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AI Inference Competition Heats Up
by Dina Genkina on 28. August 2024. at 15:07
While the dominance of Nvidia GPUs for AI training remains undisputed, we may be seeing early signs that, for AI inference, the competition is gaining on the tech giant, particularly in terms of power efficiency. The sheer performance of Nvidia’s new Blackwell chip, however, may be hard to beat.
This morning, ML Commons released the results of its latest AI inferencing competition, ML Perf Inference v4.1. This round included first-time submissions from teams using AMD Instinct accelerators, the latest Google Trillium accelerators, chips from Toronto-based startup UntetherAI, as well as a first trial for Nvidia’s new Blackwell chip. Two other companies, Cerebras and FuriosaAI, announced new inference chips but did not submit to MLPerf.
Much like an Olympic sport, MLPerf has many categories and subcategories. The one that saw the biggest number of submissions was the “datacenter-closed” category. The closed category (as opposed to open) requires submitters to run inference on a given model as-is, without significant software modification. The data center category tests submitters on bulk processing of queries, as opposed to the edge category, where minimizing latency is the focus.
Within each category, there are 9 different benchmarks, for different types of AI tasks. These include popular use cases such as image generation (think Midjourney) and LLM Q&A (think ChatGPT), as well as equally important but less heralded tasks such as image classification, object detection, and recommendation engines.
This round of the competition included a new benchmark, called Mixture of Experts. This is a growing trend in LLM deployment, where a language model is broken up into several smaller, independent language models, each fine-tuned for a particular task, such as regular conversation, solving math problems, and assisting with coding. The model can direct each query to an appropriate subset of the smaller models, or “experts”. This approach allows for less resource use per query, enabling lower cost and higher throughput, says Miroslav Hodak, MLPerf Inference Workgroup Chair and senior member of technical staff at AMD.
The winners on each benchmark within the popular datacenter-closed benchmark were still submissions based on Nvidia’s H200 GPUs and GH200 superchips, which combine GPUs and CPUs in the same package. However, a closer look at the performance results paint a more complex picture. Some of the submitters used many accelerator chips while others used just one. If we normalize the number of queries per second each submitter was able to handle by the number of accelerators used, and keep only the best performing submissions for each accelerator type, some interesting details emerge. (It’s important to note that this approach ignores the role of CPUs and interconnects.)
On a per accelerator basis, Nvidia’s Blackwell outperforms all previous chip iterations by 2.5x on the LLM Q&A task, the only benchmark it was submitted to. Untether AI’s speedAI240 Preview chip performed almost on-par with H200’s in its only submission task, image recognition. Google’s Trillium performed just over half as well as the H100 and H200s on image generation, and AMD’s Instinct performed about on-par with H100s on the LLM Q&A task.
The power of Blackwell
One of the reasons for Nvidia Blackwell’s success is its ability to run the LLM using 4-bit floating-point precision. Nvidia and its rivals have been driving down the number of bits used to represent data in portions of transformer models like ChatGPT to speed computation. Nvidia introduced 8-bit math with the H100, and this submission marks the first demonstration of 4-bit math on MLPerf benchmarks.
The greatest challenge with using such low-precision numbers is maintaining accuracy, says Nvidia’s product marketing director Dave Salvator. To maintain the high accuracy required for MLPerf submissions, the Nvidia team had to innovate significantly on software, he says.
Another important contribution to Blackwell’s success is it’s almost doubled memory bandwidth, 8 terabytes/second, compared to H200’s 4.8 terabytes/second.
Nvidia GB2800 Grace Blackwell SuperchipNvidia
Nvidia’s Blackwell submission used a single chip, but Salvator says it’s built to network and scale, and will perform best when combined with Nvidia’s NVLink interconnects. Blackwell GPUs support up to 18 NVLink 100 gigabyte-per-second connections for a total bandwidth of 1.8 terabytes per second, roughly double the interconnect bandwidth of H100s.
Salvatore argues that with the increasing size of large language models, even inferencing will require multi-GPU platforms to keep up with demand, and Blackwell is built for this eventuality. “Blackwell is a platform,” Salvator says.
Nvidia submitted their Blackwell chip-based system in the preview subcategory, meaning it is not for sale yet but is expected to be available before the next MLPerf release, six months from now.
Untether AI shines in power use and at the edge
For each benchmark, MLPerf also includes an energy measurement counterpart, which systematically tests the wall plug power that each of the systems draws while performing a task. The main event (the datacenter-closed energy category) saw only two submitters this round: Nvidia and Untether AI. While Nvidia competed in all the benchmarks, Untether only submitted for image recognition.
Submitter
Accelerator
Number of accelerators
Queries per second
Watts
Queries per second per Watt
NVIDIA
NVIDIA H200-SXM-141GB
8
480,131.00
5,013.79
95.76
UntetherAI
UntetherAI speedAI240 Slim
6
309,752.00
985.52
314.30
The startup was able to achieve this impressive efficiency by building chips with an approach it calls at-memory computing. UntetherAI’s chips are built as a grid of memory elements with small processors interspersed directly adjacent to them. The processors are parallelized, each working simultaneously with the data in the nearby memory units, thus greatly decreasing the amount of time and energy spent shuttling model data between memory and compute cores.
“What we saw was that 90 percent of the energy to do an AI workload is just moving the data from DRAM onto the cache to the processing element,” says Untether AI vice president of product Robert Beachler. “So what Untether did was turn that around ... Rather than moving the data to the compute, I’m going to move the compute to the data.”
This approach proved particularly successful in another subcategory of MLPerf: edge-closed. This category is geared towards more on-the-ground use cases, such as machine inspection on the factory floor, guided vision robotics, and autonomous vehicles—applications where low energy use and fast processing are paramount, Beachler says.
Submitter
GPU type
Number of GPUs
Single Stream Latency (ms)
Multi-Stream Latency (ms)
Samples/s
Lenovo
NVIDIA L4
2
0.39
0.75
25,600.00
Lenovo
NVIDIA L40S
2
0.33
0.53
86,304.60
UntetherAI
UntetherAI speedAI240 Preview
2
0.12
0.21
140,625.00
On the image recognition task, again the only one UntetherAI reported results for, the speedAI240 Preview chip beat NVIDIA L40S’s latency performance by 2.8x and its throughput (samples per second) by 1.6x. The startup also submitted power results in this category, but their Nvidia-accelerated competitors did not, so it is hard to make a direct comparison. However, the nominal power draw per chip for UntetherAI’s speedAI240 Preview chip is 150 Watts, while for Nvidia’s L40s it is 350 W, leading to a nominal 2.3x power reduction with improved latency.
Cerebras, Furiosa skip MLPerf but announce new chips
Furiosa’s new chip implements the basic mathematical function of AI inference, matrix multiplication, in a different, more efficient way. Furiosa
Yesterday at the IEEE Hot Chips conference at Stanford, Cerebras unveiled its own inference service. The Sunnyvale, Calif. company makes giant chips, as big as a silicon wafer will allow, thereby avoiding interconnects between chips and vastly increasing the memory bandwidth of their devices, which are mostly used to train massive neural networks. Now it has upgraded its software stack to use its latest computer CS3 for inference.
Although Cerebras did not submit to MLPerf, the company claims its platform beats an H100 by 7x and competing AI startup Groq’s chip by 2x in LLM tokens generated per second. “Today we’re in the dial up era of Gen AI,” says Cerebras CEO and cofounder Andrew Feldman. “And this is because there’s a memory bandwidth barrier. Whether it’s an H100 from Nvidia or MI 300 or TPU, they all use the same off chip memory, and it produces the same limitation. We break through this, and we do it because we’re wafer-scale.”
Hot Chips also saw an announcement from Seoul-based Furiosa, presenting their second-generation chip, RNGD (pronounced “renegade”). What differentiates Furiosa’s chip is its Tensor Contraction Processor (TCP) architecture. The basic operation in AI workloads is matrix multiplication, normally implemented as a primitive in hardware. However, the size and shape of the matrixes, more generally known as tensors, can vary widely. RNGD implements multiplication of this more generalized version, tensors, as a primitive instead. “During inference, batch sizes vary widely, so its important to utilize the inherent parallelism and data re-use from a given tensor shape,” Furiosa founder and CEO June Paik said at Hot Chips.
Although it didn’t submit to MLPerf, Furiosa compared the performance of its RNGD chip on MLPerf’s LLM summarization benchmark in-house. It performed on-par with Nvidia’s edge-oriented L40S chip while using only 185 Watts of power, compared to L40S’s 320 W. And, Paik says, the performance will improve with further software optimizations.
IBM also announced their new Spyre chip designed for enterprise generative AI workloads, to become available in the first quarter of 2025.
At least, shoppers on the AI inference chip market won’t be bored for the foreseeable future.
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Ransomware-as-a-Service Is Changing Extortion Efforts
by Margo Anderson on 28. August 2024. at 11:01
Thirty-five years ago, a misguided AIDS activist developed a piece of malware that encrypted a computer’s filenames—and asked for US $189 to obtain the key that unlocked an afflicted system. This “AIDS Trojan” holds the dubious distinction of being the world’s first piece of ransomware. In the intervening decades the encryption behind ransomware has become more sophisticated and harder to crack, and the underlying criminal enterprise has only blossomed like a terrible weed. Among the most shady of online shady businesses, ransomware has now crossed the $1 billion mark in ransoms paid out last year. Equally unfortunately, the threat today is on the rise, too. And in the same way that the “as a service” business model has sprouted up with software-as-a-service (SaaS), the ransomware field has now spawned a ransomware-as-a-service (RaaS) industry.
Guillermo Christensen is a Washington, D.C.-based lawyer at the firm K&L Gates. He’s also a former CIA officer who was detailed to the FBI to help build the intelligence program for the Bureau. He’s an instructor at the FBI’s CISO Academy—and a founding member of the Association of U.S. Cyber Forces and the National Artificial Intelligence and Cybersecurity Information Sharing Organization. IEEE Spectrum spoke with Christensen about the rise of ransomware-as-a-service as a new breed of ransomware attacks and how they can be understood—and fought.
Guillermo Christensen on...:
- How has ransomware evolved in recent years?
- How are people and companies responding to ransomware attacks?
- What is ransomware-as-a-service?
- An example of a recent ransomware attack
- How are ransomware groups evading detection and bypassing security measures?
- What are the most effective anti-ransomware tools and technologies today?
Guillermo ChristensenK&L Gates
How has the ransomware situation changed in recent years? Was there an inflection point?
Christensen: I would say, [starting in] 2022, which the defining feature of is the Russian invasion of Eastern Ukraine. I see that as a kind of a dividing line in the current situation.
[Ransomware threat actors] have shifted their approach towards the core infrastructure of companies. And in particular, there are groups now that have had remarkable success encrypting the large-scale hypervisors, these systems that basically create fake computers, virtual machines that run on servers that can be enormous in scale. So by being able to attack those resources, the threat actors are able to do massive damage, sometimes taking down an entire company’s infrastructure in one attack. And some of these are due to the fact that this kind of infrastructure is hard to keep updated to patch for vulnerabilities and things like that.
Before 2022, many of these groups did not want to attack certain kinds of targets. For example, when the Colonial Pipeline company [was attacked], there was a lot of chatter afterwards that maybe that was a mistake because that attack got a lot of attention. The FBI put a lot of resources into going after [the perpetrators]. And there was a feeling among many of the ransomware groups, “Don’t do this. We have a great business here. Don’t mess it up by making it so much more likely that the U.S. government’s going to do something about this.”
How did you know the threat actors were saying these sorts of things?
Christensen: Because we work with a lot of threat intelligence experts. And a threat intelligence expert does a lot of things. But one of the things they do is they try to inhabit the same criminal forums as these groups—to get intelligence on what are they doing, what are they developing, and things like that. It’s a little bit like espionage. And it involves creating fake personas that you insert information, and you develop credibility. The other thing is that the Russian criminal groups are pretty boisterous. They have big egos. And so they also talk a lot. They talk on Reddit. They talk to journalists. So you get information from a variety of sources. Sometimes we’ve seen the groups, for example, actually have codes of ethics, if you will, about what they will or won’t do. If they inadvertently attack a hospital, when the hospital tells them, “Hey, you attacked the hospital, and you’re supposed to not do that,” in those cases, some of these groups have decrypted the hospital’s networks without charging a fee before.
“There was a feeling among many of the ransomware groups, ‘Don’t do this. We have a great business here.’”
But that, I think, has changed. And I think it changed in the course of the war in Ukraine. Because I think a lot of the Russian groups basically now understand we are effectively at war with each other. Certainly, the Russians believe the United States is at war with them. If you look at what’s going on in Ukraine, I would say we are. Nobody declares war on each other anymore. But our weapons are being used in fighting.
And so how are people responding to ransomware attacks since the Ukraine invasion?
Christensen: So now, they’ve taken it to a much higher level, and they’re going after companies and banks. They’re going after large groups and taking down all of the infrastructure that runs everything from their enterprise systems, their ERP systems that they use for all their businesses, their emails, et cetera. And they’re also stealing their data and holding it hostage, in a sense.
They’ve gone back to, really, the ultimate pain point, which is, you can’t do what your business is supposed to do. One of the first questions we ask when we get involved in one of these situations—if we don’t know who the company is—is “What is effectively the burn rate on your business every day that you’re not able to use these systems?” And some of them take a bit of effort to understand how much it is. Usually, I’m not looking for a precise amount, just a general number. Is it a million dollars a day? Is it 5 million? Is it 10? Because whatever that amount is, that’s what you then start defining as an endpoint for what you might need to pay.
What is ransomware-as-a-service? How has it evolved? And what are its implications?
Christensen: Basically, is it’s almost like the ransomware groups created a platform, very professionally. And if you know of a way to break into a company’s systems, you approach them and you say, “I have access to this system.” They also will have people who are good at navigating the network once they’re inside. Because once you’re inside, you want to be very careful not to tip off the company that something’s happened. They’ll steal the [company's] data. Then there’ll be either the same group or someone else in that group who will create a bespoke or customized version of the encryption for that company, for that victim. And they deploy it.
Because you’re doing it at scale, the ransomware can be fairly sophisticated and updated and made better every time from the lessons they learn.
Then they have a negotiator who will negotiate the ransom. And they basically have an escrow system for the money. So when they get the ransom money, the money comes into one digital wallet—sometimes a couple, but usually one. And then it gets split up among those who participated in the event. And the people who run this platform, the ransomware-as-a-service, get the bulk of it because they did the work to set up the whole thing. But then everybody gets a cut from that.
And because you’re doing it at scale, the ransomware can be fairly sophisticated and updated and made better every time from the lessons they learn. So that’s what ransomware as a service is.
How do ransomware-as-a-service companies continue to do business?
Christensen: Effectively, they’re untouchable right now, because they’re mostly based in Russia. And they operate using infrastructure that is very hard to take down. It’s almost bulletproof. It’s not something you can go to a Google and say, “This website is criminal, take it down.” They operate in a different type of environment. That said, we have had success in taking down some of the infrastructure. So the FBI in particular working with international law enforcement has had some remarkable successes lately because they’ve been putting a lot of effort into this in taking down some of these groups. One in particular was called Hive.
They were very, very good, caused a lot of damage. And the FBI was able to infiltrate their system, get the decryption keys effectively, give those to a lot of victims. Over a period of almost six months, many, many companies that reported their attack to the FBI were able to get free decryption. A lot of companies didn’t, which is really, really foolish, and they paid. And that’s something that I often just am amazed that there are companies out there that don’t report to the FBI because there’s no downside to doing that. But there are a lot of lawyers who don’t want to report for their clients to the FBI, which I think is incredibly short-sighted.
But it takes months or years of effort. And the moment you do, these groups move somewhere else. You’re not putting them in jail very often. So basically, they just disappear and then come together somewhere else.
What’s an example of a recent ransomware attack?
Christensen: One that I think is really interesting, which I was not involved with, is the attack on a company called CDK. This one got quite a bit of publicity. So details are quite well known. CDK is a company that provides the back office services for a lot of car dealers. And so if you were trying to buy a car in the last couple of months, or were trying to get your car serviced, you went to the dealer, and they were doing nothing on their computers. It was all on paper.
It appears the threat actor then came back in and attacked a second time, this time, harming broader systems, including backups.
And this has actually had quite an effect in the auto industry. Because once you interrupt that system, it cascades. And what they did in this particular case, the ransomware group went after the core system knowing that this company would then basically take down all these other businesses. So that it was a very serious problem. The company, from what we’ve been able to read, made some serious mistakes at the front end.
The first thing is rule number one, when you have a ransomware or any kind of a compromise of your system, you first have to make sure you’ve ejected the threat actor from your system. If they’re still inside, you’ve got a big problem. So what it appears is that they realized they [were being attacked] over a weekend, I think, and they realized, “Boy, if we don’t get these systems back up and running, a lot of our customers are going to be really, really upset with us.” So they decided to restore. And when they did that, they still had the threat actor in the system.
And it appears the threat actor then came back in and attacked a second time, this time, harming broader systems, including backups. So when they did that, they essentially took the company down completely, and it’s taken them at least a month plus to recover, costing hundreds of millions of dollars.
So what could we take as lessons learned from the CDK attack?
Christensen: There are a lot of things you can do to try to reduce the risk of ransomware. But the number one at this point is you’ve got to have a good plan, and the plan has got to be tested. If the day you get hit by ransomware is the first day that your leadership team talks about ransomware or who’s going to do what, you are already so behind the curve.
It’s the planning that is essential, not the plan.
And a lot of people think, “Well, a plan. Okay. So we have a plan. We’re going to follow this checklist.” But that’s not real. You don’t follow a plan. The point of the plan is to get your people ready to be able to deal with this. It’s the planning that is essential, not the plan. And that takes a lot of effort.
I think a lot of companies, frankly, don’t have the imagination at this point to see what could happen to them in this kind of attack. Which is a pity because, in a lot of ways, they’re gambling that other people are going to get hit before them. And from my perspective, that’s not a serious business strategy. Because the prevalence of this threat is very serious. And everybody’s more or less using the same system. So you really are just gambling that they’re not going to pick you out of another 10 companies.
What are some of the new technologies and techniques that ransomware groups are using today to evade detection and to bypass security measures?
Christensen: So by and large, they mostly still use the same tried and true techniques. And that’s unfortunate because what that should tell you is that many of these companies have not improved their security based on what they should have learned. So some of the most common attack vectors, so the ways into these companies, is the fact that some part of the infrastructure is not protected by multi-factor authentication.
Companies often will say, “Well, we have multi-factor authentication on our emails, so we’re good, right?” What they forget is that they have a lot of other ways into the company’s network—mostly things like virtual private networks, remote tools, lots of things like that. And those are not protected by multi-factor authentication. And when they’re discovered, and it’s not difficult for a threat actor to find them. Because usually, if you look at, say, a listing of software that a company is using, and you can scan these things externally, you’ll see the version of a particular type of software. And you know that that software does not support multi-factor authentication perhaps, or it’s very easy to see that when you put in a password, it doesn’t prompt you for a multi-factor. Then you simply use brute force techniques, which are very effective, to guess the password, and you get in.
Everybody, practically speaking, uses the same passwords. They reuse the passwords. So it’s very common for these criminal groups that hacked, say, a large company on one level, they get all the passwords there. And then they figure out that that person is at another company, and they use that same password. Sometimes they’ll try variations. That works almost 100 percent of the time.
Is there a technology that anti-ransomware advocates and ransomware fighters are waiting for today? Or is the game more about public awareness?
Christensen: Microsoft has been very effective at taking down large bot infrastructures, working with the Department of Justice. But this needs to be done with more independence, because if the government has to bless every one of these things, well, then nothing will happen. So we need to set up a program. We allow a certain group of companies to do this. They have rules of engagement. They have to disclose everything they do. And they make money for it.
I mean, they’re going to be taking a risk, so they need to make money off it. For example, be allowed to keep half the Bitcoin they grab from these groups or something like that.
But I think what I would like to see is that these threat actors don’t sleep comfortably at night, the same way that the people fighting defense right now don’t get to sleep comfortably at night. Otherwise, they’re sitting over there being able to do whatever they want, when they want, at their initiative. In a military mindset, that’s the worst thing. When your enemy has all the initiative and can plan without any fear of repercussion, you’re really in a bad place.
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Essential Skills for IT Professionals in the AI Era
by Kumar Singirikonda on 27. August 2024. at 18:00
Artificial Intelligence is transforming industries worldwide, creating new opportunities in health care, finance, customer service, and other disciplines. But the ascendance of AI raises concerns about job displacement, especially as the technology might automate tasks traditionally done by humans.
Jobs that involve data entry, basic coding, and routine system maintenance are at risk of being eliminated—which might worry new IT professionals. AI also creates new opportunities for workers, however, such as developing and maintaining new systems, data analysis, and cybersecurity. If IT professionals enhance their skills in areas such as machine learning, natural language processing, and automation, they can remain competitive as the job market evolves.
Here are some skills IT professionals need to stay relevant, as well as advice on how to thrive and opportunities for growth in the industry.
Boosting your knowledge
One area you should become proficient in is machine learning algorithms.
I recommend learning the fundamentals such as the basics of programming and mathematics. Look for programs that require you to participate in projects and assignments that apply what you’ve learned.
Understanding data is also crucial. Learn how to collect, analyze, and interpret data using Python, R, SQL, and similar tools.
Recommended resources:
- Coursera is an online learning platform that provides classes in a large number of subjects. I suggest the introductory course on machine learning taught by Andrew Ng, a computer science adjunct professor at Stanford.
- EdX, another online platform, offers a variety of courses including ones in computer science, engineering, and business. I recommend taking the Data Science MicroMasters program, which provides a comprehensive foundation of the field, including statistical and computational tools for data analysis.
- Udacity, which is known for its nanodegree programs, offers practical, project-based tech learning experiences. Nanodegrees are certified online educational programs that teach you specialized skills in less time than traditional bachelor’s and master’s degrees. Consider the AI Programming With Python nanodegree, which covers the essential skills needed for building AI applications using programming languages such as Python, NumPy, and PyTorch.
- Fast.ai offers free courses on deep learning. Start with the Practical Deep Learning for Coders program designed for beginners. It covers state-of-the-art techniques and tools.
- Google’s free Machine Learning Crash Course provides a practical introduction to the topic using TensorFlow APIs, which are open-source machine learning libraries. The course includes exercises, interactive visualizations, and instructional videos.
Key insights into AI ethics
Understanding the ethical considerations surrounding AI technologies is crucial. Courses on AI ethics and policy provide important insights into ethical implications, government regulations, stakeholder perspectives, and AI’s potential societal, economic, and cultural impacts.
I recommend reviewing case studies to learn from real-world examples and to get a grasp of the complexities surrounding ethical decision-making. Some AI courses explore best practices adopted by organizations to mitigate risks.
It’s also critical to learn how to conduct impact assessments to evaluate the potential societal, economic, and cultural influence of AI technologies before they’re deployed. A proactive approach can help identify and address ethical issues early on.
The importance of soft skills
AI can handle data, but humans are needed for creative and strategic thinking. AI professionals need to develop their critical-thinking and problem-solving skills, as they are areas where human intelligence excels. By honing your skills, you can complement AI technology and ensure better decision-making.
Working with AI involves interdisciplinary teams, and that requires strong communication skills to collaborate effectively with diverse team members for a broader range of perspectives and innovative solutions.
The ability to communicate clearly and concisely is also crucial when explaining complex concepts or ideas to others, whether in presentations or defining a new concept in code.
Navigating the new job market
Joining professional networks and AI communities can help you connect with potential employers. Consider joining LinkedIn and GitHub, creating a personal website, and writing a blog.
Share your portfolio on LinkedIn and other professional networks to access a wider audience and to connect with potential employers.
Create a strong online presence by sharing information about your projects, writing articles, and participating in discussions about AI and related technologies. Not only does it allow you to showcase your skills and expertise, it also could attract the attention of recruiters and hiring managers.
Another way to show off your technical skills is to develop a portfolio of your AI projects, code samples, and relevant work experience. A well-curated portfolio demonstrates your capabilities to potential employers. You should update it regularly with new projects and accomplishments. If you don’t have much professional AI experience, create personal projects and tasks to showcase your abilities.
Many successful engineers attribute their achievements to the guidance of mentors. Seeking out experienced mentors can provide invaluable guidance, feedback, and industry insights. Building relationships with more seasoned engineers offers networking opportunities, and it helps you stay updated on industry trends and advancements.
Engaging with peers through study groups and professional networks is beneficial as well. It allows you to gain different perspectives and collaborate on solving problems. Connecting with other IT professionals helps deepen your understanding of AI and technology concepts while building a robust support system within the industry.
How to thrive in the AI era
The tech industry evolves rapidly, so be open to learning new skills and adapting to changes in the job market. It can demonstrate your ability to overcome challenges and stay relevant. By continuously improving your skills, you are advertising your dedication to the field and you might stand out to potential employers.
Technical interviews for IT professionals often include coding tests, AI algorithms, and machine learning concepts. You can hone your skills at online coding platforms such as LeetCode and HackerRank. The platforms can’t teach you how to code, but they can provide a place to work on and test your code.
Combining your technical skills with knowledge of other fields such as business, health care, and finance is also advised. An interdisciplinary approach can open the door to more jobs.
Outlook and opportunities
To advance in the AI field, stay informed about its applications in emerging areas such as quantum computing, biotechnology, and smart cities. Understanding such fields can give you a competitive edge and open growth opportunities.
Step outside your comfort zone by participating in AI projects aimed at addressing social issues such as climate change, health care access, and education. By applying AI for social good, you not only contribute positively to society; you also gain valuable experience and recognition.
Having expertise in AI offers numerous opportunities for entrepreneurship. You might want to consider starting your own venture or joining innovative startups leveraging AI to solve specific problems. By being part of the entrepreneurial ecosystem, you can contribute to groundbreaking solutions and potentially create a lasting impact on society. Look for funding opportunities, incubators, and accelerators that support AI-driven startups.
Practical experience is invaluable. Seek out internships or work on projects that involve AI and machine learning. Hands-on experience enhances your technical skills and provides you with practical, real-world work to showcase in job interviews. Plus, internships can lead to valuable connections and even job opportunities.
Another way to gain practical experience is by contributing to open-source AI projects. It not only would improve your skills but also would help you build your portfolio. By collaborating with other developers on open-source projects, you can gain valuable insights and feedback to further enhance your knowledge in AI and machine learning.
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Can China Challenge SpaceX's Starlink?
by Andrew Jones on 27. August 2024. at 14:00
China launched its first batch of satellites for its Qianfan megaconstellation earlier this month. It now has 18 satellites in orbit, but much more will be needed to build out this network of nearly 14,000 satellites.
Qianfan—”thousands sails” in Chinese and also referred to as Spacesail or G60—is a project run by Shanghai Spacecom Satellite Technology (SSST). Last February, the company announced it had raised 6.7 billion yuan ($943 million) in funding, with backing from Shanghai’s municipal government. This makes it a serious project, and one meant to catch up with SpaceX’s Starlink, providing global connectivity, including direct mobile connections, while also providing rural connectivity, supporting e-commerce, and bolstering national security within China.
The aim, SSST says, is to launch all 13,904 satellites by 2030. That, incredibly, works out to launching an average of just over seven satellites per day, every day, until the end of the decade.
To put this in perspective, SpaceX, with its reusable Falcon 9 rocket, has launched 6,895 satellites since the Starlink constellation’s first launch in May 2019. Of these, around 5,500 are still in orbit and operational. That works out to about 3.5 satellites launched per day.
To get off the ground, in other words, Qianfan will require both a boom in Chinese launch rates and a surge in satellite manufacturing.
China’s launch capacity is rising
China’s ability to launch spacecraft has grown vastly in recent years. The country launched 22 times in 2016, rising to 67 launches in 2023. Much of this growth has come from the creation of the country’s BeiDou navigation satellite system, the construction of a space station, and the buildout of a national space infrastructure, including large communication satellites and remote-sensing spacecraft for civilian and military uses.
However, none of the Chinese launchers used so far are reusable, and China’s four national spaceports are working at near full capacity. To build Qianfan, SSST and China will need new rockets and new spaceports. The country is working on both.
China has long heeded the rise of private space actors in the United States, such as SpaceX and Planet, and in 2014 the country began allowing private capital into some areas of the space sector. This investment started with small rockets and tiny satellites. More recently, emerging companies have been given the green light to make larger rockets.
The result is that Chinese companies such as Space Pioneer, Landspace, Deep Blue Aerospace and iSpace are closing in on first launches of medium-lift and/or reusable rockets. Landspace’s reusable stainless steel Zhuque-3, for example, will have a similar payload capacity to low Earth orbit as SpaceX’s Falcon 9, according to the company’s website. A first launch is slated for 2025.
But making rockets and flying frequently and reliably are separate issues.
“In order to increase launch throughput, additional capabilities need to be developed in the launch supply chain,” says Ian Christensen, senior director for private sector programs at the Broomfield, Colo.-based Secure World Foundation.
“In my view it is an open question whether China’s launch vehicle production capabilities can be scaled to meet the throughput necessary to deploy these constellations on the stated schedule,” he says.
The next question is, where will they launch from?
China seeks bigger spaceports
The first launch from a new commercial spaceport on the South China Sea island of Hainan is expected in the near future. It’s close to the national Wenchang spaceport, and two pads have been built so far. One is for a modified Long March 8 rocket—which is expected to play a major role in constellation launches—and the other will host rockets developed by commercial companies. Plans call for up to 10 launch pads. Recently announced plans for space industry development from Beijing and Shanghai have also been promoted to attract and boost space companies.
Yet whether these plans will translate into definitive action is an open question, according to Christensen. “Ultimately this is about resilience and quality in the supply chain: launch, satellite manufacturing, and terminal equipment,” he says. “Can the various entities involved in this project maintain production at the pace necessary to achieve the deployment schedules outlined?”
Christensen notes that many of the capabilities and entities involved in the Qianfan/G60 project are new, and the project is not vertically integrated, unlike SpaceX’s Starlink. The latter owns and controls both the launch and satellite manufacturing segments.
“[Qianfan’s] manufacturing entity was established in 2022, and its first satellite was produced in late 2023,” he says. That means fundamental questions about the satellites’ spaceworthiness and durability are as yet unanswered. “How will the products perform in space?” he says. “Will [China’s] manufacturing quality be maintained?”
Those aren’t rhetorical questions, and the answers may have wide repercussions. The first Qianfan/G60 launch, on a Long March 6A on Aug. 6, created a field of hundreds of pieces of debris when the launcher’s upper stage broke up. The accident highlights a worrying issue. Qianfan/G60 satellites will operate at 800 kilometers above Earth, about 250 km higher than Starlink satellites. This means the Qianfan satellites themselves, along with rocket stages and any debris, could remain in space for decades—well beyond their own obsolescence. And that debris would then eventually threaten spacecraft in lower orbits, as it all descends back to Earth.
The rollout of Qianfan/G60 thus has domestic and international ramifications. It heralds a rapid advance in Chinese launch and satellite capabilities. And the acceleration of China’s launch rate to keep up with the project’s ambitious timetables will likely exacerbate the already significant issues of space debris, potential collisions, impacts on astronomy (already recognized by Starlink), orbital crowding, and international cooperation and coordination in space.
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A Match Made in Yorktown Heights
by Harry Goldstein on 26. August 2024. at 20:06
It pays to have friends in fascinating places. You need look no further than the cover of this issue and the article “ IBM’s Big Bet on the Quantum-Centric Supercomputer” for evidence. The article by Ryan Mandelbaum, Antonio D. Córcoles, and Jay Gambetta came to us courtesy of the article’s illustrator, the inimitable graphic artist Carl De Torres, a longtime IEEE Spectrum contributor as well as a design and communications consultant for IBM Research.
Story ideas typically originate with Spectrum’s editors and pitches from expert authors and freelance journalists. So we were intrigued when De Torres approached Spectrum about doing an article on IBM Research’s cutting-edge work on quantum-centric supercomputing.
De Torres has been collaborating with IBM in a variety of capacities since 2009, when, while at Wired magazine creating infographics, he was asked by the ad agency Ogilvy to work on Big Blue’s advertising campaign “Let’s build a Smarter Planet.” That project went so well that De Torres struck out on his own the next year. His relationship with IBM expanded, as did his engagements with other media, such as Spectrum, Fortune, and The New York Times. “My interest in IBM quickly grew beyond helping them in a marketing capacity,” says De Torres, who owns and leads the design studio Optics Lab in Berkeley, Calif. “What I really wanted to do is get to the source of some of the smartest work happening in technology, and that was IBM Research.”
Last year, while working on visualizations of a quantum-centric supercomputer with Jay Gambetta, vice president and lead scientist of IBM Quantum at the Thomas J. Watson Research Center in Yorktown Heights, N.Y., De Torres was inspired to contact Spectrum’s creative director, Mark Montgomery, with an idea.
“I really loved this process because I got to bring together two of my favorite clients to create something really special.” —Carl De Torres
“I thought, ‘You know, I think IEEE Spectrum would love to see this work,’” De Torres told me. “So with Jay’s permission, I gave Mark a 30-second pitch. Mark liked it and ran it by the editors, and they said that it sounded very promising.” De Torres, members of the IBM Quantum team, and Spectrum editors had a call to brainstorm what the article could be. “From there everything quickly fell into place, and I worked with Spectrum and the IBM Quantum team on a visual approach to the story,” De Torres says.
As for the text, we knew it would take a deft editorial hand to help the authors explain what amounts to the peanut butter and chocolate of advanced computing. Fortunately for us, and for you, dear reader, Associate Editor Dina Genkina has a doctorate in atomic physics, in the subfield of quantum simulation. As Genkina explained to me, that speciality is “adjacent to quantum computing, but not quite the same—it’s more like the analog version of QC that’s not computationally complete.”
Genkina was thrilled to work with De Torres to make the technical illustrations both accurate and edifying. Spectrum prides itself on its tech illustrations, which De Torres notes are increasingly rare in the space-constrained era of mobile-media consumption.
“Working with Carl was so exciting,” Genkina says. “It was really his vision that made the article happen, and the scope of his ambition for the story was at times a bit terrifying. But it’s the kind of story where the illustrations make it come to life.”
De Torres was happy with the collaboration, too. “I really loved this process because I got to bring together two of my favorite clients to create something really special.”
This article appears in the September 2024 print issue.
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Erika Cruz Keeps Whirlpool’s Machines Spinning
by Edd Gent on 26. August 2024. at 15:00
Few devices are as crucial to people’s everyday lives as their household appliances. Electrical engineer Erika Cruz says it’s her mission to make sure they operate smoothly.
Cruz helps design washing machines and dryers for Whirlpool, the multinational appliance manufacturer.
Erika Cruz
Employer:
Whirlpool
Occupation:
Associate electrical engineer
Education:
Bachelor’s degree in electronics engineering, Industrial University of Santander, in Bucaramanga, Colombia
As a member of the electromechanical components team at Whirlpool’s research and engineering center in Benton Harbor, Mich., she oversees the development of timers, lid locks, humidity sensors, and other components.
More engineering goes into the machines than is obvious. Because the appliances are sold around the world, she says, they must comply with different technical and safety standards and environmental conditions. And reliability is key.
“If the washer’s door lock gets stuck and your clothes are inside, your whole day is going to be a mess,” she says.
While appliances can be taken for granted, Cruz loves that her work contributes in its own small way to the quality of life of so many.
“I love knowing that every time I’m working on a new design, the lives of millions of people will be improved by using it,” she says.
From Industrial Design to Electrical Engineering
Cruz grew up in Bucaramanga, Colombia, where her father worked as an electrical engineer, designing control systems for poultry processing plants. Her childhood home was full of electronics, and Cruz says her father taught her about technology. He paid her to organize his resistors, for example, and asked her to create short videos for work presentations about items he was designing. He also took Cruz and her sister along with him to the processing plants.
“We would go and see how the big machines worked,” she says. “It was very impressive because of their complexity and impact. That’s how I got interested in technology.”
In 2010, Cruz enrolled in Colombia’s Industrial University of Santander, in Bucaramanga, to study industrial design. But she quickly became disenchanted with the course’s focus on designing objects like fancy tables and ergonomic chairs.
“I wanted to design huge machines like my father did,” she says.
A teacher suggested that she study mechanical engineering instead. But her father was concerned about discrimination she might face in that career.
“He told me it would be difficult to get a job in the industry because mechanical engineers work with heavy machinery, and they saw women as being fragile,” Cruz says.
Her father thought electrical engineers would be more receptive to women, so she switched fields.
“I am very glad I ended up studying electronics because you can apply it to so many different fields,” Cruz says. She received a bachelor’s degree in electronics engineering in 2019.
The Road to America
While at university, Cruz signed up for a program that allowed Colombian students to work summer jobs in the United States. She held a variety of summer positions in Galveston, Texas, from 2017 to 2019, including cashier, housekeeper, and hostess.
She met her future husband in 2018, an American working at the same amusement park as she did. When she returned the following summer, they started dating, and that September they married. Since she had already received her degree, he was eager for her to move to the states permanently, but she made the difficult decision to return to Colombia.
“With the language barrier and my lack of engineering experience, I knew if I stayed in the United States, I would have to continue working jobs like housekeeping forever,” she says. “So I told my husband he had to wait for me because I was going back home to get some engineering experience.”
“I love knowing that every time I’m working on a new design, the lives of millions of people will be improved by using it.”
Cruz applied for engineering jobs in neighboring Brazil, which had more opportunities than Colombia did. In 2021, she joined Whirlpool as an electrical engineer at its R&D site in Joinville, Brazil. There, she introduced into mass production sensors and actuators provided by new suppliers.
Meanwhile, she applied for a U.S. Green Card, which would allow her to work and live permanently in the country. She received it six months after starting her job. Cruz asked her manager about transferring to one of Whirlpool’s U.S. facilities, not expecting to have any luck. Her manager set up a phone call with the manager of the components team at the company’s Benton Harbor site to discuss the request. Cruz didn’t realize that the call was actually a job interview. She was offered a position there as an electrical engineer and moved to Michigan later that year.
Designing Appliances Is Complex
Designing a new washing machine or dryer is a complex process, Cruz says. First, feedback from customers about desirable features is used to develop a high-level design. Then the product design work is divided among small teams of engineers, each responsible for a given subsystem, including hardware, software, materials, and components.
Part of Cruz’s job is to test components from different suppliers to make sure they meet safety, reliability, and performance requirements. She also writes the documentation that explains to other engineers about the components’ function and design.
Cruz then helps select the groups of components to be used in a particular application—combining, say, three temperature sensors with two humidity sensors in an optimized location to create a system that finds the best time to stop the dryer.
Building a Supportive Environment
Cruz loves her job, but her father’s fears about her entering a male-dominated field weren’t unfounded. Discrimination was worse in Colombia, she says, where she regularly experienced inappropriate comments and behavior from university classmates and teachers.
Even in the United States, she points out, “As a female engineer, you have to actually show you are able to do your job, because occasionally at the beginning of a project men are not convinced.”
In both Brazil and Michigan, Cruz says, she’s been fortunate to often end up on teams with a majority of women, who created a supportive environment. That support was particularly important when she had her first child and struggled to balance work and home life.
“It’s easier to talk to women about these struggles,” she says. “They know how it feels because they have been through it too.”
Update Your Knowledge
Working in the consumer electronics industry is rewarding, Cruz says. She loves going into a store or visiting someone’s home and seeing the machines that she’s helped build in action.
A degree in electronics engineering is a must for the field, Cruz says, but she’s also a big advocate of developing project management and critical thinking skills. She is a certified associate in project management, granted by the Project Management Institute, and has been trained in using tools that facilitate critical thinking. She says the project management program taught her how to solve problems in a more systematic way and helped her stand out in interviews.
It’s also important to constantly update your knowledge, Cruz says, “because electronics is a discipline that doesn’t stand still. Keep learning. Electronics is a science that is constantly growing.”
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NASCAR Unveils Electric Race Car Prototype
by Willie D. Jones on 25. August 2024. at 13:00
NASCAR, the stock car racing sanctioning body known for its high-octane events across the United States, is taking a significant step toward a greener future. In July, during the Chicago Street Race event, NASCAR unveiled a prototype battery-powered race car that marks the beginning of its push to decarbonize motorsports. This move is part of NASCAR’s broader strategy to achieve net-zero emissions by 2035.
The electric prototype represents a collaborative effort between NASCAR and its traditional Original Equipment Manufacturer (OEM) partners—Chevrolet, Ford, and Toyota—along with ABB, a global technology leader. Built by NASCAR engineers, the car features three 6-Phase motors from Stohl Advanced Research and Development, an Austrian specialist in electric vehicle powertrains. These motors together produce 1,000 kilowatts at peak power, equivalent to approximately 1,300 horsepower. The energy is supplied by a 78-kilowatt-hour liquid-cooled lithium-ion battery, operating at 756 volts, though the specific battery chemistry remains a closely guarded secret.
C.J. Tobin, Senior Engineer of Vehicle Systems at NASCAR and the lead engineer on the EV prototype project, explained the motivation behind the development. He told IEEE Spectrum that “The push for electric vehicles is continuing to grow, and when we started this project one and a half years ago, that growth was rapid. We wanted to showcase our ability to put an electric stock car on the track in collaboration with our OEM partners. Our racing series have always been a platform for OEMs to showcase their stock cars, and this is just another tool for them to demonstrate what they can offer to the public.”Eleftheria Kontou, a professor of civil and environmental engineering at the University of Illinois Urbana-Champaign whose primary research focus is transportation engineering, said in an interview that “It was an excellent introduction of the new technology to NASCAR fans, and I hope that the fans will be open to seeing more innovations in that space.”
John Probst, NASCAR’s SVP of Innovation and Racing Development speaks during the unveiling of the new EV prototype. Jared C. Tilton/Getty Images
The electric race car is not just about speed; it’s also about sustainability. The car’s body panels are made from ampliTex, a sustainable flax-based composite supplied by Bcomp, a Swiss manufacturer specializing in composites made from natural fibers. AmpliTex is lighter, more moldable, and more durable than traditional materials like steel or aluminum, making the car more efficient and aerodynamic.
Regenerative braking is another key feature of the electric race car. As it slows down, the car can convert some of its kinetic energy into electric charge that feeds back into the battery. This feature most advantageous on road courses like the one in Chicago and on short oval tracks like Martinsville Speedway in Virginia.“The Chicago Street Race was a great introduction for the EV prototype because it happens in a real-world setup where electric vehicles tend to thrive,” says Kontou, who also serves on the Steering Committee of the Illinois Alliance for Clean Transportation. “[It was a good venue for the car’s unveiling] because navigating the course requires more braking than is typical at many speedway tracks.”
Though the electric prototype is part of a larger NASCAR sustainability initiative, “There are no plans to use the electric vehicle in competition at this time,” a spokesman said. “The internal combustion engine plays an important role in NASCAR and there are no plans to move away from that.” So, die-hard stock-car racing fans can still anticipate the sounds and smells of V-8 engines burning gasoline as they hurtle around tracks and through street courses.“The Chicago Street Race was a great introduction for the EV prototype because it happens in a real-world setup where electric vehicles tend to thrive.” —Eleftheria Kontou, University of Illinois
In its sustainability efforts, NASCAR lags well behind Formula One, its largest rival atop the world’s motorsports hierarchy. Since 2014, Formula One’s parent organization, the Fédération Internationale de l’Automobile (FIA), has had an all-electric racing spinoff, called Formula E. For the current season, which began in July, the ABB FIA Formula E World Championship series boasts 11 teams competing in 17 races. This year’s races feature the league’s third generation of electric race cars, and a fourth generation is planned for introduction in 2026.
Asked how NASCAR plans to follow through on its pledge to make its core operations net-zero emissions by its self-imposed target date, the spokesman pointed to changes that would counterbalance the output of traditional stock cars, which are notorious for their poor fuel efficiency and high carbon emissions. Those include 100 percent renewable electricity at NASCAR-owned racetracks and facilities, and tradeoffs such as recycling and on-site charging stations for use by fans with EVs.
The spokesman also noted that NASCAR and its OEM partners are developing racing fuel that’s more sustainable in light of the fact that stock cars consume, on average, about 47 liters for every 100 km they drive (5 miles per gallon). For comparison, U.S. federal regulators announced in June that they would begin enforcing an industry-wide fleet average of approximately 5.6 liters per 100 kilometers (50.4 miles per gallon) for model year 2031 and beyond. Fortunately for NASCAR, race cars are exempt from fuel-efficiency and tailpipe-emissions rules.
While some may be tempted to compare NASCAR’s prototype racer with the cars featured in the ABB FIA Formula E World Championship, Tobin emphasized that NASCAR’s approach in designing the prototype was distinct. “Outside of us seeing that there was a series out there racing electric vehicles and seeing how things were run with Formula E, we leaned heavily on our OEMs and went with what they wanted to see at that time,” he said.
The apparently slow transition to electric vehicles in NASCAR is seen by some in the organization as both a response to environmental concerns and a proactive move to stay ahead of potential legislation that could threaten the future of motorsports. “NASCAR and our OEM partners want to be in the driver’s seat, no matter where we’re going,” says Tobin. “With the development of [the NextGen EV prototype], we wanted to showcase the modularity of the chassis and what powertrains we can build upon it—whether that be alternative fuels, battery electric power, or something unforeseen in the future…We want to continue to push the envelope.” -
Seaport Electrification Could Slash Emissions Worldwide
by Willie D. Jones on 24. August 2024. at 13:00
According to the International Maritime Organization, shipping was responsible for over 1 billion tonnes of carbon dioxide emissions in 2018. A significant share of those emissions came from seaport activities, including ship berthing, cargo handling, and transportation within port areas. In response, governments, NGOs, and environmental watchdog groups are sounding alarms and advocating for urgent measures to mitigate pollution at the world’s ports.
One of the most promising solutions for the decarbonization of port operations involves electrifying these facilities. This plan envisions ships plugging into dockside electric power rather than running their diesel-powered auxiliary generators for lighting, cargo handling, heating and cooling, accommodation, and onboard electronics. It would also call for replacing diesel-powered cranes, forklifts, and trucks that move massive shipping containers from ship to shore with battery-powered alternatives.
To delve deeper into this transformative approach, IEEE Spectrum recently spoke with John Prousalidis, a leading advocate for seaport electrification. Prousalidis, a professor of marine electrical engineering at the National Technical University of Athens, has played a pivotal role in developing standards for seaport electrification through his involvement with the IEEE, the International Electrical Commission (IEC), and the International Organization for Standardization (ISO). As vice-chair of the IEEE Marine Power Systems Coordinating Committee, he has been instrumental in advancing these ideas. Last year, Prousalidis co-authored a key paper titled “Holistic Energy Transformation of Ports: The Proteus Plan” in IEEE Electrification Magazine. In the paper, Prousalidis and his co-authors outlined their comprehensive vision for the future of port operations. The main points of the Proteus plan have been integrated in the policy document on Smart and Sustainable Ports coordinated by Prousalidis within the European Public Policy Committee Working Group on Energy; the policy document was approved in July 2024 by the IEEE Global Policy Committee.
Professor John ProusalidisJohn Prousalidis
What exactly is “cold ironing?”
John Prousalidis: Cold ironing involves shutting down a ship’s propulsion and auxiliary engines while at port, and instead, using electricity from shore to power onboard systems like air conditioning, cargo handling equipment, kitchens, and lighting. This reduces emissions because electricity from the grid, especially from renewable sources, is more environmentally friendly than burning diesel fuel on site. The technical challenges include matching the ship’s voltage and frequency with that of the local grid, which, in general, varies globally, while tackling grounding issues to protect against short circuits.IEEE, along with IEC and ISO, have developed a joint standard, 80005, which is a series of three different standards for high-voltage and low-voltage connection. It is perhaps (along with Wi-Fi, the standard for wireless communication) the “hottest” standard because all governmental bodies tend to make laws stipulating that this is the standard that all ports need to follow to supply power to ships.
How broad has adoption of this standard been?
Prousalidis: The European Union has mandated full compliance by January 1, 2030. In the United States, California led the way with similar measures in 2010. This aggressive remediation via electrification is now being adopted globally, with support from the International Maritime Organization.
Let’s talk about another interesting idea that’s part of the plan: regenerative braking on cranes. How does that work?
Prousalidis: When lowering shipping containers, cranes in regenerative braking mode convert the kinetic energy into electric charge instead of wasting it as heat. Just like when an electric vehicle is coming to a stop, the energy can be fed back into the crane’s battery, potentially saving up to 50 percent in energy costs—though a conservative estimate would be around 20 percent.
What are the estimated upfront costs for implementing cold ironing at, say, the Port of Los Angeles, which is the largest port in the United States?
Prousalidis: The cost for a turnkey solution is approximately US $1.7 million per megawatt, covering grid upgrades, infrastructure, and equipment. A rough estimate using some established rules of thumb would be about $300 million. The electrification process at that port has already begun. There are, as far as I know, about 60 or more electrical connection points for ships at berths there.
How significant would the carbon reduction from present levels be if there were complete electrification with renewable energy at the world’s 10 biggest and busiest ports?
Prousalidis: If ports fully electrify using renewable energy, the European Union’s policy could achieve a 100-percent reduction in ship emissions in the port areas. According to the IMO’s approach, which considers the energy mix of each country, it could lead to a 60-percent reduction. This significant emission reduction means lower emissions of CO2, nitrogen oxides, sulfur oxides, and particulate matter, thus reducing shipping’s contribution to global warming and lowering health risks in nearby population centers.
If all goes according to plan, and every country with port operations goes full bore toward electrification, how long do you think it will realistically take to completely decarbonize that aspect of shipping?
Prousalidis: As I said, the European Union is targeting full port electrification by 1 January 2030. However, with around 600 to 700 ports in Europe alone, and the need for grid upgrades, delays are possible. Despite this, we should focus on meeting the 2030 deadline rather than anticipating extensions. This recalls the words of Gemini and Apollo pioneer astronaut, Alan Shepard, when he explained the difference between a test pilot and a normal professional pilot: “Suppose each of them had 10 seconds before crashing. The conventional pilot would think, In 10 seconds I’m going to die. The test pilot would say to himself, I’ve got 10 seconds to save myself and save the craft.” The point is that, in a critical situation like the fight against global warming, we should focus on the time we have to solve the problem, not on what happens after time runs out. But humanity doesn’t have an eject button to press if we don’t make every effort to avoid the detrimental consequences that will come with failure of the “save the planet” projects. -
Sydney’s Tech Super-Cluster Propels Australia’s AI Industry Forward
by BESydney on 24. August 2024. at 12:00
This is a sponsored article brought to you by BESydney.
Australia has experienced a remarkable surge in AI enterprise during the past decade. Significant AI research and commercialization concentrated in Sydney drives the sector’s development nationwide and influences AI trends globally. The city’s cutting-edge AI sector sees academia, business and government converge to foster groundbreaking advancements, positioning Australia as a key player on the international stage.
Sydney – home to half of Australia’s AI companies
Sydney has been pinpointed as one of four urban super-clusters in Australia, featuring the highest number of tech firms and the most substantial research in the country.
The Geography of Australia’s Digital Industries report, commissioned by the National Science Agency, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Tech Council of Australia, found Sydney is home to 119,636 digital professionals and 81 digital technology companies listed on the Australian Stock Exchange with a combined worth of A$52 billion.
AI is infusing all areas of this tech landscape. According to CSIRO, more than 200 active AI companies operate across Greater Sydney, representing almost half of the country’s 544 AI companies.
“Sydney is the capital of AI startups for Australia and this part of Australasia”
—Toby Walsh, UNSW SydneyWith this extensive AI commercialization and collaboration in progress across Sydney, AI startups are flourishing.
“Sydney is the capital of AI startups for Australia and this part of Australasia,” according to Professor Toby Walsh, Scientia Professor of Artificial Intelligence at the Department of Computer Science and Engineering at the University of New South Wales (UNSW Sydney).
He cites robotics, AI in medicine and fintech as three areas where Sydney leads the world in AI innovation.
“As a whole, Australia punches well above its weight in the AI sector,” Professor Walsh says. “We’re easily in the top 10, and by some metrics, we’re in the top five in the world. For a country of just 25 million people, that is quite remarkable.”
Sydney’s universities at the forefront of AI research
A key to Sydney’s success in the sector is the strength of its universities, which are producing outstanding research.
In 2021, the University of Sydney (USYD), the University of New South Wales (UNSW Sydney), and the University of Technology Sydney (UTS) collectively produced more than 1000 peer-reviewed publications in artificial intelligence, contributing significantly to the field’s development.
According to CSIRO, Australia’s research and development sector has higher rates of AI adoption than global averages, with Sydney presenting the highest AI publishing intensity among Australian universities and research institutes.
Professor Aaron Quigley, Science Director and Deputy Director of CSIRO’s Data61 and Head of School in Computer Science and Engineering at UNSW Sydney, says Sydney’s AI prowess is supported by a robust educational pipeline that supplies skilled graduates to a wide range of industries that are rapidly adopting AI technologies.
“Sydney’s AI sector is backed up by the fact that you have such a large educational environment with universities like UTS, USYD and UNSW Sydney,” he says. “They rank in the top five of AI locations in Australia.”
UNSW Sydney is a heavy hitter, with more than 300 researchers applying AI across various critical fields such as hydrogen fuel catalysis, coastal monitoring, safe mining, medical diagnostics, epidemiology and stress management.
UNSW Sydney has more than 300 researchers applying AI across various critical fields such as hydrogen fuel catalysis, coastal monitoring, safe mining, medical diagnostics, epidemiology, and stress management.UNSW
UNSW Sydney’s AI Institute also has the largest concentration of academics working in AI in the country, adds Professor Walsh.
“One of the main reasons the AI Institute exists at UNSW Sydney is to be a front door to industry and government, to help translate the technology out of the laboratory and into practice,” he says.
Likewise, the Sydney Artificial Intelligence Centre at the University of Sydney, the Australian Artificial Intelligence Institute at UTS, and Macquarie University’s Centre for Applied Artificial Intelligence are producing world-leading research in collaboration with industry.
Alongside the universities, the Australian Government’s National AI Centre in Sydney, aims to support and accelerate Australia’s AI industry.
Synergies in Sydney: where tech titans converge
Sydney’s vortex of tech talent has meant exciting connections and collaborations are happening at lightning speed, allowing simultaneous growth of several high-value industries.
The intersection between quantum computing and AI will come into focus with the April 2024 announcement of a new Australian Centre for Quantum Growth at the University of Sydney. This centre will aim to build strategic and lasting relationships that drive innovation to increase the nation’s competitiveness within the field. Funded under the Australian Government’s National Quantum Strategy, it aims to promote the industry and enhance Australia’s global standing.
“There’s nowhere else in the world that you’re going to get a quantum company, a games company, and a cybersecurity company in such close proximity across this super-cluster arc located in Sydney”
—Aaron Quigley, UNSW Sydney“There’s a huge amount of experience in the quantum space in Sydney,” says Professor Quigley. “Then you have a large number of companies and researchers working in cybersecurity, so you have the cybersecurity-AI nexus as well. Then you’ve got a large number of media companies and gaming companies in Sydney, so you’ve got the interconnection between gaming and creative technologies and AI.”
“So it’s a confluence of different industry spaces, and if you come here, you can tap into these different specialisms,” he adds “There’s nowhere else in the world that you’re going to get a quantum company, a games company, and a cybersecurity company in such close proximity across this super-cluster arc located in Sydney.”
A global hub for AI innovation and collaboration
In addition to its research and industry achievements in the AI sector, Sydney is also a leading destination for AI conferences and events. The annual Women in AI Asia Pacific Conference is held in Sydney each year, adding much-needed diversity to the mix.
Additionally, the prestigious International Joint Conference on Artificial Intelligence was held in Sydney in 1991.
Overall, Sydney’s integrated approach to AI development, characterized by strong academic output, supportive government policies, and vibrant commercial activity, firmly establishes it as a leader in the global AI landscape.
To discover more about how Sydney is shaping the future of AI download the latest eBook on Sydney’s Science & Engineering industry at besydney.com.au
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Video Friday: Disney Robot Dance
by Evan Ackerman on 23. August 2024. at 17:30
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.
ICRA@40: 23–26 September 2024, ROTTERDAM, NETHERLANDS
IROS 2024: 14–18 October 2024, ABU DHABI, UAE
ICSR 2024: 23–26 October 2024, ODENSE, DENMARK
Cybathlon 2024: 25–27 October 2024, ZURICH
Enjoy today’s videos!
I think it’s time for us all to admit that some of the most interesting bipedal and humanoid research is being done by Disney.
[ Research Paper from ETH Zurich and Disney Research]
Over the past few months, Unitree G1 robot has been upgraded into a mass production version, with stronger performance, ultimate appearance, and being more in line with mass production requirements.
[ Unitree ]
This robot is from Kinisi Robotics, which was founded by Brennand Pierce, who also founded Bear Robotics. You can’t really tell from this video, but check out the website because the reach this robot has is bonkers.
Kinisi Robotics is on a mission to democratize access to advanced robotics with our latest innovation—a low-cost, dual-arm robot designed for warehouses, factories, and supermarkets. What sets our robot apart is its integration of LLM technology, enabling it to learn from demonstrations and perform complex tasks with minimal setup. Leveraging Brennand’s extensive experience in scaling robotic solutions, we’re able to produce this robot for under $20k, making it a game-changer in the industry.
[ Kinisi Robotics ]
Thanks Bren!
Finally, something that Atlas does that I am also physically capable of doing. In theory.
Okay, never mind. I don’t have those hips.
[ Boston Dynamics ]
Researchers in the Department of Mechanical Engineering at Carnegie Mellon University have created the first legged robot of its size to run, turn, push loads, and climb miniature stairs.
They say it can “run,” but I’m skeptical that there’s a flight phase unless someone sneezes nearby.
[ Carnegie Mellon University ]
The lights are cool and all, but it’s the pulsing soft skin that’s squigging me out.
[ Paper, Robotics Reports Vol.2 ]
Roofing is a difficult and dangerous enough job that it would be great if robots could take it over. It’ll be a challenge though.
[ Renovate Robotics ] via [ TechCrunch ]
Kento Kawaharazuka from JSK Robotics Laboratory at the University of Tokyo wrote in to share this paper, just accepted at RA-L, which (among other things) shows a robot using its flexible hands to identify objects through random finger motion.
[ Paper accepted by IEEE Robotics and Automation Letters ]
Thanks Kento!
It’s one thing to make robots that are reliable, and it’s another to make robots that are reliable and repairable by the end user. I don’t think iRobot gets enough credit for this.
[ iRobot ]
I like competitions where they say, “just relax and forget about the competition and show us what you can do.”
[ MBZIRC Maritime Grand Challenge ]
I kid you not, this used to be my job.
[ RoboHike ]
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Early-Career Telecom Engineer Is a Model of IEEE Engagement
by Willie D. Jones on 22. August 2024. at 18:00
There were early signs that Muhammad Hamza Ihtisham was born to excel in engineering or computer science, but family tradition initially steered him toward a career in medicine. Ihtisham’s mother and other family members were medical professionals. His father is a businessman.
Even though his father had dreamed of having a son in the engineering field, it was assumed that Ihtisham would become a doctor. And he almost did. But when he didn’t pass the medical qualifying exams in high school, he saw it as a sign to switch professions.
Muhammad Hamza Ihtisham
Employer:
Jazz in Lahore, Pakistan
Title:
Network experience specialist in radio
Member grade:
IEEE member
Alma mater:
University of the Punjab
“I asked my parents and my principal for permission to switch my focus from medicine to computer science,” he says. Although the change took effect only three months before he had to take exams, he scored high enough to place third among the computer science students at his school. He has never looked back.
Ihtisham is now a network experience specialist in radio at the largest telecommunications provider in Pakistan: Lahore-based Jazz. Ihtisham monitors, supervises, and troubleshoots nationwide wireless networks and is a team player who implements smart systems and AI-based solutions to optimize network performance.
“We are working with 2G, 3G, and 4G network supervision,” he says, “and we’re also evolving for 5G and optical fiber networks.”
Muhammad Hamza Ihtisham chats with 2018 IEEE President Jim Jefferies.Muhammad Hamza Ihtisham
Early evidence of STEM affinity
Ihtisham didn’t need much encouragement to become an engineer, he says, adding that he always has wanted to work with technology.
When he was young, his father bought him a computer with a Pentium II CPU “at a time when there were very few computers in my town,” he says.
Ihtisham’s curiosity led him to dismantle and explore its components, fostering a deeper interest in technology.
“I destroyed many motherboards and processors when I pulled them out of the computer to see how they worked,” he says. It was part of his innate tendency to get to the bottom of how things worked. “I still have that spark in me, that inner child who wants to open things up and investigate how they work.”
He earned a bachelor’s degree in electrical engineering with a specialization in telecommunications from the University of the Punjab in 2018. He returned to pursue a master’s degree in industrial engineering and management—which he received in 2022.
Thanks to his IEEE connections, Ihtisham secured his position at Jazz even before getting his bachelor’s degree. As the university’s IEEE student branch chair, Ihtisham invited an engineer from Jazz to speak to students on campus. When Ihtisham later showed up at the company for a job interview, that same engineer was the department head and immediately recognized him.
Since his college days, Ihtisham has poured time and energy into giving back to the profession through participation in IEEE. He founded his school’s student chapter and today serves as chair of the IEEE Lahore Section’s Young Professionals group. He is also the deputy lead of the global technical and operation committee of the IEEE Young Professionals mentoring program, which connects experts with mentees to help them learn and further their career.
Active IEEE student leader
Ihtisham entered college thinking he would become a computer scientist, but before long he became convinced that his true passion lay in engineering. Noticing a gap in student activities within the school’s EE department, he joined IEEE in his third semester.
Although the university was more than 150 years old, electrical engineering was a relatively new course of study there.
“My graduating class had only the 10th cohort of graduates to earn that degree from the university,” he says.
As founder of the school’s IEEE student branch, Ihtisham set about adding activities and opportunities for would-be engineers that he felt were missing. He was the branch’s first chair, organizing activities, boosting membership, and overseeing initiatives that impacted his university and the wider IEEE Lahore Section. He was then appointed a student representative for the section.
“That was a turning point for me,” he says.
He originally started volunteering with IEEE for a pragmatic purpose that served the entire engineering student body, he says, but as he settled into his new leadership roles, volunteering became a source of personal fulfillment and development.
“When I started my IEEE journey, I was not prepared. But I worked on my leadership, my behavior, and improving my soft skills. So, you could say my involvement with IEEE has transformed my personality and served as leadership training.”
For his efforts, he has been recognized with several awards including the IEEE Lahore Section’s 2018 Outstanding Volunteer for organizing student activities and conferences.
“When I started my IEEE journey, I was not well groomed,” Ihistham says. “But I worked on my leadership, my behavior, and improving my soft skills. So, you could say my involvement with IEEE has transformed my personality and served as leadership training.”
The communications and negotiating skills he picked up by networking with IEEE members across the globe have benefited him at Jazz, he says.
His dedication to IEEE didn’t end with his student years. Today his roles involve mentoring, networking, and leading initiatives to foster growth and collaboration in the engineering community.
Now his leadership skills help him manage and motivate other volunteers and mentor engineering students. He received the 2021 IEEE MGA Young Professionals Achievement Award for organizing YP activities and the 2021 IEEE IAS Young Member Service Award for virtually engaging IEEE Industry Applications Society members during the COVID-19 pandemic.
Advice for aspiring engineers
To students considering a career in electrical engineering, Ihtisham emphasizes the importance of finding the right mentors and embracing open-source collaboration. He advises discussing ideas with experts to gain valuable insights and foster innovative thinking.
His success story underscores the value of mentorship, continuous learning, and community engagement. While he was in graduate school working toward his master’s degree, he began doing research to develop an effective and reliable brain-computer interface. He talked with the medical professionals in his family for information about how the brain works but then found himself at an impasse because there were not enough datasets in Pakistan for training his machine-learning software.
He reached out to the IEEE community and found a mentor for the project at the University of New South Wales in Sydney. Their collaboration was fruitful enough that Ihtisham was invited to present a TEDx talk on what he had learned about addiction and neurofeedback.
Based on that project, he took home third prize in the IEEE IAS Chapters and Membership Department Zucker Undergraduate Student Design Contest in 2019.
Ihtisham’s journey with IEEE exemplifies the impact of dedication, mentorship, and continued learning on building an interesting and successful engineering career.
“My success is having an impact on my younger cousins,” he says. “If they want to pursue a career in engineering or another STEM field, they have someone in the family who can guide them.”
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Top Programming Languages Methodology 2024
by Stephen Cass on 22. August 2024. at 15:04
In our goal of trying to estimate a programming language’s popularity, we realized that no one can look over the shoulder of every person writing code, whether that be a child writing a Java script for a personal Minecraft server, a mobile app developer hoping to hit it big, or an aerospace engineer writing mission-critical code for a voyage to Mars. Our Top Programming Languages interactive tries to tackle the problem of estimating a language’s popularity by looking for proxy signals.
We do this by constructing measures of popularity from a variety of data sources that we believe are good proxies for active interest for each programming language. In total, we identify 63 programming languages. We then weight each data source to create an overall index of popularity, excluding some of the lowest scorers. Below, we describe the sources of data we use to get the measures, and the weighting scheme we use to produce the overall indices.
By popularity, we mean we are trying to rank languages that are in active use. We look at three different aspects of popularity: languages in active use among typical IEEE members and working software engineers (the “Spectrum” ranking), languages that are in demand by employers (the “Jobs” ranking), and languages that are in the zeitgeist (the “Trending” ranking).
We gauged the popularity of languages using the following sources for a total of eight metrics (see below). We gathered the information for all metrics in July—August 2024. The data were gathered manually to avoid results being biased due to API changes or terminations and because many of the programming language’s names (C++, Scheme) collided with common terms found in research papers and job ads or were difficult for a search engine to parse. When a large number of search results made it impractical to resolve ambiguities by examining all of the results individually, we used a sample of each data source, and determined the relevant sample size based on estimating the true mean with 95 percent confidence. Not all data sources contain information for each programming language and we interpret this information as the programming language having “no hits” (that is, not being popular).
The results from each metric are normalized to produce a relative popularity score between 0 and 1. Then the individual metrics are multiplied by a weight factor, combined, and the result renormalized to produce an aggregate popularity score.
In aggregating metrics, we hope to compensate for statistical quirks that might distort a language’s popularity score in any particular source of data. Varying the weight factors allows us to create the different results for the Spectrum, Jobs, and Trending rankings. We fully acknowledge that, while these weights are subjective, they are based on our understanding of the sources and our prior coverage of software topics. Varying the weight factors allows us to emphasize different types of popularity and produce the different rankings. We then combined each weighted data source for each program and then renormalized the resulting frequency to produce an aggregate popularity score.
The Top Programming Languages was originally created by data journalist Nick Diakopoulos. Our statistical methodology advisor is Hilary Wething. Research assistance was provided by Elizabeth Wood. Rankings are computed using R.
Google
Google is the leading search engine in the world, making it an ideal fit for estimating language popularity. We measured the number of hits for each language by searching on the template, “X programming language” (with quotation marks) and manually recorded the number of results that were returned by the search. We took the measurement in July 2024. We like this measure because it indicates the volume of online information resources about each programming language.
Stack Overflow
Stack Overflow is a popular site where programmers can ask questions about coding. We recorded the number of questions tagged to each program within the last week prior to our search (August 2024). For the Mathematica/Wolfram language, we relied on the sister “Stack” for the Mathematica platform and tallied the number of programming-related questions asked in the past week. These data were gathered manually. This measure indicates what programming languages are currently trending.
IEEE Xplore Digital Library
IEEE maintains a digital library with millions of conference and journal articles covering a wide array of scientific and engineering disciplines. We searched for articles that mention each of the languages in the template “X programming” for the years 2023 and 2024, because this is the smallest timeframe for which we could access articles. For search results that returned thousands of articles, we identified the correct sample size for a 95 percent confidence interval (usually a little over 300) and pulled that number of articles. For each language we sampled, we identified the share of articles that utilize the programming language and then multiplied the total number of articles by this share to tally the likely total number of articles that reference a given programming language. We conducted this search in July 2024. This metric captures the prevalence of the different programming languages as used and referenced in engineering scholarship.
IEEE Job Site
We measured the demand for different programming languages in job postings on the IEEE Job Site. For search results that returned thousands of listings, we identified the correct sample size for a 95 percent confidence interval (usually around 300 results) and pulled that number of job listings to manually examine. For each language we sampled, we identified the share of listings that utilize the programming language and then multiplied the total number of job listings by this share to tally the likely total number of job listings that reference a given programming language. Additionally, because some of the languages we track could be ambiguous in plain text—such as lD, Go, J, Ada, and R—we searched for job postings with those words in the job description and then manually examined the results, again sampling entries if the number of results was large. The search was conducted in July 2024. We like the IEEE Job Site for its large number of non-U.S. listings, making it an ideal to measure global popularity.
CareerBuilder
We measured the demand for different programming languages on the CareerBuilder job site. We searched for “Developer” jobs offered within the United States, as this is the most popular job title for programmers. We sampled 400 job ads and manually examined them to identify which languages employers mentioned in the postings. The search was conducted in July 2024. We like the career builder site to identify the popularity of programmer jobs in the United States.
GitHub
GitHub is a public repository for many volunteer-driven open-source software projects. We used data gathered by GitHut 2.0, which measures the top 50 languages used by the number of repositories tagged with that language and draws from GitHub’s public API. We use two metrics from GitHub: repositories that have been “starred” by users to reflect long-term interests, and the number of pull requests to indicate current activity. The data cover the second quarter of 2024. These measures indicate what languages coders choose to work in when they have a personal choice.
Trinity College Dublin Library
The library of Trinity College Dublin is one of six legal deposit libraries in Ireland and the United Kingdom. A copy must be deposited with the library of any book published or distributed in Ireland, and on request any U.K. publisher or distributor must also deposit a book. We searched for all books published in the year to date that had their subject matter categorized as computer programming and totaled the number of returns. The search was conducted in June 2024. We like this library collection because it represents a large and categorized sample of works, primarily in the English language.
Discord
Discord is popular chat-room platform where many programmers exchange information. We counted the number of tags that correspond to each language. In the case of languages that could also be names of nonprogramming topics, (many nonprogramming-related topics also have dedicated Discord servers; for example, “Julia” could refer to the programming language or the Sesame Street puppet), results were manually examined. Disboard was searched in August 2024. Disboard lists many public discord servers and many young coders use the site, contributing a different demographic of coders.
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The Top Programming Languages 2024
by Stephen Cass on 22. August 2024. at 15:03
Welcome to IEEE Spectrum’s 11th annual rankings of the most popular programming languages. As always, we combine multiple metrics from different sources to create three meta rankings. The “Spectrum” ranking is weighted towards the profile of the typical IEEE member, the “Trending” ranking seeks to spot languages that are in the zeitgeist, and the “Jobs” ranking measures what employers are looking for.
You can find a full breakdown of our methodology here, but let’s jump into our results. At the top, Python continues to cement its overall dominance, buoyed by things like popular libraries for hot fields such as AI as well as its pedagogical prominence. (For most students today, if they learn one programming language in school, it’s Python.) Python’s pretty popular with employers too, although there its lead over other general purpose languages is not as large and, like last year, it plays second fiddle to the database query language SQL, which employers like to see paired with another language. SQL popularity with employers is a natural extension of today’s emphasis on networked and cloud-based system architectures, where databases become the natural repository for all the bytes a program’s logic is chewing on.
Top Programming Languages 2024 is brought to you by the IEEE Computer Society. Get connected with the world’s largest community empowering computer science and engineering professionals.
Stalwarts like Java, Javascript, and C++ also retain high rankings, but it’s what’s going on a little further down that’s particularly interesting. Typescript—a superset of Javascript—moves up several places on all the rankings, especially for Jobs, where it climbs to fourth place, versus 11th last year. Typescript’s primary differentiator over Javascript is that it enforces static typing of variables, where the type of a variable—integer, floating point, text, and so forth—must be declared before it can be used. This allows for more error checking when Typescript programs are compiled to Javascript, and the increase in reliability has proven appealing.
Another climber is Rust, a language aimed at creating system software, like C or C++. But unlike those two languages, Rust is “memory safe”, meaning it uses a variety of techniques to ensure programs can’t write to locations in memory that they are not supposed to. Such errors are a major source of security vulnerabilities. Rust’s profile has been rising sharply, boosted by things like a February cybersecurity report from the White House calling for memory safe languages to replace C and and C++. Indeed, C’s popularity appears to be on the wane, falling from fourth to ninth place on the Spectrum ranking and from 7th to 13th on the Jobs ranking.
Two languages have entered the rankings for the first time: Apex and Solidity. Apex is designed for building business applications that use a Salesforce server as a back end, and Solidity is designed for creating smart contracts on the Ethereum blockchain.
This year also saw several languages drop out of the rankings. This doesn’t mean a language is completely dead, it just means that these languages’ signal is too weak to allow them to be meaningfully ranked. Languages that dropped out included Forth, a personal favorite of mine that’s still popular with folks building 8-bit retro systems because of its tiny footprint. A weak signal is also why we haven’t included some buzzy languages such as Zig, although those proficient in it can apparently command some high salaries.
As these other languages come and go from the rankings, I have to give the shout out to the immortals, Fortran and Cobol. Although they are around 65 years old, you can still find employers looking for programmers in both. For Fortran, this tends to be for a select group of people who are also comfortable with high-energy physics, especially the kind of high-energy physics that goes boom (and with the security clearances to match). Cobol is more broadly in demand, as many government and financial systems still rely on decades-old infrastructure—and the recent paralyzing impact of the Cloudstrike/Microsoft Windows outage incident probably hasn’t done much to encourage their replacement!
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Meet Boardwalk Robotics’ Addition to the Humanoid Workforce
by Evan Ackerman on 22. August 2024. at 12:00
Boardwalk Robotics is announcing its entry into the increasingly crowded commercial humanoid(ish) space with Alex, a “workforce transformation” humanoid upper torso designed to work in manufacturing, logistics, and maintenance.
Before we get into Alex, let me take just a minute here to straighten out how Boardwalk Robotics is related to IHMC, the Institute for Human Machine Cognition in Pensacola, Fla. IHMC is, I think it’s fair to say, somewhat legendary when it comes to bipedal robotics—its DARPA Robotics Challenge team took second place in the final event (using a Boston Dynamics DRC Atlas), and when NASA needed someone to teach the agency’s Valkyrie humanoid to walk better, they sent it to IHMC.
Boardwalk, which was founded in 2017, has been a commercial partner with IHMC when it comes to the actual building of robots. The most visible example of this to date has been IHMC’s Nadia humanoid, a research platform which Boardwalk collaborated on and built. There’s obviously a lot of crossover between IHMC and Boardwalk in terms of institutional knowledge and experience, but Alex is a commercial robot developed entirely in-house by Boardwalk.
“We’ve used Nadia to learn a lot in the realm of dynamic locomotion research, and we’re taking all that and sticking it into a manipulation platform that’s ready for commercial work,” says Brandon Shrewsbury, Boardwalk Robotics’ CTO. “With Alex, we’re focusing on the manipulation side first, getting that well established. And then picking the mobility to match the task.”
The first thing you’ll notice about Alex is that it doesn’t have legs, at least for now. Boardwalk’s theory is that for a humanoid to be practical and cost effective in the near term, legs aren’t necessary, and that there are many tasks that offer a good return on investment where a stationary pedestal or a glorified autonomous mobile robotic base would be totally fine.
“There are going to be some problem sets that require legs, but there are many problem sets that don’t,” says Robert Griffin, a technical advisor at Boardwalk. “And there aren’t very many problem sets that don’t require halfway decent manipulation capabilities. So if we can design the manipulation well from the beginning, then we won’t have to depend on legs for making a robot that’s functionally useful.”
It certainly helps that Boardwalk isn’t at all worried about developing legs: “Every time we bring up a new humanoid, it’s something like twice as fast as the previous time,” Griffin says. This will be the eighth humanoid that IHMC has been involved in bringing up—I’d tell you more about all eight of those humanoids, but some of them are so secret that even I don’t know anything about them. Legs are definitely on the road map, but they’re not done yet, and IHMC will have a hand in their development to speed things along: It turns out that already having access to a functional (top of the line, really) locomotion stack is a big head start.
Alex’s actuators are all designed in-house, and the next version will feature new grippers that allow for quicker tool changes.Boardwalk Robotics
While the humanoid space is wide open right now and competition isn’t really an issue, looking ahead, Boardwalk sees safety as one of its primary differentiators since it’s not starting out with legs, says Shrewsbury. “For a full humanoid, there’s no way to make that completely safe. If it falls, it’s going to face-plant.” By keeping Alex on a stable base, it can work closer to humans and potentially move its arms much faster while also preserving a dynamic safety zone.
Alex is available for researchers to purchase immediately.Boardwalk Robotics
Despite its upbringing in research, Alex is not intended to be a research robot. You can buy it for research purposes, if you want, but Boardwalk will be selling Alex as a commercial robot. At the moment, Boardwalk is conducting pilot programs with Alex where they’re working in partnership with select customers, with the eventual goal of transitioning to a service model. The first few sectors that Boardwalk is targeting include logistics (because, of course) and food processing, although as Boardwalk CEO Michael Morin tells us, one of the very first pilots is (appropriately enough) in aviation.
Morin, who helped to commercialize Barrett Technologies’ WAM Arm before spending some time at Vicarious Surgical as that company went public, joined Boardwalk to help them turn good engineering into a good product, which is arguably the hardest part of making useful robots (besides all the other hardest parts). “A lot of these companies are just learning about humanoids for the first time,” says Morin. “That makes the customer journey longer. But we’re putting in the effort to educate them on how this could be implemented in their world.”
If you want an Alex of your very own, Boardwalk is currently selecting commercial partners for a few more pilots. And for researchers, the robot is available right now.
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IBM’s Big Bet on the Quantum-Centric Supercomputer
by Jay Gambetta on 21. August 2024. at 13:00
Back in June 2022, Oak Ridge National Laboratory debuted Frontier—the world’s most powerful supercomputer. Frontier can perform a billion billion calculations per second. And yet there are computational problems that Frontier may never be able to solve in a reasonable amount of time.
Some of these problems are as simple as factoring a large number into primes. Others are among the most important facing Earth today, like quickly modeling complex molecules for drugs to treat emerging diseases, and developing more efficient materials for carbon capture or batteries.
However, in the next decade, we expect a new form of supercomputing to emerge unlike anything prior. Not only could it potentially tackle these problems, but we hope it’ll do so with a fraction of the cost, footprint, time, and energy. This new supercomputing paradigm will incorporate an entirely new computing architecture, one that mirrors the strange behavior of matter at the atomic level—quantum computing.
For decades, quantum computers have struggled to reach commercial viability. The quantum behaviors that power these computers are extremely sensitive to environmental noise, and difficult to scale to large enough machines to do useful calculations. But several key advances have been made in the last decade, with improvements in hardware as well as theoretical advances in how to handle noise. These advances have allowed quantum computers to finally reach a performance level where their classical counterparts are struggling to keep up, at least for some specific calculations.
For the first time, we here at IBM can see a path toward useful quantum computers, and we can begin imagining what the future of computing will look like. We don’t expect quantum computing to replace classical computing. Instead, quantum computers and classical computers will work together to run computations beyond what’s possible on either alone. Several supercomputer facilities around the world are already planning to incorporate quantum-computing hardware into their systems, including Germany’s Jupiter, Japan’s Fugaku, and Poland’s PSNC. While it has previously been called hybrid quantum-classical computing, and may go by other names, we call this vision quantum-centric supercomputing.
A Tale of Bits and Qubits
At the heart of our vision for a quantum-centric supercomputer is the quantum hardware, which we call a quantum processing unit (QPU). The power of the QPU to perform better than classical processing units in certain tasks comes from an operating principle that’s fundamentally different, one rooted in the physics of quantum mechanics.
In the standard or “classical” model of computation, we can reduce all information to strings of binary digits, bits for short, which can take on values of either 0 or 1. We can process that information using simple logic gates, like AND, OR, NOT, and NAND, which act on one or two bits at a time. The “state” of a classical computer is determined by the states of all its bits. So, if you have N bits, then the computer can be in just one of 2N states.
But a quantum computer has access to a much richer repertoire of states during computation. A quantum computer also has bits. But instead of just 0 and 1, its quantum bits— qubits—via a quantum property known as superposition, represent 0, 1, or a linear combination of both. While a digital computer can be in just one of those 2N states, a quantum computer can be in many logical states at once during the computation. And the superpositions the different qubits are in can be correlated with one another in a fundamental way, thanks to another quantum property known as entanglement. At the end of the computation, the qubit assumes just one state, chosen based on probabilities generated during the running of the quantum algorithm.
It’s not obvious how this computing paradigm can outperform the classical one. But in 1994, Peter Shor, a mathematician at MIT, discovered an algorithm that, using the quantum-computing paradigm, could divide large numbers into their prime factors exponentially faster than the best classical algorithm. Two years later, Lov Grover discovered a quantum algorithm that could find a particular entry in a database much faster than a classical one could.
Perhaps most importantly, since quantum computers follow the laws of quantum mechanics, they are the right tool for simulating the fundamentally quantum phenomena of our world, such as molecular interactions for drug discovery or materials design.
The Quantum-Centric Supercomputer’s Center
Before we can build a quantum-centric supercomputer, we have to make sure it’s capable of doing something useful. Building a capable enough QPU relies on constructing hardware that can re-create counterintuitive quantum behaviors.
Here at IBM, the basic building block of a quantum computation—the qubit—is made out of superconducting components. Each physical qubit consists of two superconducting plates, which act as a capacitor, wired to components called Josephson junctions, which act as a special lossless, nonlinear inductor.
The current flowing across Josephson junctions is quantized—fixed to discrete values. The Josephson junctions ensure that only two of those values (or their superpositions) are realistically accessible. The qubit is encoded in two current levels, one representing a 0, the other a 1. But, as mentioned, the qubit can also exist in a superposition of the 0 and 1 states.
Because superconductors need frigid temperatures to maintain superconductivity, the qubits and some of their control circuitry are held inside a specialty liquid-helium fridge called a dilution refrigerator.
We change the qubit states and couple qubits together with quantum instructions, commonly known as gates. These are a series of specially crafted microwave waveforms. A QPU includes all of the hardware responsible for accepting a set of quantum instructions—called a quantum circuit—and returning a single output represented by a binary string. The QPU includes the qubits plus components that amplify signals, the control electronics, and the classical computation required for tasks such as holding the instructions in memory, accumulating and separating signals from noise, and creating single binary outputs. We etch components like qubits, resonators for readouts, output filters, and quantum buses into a superconducting layer deposited on top of a silicon chip.
But it’s a challenge trying to control qubits at the supersensitive quantum level. External noise, noise from the electronics, and cross talk between control signals for different qubits all destroy the fragile quantum properties of the qubits. Controlling these noise sources has been key in reaching the point where we can envision useful quantum-centric supercomputers.
Getting the Quantum Stuff up to Snuff
No one has yet conclusively demonstrated quantum advantage—that is, a quantum computer that outperforms the best classical one on a real-world relevant task. Demonstrating true quantum advantage would herald a new era of computing, where previously intractable tasks would now be within reach.
Before we can approach this grandiose goal, we have to set our sights a bit lower, to a target we call quantum utility. Quantum utility is the ability of quantum hardware to outperform brute-force classical calculations of a quantum circuit. In other words, it’s the point where quantum hardware is better at doing quantum computations than a traditional computer is.
This may sound underwhelming, but it is a necessary stepping-stone on the way to quantum advantage. In recent years, the quantum community has finally reached this threshold. Demonstrating quantum utility of our QPU, which we did in 2023, has convinced us that our quantum hardware is advanced enough to merit being built into a quantum-centric supercomputer. Achieving this milestone has taken a combination of advances, including both hardware and algorithmic improvements.
Since 2019, we’ve been incorporating advances in semiconductor fabrication to introduce 3D integration to our chips. This gave us access to qubits from a controller chip placed below the qubit plane to reduce the wiring on the chip, a potential source of noise. We also introduced readout multiplexing, which allows us to access the information from several qubits with a single wire, drastically reducing the amount of hardware we have to put in the dilution refrigerator.
In 2023, we implemented a new way to perform quantum gates—the steps of a program that change the value of the qubits—on our hardware, using components called tunable couplers. Previously, we prevented cross talk by fabricating the qubits that respond to different frequencies so that they wouldn’t react to microwave pulses meant for other qubits. But this made it too difficult for the qubits to perform the essential task of talking to one another, and it also made the processors slow. With tunable couplers, we don’t need the frequency-specific fabrication. Instead, we introduced a sort of “on-off” switch, using magnetic fields to decide whether or not a qubit should talk to another qubit. The result: We virtually eliminated cross-talk errors between qubits, allowing us to run much faster, more reliable gates.
As our hardware improved, we also demonstrated that we could deal with some noise using an error mitigation algorithm. Error mitigation can be done in many ways. In our case, we run quantum programs, analyze how the noise in our system changes the program outputs, and then create a noise model. Then we can use classical computing and our noise model to recover what a noise-free result would look like. The surrounding hardware and software of our quantum computer therefore includes classical computing capable of performing error mitigation, suppression, and eventually, error correction.
Alongside ever-improving hardware advances, we teamed up with the University of California, Berkeley, to demonstrate in 2023 that a quantum computer running our 127-qubit quantum chip, Eagle, could run circuits beyond the ability of brute-force classical simulation—that is, methods where the classical computer exactly simulates the quantum computer in order to run the circuit, reaching quantum utility. And we did so for a real condensed-matter physics problem—namely, finding the value of a property called magnetization for a system of simplified atoms with a structure that looked like the layout of our processors’ qubits.
Left: A quantum processing unit is more than just a chip. It includes the interconnects, amplifiers, and signal filtering. It also requires the classical hardware, including the room-temperature classical computers needed to receive and apply instructions and return outputs. Right: At the heart of an IBM quantum computer is a multilayer semiconductor chip etched with superconducting circuits. These circuits comprise the qubits used to perform calculations. Chips are divided into a layer with the qubits, a layer with resonators for readout, and multiple layers of wiring for input and output.
Error Correction to the Rescue
We were able to demonstrate the ability of our quantum hardware outperforming brute-force classical simulation without leveraging the most powerful area of quantum-computing theory: quantum error correction.
Unlike error mitigation, which deals with noise after a computation, quantum error correction can remove noise as it arises during the process. And it works for a more general kind of noise; you don’t need to figure out a specific noise model first. Plus, while error mitigation is limited in its ability to scale as the complexity of quantum circuits grows, error correction will continue to work at large scales.
Error Correction
But quantum error correction comes at a huge cost: It requires more qubits, more connectivity, and more gates. For every qubit you want to compute with, you may need many more to enable error correction. Recent advances in improving hardware and finding better error-correcting codes have allowed us to envision an error-corrected supercomputer that can make those costs worthwhile.
Quantum error-correcting schemes are a bit more involved than error correction in traditional binary computers. To work at all, these quantum schemes require that the hardware error rate is below a certain threshold. Since quantum error correction’s inception, theorists have devised new codes with more relaxed thresholds, while quantum-computer engineers have developed better-performing systems. But there hasn’t yet been a quantum computer capable of using error correction to perform large-scale calculations.
Meanwhile, error-correction theory has continued to advance. One promising finding by Moscow State University physicists Pavel Panteleev and Gleb Kalachev inspired us to pursue a new kind of error-correcting code for our systems. Their 2021 paper demonstrated the theoretical existence of “good codes,” codes where the number of extra qubits required to perform error correction scales more favorably.
This led to an explosion of research into a family of codes called quantum low-density parity check codes, or qLDPC codes. Earlier this year, our team published a qLDPC code with an error threshold high enough that we could conceivably implement it on near-term quantum computers; the amount of required connectivity between qubits was only slightly beyond what our hardware already supplies. This code would need only a tenth the number of qubits as previous methods to achieve error correction at the same level.
These theoretical developments allow us to envision an error-corrected quantum computer at experimentally accessible scales, provided we can connect enough quantum processing power together, and leverage classical computing as much as possible.
Hybrid Classical-Quantum Computers for the Win
To take advantage of error correction, and to reach large enough scales to solve human-relevant problems with quantum computers, we need to build larger QPUs or connect multiple QPUs together. We also need to incorporate classical computing with the quantum system.
Quantum-centric supercomputers will include thousands of error-corrected qubits to unlock the full power of quantum computers. Here’s how we’ll get there.
2024
Heron
→ 156 qubits
→ 5K gates before errors set in
2025
Flamingo
→ Introduce l-couplers between chips
→ Connect 7 chips for 7 x 156 = 1,092 qubits
→ 5K gates before errors set in
2027
Flamingo
→ l-couplers between chips
→ 7 x 156 = 1,092 qubits
→ Improved hardware and error mitigation
→ 10K gates before errors set in
2029
Starling
→ 200 qubits
→ l-, m-, and c-couplers combined
→ Error correction
→ 100M gates
2030
BlueJay
→ 2,000 qubits
→ Error correction
→ 1B gates
Last year, we released a machine we call the IBM Quantum System Two, which we can use to start prototyping error mitigation and error correction in a scalable quantum computing system. System Two relies on larger, modular cryostats, allowing us to place multiple quantum processors into a single refrigerator with short-range interconnects, and then combine multiple fridges into a bigger system, kind of like adding more racks to a traditional supercomputer.
Along with the System Two release, we also detailed a 10-year plan for realizing our vision. Much of the early hardware work on that road map has to do with interconnects. We’re still developing the interconnects required to connect quantum chips into larger chips like Lego blocks, which we call m-couplers. We’re also developing interconnects to transfer quantum information between more distant chips, called l-couplers. We hope to prototype both m- and l-couplers by the end of this year. We’re also developing on-chip couplers that link qubits on the same chip that are more distant than their nearest neighbors—a requirement of our newly developed error-correction code. We plan to deliver this c-coupler by the end of 2026. In the meantime, we’ll be improving error mitigation so that by 2028, we can run a quantum program across seven parallel quantum chips, each chip capable of performing up to 15,000 accurate gates before the errors set in, on 156 qubits.
We’re also continuing to advance error correction. Our theorists are always looking for codes that require fewer extra qubits for more error-correcting power and allow for higher error thresholds. We must also determine the best way to run operations on information that’s encoded into the error-correcting code, and then decode that information in real time. We hope to demonstrate those by the end of 2028. That way, in 2029, we can debut our first quantum computer incorporating both error mitigation and error correction that can run up to 100 million gates until the errors take hold, on 200 qubits. Further advances in error correction will allow us to run a billion gates on 2,000 qubits by 2033.
Knitting Together a Quantum-Centric Supercomputer
The ability to mitigate and correct errors removes a major roadblock in the way of full-scale quantum computing. But we still don’t think it’ll be enough to tackle the largest, most valuable problems. For that reason, we’ve also introduced a new way of running algorithms, where multiple quantum circuits and distributed classical computing are woven together into a quantum-centric supercomputer.
Many envision the “quantum computer” as a single QPU, working on its own to run programs with billions of operations on millions of physical qubits. Instead, we envision computers incorporating multiple QPUs, running quantum circuits in parallel with distributed classical computers.
Combining the strengths of quantum and classical
Quantum-centric supercomputing leverages quantum and classical resources in parallelized workloads to run computations larger than what was possible before. A quantum-centric supercomputer is a system optimized to orchestrate work across the quantum computers and advanced classical compute clusters in the same data center.
Recent work has demonstrated techniques that let us run quantum circuits much more efficiently by incorporating classical computing with quantum processing. These techniques, called circuit knitting, break down a single quantum-computing problem into multiple quantum-computing problems and then run them in parallel on quantum processors. And then a combination of quantum and classical computers knit the circuit results together for the final answer.
Another technique uses the classical computer to run all but the core, intrinsically quantum part of the calculation. It is this last vision that we believe will realize quantum advantage first.
Therefore, a quantum computer doesn’t just include one quantum processor, its control electronics, and its dilution refrigerator—it also includes the classical processing required to perform error correction, and error mitigation.
We haven’t realized a fully integrated quantum-centric supercomputer yet. But we’re laying the groundwork with System Two, and Qiskit, our full-stack quantum-computing software for running large quantum workloads. We are building middleware capable of managing circuit knitting, and of provisioning the appropriate computing resources when and where they’re required. The next step is to mature our hardware and software infrastructure so that quantum and classical can extend one another to do things beyond the capabilities of either.
Today’s quantum computers are now scientific tools capable of running programs beyond the brute-force ability of classical simulation, at least when simulating certain quantum systems. But we must continue improving both our quantum and classical infrastructure so that, combined, it’s capable of speeding up solutions for problems relevant to humanity. With that in mind, we hope that the broader computing community will continue researching new algorithms incorporating circuit knitting, parallelized quantum circuits, and error mitigation in order to find use cases that can benefit from quantum in the near term.
And we look forward to a day when the Top 500 list of most powerful supercomputers will include machines that have quantum processors at their hearts.
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Ansys SimAI Software Predicts Fully Transient Vehicle Crash Outcomes
by Ansys on 20. August 2024. at 18:09
The Ansys SimAI™ cloud-enabled generative artificial intelligence (AI) platform combines the predictive accuracy of Ansys simulation with the speed of generative AI. Because of the software’s versatile underlying neural networks, it can extend to many types of simulation, including structural applications.
This white paper shows how the SimAI cloud-based software applies to highly nonlinear, transient structural simulations, such as automobile crashes, and includes:- Vehicle kinematics and deformation
- Forces acting upon the vehicle
- How it interacts with its environment
- How understanding the changing and rapid sequence of events helps predict outcomes
These simulations can reduce the potential for occupant injuries and the severity of vehicle damage and help understand the crash’s overall dynamics. Ultimately, this leads to safer automotive design.
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IEEE Introduces Digital Certificates Documenting Volunteer Roles
by Joanna Goodrich on 20. August 2024. at 18:00
IEEE Collabratec has made it easier for volunteers to display their IEEE positions. The online networking platform released a new benefit this year for its users: digital certificates for IEEE volunteering. They reflect contributions made to the organization, such as leading a committee or organizing an event.
Members can download the certificates and add them to their LinkedIn profile or résumé. Volunteers also can print their certificates to frame and display in their office.
Each individualized document includes the person’s name, the position they’ve held, and the years served. Every position held has its own certificate. The member’s list of roles is updated annually.
The feature is a result of a top recommendation to improve volunteer recognition made by delegates at the 2023 IEEE Sections Congress, according to Deepak Mathur. The senior member is vice president of IEEE Member and Geographic Activities. The new feature “respects the time and effort of our volunteers and is a testament to the power and versatility of the Collabratec platform,” Mathur said in an announcement.
Members can download their certificates by selecting the Certificates tab on their Collabratec page and scrolling to each of their positions.
To learn more about IEEE Collabratec, check out the user guide, FAQs, and users’ forum.
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Nasir Ahmed: An Unsung Hero of Digital Media
by Willie D. Jones on 19. August 2024. at 12:00
Stop for a second and think about the Internet without digital images or video. There would be no faces on Facebook. Instagram and TikTok probably wouldn’t exist. Those Zoom meetings that took the place of in-person gatherings for school or work during the height of the COVID-19 pandemic? Not an option.
Digital audio’s place in our Internet-connected world is just as important as still images and video. It has changed the music business—from production to distribution to the way fans buy, collect, and store their favorite songs.
What do those millions of profiles on LinkedIn, dating apps, and social media platforms (and the inexhaustible selection of music available for download online) have in common? They rely on a compression algorithm called the discrete cosine transform, or DCT, which played a major role in allowing digital files to be transmitted across computer networks.
“DCT has been one of the key components of many past image- and video-coding algorithms for more than three decades,” says Touradj Ebrahimi, a professor at Ecole Polytechnique Fédérale de Lausanne, in Switzerland, who currently serves as chairman of the JPEG standardization committee. “Only a few image-compression standards not using DCT exist today,” he adds.
The Internet applications people use every day but largely take for granted were made possible by scientists and engineers who, for the most part, toiled in anonymity. One such “hidden figure” is Nasir Ahmed, the Indian-American engineer who figured out an elegant way to cut down the size of digital image files without sacrificing their most critical visual details.
Ahmed published his seminal paper about the discrete cosine transform compression algorithm he invented in 1974, a time when the fledgling Internet was exclusively dial-up and text-based. There were no pictures accompanying the words, nor could there have been, because Internet data was transmitted over standard copper telephone landlines, which was a major limitation on speed and bandwidth.
“Only a few image-compression standards not using DCT exist today.” –Touradj Ebrahimi, EPFL
These days, with the benefit of superfast chips and optical-fiber networks, data download speeds for a laptop with a fiber connection reach 1 gigabit per second. So, a music lover can download a 4-minute song to their laptop (or more likely a smartphone) in a second or two. In the dial-up era, when Internet users’ download speeds topped out at 56 kilobits per second (and were usually only half that fast), pulling down the same song from a server would have taken nearly all day. Getting a picture to appear on a computer’s screen was a process akin to watching grass grow.
Ahmed was convinced there had to be a way to cut down the size of digital files and speed up the process. He set off on a quest to represent with ones and zeros what is critical to an image being legible, while tossing aside the bits that are less important. The answer, which built on the earlier work of mathematician and information-theory pioneer Claude Shannon, took a while to come into focus. But because of Ahmed’s determination and unwavering belief in the value of what he was doing, he persevered even after others told him that it was not worth the effort.
Raised to Love Technology
It seemed almost preordained that Ahmed would have a career in one of the STEM fields. Nasir, who was born in Bengaluru, India, in 1940, was raised by his maternal grandparents. Ahmed’s grandfather was an electrical engineer who told him that he had been sent to the United States in 1919 to work at General Electric‘s location in Schenectady, N.Y. He shared tales of his time in the United States with his grandson and encouraged young Nasir to emigrate there. In 1961, after earning a bachelor’s degree in electrical engineering at the University of Visvesvaraya College of Engineering, in Bengaluru, Ahmed did just that, leaving India that fall for graduate school at the University of New Mexico, in Albuquerque. Ahmed earned a master’s degree and a Ph.D. in electrical engineering in 1963 and 1966, respectively.
During his first year in Albuquerque, he met Esther Parente, a graduate student from Argentina. They soon became inseparable and were married while he was working toward his doctorate. Sixty years later, they are still together.
The Seed of an Idea
In 1966, Ahmed, fresh out of grad school with his Ph.D., was hired as a principal research engineer at Honeywell’s newly created computer division. While there, Ahmed was first exposed to Walsh functions, a technique for analyzing digital representations of analog signals. The fast algorithms that could be created based on Walsh functions had many potential applications. Ahmed focused on using these signal-processing and analysis techniques to reduce the file size of a digital image without losing too much of the visual detail in the uncompressed version.
That research focus remained his primary interest when he returned to academia, taking a job as a professor in the electrical and computer engineering department at Kansas State University, in 1968.
Ahmed, like dozens of other researchers around the globe, was obsessed with finding the answer to a single question: How do you create a mathematical formula for deciphering which of the ones and zeros that represent a digital image need to be kept and which can be thrown away? The things he’d learned at Honeywell gave him a framework for understanding the elements of the problem and how to attack it. But the majority of the credit for the eventual breakthrough has to go to Ahmed’s steely determination and willingness to take a gamble on himself.
In 1972, he sought grant funding that would let him afford to spend the months between Kansas State’s spring and fall semesters furthering his ideas. He applied for a U.S. National Science Foundation grant, but was denied. Ahmed recalls the moment: “I had a strong intuition that I could find an efficient way to compress digital signal data. But to my surprise, the reviewers said the idea was too simple, so they rejected the proposal.”
Undaunted, Ahmed and his wife worked to make the salary he earned during the nine-month school year last through the summer so he could focus on his research. Money was tight, the couple recalls, but that moment of financial belt-tightening only seemed to heighten Ahmed’s industriousness. They persevered, and Ahmed’s long days and late nights in the lab eventually yielded the desired result.
DCT Compression Comes Together
Ahmed took a technique for turning the array of image-processing data representing an image’s pixels into a waveform, effectively rendering it as a series of waves with oscillating frequencies, and combined it with cosine functions that were already being used to model phenomena such as light waves, sound waves, and electric current. The result was a long string of numbers with values bounded by 1 and –1. Ahmed realized that by quantizing this string of values and performing a Fourier transformation to break the function into its constituent frequencies, each pixel’s data could be represented in a way that was helpful for deciding what data points must be kept and what could be omitted. Ahmed observed that the lower-frequency waves corresponded to the necessary or “high information” regions of the image, while the higher-frequency waves represented the bits that were less important and could therefore be approximated. The compressed-image files he and his team produced were one-tenth the size of the originals. What’s more, the process could be reversed, and a shrunken data file would yield an image that was sufficiently similar to the original.
After another two years of laborious testing, with he and his two collaborators running computer programs written on decks of data punch cards, the trio published a paper in IEEE Transactions On Computers titled “Discrete Cosine Transform” in January 1974. Though the paper’s publication did not make it immediately clear, the worldwide search for a reliable method of doing the lossy compression that Claude Shannon had postulated in the 1940s was over.
JPEGs, MPEGs, and More
It wasn’t until 1983 that the International Organization for Standardization (ISO) began working on the technology that would allow photo-quality images to accompany text on the screens of computer terminals. To that end, ISO established the Joint Photographic Experts Group, better known by the ubiquitous acronym JPEG. By the time the first JPEG standard was published in 1992, DCT and advances made by a cadre of other researchers had come to be recognized by the group as basic elements of their method for the digital compression and coding of still images. “This is the beauty of standardization, where several dozen bright minds are behind the success of advances such as JPEG,” says Ebrahimi.
And because video can be described as a succession of still images, Ahmed’s technique was also well suited to making video files smaller. DCT was the compression technique of choice when ISO and the international Electrotechnical Commission (IEC) established the Moving Picture Experts Group, or MPEG, for the compression and coding of audio, video, graphics, and genomic data in 1988. When the first MPEG standard was published in 1993, the World Wide Web that now includes Google Maps, dating apps, and e-commerce businesses was just four years old.
The ramping up of computer speeds and network bandwidth during that decade—along with the ability to transmit pictures and video via much smaller files—quickly transformed the Internet before anyone knew that Amazon would eventually let readers judge millions of books by their covers.
Having solved the problem that had monopolized his time and attention for several years, Ahmed resumed his career in academia. In 1993, the year the first MPEG standard went on the books, Ahmed left Kansas State and returned to the University of New Mexico. There he was a presidential professor of electrical and computer engineering until 1989, when he was promoted to chair of the ECE department. Five years after that, he became dean of UNM’s school of engineering. Ahmed held that post for two years until he was named associate provost for research and dean of graduate studies. He stayed in that job until he retired from the university in 2001 and was named professor emeritus.
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Taiwan Reboots Its Solar-Power Fishponds
by Peter Fairley on 19. August 2024. at 12:00
A maze of brackish and freshwater ponds covers Taiwan’s coastal plain, supporting aquaculture operations that produce roughly NT $30 billion (US $920 million) worth of seafood every year. Taiwan’s government is hoping that the more than 400 square kilometers of fishponds can simultaneously produce a second harvest: solar power.
What is aquavoltaics?
That’s the impetus behind the new 42.9-megawatt aquavoltaics facility in the southern city of Tainan. To build it, Taipei-based Hongde Renewable Energy bought 57.6 hectares of abandoned land in Tainan’s fishpond-rich Qigu district, created earthen berms to delineate the two dozen ponds, and installed solar panels along the berms and over six reservoir ponds.
Tony Chang, general manager of the Hongde subsidiary Star Aquaculture, says 18 of the ponds are stocked with mullet (prized for their roe) and shrimp, while milkfish help clean the water in the reservoir ponds. In 2023, the first full year of operation, Chang says his team harvested over 100,000 kilograms of seafood. This August, they began stocking a cavernous indoor facility, also festooned with photovoltaics, to cultivate white-legged shrimp.
A number of other countries have been experimenting with aquavoltaics, including China, Chile, Bangladesh, and Norway, extending the concept to large solar arrays floating on rivers and bays. But nowhere else is the pairing of aquaculture and solar power seen as so crucial to the economy. Taiwan is striving to massively expand renewable generation to sustain its semiconductor fabs, and solar is expected to play a large role. But on this densely populated island—slightly larger than Maryland, smaller than the Netherlands—there’s not a lot of open space to install solar panels. The fishponds are hard to ignore. By the end of 2025, the government is looking to install 4.4 gigawatts of aquavoltaics to help meet its goal of 20 GW of solar generation.
Is Taiwan’s aquavoltaics plan unrealistic?
Meanwhile, though, solar developers are struggling to deliver on Taiwan’s ambitious goals, even as some projections suggest Taiwan will need over eight times more solar by 2050. And aquavoltaics in particular have come under scrutiny from environmental groups. In 2020, for example, reporter Cai Jiashan visited 100 solar plants built on agricultural land, including fishponds, and found dozens of cases where solar developers built more solar capacity than the law intended, or secured permits based on promises of continued farming that weren’t kept.
Star Aquaculture grows milkfish to help clean water for its breeding ponds.HDRenewables
On 7 July 2020, Taiwan’s Ministry of Agriculture responded by restricting solar development on farmland, in what the solar industry called the “Double-Seven Incident.” Many aquavoltaic projects were canceled while others were delayed. The latter included a 10-MW facility in Tainan that Google had announced to great fanfare in 2019 as its first renewable-energy investment in Asia, to supply power for the company’s Taiwan data centers. The array finally started up in 2023, three years behind schedule.
Critics of Taiwan’s renewed aquavoltaic plans thus see the government’s goal as unrealistic. Yuping Chen, executive director of the Taiwan Environment and Planning Association, a Taipei-based nonprofit dedicated to resolving conflicts between solar energy and agriculture, says of aquavoltaics, “It is claimed to be crucial by the government, but it’s impossible to realize.”
How aquavoltaics could revive fishing, boost revenue
Solar developers and government officials who endorse aquavoltaics argue that such projects could revive the island’s traditional fishing community. Taiwan’s fishing villages are aging and shrinking as younger people take city jobs. Climate change has also taken a toll. Severe storms damage fishpond embankments, while extreme heat and rainfall stress the fish.
4.4
Gigawatts of aquavoltaics that Taiwan wants to install by the end of 2025
Solar development could help reverse these trends. Several recent studies examining fishponds in Taiwan found that adding solar improves profitability, providing an opportunity to reinvigorate communities if agrivoltaic investors share their returns. Alan Wu, deputy director of the Green Energy Initiative at Taiwan’s Industrial Technology Research Institute, says the Hsinchu-based lab has opened a research station in Tainan to connect solar and aquaculture firms. ITRI is helping aquavoltaics facilities boost their revenues by figuring out how they can raise “species of high economic value that are normally more difficult to raise,” Wu says.
Such high-value products include the 27,000 pieces of sun-dried mullet roe that Hongde Renewable Energy’s Tainan site produced last year. The new indoor facility, meanwhile, should boost yields of the relatively pricey whiteleg shrimp. Chang expects the indoor harvests to fetch $500,000 to $600,000 annually, compared to $800,000 to $900,000 from the larger outdoor ponds.
The solar roof over the 100,000-liter indoor growth tanks protects the 2.7 million shrimp against weather and bird droppings. Chang says a patent-pending drain mechanically removes waste from each tank, and also sucks out the shrimp when they’re ready for harvest.
Land that Star Aquaculture set aside for wildlife now attracts endangered birds like the black-faced spoonbill [left] and the oriental stork [right].iStock (2)
The company has also set aside 9 percent of the site for wildlife, in response to concerns from conservationists. “Egrets, endangered oriental storks, and black-faced spoonbills continue to use the site,” Chang says. “If it was all covered with PV, it could impact their habitat.”
Such measures may not satisfy environmentalists, though. In a review published last month, researchers at Fudan University in Shanghai and two Chinese power firms concluded that China’s floating aquavoltaic installations—some of which already span 5 square kilometers—will “inevitably” alter the marine environment.
Aquavoltaic facilities that are entirely indoors may be an even harder sell as they scale up. Toshiba is backing such a plant in Tainan, to generate 120 MW for an unspecified “semiconductor manufacturer,” with plans for a 360-MW expansion. The resulting buildings could exclude wildlife from 5 square kilometers of habitat. Indoor projects could compensate by protecting land elsewhere. But, as Chen of the Taiwan Environment and Planning Association notes, developers of such sites may not take such measures unless they’re required by law to do so.
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Electrostatic Motors Reach the Macro Scale
by Glenn Zorpette on 18. August 2024. at 12:00
It’s a pretty sure bet that you couldn’t get through a typical day without the direct support of dozens of electric motors. They’re in all of your appliances not powered by a hand crank, in the climate-control systems that keep you comfortable, and in the pumps, fans, and window controls of your car. And although there are many different kinds of electric motors, every single one of them, from the 200-kilowatt traction motor in your electric vehicle to the stepper motor in your quartz wristwatch, exploits the exact same physical phenomenon: electromagnetism.
For decades, however, engineers have been tantalized by the virtues of motors based on an entirely different principle: electrostatics. In some applications, these motors could offer an overall boost in efficiency ranging from 30 percent to close to 100 percent, according to experiment-based analysis. And, perhaps even better, they would use only cheap, plentiful materials, rather than the rare-earth elements, special steel alloys, and copious quantities of copper found in conventional motors.
“Electrification has its sustainability challenges,” notes Daniel Ludois, a professor of electrical engineering at the University of Wisconsin in Madison. But “an electrostatic motor doesn’t need windings, doesn’t need magnets, and it doesn’t need any of the critical materials that a conventional machine needs.”
Such advantages prompted Ludois to cofound a company, C-Motive Technologies, to build macro-scale electrostatic motors. “We make our machines out of aluminum and plastic or fiberglass,” he says. Their current prototype is capable of delivering torque as high as 18 newton meters and power at 360 watts (0.5 horsepower)—characteristics they claim are “the highest torque and power measurements for any rotating electrostatic machine.”
The results are reported in a paper, “Synchronous Electrostatic Machines for Direct Drive Industrial Applications,” to be presented at the 2024 IEEE Energy Conversion Congress and Exposition, which will be held from 20 to 24 October in Phoenix, Ariz. In the paper, Ludois and four colleagues describe an electrostatic machine they built, which they describe as the first such machine capable of “driving a load performing industrial work, in this case, a constant-pressure pump system.”
Making Electrostatic Motors Bigger
The machine, which is hundreds of times more powerful than any previous electrostatic motor, is “competitive with or superior to air-cooled magnetic machinery at the fractional [horsepower] scale,” the authors add. The global market for fractional horsepower motors is more than US $8.7 billion, according to consultancy Business Research Insights.
C-Motive’s 360-watt motor has a half dozen each of rotors and stators, shown in yellow in this cutaway illustration.C-Motive Technologies
Achieving macro scale wasn’t easy. Electrostatic motors have been available for years, but today, these are tiny units with power output measured in milliwatts. “Electrostatic motors are amazing once you get below about the millimeter scale, and they get better and better as they get smaller and smaller,” says Philip Krein, a professor of electrical engineering at the University of Illinois Urbana-Champaign. “There’s a crossover at which they are better than magnetic motors.” (Krein does not have any financial connection to C-Motive.)
For larger motors, however, the opposite is true. “At macro scale, electromagnetism wins, is the textbook answer,” notes Ludois. “Well, we’ve decided to challenge that wisdom.”
For this quest he and his team found inspiration in a lesser-known accomplishment of one of the United States’ founding fathers. “The fact is that Benjamin Franklin built and demonstrated a macroscopic electrostatic motor in 1747,” says Krein. “He actually used the motor as a rotisserie to grill a turkey on a riverbank in Philadelphia” (a fact unearthed by the late historian I. Bernard Cohen for his 1990 book Benjamin Franklin’s Science ).
Krein explains that the fundamental challenge in attempting to scale electrostatic motors to the macro world is energy density. “The energy density you can get in air at a reasonable scale with an electric-field system is much, much lower—many orders of magnitude lower—than the density you can get with an electromagnetic system.” Here the phrase “in air” refers to the volume within the motor, called the “air gap,” where the machine’s fields (magnetic for the conventional motor, electric for the electrostatic one) are deployed. It straddles the machine’s key components: the rotor and the stator.
Let’s unpack that. A conventional electric motor works because a rotating magnetic field, set up in a fixed structure called a stator, engages with the magnetic field of another structure called a rotor, causing that rotor to spin. The force involved is called the Lorentz force. But what makes an electrostatic machine go ‘round is an entirely different force, called the Coulomb force. This is the attractive or repulsive physical force between opposite or like electrical charges.
Overcoming the Air Gap Problem
C-Motive’s motor uses nonconductive rotor and stator disks on which have been deposited many thin, closely spaced conductors radiating outward from the disk’s center, like spokes in a bicycle wheel. Precisely timed electrostatic charges applied to these “spokes” create two waves of voltage, one in the stator and another in the rotor. The phase difference between the rotor and stator waves is timed and controlled to maximize the torque in the rotor caused by this sequence of attraction and repulsion among the spokes. To further wring as much torque as possible, the machine has half a dozen each of rotors and stators, alternating and stacked like compact discs on a spindle.
The 360-watt motor is hundreds of times more powerful than previous electrostatic motors, which have power output generally measured in milliwatts.C-Motive Technologies
The machine would be feeble if the dielectric between the charges was air. As a dielectric, air has low permittivity, meaning that an electric field in air can not store much energy. Air also has a relatively low breakdown field strength, meaning that air can support only a fairly weak electric field before it breaks down and conducts current in a blazing arc. So one of the team’s greatest challenges was producing a dielectric fluid that has a much higher permittivity and breakdown field strength than air, and that was also environmentally friendly and nontoxic. To minimize friction, this fluid also had to have very low viscosity, because the rotors would be spinning in it. A dielectric with high permittivity concentrates the electric field between oppositely charged electrodes, enabling greater energy to be stored in the space between them. After screening hundreds of candidates over several years, the C-Motive team succeeded in producing an organic liquid dielectric with low viscosity and a relative permittivity in the low 20s. For comparison, the relative permittivity of air is 1.
Another challenge was supplying the 2,000 volts their machine needs to operate. High voltages are necessary to create the intense electric fields between the rotors and stators. To precisely control these fields, C-Motive was able to take advantage of the availability of inexpensive and stupendously capable power electronics, according to Ludois. For their most recent motor, they developed a drive system based on readily available 4.5-kilovolt insulated-gate bipolar transistors, but the rate of advancement in power semiconductors means they have many attractive choices here, and will have even more in the near future.
Ludois reports that C-Motive is now testing a 750-watt (1 hp) motor in applications with potential customers. Their next machines will be in the range of 750 to 3,750 watts (1 to 5 hp), he adds. These will be powerful enough for an expanded range of applications in industrial automation, manufacturing, and heating, ventilating, and air conditioning.
It’s been a gratifying ride for Ludois. “For me, a point of creative pride is that my team and I are working on something radically different that, I hope, over the long term, will open up other avenues for other folks to contribute.”
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Video Friday: Silly Robot Dog Jump
by Evan Ackerman on 16. August 2024. at 16:45
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.
ICRA@40: 23–26 September 2024, ROTTERDAM, NETHERLANDS
IROS 2024: 14–18 October 2024, ABU DHABI, UAE
ICSR 2024: 23–26 October 2024, ODENSE, DENMARK
Cybathlon 2024: 25–27 October 2024, ZURICH
Enjoy today’s videos!
The title of this video is “Silly Robot Dog Jump” and that’s probably more than you need to know.
[ Deep Robotics ]
It’ll be great when robots are reliably autonomous, but until they get there, collaborative capabilities are a must.
[ Robust AI ]
I am so INCREDIBLY EXCITED for this.
[ IIT Instituto Italiano di Tecnologia ]
In this 3 minutes long one-take video, the LimX Dynamics CL-1 takes on the challenge of continuous heavy objects loading among shelves in a simulated warehouse, showcasing the advantages of the general-purpose form factor of humanoid robots.
[ LimX Dynamics ]
Birds, bats and many insects can tuck their wings against their bodies when at rest and deploy them to power flight. Whereas birds and bats use well-developed pectoral and wing muscles, how insects control their wing deployment and retraction remains unclear because this varies among insect species. Here we demonstrate that rhinoceros beetles can effortlessly deploy their hindwings without necessitating muscular activity. We validated the hypothesis using a flapping microrobot that passively deployed its wings for stable, controlled flight and retracted them neatly upon landing, demonstrating a simple, yet effective, approach to the design of insect-like flying micromachines.
[ Nature ]
Agility Robotics’ CTO, Pras Velagapudi, talks about data collection, and specifically about the different kinds we collect from our real-world robot deployments and generally what that data is used for.
[ Agility Robotics ]
Robots that try really hard but are bad at things are utterly charming.
[ University of Tokyo JSK Lab ]
The DARPA Triage Challenge unsurprisingly has a bunch of robots in it.
[ DARPA ]
The Cobalt security robot has been around for a while, but I have to say, the design really holds up—it’s a good looking robot.
[ Cobalt AI ]
All robots that enter elevators should be programmed to gently sway back and forth to the elevator music. Even if there’s no elevator music.
[ Somatic ]
ABB Robotics and the Texas Children’s Hospital have developed a groundbreaking lab automation solution using ABB’s YuMi® cobot to transfer fruit flies (Drosophila melanogaster) used in the study for developing new drugs for neurological conditions such as Alzheimer’s, Huntington’s and Parkinson’s.
[ ABB ]
Extend Robotics are building embodied AI enabling highly flexible automation for real-world physical tasks. The system features intuitive immersive interface enabling tele-operation, supervision and training AI models.
[ Extend Robotics ]
The recorded livestream of RSS 2024 is now online, in case you missed anything.
[ RSS 2024 ]
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IEEE and Keysight Team Up to Teach Kids About Electronics
by Robert Schneider on 15. August 2024. at 18:00
IEEE TryEngineering has partnered with Keysight Technologies to develop lesson plans focused on electronics and power simulation. Keysight provides hardware, software, and services to a wide variety of industries, particularly in the area of electronic measurement.
IEEE TryEngineering, an IEEE Educational Activities program, empowers educators to foster the next generation of technology innovators through free, online access to culturally relevant, developmentally appropriate, and educationally sound instructional resources for teachers and community volunteers.
The lesson plans cover a variety of STEM topics, experience levels, and age ranges. Educators should be able to find an applicable topic for their students, regardless of their grade level or interests.
Lesson plans on circuits
There are already a number of lesson plans available through the Keysight partnership that introduce students to electrical concepts, with more being developed. The most popular one thus far is Series and Parallel Circuits, which has been viewed more than 100 times each month. Teams of pupils predict the difference between a parallel and serial circuit design by building examples using wires, light bulbs, and batteries.
“TryEngineering is proud to be Keysight’s partner in attaining the ambitious goal of bringing engineering lessons to 1 million students in 2024.” —Debra Gulick
The newest of the Keysight-sponsored lesson plans, Light Up Name Badge, teaches the basics of circuitry, such as the components of a circuit, series and parallel circuits, and electronic component symbols. Students can apply their newfound knowledge in a design challenge wherein they create a light-up badge with their name.
Developing a workforce through STEM outreach
“Keysight’s commitment to workforce development through preuniversity STEM outreach makes it an ideal partner for IEEE TryEngineering,” says Debra Gulick, director of student and academic education programs for IEEE Educational Activities.
In addition, Keysight’s corporate social responsibility vision to build a better planet by accelerating innovation to connect and secure the world while employing a global business framework of ethical, environmentally sustainable, and socially responsible operations makes it a suitable IEEE partner.
“TryEngineering is proud to be Keysight’s partner in attaining the ambitious goal of bringing engineering lessons to 1 million students in 2024,” Gulick says.
The IEEE STEM Summit, a three-day virtual event in October for IEEE volunteers and educators, is expected to include a session highlighting Keysight’s lesson plans.
Educators and volunteers engaged in outreach activities with students can learn more on the Keysight TryEngineering partnership page.
The arrangement with Keysight was made possible with support from the IEEE Foundation. -
Optical Metasurfaces Shine a Light on Li-Fi, Lidar
by Margo Anderson on 15. August 2024. at 14:00
A new, tunable smart surface can transform a single pulse of light into multiple beams, each aimed in different directions. The proof-of-principle development opens the door to a range of innovations in communications, imaging, sensing, and medicine.
The research comes out of the Caltech lab of Harry Atwater, a professor of applied physics and materials science, and is possible due to a type of nano-engineered material called a metasurface. “These are artificially designed surfaces which basically consist of nanostructured patterns,” says Prachi Thureja, a graduate student in Atwater’s group. “So it’s an array of nanostructures, and each nanostructure essentially allows us to locally control the properties of light.”
The surface can be reconfigured up to millions of times per second to change how it is locally controlling light. That’s rapid enough to manipulate and redirect light for applications in optical data transmission such as optical space communications and Li-Fi, as well as lidar.
“[The metasurface] brings unprecedented freedom in controlling light,” says Alex M.H. Wong, an associate professor of electrical engineering at the City University of Hong Kong. “The ability to do this means one can migrate existing wireless technologies into the optical regime. Li-Fi and LIDAR serve as prime examples.”
Metasurfaces remove the need for lenses and mirrors
Manipulating and redirecting beams of light typically involves a range of conventional lenses and mirrors. These lenses and mirrors might be microscopic in size, but they’re still using optical properties of materials like Snell’s Law, which describes the progress of a wavefront through different materials and how that wavefront is redirected—or refracted—according to the properties of the material itself.
By contrast, the new work offers the prospect of electrically manipulating a material’s optical properties via a semiconducting material. Combined with nano-scaled mirror elements, the flat, microscopic devices can be made to behave like a lens, without requiring lengths of curved or bent glass. And the new metasurface’s optical properties can be switched millions of times per second using electrical signals.
“The difference with our device is by applying different voltages across the device, we can change the profile of light coming off of the mirror, even though physically it’s not moving,” says paper co-author Jared Sisler—also a graduate student in Atwater’s group. “And then we can steer the light like it’s an electrically reprogrammable mirror.”
The device itself, a chip that measures 120 micrometers on each side, achieves its light-manipulating capabilities with an embedded surface of tiny gold antennas in a semiconductor layer of indium tin oxide. Manipulating the voltages across the semiconductor alters the material’s capacity to bend light—also known as its index of refraction. Between the reflection of the gold mirror elements and the tunable refractive capacity of the semiconductor, a lot of rapidly-tunable light manipulation becomes possible.
“I think the whole idea of using a solid-state metasurface or optical device to steer light in space and also use that for encoding information—I mean, there’s nothing like that that exists right now,” Sisler says. “So I mean, technically, you can send more information if you can achieve higher modulation rates. But since it’s kind of a new domain, the performance of our device is more just to show the principle.”
Metasurfaces open up plenty of new possibilities
The principle, says Wong, suggests a wide array of future technologies on the back of what he says are likely near-term metasurface developments and discoveries.
“The metasurface [can] be flat, ultrathin, and lightweight while it attains the functions normally achieved by a series of carefully curved lenses,” Wong says. “Scientists are currently still unlocking the vast possibilities the metasurface has available to us.
“With improvements in nanofabrication, elements with small feature sizes much smaller than the wavelength are now reliably fabricable,” Wong continues. “Many functionalities of the metasurface are being routinely demonstrated, benefiting not just communication but also imaging, sensing, and medicine, among other fields... I know that in addition to interest from academia, various players from industry are also deeply interested and making sizable investments in pushing this technology toward commercialization.”
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NIST Announces Post-Quantum Cryptography Standards
by Dina Genkina on 13. August 2024. at 10:01
Today, almost all data on the Internet, including bank transactions, medical records, and secure chats, is protected with an encryption scheme called RSA (named after its creators Rivest, Shamir, and Adleman). This scheme is based on a simple fact—it is virtually impossible to calculate the prime factors of a large number in a reasonable amount of time, even on the world’s most powerful supercomputer. Unfortunately, large quantum computers, if and when they are built, would find this task a breeze, thus undermining the security of the entire Internet.
Luckily, quantum computers are only better than classical ones at a select class of problems, and there are plenty of encryption schemes where quantum computers don’t offer any advantage. Today, the U.S. National Institute of Standards and Technology (NIST) announced the standardization of three post-quantum cryptography encryption schemes. With these standards in hand, NIST is encouraging computer system administrators to begin transitioning to post-quantum security as soon as possible.
“Now our task is to replace the protocol in every device, which is not an easy task.” —Lily Chen, NIST
These standards are likely to be a big element of the Internet’s future. NIST’s previous cryptography standards, developed in the 1970s, are used in almost all devices, including Internet routers, phones, and laptops, says Lily Chen, head of the cryptography group at NIST who lead the standardization process. But adoption will not happen overnight.
“Today, public key cryptography is used everywhere in every device,” Chen says. “Now our task is to replace the protocol in every device, which is not an easy task.”
Why we need post-quantum cryptography now
Most experts believe large-scale quantum computers won’t be built for at least another decade. So why is NIST worried about this now? There are two main reasons.
First, many devices that use RSA security, like cars and some IoT devices, are expected to remain in use for at least a decade. So they need to be equipped with quantum-safe cryptography before they are released into the field.
“For us, it’s not an option to just wait and see what happens. We want to be ready and implement solutions as soon as possible.” —Richard Marty, LGT Financial Services
Second, a nefarious individual could potentially download and store encrypted data today, and decrypt it once a large enough quantum computer comes online. This concept is called “harvest now, decrypt later“ and by its nature, it poses a threat to sensitive data now, even if that data can only be cracked in the future.
Security experts in various industries are starting to take the threat of quantum computers seriously, says Joost Renes, principal security architect and cryptographer at NXP Semiconductors. “Back in 2017, 2018, people would ask ‘What’s a quantum computer?’” Renes says. “Now, they’re asking ‘When will the PQC standards come out and which one should we implement?’”
Richard Marty, chief technology officer at LGT Financial Services, agrees. “For us, it’s not an option to just wait and see what happens. We want to be ready and implement solutions as soon as possible, to avoid harvest now and decrypt later.”
NIST’s competition for the best quantum-safe algorithm
NIST announced a public competition for the best PQC algorithm back in 2016. They received a whopping 82 submissions from teams in 25 different countries. Since then, NIST has gone through 4 elimination rounds, finally whittling the pool down to four algorithms in 2022.
This lengthy process was a community-wide effort, with NIST taking input from the cryptographic research community, industry, and government stakeholders. “Industry has provided very valuable feedback,” says NIST’s Chen.
These four winning algorithms had intense-sounding names: CRYSTALS-Kyber, CRYSTALS-Dilithium, Sphincs+, and FALCON. Sadly, the names did not survive standardization: The algorithms are now known as Federal Information Processing Standard (FIPS) 203 through 206. FIPS 203, 204, and 205 are the focus of today’s announcement from NIST. FIPS 206, the algorithm previously known as FALCON, is expected to be standardized in late 2024.
The algorithms fall into two categories: general encryption, used to protect information transferred via a public network, and digital signature, used to authenticate individuals. Digital signatures are essential for preventing malware attacks, says Chen.
Every cryptography protocol is based on a math problem that’s hard to solve but easy to check once you have the correct answer. For RSA, it’s factoring large numbers into two primes—it’s hard to figure out what those two primes are (for a classical computer), but once you have one it’s straightforward to divide and get the other.
“We have a few instances of [PQC], but for a full transition, I couldn’t give you a number, but there’s a lot to do.” —Richard Marty, LGT Financial Services
Two out of the three schemes already standardized by NIST, FIPS 203 and FIPS 204 (as well as the upcoming FIPS 206), are based on another hard problem, called lattice cryptography. Lattice cryptography rests on the tricky problem of finding the lowest common multiple among a set of numbers. Usually, this is implemented in many dimensions, or on a lattice, where the least common multiple is a vector.
The third standardized scheme, FIPS 205, is based on hash functions—in other words, converting a message to an encrypted string that’s difficult to reverse
The standards include the encryption algorithms’ computer code, instructions for how to implement it, and intended uses. There are three levels of security for each protocol, designed to future-proof the standards in case some weaknesses or vulnerabilities are found in the algorithms.
Lattice cryptography survives alarms over vulnerabilities
Earlier this year, a pre-print published to the arXiv alarmed the PQC community. The paper, authored by Yilei Chen of Tsinghua University in Beijing, claimed to show that lattice-based cryptography, the basis of two out of the three NIST protocols, was not, in fact, immune to quantum attacks. On further inspection, Yilei Chen’s argument turned out to have a flaw—and lattice cryptography is still believed to be secure against quantum attacks.
On the one hand, this incident highlights the central problem at the heart of all cryptography schemes: There is no proof that any of the math problems the schemes are based on are actually “hard.” The only proof, even for the standard RSA algorithms, is that people have been trying to break the encryption for a long time, and have all failed. Since post-quantum cryptography standards, including lattice cryptography, are newer, there is less certainty that no one will find a way to break them.
That said, the failure of this latest attempt only builds on the algorithm’s credibility. The flaw in the paper’s argument was discovered within a week, signaling that there is an active community of experts working on this problem. “The result of that paper is not valid, that means the pedigree of the lattice-based cryptography is still secure,” says NIST’s Lily Chen (no relation to Tsinghua University’s Yilei Chen). “People have tried hard to break this algorithm. A lot of people are trying, they try very hard, and this actually gives us confidence.”
NIST’s announcement is exciting, but the work of transitioning all devices to the new standards has only just begun. It is going to take time, and money, to fully protect the world from the threat of future quantum computers.
“We’ve spent 18 months on the transition and spent about half a million dollars on it,” says Marty of LGT Financial Services. “We have a few instances of [PQC], but for a full transition, I couldn’t give you a number, but there’s a lot to do.”
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Level Up Your Leadership Skills with IEEE Courses
by Nicholas Spada on 12. August 2024. at 19:00
Author and leadership expert John C. Maxwell famously said, “The single biggest way to impact an organization is to focus on leadership development. There is almost no limit to the potential of an organization that recruits good people, raises them up as leaders, and continually develops them.”
Experts confirm that there are clear benefits to fostering leadership by encouraging employees’ professional growth and nurturing and developing company leaders. A culture of leadership development and innovation boosts employee engagement by 20 percent to 25 percent, according to an analysis in the Journal of Applied Psychology. Companies are 22 percent more profitable, on average, when they engage their employees by building a culture of leadership, innovation, and recognition, according to Zippia research.
Developing professionals into strong leaders can have a lasting impact on a company, and the IEEE Professional Development Suite can help make it possible. The training programs in the suite help aspiring technology leaders who want to develop their essential business and management skills. Programs include IEEE Leading Technical Teams, the IEEE | Rutgers Online Mini-MBA for Engineers and Technical Professionals, and the Intensive Wireless Communications and Advanced Topics in Wireless courses offered by the IEEE Communications Society. IEEE also offers topical courses through its eLearning Library.
Tips for leading teams
IEEE Leading Technical Teams is a live, six-hour course offered both in person and virtually. Addressing challenges that come with leading groups, it is designed for team leaders, managers, and directors of engineering and technical teams.
“Participating benefited me and my employer by enhancing my leadership skills in inspiring others to achieve the goals of our organization,” says Stephen Wilkowski, a system test engineer at CACI International in Reston, Va., who completed the training. “I found the leadership practices assessment to be very valuable, as I appreciated the anonymous feedback received from those who I work with. I would recommend the training to anyone desiring to improve their leadership skills.”
Attendees participate in the 360° Leadership Practices Inventory, a tool that solicits confidential feedback on the participant’s strengths and opportunities for improvement from their team members and managers. The program encompasses instructor-led exercises and case studies demonstrating the application of best practices to workplace challenges.
Participants learn the “five practices of exemplary leadership” and receive valuable peer coaching.
To learn more about in-person and virtual options for individuals and companies, complete this form.
A mini-MBA for technologists
The 12-week IEEE | Rutgers Online Mini-MBA for Engineers and Technical Professionals program covers business strategy, new product development management, financial analysis, sales and marketing, and leadership. It includes a combination of expert instruction, peer interaction, self-paced video lessons, interactive assessments, live office hours, and hands-on capstone project experience. The program offers flexible learning opportunities for individual learners as well as customized company cohort options.
Developing professionals into strong leaders can have a lasting impact on a company, and the IEEE Professional Development Suite can help make that possible.
“The mini-MBA was a great opportunity to explore other areas of business that I don’t typically encounter,” says graduate Jonathan Bentz, a senior manager at Nvidia. “I have a customer-facing technical role, and the mini-MBA allowed me to get a taste of the full realm of business leadership.”
For more information, see IEEE | Rutgers Online Mini-MBA for Engineers and Technical Professionals.
Training on wireless communications
The Intensive Wireless Communications and the Advanced Topics in Wireless course series are exclusively presented by the IEEE Communications Society.
The Intensive Wireless interactive live course provides training necessary to stay on top of key developments in the dynamic, rapidly evolving communications industry. Designed for those with an engineering background who want to enhance their knowledge of wireless communication technologies, the series is an ideal way to train individual employees or your entire team at once.
The Advanced Topics in Wireless series is for engineers and technical professionals with a working knowledge of wireless who are looking to enhance their skill set. The series dives into recent advancements, applications, and use cases in emerging connectivity.
Participants in the live, online course series develop a comprehensive view of 5G/NR technology, as well as an understanding of the implementation of all the ITU-specified use case categories such as enhanced mobile broadband, mIoT, and ultra-reliable low-latency communication. The series also provides a robust foundation on the network architecture and the evolution of technology, which enables fully open radio access networks.
Learn more about the Advanced Topics in Wireless Course Series by completing this form.
Topics in the eLearning Library
Tailored for professionals, faculty, and students, the IEEE eLearning Library taps into a wealth of expertise from the organization’s global network of more than 450,000 industry and academia members. Courses cover a wide variety of disciplines including artificial intelligence, blockchain technology, cyber and data security, power and energy, telecommunications, and IEEE standards.
You can help foster growth and leadership skills for your organization by offering employees access to hundreds of courses. Start exploring the library by filling out this form.
Completion of course programs offers learners the ability to earn IEEE certificates bearing professional development hours, continuing education units, and digital badges.
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Amazon Vies for Nuclear-Powered Data Center
by Andrew Moseman on 12. August 2024. at 18:36
When Amazon Web Services paid US $650 million in March for another data center to add to its armada, the tech giant thought it was buying a steady supply of nuclear energy to power it, too. The Susquehanna Steam Electric Station outside of Berick, Pennsylvania, which generates 2.5 gigawatts of nuclear power, sits adjacent to the humming data center and had been directly powering it since the center opened in 2023.
After striking the deal, Amazon wanted to change the terms of its original agreement to buy 180 megawatts of additional power directly from the nuclear plant. Susquehanna agreed to sell it. But third parties weren’t happy about that, and their deal has become bogged down in a regulatory battle that will likely set a precedent for data centers, cryptocurrency mining operations, and other computing facilities with voracious appetites for clean electricity.
Putting a data center right next to a power plant so that it can draw electricity from it directly, rather than from the grid, is becoming more common as data centers seek out cheap, steady, carbon-free power. Proposals for co-locating data centers next to nuclear power have popped up in New Jersey, Texas, Ohio, and elsewhere. Sweden is considering using small modular reactors to power future data centers.
However, co-location raises questions about equity and energy security, because directly-connected data centers can avoid paying fees that would otherwise help maintain grids. They also hog hundreds of megawatts that could be going elsewhere.
“They’re effectively going behind the meter and taking that capacity off of the grid that would otherwise serve all customers,” says Tony Clark, a senior advisor at the law firm Wilkinson Barker Knauer and a former commissioner at the Federal Energy Regulatory Commission (FERC), who has testified to a U.S. House subcommittee on the subject.
Amazon’s nuclear power deal meets hurdles
The dust-up over the Amazon-Susquehanna agreement started in June, after Amazon subsidiary Amazon Web Services filed a notice to change its interconnection service agreement (ISA) in order to buy more nuclear power from Susquehanna’s parent company, Talen Energy. Amazon wanted to increase the amount of behind-the-meter power it buys from the plant from 300 MW to 480 MW. Shortly after it requested the change, utility giants Exelon and American Electric Power (AEP), filed a protest against the agreement and asked FERC to hold a hearing on the matter.
Their complaint: the deal between Amazon and the nuclear plant would hurt a third party, namely all the customers who buy power from AEP or Exelon utilities. The protest document argues that the arrangement would shift up to $140 million in extra costs onto the people of Pennsylvania, New Jersey, and other states served by PJM, a regional transmission organization that oversees the grid in those areas. “Multiplied by the many similar projects on the drawing board, it is apparent that this unsupported filing has huge financial consequences that should not be imposed on ratepayers without sufficient process to determine and evaluate what is really going on,” their complaint says.
Susquehanna dismissed the argument, effectively saying that its deal with Amazon is none of AEP and Exelon’s business. “It is an unlawful attempt to hijack this limited [ISA] amendment proceeding that they have no stake in and turn it into an ad hoc national referendum on the future of data center load,” Susquehanna’s statement said. (AEP, Exelon, Talen/Susquehanna, and Amazon all declined to comment for this story.)
More disputes like this will likely follow as more data centers co-locate with clean energy. Kevin Schneider, a power system expert at Pacific Northwest National Laboratory and research professor at Washington State University, says it’s only natural that data center operators want the constant, consistent nature of nuclear power. “If you look at the base load nature of nuclear, you basically run it up to a power level and leave it there. It can be well aligned with a server farm.”
Data center operators are also exploring energy options from solar and wind, but these energy sources would have a difficult time matching the constancy of nuclear, even with grid storage to help even out their supply. So giant tech firms look to nuclear to keep their servers running without burning fossil fuels, and use that to trumpet their carbon-free achievements, as Amazon did when it bought the data center in Pennsylvania. “Whether you’re talking about Google or Apple or Microsoft or any of those companies, they tend to have corporate sustainability goals. Being served by a nuclear unit looks great on their corporate carbon balance sheet,” Clark says.
Costs of data centers seeking nuclear energy
Yet such arrangements could have major consequences for other energy customers, Clark argues. For one, directing all the energy from a nuclear plant to a data center is, fundamentally, no different than retiring that plant and taking it offline. “It’s just a huge chunk of capacity leaving the system,” he says, resulting in higher prices and less energy supply for everyone else.
Another issue is the “behind-the-meter” aspect of these kinds of deals. A data center could just connect to the grid and draw from the same supply as everyone else, Clark says. But by connecting directly to the power plant, the center’s owner avoids paying the administrative fees that are used to maintain the grid and grow its infrastructure. Those costs could then get passed on to businesses and residents who have to buy power from the grid. “There’s just a whole list of charges that get assessed through the network service that if you don’t connect through the network, you don’t have to pay,” Clark says. “And those charges are the part of the bill that will go up” for everyone else.
Even the “carbon-free” public relations talking points that come with co-location may be suspect in some cases. In Washington State, where Schneider works, new data centers are being planted next to the region’s abundant hydropower stations, and they’re using so much of that energy that parts of the state are considering adding more fossil fuel capacity to make ends meet. This results in a “zero-emissions shell game,” Clark wrote in a white paper on the subject.
These early cases are likely only the beginning. A report posted in May from the Electric Power Research Institute predicts energy demand from data centers will double by 2030, a leap driven by the fact that AI queries need ten times more energy than traditional internet searches. The International Energy Agency puts the timeline for doubling sooner–in 2026. Data centers, AI, and the cryptocurrency sector consumed an estimated 460 terawatt-hours (TWh) in 2022, and could reach more than 1000 TWh in 2026, the agency predicts.
Data centers face energy supply challenges
New data centers can be built in a matter of months, but it takes years to build utility-scale power projects, says Poorvi Patel, manager of strategic insights at Electric Power Research Institute and contributor to the report. The potential for unsustainable growth in electricity needs has put grid operators on alert, and in some cases has sent them sounding the alarm. Eirgrid, a state-owned transmission operator in Ireland, last week warned of a “mass exodus” of data centers in Ireland if it can’t connect new sources of energy.
There’s only so much existing nuclear power to go around, and enormous logistical and regulatory roadblocks to building more. So data center operators and tech giants are looking for creative solutions. Some are considering small modular reactors (SMRs)–which are advanced nuclear reactors with much smaller operating capacities than conventional reactors. Nano Nuclear Energy, which is developing microreactors–a particularly small type of SMR–last month announced an agreement with Blockfusion to explore the possibility of powering a currently defunct cryptomining facility in Niagara Falls, New York.
“To me, it does seem like a space where, if big tech has a voracious electric power needs and they really want that 24/7, carbon-free power, nuclear does seem to be the answer,” Clark says. “They also have the balance sheets to be able to do some of the risk mitigation that might make it attractive to get an SMR up and running.”
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Photonic Chip Cuts Cost of Hunting Exoplanets
by Rachel Berkowitz on 12. August 2024. at 13:01
At 6.5 meters in diameter, the James Webb Space Telescope’s primary mirror captures more light than any telescope that’s ever been launched from Earth. But not every astronomer has US $10 billion to spend on a space telescope. So to help bring the cost of space-based astronomy down, researchers at the National Research Council of Canada in Ottawa are working on a way to process starlight on a tiny optical chip. Ross Cheriton, a photonics researcher there, and his students built and tested a CubeSat prototype with a new kind of photonic chip. The goal is to lower the barrier to entry for astronomical science using swarms of lower-cost spacecraft.
“We hope to enable smaller space telescopes to do big science using highly compact instrument-on-chips,” Cheriton says, who is also affiliated with the Quantum and Nanotechnology Research Centre in Ottawa.
Photonics integrated circuits (PICs) use light instead of electricity to process information, and they’re in wide use slinging trillions and trillions of bits around data centers. But only recently have astronomers begun to examine how to use them to push the boundaries of what can be learned about the universe.
Ground-based telescopes are plagued by Earth’s atmosphere, where turbulence blurs incoming light, making it difficult to focus it onto a camera chip. In outer space, telescopes can peer at extremely faint objects in non-visible wavelengths without correcting for the impact of turbulence. That’s where Cheriton aims to boldly go with a PIC filter that detects very subtle gas signatures during an exoplanet “eclipse” called a transit.
The main motivation for putting photonic chips in space is to reduce the size, weight, and cost of components, because it can be produced en masse in a semiconductor foundry. “The dream is a purely fiber and chip-based instrument with no other optics,” says Cheriton. Replacing filters, lenses, and mirrors with a chip also improves stability and scalability compared to ordinary optical parts.
CubeSats—inexpensive, small, and standardized satellites—have proved to be a cost-effective way of deploying small instrument payloads. “The compact nature of PICs is a perfect match for CubeSats to study bright exoplanet systems James Webb doesn’t have time to stare at,” says Cheriton.
For a total mission cost of less than $1 million—compared to the Webb’s $10 billion—an eventual CubeSat mission could stare at a star for days to weeks while it waits for a planet to cross the field of view. Then, it would look for slight changes in the star’s spectrum that are associated with how the planet’s atmosphere absorbs light—telltale evidence of gasses of a biological origin.
Smaller spectroscopy
As a proof-of-concept, Cheriton guided a team of undergraduate students who spent eight months designing and integrating a PIC into a custom 3U CubeSat (10 centimeter x 10 cm x 30 cm) platform. Their silicon nitride photonic circuit sensor proved itself capable of detecting the absorption signatures of CO2 in incoming light.
In their design, light entering the CubeSat’s collimating lens gets focused into a fiber and then pushed to the photonic chip. It enters an etched set of waveguides that includes a ring resonator. Here, light having a specific set of wavelengths builds in intensity over multiple trips around the ring, and is then output to a detector. Because only a select few wavelengths constructively interfere—those chosen to match a gas’s absorption spectrum—the ring serves as a comb-like filter. After the light goes through the ring resonator, the signal from the waveguide gets passed to an output fiber and onto a camera connected to a Raspberry Pi computer for processing. A single pixel’s intensity therefore serves as a reading for a gas’s presence.
Light travels through a waveguide on a photonic integrated circuit.Teseract
Because it’s built on a chip, the sensor could be multiplexed for observing several objects or sense different gasses simultaneously. Additionally, all the light falling on a single pixel means that the signal is more sensitive than a traditional spectrometer, says Cheriton. Moreover, instead of hunting for peaks in a full spectrum, the technology looks for how well the absorption spectrum matches that of a specific gas, a more efficient process. “If something is in space, you don’t want to send gigabytes of data home if you don’t have to,” he says.
Space travel is still a long way off for the astrophotonic CubeSat. The current design does not use space-qualified components. But Cheriton’s students tested it in the lab for red light (635 nm) and CO2 in a gas cell. They used a “ground station” computer to transmit all commands and receive all results—and to monitor the photovoltaics and collect data from the flight control sensors onboard their CubeSat.
Next, the team plans to test whether their sensor can detect oxygen with the silicon nitride chip, a material that was chosen for its transparency to the gas’s 760 nm wavelength. Success would leave them well positioned to meet what Cheriton calls the next huge milestone for astronomers: looking for an earth-like planet with oxygen.
The work was presented at the Optica (formerly Optical Society of America) Advanced Photonics conference in July.
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Andrew Ng: Unbiggen AI
by Eliza Strickland on 09. February 2022. at 15:31
Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A.
Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias.
Andrew Ng on...
- What’s next for really big models
- The career advice he didn’t listen to
- Defining the data-centric AI movement
- Synthetic data
- Why Landing AI asks its customers to do the work
The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way?
Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions.
When you say you want a foundation model for computer vision, what do you mean by that?
Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them.
What needs to happen for someone to build a foundation model for video?
Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision.
Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries.
It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users.
Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation.
“In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.”
—Andrew Ng, CEO & Founder, Landing AII remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince.
I expect they’re both convinced now.
Ng: I think so, yes.
Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.”
How do you define data-centric AI, and why do you consider it a movement?
Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data.
When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline.
The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up.
You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them?
Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.
When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set?
Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system.
“Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.”
—Andrew NgFor example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance.
Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training?
Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle.
One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way.
When you talk about engineering the data, what do you mean exactly?
Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.
For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow.
What about using synthetic data, is that often a good solution?
Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development.
Do you mean that synthetic data would allow you to try the model on more data sets?
Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category.
“In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.”
—Andrew NgSynthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data.
To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment?
Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data.
One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory.
How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up?
Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations.
In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists?
So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work.
Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains.
Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement?
Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it.
This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”
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How AI Will Change Chip Design
by Rina Diane Caballar on 08. February 2022. at 14:00
The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process.
Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version.
But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform.
How is AI currently being used to design the next generation of chips?
Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider.
Heather GorrMathWorks
Then, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI.
What are the benefits of using AI for chip design?
Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design.
So it’s like having a digital twin in a sense?
Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end.
So, it’s going to be more efficient and, as you said, cheaper?
Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering.
We’ve talked about the benefits. How about the drawbacks?
Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years.
Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together.
One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge.
How can engineers use AI to better prepare and extract insights from hardware or sensor data?
Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start.
One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI.
What should engineers and designers consider when using AI for chip design?
Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team.
How do you think AI will affect chip designers’ jobs?
Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip.
How do you envision the future of AI and chip design?
Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.
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Atomically Thin Materials Significantly Shrink Qubits
by Dexter Johnson on 07. February 2022. at 16:12
Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.
IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability.
Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100.
“We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.”
The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit.
Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C).
Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT
In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.
As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.
In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.
“We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics.
On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas.
While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor.
“What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.”
This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits.
“The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang.
Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.