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Peering Into the Black Box: The Rise of Representation Engineering
Manage episode 448992995 series 3351512
IVANCAST PODCAST에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 IVANCAST PODCAST 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Join us in SHIFTERLABS’ latest experimental podcast series powered by Notebook LM, where we bridge research and conversation to illuminate groundbreaking ideas in AI. In this episode, we dive into “Representation Engineering: A Top-Down Approach to AI Transparency,” an insightful paper from the Center for AI Safety, Carnegie Mellon University, Stanford, and other leading institutions. This research redefines how we view transparency in deep learning by shifting the focus from neurons and circuits to high-level representations.
Discover how Representation Engineering (RepE) introduces new methods for reading and controlling cognitive processes in AI models, offering innovative solutions to challenges like honesty, hallucination detection, and fairness. We explore its applications across essential safety domains, including model control and ethical behavior. Tune in to learn how these advances could shape a future of AI that is more transparent, accountable, and aligned with human values.
This series is part of SHIFTERLABS’ ongoing commitment to pushing the boundaries of educational technology and fostering discussions at the intersection of research, technology, and responsible innovation.
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