I disagree too (despite being a researcher in the general neighborhood), but I find the usual comparison with a human brain to be inaccurate. We don’t trust a p

I disagree too (despite being a researcher in the general neighborhood), but I find the usual comparison with a human brain to be inaccurate. We don’t trust a person just because of their track record, the fact that they are human to begin with already comes with a lot of baggage. I think the best analogy is with a very complicated piece of hardware. That said, someone has to have a good understanding of what is happening (not necessarily the end user). In my experience neural networks today are basically like compiled software which source code has been lost. Every act of debugging and fixing has to include also reverse engineering (I’m talking about the actual network weights, not the e.g. PyTorch code). It’s too inefficient and cumbersome, and as they become more and more common, statistics says the failures will become more often and more severe. When do we, for example, decide to put the first NN on an airplane cockpit?