That is absolutely the worst possible way to read my comment. They're not bad because sometimes they fail, they're bad because sometimes they fail and we know exactly what is wrong with them, but we can't fix them because they don't take advice.

There is no way to tell the Tesla vision NN, "hey, when you see this pattern and you're confused about which path to take, it is better to take one incorrectly than it is to run into a concrete divider". We know exactly what the problem is, but there is no interface with a NN to tell it to do something, other than to just keep training it with more data. And once you realize that your only interface to get better outcomes is to wildly manipulate the training dataset, then you haven't made software engineering better, you've made data engineering worse.

Take notice of something important: all of the domains where Neural Networks have been wildly successful are domains that are wildly underspecified. Take language for example. Grammar rules, vocabulary, pronunciation, and even meanings of words are constantly changing. There is no possibility of ever having a formal definition of any language, let alone all of them. Or vision...where the only formal definition of anything is what color of light it reflects in a particular angle. Again, no formal definition of anything.

But the shortest path from A to B? That problem has a formal definition, and no neural network has even come close to the accuracy of A star or Djikstras. The minimum cost solution to a Multi-Commodity Flow Problem? That problem has a formal definition, but no Neural Network has come close to the accuracy of a Simplex method's solution.

Tautological arguments about percentage accuracy might give the edge to Neural Nets in some domains, but not all of them, and for that reason they completely miss the point.

1. Percentage accuracy is only half of the thing that actually matters. Without the cost of being wrong taken into account, percentage accuracy will totally fuck you over hard. Here's a game: you can choose between two algorithms, one that has 90% accuracy with a 10% chance of smelling gross, or a 99.99% accuracy with a 0.01% chance of your body been shaved down to bone over a thousand cuts from a vegetable peeler. Which would you choose?

2. Sometimes absolute accuracy matters. We have formal systems for absolute accuracy. We have symbolic logic for absolute accuracy. We have deterministic systems for absolute accuracy. If the best that I can get from a neural network is a percentage accuracy, then it has already failed a test of general applicability. How many years and how many computers and how much data would we have to feed into how big of a neural network in order to get to E = MC^2 with perfect accuracy?

3. Even if percentage accuracy matters, time-relative accuracy matters even more. With a Neural Network, if you need to get better accuracy, how do you do it? You should see actual machine learning practitioners try to solve these problems. They literally try to deconstruct the black box, trying to figure out how different neurons are weighted, and what input data can be altered to result in a different weighting. It's a clusterfuck, and it slows progress to a halt. We've known exactly what was wrong with the Tesla Vision NN for over half a decade, but actually fixing it has completely stalled because of the fact that it is a black box, and can only be fixed like black boxes. This is systems theory 101: you can't fix systems that you can't understand.