>We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.

I think these lessons are less appropriate as our hardware and our understanding of neural networks improve. An agent which is able to [self] learn complex probabilistic relationships between inputs and outputs (i.e. heuristics) requires a minimum complexity/performance, both in hardware and neural network design, before any sort of useful[self] learning is possible. We've only recently crossed that threshold (5-10 years ago)

>The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin

Admittedly, I'm not quite sure of the author's point. They seem to indicate that there is a trade-off between spending time optimizing the architecture and baking in human knowledge.

If that's the case, I would argue that there is an impending perspective shift in the field of ML, wherein "human knowledge" is not something to hardcode explicitly, but instead is implicitly delivered through a combination of appropriate data curation and design of neural networks which are primed to learn certain relationships.

That's the future and we're just collectively starting down that path - it will take some time for the relevant human knowledge to accumulate.