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> What are non edge-case examples of the symbolic approach not working? ... It should be possible to infer using a symbolic approach, or more simply just provid

by ekidd 10y ago

> What are non edge-case examples of the symbolic approach not working? ... It should be possible to infer using a symbolic approach, or more simply just provide a new definition. I've met some really really brilliant people who've been banging their head against that particular wall since the 1980s. The MIT AI Lab crew, for example, poured untold brainpower into symbolic inference. There was the whole "expert systems" movement. This all failed miserably, in disgrace, because nobody could ever get it to work, and "AI" became a dirty word. Later, there was Cyc, which was hyped on and off throughout the 90s. http://www.cyc.com/ http://www.cyc.com/ After that, there were people who tried to reason over RDF tuples, which didn't work either: http://www.shirky.com/writings/herecomeseverybody/semantic_syllogism.html http://www.shirky.com/writings/herecomeseverybody/semantic_s... This idea pops up every 5 or 10 years and wastes a generation of brainpower. It never works. And let me be clear: There have been some terrifyingly brilliant people who were convinced that it ought to work, and who spent years of their life on it. Meanwhile, any joker who can code up Bayes theorem or single value decomposition can get some results in a couple of weeks. Probability and statistics get results (as do more advanced numeric techniques). Logic deduction fails. I'm not even sure I could explain why. But I encourage you to think long and deeply on what Norvig has written on this subject. Or just buy Norvig's two AI textbooks (written before and after he discovered the joys of probability, basically), do some exercises, and compare the results you get.

rvense 10y ago
> I've met some really really brilliant people who've been banging their head against that particular wall since the 1980s. The idea of breaking meaning into an inventory of discrete components like this is at least as old as Hjelmslev, I think even Saussure touches on it. Part of it seems to me to be that you're breaking down words into words. Even if you write it in uppercase and call it a primitive, you don't have to spend too much time on cognitive linguistics to know that there's really nothing primitive about MAN or MONARCH...
- DonaldFisk 10y ago
  At that point, you do need some sort of grounding, e.g. a human reading the text, or a neural network embedded in a robot. It isn't symbols all the way down.
srssays 10y ago
It's clear in hindsight. Natural language is the serialisation of human thought. Humans don't think symbolically in terms of rigorous logical statements. We stretch and play with definitions, sometimes making up a new definition on the spot, and relying on context and our shared experiences for the other person to figure out the meaning. Statistics isn't sufficient for fully understanding natural language (for that, a computer would have to go out and experience the world in the first person). But it is necessary.

by ekidd 10y ago

rvense 10y ago

> I've met some really really brilliant people who've been banging their head against that particular wall since the 1980s. The idea of breaking meaning into an inventory of discrete components like this is at least as old as Hjelmslev, I think even Saussure touches on it. Part of it seems to me to be that you're breaking down words into words. Even if you write it in uppercase and call it a primitive, you don't have to spend too much time on cognitive linguistics to know that there's really nothing primitive about MAN or MONARCH...

DonaldFisk 10y ago
At that point, you do need some sort of grounding, e.g. a human reading the text, or a neural network embedded in a robot. It isn't symbols all the way down.

srssays 10y ago

It's clear in hindsight. Natural language is the serialisation of human thought. Humans don't think symbolically in terms of rigorous logical statements. We stretch and play with definitions, sometimes making up a new definition on the spot, and relying on context and our shared experiences for the other person to figure out the meaning. Statistics isn't sufficient for fully understanding natural language (for that, a computer would have to go out and experience the world in the first person). But it is necessary.