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Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.
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.
An AI Measurement Science (AIMS) Seminar Series
An AI Measurement Science (AIMS) Seminar Series
The yield, however, doesn’t seem equally distributed to all who aspire to repeat the success of others in their respective domains, due to the data themselves. A selected few are running away with the infrastructure and the competence they’ve built over time to collect and process the data, leaving many others behind. For some, it’s a struggle to find ways how to get them in the first place, and for some others it’s about figuring out what to do with them. And while many give their data away without knowing what they get in return, the growing awareness of the issue by the public and the thought leaders is being materialized into new regulations and suggestions on how the data should be governed and shared. In this seminar, Bongjun Ko, an AI Engineering Fellow at Stanford HAI, would like to share his thoughts on the this issue, drawing from the experience as an engineer who’s been trying to overcome the lack of data when building data-driven solutions, and as an individual who’s been providing the “new oil in 21st century”. Some of the open questions he would like to cast include: What can you do to remain competitive without data? Is data really a new oil? How much is a piece of data worth, and can it be measured?