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The Best Way to Achieve AGI Is to Invent It

2024/11/4
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Pedro Domingos
长期机器学习研究者和华盛顿大学计算机科学教授Emeritus,专注于机器学习、人工智能和数据科学的研究和统一算法的探索。
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Pedro Domingos认为,实现AGI的关键在于基础性的创新想法,而不是仅仅依赖于规模化。他指出,当前的AI研究过于同质化,缺乏多样性,容易陷入局部最优。过度关注Transformer模型等现有技术,虽然能带来一定的改进,但不足以实现人类水平的智能。他强调AI发展是一个长期的过程,需要更多样化的研究方向和方法。Martin Casado则主要扮演提问者的角色,引导Pedro Domingos阐述其观点,并就一些关键问题进行深入探讨。

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Pedro Domingos discusses the misconception that AI progress is a sprint, emphasizing the need for fundamental new ideas and the diversity of research directions.
  • AI progress is more like a marathon than a sprint.
  • Current research is more focused but less diverse.
  • Even incremental improvements in AI can have significant impacts.

Shownotes Transcript

Longtime machine-learning researcher, and University of Washington Professor Emeritus, Pedro Domingos joins a16z General Partner Martin Casado to discuss the state of artificial intelligence, whether we're really on a path toward AGI, and the value of expressing unpopular opinions.  It's a very insightful discussion as we head into an era of mainstream AI adoption, and ask big questions about how to ramp up progress and diversify research directions.

Here's an excerpt of Pedro sharing his thoughts on the increasing cost of frontier models and whether that's the right direction:

*"if you believe the scaling laws hold and the scaling laws will take us to human-level intelligence, then, hey, it's worth a lot of investment. That's one part, but that may be wrong. The other part, however, is that to do that, we need exploding amounts of compute. *

"If if I had to predict what's going to happen, it's that we do not need a trillion dollars to reach AGI at all. So if you spend a trillion dollars reaching AGI, this is a very bad investment."

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