• Chris Creates With AI

  • 著者: Chris Tansey
  • ポッドキャスト

Chris Creates With AI

著者: Chris Tansey
  • サマリー

  • Join Chris as he explores the fascinating world of artificial intelligence and its creative applications. Each season, dive deep into a different aspect of AI technology, from mastering prompt engineering to unlocking the potential of large language models.

    Whether you're an AI enthusiast, a curious beginner, or a seasoned professional, "Chris Creates With AI" offers insights, practical tips, and engaging discussions that will expand your understanding of AI's role in creative and problem-solving processes.

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あらすじ・解説

Join Chris as he explores the fascinating world of artificial intelligence and its creative applications. Each season, dive deep into a different aspect of AI technology, from mastering prompt engineering to unlocking the potential of large language models.

Whether you're an AI enthusiast, a curious beginner, or a seasoned professional, "Chris Creates With AI" offers insights, practical tips, and engaging discussions that will expand your understanding of AI's role in creative and problem-solving processes.

エピソード
  • Diving Prompt First: Self Consistency
    2024/10/11

    We discuss a technique called self-consistency which enhances the reasoning capabilities of large language models (LLMs). This technique involves prompting an LLM to generate multiple reasoning paths for a question and then selecting the most consistent answer among these paths. This method improves the accuracy and reliability of LLMs, particularly for tasks requiring complex reasoning, such as arithmetic and commonsense reasoning.

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    10 分
  • Diving Prompt First: Automatic Reasoning and Tool-use (ART)
    2024/10/10

    ART addresses the limitations of traditional Chain-of-Thought (CoT) prompting by enabling LLMs to decompose tasks into multiple steps and utilize external resources, ultimately improving their performance on tasks requiring reasoning and complex problem-solving.

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    11 分
  • Diving Prompt First: ReAct (Reason + Act)
    2024/10/09

    ReAct (Reason + Act) is a prompting technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to reason, plan, and interact with external tools and data sources. This technique aims to overcome the limitations of traditional LLMs, which are restricted to their training data, leading to more accurate, reliable, and sophisticated applications.

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    12 分

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