Reminds me of those videos from the 1950s that predicted what life would look like in the year 2000. He's right about cleaning datasets being an entire job its

Reminds me of those videos from the 1950s that predicted what life would look like in the year 2000. He's right about cleaning datasets being an entire job itself. But that should have been a red flag for his conclusion even then. Scaling anything means scaling costs. So, the more you want your neurochip to do that it isn't capable of doing by design, you need to do before or after it's done its task(s). That's what's between the lines of what he mentioned of "silent fails." If you don't want bias in your output, and your design isn't capable of vetting bias, you need to do the work of vetting bias before you pass that dataset off. That means you need an entire model defining bias, predicting impact and constraints on outputs. I've said it before and will continue saying it 'til I'm apparently blue in the face: The complexity of a solution is dependent on the complexity of the problem it solves. I get that simple things feel attractive because they require less effort. We don't really reduce effort so much as we shift it from wholly solutionizing to partly solutionizing and mostly trying to continue to partly solutionize.