Past Performance is Not Indicative of Future Results (2020)

>There is general knowledge about the world for humans to know that there isn’t a giant human in the sky no matter how good the face looks.

Is there? Humans used to think the gods were literally watching them from the sky and the constellations were actual creatures sent into the night. So this seems learned behavior from data rather than some inherent part of human thinking.

>Train it with as many images as you want and as long as a good enough face shows up, the model is going to have a positive match.

So will a human if something is close enough to a face. A shadow at night for example might look just like a human face. Children will often think there's a monster in the room or under their bed.

> I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI

> I share the skepticism towards any progress towards 'general AI' - I don't think that we're remotely close or even on the right path in any way.

This isn't how science works though. Quoting the wikipedia page for Thomas Kuhn's "The Structure of Scientific Revolutions" (https://en.wikipedia.org/wiki/The_Structure_of_Scientific_Re...):

"Kuhn challenged the then prevailing view of progress in science in which scientific progress was viewed as "development-by-accumulation" of accepted facts and theories. Kuhn argued for an episodic model in which periods of conceptual continuity where there is cumulative progress, which Kuhn referred to as periods of "normal science", were interrupted by periods of revolutionary science."

I think this is the accepted model in the philosophy of science since the 1970s. That's why I find this argument about AI so strange, especially when it comes from respected science writers.

The idea that accumulated progress along the current path is insufficient for a breakthrough like AGI is almost obviously true. Your second point is important here. Most researchers aren't concerned with AGI because incremental ML and AI research is interesting and useful in its own right.

We can't predict when the next paradigm shift in AI will occur. So it's a bit absurd to be optimistic or skeptical. When that shift happens we don't know if it will catapult us straight to AGI or be another stepping stone on a potentially infinite series of breakthroughs that never reaches AGI. To think of it any other way is contrary to what we know about how science works. I find it odd how much ink is being spent on this question by journalists.

Excellent. One problem in my mind that I don't see discussed enough -- and also not in your other post -- is that there is a large divide between those who use the technology (the cops in this case) and those who supply it, and there is no accountability in any of the two groups when something goes wrong. Like you write in your other post, "the system works (according to an objective function which maximizes arrests.)", and that is as far as the engineer goes. On the other hand, the cop picks up the technology and blindly applies it. To make any improvement to the system would require both groups to work together, but as far as I know, that is not happening. A recent example can be found in the adventures of Clearview AI. So from that perspective, I do think that the engineers (and the cops, and everybody else) are out to lunch, each doing their own work in a bubble and not paying enough attention to (or caring about) the side effects of the applications of this technology.

Also, the lack of thought and accountability that I mention above I think is fairly general from my experience, even outside of policing. That is why I don't generally agree with the lunch statement. Guys are having a hell of a party as far as I can tell -- at the expense of horror stories suffered by the victims of these systems.

Well, we seem to experience such things in a split second, and then we correct ourselves. We use some kind of reasoning to double-check suspicious sensory experiences.

(I was thinking of this when I was driving in a new place. Suddenly it looked like the road ended abruptly and I got ready to act, but of course it didn't end and I realized that just a split second later.)

I have a PhD in neural networks, haven't used it in many a year, but some of the knowledge is still there. Some of the memories of racking my brains to understand what the hell is going on are still there, too.

It is easy to have a theory of what is going on, to model the processes of how things are playing out inside the system, to make external predictions of the system, and to be utterly wrong.

Not because your model is wrong, but because either the boundary conditions were unexpected, or there was an anti-pattern in the data, or because the underlying assumptions of the model were violated by the data (in my case, this happened once when all the data was taken in the Southern Hemisphere...)

In all these cases, you can know what you're doing, you can know the boundaries of what what you're working with, and you can get results that surprise you. It's called "research" for a reason.

The model can also be ridiculously complex. Some of the equations I was dealing with took several lines to write down, and then only because I was substituting in other, complicated expressions to reduce the apparent complexity. It's easy to make mistakes - and so you can know what you're doing, and the boundaries that you're working with, and still have a mistake in the model that leads to a mistake in the data ... garbage in, garbage out.

In short, this shit is hard, yo!