A fun read, but the title is somewhat misleading. Excerpts from the paper: This is why (understanding how) AI (works) is hard. This is why phy

A fun read, but the title is somewhat misleading. Excerpts from the paper: This is why (understanding how) AI (works) is hard. This is why physics is (able to offer) simple (explanations for seemingly complicated natural phenomena) The author is arguing that: 1. "top-down analysis" to deep learning is ineffective due to the nature AI. 2. It should be possible to use the "sparsity" principle from physics as "a guiding principle of deep learning theory" Where the "sparsity" principle dicit: The number of parameters defining a theory should be far less than the number of experiments that such a theory describes .. Personally, I'm skeptical. I prefer to understand whole theater of ANN as an attempt to optimize neural-net for practicality. Even deep learning is a technique for structuring the network for efficient training. I thinks so because even the most dumbest neural network - layers with high connectivity - can perform certain tasks, if we train it really really hard with fingers crossed. To avoid uncertainty and improve performance, it's necessary to structure the network. This cuts unnecessary computation, and prevents over-fitting and over-labeling. That is, we have to inject bias to network, and this makes any bottom-up analysis biased and less generalizable.