hharrison

To respond to a bunch of other posters here:

There's a fundamental difference between data scientist and statistician, I think. I see statistics as an academic discipline and data science as an applied discipline.

More concretely, the statistics approach is: formulate question --> formulate hypothesis --> collect data in a controlled environment under a specific set of assumptions (i.e., perform an experiment) --> determine probability of the data given the hypothesis (and assumptions).

While the data science approach is: hey look, we already have all this data --> generate predictions --> collect more data --> refine predictions.

Of course, that's an over-generalization. But I think the different emphasis on hypothesis testing vs. machine learning/data mining is fundamental.

The same thing can be done in bash:

## arrow up

"\e[A":history-search-backward

## arrow down

"\e[B":history-search-forward

(in .inputrc)

You're right, and flipping through my copy of I Am a Strange Loop for the first time in a while I find some things that are reminiscent of non-computational theories. I guess I was/am caught up on the computationalism vs. non-computationalism debate and am conflating GOFAI with everyone working in a computationalist framework. Hofstadter pushes the boundaries, but he's still a computationalist.

For someone who takes the idea of a system that's impossible to model further, IMO, check out the biologist Robert Rosen's Life Itself and Essays on Life Itself. He pushes more in the direction of complex systems theory than self-reference (maybe a physics version of the same idea?) but his writings are brilliant. Less fun than Hofstadter's, but more rigorous and with a necessary appreciation of the biological and physical sciences. Anyways, I'm rambling.

Well, you said "you cannot not reduce it [AI] to statistics". And I'm saying, not only is it possible to not reduce AI to statistics, it is desirable. It's the reduce part that I have a hard time with.

I agree with your point, but the solution isn't to instead model the underlying biological nuts and bolts (assuming this is what you mean by "underlying behavior of human brains"). Even if those approaches approximated human behavior (which they don't), it wouldn't be all that informative. Just as creating "artificial" intelligence by growing a brain in a petri dish wouldn't be informative either. (Well, it wouldn't be informative as to what the brain's job description is, although we could presumably learn plenty from of other things from the exercise).

The better solution is to come up with creative experiments (with human experiments) that can arbitrate between competing implementations. With modern technologies like virtual reality, you can create almost any counterfactual situation and that to decide between theories that make the same predictions "in the real world".

I haven't checked any of his recent projects, but from his books he's firmly rooted in the symbolic tradition. He adds some interesting wrinkles, sure, but wouldn't he still be diametrically opposed to someone like Rodney Brooks? http://www.sciencedirect.com/science/article/pii/00043702919... (Intelligence Without Representation)

Well you might say Wundt was the father of structuralism and James the father of functionalism - a deep divide which is still felt today. I suppose this shows the author's bias, implicit or not.

There's a huge middle ground between reducing cognition to statistics (as you seem to be proposing) and "abstracting away" the statistics. You seem to be accusing any approach that's not "statistics all the way down" of ignoring the statistical nature of the mind. When in fact, there are many approaches to cognition which make heavy uses of advances in statistical theory without reducing cognition to statistics.

What you're proposing is an overly reductionist ontology, IMO, and yet I agree that statistics are important, if not critical. (Hint: not the boring linear kind of statistics that you so often see in Big Data)

There are more divisions than the article can get into, of course. I think the main one is between AI to solve engineering problems and AI to better understand the mind. The article is about doing the latter when everyone else's definition of AI is the former.

What you're talking about, I think, is various approaches within the latter group of researchers. I defended Hofstadter in another reply because I find his goals worthwhile in and of themselves - in a "basic research" sense. Discarding anything that's not an optimal solution - the attitude taken by a couple of responses here - ignores a whole lot of interesting science, and as a scientist it bothers me quite a bit.

That said, once we're talking about the goal of understanding the human mind, GOFAI is, to be sure, incredibly old-fashioned, and you won't find me defending Hofstadter anymore as far as his approach. His goal is a worthwhile one, but you're right that his approach shouldn't really be considered an 'underdog'.

Personally, I think the best hope for understanding what intelligence is, in a general sense, comes from non-equilibrium thermodynamics, as in the sort of research goin on here: http://www.tandfonline.com/toc/heco20/24/1#.UmlRN_mfihM

but that's a can of worms for another post.

As an aside, regarding your last comment, I completely disagree on your view of philsoophy. It may not have produced results but it has guided science. But I agree with the thrust of your sentiment: an AI researcher with the goal of understanding the human mind should be spending as much time studying humans (as in, doing Psych or Cognitive Science experiments) as programming AIs.

Maybe the article doesn't get it across, but the anti-establishment crowd doesn't think that doing things the "human way" is always the best solution. In fact, they acknowledge that it's often, even usually, not. Hofstadter does not want to replace (e.g.) GPS AI with human-style intelligence, or even more ridiculous, replace the AI that flies airplanes with one that gets bored and does crossword puzzles instead of paying attention. Instead he wants to understand human-style intelligence because that opens doors to tacking really interesting scientific/philosophical problems like personality, self, consciousness, autonomy, etc.

In short, the camps have different goals. One is trying to solve problems optimally. The other is trying to understand what intelligence means, biologically. AI as a sub-discipline of Engineering, vs. AI as a sub-discipline of Cognitive Science, perhaps.