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Generalisation vs Reasoning

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Manage episode 399856834 series 1993932
Indhold leveret af AI-Podden with Ather Gattami and Ather Gattami. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af AI-Podden with Ather Gattami and Ather Gattami eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Reinforcement learning is a type of machine learning that allows agents to learn by interacting with their environment. DeepMind researcher, David Abel is interested in using reinforcement learning to understand and build intelligent agents. One of the big questions in AI is what exactly "intelligence" is. Another big question is how to build intelligent agents that can reason and solve problems. Abel believes that it is important to achieve conceptual clarity in AI.

Abel's research focuses on AI's core scientific questions, emphasizing the need for conceptual clarity about intelligent behavior and agents. David express skepticism about creating AI with certain reward functions, suggesting that multi-criteria objectives could better align AI with human interests.

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132 episoder

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Generalisation vs Reasoning

AI-podden

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Manage episode 399856834 series 1993932
Indhold leveret af AI-Podden with Ather Gattami and Ather Gattami. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af AI-Podden with Ather Gattami and Ather Gattami eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Reinforcement learning is a type of machine learning that allows agents to learn by interacting with their environment. DeepMind researcher, David Abel is interested in using reinforcement learning to understand and build intelligent agents. One of the big questions in AI is what exactly "intelligence" is. Another big question is how to build intelligent agents that can reason and solve problems. Abel believes that it is important to achieve conceptual clarity in AI.

Abel's research focuses on AI's core scientific questions, emphasizing the need for conceptual clarity about intelligent behavior and agents. David express skepticism about creating AI with certain reward functions, suggesting that multi-criteria objectives could better align AI with human interests.

Don't forget to subscribe and follow us on Linkedin.
  continue reading

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