HAI faculty share their predictions for the coming year.


Copyright: hai.stanford.edu – “What to Expect in 2023 in AI”


This year’s biggest headline might have been generative AI, but what should we expect from the field in 2023? Four Stanford HAI faculty members describe what they expect the biggest advances, opportunities, and challenges will be for the coming year.

Better Foundation Models

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.”

One area he expects to see growth: sparse models. A sparse model is a way of representing complex data in a more efficient or compact way, which can be faster to compute and require less memory to store.

“Generally, I expect to see algorithmic advances that let you have more scale,” he says.

Video’s Generative Moment

While text and image generative AI was this year’s big story, video will be a big focus in 2023, says Percy Liang, associate professor of computer science and director of Stanford HAI’s Center for Research on Foundation Models. “Capturing long-range dependencies is challenging, but technology will continue to get better, at least with shorter videos to start,” he says. “We may be getting to a point next year where we won’t be able to distinguish whether a human or computer generated a video. Up to today, if you watch a video, you expect it to be real, but we’re seeing that hard line start to evaporate.”[…]

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