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In this talk, Pamela Chen, 2020 Human-Centered AI and JSK Journalism Fellow at Stanford, shares her experiences leading an editorial team at Instagram as the company scaled content discovery to serve more than 1 billion monthly active users. Spoiler alert: it doesn’t go as planned.
How do AI agents influence knowledge work? This paper finds that agents shift worker effort from implementation to supervision, which especially benefits verifiable work and expert workers. I use data from the coding platform Cursor to study agents in software production.
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How do AI agents influence knowledge work? This paper finds that agents shift worker effort from implementation to supervision, which especially benefits verifiable work and expert workers. I use data from the coding platform Cursor to study agents in software production.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
As content platforms began to rely on machine learning to personalize recommendations, they inadvertently enabled a new human creator class who specialized in going viral via the machine. The ranking algorithms got better at predicting the content we respond to, and these viral meme creators got better at achieving the response the algorithms were designed to predict. This emerging feedback loop, combined with the hard learnings of scaling content discovery, has led to new questions.
Who are the players behind “the power of memes” to both expand the global cultural zeitgeist and enable targeted disinformation campaigns at astounding scale? And how can better understanding the ecosystem change the way we approach the design of human-centered AI in content curation systems today?
