Well actually we are finding today newtonian physics theory is actually wrong or at least incomplete so THAT is a pretty shitty argument.

Wrong. Again, I don't think you understood what I'm saying. It's incomplete at very specific cases, very low level like quantum mechanics and relativity. For typical engineering problems, classical mechanics, and for a lot of cases, it's pretty much all you need (ie. REAL WORLD PROBLEMS).

my point is that we really don't need one. we can make various specialized networks for specific tasks and those can outperform us in lots of cases

I've never seen a neural network out perform humans in a practical setting. They don't work in practical real world settings. Object recognition itself is still a major open task, it's nowhere close to human level. People have been stuck on it for 50 years, with the same problem. The only difference right now is that you have compute and large amounts of training data. Mathematically, there has been no progress. To emphasize, THEY DON'T WORK EVEN ON SPECIFIC TASKS!!!

There are better people who've shown this within the deep learning community:

https://blog.piekniewski.info/2019/03/12/a-short-story-of-silicon-valleys-affair-with-ai/

https://blog.piekniewski.info/2018/06/06/ai-winter-addendum/

https://blog.piekniewski.info/2018/05/28/ai-winter-is-well-on-its-way/

Every obnoxious claims I've seen by people like Andrew Ng get debunked. These models haven't replaced anyone, and the claims you are hearing are just in academic settings, not in practise. DeepMind and Google Brain Project will go the way of IBM Watson. Unusable in practical settings.

This field has never been stronger and moving forward quickly

What progress? We are using the same ideas that didn't work from ~50 years ago. Deep learning is just a form of regression, back propagation is just a variation on Newton-Raphson's method. Deep reinforcement learning is no different. All of these techniques assume a stationary world.