The joint lawsuit accuses the company of "capitalizing and profiting" from the unauthorized use of their content for ChatGPT. The legal action was filed in the Ontario Superior Court of Justice. The plaintiffs include CBC/Radio-Canada, Postmedia, Metroland, the Toronto Star, the Globe and Mail and The Canadian Press. They're seeking punitive damages from OpenAI, payments for any profits the ChatGPT creator made from using their news articles and a ban on further use of their content. "OpenAI is capitalizing and profiting from the use of this content, without getting permission or compensating content owners."

The first Neuralink clinical trials outside the US will take place in Canada. Neuralink has secured Health Canada's approval to launch human trials in the country, with the Toronto Western Hospital being the "first and exclusive surgical site" for the procedure. The company first opened its Canadian patient registry in March this year, but now it's actively looking for potential participants. "Recruitment is now open," it has announced on X. Under the CAN-PRIME study, Neuralink will embed its implant in the brain of the participant so that it can interpret their neural activity.

The streets of London, New York, Dublin and Toronto have been full of thousands of people hoping to try and win celebrity lookalike contests in recent weeks. The phenomenon started last month, when Timothee Chalamet doppelgangers flocked to New York to try and win 50 for their resemblance to the Dune star. Since then, we've seen a Paul Mescal contest in Dublin, a Harry Styles contest in London, and even a Dev Patel contest in San Francisco. Amid the madness, you might be asking yourself - could I ever win a celebrity lookalike contest? Thankfully, help is at hand to answer this question, in the form of an app called Star by Face.

Once upon a time, water flowed across the surface of Mars. Waves lapped against shorelines, strong winds gusted and howled, and driving rain fell from thick, cloudy skies. Mars is about half the diameter of Earth, and that's where things went wrong. The Martian core cooled quickly, soon leaving the planet without a magnetic field. This, in turn, left it vulnerable to the solar wind, which swept away much of its atmosphere. Without a critical shield from the sun's ultraviolet rays, Mars could not retain its heat.

Imagine having a robot that can collaborate with a human scientist on a chemistry experiment, says Alán Aspuru-Guzik, a chemist, computer scientist, and materials scientist at the University of Toronto, who is one of the project's leaders. Aspuru-Guzik's vision is to elevate traditional lab automation to "eventually make an AI scientist," one that can perform and troubleshoot an experiment and even offer feedback on the results. Aspuru-Guzik and his team designed Organa to be flexible. That means that instead of performing only one task or one part of an experiment as a typical fixed automation system would, it can perform a multistep experiment on cue. The system is also equipped with visualization tools that can monitor progress and provide feedback on how the experiment is going.

In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all autoencoders which use mini-batch statistics to directly enforce a lifetime sparsity in the activations of the hidden units. We then propose the convolutional winner-take-all autoencoder which combines the benefits of convolutional architectures and autoencoders for learning shift-invariant sparse representations. We describe a way to train convolutional autoencoders layer by layer, where in addition to lifetime sparsity, a spatial sparsity within each feature map is achieved using winner-take-all activation functions. We will show that winner-take-all autoencoders can be used to to learn deep sparse representations from the MNIST, CIFAR-10, ImageNet, Street View House Numbers and Toronto Face datasets, and achieve competitive classification performance.

Alex Galvagni, CEO of Age of Learning and a former artificial intelligence researcher with NASA, says advances in AI now make it possible to deliver to children "a personalized and supportive" experience in education. This photo combo shows the 2024 Nobel Prize winners in Physics, professor John Hopfield, left, of Princeton University, and professor Geoffrey Hinton, of the University of Toronto, on Tuesday, Oct. 8, 2024. FOUNDATIONAL WORK: Two pioneers of artificial intelligence -- John Hopfield and Geoffrey Hinton -- won the Nobel Prize in physics Tuesday for helping to create the building blocks of machine learning that are revolutionizing the way we work and live, but also create new threats for humanity. UBER EV: Ride-sharing platform Uber on Tuesday announced that the company is taking new steps to promote the use of electric vehicles (EVs) on its platform. 'BETTER JOB': The United Nations (U.N.) advisory body on artificial intelligence (AI) last week issued seven recommendations to address AI-related risks, but an expert told Fox News Digital the points do not cover critical areas of concern.

Potential based reward shaping is a powerful technique for accelerating convergence of reinforcement learning algorithms. Typically, such information includes an estimate of the optimal value function and is often provided by a human expert or other sources of domain knowledge. However, this information is often biased or inaccurate and can mislead many reinforcement learning algorithms. In this paper, we apply Bayesian Model Combination with multiple experts in a way that learns to trust a good combination of experts as training progresses. This approach is both computationally efficient and general, and is shown numerically to improve convergence across discrete and continuous domains and different reinforcement learning algorithms.

Two pioneers of artificial intelligence – John Hopfield and Geoffrey Hinton – won the Nobel Prize in physics Oct. 8 for helping create the building blocks of machine learning that is revolutionizing the way we work and live but also creates new threats to humanity, one of the winners said. Mr. Hinton, who is known as the Godfather of artificial intelligence, is a citizen of Canada and Britain who works at the University of Toronto, and Mr. Hopfield is an American working at Princeton. "This year's two Nobel Laureates in physics have used tools from physics to develop methods that are the foundation of today's powerful machine learning," the Nobel committee said in a press release. Ellen Moons, a member of the Nobel committee at the Royal Swedish Academy of Sciences, said the two laureates "used fundamental concepts from statistical physics to design artificial neural networks that function as associative memories and find patterns in large data sets." She said that such networks have been used to advance research in physics and "have also become part of our daily lives, for instance in facial recognition and language translation. Mr. Hinton predicted that AI will end up having a "huge influence" on civilization, bringing improvements in productivity and health care. "It would be comparable with the Industrial Revolution," he said in the open call with reporters and the officials from the Royal Swedish Academy of Sciences. "Instead of exceeding people in physical strength, it's going to exceed people in intellectual ability.

Alex Galvagni, CEO of Age of Learning and a former artificial intelligence researcher with NASA, says advances in AI now make it possible to deliver to children "a personalized and supportive" experience in education. Two pioneers of artificial intelligence -- John Hopfield and Geoffrey Hinton -- won the Nobel Prize in physics Tuesday for helping create the building blocks of machine learning that is revolutionizing the way we work and live but also creates new threats for humanity. Hinton, who is known as the godfather of artificial intelligence, is a citizen of Canada and Britain who works at the University of Toronto, and Hopfield is an American working at Princeton. "These two gentlemen were really the pioneers," said Nobel physics committee member Mark Pearce. "They ... did the fundamental work, based on physical understanding which has led to the revolution we see today in machine learning and artificial intelligence."