Computer Science

Dalla Lana School of Public Health

Ecology and Evolutionary Biology

Faculty of Applied Science & Engineering

Hospital for Sick Children

Laboratory Medicine and Pathobiology

Leslie Dan Faculty of Pharmacy

Mathematics

Physics

Psychology

University Health Network

UTIAS

Subheadline

From better batteries to preventing memory loss, nearly four dozen projects at U of T and its partner hospitals are being supported by the Ontario Research Fund

Researchers in the ��Ƶ’s Thermal Management Systems (TMS) Laboratory are working to improve the way battery systems handle heat and develop structural battery pack components. 

“Whether they are being used for electric vehicles or for stationary energy storage systems that reduce strain on the grid, lithium-ion batteries are transforming the way we use electricity,” said Carlos Da Silva, senior research associate at the TMS Lab in the Faculty of Applied Science & Engineering and executive director of U of T’s Electrification Hub.

“Unfortunately, today’s batteries are still sensitive to temperature: if they get too cold or too hot, it can degrade their performance and even present safety risks. We are working on new technologies that make batteries more resilient to thermal fluctuations.”

The battery-related research is among nearly four dozen projects at U of T and its partner hospitals that are receiving almost $35 million in support through the Ontario Research Fund – Research Excellence (ORF-RE) and the Ontario Research Fund – Small Infrastructure (ORF-SIF). (See the full list of projects and their principal researchers below).

"Research at the ��Ƶ and at all universities and colleges across Ontario is the foundation of the province’s competitiveness now and in the future,” said Leah Cowen, U of T’s vice-president, research and innovation, and strategic initiatives.

“This investment protects and advances cutting-edge, made-in-Ontario research in important economic sectors and helps ensure universities can continue to train, attract and retain the world’s top talent."

At U of T Engineering’s TMS Lab, researchers led by Cristina Amon, a University Professor in the department of mechanical and industrial engineering, are working on two funded projects. They are developing advanced computational modelling and digital twin methodologies that predict and optimize how heat flows through battery packs. The methodologies are carefully calibrated and validated through industry-relevant experiments in the lab.

Senior Research Associate Carlos Da Silva, left, and University Professor Cristina Amon, right, chat in the Faculty of Applied Science & Engineering's Thermal Management Systems Laboratory (photo by Aaron Demeter)

These methodologies will help battery designers anticipate and prevent thermal management challenges before they arise. It can also enable them to optimize the design and deployment of fire mitigation measures, such as ultra-thin heat barriers, within their battery systems.

The team is also collaborating with Ford Canada and several other companies in the energy storage space. For example, they have worked with Jule (powered by eCAMION) on the development of direct current electric vehicle fast chargers with integrated battery energy storage systems, one of which was recently unveiled on the U of T campus.

“We are grateful for this ORF-RE funding, which will accelerate our research and help us further expand our partnerships, ensuring that battery thermal innovations have a seamless transition from the lab to the marketplace,” Amon said.

“As a result of this work, the next generation of batteries will be safer and more resilient than ever before, which is especially important in colder climates like ours here in Ontario.” 

Ontario Research Fund – Research Excellence:

Cristina Amon in the department of mechanical & industrial engineering in the Faculty of Applied Science & Engineering – Powering Ontario’s grid transformation and electric vehicle fast charging with thermally resilient battery energy storage & Next-gen electric vehicle battery systems: Lightweight, thermally performant and fire safe for all climates
Morgan Barense in the department of psychology in the Faculty of Arts & Science – HippoCamera: Digital memory rehabilitation to combat memory loss
Aimy Bazylak in the department of mechanical & industrial engineering in the Faculty of Applied Science and Engineering – RECYCLEAN: Critical minerals recycling & re-manufacturing for the energy transition
Ian Connell at University Health Network and the department of medical biophysics in the Temerty Faculty of Medicine – MRI-compatible innovations for neuromodulation
Simon Graham at Sunnybrook Health Sciences Centre and the department of medical biophysics in the Temerty Faculty of Medicine – Technological innovations for clinical MRI of the brain at 7 tesla
Clinton Groth in the Institute for Aerospace Studies in the Faculty of Applied Science & Engineering – Hydrogen as a sustainable aviation fuel – combustion research to remove impediments to adoption in gas turbine engines
James Kennedy at Centre for Addiction and Mental Health and the department of psychiatry in the Temerty Faculty of Medicine – Clinical utility and enhancements of a pharmacogenomic decision support tool for mental health patients
Shaf Keshavjee at University Health Network and the department of surgery in the Temerty Faculty of Medicine – Advanced solutions to human lung preservation and assessment using artificial intelligence
Aviad Levis in the department of computer science in the Faculty of Arts & Science – AI and quantum enhanced astronomy
JoAnne McLaurin at Sunnybrook Health Sciences Centre and the department of laboratory medicine & pathobiology in the Temerty Faculty of Medicine – Conversion of astrocytes to neurons to treat neurodegenerative diseases of the brain and the eye
R. J. Dwayne Miller in the department of chemistry in the Faculty of Arts & Science – PicoSecond InfraRed Laser (PIRL) “cancer knife” with complete biodiagnostics via spatial imaging mass spectrometry
Javad Mostaghimi in the department of mechanical & industrial engineering in the Faculty of Applied Science & Engineering – A new generation of compact, transportable mass spectrometers for rapid, in-field sample analysi
Xiao Yu (Shirley) Wu in the Leslie Dan Faculty of Pharmacy – Molecular dynamics modeling and screening of excipients for designing amorphous solid dispersion formulations of poorly–soluble drugs

Ontario Research Fund – Small Infrastructure Fund:

Celina Baines in the department of ecology & evolutionary biology in the Faculty of Arts & Science – Impacts of environmental change on organismal movement
Sergio de la Barrera in the department of physics in the Faculty of Arts & Science – Facility for quantum materials and device assembly from atomically thin van der Waals layers
Michelle Bendeck in the department of laboratory medicine & pathobiology in the Temerty Faculty of Medicine – 4D quantitative cardiovascular physiology centre
Laurent Bozec in the department of laboratory medicine & pathobiology in the Temerty Faculty of Medicine – 21st Century challenge for Dentistry: Breaking the cycle of irreversible dental tissue loss
Mark Chiew at Sunnybrook Health Sciences Centre and the department of medical biophysics in the Temerty Faculty of Medicine – Next generation computational MRI for rapid neuroimaging and image-guided therapy
Haissi Cui in the department of chemistry in the Faculty of Arts & Science – A molecule to mouse approach to study the intracellular localization of genetic code interpretation in mammalian cells
Andy Kin On DeVeale at the University Health Network and the Dalla Lana School of Public Health – Sarcopenia and musculoskeletal interactions (sami) collaborative hub
Ali Dolatabadi in the department of mechanical & industrial engineering in the Faculty of Applied Science & Engineering – Advanced cold spray facility
Spencer Freeman at the Hospital for Sick Children and the department of biochemistry in the Temerty Faculty of Medicine – Imaging biophysical determinants of the innate immune response
Liisa Galea at the Centre for Addiction and Mental Health and the Institute of Medical Science in the Temerty Faculty of Medicine – Sex and sex-specific factors influencing brain health across the lifespan
Maged Goubran at Sunnybrook Health Sciences Centre and the department of medical biophysics in the Temerty Faculty of Medicine – AI platform for mapping, tracking and predicting circuit alterations in Alzheimer’s disease
Eitan Grinspun in the departments of computer science and department of mathematics in the Faculty of Arts & Science – A computer graphics perspective on entanglement of slender structures
Levon Halabelian in the Department of Pharmacology and Toxicology in the Temerty Faculty of Medicine – Enabling a high-throughput drug discovery pipeline for targeting disease-related human proteins
Ziqing Hong in the department of physics in the Faculty of Arts & Science – Ultra-sensitive cryogenic detector development for dark matter and neutrino experiments
Eno Hysi at the Unity Health Toronto and the department of medical biophysics in the Temerty Faculty of Medicine – Structural and functional assessments of diabetic skin microvasculature using photoacoustic imaging
Lewis Kay in the department of biochemistry in the Temerty Faculty of Medicine – Helium recovery system for the biomolecular NMR facility
Xiang Li in the department of chemistry and the department of physic in the Faculty of Arts & Science – Real-time multi-faceted probes of quantum materials
Qian Lin in the department of cell & systems biology in the Faculty of Arts & Science – 2p-RAM for whole-brain single-neuron imaging of behaving zebrafish to study neural mechanisms of cognitive behaviours
Xilin Liu in the Edward S. Rogers Sr. department of electrical and computer engineering in the Faculty of Applied Science & Engineering – Integrated circuits for wireless brain implants with multi-modal neural interfaces
Stephen Lye at the Sinai Health System and the department of physiology in the Temerty Faculty of Medicine – Healthy Life Trajectories Initiative (HeLTI) analytics platform
Caitlin Maikawa in the Institute of Biomedical Engineering in the Faculty of Applied Science & Engineering – Biointerfacing materials for drug delivery lab
Emma Master in the department of chemical engineering & applied chemistry in the Faculty of Applied Science & Engineering – Accelerating biomanufacturing innovation through enhanced capacity for scale-up and downstream bioprocess engineering
Roman Melnyk at the Hospital for Sick Children and the department of biochemistry in the Temerty Faculty of Medicine – The H-SCREEN: A platform for high throughput and high content imaging-based small molecule screens for disease modulation
Juan Mena-Parra in the department of astronomy & astrophysics in the Faculty of Arts & Science – An advanced laboratory to enable novel radio telescopes for cosmology and time-domain astrophysics
Seyed Mohamad Moosavi in the department of chemical engineering and applied chemistry in the Faculty of Applied Science & Engineering – Machine learning for nanoporous materials design
Enid Montague in the department of mechanical & industrial engineering in the Faculty of Applied Science & Engineering – Automation and equity in healthcare laboratory
Michael Norris in the department of biochemistry in the Temerty Faculty of Medicine – Infrastructure for structural and functional virology research hub
Amaya Perez-Brumer in the Dalla Lana School of Public Health – 3P lab: Centering power, privilege and positionality for health equity research
Monica Ramsey in the department of anthropology at the ��Ƶ Mississauga – Ramsey Laboratory for Environmental Archaeology (RLEA): How human-environment interactions shaped plant-food
Arneet Saltzman in the department of cell & systems biology in the in the Faculty of Arts & Science – Heterochromatin regulation in development and inheritance
Mina Tadrous in the Leslie Dan Faculty of Pharmacy – Developing a centre for real-world evidence to improve the use of medications for Canadians
Shurui Zhou in the department of electrical & computer engineering in the Faculty of Applied Science & Engineering – Improving collaboration efficiency for fork-based software development
Olena Zhulyn at the Hospital for Sick Children and the department of molecular genetics in the Temerty Faculty of Medicine – Targeting translation for tissue regeneration and repair
Christoph Zrenner at the Centre for Addiction and Mental Health and the Institute of Biomedical Engineering in the Faculty of Applied Science & Engineering – Next-generation real-time closed-loop personalized neurostimulation

U of T’s ��Ƶ receives Nobel medal, diploma at Stockholm ceremony

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

U of T’s ��Ƶ receives Nobel medal, diploma at Stockholm ceremony

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

Cutline

U of T University Professor Emeritus ��Ƶ 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

Subheadline

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

The ��Ƶ’s ��Ƶ 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.

��Ƶ 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 U of T 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.

“[��Ƶ] 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.

U of T 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 U of T 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 U of T’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.”

U of T 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 U of T Mississauga, where community members congregated to watch the livestream in the atrium of the Communication Culture & Technology Building, and at U of T 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 U of T 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

U of T’s ��Ƶ delivers Nobel lecture alongside co-laureate

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

U of T’s ��Ƶ delivers Nobel lecture alongside co-laureate

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

Cutline

U of T's ��Ƶ, 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)

Topic

Schwartz Reisman Institute for Technology and Society

Leah Cowen

Faculty of Applied Science & Engineering

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 ��Ƶ’s ��Ƶ 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 U of T 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 U of T, 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 U of T 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.

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

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

Moderated by Leah Cowen, U of T’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 U of T’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.

U of T 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 U of T 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 U of T’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.”

��Ƶ shares 2024 VinFuture Grand Prize for work on deep learning

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

��Ƶ shares 2024 VinFuture Grand Prize for work on deep learning

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

Cutline

(photo by Johnny Guatto)

Topic

Schwartz Reisman Institute for Technology and Society

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

��Ƶ, University Professor emeritus of computer science at the ��Ƶ 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 ��Ƶ couldn’t be more proud of University Professor Emeritus Geoff Hinton,” said Leah Cowen, U of T’s vice-president, research and innovation, and strategic initiatives. “When Geoff arrived at U of T 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 U of T 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 U of T’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 U of T 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.

Light in a bottle: U of T researchers use AI to capture photons in motion

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

Light in a bottle: U of T 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 ��Ƶ 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 ��Ƶ 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 ��Ƶ’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 ��Ƶ, “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 ��Ƶ

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

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

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

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

Mariam Matti

Topic

Our Community

Alumni

Statistical Sciences

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

In the wake of ��Ƶ’s 2024 Nobel Prize in Physics, former students and colleagues from the ��Ƶ 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.’”

��Ƶ 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' ��Ƶ’s Nobel Prize makes headlines around the world

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

‘Godfather of AI' ��Ƶ’s Nobel Prize makes headlines around the world

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

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(photo by Polina Teif)

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News of ��Ƶ’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 ��Ƶ, 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 ��Ƶ after Nobel win

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

Congratulations pour in for ��Ƶ after Nobel win

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

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(Photo by Johnny Guatto/��Ƶ)

Mariam Matti

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Our Community

Alumni

University Health Network

Vector Institute

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“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 ��Ƶ’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 ��Ƶ 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 ��Ƶ 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 ��Ƶ 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

��Ƶ 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 ��Ƶ, 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 ��Ƶ 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 ��Ƶ’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 ��Ƶ 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 ��Ƶ 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

��Ƶ 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 ��Ƶ 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 ��Ƶ 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. ��Ƶ 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

��Ƶ wins Nobel Prize in Physics

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

��Ƶ wins Nobel Prize in Physics

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

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(Photo by Johnny Guatto/��Ƶ)

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Physics