Computer Science

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.

Math grad says U of T taught him to 'learn how to learn'

rahul.kalvapalle — Wed, 06 Nov 2024 20:15:18 +0000

Math grad says U of T taught him to 'learn how to learn'

rahul.kalvapalle Wed, 11/06/2024 - 15:15

Cutline

Sean Fridkin, who earned a bachelor's in math and computer science, counts University Professor Emeritus and Nobel Prize-winner Geoffrey Hinton among his inspirations (supplied image)

Topic

Mathematics

Trinity College

Undergraduate Students

Subheadline

Sean Fridkin, 18, completed his high school and undergraduate studies in a total of four years

Some might view mathematics as an abstract discipline, but Sean Fridkin sees it as way to understand the world and solve problems – and he’s getting an early start.

Fridkin recently crossed the stage in the ��߲ݴ�ý’s Convocation Hall to receive his honours bachelor of science in mathematics with a specialist in computer science, completing his high school and undergraduate studies in a total of four years.

Born in Israel, Fridkin spent most of his childhood and formative years in Bradford West Gwillimbury, Ont., where he managed to complete high school in two years after qualifying for a gifted students’ program.

He says he wanted to attend U of T because it’s the top university in Canada. “U of T has the best faculty by far,” he said, citing among his inspirations University Professor Emeritus and Nobel Prize winner Geoffrey Hinton, “and you have opportunities to learn and take a bunch of different courses.

"It’s also a very prestigious program, so in terms of job prospects, it’s really good. Plus, the people are great ... it’s a great place to learn, grow and network.”

Fridkin spoke to U of T News about his interest in math and computer science, plans for the future and thoughts on how students can get the most out of their undergraduate years:

How did you become interested in mathematics and computer science?

I think math is about trying to understand the world. All around, you have things going on, things happening, and math is seeing the patterns and getting a deeper understanding for what’s going on. That’s always interested me. I think it interests most people – they just don’t know that it’s called math and there’s a language for it.

The way I did my math major is I took a bunch of courses in different fields, but the main thing that unifies them is they teach you to think about the world in different ways. You have classes about the closeness of objects, analysis, topology, you’ve got to think about different types of infinities … You expand the way in which you think, and that interested me more than any specific path within mathematics.

In terms of computer science, I really like numerical methods, where you try to approximate – given some data – and interpolate with models to predict real things. I find machine learning numerical methods really fun.

What was your approach to learning while at U of T?

The number one goal for me in university was to learn how to learn – and learn how to think about new things and discover new ways of thinking. That’s the thing U of T gave me. You get the opportunity to talk to lots of different people, lots of different perspectives and learn from professors who are very experienced in the field and often have a unique way of thinking about different problems.

Also, with the assignments, you have to push yourself and that’s what I enjoyed about it. It was a little different from high school, where an assignment might take you 30 minutes. Here, for some of the math classes, I would have to initially spend maybe a couple of days on one assignment. And I learned a lot from that. I think it’s going to impact me, not only as I go further in my career, but also in life.

What are your plans for your career and education going forward?

I’m looking for computer science roles. I want to learn from interesting people and work on interesting problems. That’s the main goal. I don’t have any specific field that I’m going for within computer science – just interesting problems and people who I know I’ll be able to learn from and who I admire and respect.

I really enjoy learning, but I think most learning can be done informally, on your own or with good people at a company. But certainly, if there’s something interesting that I want to learn more about and that I can’t do on my own, I would 100 per cent go back to school.

Long term, I want to transition to something in the machine learning world.

What would be your advice to students starting university?

I think the actual coursework is secondary to what you do in university. You should learn how to approach new problems ... For me, since I was going into math and computer science, I read a little bit of math stuff and I mostly worked on brain teaser problems. That helped me much more than any domain knowledge in any of the fields.

As soon as you build that thought process in your head – as soon as that becomes your natural way of thinking – everything becomes so much easier.

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

Nobel Prize

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.”

‘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

Nobel Prize

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

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

Nobel Prize

Physics

Temerty Faculty of Medicine

Subheadline

A U of T University Professor Emeritus, Hinton shared the honour with Princeton University's John J. Hopfield for discoveries and inventions that enable machine learning with artificial neural networks

Geoffrey Hinton, a University Professor Emeritus of computer science at the ��߲ݴ�ý, has won the 2024 Nobel Prize in Physics.

Widely regarded as the “godfather of AI,” Hinton shared the prize with John J. Hopfield of Princeton University for foundational discoveries and inventions that enable machine learning with artificial neural networks.

Hinton said he was “flabbergasted” at the honour as messages poured in from around the world.

“I had no expectations of this,” he told U of T News shortly after the win was announced in Stockholm Tuesday morning. “I am extremely surprised and I'm honoured to be included.”

He later told reporters at a press conference he was “in a cheap hotel in California” with no Internet and a poor phone connection when he was notified about his Nobel Prize.

“I was going to get an MRI scan today, but I think I’m going to have to cancel that.”

Hinton and Hopfield are credited with wielding tools from physics to advance basic research in the field. Specifically, Hopfield created an associative memory that can store and reconstruct images in data, while Hinton invented a way to find properties in data and perform tasks such as identifying specific elements in pictures.

“On behalf of the ��߲ݴ�ý, I am absolutely delighted to congratulate University Professor Emeritus Geoffrey Hinton on receiving the 2024 Nobel Prize in Physics,” said U of T President Meric Gertler. “The U of T community is immensely proud of his historic accomplishment.”

Hinton was selected for the high-profile award for his use of the Hopfield network – invented by his co-laureate – as the foundation for a new network called the Boltzmann machine that can learn to recognize elements within a given type of data.

The Boltzmann machine can classify images and generate new examples of the pattern on which it was trained, with Hinton and his graduate students later building on this work to help usher in today’s rapid development of machine learning – a technology that now underpins a host of applications ranging from large language models such as ChatGPT to self-driving cars.

Johan Jarnestad/The Royal Swedish Academy of Sciences

“The laureates’ work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties,” said Ellen Moons, chair of the Nobel Committee for Physics.

The win by Hinton and Hopfield was covered by media and other organizations around the globe, with The New York Times describing the Nobel committee’s decision as “an acknowledgement of AI’s growing significance in the way people live and work,” and the prestigious journal Nature noting Hinton’s innovations now “form the basis of many state-of-the-art AI tools.”

Hinton joined U of T as a professor of computer science in 1987 after working in various universities in the U.K., where he was born, and in the United States. He went on to be named a University Professor – U of T’s highest academic appointment – in 2006.

Driven by a desire to understand the human brain, Hinton and his graduate students built on his early efforts with an array of developments that paved the way for an explosion in deep learning. One of the first cohort of researchers supported by the Canadian Institute for Advanced Research (CIFAR), Hinton’s work helped catapult Canada to its current status as a global leader in AI development.

The Royal Swedish Academy of Sciences, which awards the Nobel Prize in Physics, noted Hinton persisted with his research even as the scientific community lost interest in artificial neural networks during the 1990s, and ultimately “helped start the new explosion of exciting results” in the 2000s.

Hinton, for his part, said during a U of T press conference Tuesday evening that his achievements wouldn’t have been possible without support for curiosity-based research – something he said Canada was good at.

He added that his shock at winning the Nobel stemmed from the fact that, while his work has drawn on statistical physics, he isn’t a physicist himself – and even “dropped out of physics after my first year in university because I couldn’t do the complicated math.”

He also said that he plans to donate the money associated with the prize to various charities, including one that provides jobs for neurodiverse young adults.

Hinton likened the influence of AI to that of the Industrial Revolution during a virtual press conference with the academy earlier in the day – “But instead of exceeding people in physical strength, it’s going to exceed people in intellectual ability.”

He added that the rise of AI “is going to be wonderful in many respects,” citing health care and workplace productivity as two areas poised to benefit hugely from the technology. “But we also have to worry about a number of possible bad consequences, particularly the threat of these things getting out of control,” Hinton said.

In early 2023, Hinton quit his job at Google and focused on sounding the alarm about the risks of rapid and unfettered AI development. He outlined his reasoning in a 46-minute U of T video last year, urging young researchers to focus their efforts on the emerging field of AI safety – a message he repeated in media interviews following his Nobel win.

He has continued to tackle the issue at lectures and public appearances around the world, including at U of T and at Cambridge University, his alma mater.

“I am thrilled Geoffrey Hinton, an esteemed colleague and dear friend has been awarded the Nobel Prize in Physics,” said Melanie Woodin, dean of U of T’s Faculty of Arts & Science.

“Geoff is an historic visionary whose groundbreaking work in deep learning and neural networks has made U of T and the Toronto region a leading global centre for AI. And it speaks volumes about his integrity that while he helped lay the foundation for the artificial intelligence revolution, he is also one of the leading voices urging that we develop this technology responsibly and ethically.”

Similarly, Prime Minister Justin Trudeau lauded Hinton for his efforts to realize responsible AI development, releasing a statement and writing on X: “Geoffrey, we’re glad to have a mind like yours developing safe and responsible AI for the world.”

Geoffrey Hinton delivers a lecture about responsible AI to U of T students and faculty (photo by Johnny Guatto)

Hinton, who is co-founder and chief scientific adviser at the Vector Institute in Toronto, joins an illustrious list of past Nobel Prize in Physics winners that includes Albert Einstein and Marie Curie (who also won a Nobel in chemistry). The prestigious award is the latest in a long list of accolades for Hinton. They include the Association for Computing Machinery’s A.M. Turing Award – widely considered “the Nobel Prize of computing” – in 2019 alongside collaborators Yann LeCun and Yoshua Bengio.

Hinton is the fourth U of T faculty member to win a Nobel Prize over the years.

Sir Frederick Banting and J.J.R Macleod won a Nobel Prize in Physiology or Medicine for their work with Charles Best in 1923 to isolate insulin. In 1986, John Polanyi was one of three winners of the Nobel Prize in Chemistry for the development of the new field of reaction dynamics.

Other members of the U of T community, including several alumni, have received or been associated with the international honour.

Oliver Smithies, a past professor at U of T, was a joint winner of the Nobel Prize in Physiology or Medicine in 2007 for discovering the “principles for introducing specific gene modifications in mice by the use of embryonic stem cells.”

Also in 2007, Professor Robert Jefferies shared in the Nobel Peace Prize awarded to the Intergovernmental Panel on Climate Change (IPCC), in which he was a key Canadian representative as an international leader in Arctic science and global change biology.

In 1999, U of T Professor James Orbinski accepted the Nobel Peace Prize on behalf of Doctors Without Borders, which was recognized for its humanitarian work.

Anti-nuclear activist and U of T alumna Setsuko Thurlow accepted the Nobel Peace Prize in Norway in 2017 on behalf the International Campaign to Abolish Nuclear Weapons (ICAN).

In 2001, Michael Spence, an alumnus of ��߲ݴ�ý Schools, was one of three joint winners of the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel for his contributions to analyses of markets with asymmetrical information.

Bertram Brockhouse, who completed two degrees at U of T, was a co-winner of the Nobel Prize in Physics in 1994 for the development of neutron scattering techniques for studies of condensed matter.

Arthur Schawlow, an alumnus, was one of three winners of the same prize in 1981 for his contribution to the development of laser spectroscopy.

In 1998, U of T alumnus Walter Kohn was a co-winner of the Nobel Prize in Chemistry for development of the density-functional theory.

Former Prime Minister Lester B. Pearson, who received a bachelor’s degree from U of T, won the Nobel Peace Prize in 1957.

U of T researchers develop AI model to predict 'very dynamic' peptide structures

Christopher.Sorensen — Thu, 15 Aug 2024 12:54:41 +0000

U of T researchers develop AI model to predict 'very dynamic' peptide structures

Christopher.Sorensen Thu, 08/15/2024 - 08:54

Cutline

PhD Graduate Osama Abdin and Professor Philip M. Kim developed a deep-learning model that can predict all possible shapes of peptides, which are are of keen interest to researchers who are developing therapeutics (supplied image)

Anika Hazra

Topic

Breaking Research

Donnelly Centre for Cellular & Biomolecular Research

Subheadline

The new model expands on the capabilities of Google DeepMind's AlphaFold, the leading AI system for predicting protein structures

Researchers at the ��߲ݴ�ý have developed a deep-learning model that can predict all possible shapes of peptides – chains of amino acids that are shorter than proteins, but perform similar biological functions.

Called PepFlow, the model combines machine learning and physics to model the range of folding patterns that a peptide can assume based on its energy landscape.

Peptides, unlike proteins, are dynamic molecules that can take on a range of conformations. They are involved in many biological processes that are of keen interest to researchers who are developing therapeutics.

“We haven’t been able to model the full range of conformations for peptides until now,” said Osama Abdin, first author on the study and recent PhD graduate of molecular genetics at U of T’s Donnelly Centre for Cellular and Biomolecular Research. “PepFlow leverages deep-learning to capture the precise and accurate conformations of a peptide within minutes.

“There’s potential with this model to inform drug development through the design of peptides that act as binders.”

The study was recently published in the journal Nature Machine Intelligence.

A peptide’s role in the human body is directly linked to how it folds since its 3D structure determines the way it binds and interacts with other molecules.

“Peptides were the focus of the PepFlow model because they are very important biological molecules and they are naturally very dynamic, so we need to model their different conformations to understand their function,” said Philip M. Kim, the study’s principal investigator and a professor at the Donnelly Centre. “They’re also important as therapeutics, as can be seen by the GLP1 analogues, like Ozempic, used to treat diabetes and obesity.”

Peptides are also cheaper to produce than their larger protein counterparts, said Kim, who is also a professor of computer science in U of T’s Faculty of Arts & Science and a professor of molecular genetics in the Temerty Faculty of Medicine.

The new model expands on the capabilities of AlphaFold, the leading Google DeepMind AI system for predicting protein structure. It does this by generating a range of conformations for a given peptide. Taking inspiration from highly advanced physics-based machine learning models, PepFlow can also model peptide structures that take on unusual formations, including the ring-like structure that results from a process called macrocyclization. Peptide macrocycles are currently a highly promising venue for drug development.

“It took two-and-a-half years to develop PepFlow and one month to train it, but it was worthwhile to move to the next frontier beyond models that only predict one structure of a peptide,” Abdin said.

There are, however, limitations given that PepFlow represents the first version of a new model. The study authors noted a number of ways in which PepFlow could be improved, including training the model with explicit data for solvent atoms, which would dissolve the peptides to form a solution, and for constraints on the distance between atoms in ring-like structures.

Yet, even as a first version, the researchers say PepFlow is a comprehensive and efficient model with potential for furthering the development of treatments that depend on peptide binding to activate or inhibit biological processes.

“Modelling with PepFlow offers insight into the real energy landscape of peptides,” said Abdin.

The research was supported by the Canadian Institutes of Health Research and the Natural Sciences and Engineering Research Council of Canada.

Learning rewired: U of T researcher sparks kids’ interest in tech with animatronic critters

bresgead — Tue, 16 Jul 2024 18:22:39 +0000

Learning rewired: U of T researcher sparks kids’ interest in tech with animatronic critters

bresgead Tue, 07/16/2024 - 14:22

Cutline

Paul Dietz, a distinguished engineer in residence and director of fabrication in U of T’s computer science department, hopes his paper animatronic creations can engage more kids in STEM through the power of storytelling (photo by Polina Teif)

Topic

Ontario Institute for Studies in Education

Robotics

STEM

Subheadline

Paul Dietz says robotic paper creations are a creative – and more inclusive – way to get kids interested in STEM fields

Could a talking paper octopus be the key to igniting kids' curiosity about technology?

��߲ݴ�ý engineer Paul Dietz certainly thinks so. With the help of a menagerie of mechanically controlled puppets, he has a plan to help students learn to think creatively across a wide range of fields.

All it takes is some simple circuitry, a few arts and crafts supplies – and a lot of imagination.

A distinguished engineer in residence and director of fabrication in the Faculty of Arts and Science’s computer science department, Dietz is the whimsical mind behind the Animatronics Workshop. The program collaborates with schools to provide opportunities for children to create, design and build their own robotic shows.

Dietz has been partnering with schools where kids create their own animatronic stories – from staging pre-programmed puppet shows to hosting Q-and-As with Shakespeare – departing from the competition-based competitions typical of many youth robotics efforts.

Dietz’s program has been his passion project for a decade and a half, developed on the side while he worked day jobs engineering innovations for companies like Microsoft, Mitsubishi and Disney, as well as his own startups.

Now, at U of T, Dietz is focusing on bringing accessible and affordable animatronics to classrooms across Canada. The goal, he says, is to teach kids to use technology as a tool for storytelling, dismantling what he sees as a false divide between the arts and sciences.

“One of the first participants in this program was a young girl who was really into writing creative stories and really loved science. And she saw these as two conflicting parts of her world,” says Dietz, who is also a faculty affiliate at the Schwartz Reisman Institute for Technology and Society.

“After what she did in animatronics, it suddenly dawned on her that you can do both. If you do engineering right, it is a creative art.”

In a capstone course on physical computing in K-12, Dietz encouraged undergraduate students to explore how computer-based systems can bring stories to life in the classroom (photo by Polina Teif)

Dietz had a similar realization as a teenager in the late 1970s, when a behind-the-scenes tour of Walt Disney Imagineering got him tinkering with an animatronic robot penguin.

This early fusion of technical skills and storytelling sensibilities set Dietz on a path that turned flights of imagination into real-world breakthroughs that shape our engagement with technology.

A prolific inventor and researcher, Dietz is best known for co-creating an early progenitor of the multi-touch display technology that’s ubiquitous in today’s smartphones and tablets. Other innovations include 'Pal Mickey,' an interactive plush toy that guided visitors through Disney theme parks, and parallel reality displays that allow multiple viewers to see individualized content on the same screen.

Dietz says his storied career debunks the common misconception – often reinforced in schools – that creativity is exclusive to artistic pursuits, while science is the domain of strict rationality, where there are prescribed methods of inquiry to arrive at a single correct answer.

As Dietz sees it, weaving a narrative and programming a robot are propelled by the same creative impulse – they just exercise different skills. He believes a well-rounded education should equip students with a diverse arsenal of tools to explore new ideas.

“If you’re an artist, you have to learn the mechanics of sculpting or painting or whatever your medium is,” he says. “We should be looking at engineering and technology as those tools, and the key is … learning how to use them creatively to achieve things that are actually positive for our society.”

The universal appeal of storytelling also serves to make technology accessible and exciting to kids of all ages and genders, Dietz adds.

Bridging the gender divide in STEM has been core to Dietz’s animatronics mission since its inception.

When his daughter was in middle school, Dietz took her to a robotics competition – but she was turned off by the contest, which seemed pointless to her. However, when the two of them worked together on an animatronic raccoon, he saw her passion for creating ignite.

“This light bulb went off in my head: Maybe the problem isn’t that we’re doing tech,” says Dietz. “Maybe kids like my daughter need to see some application that makes sense to them – like telling a story.”

Kids at the Eric Jackman Institute of Child Study are encouraged to develop creative and computer science skills (photo courtesy of JICS)

Over the years, Dietz has partnered with several schools to set up animatronics workshops that attracted an even number of boys and girls and ensured every kid participated in all aspects of the projects – from storytelling and character design to robot building and programming.

But as his career took him across the U.S., Dietz found it difficult to sustain and replicate the success of the programs because of the prohibitive costs of full-scale animatronic robots and the significant technical expertise required from teachers.

At U of T, Dietz is working to bring animatronics to schools of all resources, allowing students to develop creative and computer science skills by harnessing the endless storytelling possibilities of paper.

Undergraduate students demo an interactive diorama during a capstone showcase at the Bahen Centre for Information Technology (photo by Polina Teif)

At the Dr. Eric Jackman Institute of Child Study (JICS) at U of T’s Ontario Institute for Studies in Education, students from kindergarten through Grade 6 have put Dietz’s paper animatronics kits to the test, bringing characters to life with kinetic, vocal creations.

The laboratory school has hosted a series of pilot projects where kids fashioned characters out of construction paper, recorded voices and wired motorized movements to animate creations ranging from a chomping, sharp-toothed maw to a bouncing kitten.

Dietz hopes the pilot program at JICS, pictured, can be scaled up to schools across the country (photo courtesy of JICS)

Nick Song, a special education and technology teacher at JICS, says he sees enormous educational potential for paper animatronics to engage students in hands-on, interactive learning that simultaneously develops technology skills and fosters creative expression.

“The kids love doing things with technology because it gives them a really cool feedback loop where they can try something and see it work immediately,” says Song. “All of this is very motivating for kids, seeing something pick up their voice and start moving, and you almost feel like it’s coming to life.”

Building on the pilots at JICS, Dietz is aiming to scale up the program to schools across the country in hopes of nurturing the next generation of out-of-the-box innovators.

“It’s very different from the technical work that I’ve generally done … but it feels very right,” says Dietz. “I think we’re doing something important for Canada.”

With U of T innovators front and centre, Collision conference wraps up five-year Toronto run

rahul.kalvapalle — Fri, 21 Jun 2024 14:52:23 +0000

With U of T innovators front and centre, Collision conference wraps up five-year Toronto run

rahul.kalvapalle Fri, 06/21/2024 - 10:52

Cutline

U of T alumna Nuha Siddiqui, co-founder and CEO of Erthos, on stage at the Collision tech conference (photo by Johnny Guatto)

Rahul Kalvapalle

Adina Bresge

Topic

Institutional Strategic Initiatives

U of T Entrepreneurship

Innovation & Entrepreneurship

Innovations & Partnerships Office

Startups

Budding entrepreneurs, leading scientists and future business leaders from the ��߲ݴ�ý community played a leading role at the 2024 Collision tech conference in downtown Toronto.

Running over three days this week, the conference drew some 40,000 attendees from across the spheres of tech, business and media, including more than 1,600 startups and 700 investors.

The rapid development of artificial intelligence technologies and their impact on business and society were key themes for many of the conference’s keynotes and exhibits – so it was no surprise that U of T’s AI luminaries were front and centre.

They included U of T alumnus Aidan Gomez, co-founder of language processing startup Cohere – which has raised hundreds of millions from investors and generated significant industry buzz.

He urged businesses to commit to adopting AI tools to support their workers.

“Making sure that you’re delivering the tools that your employees need to be competitive and effective is crucial,” Gomez said during his talk on Tuesday.

He added that augmentation of workforces with AI co-pilots and assistants is inevitable – including in industries that might not stand out as obvious adopters of the technology. He shared the example of a natural resources insurance firm that built an AI co-pilot – powered by Cohere – to help their actuaries speed up their research, craft more accurate bids and win more contracts.

“I never would have thought a natural resources insurance company would be adopting LLMs [large language models], but they are, and it’s having an impact. It’s actually helping them win more business,” Gomez said. “So I think the technology is completely horizontal.”

Gomez also cited the medical sector – particularly, drug discovery – as another area that’s poised to benefit massively from AI advances.

Aidan Gomez, a U of T alumnus, talked about how AI will be used to augment the workforce (photo by Polina Teif)

University Professor Emeritus Geoffrey Hinton, the influential computer scientist often dubbed the “godfather of AI,” also identified medical care and productivity as two key areas that will see significant improvements thanks to AI. However, much of his discussion, titled "Can We Control AI?", focused on his previously-cited concerns about how AI development could ultimately wrest control from humans given the current race to develop the technology and the absence of sufficient safeguards.

“Even if I’m totally benevolent and I just want to achieve what you asked me to achieve, I’ll realize that if I get more control, it will be easier to do that,” Hinton said of AI agents.

“And actually, if these things are much smarter than us, they’ll realize: Just take the control away from people and it will be much more efficient … and that seems to be like a very slippery path.”

Geoffrey Hinton warned of the existential dangers posed by unchecked AI development (photo by Johnny Guatto)

Gomez, for his part, said he doesn’t believe AI poses a serious threat.

“The notion that the technology is going to start self-improving, that it’s going to start manipulating people, that it’s going to take over, seize power and displace humans: that’s a sci-fi narrative,” he said. “I am empathetic to it – we’ve been writing stories about that exact scenario for decades, since before computers, and so it’s very deeply embedded in our cultural brainstems ... I just don’t think it’s true.”

Earlier at the conference, Raquel Urtasun, founder of self-driving trucking startup Waabi, spoke about generative AI and how Waabi is applying the technology to autonomous trucks. Her keynote took place following the company’s announcement that it raised US$200 million in Series B funding to support the deployment of driverless trucks in 2025.

“Everything will be controlled by generative AI systems inside the vehicle and nothing else. This is a breakthrough for the industry, where such a thing has never happened before,” said Urtasun, a professor in the department of computer science in U of T's Faculty of Arts & Science.

Raquel Urtasun spoke about her self-driving truck startup Waabi (photo by Polina Teif)

The conference also featured demos from other promising U of T startups including Erthos, which has invented sustainable alternatives to plastics and is now using machine learning to accelerate biomaterials discovery.

“Our platform allows us to design effective biomaterials five times faster and with 92 per cent less cost compared to our industry,” said co-founder and CEO Nuha Siddiqui.

Collision’s final year in Toronto was expected to draw some 40,000 attendees (photo by Johnny Guatto)

During the conference, U of T’s booth near the main stage featured exhibits from campus-linked accelerators, institutional strategic initiatives, academic divisions and the Innovations and Partnerships Office. U of T’s Black Founders Network was also at Collision as one of the organizers of the Black Innovation Zone.

Collision 2024 marked the fifth and final edition of the annual conference in Toronto.

AI safety, cybersecurity experts take on key roles at Schwartz Reisman Institute for Technology and Society

Christopher.Sorensen — Fri, 14 Jun 2024 15:18:33 +0000

AI safety, cybersecurity experts take on key roles at Schwartz Reisman Institute for Technology and Society

Christopher.Sorensen Fri, 06/14/2024 - 11:18

Cutline

From left to right: David Duvenaud and Roger Grosse have been appointed Schwartz Reisman Chairs in Technology and Society; David Lie has been appointed director of the Schwartz Reisman Institute for Technology and Society (supplied images)

Adina Bresge

Topic

Schwartz Reisman Institute for Technology and Society

Faculty of Applied Science & Engineering

Faculty of Law