Will LLMs scale all the way to AGI?
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Scaling large language models (LLMs) towards artificial general intelligence (AGI) involves complex challenges and uncertainties. Experts have discussed various aspects of this issue:
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Inverse Scaling Phenomena: from the Mindscape Podcast expresses skepticism about the idea that massively scaled LLMs inherently exhibit "power-seeking" behaviors, a concern in AI alignment. His findings suggest such behaviors, indicative of AGI potential, have not been observed, implying a limitation in scaling LLMs straight to AGI 1.
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Components Beyond LLMs: , discussing on the Lex Fridman Podcast, contends that while LLMs have achieved remarkable capabilities, components beyond the current models, possibly including elements like 'embodiment', might be necessary. He sees AI more as an enhancement to human capabilities rather than full AGI 2.
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Existential Risks and Abilities: On the Data Skeptic Podcast, reflects the broad spectrum of capabilities and existential risks associated with AGI. He recognizes LLMs' part in shaping AGI but emphasizes the unknowns about integrating essential skills like rational thinking and emotions into AGI 3.
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Technical Needs for Scaling: The dialogue between Mindscape Podcast guests addresses scaling LLMs. It's pointed out that despite significant advances, the mistakes made by existing models are systematic and unlikely to be resolved by merely increasing data. The discussion hints at the necessity of structural changes in AI systems to move towards AGI 4.
Overall, while LLMs contribute significantly to the field of AI, their direct path to developing AGI is fraught with technical, philosophical, and practical challenges. The consensus suggests that additional innovations and components will be essential for achieving AGI.
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