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Towards Understanding and Building Embodied Intelligence
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
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The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
In contrast to embodied intelligence, which is common in nature, the recent progress in AI has been disembodied. Animals display remarkable degrees of embodied intelligence by leveraging their evolved morphologies to learn complex tasks. In this talk, I will argue that intelligent behavior is a function of the brain, morphology, and the environment. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, partially due to the substantial challenge of performing large-scale in silico experiments on evolution and learning. To address this, I will introduce a new framework called DERL which enables us to evolve agents with diverse morphologies to learn hard locomotion and manipulation tasks in complex environments, and reveals insights into relations between environmental physics, embodied intelligence, and the evolution of rapid learning.
