Oh please. Dumb word games isn't going to make the "is this a bird" AI approach explainable. https://xkcd.com/1425/

Even the "simple" AI's with only a few TBs of data for training still have low/no understanding what's going on to identify the features and how they relate. Sure, we can tell this neuron identifies this squiggle, but what does it mean? No fucking clue. It also shows us collectively that most of these "hard" problems we have no clue how to understand them. And although AI allows us to "do it", it's a bad hack for actually understanding, optimizing, and doing.

> That it works without you understanding it is a failure of your understanding not of the approach.

Cool. Then point me towards the tools to explain how weights in a moderate-ly sized NN are created with training data, and associated with each other and how they find the features in question.

Oh yeah. Those tools don't exist.

You seem to find the explanation unsatisfying and are getting mad at the tools. The program that defines how you recognize a bird is defined in the connectivity of the neurons in your eye and your brain. The circuit is the program.

It's not at all clear that there is or even should be a deeper explanation than "a function with sufficiently many parameters can approximate any other function with only a small change to those parameters, and thus allows tractable solving" - whether by gradient descent or some hokey approximation our neurons are probably doing.

We have even less of an idea how our own system of "understanding" works than we do of neural networks "understanding". If you're going to be mad at tools that don't provide satisfying explanations for how they work, then look no further than your own brain. Are you going to give up on thinking, just because when you look at it closely you realize you have no idea how on earth you're even doing it?

There’s no helping the incurious, but there are indeed reams of explorations unto weights and their explanations.

One of my personal favourites is Zeiler and Fergus https://arxiv.org/abs/1311.2901

Tom Zahavy’s “Greying the black box” on deep RL comes to mind too https://arxiv.org/abs/1602.02658

OpenAI has their Microscope tool for investigating individual neurons https://openai.com/research/microscope; more recently OpenAI shared research and tooling leveraging a powerful language model to explain a smaller model’s neurons. https://openai.com/research/language-models-can-explain-neur...

For a true toy box exploration playground.tensorflow.org is worth a play

These may or may not satisfy your curiosities — your perfect tool may not exist…yet. I would argue that the sampling of extant work above are indicative that it and more are possible.