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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.

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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 AI

I 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.”

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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 Ng

For 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.

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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 Ng

Synthetic 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.

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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.

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This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”

“Cover, bring to a boil, then reduce heat. Simmer for 20 minutes.” These directions seem simple enough, and yet I have messed up many, many pots of rice over the years. My sympathies to anyone who’s ever had to boil rice on a stovetop, cook it in a clay pot over a kerosene or charcoal burner, or prepare it in a cast-iron cauldron. All hail the 1955 invention of the automatic rice cooker!

How the automatic rice cooker was invented

It isn’t often that housewives get credit in the annals of invention, but in the story of the automatic rice cooker, a woman takes center stage. That happened only after the first attempts at electrifying rice cooking, starting in the 1920s, turned out to be utter failures. Matsushita, Mitsubishi, and Sony all experimented with variations of placing electric heating coils inside wooden tubs or aluminum pots, but none of these cookers automatically switched off when the rice was done. The human cook—almost always a wife or daughter—still had to pay attention to avoid burning the rice. These electric rice cookers didn’t save any real time or effort, and they sold poorly.

But Shogo Yamada, the energetic development manager of the electric appliance division for Toshiba, became convinced that his company could do better. In post–World War II Japan, he was demonstrating and selling electric washing machines all over the country. When he took a break from his sales pitch and actually talked to women about their daily household labors, he discovered that cooking rice—not laundry—was their most challenging chore. Rice was a mainstay of the Japanese diet, and women had to prepare it up to three times a day. It took hours of work, starting with getting up by 5:00 am to fan the flames of a kamado, a traditional earthenware stove fueled by charcoal or wood on which the rice pot was heated. The inability to properly mind the flame could earn a woman the label of “failed housewife.”

In 1951, Yamada became the cheerleader of the rice cooker within Toshiba, which was understandably skittish given the past failures of other companies. To develop the product, he turned to Yoshitada Minami, the manager of a small family factory that produced electric water heaters for Toshiba. The water-heater business wasn’t great, and the factory was on the brink of bankruptcy.

How Sources Influence the Telling of History

As someone who does a lot of research online, I often come across websites that tell very interesting histories, but without any citations. It takes only a little bit of digging before I find entire passages copied and pasted from one site to another, and so I spend a tremendous amount of time trying to track down the original source. Accounts of popular consumer products, such as the rice cooker, are particularly prone to this problem. That’s not to say that popular accounts are necessarily wrong; plus they are often much more engaging than boring academic pieces. This is just me offering a note of caution because every story offers a different perspective depending on its sources.

For example, many popular blogs sing the praises of Fumiko Minami and her tireless contributions to the development of the rice maker. But in my research, I found no mention of Minami before Helen Macnaughtan’s 2012 book chapter, “Building up Steam as Consumers: Women, Rice Cookers and the Consumption of Everyday Household Goods in Japan,” which itself was based on episode 42 of the Project X: Challengers documentary series that was produced by NHK and aired in 2002.

If instead I had relied solely on the description of the rice cooker’s early development provided by the Toshiba Science Museum (here’s an archived page from 2007), this month’s column would have offered a detailed technical description of how uncooked rice has a crystalline structure, but as it cooks, it becomes a gelatinized starch. The museum’s website notes that few engineers had ever considered the nature of cooking rice before the rice-cooker project, and it refers simply to the “project team” that discovered the process. There’s no mention of Fumiko.

Both stories are factually correct, but they emphasize different details. Sometimes it’s worth asking who is part of the “project team” because the answer might surprise you. —A.M.

Although Minami understood the basic technical principles for an electric rice cooker, he didn’t know or appreciate the finer details of preparing perfect rice. And so Minami turned to his wife, Fumiko.

Fumiko, the mother of six children, spent five years researching and testing to document the ideal recipe. She continued to make rice three times a day, carefully measuring water-to-rice ratios, noting temperatures and timings, and prototyping rice-cooker designs. Conventional wisdom was that the heat source needed to be adjusted continuously to guarantee fluffy rice, but Fumiko found that heating the water and rice to a boil and then cooking for exactly 20 minutes produced consistently good results.

But how would an automatic rice cooker know when the 20 minutes was up? A suggestion came from Toshiba engineers. A working model based on a double boiler (a pot within a pot for indirect heating) used evaporation to mark time. While the rice cooked in the inset pot, a bimetallic switch measured the temperature in the external pot. Boiling water would hold at a constant 100 °C, but once it had evaporated, the temperature would soar. When the internal temperature of the double boiler surpassed 100 °C, the switch would bend and cut the circuit. One cup of boiling water in the external pot took 20 minutes to evaporate. The same basic principle is still used in modern cookers.

Yamada wanted to ensure that the rice cooker worked in all climates, so Fumiko tested various prototypes in extreme conditions: on her rooftop in cold winters and scorching summers and near steamy bathrooms to mimic high humidity. When Fumiko became ill from testing outside, her children pitched in to help. None of the aluminum and glass prototypes, it turned out, could maintain their internal temperature in cold weather. The final design drew inspiration from the Hokkaidō region, Japan’s northernmost prefecture. Yamada had seen insulated cooking pots there, so the Minami family tried covering the rice cooker with a triple-layered iron exterior. It worked.

How Toshiba sold its automatic rice cooker

Toshiba’s automatic rice cooker went on sale on 10 December 1955, but initially, sales were slow. It didn’t help that the rice cooker was priced at 3,200 yen, about a third of the average Japanese monthly salary. It took some salesmanship to convince women they needed the new appliance. This was Yamada’s time to shine. He demonstrated using the rice cooker to prepare takikomi gohan, a rice dish seasoned with dashi, soy sauce, and a selection of meats and vegetables. When the dish was cooked in a traditional kamado, the soy sauce often burned, making the rather simple dish difficult to master. Women who saw Yamada’s demo were impressed with the ease offered by the rice cooker.

Another clever sales technique was to get electricity companies to serve as Toshiba distributors. At the time, Japan was facing a national power surplus stemming from the widespread replacement of carbon-filament lightbulbs with more efficient tungsten ones. The energy savings were so remarkable that operations at half of the country’s power plants had to be curtailed. But with utilities distributing Toshiba rice cookers, increased demand for electricity was baked in.

Within a year, Toshiba was selling more than 200,000 rice cookers a month. Many of them came from the Minamis’ factory, which was rescued from near-bankruptcy in the process.

How the automatic rice cooker conquered the world

From there, the story becomes an international one with complex localization issues. Japanese sushi rice is not the same as Thai sticky rice which is not the same as Persian tahdig, Indian basmati, Italian risotto, or Spanish paella. You see where I’m going with this. Every culture that has a unique rice dish almost always uses its own regional rice with its own preparation preferences. And so countries wanted their own type of automatic electric rice cooker (although some rejected automation in favor of traditional cooking methods).

Yoshiko Nakano, a professor at the University of Hong Kong, wrote a book in 2009 about the localized/globalized nature of rice cookers. Where There Are Asians, There Are Rice Cookers traces the popularization of the rice cooker from Japan to China and then the world by way of Hong Kong. One of the key differences between the Japanese and Chinese rice cooker is that the latter has a glass lid, which Chinese cooks demanded so they could see when to add sausage. More innovation and diversification followed. Modern rice cookers have settings to give Iranians crispy rice at the bottom of the pot, one to let Thai customers cook noodles, one for perfect rice porridge, and one for steel-cut oats.

My friend Hyungsub Choi, in his 2022 article “Before Localization: The Story of the Electric Rice Cooker in South Korea,” pushes back a bit on Nakano’s argument that countries were insistent on tailoring cookers to their tastes. From 1965, when the first domestic rice cooker appeared in South Korea, to the early 1990s, Korean manufacturers engaged in “conscious copying,” Choi argues. That is, they didn’t bother with either innovation or adaptation. As a result, most Koreans had to put up with inferior domestic models. Even after the Korean government made it a national goal to build a better rice cooker, manufacturers failed to deliver one, perhaps because none of the engineers involved knew how to cook rice. It’s a good reminder that the history of technology is not always the story of innovation and progress.

Eventually, the Asian diaspora brought the rice cooker to all parts of the globe, including South Carolina, where I now live and which coincidentally has a long history of rice cultivation. I bought my first rice cooker on a whim, but not for its rice-cooking ability. I was intrigued by the yogurt-making function. Similar to rice, yogurt requires a constant temperature over a specific length of time. Although successful, my yogurt experiment was fleeting—store-bought was just too convenient. But the rice cooking blew my mind. Perfect rice. Every. Single. Time. I am never going back to overflowing pots of starchy water.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the November 2024 print issue as “The Automatic Rice Cooker’s Unlikely Inventor.”

References

Helen Macnaughtan’s 2012 book chapter, “Building up Steam as Consumers: Women, Rice Cookers and the Consumption of Everyday Household Goods in Japan,” was a great resource in understanding the development of the Toshiba ER-4. The chapter appeared in The Historical Consumer: Consumption and Everyday Life in Japan, 1850-2000, edited by Penelope Francks and Janet Hunter (Palgrave Macmillan).

Yoshiko Nakano’s book Where There are Asians, There are Rice Cookers (Hong Kong University Press, 2009) takes the story much further with her focus on the National (Panasonic) rice cooker and its adaptation and adoption around the world.

The Toshiba Science Museum, in Kawasaki, Japan, where we sourced our main image of the original ER-4, closed to the public in June. I do not know what the future holds for its collections, but luckily some of its Web pages have been archived to continue to help researchers like me.

As the philanthropic partner of IEEE, the IEEE Foundation expands the organization’s charitable body of work by inspiring philanthropic engagement that ignites a donor’s innermost interests and values.

One way the Foundation does so is by partnering with IEEE units to create memorial funds, which pay tribute to members, family, friends, teachers, professors, students, and others. This type of giving honors someone special while also supporting future generations of engineers and celebrating innovation.

Below are three recently created memorial funds that not only have made an impact on their beneficiaries and perpetuated the legacy of the namesake but also have a deep meaning for those who launched them.

EPICS in IEEE Fischer Mertel Community of Projects

The EPICS in IEEE Fischer Mertel Community of Projects was established to support projects “designed to inspire multidisciplinary teams of engineering students to collaborate and engineer solutions to address local community needs.”

The fund was created by the children of Joe Fischer and Herb Mertel to honor their fathers’ passion for mentoring students. Longtime IEEE members, Fischer and Mertel were active with the IEEE Electromagnetic Compatibility Society. Fischer was the society’s 1972 president and served on its board of directors for six years. Mertel served on the society’s board from 1979 to 1983 and again from 1989 to 1993.

“The EPICS in IEEE Fischer Mertel Community of Projects was established to inspire and support outstanding engineering ideas and efforts that help communities worldwide,” says Tina Mertel, Herb’s daughter. “Joe Fischer and my father had a lifelong friendship and excelled as engineering leaders and founders of their respective companies [Fischer Custom Communications and EMACO]. I think that my father would have been proud to know that their friendship and work are being honored in this way.”

The nine projects supported thus far have the potential to impact more than 104,000 people because of the work and collaboration of 190 students worldwide. The projects funded are intended to represent at least two of the EPICS in IEEE’s focus categories: education and outreach; human services; environmental; and access and abilities.

Here are a few of the projects:

• The Engineering Outreach at San Diego K–12 Schools project aims to bridge the city’s STEM education gap by sending IEEE members to schools to teach project-based lessons in mechanical, aerospace, electrical and computer engineering, as well as computer science. The project is led by the IEEE–Eta Kappa Nu honor society’s Kappa Psi chapter at the University of California San Diego and the San Diego Unified School District.
• Students from the Sahrdaya College of Engineering and Technology student branch in Kodakara, India, are developing an exoskeleton for nurses to support their lumbar spine region.
• Volunteers from the IEEE Uganda Section and local engineering students are designing and fabricating a stationary bicycle to act as a generator, providing power to families living in underserved communities. The goal of the project is to reduce air pollution caused by generators and supply reliable, affordable power to people in need.
• The IEEE Colombian Caribbean Section’s Increasing Inclusion of Visually Impaired People with a Mobile Application for English Learning project aims to ensure visually impaired students can learn to read, write, and speak English alongside their peers. The section’s members are developing a mobile app to help accomplish their goal.

IEEE AESS Michael C. Wicks Radar Student Travel Grant

The IEEE Michael C. Wicks Radar Student Travel Grant was established by IEEE Fellow Michael Wicks prior to his death in 2022. The grant provides travel support for graduate students who are the primary authors on a paper being presented at the annual IEEE Radar Conference. Wicks was an electronics engineer and a radio industry leader who was known for developing knowledge-based space-time adaptive processing. He believed in investing in the next generation and he wanted to provide an opportunity for that to happen.Ten graduate students have been awarded the Wicks grant to date. This year two students from Region 8 (Africa, Europe, Middle East) and two students from Region 10 (Asia and Pacific) were able to travel to Denver to attend the IEEE Radar Conference and present their research. The papers they presented are “Target Shape Reconstruction From Multi-Perspective Shadows in Drone-Borne SAR Systems” and “Design of Convolutional Neural Networks for Classification of Ships from ISAR Images.”

Life Fellow Fumio Koyama and IEEE Fellow Constance J. Chang-Hasnain proudly display their IEEE Nick Holonyak, Jr. Medal for Semiconductor Optoelectronic Technologies at this year’s IEEE Honors Ceremony. They are accompanied by IEEE President-Elect Kathleen Kramer and IEEE President Tom Coughlin.Robb Cohen

IEEE Nick Holonyak Jr. Medal for Semiconductor Optoelectronic Technologies

The IEEE Nick Holonyak Jr. Medal for Semiconductor Optoelectronic Technologies was created with a memorial fund supported by some of Holonyak’s former graduate students to honor his work as a professor and mentor. Presented on behalf of the IEEE Board of Directors, the medal recognizes outstanding contributions to semiconductor optoelectronic devices and systems including high-energy-efficiency semiconductor devices and electronics.

Holonyak was a prolific inventor and longtime professor of electrical engineering and physics. In 1962, while working as a scientist at General Electric’s Advanced Semiconductor Laboratory in Syracuse, N.Y., he invented the first practical visible-spectrum LED and laser diode. His innovations are the basis of the devices now used in high-efficiency light bulbs and laser diodes. He left GE in 1963 to join the University of Illinois Urbana-Champaign as a professor of electrical engineering and physics at the invitation of John Bardeen, his Ph.D. advisor and a two-time Nobel Prize winner in physics. Holonyak retired from UIUC in 2013 but continued research collaborations at the university with young faculty members.

“In addition to his remarkable technical contributions, he was an excellent teacher and mentor to graduate students and young electrical engineers,” says Russell Dupuis, one of his doctoral students. “The impact of his innovations has improved the lives of most people on the earth, and this impact will only increase with time. It was my great honor to be one of his students and to help create this important IEEE medal to ensure that his work will be remembered in the future.”

The award was presented for the first time at this year’s IEEE Honors Ceremony, in Boston, to IEEE Fellow Constance Chang-Hasnain and Life Fellow Fumio Koyama for “pioneering contributions to vertical cavity surface-emitting laser (VCSEL) and VCSEL-based photonics for optical communications and sensing.”

Establishing a memorial fund through the IEEE Foundation is a gratifying way to recognize someone who has touched your life while also advancing technology for humanity. If you are interested in learning more about memorial and tribute funds, reach out to the IEEE Foundation team: donate@ieee.org.

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