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U of T Study Suggests Larger Datasets Aren't Always Better for Training AI Models – India Education | Latest Education News | Global Educational News

A new study by researchers at the University of Toronto suggests that one of the fundamental assumptions of deep learning artificial intelligence models – that they require enormous amounts of training data to make accurate predictions – may not be a ....

Jason Hattrick , Kangming Li , University Of Toronto , Nature Communications , Applied Science , Open Catalyst Project , Materials Project , Open Quantum Materials Database ,

How Google DeepMind's AI breakthrough could revolutionise chip, battery development | Explained News

AI researchers have predicted the structures of more than 2 million new materials, and published a list of the 381,000 most stable ones. They say their work is currently equivalent to nearly 800 years of knowledge. ....

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Google AI Finds 800 Years of Industrial Materials in One Click

Google AI Finds 800 Years of Industrial Materials in One Click
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New study finds bigger datasets might not always be better for AI models

New study finds bigger datasets might not always be better for AI models
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Kangming Li , Jason Hattrick , Nature Communications , University Of Toronto Engineering , Toronto Engineering , Jason Hattrick Simpers , Open Catalyst Project , Materials Project , Open Quantum Materials Database ,

Toronto Study: Bigger Datasets Not Always Better for AI

Toronto Study: Bigger Datasets Not Always Better for AI
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Kangming Li , Jason Hattrick , Nature Communications , University Of Toronto Engineering , Toronto Engineering , Jason Hattrick Simpers , Open Catalyst Project , Materials Project , Open Quantum Materials Database ,