Bitter Lesson


In a 2019 blog post, Sutton observed that the most successful approaches in AI have been those that are sufficiently general such that they “continue to scale with increased computation.” 

In other words, clever, bespoke solutions engineered to tackle specific problems tend to lose out to general-purpose solutions that can be deployed at a massive scale of internet-sized data (trillions of data points) and brain-sized artificial neural networks (trillions of model parameters or “synaptic weights”). 

Sutton suggested we need to recognize that “the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries.” In other words, embrace complexity.

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