To solve AI's energy crisis, 'rethink the entire stack from electrons to algorithms', says Stanford prof

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Think biologically not digitally to go from megawatts to watts, HAI gathering told

The Stanford Institute for Human-Centered Artificial Intelligence on Wednesday celebrated five years of cat herding, which is to say shepherding the responsible development of machine learning.

Simply put, the human brain is orders of magnitude more energy efficient than silicon-based processors, to say nothing about wetware's evident intellectual superiority and ability to reason and learn.

"Biology is completely different. The final answer is just good enough and all the intermediate sets are slow, noisy, and unreliable. But not so unreliable that the final answer isn't just good enough for what's required ... So I think we have to rethink the entire technology stack from electrons to algorithms in order to really go from megawatts to watts."for energy is a critical problem that needs to be solved.

 

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