Everyone has a dream. But sometimes there’s a gap between where we are and where we want to be. True, there are some people who can bridge that gap easily, on their own, but all of us need a little help at some point. A little boost. An accountability partner. A Snooze Squad. In each episode, the Snooze Squad will strategize an action plan for people to face their fears. Guests will transform their own perception of their potential and walk away a few inches closer to who they want to become ...
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Data Dignity and the Inversion of AI
MP4•Maison d'episode
Manage episode 386536488 series 2578515
Contenu fourni par UCTV. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par UCTV ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.
In this program, Jaron Lanier, Microsoft's prime unifying scientist, discusses a piece he published in The New Yorker (“There Is No AI”) about applying data dignity ideas to artificial intelligence. Lanier argues that large-model AI can be reconceived as a social collaboration by the people who provide data to the model in the form of text, images and other modalities. This is a figure/ground inversion of the usual conception of AI as being a participant or collaborator in its own right. Explanations of model results and behaviors would then center around the relative influence of specific inputs through a provenance calculation mechanism. This formulation suggests new and different strategies for long-term economics in the context of high-performance AI, as well as more concrete approaches to many safety, fairness and alignment questions. This program is co-hosted with the UC Berkeley College of Computing, Data Science, and Society and the UC Berkeley Artificial Intelligence Research (BAIR) Lab. The CITRIS Research Exchange delivers fresh perspectives on information technology and society from distinguished academic, industry and civic leaders. Series: "Data Science Channel" [Science] [Show ID: 39326]
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116 episodes
MP4•Maison d'episode
Manage episode 386536488 series 2578515
Contenu fourni par UCTV. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par UCTV ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.
In this program, Jaron Lanier, Microsoft's prime unifying scientist, discusses a piece he published in The New Yorker (“There Is No AI”) about applying data dignity ideas to artificial intelligence. Lanier argues that large-model AI can be reconceived as a social collaboration by the people who provide data to the model in the form of text, images and other modalities. This is a figure/ground inversion of the usual conception of AI as being a participant or collaborator in its own right. Explanations of model results and behaviors would then center around the relative influence of specific inputs through a provenance calculation mechanism. This formulation suggests new and different strategies for long-term economics in the context of high-performance AI, as well as more concrete approaches to many safety, fairness and alignment questions. This program is co-hosted with the UC Berkeley College of Computing, Data Science, and Society and the UC Berkeley Artificial Intelligence Research (BAIR) Lab. The CITRIS Research Exchange delivers fresh perspectives on information technology and society from distinguished academic, industry and civic leaders. Series: "Data Science Channel" [Science] [Show ID: 39326]
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116 episodes
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