This is an incredibly negative piece- at least on the first half. The premise is- Deep Learning is hitting a wall. That is not true. There are hundreds of appl

This is an incredibly negative piece- at least on the first half. The premise is- Deep Learning is hitting a wall. That is not true. There are hundreds of applications out there that can be disrupted with Deep Learning. The problem is that very few people are trying those. Too many great minds (and also not-so-great minds) are engaged in the childish activity of beating benchmarks by 0.03% and getting publications. Besides that, another problem is big tech. They seem to think they can get "AI" just by burning bigger piles of money. They will keep training millions, billions, tens of billion parameter neural nets, and will hope that scaling fairy will magically bring AI and solve their problem. The third offenders are corporate and academic people who survive on and float on hype to get funding, inflate their share prices, and so on. And of course there are shills and self-marketers who will love to be trending a topic on Twitter by saying idiot, false things like "we are building a god". I mean- come one! Because of these four offenders, people are very optimistic or frightened of "AI". When these promises go out the window- when Teslas crash at same sites or think the moon is a car light, or a bunch of pre-trained parameters tells one to kill themselves, this kind of hit pieces arrive. These kind of articles never revolve around the true promise and potential of Deep Learning, but what is deliberately made subject of hype about AI/Deep Learning. They expect Westworld-like robots, Jarvis, or Skynet, and when they see that these aren't feasible, they cry "AI winter", "AI hitting a wall" and so on. This is so boring and unoriginal to watch. And this brings real danger to potentially fruitful AI research. Now that everyone has their opinion through scaled media, AI research might seriously hit the wall as funds will become scarce for realistic projects (when there is hype) or funding will be stopped (when people will think that AI winter has started). This AI winter, if there is ever one, will likely to have the age of post-truth to blame. ____ This was about the premise. They then proceed to preach neuro-symboloc AI. As an all-out solution- to be used everywhere. This is overly simplistic. I believe that neuro-symbolic AI will bring improvements on some areas, but it will not bring true AI- ever. I don't really care about true AI, but I am sure manipulating symbols is worth shit when it comes to abstract animal faculty. Or something more complicated than usual. Hell, we don't even know whether some math theorems are falsifiable. Gödel knew this in the last century. So did Turing. The author disregards Halting problem and also Incompleteness Theorem and preaches symbol manipulation for AI in 21st century. That's low. ____ > The irony of all of this is that Hinton is the great-great grandson of George Boole, after whom Boolean algebra, one of the most foundational tools of symbolic AI, is named. Cool fact, thanks. ____ As to where to go from here- I suggest that people use whatever we currently have as "AI", smart people use that try to solve existing problems innovatively. Deep Learning is so general. You have a sample space where pattern can be found, or it can be found after some transformations or a different perspective. Use that pattern to solve problems. This is Deep Learning and this is so general! It can be applied in so many places! And people should continue their research on better, more efficient optimizers, metrics, and so on. Those make the world better. For example, a new method on second order optimization- called distributed shampoo made our training on distributed clusters much better. We could not get the loss to go down consistently. But when we started out using that- the problem disappeared. That paper only has 17 citations ON Google Scholar. But that did solve a huge problem. This kind of research is what people should focus on. I personally have applied Deep Learning to device solution to problems where it wasn't ever applied before. Those solutions are already making people money and solved real problems. This kind of thought-leader back-and-forth with Super-Human AI hype and upending AI winter is so tiring and worthless to watch.