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Comment by stiff | Hacker News Reader

This is a bad article, especially for a technical audience. It romanticizes things a lot, as journalists have to, to keep up the readership rates, but it doesn't make for a very balanced judgement. This kind of debate is going on and on, you can read a much more reasonable account here:

http://norvig.com/chomsky.html

I find the analogy to Einstein at the end of article especially funny. I think it's much more likely that people will look upon current defenders of "good old fashioned AI" like they now do upon people who still looked for ether after Einstein's discoveries.

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I think this was an exceptionally good article, because the writer has a far better understanding of Hofstadter's work than most journalists do of science topics (also better than most commenters in this thread, of Hofstadter's work).

In particular, how often do you see a journalist with this much insight?

> When you read Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, which describes in detail this architecture and the logic and mechanics of the programs that use it, you wonder whether maybe Hofstadter got famous for the wrong book.

FCCA is really brilliant.

100% agreed. GEB is a great book, but Hofstadter hasn't been relevant in this field for a while now (AFAIK). Good old fashioned AI approaches show what a giant blind spot our minds are for our species, how little introspection goes on even within the brightest minds. Modern approaches to AI/ML have yielded tangible results and moved the field in the right direction, dismissing those results makes anyone look ridiculous in 2013.

Pure ML approaches are doomed to end up in a tar pit of AI mysticism. A statistical learning model can and will conclude that the thunder gods make the crops grow. People like Douglas Hofstadter and Eliezer Yudkowsky will have to use classical AI approaches to train our AI children in science and rationality.

Not sure what AI mysticism is, nor how you arrived at the conclusion that statistical learning models will come up with thunder gods.

My uninformed guess is that they mean that a statistical AI system might come to similar conclusions as humans such as concluding that there exist thunder gods, while a non statistical type (one with just "reason") would not come to that conclusion about thunder gods, and as such would not as accurately model humans.

Simple models of correlation and causation often make bizarre predictions, like the famous conclusion that pirates cause global warming. A statistical learning system cannot step outside its own predictions to fix such problems.

A statistical learning model concluding that 'the thunder gods make the crops grow' would not make it a failure. Indeed that's what people thought for thousands of years!

I think you're confusing good AI with being 'smarter than humans'.

An AI can still pass the Turing test if it believes in Scientology or any of the other nonsesne beliefs that humans have.

That's fine if you want an AI hunter-gatherer. To have an AI scientist or engineer you need a rational thinking layer on top of the ML layer, so it can create hypotheses and try to falsify them. I predict the rational thinking layer will be built with good old fashioned AI: symbol processing, theorem proving, etc. Getting this to talk to the fuzzy ML layer will be a challenge.

I wouldn't call the article "bad", but I do think it loses points for furthering a "feud" which just need not exist. Norvig-style AI is incredibly useful in the here and now to solve real-world problems. Hofstadter-style AI is driven by a deep desire to exorcize one of the few remaining "ghosts" in our otherwise natural world. It is an attempt to REMOVE the "ether" of thought stuff, not to hide in it.

Perhaps a terminology change is in order? I would call what people like Norvig and Hinton and Ng do "machine learning", while I would probably use "artificial sentience" to describe the Hofstadter-Dennet camp, as that seems to more closely capture their ultimate goal.

The world needs both approaches, and hopefully the time will come when the gap is bridged.

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