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Scaling in the service of reasoning & model-based ML - Yoshua Bengio

yoshuabengio.org · 2,601 words · saved by 1 readers

Co-written with my PhD student Edward J. Hu. Scaling seems to work really well. We must be cautious in pursuing research directions that build knowledge…

Co-written with my PhD student Edward J. Hu . Scaling seems to work really well. We must be cautious in pursuing research directions that build knowledge directly into AI systems at the expense of scalability . In fact, even nature builds intelligence on top of large-scale biological neural networks . However, current large-scale systems still exhibit significant factual errors and unpredictable behavior when deployed. While these errors might improve with short-term solutions like more filters, better retrievers, and smarter prompts, these systems do not think like humans do, as indicated by

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