Someone out there is probably experimenting with something world-changing, and has all the ingredients except for a few more iterations of Moore's Law. It would feel a lot like working on deep learning in 1990. If you think you might be on this path, it's worth studying the history.
These are basically the only outliers who claim that automatic differentiation was invented by Linnainmaa alone. Many people invented AD at the same time, and Linnainmaa was not the first. Simply naming one person is a huge disservice to the community and shows that this is just propaganda, as much of Schmidhuber's stuff is.
1. First Very Deep NNs -> This is false. Schmidhuber did not create the first deep network in 1991. This dates back at least to Fukushima for the theory and LeCun in 1989 practically.
2. Compressing / Distilling one NN into Another. Lots of people did this before 1991.
3. The Fundamental Deep Learning Problem: Vanishing / Exploding Gradients. They did publish an analysis of this, that's true.
4. Long Short-Term Memory (LSTM) Recurrent Networks. No, this was 1997.
5. Artificial Curiosity Through Adversarial Generative NNs. Absolutely not. Andrew G Barto, Richard S Sutton, and Charles W Anderson. Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems. IEEE Trans. on Systems, Man, and Cybernetics, (5):834– 846, 1983
6. Artificial Curiosity Through NNs That Maximize Learning Progress (1991) I have nothing to say to this. This isn't something that worked back in 1991 and it's not something that works today.
7. Adversarial Networks for Unsupervised Data Modeling (1991) This isn't the same idea as GANs. The idea as presented in the paper doesn't work.
8. End-To-End-Differentiable Fast Weights: NNs Learn to Program NNs (1991). Already existed and the idea as presented in the original paper doesn't work.
9. Learning Sequential Attention with NNs (1990). He uses the word attention but it's not the same mechanism as the one we use today which dates to 2010. This did not invent attention in any way.
10. Hierarchical Reinforcement Learning (1990). Their 1990 does not do hierarchical RL, their 1991 paper does something like it. This is at least contemporary with Learning to Select a Model in a Changing World by Mieczyslaw M.Kokar and Spiridon A.Reveliotis.
Please stop posting the ravings on a person who is trying to steal other people's work.
Well, now it seems that you lie here since you avoid the well-known fact that Schmidhuber attributes the first deep NNS back to the 60ies and 70ies. The same goes for many of your other points.
Quoting the blog post:
"Of course, Deep Learning in feedforward NNs started much earlier, with Ivakhnenko & Lapa, who published the first general, working learning algorithms for deep multilayer perceptrons with arbitrarily many layers back in 1965 [DEEP1]. For example, Ivakhnenko's paper from 1971 [DEEP2] already described a Deep Learning net with 8 layers, trained by a highly cited method still popular in the new millennium [DL2]."
Let's try to be fair and objective here. You may have an axe to grind with Schmidhuber, but that does not give you the right to take things out of context.
- lowering the price of each chip - you can get that by more automation.
- lowering the cost of energy used by a chip - you can have that by raise of renewable energy generation and its decentralisation (and again, more automation).
The point is that automation caused by AI will start a reinforcing feedback loop where more and more work can be done more cheaply, speeding up automation itself too.
there isn't much evidence that AI has accelerated the rate of automation, and people have been saying this about information technology for the last 4 decades already. By any account, automation and growth contribution of the technologies are low by historical standards.
The primary mechanism that has kept Moore's law alive up until now is miniaturization of transistors and we're going to run into a wall on that front pretty soon.
However, in broader economic terms, I think the idea that AI may 'accelerate' the world in general is largely indirect: for instance, by saving time and money in other areas of life (because better tools, cheaper means, infra, etc), people become more able to perform their job. There are obviously diminishing returns to such optimization, as to any natural/economic process.