What To Expect in 2023 in AI

Content Originally Published on Stanford University Human-Centered Artificial Intelligence

Foundation models – giant models that can be used for a variety of downstream tasks without additional training – have been seeing huge progress, and that will only improve next year, says Chris Manning, the Thomas M. Siebel Professor in Machine Learning in the School of Engineering, professor of linguistics and of computer science, director of the Stanford Artificial Intelligence Laboratory, and associate director of Stanford HAI. He expects to see improvements in data and data curation – “not just bigger data collections, but large efforts into improving the quality of the data and cleaning out toxic or biased information that comes from random trawls of the web.” Keep reading to learn what Four Stanford HAI facutly members expect the biggest advances, opportunities and chllenges will be in the coming year.

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