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Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.

Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.

In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
Please note that this event is open to Stanford affiliates only.
Please note that this event is open to Stanford affiliates only.
From improving medical diagnosis to optimizing supply chains, AI holds the promise of transforming every industry. However, many companies and teams--particularly ones outside the consumer internet industry--are still struggling to take research breakthroughs or promising proof-of-concept demonstrations and turn them into practical production deployments. How can researchers and AI professionals help bridge this gap? In this talk, I’ll describe some key challenges facing AI deployments, and also discuss some solutions, ranging from techniques for working with small data, to improving algorithms’ robustness and generalizability, to systematically planning out the full-cycle of machine learning projects.