Artificial Intelligence

Graduate Students

Subheadline

A novel AI algorithm simulates what an ultra-fast scene – such as a pulse of light speeding through a pop bottle – would look like from any vantage point

Close your eyes and picture the iconic “bullet time” scene from The Matrix – the one where Neo, played by Keanu Reeves, dodges bullets in slow motion. Now imagine being able to witness the same effect, but instead of speeding bullets, you’re watching something that moves one million times faster: light itself.

Computer scientists from the ��߲ݴ�ý have built an advanced camera setup that can visualize light in motion from any perspective, opening avenues for further inquiry into new types of 3D sensing techniques.

The researchers developed a sophisticated AI algorithm that can simulate what an ultra-fast scene – a pulse of light speeding through a pop bottle or bouncing off a mirror – would look like from any vantage point.

David Lindell (supplied image)

David Lindell, an assistant professor in the department of computer science in the Faculty of Arts & Science, says the feat requires the ability to generate videos where the camera appears to “fly” alongside the very photons of light as they travel.

“Our technology can capture and visualize the actual propagation of light with the same dramatic, slowed-down detail,” says Lindell. “We get a glimpse of the world at speed-of-light timescales that are normally invisible.”

The researchers believe the approach, which was recently presented at the 2024 European Conference on Computer Vision, can unlock new capabilities in several important research areas, including: advanced sensing capabilities such as non-line-of-sight imaging, a method that allows viewers to “see” around corners or behind obstacles using multiple bounces of light; imaging through scattering media, such as fog, smoke, biological tissues or turbid water; and 3D reconstruction, where understanding the behaviour of light that scatters multiple times is critical.

In addition to Lindell, the research team included U of T computer science PhD student Anagh Malik, fourth-year engineering science undergraduate Noah Juravsky, Professor Kyros Kutulakos and Stanford University Associate Professor Gordon Wetzstein and PhD student Ryan Po.

The researchers’ key innovation lies in the AI algorithm they developed to visualize ultrafast videos from any viewpoint – a challenge known in computer vision as “novel view synthesis.”

Traditionally, novel view synthesis methods are designed for images or videos captured with regular cameras. However, the researchers extended this concept to handle data captured by an ultra-fast camera operating at speeds comparable to light, which posed unique challenges – including the need for their algorithm to account for the speed of light and model how it propagates through a scene.

Through their work, researchers observed a moving-camera visualization of light in motion, including refracting through water, bouncing off a mirror or scattering off a surface. They also demonstrated how to visualize phenomena that only occur at a significant portion of the speed of light, as predicted by Albert Einstein. For example, they visualize the “searchlight effect” that makes objects brighter when moving toward an observer, and “length contraction,” where fast-moving objects look shorter in the direction they are travelling. The researchers were also able to create a way to see how objects would appear to contract in length when moving at such high speeds.

Anagh Malik (supplied image)

While current algorithms for processing ultra-fast videos typically focus on analyzing a single video from a single viewpoint, the researchers say their work is the first to extend this analysis to multi-view light-in-flight videos, allowing for the study of how light propagates from multiple perspectives.

“Our multi-view light-in-flight videos serve as a powerful educational tool, offering a unique way to teach the physics of light transport,” says Malik. “By visually capturing how light behaves in real-time – whether refracting through a material or reflecting off a surface – we can get a more intuitive understanding of the motion of light through a scene.

“Additionally, our technology could inspire creative applications in the arts, such as filmmaking or interactive installations, where the beauty of light transport can be used to create new types of visual effects or immersive experiences.”

The research also holds significant potential for improving LIDAR (Light Detection and Ranging) sensor technology used in autonomous vehicles. Typically, these sensors process data to immediately create 3D images right away. But the researchers’ work suggests the potential to store the raw data, including detailed light patterns, to help create systems that perform better than conventional LIDAR to see more details, look through obstacles and understand materials better.

While the researchers’ project focused on visualizing how light moves through a scene from any direction, they note that carries “hidden information” about the shape and appearance of everything it touches. As the researchers look to their next steps, they want to unlock this information by developing a method that uses multi-view light-in-flight videos to reconstruct the 3D geometry and appearance of the entire scene.

“This means we could potentially create incredibly detailed, three-dimensional models of objects and environments – just by watching how light travels through them,” Lindell says.

From AI to Atari: What it's like to work with Nobel Prize-winner Geoffrey Hinton

mattimar — Fri, 25 Oct 2024 14:13:56 +0000

From AI to Atari: What it's like to work with Nobel Prize-winner Geoffrey Hinton

mattimar Fri, 10/25/2024 - 10:13

Cutline

From left: Chris Maddison, Nick Frosst and Kevin Swersky at a recent event celebrating U of T University Professor Emeritus Geoffrey Hinton's 2024 Nobel Prize in Physics (photo by Johnny Guatto)

Mariam Matti

Topic

Alumni

department of mechanical and industrial engineering

Statistical Sciences

Subheadline

Three former students who worked with the "godfather of AI" recall his passionate and playful approach to research

In the wake of Geoffrey Hinton’s 2024 Nobel Prize in Physics, former students and colleagues from the ��߲ݴ�ý are sharing their favourite anecdotes about the “godfather of AI” – including one involving the classic Atari video game Asteroids.

Nick Frosst (photo by Nina Haikara)

Nick Frosst, a U of T alumnus and co-founder of generative AI startup Cohere, said Hinton, University Professor Emeritus of computer science, once spoke of an intense, button-mashing session that left him with nerve damage.

“This kind of explains, perhaps, the way in which he types, which is still two fingers at a time,” said Frosst, who began working with Hinton as a U of T undergraduate student and was his first employee at Google Brain.

He shared the story at a recent event hosted by the department of computer science in the Faculty of Arts & Science, saying it offered a telling glimpse into Hinton’s character.

“It’s that fever-pitched intensity, passion and playfulness that he brings to everything … He found something that was fun and engaging and he played it until it damaged a finger and then he continued to push for it.”

Frosst said he also appreciates Hinton’s thoughtful consideration about the potential negative consequences of the revolutionary technology he helped create and praised him for looking past formal qualifications to spot potential and creativity.

“I don't have a master's degree or a PhD, but he was willing to work with me and I saw that in the types of people he brought into Google Brain to work with him,” Frosst said.

“He took lots of chances on people and gave them the time of day once they were there. And for that, I'll always be thankful and deeply privileged, and honoured, to have him in my life.”

Other former students at U of T tell similar stories.

Chris Maddison (supplied image)

Chris Maddison, now an assistant professor in U of T’s departments of computer science and statistical sciences in the Faculty of Arts & Science, was also an undergraduate student when he started working with Hinton in 2011.

He also painted a picture of Hinton’s vibrant office – where everyone knew when he had a new idea.

“The excitement, the joy radiated out of his office down the hall. The air was buzzing with possibility,” said Maddison. “He was famous for bursting into a room and pronouncing that, he now finally, after all these years, understood how the brain worked.”

Kevin Swersky (photo by Johnny Guatto)

Kevin Swersky, a research scientist at Google DeepMind, worked with Hinton as a graduate student at U of T and similarly described visiting Hinton as a memorable academic experience.

“Normally when you go to a supervisor's office, you give them a progress update. You go through what your latest results are, you talk about a couple of your ideas and you get some feedback,” he said.

“Going to Geoff’s office was a completely different story. He would be telling you what his latest idea was. He would show you his latest results. And his whole thing was just that he was really excited about it, and his hope was to inspire you enough to start running with it.”

He added that he was particularly inspired by Hinton’s focus on small, solvable puzzles that would ultimately lead to significant breakthroughs over time.

“Geoff would think completely intuitively – like the universe was a puzzle and he was just kind of figuring out where all the pieces went, and the math would always follow whatever he was talking about,” he said.

He also remarked on Hinton’s kindness.

“He offered to put me up for a few weeks,” he said of a time when he found himself looking for a place to stay in Toronto. “He offered to go and get dishes. I was thinking to myself, ‘Wow, Geoff Hinton wants to go out shopping for dishes for me so that I can be comfortable for a few weeks.’”

Geoffrey Hinton shakes hand at a recent U of T event celebrating his Nobel Prize (photo by Mac Pattanasuttinont)

Frosst, too, has a Hinton home-making story: the AI luminary built him a desk from scratch.

“He's a carpenter,” Frosst said. “It’s a small wooden desk that fits in the corner of my room at home.

“That's where I keep my computer and work from.”

U of T experts use machine learning to analyze where bike lanes should be located for maximum benefit

Christopher.Sorensen — Wed, 23 Oct 2024 14:07:11 +0000

U of T experts use machine learning to analyze where bike lanes should be located for maximum benefit

Christopher.Sorensen Wed, 10/23/2024 - 10:07

Cutline

Researchers from U of T's Faculty of Applied Science & Engineering used novel computing approaches to compare utilitarian and equity-driven approaches toward expansion of protected bike lanes (photo by Michelle Mengsu Chang/Toronto Star via Getty Images)

Tyler Irving

Topic

Breaking Research

Faculty of Applied Science & Engineering

Cities

Sustainability

Subheadline

“If you optimize for equity, you get a map that is more spread out and less concentrated in the downtown areas"

A team of researchers from the department of civil and mineral engineering in the ��߲ݴ�ý’s Faculty of Applied Science & Engineering are wielding machine learning to understand where cycling infrastructure should be located in order to benefit the most people.

In a paper published in the Journal of Transport Geography, researchers used novel computing approaches to compare two strategies for expansion of protected bike lanes – using Toronto as a model.

“Right now, some people have really good access to protected biking infrastructure: they can bike to work, to the grocery store or to entertainment venues,” says post-doctoral fellow and lead author Madeleine Bonsma-Fisher, who previously researched interactions between bacteria and viruses before applying her data analysis skills to active transportation. “More lanes could increase the number of destinations they can reach, and previous work shows that will increase the number of cycle trips taken.

“However, many people have little or no access to protected cycling infrastructure at all, limiting their ability to get around. This raises a question: is it better to maximize the number of connected destinations and potential trips overall, or is it more important to focus on maximizing the number of people who can benefit from access to the network?”

To delve into the question, Bonsma-Fisher and co-authors used machine learning and optimization, a challenge that required them to explore new computational approaches.

“This kind of optimization problem is what’s called an ‘NP-hard’ problem, which means that the computing power needed to solve it scales very quickly along with the size of the network,” says Shoshanna Saxe¸ associate professor in the department of civil and mineral engineering and one of Bonsma-Fisher’s two co-supervisors alongside Professor Timothy Chan of the department of mechanical and industrial engineering. “If you used a traditional optimization algorithm on a city the size of Toronto, everything would just crash.”

To get around the problem, PhD student Bo Lin invented a machine learning model capable of considering millions of combinations of over a thousand different infrastructure projects in order to test where the most impactful places are to build new cycling infrastructure.

Using Toronto as a stand-in for any large, automobile-oriented North American city, the team generated maps of future bike lane networks along major streets, optimized according to two broad types of strategies.

The first strategy, dubbed the utilitarian approach, focused on maximizing the number of trips that could be taken using only routes with protected bike lanes in under 30 minutes – without regard for who those trips were taken by.

The second, an equity-based strategy, sought to maximize the number of people who had at least some connection to the network.

“If you optimize for equity, you get a map that is more spread out and less concentrated in the downtown areas,” says Bonsma-Fisher. “You do get more parts of the city that have a minimum of accessibility by bike, but you also get a somewhat smaller overall gain in average accessibility.”

This results in a trade-off, says Saxe. “This trade-off is temporary, assuming we will eventually have a full cycling network across the city, but it is meaningful for how we do things in the meantime and could last a long time given ongoing challenges to building cycling infrastructure.”

Another key finding was that certain routes appeared to be essential no matter what strategy was pursued – for example, protected bike lanes along Bloor Street West.

“Those bike lanes benefit even people who don’t live near them and are a critical trunk to maximizing both the equity and utility of the bike network. Their impact is so consistent across models that it challenges the idea that bike lanes are a local issue, affecting only the people close by,” Saxe says. “Optimized infrastructure repeatedly turns out in our model to serve neighbourhoods quite a distance away.”

The team is already sharing their data with Toronto’s city planners to help inform ongoing decisions about infrastructure investments. Going forward, the researchers hope to apply their analysis to other cities as well.

“No matter what your local issues or what choices you end up making, it’s really important to have a clear understanding of what goals you are aiming for and check if you are meeting them,” says Bonsma-Fisher.

“This kind of analysis can provide an evidence-based, data-driven approach to answering these tough questions.”

U of T receives $52 million to upgrade SciNet supercomputer

Christopher.Sorensen — Tue, 22 Oct 2024 15:54:56 +0000

U of T receives $52 million to upgrade SciNet supercomputer

Christopher.Sorensen Tue, 10/22/2024 - 11:54

Cutline

From left: Charmaine Dean, Timothy Chan, Bryan May, Nolan Quinn, George Ross, Bardish Chagger, Ranil Sonnadara and Eleanor McMahon (supplied image)

Adam Elliott Segal

Topic

SciNet

Faculty of Applied Science & Engineering

Subheadline

The upgraded supercomputer, housed at U of T and available to researchers across Canada, will boast roughly three times the computing power of its predecessor

The ��߲ݴ�ý will receive more than $52 million to upgrade one of the fastest supercomputers in Canada – a shared resource housed at U of T that allows researchers across the country to address key challenges in areas such as health care, drug discovery, sustainable transportation, AI and advanced manufacturing.

In a recent announcement, the Digital Research Alliance of Canada – a non-profit organization funded by the Canadian government – and the Government of Ontario committed to investing more than $95 million into advanced research computing (ARC) in Ontario at host sites at U of T and the University of Waterloo.

The more than $52 million earmarked for U of T’s SciNet – which includes matching funds from the province and Compute Ontario – will go toward replacing the Niagara supercomputer with a new computer network with roughly three times the raw computing power, more GPU capacity and storage boosted by an estimated 80 per cent.

“This computational infrastructure is critical for our community and for the Canadian research community at large – from the biomedical sciences to aerospace manufacturing,” said Timothy Chan, U of T’s associate vice-president and vice-provost, strategic initiatives and a professor in the department of mechanical and industrial engineering in the Faculty of Applied Science & Engineering.

“It supports research in all fields, plus users benefit from the extensive education and training programming offered by SciNet.”

He added that the investment supports Canada’s desire to lead in advanced technologies, and that it comes on the heels of U of T University Professor Emeritus Geoffrey Hinton being awarded the 2024 Nobel Prize in Physics for laying the foundations for today’s AI boom through his seminal work on deep learning.

“By expanding Canada’s supercomputing capabilities, we ensure that the country continues to excel in science and research while staying competitive on the global stage,” François-Phillipe Champagne, Canada’s minister of innovation, science and industry, said in a statement.

Chan, for his part, said the shared computing infrastructure, which will utilize a more sustainable, state-of-the-art cooling system, is a savvy use of public funding.

“These investments result in cost savings,” he said. “Ontario’s publicly funded ARC ecosystem costs users 80 per cent less than commercial cloud systems.”

He said students and researchers across Canada should be encouraged by the news.

“It's a unique resource,” he said. “If my students are doing computational research, they can use the cloud, which is expensive. Or they can build their own computer to do it, but they’re not going to have the same kind of power, scale or expert support as something like SciNet.

“It plays a big role in being able to speed up research, speed up discovery with whatever they're working on and access more computational memory, more storage and more computational power.”

For example, Chan says students can run an algorithm at much faster speeds and test multiple algorithms at the same time – something that’s not always possible on local machines.

“Equipping the next generation with job-ready skills in high-performance computing, machine learning and AI is critical to industries including manufacturing, automotive, finance, and the life sciences. When we invest in advanced research computing, we help our students get skilled jobs and attract highly skilled workers to Ontario.”

‘One of the great minds of the 21st century’: U of T celebrates Geoffrey Hinton’s Nobel Prize

rahul.kalvapalle — Thu, 17 Oct 2024 17:24:21 +0000

‘One of the great minds of the 21st century’: U of T celebrates Geoffrey Hinton’s Nobel Prize

rahul.kalvapalle Thu, 10/17/2024 - 13:24

Cutline

University Professor Emeritus Geoffrey Hinton, who won the 2024 Nobel Prize in Physics, attends a celebration event held at the Schwartz Reisman Innovation Campus (photo by Mac Pattanasuttinont)

Rahul Kalvapalle

Topic

Schwartz Reisman Innovation Campus

Leah Cowen

Wesley Hall

Schwartz Reisman Institute for Technology and Society

Department of Computer Science

CIFAR

Faculty of Applied Science & Engineering

Subheadline

The 2024 co-winner of the Nobel Prize in Physics - known to many as the "godfather of AI" - was honoured at an event attended by a who's who of the Toronto research community

There were standing ovations, peals of laughter and even a few tears as the ��߲ݴ�ý welcomed Geoffrey Hinton back to campus after he won the 2024 Nobel Prize in Physics.

A University Professor Emeritus of computer science, Hinton traded the “cheap hotel room in California,” where he received the life-changing news, for an emotional reception in the Schwartz Reisman Innovation Campus’s airy event hall.

The Oct. 10 event drew U of T leaders, supporters, dignitaries and other luminaries. They included Deputy Prime Minister Chrystia Freeland, University Professor Emeritus John Polanyi, who won the Nobel Prize in Chemistry in 1986, and Massey College Principal James Orbinski, who accepted the Nobel Peace Prize on behalf of Doctors Without Borders in 1999.

Several of Hinton’s many collaborators and proteges also attended the event – not to mention students who were simply eager to catch a glimpse of the “godfather of AI.”

Hinton chats with U of T Scarborough Professor David Fleet and Google Research Scientists Sara Sabour and Daniel Watson (photo by Johnny Guatto)

Though sleep deprived, Hinton regaled the audience with fond recollections of his early years as an AI researcher, snapshots of his trademark dry humour and warm expressions of gratitude for mentors, collaborators and, of course, his many students.

“I’ve been blessed to have brilliant graduate students and post-docs,” Hinton said.

“I had a principle when selecting graduate students: ‘If they’re not smarter than me, what’s the point?’ And I’ve had quite a number of graduate students who were smarter than me.

“They did things I wouldn’t have been able to do, so I’d like to thank them.”

He said two figures in particular played a huge role in the work that led to his Nobel Prize, which he shared with Princeton University’s John J. Hopfield. The first was Terry Sejnowsky, a computational neuroscientist and former student of Hopfield’s, who worked with Hinton on Boltzmann machines – a period Hinton described as “the most happy research time of my life.”

He also praised the contributions of the late David Rumelhart, a psychologist at Stanford University, who worked with him to develop backpropagation algorithms, a key breakthrough.

“[He] should have won the Nobel Prize,” Hinton said. “But unfortunately, Dave got a horrible brain disease and he died quite young.” (The Nobel organization doesn’t award the honour posthumously).

Hinton poses for a picture with Assistant Professor Chris Maddison, who was one of the last graduate students Hinton supervised (photo by Mac Pattanasuttinont)

Chris Maddison joined Hinton’s research group as an undergraduate and was one of the last students he supervised. Now an assistant professor in U of T’s departments of computer science and statistical sciences in the Faculty of Arts & Science, Maddison lauded Hinton as “a steadfast mentor and supporter who saw strength in me that I didn’t see” and said one of his greatest attributes was his sheer enthusiasm.

“No matter what’s going on, you can find him in the lab on Sundays at 8 p.m. playing with his MATLAB scripts like a child playing with Legos,” Maddison said. “He never lost that child-like sense of wonder that buoyed him and the group.”

U of T President Meric Gertler said, via video message from Indonesia, where he was on university business, that Hinton is “one of the great minds of the 21st century” (photo by Johnny Guatto)

U of T President Meric Gertler, who was in Indonesia on university business when the prize was announced, hailed Hinton in a video message as “one of the great minds of the 21st century” and someone who “literally created new ways of thinking about thinking and learning.”

He noted that Hinton’s AI leadership extends to the pressing question of responsible and safe development of the technology.

“With his Nobel win, he’s now perfectly positioned to amplify this concern on a world stage.” President Gertler said.

Hinton shares a laugh with University Professor Molly Shoichet of the Faculty of Applied Science & Engineering (photo by Johnny Guatto)

The event’s guest list served as a reminder of Hinton’s outsized influence on the research community in Toronto and beyond, drawing key figures from: the Vector Institute, where Hinton is co-founder and chief scientific adviser; the Schwartz Reisman Institute of Technology and Society, where he sits on the advisory board; and CIFAR (Canadian Institute for Advanced Research), where Hinton is an adviser and longtime fellow.

Hinton poses for a photo with Leah Cowen, U of T’s vice-president, research and innovation, and strategic initiatives (photo by Johnny Guatto)

Leah Cowen, U of T’s vice-president, research and innovation, and strategic initiatives, reminded the accomplished audience that Hinton’s historic achievement – both the Nobel Prize and the AI revolution he helped spark – was the result of years toiling in an “unpromising backwater” of AI research.

“It is tempting to think that it happened almost overnight, but it didn’t,” Cowen said.

University Professors Emeriti – and fellow Nobel Prize-winners – Geoffrey Hinton and John Polanyi have their photo taken together (photo by Johnny Guatto)

Hinton took time to reflect on periods of personal struggle and tragedy – and thank those who helped him in his hour of need.

When his wife had an incurable form of cancer, he recalled how U of T President Emeritus David Naylor, a physician, medical researcher and former dean of the Temerty Faculty of Medicine, carried out research on a possible alternative treatment that was being explored – incorporating input from top medical experts – and presented him with a report of his findings.

“He’s a tremendous human being,” Hinton said of Naylor, who was in the audience.

Hinton shares a moment with U of T President Emeritus David Naylor (photo by Johnny Guatto)

Hinton also recalled how, when his first wife became ill in 1993 – also with cancer – his post-doctoral trainee Peter Dayan, now a director at the Max Planck Institute for Biological Cybernetics, stepped in to advise Hinton’s graduate students, giving him time to care for his ailing spouse.

“At times like this, you remember the people who helped you most when things were very difficult,” Hinton said.

He later pointed out that Dayan went on to supervise one of this year’s winners of the Nobel Prize in Chemistry, Demis Hassabis, joking that this made Dayan “the meat in a Nobel sandwich.”

Deputy Prime Minister Chrystia Freeland said Canada was lucky to have Hinton, who was born in the U.K., and thanked his daughter for sharing her father with the country, with science and the world (photo by Johnny Guatto)

For Minister Freeland, Hinton’s award showcased the value of ideas and of fundamental research. She said Hinton’s Nobel Prize sent waves of pride across Canada, which she said was “lucky as a country” that Hinton arrived on its shores back in 1987.

“Geoff shows that you can be a really brilliant intellectual and also a really great human being who cares about his community and his country,” Freeland said. “I am constantly struck by how Geoff thinks about the bigger implications of his ideas and how Geoff thinks about really wanting to make Canada and the world a better place.”

Hinton chats with Melanie Woodin, dean of the Faculty of Arts & Science, who thanked him for his friendship and mentorship – and his dedication to science and scholarship (photo by Johnny Guatto)

Following the program – which also featured remarks by U of T Chancellor Wes Hall, Faculty of Arts & Science Dean Melanie Woodin and Christine Szustaczek, U of T’s vice-president, communications – Hinton mingled with members of the audience, shook hands with students and caught up with former colleagues and trainees.

Clockwise from top left: Christine Szustaczek, Wes Hall, Chris Maddison, Leah Cowen, Geoffrey Hinton and Melanie Woodin (photo by Johnny Guatto)

Brendan Frey, professor in the Faculty of Applied Science & Engineering and CEO of AI-powered therapeutics startup Deep Genomics, said he was one of many who earned his PhD under Hinton’s supervision.

“I think of Geoff as the father of a community that includes myself, other graduate students and all the people who didn’t believe but then came to believe – and he inspired all of us,” said Frey, who shared a hug with his former supervisor following the event.

“I’m really happy for him.”

In his words: Geoffrey Hinton reflects on his Nobel Prize win

rahul.kalvapalle — Thu, 10 Oct 2024 16:26:59 +0000

In his words: Geoffrey Hinton reflects on his Nobel Prize win

rahul.kalvapalle Thu, 10/10/2024 - 12:26

Cutline

Geoffrey Hinton, University Professor Emeritus of computer science at the ��߲ݴ�ý and winner of the 2024 Nobel Prize in Physics, takes questions during a virtual press conference hosted by U of T (image via YouTube)

Rahul Kalvapalle

Topic

Global Lens

Department of Computer Science

Global

President Meric Gertler

Subheadline

"If you believe in something, don’t give up on it until you understand why that belief is wrong"

Empower curiosity-driven research. Follow your convictions. Think not just about how to advance technology, but how to direct its use for good.

These were among the key messages delivered by Geoffrey Hinton, University Professor Emeritus of computer science at the ��߲ݴ�ý and winner of the 2024 Nobel Prize in Physics, during an Oct. 8 press conference held by the university to mark his historic award.

Widely regarded as “the godfather of AI,” Hinton was named a co-winner of the prize – alongside John J. Hopfield of Princeton University – for his work on Boltzmann machines and artificial neural networks, which laid the groundwork for advancements in AI and stimulated new research directions in physics.

U of T President Meric Gertler hailed Hinton for having “a profound impact on multiple fields and disciplines,” crediting “his leadership and exemplary mentorship of young scholars” with helping U of T become a global leader in AI and machine learning.

“I think one cannot overstate the impact of a win like this on the ability of Canada, Toronto and the ��߲ݴ�ý to be able to welcome talented newcomers, great students and wonderful faculty from across the country and around the world because of the recognition that arises with Geoff’s win,” President Gertler said.

For his part, Hinton echoed his remarks from earlier in the day that he was “flabbergasted” to receive the prize and pleased that the Nobel committee recognized the advancements in artificial neural networks.

He also answered questions about his influences, legacy and how it feels to go from being an obscure researcher who toiled in a largely forsaken field to a Nobel Laureate – and his advice for researchers who hope to one day follow in his footsteps.

Here are five key themes that emerged from Hinton's news conference:

His legacy

“I’m hoping AI will lead to tremendous benefits, to tremendous increases in productivity and to a better life for everybody. I’m convinced that it will do that in health care.

“My worry is that it may also lead to bad things, and in particular, when we get things more intelligent than ourselves, no one really knows whether we’re going to be able to control them.

“We don’t know how to avoid [catastrophic AI scenarios] at present. That’s why we urgently need more research. So I’m advocating that our best young researchers, or many of them, should work on AI safety and governments should force large companies to provide the computational facilities they need to do that.”

A collaborative effort

“I think of the prize as a recognition of a large community of people who worked on artificial neural networks for many years.

“I’d particularly like to acknowledge my two main mentors: David Rumelhart, with whom I worked on the backpropagation algorithm … and my colleague Terry Sejnowsky, who I worked with a lot in the 1980s on Boltzmann machines and who taught me a lot about the brain.

“I’d also like to acknowledge my students. I was particularly fortunate to have many clever students, much cleverer than me, who actually made things work. They’ve gone on to do great things.

“I should also acknowledge Yoshua Bengio and Yann LeCun who were close colleagues and very instrumental in developing this whole field.”

Canada’s research strengths

“I think the main thing about Canada as a place to do research is there isn’t as much money as there is in the U.S., but it uses its money quite wisely.

“In particular, the main funding council for this type of research, called NSERC, uses money for basic curiosity-driven research, and all of these advances in neural networks came out of basic curiosity-driven research – not out of throwing money at applied problems, but out of letting scientists follow their curiosity to try and understand things. And Canada’s quite good at that.”

Many thought he was wasting his time

“It was a lot of fun doing the research, but it was slightly annoying that many people – in fact, most people in the field of AI – said that neural networks would never work.

"They were very confident these things were a waste of time and we would never be able to learn complicated things – for example, understanding natural language – using neural networks. And they were wrong."

Believe in your ideas

"My message is this: if you believe in something, don’t give up on it until you understand why that belief is wrong.

"Often, you believe in things and you eventually figure out why that’s a wrong thing to believe in. But so long as you believe in something and you can’t see why that’s wrong – like, ‘the brain has to work somehow so we have to figure out how it learns the connection strengths to make it work’ – keep working on it and don’t let people tell you it’s nonsense if you can’t see why it’s nonsense."

‘Godfather of AI' Geoffrey Hinton’s Nobel Prize makes headlines around the world

Christopher.Sorensen — Wed, 09 Oct 2024 19:24:35 +0000

‘Godfather of AI' Geoffrey Hinton’s Nobel Prize makes headlines around the world

Christopher.Sorensen Wed, 10/09/2024 - 15:24

Cutline

(photo by Polina Teif)

Topic

Global Lens

News of Geoffrey Hinton’s Nobel Prize in Physics travelled around the globe, making international headlines for the British-Canadian scientist known as the “godfather of AI.”

A University Professor Emeritus of computer science at the ��߲ݴ�ý, Hinton won the prize jointly with Princeton’s John J. Hopfield for early discoveries and inventions in the realm of physics that laid the groundwork for today’s artificial intelligence boom.

Hinton told The New York Times he was, “shocked and amazed and flabbergasted. I never expected it.”

After receiving the call from Stockholm in the wee hours, Hinton, who was in California, spent much of the day attending press conferences and speaking with reporters from a “cheap hotel room with no internet,” even oscillating mid-interview between the Times and the BBC.

Nevertheless, he seized the moment to emphasize both the promise and pitfalls of AI-based technologies.

“It’s going to be like the Industrial Revolution – but instead of our physical capabilities, it’s going to exceed our intellectual capabilities," he told the BBC. “… but I worry that the overall consequences of this might be systems that are more intelligent than us that might eventually take control.”

Independent UK acknowledged the unusual nature of Hinton’s win in the physics category.

“I’m not a physicist, I have very high respect for physics,” Hinton said. “I dropped out of physics after my first year at university because I couldn’t do the complicated math. So, getting an award in physics was very surprising to me. I’m very pleased that the Nobel committee recognised that there’s been huge progress in the area of artificial neural networks.”

Closer to home, Hinton’s award was lauded in the national media, from CBC: The National – which highlighted his emphasis on the importance of supporting curiosity-driven research and his role in training many of today’s AI leaders – to the front pages of The Toronto Star and The Globe and Mail.

“I want to emphasize that AI is going to do tremendous good,” Hinton told the Globe after receiving the news about the win. “In areas like health care, it’s going to be amazing. That’s why its development is never going to be stopped. The real question is can we keep it safe?”

Read Hinton’s interview in the New York Times (paywall)

Read the BBC story

Read the Globe and Mail story (paywall)

Watch CBC: The National’s report

Congratulations pour in for Geoffrey Hinton after Nobel win

mattimar — Tue, 08 Oct 2024 19:43:16 +0000

Congratulations pour in for Geoffrey Hinton after Nobel win

mattimar Tue, 10/08/2024 - 15:43

Cutline

(Photo by Johnny Guatto/��߲ݴ�ý)

Mariam Matti

Topic

Alumni

University Health Network

Vector Institute

Subheadline

“You always knew when Geoff had a new idea. The excitement, the joy radiated out of his office down the hall. The air was buzzing with possibility"

Reactions to Geoffrey Hinton’s Nobel Prize win began almost immediately after it was announced Tuesday morning.

Known as the “godfather of AI,” the University Professor Emeritus of computer science at the ��߲ݴ�ý shared the Nobel Prize in Physics with John J. Hopfield of Princeton University for groundbreaking work that laid the foundation for machine learning using artificial neural networks.

U of T’s Geoffrey Hinton and Princeton’s John J. Hopfield are pictured during the announcement for the 2024 Nobel Prize in Physics (photo by Jonathan Nackstrand/AFP via Getty Images)

At U of T, students, faculty and staff gathered at an event hosted by the department of computer science in the Faculty of Arts & Science, which Hinton joined as a professor in 1987.

Those in attendance described a revered and beloved figure, citing Hinton’s determination, playful approach to research and excitement for new ideas.

"I really don’t think there’s anyone more deserving of this recognition," said Chris Maddison, assistant professor in U of T’s departments of computer science and statistical sciences, and one of Hinton’s former students.

"Geoff was a fantastic adviser. He worked really, really hard to remove the barriers for his students and he was laser focused on ideas and building his own understanding."

Hinton also received congratulations from universities and other research organizations around the world, including the Royal Society, the Alan Turing Institute and Cambridge University, Hinton’s alma mater.

Meanwhile, friends, colleagues and leaders in politics and business took to social media to express their congratulations for Hinton’s remarkable achievement.

Here’s a snapshot of what some of them said:

From left: Melanie Woodin, Chris Maddison, Nick Frosst, Kevin Swersky and Eyal de Lara (photo by Johnny Guatto)

“Congratulations to Geoffrey Hinton on this fantastic recognition which is a testament to the importance of supporting basic research and the long journey that can lead to profound discoveries like deep learning that forever change our world. Hinton's phenomenal work has seeded new and innovative research by his former students and many around the world who are using AI to solve global challenges in areas like medicine and climate change.“

– Leah Cowen, vice-president, research and innovation, and strategic initiatives at U of T

“I would say his approach to science has stuck with me. Everything I know about how to do science, I mostly learned from him. It's his curiosity and playfulness ... that has been most impactful. I would also say his thoughtfulness, thinking about the consequence of the technology and how it affects society is something that we have taken seriously at Cohere as well.”

– Nick Frosst, U of T alumnus and co-founder of Cohere

“You always knew when Geoff had a new idea. The excitement, the joy radiated out of his office down the hall. The air was buzzing with possibility. He was famous for bursting into a room and pronouncing that, he now finally, after all these years, understood how the brain worked. Above all, he understood that research was a human endeavor. Research is really hard. It becomes personal. It's intertwined with tragedies and compromises. I saw him go through some of his own when I was a student in the group, and I went through my own. I remember when I was going through some health challenges, I went to him and I said, 'Geoff, sometimes it's really hard to go on.’ And he looked at me and he said, ’But we're not going to let that slow us down, will we?’"

– Chris Maddison, assistant professor in U of T’s departments of computer science and statistical sciences, Faculty of Arts & Science

U of T is the only computer science department with a Nobel Prize winner, says Professor Michael Brudno (photo by Johnny Guatto)

“It's a once-in-a-lifetime opportunity to celebrate an award for somebody who has made huge contributions – obviously in AI broadly, [and] to this department. No other computer science department can say that they have a Nobel Laureate. But I think beyond that, it really goes back to show how the birthplace of modern AI is Toronto, how this is the place where it all started and how it's upon us as the AI faculty in this department to continue this legacy.”

– Michael Brudno, professor in U of T’s department of computer science, acting vice-dean, graduate education in Faculty of Arts & Science, a chief data scientist at University Health Network and a faculty member at the Vector Institute

Geoffrey Hinton shares a laugh with fellow AI researcher Raquel Urtasun, left, at the Vector Institute’s opening in 2017 (photo by Lisa Lightbourn)

"Geoff is the most influential person in AI, and our field would certainly not be the same without his immense contributions. He is also a tremendous educator, and has mentored many students and postdocs who have gone on to become very influential in the field of AI. Geoff cares deeply about the Toronto and Canadian ecosystem and was the driving force in the formation of the Vector Institute, which we co-founded together. Through our time as colleagues at the ��߲ݴ�ý, I've deeply admired his commitment to the advancement of AI for good. This recognition is well-deserved and acknowledges his decades of work leading AI innovation and building the foundation for the AI revolution that is happening today.”

– Raquel Urtasun, professor in U of T’s department of computer science, faculty member and co-founder at the Vector Institute, founder and CEO of Waabi

“I was the first to call Geoff Hinton “Godfather of Deep Learning,” which later became “Godfather of AI.” Thrilled to see him win the Nobel prize together with John Hopfield for AI. Congrats @geoffreyhinton.”

– Andrew Ng, co-founder of Coursera, founder GoogleBrain, former chief scientist at Baidu

Prime Minister Justin Trudeau speaks with Geoffrey Hinton at a 2019 event (photo by Nick Iwanyshyn)

“Dr. Hinton is a stalwart in his field. Celebrated as one of the ‘Godfathers of AI’, he brings decades of leading expertise in AI research as a Professor Emeritus at the ��߲ݴ�ý’s Department of Computer Science, and the current Chief Scientific Advisor at the Vector Institute in Toronto – one of our three national AI institutes. Dr. Hinton is also an outspoken advocate for the responsible development and adoption of AI, educating the world about the benefits and challenges this technology poses.

“Canada is at the forefront of AI technology thanks to trailblazers like Dr. Hinton. His curiosity for discovery and contributions to innovation will inspire generations to come. On behalf of all Canadians, I congratulate him on his remarkable achievement.”

– Justin Trudeau, Prime Minister of Canada

A.M. Turing Award winners Yann LeCun, left, Yoshua Bengio, middle, and Geoffrey Hinton at an AI summit in Montreal (photo courtesy of RE•WORK)

"@HopfieldJohn and @geoffreyhinton, along with collaborators, have created a beautiful and insightful bridge between physics and AI. They invented neural networks that were not only inspired by the brain, but also by central notions in physics such as energy, temperature, system dynamics, energy barriers, the role of randomness and noise, connecting the local properties, e.g., of atoms or neurons, to global ones like entropy and attractors. And they went beyond the physics to show how these ideas could give rise to memory, learning and generative models; concepts which are still at the forefront of modern AI research. Their ideas inspired me so profoundly that I decided to choose learning in neural networks for my own research as a graduate student. They motivated me to look for abstract principles that could be as simple as the laws of physics, but could explain biological as well as artificial intelligence. I'm truly delighted for them and for our field."

– Yoshua Bengio, professor at Université de Montréal and co-winner of the A.M Turing Award with Geoffrey Hinton and Yann LeCun

“It was the first thing I saw in the morning. I opened my phone, and it was the headline of The New York Times and I was like, ‘Yes!’ I was very excited. I saw his perseverance – he always goes with what he believes, not what the flow is – and it's quite inspirational for me. That’s what I look for, especially in the health care area. I want to do something meaningful, something big.”

–Tina Behrouzi, second year PhD student in U of T’s department of computer science

Geoffrey Hinton poses with graduate students Ilya Sutskever, left, and Alex Krizhevsky, right, in 2013 (photo by Johnny Guatto)

“Congratulations to @geoffreyhinton for winning the Nobel Prize in physics!!”

– Ilya Sutskever, U of T alumnus, co-founder of OpenAI and co-founder and chief scientist at Safe Superintelligence

“Going to Geoff’s office was always fun. Normally when you go to a supervisor's office, you give them a progress update. You go through what your latest results are, you talk about a couple of your ideas and you get some feedback. Going to Geoff’s office was a completely different story. He would be telling you what his latest idea was. He would show you his latest results. And his whole thing was just that he was really excited about it, and his hope was to inspire you enough to start running with it.”

– Kevin Swersky, U of T alumnus and research scientist at Google DeepMind

“[Hinton's] pioneering research at the ��߲ݴ�ý not only revolutionized the field of AI but has also been instrumental in establishing Canada as a global powerhouse in AI research and innovation."

– Tony Gaffney, president and CEO of the Vector Institute

Fei-Fei Li and Geoffrey Hinton speak at a 2023 event in Toronto (photo by Polina Teif)

“This is beyond exciting! #AI’s far reaching impact is just beginning.”

– Fei-Fei Li, professor of computer science at Stanford University and co-director of Stanford’s Human-Centered AI Institute

“Congratulations to @geoffreyhinton, University Professor Emeritus at@UofT, on winning the 2024 Nobel Prize in Physics! Widely regarded as the “godfather of AI,” Hinton’s pioneering work in artificial neural networks has transformed the global AI landscape. Dr. Hinton’s achievement highlights the best of #Ontario’s world-class talent and thriving AI ecosystem, driving innovation and shaping the future of critical #technology!

– Victor Fedeli, Ontario’s minister of economic development, job creation and trade

"On behalf of the Department and the University, we are very proud to acknowledge Geoff's global achievements and this international recognition. His contributions to machine learning and artificial intelligence have benefited virtually every discipline in science, engineering, social sciences and medicine. As we celebrate the department’s 60th anniversary, this award embodies six decades of impact and innovation in computer science and technology.”

– Eyal de Lara, professor and chair of U of T’s department of computer science, Faculty of Arts & Science

“Heartfelt congratulations to Geoff Hinton @geoffreyhinton on winning the Nobel Prize! What an incredible honor! I feel deeply privileged to have had the opportunity to be your PhD student, work with you, and learn from you.”

– Russ Salakhutdinov, professor of computer science at Carnegie Mellon University

Hinton approached his research with an “almost childlike playfulness,” says Graduate student Ujan Sen (photo by Johnny Guatto)

“Waking up today, seeing that news, just motivates me even more and reaffirms the decision that coming to U of T was the absolutely the best decision I could have taken. I think one of the people who had previously worked with him mentioned something along the lines of: Geoff didn't really care too much about pedigree. He cared about ideas, regardless if you have a master's or PhD. And the way he approached his research and the almost childlike playfulness and innocence he had with sort of getting to the answer is something that I really resonate with.”

– Ujan Sen, master’s student in U of T’s department of computer science, Faculty of Arts & Science

"I want to extend my heartfelt congratulations to Dr. Geoffrey Hinton on winning the 2024 Nobel Prize in Physics. His groundbreaking work in machine learning has made Canada world-renowned in the field of AI. He is a true inspiration for the next generation of Canadian researchers!"

– François-Philippe Champagne, Canada’s minister of innovation, science and industry

“I’m originally a chemist. It was a surprising thing for me that a Nobel Prize is being awarded to a computer scientist. This is a big moment for computer science. I think it will bring recognition to physics-based approaches and core thinking.

“I think it’s great that some universities like U of T are willing to believe in crazy ideas. People need to encourage outside-the-box thinking.”

– Ella Rajaonson, PhD student in the Matter Lab with U of T professor Alán Aspuru-Guzik

“Congratulations!”

– Olivia Chow, mayor of Toronto

Geoffrey Hinton wins Nobel Prize in Physics

davidlee1 — Tue, 08 Oct 2024 19:35:09 +0000

Geoffrey Hinton wins Nobel Prize in Physics

davidlee1 Tue, 10/08/2024 - 15:35

Cutline

(Photo by Johnny Guatto/��߲ݴ�ý)

Rahul Kalvapalle

Topic

Global Lens

Physics