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Applicability vs. job displacement: further notes on our recent research on AI and occupations

8月 21, 2025
Recently, we released a paper Working with AI: Measuring the Occupational Implications of Generative AI that studied what occupations might find AI chatbots useful, and to what degree. The paper sparked significant discussion, which is no surprise since people care deeply about the future of AI and jobs--that’s part of why we think it’s important to study these topics.
Three white icons on a gradient background transitioning from blue to green. From left to right: a network structure with connected circles, an upward-trending line graph with bars and an arrow, and a checklist with horizontal lines and checkmarks.

最近の投稿

  1. Three white icons on a gradient background transitioning from blue to green. From left to right: a network structure with connected circles, an upward-trending line graph with bars and an arrow, and a checklist with horizontal lines and checkmarks.

    Applicability vs. job displacement: further notes on our recent research on AI and occupations 

    8月 21, 2025

    Recently, we released a paper Working with AI: Measuring the Occupational Implications of Generative AI that studied what occupations might find AI chatbots useful, and to what degree. The paper sparked significant discussion, which is no surprise since people care deeply about the future of AI and jobs--that’s part of why we think it’s important to study these topics.

  5. Stylized digital illustration of a multi-layered circuit board. A glowing blue microchip sits at the top center, with intricate circuitry radiating outward. Beneath it, four stacked layers transition in color from blue to orange, each featuring circuit-like patterns. Smaller rectangular and circular components are connected around the layers, all set against a dark background with scattered geometric shapes.

    Project Ire autonomously identifies malware at scale 

    8月 5, 2025

    Designed to classify software without context, Project Ire replicates the gold standard in malware analysis through reverse engineering. It streamlines a complex, expert-driven process, making large-scale malware detection faster & more consistent.

  9. CollabLLM blog hero | flowchart diagram starting in the upper left corner with an icon of two overlapping chat bubbles; arrow pointing right to an LLM network node icon; branching down to show three simulated users; right arrow to a "Reward" box

    CollabLLM: Teaching LLMs to collaborate with users 

    7月 15, 2025

    Recipient of an ICML 2025 Outstanding Paper Award, CollabLLM improves how LLMs collaborate with users, including knowing when to ask questions and how to adapt tone and communication style to different situations. This approach helps move AI toward more user-centric and trustworthy systems.

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