AI = means you don't understand how to quantify the problem
AI is a great hack to quickly and unknowingly solve classification problems and other similar things. But you trade massive datasets for explainability. And that's a pretty bitter pill to swallow.
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.
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?
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.
If you look at one of the most widely used text books machine learning is one of seven chapters, and not all of that is deep or supervised learning.
In my view calling a solution "neural" and "AI" because you are optimizing a stack of relus, and then turning around and solving the same (or almost the same) problem by optimizing a bunch of Gaussian model parameters on the same data, and then saying that is "not AI" and "at least it's not neural!" is just a very specious and unnecessary way to delineate things imho. They're just two solutions to the same problem, both data driven, both generating "models".. they have differences, just not differences that I think warranted such a comment in the blog, and moreover I thought it weird to attribute some kind of normative evaluation based on how you categorize the solution.