AI winter is well on its way

I think the most important question is what 'winter' really means in this context. The new concepts in AI tend to follow the hype cycle so the disillusionment will certainly come. One thing is the general public see the amazing things Tesla or Google do with deep learning and extrapolate this thinking we're on the brink of creating artificial general intelligence. The disappointment will be even bigger if DL fails to deliver its promises like self-driving cars.

Of course the situation now is different than 30 years ago because AI has proved to be effective in many areas so the research won't just stop. The way I understand this 'AI winter' is that deep learning might be the current local maximum of AI techniques and will soon reach the dead end where tweaking neural networks won't lead to any real progress.

I know right, the school I went to wasn't exactly the best.

I skimmed through sections 4 & 5, optimization processing was difficult to understand.

When I was in elementary school, I remember pitying the mentally disabled children, knowing their financial success was destined, so I connected with the g-factor definition. I really think general intelligence is more of a sense of all clusters awareness, whether it be social or cognitive. I've met tons of great students in Math courses who simply cannot converse with the general public. I've also met tons of people on the streets of my city who would have a difficult time understanding high school algebra.

As for Section 5, I do think the rise of AI over humanity is completely in our grasp. I really should take a course on the subject before I sound like the people that I'm criticizing for ignorance, but from a general perspective, I cannot see it outside of our control. As Eliezer said, we can make predictions, but only time will clear the fog.

There are two sides to this:

- how good humans are in detecting cancer (hint: not very good) and if having an automated system even as a "second opinion" next to an expert might not be useful?

- there are metrics for capturing true/false positives/negatives one can focus on during learning optimization

From studies you might have noticed that expert radiologists have e.g. F1-score at 0.45 and on average they score 0.39, which sounds really bad. Your system manages to push average to 0.44, which might be worse than the best radiologist out there, but better than an average radiologist [1]. Is this really being oversold? (I am not addressing possible problems with overly optimistic datasets etc. which are real concerns)

[1] https://stanfordmlgroup.github.io/projects/chexnet/

AI winters are a result of a massive disparity between the expectations of the general public and the reality of where the technology currently sits. Just like an asset bubble, the value of the industry as a whole pops as people collectively realize that AI, while not being worthless, is worth significantly less than they thought.

Understand that in pop-sci circles over the past several years the general public is being exposed to stories warning about the singularity by well respected people like Stephen Hawking and Elon Musk (http://time.com/3614349/artificial-intelligence-singularity-...). Autonomous vehicles are on the roads and Boston Dynamics is showing very real robot demonstrations. Deep learning is breaking records in what we thought was possible with machine learning. All of this progress has excited an irrational exuberance in the general public.

But people don't have a good concept of what these technologies can't do, mainly because researchers, business people, and journalists don't want to tell them--they want the money and attention. But eventually the general public wises up to the unfulfillment of expectations, and drives their attention elsewhere. Here we have the AI winter.

I don't get the sentiment of the article either. I can't speak for researchers but software engineers are living through very exciting times.

  State of the art in numbers:
  Image Classification - ~$55, 9hrs (ImageNet)
  Object Detection - ~$40, 6hrs (COCO)
  Machine Translation - ~$40, 6hrs (WMT '14 EN-DE)
  Question Answering - ~$5, 0.8hrs (SQuAD)
  Speech recognition - ~$90, 13hrs (LibriSpeech)
  Language Modeling - ~$490, 74hrs (LM1B)

"If you think Deep (Reinforcement) Learning is going to solve AGI, you are out of luck" --

I don't know. Duplex equipped with a way to minimize his own uncertainties sounds quite scary.