Artificial Intelligence

Department of Computer Science

It was a whirlwind week for the University of Toronto’s Geoffrey Hinton, who travelled to Sweden to officially accept the 2024 Nobel Prize in Physics alongside a jam-packed schedule of receptions, lectures, talks, ceremonies, banquets and media engagements.

The ��˾��ֱ�� University Professor emeritus of computer science known as the “godfather of AI” received the prestigious award during a formal ceremony at Stockholm Concert Hall — the main event during Nobel Week, held from Dec. 6 to 12 in Stockholm and Oslo.

He shared the honour with John J. Hopfield of Princeton University for foundational work that paved the way for today's rapid advances in artificial intelligence, which some have called “the next industrial revolution.”

From the days leading up to the ceremony to the celebrations overseas and back home in Toronto, here are a few highlights and behind-the-scenes glimpses of a historic moment for Hinton and the rest of the ��˾��ֱ�� community.

(Photo by Jennifer Cressman)

After arriving in Stockholm on Dec. 5, Hinton takes part in a panel discussion at the Royal Swedish Academy of Engineering Sciences (IVA) on AI development, humanity and the future. He is joined by IVA fellows Kristina Höök, Anders Sandberg and Staffan Truvé, and moderator Anette Novak.

(© Nobel Prize Outreach / Nanaka Adachi)

On Dec. 6, Hinton signs the guest book at the Nobel Prize Museum, where he donated an early Boltzmann machine – a chip about the size of a postage stamp that can be used to recognize elements in data.

(Photos by © Nobel Prize Outreach / Clément Morin, left, and at right, ��˾��ֱ�� staff)

Continuing a Nobel tradition dating back to the event’s 100th anniversary in 2001, Hinton signs the underside of a chair at the Nobel Prize Museum’s restaurant, joining the names of fellow laureates etched into history.

(Photos by Jennifer Cressman)

During a news conference at the Royal Swedish Academy of Sciences on Dec. 7, Hinton pauses to snap a personal photo, capturing a moment of his historic week.

(Photo by ��˾��ֱ�� staff)

In his Nobel Prize lecture in physics on Dec. 8, Hinton talks about how decades of his fundamental research, and that of his co-laureate, paved the way for the development of artificial neural networks and machine learning.

Hinton explains the significance of the Boltzmann machine, which he based on an invention of his co-laureate that was known as the Hopfield network.

(Photo by Pontus Lundahl/TT News Agency/AFP via Getty Images)

Hinton and John J. Hopfield shake hands after delivering their Nobel Prize lectures.

(© Nobel Prize Outreach / Clément Morin)

Since the 1960s, the laureates have participated in a roundtable discussion for television’s Nobel Minds. At the taping on Dec. 9, Hinton was joined by David Baker, Demis Hassabis, Gary Ruvkun, Daron Acemoglu and James A. Robinson.

The following day, Hinton officially accepted his Nobel Prize in Physics.

(Photo by Jonas Borg)

Hinton poses with Ulrika Gustafsson, his Nobel attaché, prior to the formal ceremony on Dec. 10.

The event, which was livestreamed from Stockholm Concert Hall, was watched around the world.

Congratulations to Canada’s own Geoffrey Hinton — “Godfather of AI”, Professor Emeritus, and now, Nobel laureate. pic.twitter.com/CON0AtXokq
— Justin Trudeau (@JustinTrudeau) December 10, 2024

(Photos by Diana Tyszko, Matt Hintsa, Sobica Vinayagamoorthy and Shauna Rempel)

On Dec. 10 in Toronto, ��˾��ֱ�� community members shared in the moment via Nobel ceremony watch parties that were held across ��˾��ֱ��’s three campuses.

(© Nobel Prize Outreach / Nanaka Adachi)

Hinton receives his Nobel Prize from King Carl XVI Gustaf of Sweden during the ceremony.

(Photo by Jonathan Nackstrand/AFP via Getty Images)

Hinton sits next to Anna Sjöström Douagi, acting CEO of the Nobel Foundation, during the Nobel Prize banquet on Dec. 10.

“If the benefits of the increased productivity can be shared equally, it will be a wonderful advance for all of humanity,” Hinton told the audience, before repeating his warnings about the near- and longer-term dangers posed by rapid, unfettered progress of the technology. See the complete text of Hinton's acceptance speech.

��˾��ֱ��’s Geoffrey Hinton receives Nobel medal, diploma at Stockholm ceremony

Christopher.Sorensen — Tue, 10 Dec 2024 20:05:05 +0000

��˾��ֱ��’s Geoffrey Hinton receives Nobel medal, diploma at Stockholm ceremony

Christopher.Sorensen Tue, 12/10/2024 - 15:05

Cutline

��˾��ֱ�� University Professor Emeritus Geoffrey Hinton receives his Nobel Prize in Physics from King Carl XVI Gustaf of Sweden during the Nobel Prize award ceremony in Stockholm (photo by Henrik Montgomery/TT News Agency/AFP/Getty Images)

Topic

Global Lens

Subheadline

“I think we all feel an enormous amount of pride in the fact that [��˾��ֱ��] is one of the places where modern machine learning was really born - in large part due to Geoff Hinton”

The University of Toronto’s Geoffrey Hinton officially accepted his Nobel Prize in Physics during a formal ceremony at Stockholm Concert Hall that included orchestra music, an opera singer and a stage lined with bouquets of flowers.

Wearing white tie and tails, Hinton strode across the dark blue stage emblazoned with the words “The Nobel Prize” to receive his Nobel medal and diploma from King Carl XVI Gustaf of Sweden. Then, as the Royal Stockholm Philharmonic Orchestra sounded a few triumphant notes, he bowed to the Swedish royal family, the audience and returned to his seat at the side of the stage.

Geoffrey Hinton looks out over the audience after King Carl XVI Gustaf of Sweden presented him with his award (photo by Henrik Montgomery/TT News Agency/AFP/Getty Images)

And just like that, decades of scholarly endeavour in artificial intelligence (AI) – which began on the fringe of the AI research community and is now poised to forever change modern life – culminated in the ��˾��ֱ�� University Professor emeritus of computer science known as the “godfather of AI” receiving humanity’s most prestigious award for scientific achievement.

Hinton shared the 2024 Nobel Prize in Physics with John J. Hopfield of Princeton University, who made a similar journey inside the concert hall a few moments earlier. The pair were awarded the prize for discoveries and inventions that enabled machine learning with artificial neural networks, laying the groundwork for today’s AI boom.

“[Geoffrey Hinton] pioneered the efforts to establish deep and dense neural networks,” said Ellen Moons, the chair of the Nobel physics committee, during her introductory remarks. “Such networks are effective in sorting and interpreting large amounts of data and self-improve based on the accuracy of the result.

“Today, artificial neural networks are powerful tools in research fields spanning physics, chemistry and medicine, as well as in daily life.”

In fact, the three winners of the 2024 Nobel Prize in Chemistry – who were seated to Hinton’s left – drew heavily on AI for their work in predicting protein structures and computational design of proteins.

��˾��ֱ�� President Meric Gertler snapped this photo of the Nobel laureates from his seat in the concert hall (photo by Meric Gertler)

Back in Toronto, members of the ��˾��ֱ�� community gathered at watch parties on all three campuses to take in the livestream of the awards ceremony – a historic moment for the university.

On the St. George campus, more than one hundred gathered to watch and cheer in the main lobby of the Schwartz Reisman Innovation Campus – home to ��˾��ֱ��’s Schwartz Reisman Institute for Technology and Society and the Vector Institute for Artificial Intelligence, where Hinton serves as an advisory board member and chief scientific adviser, respectively.

Among the gathered audience was Michael Guerzhoy, an assistant professor, teaching stream, of engineering science and mechanical and industrial engineering in the Faculty of Applied Science & Engineering who took Hinton’s course on neural networks and machine learning in 2005 – before going on to teach the same course himself several years later.

“I think we all feel an enormous amount of pride in the fact that this is one of the places where modern machine learning was really born, in large part due to Geoff Hinton,” Guerzhoy said. “I think a lot of us owe a personal debt of gratitude to him.”

��˾��ֱ�� community members gather in the lobby of the Schwartz Reisman Innovation Campus to watch the ceremony (photo by Matt Hintsa)

Fourth-year undergraduate student James Wang said he was “in awe” as he watched Hinton receive his Nobel Prize.

“It makes you feel inspired. You want to be like him, you want to attain the highest level of achievement that anybody can achieve – and not just to do it for your sake, but also for the academic community at large,” Wang said.

There were similar expressions of pride and jubilation at ��˾��ֱ�� Mississauga, where community members congregated to watch the livestream in the atrium of the Communication Culture & Technology Building, and at ��˾��ֱ�� Scarborough, where a watch party was organized in the Meeting Place.

As per tradition, the awarding of the Nobel Prize medal and diploma took place on Dec. 10, the anniversary of the death of Alfred Nobel who, in his 1885 will, bequeathed his fortune to create the award.

Attendees at ��˾��ֱ�� Scarborough's Nobel watch party hold up signs (photo by Sobica Vinayagamoorthy)

The gold medal for the Nobel Prize in Physics depicts nature in the form of a goddess resembling Isis, her face covered by a veil held up by the Genius of Science. Inscribed are the Latin words, “Inventas vitam iuvat excoluisse per artes,”which are translated to: “It is beneficial to have improved (human) life through discovered arts.”

The Nobel diploma is crafted on handmade paper and bound in a dark blue leather cover.

The Nobel Prize diploma features a unique work of art, with the design decided by the prize-awarding bodies (© Nobel Prize Outreach. Photo: Dan Lepp Artist: Lars Eje Larsson Calligrapher: Marie A. Györi Book binder: Leonard Gustafssons Bokbinderi AB )

Later in the evening, Hinton joined his fellow Nobel laureates, their families, friends and dignitaries for a banquet at Stockholm City Hall.

In an interview with Swedish national broadcaster SVT on the sidelines of the event, Hinton said it was “very exciting but also very exhausting” to take part in the day’s Nobel events.

Reflecting on his upbringing and career, Hinton said his school years – raised as an atheist who went to a Christian school – helped prepare him for his early years as a cognitive scientist working in the then-peripheral area of artificial intelligence.

“From a young age, I was surrounded at school, everybody else had a different opinion and I thought they were wrong – that was very useful when I was studying neural nets,” Hinton said.

“For many, many years, there were only a few of us who believed in neural nets. Everybody else said this was a ridiculous thing to study. And it turned out we were right.”

– with files from Adam Elliot Segal

��˾��ֱ��’s Geoffrey Hinton delivers Nobel lecture alongside co-laureate

Christopher.Sorensen — Mon, 09 Dec 2024 20:48:27 +0000

��˾��ֱ��’s Geoffrey Hinton delivers Nobel lecture alongside co-laureate

Christopher.Sorensen Mon, 12/09/2024 - 15:48

Cutline

��˾��ֱ��'s Geoffrey Hinton, left, and John J. Hopfield of Princeton University, right, who share the 2024 Nobel Prize in Physics, shake hands after their Nobel lectures in Stockholm (photo by Pontus Lundahl/TT News Agency/AFP via Getty Images)

Schwartz Reisman Institute for Technology and Society

Topic

Global Lens

Leah Cowen

Subheadline

In Stockholm for a series of Nobel Week events, the “godfather of AI” will officially accept his Nobel Prize in Physics at a ceremony on Dec. 10

The University of Toronto’s Geoffrey Hinton took to the stage at the Swedish Academy in Stockholm this weekend – two days before he officially accepts his Nobel Prize in Physics for fundamental work in AI – to deliver a lecture on the inventions and discoveries that led to him being given the prestigious award.

“Today, I’m going to do something very foolish – I’m going to try and describe a complicated technical idea for a general audience, without using any equations,” said Hinton, a ��˾��ֱ�� University Professor emeritus of computer science, prompting laughter from the audience.

The “godfather of AI” then proceeded to outline how decades of his fundamental research, and that of his co-laureate John J. Hopfield of Princeton University, enabled the development of artificial neural networks and machine learning – technologies that underpin today’s AI revolution.

The Nobel lectures are among the highlights of Nobel Week, which runs from Dec. 6-12 in Stockholm and Oslo and includes award ceremonies, banquets, media engagements and commemorations at the Nobel Museum. There is also a series of Nobel Week events taking place at ��˾��ֱ��, including watch parties on all three campuses for the livestream of the Dec. 10 award ceremony.

Sunday’s first Nobel Prize lecture in physics was delivered by Hopfield, who shared how his fascination with the workings of the human brain inspired his development of the Hopfield network – an associative memory that can store and reconstruct patterns in data.

“How mind emerges from brain is, to me, the deepest question posed by our humanity,” Hopfield said.

When it was Hinton’s turn to take the stage, he described how he and Terry Sejnowski – one of Hopfield’s students – came upon a novel use of Hopfield nets: “Instead of using them to store memories, we could use them to construct interpretations of sensory input,” Hinton said.

He then went on to discuss the resulting Boltzmann machine, a type of neural network that is capable of recognizing elements within data.

Hinton discusses the significance of the Boltzmann machine (photo by ��˾��ֱ�� staff)

Yet, despite its promise, the original Boltzmann machine was too slow, Hinton said, and it wasn’t until several years later that he came up with “restricted Boltzmann machines,” which impose limitations on connections between neurons in order to increase system efficiency – a development that would prove pivotal in training deep neural networks (Hinton donated an early Boltzmann chip, about the size of a postage stamp, to the Nobel Prize Museum).

Following the lecture, Hinton was joined on stage by Hopfield, with the pair sharing a vigorous handshake and posing for photos.

��˾��ֱ�� brought together a panel of experts in Stockholm to discuss AI research and development (photo by Jonas Borg)

Separately, ��˾��ֱ�� convened an expert panel in Stockholm on Monday about the direction of AI research and development.

Moderated by Leah Cowen, ��˾��ֱ��’s vice-president, research and innovation, and strategic initiatives, the panel included: Eyal de Lara, a professor and chair of the department of computer science in ��˾��ֱ��’s Faculty of Arts & Science; David Lie, director of the Schwartz Reisman Institute for Technology and Society and a professor in the Edward S. Rogers Sr. department of electrical and computer engineering in the Faculty of Applied Science & Engineering; Tony Gaffney, president and CEO of the Vector Institute for Artificial Intelligence; and Amy Loutfi, professor of computer science and pro-vice-chancellor for AI at Örebro University in Sweden.

The conversation touched on areas including the promising applications of AI, how responsible deployment of AI can mitigate the technology’s potential pitfalls and implications of AI’s rise on education.

��˾��ֱ�� President Meric Gertler said that the AI breakthroughs fostered by Hinton’s research were made possible by Canada’s longstanding support of basic research.

“Geoff was interested in the novel but unproven concept of artificial neural networks, an area that was sometimes described as the ‘unpromising backwater’ of AI research,” President Gertler said in his remarks introducing the panel, noting that Hinton joined ��˾��ֱ�� in 1987 and was one of the first scholars to receive support from the Canadian Institute for Advanced Research (CIFAR).

“Canada was investing in brilliant people, their ideas and their students – and those investments have paid off many years later.”

Canada was also the first country to launch a national AI roadmap, President Gertler said, in the form of the Pan-Canadian Artificial Intelligence Strategy – which funds three national AI institutes including the Vector Institute for Artificial Intelligence, which is now housed in ��˾��ֱ��’s new Schwartz Reisman Innovation Campus. The state-of-the-art building also hosts the Schwartz Reisman Institute for Technology and Society, which is at the forefront of research and thought leadership on AI safety and responsible development, with Hinton one of its advisory board members.

“In short, Canada has played a key role in launching and driving the AI revolution and we’re a world leader in understanding and promoting safe, human-centred AI,” President Gertler said.

The theme of responsible AI was also brought up during a Q-and-A with Hinton, who revealed that the remarkable information-sharing abilities of large language models played a big role in sparking his now oft-repeated concerns around the current pace of the technology’s development.

“That’s when I came to realize that the fact that they’re so much better at sharing probably means that digital intelligence is just a better form of intelligence than us – and that’s what got me so worried,” he said.

Asked what responsible AI regulation looks like, Hinton said there must first be a consensus around solving the problem from a scientific standpoint – not unlike the scientific consensus that has emerged around climate change.

“Like the early days of climate change, the first thing to do is figure out what’s causing it and get scientific agreement on how you can fix it. Then, the second thing to do is get the politicians to do something about it … but here, we haven’t finished the first thing yet.”

Geoffrey Hinton shares 2024 VinFuture Grand Prize for work on deep learning

Christopher.Sorensen — Fri, 06 Dec 2024 19:32:41 +0000

Geoffrey Hinton shares 2024 VinFuture Grand Prize for work on deep learning

Christopher.Sorensen Fri, 12/06/2024 - 14:32

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(photo by Johnny Guatto)

Schwartz Reisman Institute for Technology and Society

Topic

Global Lens

Leah Cowen

Awards

Vector Institute

Subheadline

Known as the "godfather of AI," Hinton was one of five people honoured with the US$3-million prize awarded by Vietnam's VinFuture Foundation

Geoffrey Hinton, University Professor emeritus of computer science at the University of Toronto and winner of the 2024 Nobel Prize in Physics, has added another prestigious award to his collection: the 2024 VinFuture Grand Prize.

The US$3-million prize, awarded by the not-for-profit VinFuture Foundation in Vietnam, recognizes seminal scientific advancements that are improving the quality of human life and forging a more equitable and sustainable world.

Hinton, known as the “godfather of AI,” shares the prize with Yoshua Bengio, Jen-Hsun (Jensen) Huang, Yann LeCun and Fei-Fei Li. The five are honoured for their contributions to the development and adoption of deep learning, which is revolutionizing fields ranging from biomedical research and transportation to manufacturing, clean energy and agriculture.

“The University of Toronto couldn’t be more proud of University Professor Emeritus Geoff Hinton,” said Leah Cowen, ��˾��ֱ��’s vice-president, research and innovation, and strategic initiatives. “When Geoff arrived at ��˾��ֱ�� nearly 40 years ago, his research into neural networks – influenced by his ideas about how the brain learns – was considered an AI backwater, if it was considered at all.

“Fast-forward to today, and his seminal work is transforming our world in ways that we’ve only begun to grasp. This award is a testament to the power of curiosity-driven research – and the impact of investing in brilliant, curious researchers.”

The VinFuture Foundation noted that Hinton and Bengio – a professor at the Université de Montréal – were awarded the prize for their research on neural networks and deep learning algorithms. LeCun, the chief AI scientist for Facebook AI Research who was one of Hinton’s post-doctoral students at ��˾��ֱ�� in the1980s, was recognized for helping develop convolutional neural networks for computer vision. Huang, the co-founder and CEO of NVIDIA, was recognized for his work on computational architectures for deep learning and accelerated computing, while Li, a professor at Stanford University, was celebrated for contributions to computer vision and large-scale image recognition.

In a video message, Hinton, an adviser at ��˾��ֱ��’s Schwartz Reisman Institute for Technology and Society who is also co-founder and chief scientific adviser at the Vector Institute for Artificial Intelligence in Toronto, said he was honoured to share the award with such an esteemed group of researchers – including his fellow A.M Turing Award-winners Bengio and LeCun.

“The three of us spent our whole lives developing the technology of neural networks,” Hinton said in remarks that noted the collective nature of the achievement.

“I’m very happy to see that the VinFuture Prize recognizes the contributions of Jensen Huang in developing the kind of compute and software required for artificial intelligence, and Fei-Fei Li in providing the big data that was needed to prove that it worked.”

Now in its fourth year, the VinFuture Prize was created with the goal of celebrating scientific and technological innovations that are aligned with the United Nations’ 17 Sustainable Development Goals, which include poverty alleviation, renewable energy, ending hunger and improving health.

Friday’s award ceremony was attended by Vietnam’s Prime Minister Pham Minh Chinh and distinguished leaders, scientists and diplomats from around the world. It was broadcast live on Vietnamese television and streamed online.

“It’s terrific to see this recognition of Geoff’s extraordinary work and its potential to help address some of the world’s greatest challenges,” said Cowen. “Researchers around the globe – including a number of his former students – are using AI to help solve some of the most pressing challenges of our time in areas such as health care, sustainability and climate change.”

The prize comes as Hinton prepares to officially accept his Nobel Prize in Physics in Sweden on Dec. 10. He shared the Nobel with John J. Hopfield of Princeton University for foundational discoveries and inventions that enable machine learning with artificial neural networks.

Hinton, who joined ��˾��ֱ�� in 1987 after working in universities in the U.K. and U.S., has said that he plans to leverage his growing fame to spread awareness of the dangers of unchecked AI development – a warning he first shared after leaving a research position at Google in early 2023.

As his public stature grows, ‘Godfather of AI’ Geoffrey Hinton heads to Stockholm to accept his Nobel Prize

lanthierj — Wed, 04 Dec 2024 17:56:55 +0000

As his public stature grows, ‘Godfather of AI’ Geoffrey Hinton heads to Stockholm to accept his Nobel Prize

lanthierj Wed, 12/04/2024 - 12:56

Cutline

University Professor Emeritus Geoffrey Hinton will receive the 2024 Nobel Prize in Physics on Dec. 10 (photo by Johnny Guatto)

Chris Sorensen

Topic

Department of Computer Science

“Godfather of AI” Geoffrey Hinton was already on his way to becoming a household name when he won the 2024 Nobel Prize in Physics for foundational work leading to today’s artificial intelligence boom.

Two months later, his celebrity has hit a whole new level.

The University Professor emeritus at the University of Toronto says he now gets recognized on the street and that strangers regularly ask him for selfies. On a recent flight to Toronto, one flight attendant even grabbed the intercom to announce his presence on the plane. That’s in addition to a torrent of requests to speak to media, appear on podcasts and read academic papers.

As he prepares to travel to Stockholm on Dec. 10 to officially accept the honour alongside co-winner John J. Hopfield of Princeton University – which will be celebrated via watch parties and other “Nobel Week” events at ��˾��ֱ�� – Hinton says he plans to put his growing fame to good use.

“It will be useful when I talk about AI risks,” he says, referring to the warning bell he began sounding in 2023 about the potential existential threat posed by rapid and unchecked AI development.

“It will make people take me more seriously.”

For example, he was recently invited to be part of a webinar with Nobel Peace Prize laureates on whether AI should be used to decide if and when to launch nuclear weapons – a foreboding new twist on a Cold War-era threat that has preoccupied fellow ��˾��ֱ�� Nobel laureate John Polanyi, a University Professor emeritus of chemistry.

“I think it’s a bad idea,” Hinton says for the record.

Yet, as scary as such a scenario is to contemplate, Hinton has focused on what he considers an equally grave threat to humanity: the moment when machine intelligence surpasses that of our own.

That’s why he’s called on governments to develop regulations to guide AI development and deployment. It’s also why he’s urging companies to devote more funding to AI safety research as they rush to explore the myriad ways the technology can be used to make our lives better – from finding cures for deadly diseases to discovering new materials to help combat climate change.

At ��˾��ֱ��, Hinton has also taken on an advisory role at the Schwartz Reisman Institute of Technology and Society, where researchers are at the forefront of exploring AI safety and other issues around the adoption of new technologies. In particular, he highlights the work of Roger Grosse and David Duvenaud – both AI safety experts who are associate professors in the department of computer science in the Faculty of Arts & Science and Schwartz Reisman Chairs in Technology and Society (he says Grosse convinced him to go public with his concerns last year after he left a senior research position at Google).

The institute and ��˾��ֱ�� more broadly have an opportunity to become a world leader in figuring out how to guard against AI threats, he says. “I think that can be a world-class centre for figuring out whether there’s a way to make a superintelligence – which we all think is coming – not want to take control.”

Hinton’s moment in the spotlight has been a long time coming – and is testament to his curious mind, persistent nature and willingness to go against the grain.

As a child attending a “mildly Christian school” in England, he says he often felt like an outsider because he refused to accept the idea of a god without evidence (he remains an atheist). Decades later, Hinton again found himself on the fringe as he and a handful of researchers, including the Salk Institute for Biological Science’s Terry Sejnowsky, who did his PhD research under Hopfield, explored the idea that the human brain was essentially a bunch of connection strengths between neurons – not a series of logical expressions, as many in the field then believed.

That key insight now forms the basis of today neural nets.

“There were many times when I could easily have given up and sort of joined the mainstream,” Hinton says. “But I think my experience as a child made me far more resistant to that.”

A self-described cognitive scientist who works in the field of computer science, Hinton says he was surprised to win a Nobel Prize in Physics. But the Nobel committee noted he used ideas from physics to create the Boltzmann machine, which can be used to recognize elements in data. The Boltzmann machine, in turn, was based on the Hopfield network, which was invented by his co-laureate.

(photo by Johnny Guatto)

Hinton is donating an early Boltzmann chip, about the size of a postage stamp, to the Nobel Prize Museum – and has decided to use the money from his win to set up a prize for young researchers at the annual Conference on Neural Information Processing Systems. He has also donated to a Canadian charity that supports works with Indigenous communities to address water challenges, and has plans to give to another that supports neurodiverse young adults.

For young researchers hoping to follow in his footsteps? Hinton advises: focus on a problem that really excites you, don’t become swayed by conventional thinking, persevere until you know you’re wrong – and feel free to wander between different research disciplines.

“If you're really interested in chasing a criminal, you don't stop at a state border,” he says. “That's a stupid thing to do – so, the boundaries of fields, you just ignore them.”

Light in a bottle: ��˾��ֱ�� researchers use AI to capture photons in motion

Christopher.Sorensen — Tue, 19 Nov 2024 15:07:26 +0000

Light in a bottle: ��˾��ֱ�� researchers use AI to capture photons in motion

Christopher.Sorensen Tue, 11/19/2024 - 10:07

Cutline

A scene rendered using videos from an ultra-high-speed camera shows a pulse of light travelling through a pop bottle, scattering off liquid, hitting the ground, focusing on the cap and reflecting back (supplied image)

Krystle Hewitt

Topic

Breaking Research

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 University of Toronto 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 ��˾��ֱ�� 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 ��˾��ֱ�� University Professor Emeritus Geoffrey Hinton's 2024 Nobel Prize in Physics (photo by Johnny Guatto)

Mariam Matti

Topic

��˾��ֱ��

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 University of Toronto 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 ��˾��ֱ�� 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 ��˾��ֱ�� 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 ��˾��ֱ�� tell similar stories.

Chris Maddison (supplied image)

Chris Maddison, now an assistant professor in ��˾��ֱ��’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 ��˾��ֱ�� 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 ��˾��ֱ�� 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.”

��˾��ֱ�� 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

��˾��ֱ�� 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 ��˾��ֱ��'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

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 University of Toronto’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.”

��˾��ֱ�� receives $52 million to upgrade SciNet supercomputer

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

��˾��ֱ�� 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

Subheadline

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

The University of Toronto will receive more than $52 million to upgrade one of the fastest supercomputers in Canada – a shared resource housed at ��˾��ֱ�� 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 ��˾��ֱ�� and the University of Waterloo.

The more than $52 million earmarked for ��˾��ֱ��’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, ��˾��ֱ��’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 ��˾��ֱ�� 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’: ��˾��ֱ�� 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’: ��˾��ֱ�� 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)

Topic

Schwartz Reisman Innovation Campus

Leah Cowen

Wesley Hall

Schwartz Reisman Institute for Technology and Society

Department of Computer Science

CIFAR