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cover of episode 878: In Case You Missed It in March 2025

878: In Case You Missed It in March 2025

2025/4/11
logo of podcast Super Data Science: ML & AI Podcast with Jon Krohn

Super Data Science: ML & AI Podcast with Jon Krohn
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Andrey Burkov
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Natalie Monbiot
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Richmond Alake
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Andrey Burkov: 我认为实现AGI的关键在于理解人类区别于其他生物的无限期规划能力。LLM等神经网络目前是反应式的，缺乏真正的规划能力和对自身知识掌握程度的认知。它们无法区分确信的知识和推测，即使尝试通过微调模型来改进，效果仍然不佳。 要实现AGI，我们需要弄清楚是什么让我们能够进行无限期的规划。如果我们能解答这个问题，就能向AGI迈进一大步。此外，人类还具备对自身知识掌握程度的感知能力，LLM缺乏这种能力，导致其在回答问题时无法区分确信度和推测，目前尝试通过微调模型来解决这个问题，但效果不佳。 Natalie Monbiot: AI是人类认知进化的一部分，但它并非全部。人类应专注于自身独特的优势——体验式学习和创造力，与AI协作，而非被其取代。在AI时代，我们应该更加关注人类体验和创造力，因为AI无法复制人类的经验，这应该激励我们去探索那些依赖于人类与AI差异的技能和机会。

Deep Dive

Shownotes Transcript

AI stacks, AGI, training neural networks, and AI authenticity: Jon Krohn rounds up his interviews from March with this episode of “In Case You Missed It”. In his favorite clips from the month, he speaks to Andriy Burkov (Episode 867), Natalie Monbiot (Episode 873), Richmond Alake (Episode 871) and Varun Godbole (Episode 869).

Additional materials: www.superdatascience.com/878

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