Researchers: Are we on the cusp of an ‘AI winter’?

I think we're unfortunately fixated on a very literal reading of the famous Turing test (i.e. cleverly emulating humans = intelligence).

Consider language, for instance. Dolphin communication is intelligent, but does not emulate humans well; whereas the computer program ELIZA (1964) lacked intelligence, but was able to emulate humans well enough to entertain many people for quite some time.

Our current state-of-the-art NLP is - after copious research, talent, and computation - able to emulate human language somewhat better than ELIZA. But is it intelligence? There's certainly a lot of complexity involved, and neural networks show some interesting building-block patterns, but the lack of these algorithms' ability to generalize into new spaces, grow our fundamental understanding of the world around us, or really do anything besides pretend to be a human makes one wonder whether our current "AI" is just a (very good) party trick - a better version of ELIZA.

The AI summer/winter cycle feels to me like something similar to a search algorithm, we have a phase of exploration, which seem to have slower, if any, progress and no one is very sure what's the next big thing so they perhaps start trying many things (the winter "skepticism") and eventually someone finds some breakthrough and gets everyone to an exploitation phase in which everyone knows where to invest and comparatively small effort is required to create progress (the summer "hype"). And eventually all low-hanging fruits are over and the search seems to converge to a local maximum and again larger exploration is required.

So maybe the winter is just as important as the summer. Each winter lead to a summer with different focus points (specialist systems and logic followed by neural networks, bayesian models and SVMs and finally deep learning). And after each cycle we have more and more tools, each more useful than the last. And also maybe the key to avoid this strict cycle would be to encourage more exploration during the exploitation phase, giving full support to both incremental ideals that improve on the state of the art and (potentially) revolutionary ideas that give poor immediate results but create new venues to investigate.

Of course that's a simplification and there are many aspects to it, including data availability, hardware and tooling that can easily prevent brilliant ideas that were had too soon.

It's all about precision heuristics, derived from joint probabilities of inputs and outputs. That, by and large, is how I am increasingly coming to understand the power of neural networks.

Imagine you are given a picture of a candle, overlaid with a grid, and asked to fill in, with colored pencils, colors for the air surrounding the candle representing relative temperature. Of course a human utilizes intuition to rapidly assign high temperature to the flame and decreasing temperature with increasing distance.

A "dumb" finite method would need, even for such a relatively simple problem (for a human), to perform calculations for a series of time steps in each grid until some steady state condition to arrive at a much more precise but still overall similar coloring of the grid cells. You can do the same task much more quickly because you have developed intuition of the physics, which is to say you have learned heuristics which capture the general trends of the problem (air is hot close to a flame and cold far away).

Neural nets take the best of both worlds - by effectively learning probability relationships between input and output pixels, they internalize heuristic approaches to produce outputs approaching finite method accuracies at a fraction of the computation. There's a lot of waste that can be optimized out of finite computation by hardcoding rules (heuristics), but doing so for real problems is impractical. Neural nets learn these rules through training - a far simpler task is organizing the data to teach the net the right trends; much like designing lessons for a child to teach a predictive ability.

That sounds like incremental efficiency improvements, not something that will radically change people's lives. Not that that's not valuable and may help us keep economic growth going at reasonable clip but it doesn't seem like the type of thing that will change the character of most people's lives in the way that the Internet and smartphones have.

> and then hand off to human telepresence control. It wouldn't surprise me if the first self-driving systems worked that way.

The issue is that humans fare badly in situations where they don't have to pay attention until they suddenly have a short time to react before disaster, and that's exactly the problem that "supervised" self-driving systems have been suffering from when they crash.

And if you have to pay constant attention to the road anyway, I'd rather be driving myself. What's going to win won't be self-driving tech, it will be driver assist, like advanced anti-lock brakes, or self-adjusting cruise control.