Exploring Weight Agnostic Neural Networks

He covers a lot of relevant and interesting topics, including how he tries make it less of a black box when designing their courses, and also how it has the potential to confer increased rather than decreased insight into what's going on in a dataset.

I don't personally know much about ML, but I think even though there will still be probably an opaque aspect in many cases for a while to come, immense value will still be continually gleaned, as long as people are aware of the limitations. If you accept something is a black box and don't oversell it, a black box is better than no box.

All of our own brains are far more of a black box than any deep learning model, in many capacities. But we still use it daily for meat-machine learning, to great success, and can still tune the parameters a bit to improve outcomes, even if we very often don't really understand exactly what we're tuning or why it seems to cause certain effects for some brains (or why it doesn't have those effects for other brains). Consciousness may be the biggest black box of them all, but here we are all talking and making nearly non-stop use of it.

I agree it's very important to try our hardest to reach a deeper understanding, but it's kind of like psychiatry vs. neuroscience, or experimental quantum physicists who "shut up and calculate" vs. theoretical quantum physicists who actually want to know what's really going on here at the most fundamental level beyond the useful black box of quantum behavior. While we're trying to solve the hard problems of deep understanding, we can make practical use of what we have in the meantime. We need both kinds of fields and people.

If future AI architectures and ideas lead to some degree of convergence with a biological brain, I wonder if the black box problem might become amplified. Maybe one part of the solution is to not use the brain as the model to aspire to, and to eventually seek out alternative avenues to higher, and eventually general, intelligence? (Or maybe I'm completely talking out of my ass, because I'm not at all a researcher or practitioner. I'd appreciate any input from experts.)

how many floats are there? 2 lengths, 2 initial angles, a 2-dimensional velocity, a mass, and a gravitational field strength? thats like 8 floats...

But I'm no expert and hardly even an amateur, so maybe it is a similar kind of thing here with ML. And I know randomized optimization is a big thing in ML, though I'm not sure to what extent that could be analogized with randomizing Markov model probabilities.

consider the task of computing a gradient at a point p0 = in an N-dimensional space.

the naive approach was for a long time: compute the value of the score at p0, then for each coordinate compute the score for the same point but shifted a delta in the direction of that coordinate, i.e. the i-th component is computed as:

component_i = (score( ) - score (p0))/delta

thats N+1 evaluations or trials of the score to compute the final gradient

notice how reminescent this is of natural selection: the average of the last generation p0 is used to generate ~N trials, which then result in the average of the next generation shifting somewhat.

compare reverse mode automatic differentiation to calculate a gradient: one forward pass of the computation with one backward pass...

I am not complaining about the complexity of the fitness or scoring function, I am complaining about trial and error approaches, when we have discovered a rocket for differentiation!