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#12 Implementing AI Agents—From Vision to Execution

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Manage episode 459716318 series 3607822
Innhold levert av Andrew Psaltis. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Andrew Psaltis eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

In this episode of 'AI Demystified for Executives,' host Andrew Psaltis provides a comprehensive guide on implementing AI agents within organizations. The discussion includes aligning AI strategies with business goals, scoping use cases, and identifying tasks suitable for automation. Practical advice is offered on the build versus buy dilemma, pilot programs for quick wins, and developing internal expertise. The importance of selecting the right large language model (LLM), integrating tools, and continuous learning for AI agents is emphasized. Additionally, the episode addresses ethical considerations, regulatory compliance, and the development of governance policies to ensure responsible AI deployment. Real-world examples and research references are provided to support these strategies.
References:

StreamBench - Towards Benchmarking Continuous Improvement of Language Agents

Code - https://stream-bench.github.io/
Paper: https://arxiv.org/abs/2406.08747

GTA: A Benchmark for General Tool Agents

Code - https://github.com/open-compass/GTA
Paper - https://arxiv.org/abs/2407.08713

  continue reading

13 episoder

Artwork
iconDel
 
Manage episode 459716318 series 3607822
Innhold levert av Andrew Psaltis. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Andrew Psaltis eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

In this episode of 'AI Demystified for Executives,' host Andrew Psaltis provides a comprehensive guide on implementing AI agents within organizations. The discussion includes aligning AI strategies with business goals, scoping use cases, and identifying tasks suitable for automation. Practical advice is offered on the build versus buy dilemma, pilot programs for quick wins, and developing internal expertise. The importance of selecting the right large language model (LLM), integrating tools, and continuous learning for AI agents is emphasized. Additionally, the episode addresses ethical considerations, regulatory compliance, and the development of governance policies to ensure responsible AI deployment. Real-world examples and research references are provided to support these strategies.
References:

StreamBench - Towards Benchmarking Continuous Improvement of Language Agents

Code - https://stream-bench.github.io/
Paper: https://arxiv.org/abs/2406.08747

GTA: A Benchmark for General Tool Agents

Code - https://github.com/open-compass/GTA
Paper - https://arxiv.org/abs/2407.08713

  continue reading

13 episoder

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