‘Must-Read’ AI Papers Suggested by Experts — Pt 2

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To be honest, I don’t believe in singling out any one paper as being more important than the rest, since I think all papers build on each other, and we should acknowledge science as a collaborative effort. I will say that there are some papers I’ve enjoyed reading more than others, and that I’ve learned from, but others might have different experiences, based on their interest and background. That said, I’ve enjoyed reading the following:


This paper advances a general computational framework for reward that places it in an evolutionary context, formulating a notion of an optimal reward function given a fitness function and some distribution of environments. Novel results from computational experiments show how traditional notions of extrinsically and intrinsically motivated behaviors may emerge from such optimal reward functions. You can read this paper here.


This paper reviews progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. Read more on this paper here.

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This post was originally published by at Medium [AI]

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