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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.
Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
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Despite the rapid adoption of LLM chatbots, little is known about how they are used. We approach this question theoretically and empirically, modeling a user who chooses whether to complete a task herself, ask the chatbot for information that reduces decision noise, or delegate execution to the chatbot...
AI coding agents now complete multi-hour coding benchmarks with roughly 50% reliability, yet a randomized trial found experienced open-source developers took about 19% longer when allowed frontier AI tools than when tools were disallowed...
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AI coding agents now complete multi-hour coding benchmarks with roughly 50% reliability, yet a randomized trial found experienced open-source developers took about 19% longer when allowed frontier AI tools than when tools were disallowed...
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
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?