Artificial Intelligence

��˾��ֱ�� researchers develop AI model to predict 'very dynamic' peptide structures

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

��˾��ֱ�� researchers develop AI model to predict 'very dynamic' peptide structures

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

Cutline

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

Anika Hazra

Topic

Breaking Research

Temerty Faculty of Medicine

Donnelly Centre for Cellular & Biomolecular Research

��˾��ֱ��

Artificial Intelligence

Computer Science

Research & Innovation

Subheadline

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

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

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

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

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

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

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

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

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

Peptides are also cheaper to produce than their larger protein counterparts, said Kim, who is also a professor of computer science in ��˾��ֱ��’s Faculty of Arts & Science and a professor of molecular genetics in the Temerty Faculty of Medicine.

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

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

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

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

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

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

��˾��ֱ�� team takes top spot in self-driving car challenge for 6th time in 7 years

Christopher.Sorensen — Wed, 07 Aug 2024 17:42:55 +0000

��˾��ֱ�� team takes top spot in self-driving car challenge for 6th time in 7 years

Christopher.Sorensen Wed, 08/07/2024 - 13:42

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As part of the competition, the ��˾��ֱ�� team’s autonomous vehicle had to react to obstacles such as a fake deer moving across the road (photo courtesy of aUToronto)

Safa Jinje

Topic

Our Community

��˾��ֱ��

Artificial Intelligence

Faculty of Applied Science & Engineering

Graduate Students

Research & Innovation

Self-Driving Cars

Undergraduate Students

UTIAS

Subheadline

"Each time we saw an obstacle – a stop sign, a red light, the railroad bar coming down – and the car reacted by stopping and then continuing, we let out a big cheer or a sigh of relief"

A team from the University of Toronto has placed first for sixth time in seven years in a North American self-driving car competition.

After finishing in second place last year, the aUToronto team returned to the top spot at the 2024 SAE AutoDrive Challenge II, which was held in June at the Mcity Test Facility in Ann Arbor, Mich.

The aUToronto team competed against nine other teams from across Canada and the United States.

“Through the AutoDrive Challenge, we are preparing the next generation of engineers to head into the industry, to keep pushing towards the challenging goal of reaching Level 4 autonomous driving,” says Tim Barfoot, a professor at the ��˾��ֱ�� Institute for Aerospace Studies (UTIAS) in the Faculty of Applied Science & Engineering and one of the team’s academic advisers.

“The team did another excellent job this year.”

The team approached the competition by going back to first principles to ensure they had a reliable and robust system, says Kelvin Cui, a ��˾��ֱ�� Engineering alumnus and the team’s principal.

He joined aUToronto last fall after five years with the University of Toronto Formula Racing team, where he founded the “driverless” division.

“We looked at what was going to get us the most points at competition and made sure that we were not overbuilding our system and adding too much complexity,” he says.

This meant pushing for additional testing time at UTIAS and achieving more than 900 kilometres of system testing prior to the competition.

The team placed first out of 10 teams from institutions across the United States and Canada (photo courtesy of aUToronto)

A partnership with the AutoDrive team from Queen’s University was instrumental to aUToronto’s preparation. The aUToronto team drove Artemis, their autonomous vehicle, to Kingston, Ont. to assess the system at Queen’s testing facility, which features intersections and electronic streetlights.

“We added radar to our vehicle as a new sensor, so we needed to be aware of all the sensor failure modes,” says third-year Engineering Science student Robert Ren.

“A lot of our testing time went into making sure that including radar didn’t break anything else in our system, and that it could handle any sensor failure cases.”

Including radar sensors in the vehicle’s perception system allowed it to measure the motion of objects directly, which is not possible with light detection and ranging (LiDAR) sensors. 

“Radar can help with adverse weather object detections,” adds Ren. “So, if the vehicle is operating under heavy rain or fog, the LiDAR is going to be limited, but the radio waves from radar can help the vehicle see what objects are in front and what objects are moving. This enables it to make good decisions when driving in uncertain scenarios.” 

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In an event where both LiDAR and radar sensors fail, the aUToronto system can still rely on visual cameras to perform object tracking. This made the team’s object tracker much more robust compared to last year when the team experienced sensor failure during a dynamic event.

Brian Cheong, a ��˾��ֱ�� Engineering master’s student who has been a member of aUToronto since 2021, acted as technical director of the autonomy team this year – part of a new leadership structure introduced by Cui.  

“In the past, it was a lot of work for our team’s principal to keep track of all the systems,” Cheong says. “So instead of having to work directly with all 15 sub teams, Kelvin created groups of sub teams that we called stacks, and each stack had a director.”

The restructuring and technical innovations paid off, with aUToronto completing its first clean sweep in the AutoDrive Challenge II, placing first in all static and dynamic events, including the concept design presentation and intersection challenge.

“The intersection challenge was a big highlight for us,” says Cheong. “Kelvin and Robert were in the car, and I was on the sidelines watching with the rest of the team. Each time we saw an obstacle – a stop sign, a red light, the railroad bar coming down – and the car reacted by stopping and then continuing, we let out a big cheer or a sigh of relief.

“And then we were all silent as the car approached the final obstacle, which was a deer. We watched as Artemis slowed down to a stop and the deer moved by. Then we screamed and cheered, and we could hear cheering from inside the car.”

“Our success is entirely a team effort,” adds Cui. “It was not smooth sailing before the competition. The only reason we won is because everybody put in so much effort to test our vehicle every day.

“That’s how we were able to get this reliable system across the line.” 

What makes a chess move brilliant? Researchers use AI to find out

Christopher.Sorensen — Wed, 07 Aug 2024 17:25:51 +0000

What makes a chess move brilliant? Researchers use AI to find out

Christopher.Sorensen Wed, 08/07/2024 - 13:25

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��˾��ֱ�� Engineering researchers Kamron Zaidi, left, and Michael Guerzhoy, right, use game trees and deep neural networks to enable chess engines to recognize brilliant moves (photo by Safa Jinje)

Safa Jinje

Topic

Breaking Research

��˾��ֱ��

Artificial Intelligence

Faculty of Applied Science & Engineering

Mechanical & Industrial Engineering

Research & Innovation

Subheadline

AI system developed by ��˾��ֱ�� researchers is being used to study human creativity and make a chess computer that is more entertaining to play against

Researchers at the University of Toronto have designed a new AI model that understands how humans perceive creativity in chess.

In a recent paper presented at an international conference, researchers in ��˾��ֱ��’s Faculty of Applied Science & Engineering describe how they used techniques such as game trees and deep neural networks to enable chess engines to recognize brilliant moves.

The development could lead to chess engines that can find the most creative and clever path to victory in game, rather than just making moves to maximize win rates. That, in turn, could have implications for other AI systems tasked with creative endeavours.

“A chess move can be perceived as brilliant, or creative, when the strategic payoff isn’t clear at first, but in retrospect the player had to follow a precise path in gaming out all the possibilities to see so far into the future,” says paper co-author Michael Guerzhoy, an assistant professor, teaching stream, of mechanical and industrial engineering and engineering science who wrote about the research on his Substack.

“We wanted our system to understand human perception of what constitutes brilliance in chess and distinguish that from just winning.”

Most of the current research into chess AI is focused on enabling moves that create a higher chance of winning. But this doesn’t always make for an exciting game.

Skilled human chess players, on the other hand, can play in a more dramatic or imaginative way by making moves that may break traditional rules –for example, sacrificing a piece in a way that may initially look like a mistake, but ultimately, paves the way to a win.

A chess board depicts a move from the “Game of the Century” in 1956, when future American chess grandmaster Bobby Fischer (black) sacrificed his queen in a move that was celebrated as brilliant (photo by Safa Jinje)

The team worked with Leela Chess Zero, a top chess engine that learns through self-play and has played over 1.6 billion games against itself. They also employed Maia, a human-like neural network chess engine developed by ��˾��ֱ�� computer science researchers.

“We used the two neural network chess engines to create our game trees at different levels of depth in a game,” says paper co-author Kamron Zaidi, a recent ��˾��ֱ�� Engineering graduate.

“Using these game trees, we extracted many different features from it. We then fed the features into a neural network that we trained on the Lichess database of online chess games, which are labelled by human users of the database.”

A game tree in chess represents the current state of a chess board along with all the possible moves and counter moves that can occur. Each board position is represented as a node and the game tree can be expanded on until the game is either won, drawn or lost.

The researchers began with small game trees then slowly increased the size, adding more nodes to the tree. They found that when the neural network looks at all the game tree features and makes a prediction as to whether the move is brilliant or not, it reached an accuracy rate of 79 per cent using the test data set.

The research – based on Zaidi’s undergraduate engineering science thesis, which was supervised by Guerzhoy – was presented at the International Conference on Computational Creativity in Jönköping, Sweden.

“There were people from all over the world presenting research on more traditional aspects of creativity, but we were all focused on the same thing, which is, ‘How can we use AI to enhance our interactions and understandings of creativity?’” says Zaidi. 

The work has also received media coverage in outlets, including New Scientist, where English chess grandmaster Matthew Sadler says that a model that can understand brilliance could be used as a training tool for professionals and potentially lead to a more entertaining engine opponent for amateur players.

The team sees their system as having broad applicability when it comes to perception of creativity and brilliance.

“One of the biggest areas that is of interest to me is characterizing what we perceive as creativity,” says Guerzhoy.

“Not just in board games but in other creative endeavours, including music and art, where there is a formal framework and rules that need to be followed. Highly creative work involves planning in advance and gaming out the possibilities.

“But everyone I’ve talked to since the paper came out wants to know when they can play against our brilliant chess engine. So, I think making that possible is the obvious next step for us.”

With ��˾��ֱ�� innovators front and centre, Collision conference wraps up five-year Toronto run

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

With ��˾��ֱ�� innovators front and centre, Collision conference wraps up five-year Toronto run

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

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

Rahul Kalvapalle

Adina Bresge

Topic

Our Community

Institutional Strategic Initiatives

��˾��ֱ�� Entrepreneurship

��˾��ֱ��

Artificial Intelligence

Computer Science

Faculty of Arts & Science

Geoffrey Hinton

Innovation & Entrepreneurship

Innovations & Partnerships Office

Research & Innovation

Startups

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

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

The rapid development of artificial intelligence technologies and their impact on business and society were key themes for many of the conference’s keynotes and exhibits – so it was no surprise that ��˾��ֱ��’s AI luminaries were front and centre.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

“Everything will be controlled by generative AI systems inside the vehicle and nothing else. This is a breakthrough for the industry, where such a thing has never happened before,” said Urtasun, a professor in the department of computer science in ��˾��ֱ��'s Faculty of Arts & Science.

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

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

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

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

During the conference, ��˾��ֱ��’s booth near the main stage featured exhibits from campus-linked accelerators, institutional strategic initiatives, academic divisions and the Innovations and Partnerships Office. ��˾��ֱ��’s Black Founders Network was also at Collision as one of the organizers of the Black Innovation Zone.

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

Waabi, founded by ��˾��ֱ��'s Raquel Urtasun, raises US$200 million to launch self-driving trucks

lanthierj — Wed, 19 Jun 2024 20:26:16 +0000

Waabi, founded by ��˾��ֱ��'s Raquel Urtasun, raises US$200 million to launch self-driving trucks

lanthierj Wed, 06/19/2024 - 16:26

Cutline

(photo by Nick Iwanyshyn)

Rahul Kalvapalle

Topic

Global Lens

Artificial Intelligence

Department of Computer Science

Entrepreneurship

Faculty of Arts & Science

Waabi, a self-driving trucking startup founded by University of Toronto artificial intelligence (AI) expert Raquel Urtasun, has raised US$200 million in series B funding to support the deployment of fully autonomous, AI-powered trucks in 2025.

The funding round was led by previous investors Uber Technologies Inc.– where Urtasun previously worked as chief scientist of the self-driving division – and Khosla Ventures and includes an array of other high-profile strategic investors including NVIDIA Corp., Volvo Group and Porsche Automobil Holding.

The latest funding brings total investment in Waabi to more than C$380 million and will be used to expand the Toronto-headquartered company’s team in both Canada and the U.S., as well as to launch driverless commercial deliveries in Texas by next year.

(photo Nick Iwanyshyn)

Urtasun, a professor in the department of computer science at ��˾��ֱ��’s Faculty of Arts & Science and co-founder of the Vector Institute for Artificial Intelligence, said Waabi’s end-to-end AI system is advancing self-driving technologies to frontiers beyond the reach of other industry players thanks to its unique ability to carry out complex reasoning.

“What we have at Waabi is a technology that brings generative AI to the physical world for the first time, where the idea is that you have a single AI system that is able to reason like a human does, and is able to generalize to situations everything that might happen on the road – including things that it has never seen before,” she said.

“It does so in a way that is interpretable, so you can validate and verify the system, and provably safe, which is very important as you deploy these massive robots in the real world.”

Paired with Waabi’s advanced simulator, the AI system reduces the need for time-consuming road testing, Urtasun explained.

The announcement came hours before Urtasun took to the main stage at the Collision tech conference in Toronto to deliver a talk on generative AI. Her remarks touched on the technological underpinnings of generative AI and future applications, and outlined how Waabi is bringing generative AI to the physical world – starting with trucking.

Raquel Urtasun at the 2024 edition of Collision (photo by Polina Teif)

Urtasun is one of several experts from ��˾��ֱ��’s technology, innovation and entrepreneurship ecosystem who are speaking at Collision. Others include University Professor Emeritus Geoffrey Hinton, a world-renowned AI luminary and investor in Waabi.

“Self-driving technology is a prime example of how AI can dramatically improve our lives,” Hinton said in a news release. “Raquel and Waabi are at the forefront of innovation, developing a revolutionary approach that radically changes the way autonomous systems work and leads to safer and more efficient solutions.”

Earlier in the week, Urtasun brought one of Waabi’s trucks to the St. George campus and showcased some of its capabilities to Melanie Woodin, dean of the Faculty of Arts & Science, Tony Gaffney, president and CEO of the Vector Institute, and Michael Brudno, professor in the department of computer science and chief data scientist at the University Health Network.

From left: Melanie Woodin, Raquel Urtasun, Tony Gaffney and Michael Brudno (photo by Nick Iwanyshyn)

Urtasun underscored the importance of the company being headquartered in Toronto. “We’re an AI company and Toronto has always been at the forefront of AI,” Urtasun said. “There’s tremendous talent here, a busy ecosystem, and for me it’s important to be in Canada, where I’m very aligned with the values of the country as well.”

Reflecting on her journey at ��˾��ֱ��, where she started as an assistant professor in 2014, Urtasun said she initially assumed she would “just be an academic doing research for the rest of my life” – but soon realized that involvement in industry would be critical to advancing AI technologies for use in the real world.

“Three years ago, I saw a tremendous opportunity to start a new company and what you see today is the fruit of that, where we’re really close to deployment on public roads without a human [driver],” said Urtasun.

“It’s amazing – not just for Waabi, not just for Canada, but for the industry at large.”

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

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

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

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

Cutline

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

Adina Bresge

Topic

Our Community

Schwartz Reisman Institute for Technology and Society

Artificial Intelligence

Computer Science

Faculty of Applied Science & Engineering

Faculty of Arts & Science

Faculty of Law

Research & Innovation

Vector Institute

Subheadline

Roger Grosse and David Duvenaud named Schwartz Reisman Chairs in Technology and Society, while David Lie becomes the institute’s new director

A leading expert in cybersecurity and two renowned AI safety researchers are set to take on leading roles at the University of Toronto’s Schwartz Reisman Institute for Technology and Society.

David Lie, who is known for his seminal work that led to modern trusted execution processor architectures, has been named the new director of the Schwartz Reisman Institute (SRI), which aims to explore and address the ethical and societal implications of artificial intelligence and other emerging technologies.

His four-year appointment, which takes effect July 1, coincides with two renowned experts in AI safety – Roger Grosse and David Duvenaud – being named Schwartz Reisman Chairs in Technology and Society for five-year terms.

“I think one of the top priorities is ensuring that SRI and ��˾��ֱ�� are the primary places in Canada – and perhaps in the world – for AI safety discussion and research,” says Lie, a professor in the Faculty of Applied Science & Engineering’s Edward S. Rogers Sr. department of electrical and computer engineering.

“My vision is to make us one of the leaders. Canada has already contributed greatly to machine learning and AI through the contributions of previous scholars like [University Professor Emeritus] Geoffrey Hinton, and I think we have a very strong role to play in this important technology going forward.”

The appointments come as inaugural director and chair Gillian Hadfield prepares to conclude her term as chair this month (she stepped down as director at the end of last year). The institute, created following a historic gift in 2019 from business leaders Gerald Schwartz and Heather Reisman, brings together experts from disciplines across ��˾��ֱ��’s three campuses to steer AI development to prioritize safety and human welfare.

“We are thrilled to welcome David Lie, Roger Grosse and David Duvenaud to their new roles at the Schwartz Reisman Institute,” says Melanie Woodin, dean of the Faculty of Arts & Science. “Their expertise and leadership will be instrumental in fostering the interdisciplinary collaboration needed for the University of Toronto to remain at the forefront of technological innovation that benefits humanity.”

Lie, who has served as a research lead at SRI and holds cross-appointments in the department of computer science and the Faculty of Law, says his decades of research on making computer systems more secure and trustworthy – including contributions to computer architecture, formal verification, techniques using operating systems and networking – have equipped him to tackle the complex issues posed by AI, which will require researchers to anticipate and adapt to the unexpected.

“As AI become more powerful, they may do things – or are already doing things – that we didn’t anticipate or expect,” says Lie. “Bringing cybersecurity skills, thinking and tools into the AI safety discussion will be absolutely critical to solving the problem.”

Lie emphasizes that interdisciplinary collaboration is key to addressing potential AI disruption, noting that it has been pivotal in his own research and other roles.

His current research focuses on securing mobile platforms, cloud computing security and bridging the divide between technology and policy. He is also an associate director at the Data Sciences Institute, a ��˾��ֱ�� institutional strategic initiative, a faculty affiliate at the Vector Institute for Artificial Intelligence and a senior fellow at Massey College.

“It’s really one of the things that I love about a place like ��˾��ֱ��, because it's big and you have experts in every imaginable field to collaborate with,” he says. “I feel very strongly that we can always accomplish way more together than we can individually. That's true for people, but that's also true for disciplines.”

As incoming Schwartz Reisman Chairs in Technology and Society, Grosse and Duvenaud have vital roles to play in driving SRI’s research agenda and sharing its findings with the world, says Lie.

“One of the main ways universities contribute to society is through research, but we also contribute through discourse; we contribute by translating knowledge and providing that to policymakers, decision-makers and stakeholders,” he says. “I see SRI playing an important part in these roles.”

Both Grosse and Duvenaud are associate professors of computer science in the Faculty of Arts & Science, faculty affiliates at SRI, founding members of the Vector Institute and Canada CIFAR AI chairs – and both are working at San Francisco-based Anthropic, a research company focused on AI safety and alignment.

Grosse, whose research applies our understanding of deep learning to the safety and alignment of AI systems, says academia has an essential role to play in guiding AI development by looking beyond short-term incentives to ask how these technologies can be safely and ethically integrated for the long-term benefit of humanity.

“I'm very excited to be able to understand and mitigate catastrophic risks from AI, to be part of an interdisciplinary community that's especially well positioned to make progress in these issues, and I really appreciate the leadership that donors are showing and supporting this work,” he says.

“I think academia is great for being able to ask the more fundamental questions, to carry out maybe more forward-looking research that might not be directly on a company's critical path, but will contribute to safety efforts at many different organizations.”

Duvenaud’s research, meanwhile, focuses on probabilistic deep learning, artificial general intelligence governance and dangerous capabilities evaluation.

He envisions SRI as a “centre of gravity” where academics, industry members, government leaders and other stakeholders can engage with each other and shape the future of AI technologies.

“The idea is that by having this institute dedicated to this direction, we’ll be able to do things like host visitors and engage with academics from all sorts of disciplines –such as law, economics, and other parts of civil society – so that, ultimately, when policy discussions come up, we’ll be equipped and credible as people who can help governments navigate these decisions,” says Duvenaud, who is cross appointed to the department of statistical sciences.

Sheila McIlraith, an associate director and research lead at SRI, professor of computer science in the Faculty of Arts & Science, and a Canada CIFAR AI Chair at the Vector Institute, underlines the importance of rallying diverse disciplinary experts from across ��˾��ֱ�� to address the opportunities and challenges that AI will wield in the coming years.

“AI is no longer the sole purview of computer scientists. It is reshaping the way we live, work, and interact with each other, and it will take experts from a broad range of disciplines to help ensure that AI is developed and deployed for the benefit of humanity, and that Canada adapts swiftly to protect our institutions," says McIlraith, who is an expert in AI safety research herself.

“Threats are already upon us; now is the time to act.”

��˾��ֱ��’s AI thought leaders take centre stage at Collision 2024

Christopher.Sorensen — Fri, 14 Jun 2024 14:14:37 +0000

��˾��ֱ��’s AI thought leaders take centre stage at Collision 2024

Christopher.Sorensen Fri, 06/14/2024 - 10:14

Cutline

Left to right: Geoffrey Hinton, Aidan Gomez and Raquel Urtasun – all ��˾��ֱ�� community members who are leaders in the AI field – are among the most anticipated speakers this year's Collision tech conference (photos by Johnny Guatto, Piaras Ó Mídheach/Collision via Sportsfile and Polina Teif)

Mariam Matti

Topic

Our Community

��˾��ֱ�� Entrepreneurship

��˾��ֱ��

Artificial Intelligence

Computer Science

Creative Destruction Lab

Entrepreneurship

Geoffrey Hinton

Research & Innovation

Startups

Subheadline

“��˾��ֱ�� is Canada's leading engine for research-based tech and AI startups and talent - and you see that reflected in both the lineup for Collision speakers, and the entrepreneurs who attend”

Some of the most anticipated speakers at this year’s Collision tech conference are artificial intelligence luminaries from the University of Toronto – and they will be sharing their insights on how the revolutionary technology is poised to transform industry and society.

Known as the "godfather of AI," Geoffrey Hinton, a cognitive psychologist and ��˾��ֱ�� University Professor Emeritus of computer science, will host a session on June 19 titled “Can We Control AI?” that explores the potential benefits and risks inherent in AI advancement.

Aidan Gomez, one of Hinton’s former interns at Google Brain, is speaking on June 18 about the real-world impact of AI. He’s CEO and co-founder at Cohere, which he and two ��˾��ֱ�� alumni founded, and which has been dubbed one of the “buzziest AI startups around right now.”

And Raquel Urtasun, a ��˾��ֱ�� professor of computer science and the founder and CEO of self-driving truck company Waabi, which launched with one of the largest rounds of seed funding in Canadian history, will share her insights on the trajectory of AI-driven transportation solutions during a session on June 19.

Jon French (photo by Brian de Rivera Simon)

He adds that the conference also gives the university an opportunity to promote “our innovative ecosystem and connect with corporate partners from around the world and investors looking to grow their pipelines, governments and academic institutions at home and aboard.”

Anticipated to draw more than 40,000 attendees, Collision runs from June 17 to 20 at the Enercare Centre in Toronto and will feature startup founders, business leaders, investors, scientists, journalists and celebrities, including tennis star Maria Sharapova.

Before the conference officially kicks off, ��˾��ֱ�� Entrepreneurship will host two tours of the Schwartz Reisman Innovation Campus in partnership with the City of Toronto and the Government of Ontario. It will also have a booth at Collision to showcase ��˾��ֱ��’s entrepreneurial ecosystem, featuring self-driving lab and robot demos from the Acceleration Consortium and CRAFT Microfluidics – both ��˾��ֱ�� institutional strategic initiatives – and a “Startup Spotlight.”

��˾��ֱ��’s Black Founders Network is part of a group of community organizers leading the Black Innovation Zone, which will have its own booth with programming for Black innovators.

French says the conference provides ��˾��ֱ�� startups and entrepreneurs a chance to pitch their ideas and connect with global mentors, advisers, investors, partners and peers.

“For many founders, it is an eye-opening conference that gets them thinking bigger about the markets they serve and often leads to participating in programs and opportunities in geographies around the world,” he says.

Nuha Siddiqui (photo by Nick Iwanyshyn)

Nuha Siddiqui is among the ��˾��ֱ��-connected entrepreneurs participating in Collision this year. She is the co-founder and CEO of Erthos, which has developed a plant-based resin that can be used as a substitute for traditional plastics when manufacturing everyday products. She launched the company with Kritika Tyagi while they were studying at the university and received support from ��˾��ֱ��’s entrepreneurship community, including the Creative Destruction Lab at the Rotman School of Management.

Siddiqui said she’s “incredibly thankful” for ��˾��ֱ��’s continued support to Erthos’s growth.

“My co-founder and I started Erthos years ago while we were still students, and since graduating, ��˾��ֱ�� has continued to broaden our network, contribute to meaningful media coverage and remain active champions of our success,” she says.

“It's an absolute honour to represent the entrepreneurship community on the main stage at Collision this year and to have the opportunity to amplify our mission towards building a more material-conscious future."

Here are just a few other ��˾��ֱ�� faculty, alumni and other community members who will be shaping the dialogue around innovation, tech and entrepreneurship at this year’s conference:

Christian Weedbrook, founder and CEO at Xanadu and a former ��˾��ֱ�� post-doctoral researcher, will discuss the future of data centres and how quantum computing can be leveraged to avoid future economic and climate issues.
Mike Murchison, ��˾��ֱ�� alum and founding CEO of Ada, which received support from the Creative Destruction Lab, will discuss the evolution of AI customer service agents and their implications for brands and consumers.
Curtis VanWalleghem, co-founder and CEO at Hydrostor and a ��˾��ֱ�� alumnus, will speak about Hydrostor’s innovative technology that utilizes underground spaces for a sustainable long-term energy storage.
David Wong, chief product officer at Thomson Reuters and ��˾��ֱ�� alumnus, will explore the transformative power of AI and offer insights on how organizations can unlock its full potential for business operations.
Mara Reiff, ��˾��ֱ�� alumna, chief customer officer and interim co-CEO at Freshbooks, and Dan Richards, assistant professor, teaching stream, at the Rotman School of Management, will discuss how to empower small business owners to make smart decisions.

��˾��ֱ�� researchers enhance object-tracking abilities of self-driving cars

rahul.kalvapalle — Wed, 29 May 2024 14:59:42 +0000

��˾��ֱ�� researchers enhance object-tracking abilities of self-driving cars

rahul.kalvapalle Wed, 05/29/2024 - 10:59

Cutline

Sandro Papais, a PhD student, is the co-author of a new paper that introduces a graph-based optimization method to improve object tracking for self-driving cars (photo courtesy of aUToronto)

Safa Jinje

Topic

Breaking Research

Artificial Intelligence

Faculty of Applied Science & Engineering

Subheadline

The new tools could help robotic systems of autonomous vehicles better track the position and motion of vehicles, pedestrians and cyclists

Researchers at the University of Toronto Institute for Aerospace Studies (UTIAS) have introduced a pair of high-tech tools that could improve the safety and reliability of autonomous vehicles by enhancing the reasoning ability of their robotic systems.

The innovations address multi-object tracking, a process used by robotic systems to track the position and motion of objects – including vehicles, pedestrians and cyclists – to plan the path of self-driving cars in densely populated areas.

Tracking information is collected from computer vision sensors (2D camera images and 3D LIDAR scans) and filtered at each time stamp, 10 times a second, to predict the future movement of moving objects.

“Once processed, it allows the robot to develop some reasoning about its environment. For example, there is a human crossing the street at the intersection, or a cyclist changing lanes up ahead,” says Sandro Papais, a PhD student in UTIAS in the Faculty of Applied Science & Engineering. "At each time stamp, the robot’s software tries to link the current detections with objects it saw in the past, but it can only go back so far in time.”

In a new paper presented at the 2024 International Conference on Robotics and Automation in Yokohama, Japan, Papais and co-authors Robert Ren, a third-year engineering science student, and Professor Steven Waslander, director of UTIAS’s Toronto Robotics and AI Laboratory, introduce Sliding Window Tracker (SWTrack) – a graph-based optimization method that uses additional temporal information to prevent missed objects.

The tool is designed to improve the performance of tracking methods, particularly when objects are occluded from the robot’s point of view.

A visualization of a nuScenes dataset used by the researchers. The image is a mosaic of the six different camera views around the car with the object bounding boxes rendered overtop of the images (image courtesy of the Toronto Robotics and AI Laboratory)

“SWTrack widens how far into the past a robot considers when planning,” says Papais. “So instead of being limited by what it just saw one frame ago and what is happening now, it can look over the past five seconds and then try to reason through all the different things it has seen.” 

The team tested, trained and validated their algorithm on field data obtained through nuScenes, a public, large-scale dataset for autonomous driving vehicles that have operated on roads in cities around the world. The data includes human annotations that the team used to benchmark the performance of SWTrack.

They found that each time they extended the temporal window, to a maximum of five seconds, the tracking performance got better. But past five seconds, the algorithm’s performance was slowed by computation time.

“Most tracking algorithms would have a tough time reasoning over some of these temporal gaps. But in our case, we were able to validate that we can track over these longer periods of time and maintain more consistent tracking for dynamic objects around us,” says Papais.

Papais says he’s looking forward to building on the idea of improving robot memory and extending it to other areas of robotics infrastructure. “This is just the beginning,” he says. “We’re working on the tracking problem, but also other robot problems, where we can incorporate more temporal information to enhance perception and robotic reasoning.”

Another paper, co-authored by master’s student Chang Won (John) Lee and Waslander, introduces UncertaintyTrack, a collection of extensions for 2D tracking-by-detection methods that leverages probabilistic object detection.

“Probabilistic object detection quantifies the uncertainty estimates of object detection,” explains Lee. “The key thing here is that for safety-critical tasks, you want to be able to know when the predicted detections are likely to cause errors in downstream tasks such as multi-object tracking. These errors can occur because of low-lighting conditions or heavy object occlusion.

“Uncertainty estimates give us an idea of when the model is in doubt, that is, when it is highly likely to give errors in predictions. But there’s this gap because probabilistic object detectors aren’t currently used in multi-tracking object tracking.” 

Lee worked on the paper as part of his undergraduate thesis in engineering science. Now a master’s student in Waslander’s lab, he is researching visual anomaly detection for the Canadarm3, Canada’s contribution to the U.S.-led Gateway lunar outpost. “In my current research, we are aiming to come up with a deep-learning-based method that detects objects floating in space that pose a potential risk to the robotic arm,” Lee says.

Waslander says the advancements outlined in the two papers build on work that his lab has been focusing on for a number of years.

“[The Toronto Robotics and AI Laboratory] has been working on assessing perception uncertainty and expanding temporal reasoning for robotics for multiple years now, as they are the key roadblocks to deploying robots in the open world more broadly,” Waslander says.

“We desperately need AI methods that can understand the persistence of objects over time, and ones that are aware of their own limitations and will stop and reason when something new or unexpected appears in their path. This is what our research aims to do.”

What Now? AI, Episode 5: This Is Not Real

mattimar — Thu, 23 May 2024 19:33:08 +0000

What Now? AI, Episode 5: This Is Not Real mattimar Thu, 05/23/2024 - 15:33

Topic

Our Community

Temerty Faculty of Medicine

What Now? AI

Schwartz Reisman Institute for Technology and Society

Munk School of Global Affairs & Public Policy

Artificial Intelligence

Faculty of Arts & Science

Faculty of Information

Political Science

��˾��ֱ�� Mississauga

Artificial intelligence presents new opportunities to strengthen democracy even as it threatens to cast a shadow over election integrity and further the spread of misinformation.

In the fifth episode of What Now? AI, University of Toronto hosts Beth Coleman and Rahul Krishnan are joined by experts Harper Reed and Peter Loewen, who is also from ��˾��ֱ��, to explore the impact of AI on the political realm.

Listen to episode five on Apple, Spotify, SoundCloud, iHeartRadio and Amazon. Watch episode five on YouTube.

Loewen, director of ��˾��ֱ��’s Munk School of Global Affairs & Public Policy and a professor in the department of political science in the Faculty of Arts & Science, explains how AI removes the human touch from politics, potentially making the public uneasy.

“We still don't like the fact that it might be a machine that we're talking to,” said Loewen, who is also the associate director of the Schwartz Reisman Institute for Technology and Society.

“But then if you layer on this dimension of not knowing if this is actually the campaign that’s doing it, I think that’s probably orders of magnitude worse because what it does is it takes us from the realm of kind of feeling uneasy about something into feeling like this thing is corrupted.”

Reed, meanwhile, spoke about his experience as the chief technology officer on former U.S. president Barack Obama’s re-election campaign in 2012.

“The technology we built was not about convincing someone at the time that Mitt Romney was a bad person or a good person,” said Reed during a conversation with Coleman about AI and democracy that was filmed live at the Schwartz Reisman Institute for Technology and Society’s annual conference Absolutely Interdisciplinary, a portion of which was used in the podcast episode.

“The tech was more about making sure you got to vote.”

When asked about the biggest threats to AI and democracy, Reed emphasized that he is less worried about the technology itself and more with ensuring it’s beneficial to societal use.

“I’m worried about who has access to it and how they are using it.”

About the hosts:

Beth Coleman is an associate professor at ��˾��ֱ�� Mississauga’s Institute of Communication, Culture, Information and Technology and the Faculty of Information. She is also a research lead on AI policy and praxis at the Schwartz Reisman Institute for Technology and Society. Coleman authored Reality Was Whatever Happened: Octavia Butler AI and Other Possible Worlds using art and generative AI.

Rahul Krishnan is an assistant professor in ��˾��ֱ��’s department of computer science in the Faculty of Arts & Science and department of laboratory medicine and pathobiology in the Temerty Faculty of Medicine. He is a Canada CIFAR Chair at the Vector Institute, a faculty affiliate at the Schwartz Reisman Institute for Technology and Society and a faculty member at the Temerty Centre for AI Research and Education in Medicine (T-CAIREM).

Note: The artwork in the background of Peter Loewen’s interview belong to the Mirvish Family’s private collection. The large image, titled Floating Free, is by K.M. Graham. The smaller image is untitled and by the same artist.

Artificial Intelligence (AI)

arasar14 — Tue, 14 May 2024 14:12:43 +0000

Artificial Intelligence (AI) arasar14 Tue, 05/14/2024 - 10:12

URL

https://ai.utoronto.ca/

Artificial Intelligence

��˾��ֱ�� researchers develop AI model to predict 'very dynamic' peptide structures

��˾��ֱ�� team takes top spot in self-driving car challenge for 6th time in 7 years

What makes a chess move brilliant? Researchers use AI to find out

With ��˾��ֱ�� innovators front and centre, Collision conference wraps up five-year Toronto run

Waabi, founded by ��˾��ֱ��'s Raquel Urtasun, raises US$200 million to launch self-driving trucks

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

��˾��ֱ��’s AI thought leaders take centre stage at Collision 2024

��˾��ֱ�� researchers enhance object-tracking abilities of self-driving cars

What Now? AI, Episode 5: This Is Not Real

About the hosts:

Artificial Intelligence (AI)

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