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Interview with Kate Larson: Talking multi-agent systems and collective decision-making

ai, AI (Artificial Intelligence), Artificial Intelligence, IJCAI, IJCAI2025, opinions

What if AI were designed not only to optimize choices for individuals, but to help groups reach decisions together? At IJCAI 2025 in Montreal, I had the pleasure of speaking with Professor Kate Larson of the University of Waterloo, a leading expert in multi-agent systems whose research explores how AI can support collective decision-making. In […]

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California governor Gavin Newsom accuses TikTok of suppressing content critical of Trump

ai, AI (Artificial Intelligence), Artificial Intelligence, Donald Trump, Gavin Newsom, Technology, TikTok, Trump administration, US news

Newsom launched a review of the platform, despite TikTok saying a systems failure was responsible for the issueCalifornia governor Gavin Newsom has accused TikTok of suppressing content critical of president Donald Trump, as he launched a review of the platform’s content moderation practices to determine if they violated state law, even as the platform blamed

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In-House vs Crowdsourced vs Outsourced Data Labeling: Pros, Cons, & the “Right Fit” Framework

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Annotation, Data Annotation / Labeling, Data Labeling, Shaip Blogs

Choosing a data labeling model looks simple on paper: hire a team, use a crowd, or outsource to a provider. In practice, it’s one of the most leverage-heavy decisions you’ll make—because labeling affects model accuracy, iteration speed, and the amount of engineering time you burn on rework. Organizations often notice labeling problems after model performance

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