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Cyc

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Re: Cyc

#61

Post by Jeff_Brown »

vsskanth wrote: post #7

Is it possible that the inherent context dependent ambiguities of human language make knowledge-based inference so difficult since most current knowledge is stored in human language ? tangential question: is there a standard language for "knowledge", like how we describe math for "computation" ? Are a part of our brains essentially compilers from human language to an internal definition of "knowledge" that leads to c…

Attempto Controlled English[1] is a cool project: it's a formal language, but a subset of ordinary English.

My own Hode, described in an earlier comment[2], makes it easy for anyone who speaks some natural language to enter and query arbitrary structured data.

[1] https://en.wikipedia.org/wiki/Attempto_Controlled_English

[2] https://news.ycombinator.com/item?id=21784617

Re: Cyc

#62

Post by yters »

xamuel wrote: post #48
yters wrote: post #36

Why is there never any fundamental research whether human intelligence is even computable? All these huge, expensive projects based on an untested premise.

There has been some philosophical speculating but that's generally not very actionable, with people clinging to either side of the question. On the practical side, it's the sort of thing which you can't just throw money at and make progress. Ok, you have $100mil to research whether human intelligence is computable. What do you do? Hire lots of humans and assign them noncomputable tasks and tap your foot waiting for o…

But that's the sort of thing that should be researched: is the question scientifically answerable? The answer is not obviously no. I can think of ways to scientifically test for noncomputability. If I can then certainly much smarter and knowledgeable poeple can. People just assume like yourself it is not and throw lots of money at a certain assumption. If the assumption is wrong, not only is AGI a dead end, but "human in the loop" computation should be a huge win.

Re: Cyc

#63

Post by yters »

radeklew wrote: post #40
yters wrote: post #36

Why is there never any fundamental research whether human intelligence is even computable? All these huge, expensive projects based on an untested premise.

Why wouldn't it be? It seems to me that at worst we would have to wait for computers to become as powerful and complex as a human brain, and then simulating human intelligence would be a matter of accurately modelling the connections. Is there doubt as to whether a neuron can be represented computationally?

The mind may be nonphysical.

Re: Cyc

#64

Post by yters »

Ididntdothis wrote: post #39
yters wrote: post #36

Why is there never any fundamental research whether human intelligence is even computable? All these huge, expensive projects based on an untested premise.

I think it’s pretty certain that we can improve a lot. If that leads to human intelligence or something else we don’t know. But it’s worth working on improving things and trying different approaches even if the final result isn’t known.

But there might be even better approaches if human intelligence is not computable. E.g. if the mind is a halting oracles that can get us all kinds of cool things.

Re: Cyc

#65

Post by goatlover »

The_rationalist wrote: post #20
lidHanteyk wrote: post #2

I think that this particular topic is evergreen because people are perennially surprised that this technology, which seems so reasonable and advanced at first blush, has failed to be useful in practice.

There's still no complete explanation on why it is a failure. What are the technical difficulties they can't overcome?

Maybe because the world and human knowledge are incredibly complex and difficult things to put into logical relations well enough to achieve more success?

Consider that humans learn though having bodies to explore the world with, while forming a variety of social relations to learn the culture. Which is very different from encoding a bunch of rules to make up an intelligence.

Re: Cyc

#66

Post by catpolice »

m0zg wrote: post #55
catpolice wrote: post #15

I worked for Cycorp for a few years recently. AMA, I guess? I obviously won't give away any secrets (e.g. business partners, finer grained details of how the inference engine works), but I can talk about the company culture, some high level technical things and the interpretation of the project that different people at the company have that makes it seem more viable than you might guess from the outside. There were s…

How do you deal with the fact that human knowledge is probabilistic? I.e. that it's actually mostly "belief" rather than a "fact", and the "correct" answer heavily depends on the context in a somewhat Bayesian way. Best I can tell we don't yet have math to model this in any kind of a comprehensive way.

Cyc has a few ways of dealing with fallibility.

Cyc doesn't do anything Bayesian like assigning specific probabilities to individual beliefs - IIRC they tried something like that and it had the problem where nobody felt very confident about attaching any particular precise number to priors and also the inference chains can be so long and involve so many assertions that anything less than 1 probability for most assertions would result in conclusions with very low confidence levels.

As to what they actually do, there are a few approaches.

I know that for one thing, there are coarse grained epistemic levels of belief built into the representation system - some predicates have "HighLikelihoodOf___" or "LowLikelihoodOf___" versions that enable very rough probabilistic reasoning that (it's argued - I have no position on this) is actually closer to the kind of folk-probabilistic thinking that humans actually do.

Also Cyc can use non-monotonic logic, which I think is relatively unique for commercial inference engines. I'm not going to give the best explanation here, but effectively, Cyc can assume that some assertions are "generally" true but may have certain exceptions, which makes it easy to express a lot of facts in a way that's similar to human reasoning. In general, mammals don't lay eggs. So you can assert that mammals don't lay eggs. But you can also assert that statement is non-monotonic and has exceptions (e.g. Platypuses).

Finally, and this isn't actually strictly about probabilistic reasoning, but helps represent different kinds of non-absolute reasoning: knowledge in Cyc is always contextualized. The knowledge base is divided up into "microtheories" of contexts where assertions are given to hold as if they're both true and relevant - very little is assumed to be always true across the board. This allows them to represent a lot of different topics, conflicting theories or even fictional worlds - there are various microtheories used for reasoning events in about popular media franchises, where the same laws of physics might not apply.

Re: Cyc

#67

Post by Jeff_Brown »

xamuel wrote: post #51
lidHanteyk wrote: post #2

I think that this particular topic is evergreen because people are perennially surprised that this technology, which seems so reasonable and advanced at first blush, has failed to be useful in practice.

I spent a year doing an ontology postdoc. I can't speak for Cyc, but from what I saw of the ontology world, there are a lot of charlatans and people who are using it as a buzzword to make grant proposals sexier. Whether or not there's real potential in the technology, that kind of environment surely isn't conducive to achieving said potential. An outsider trying to peek into the field is immediately swamped by vast o…

This supply-side opacity is definitely a problem. There seems to be a corresponding demand-side problem, that clients often don't know quite what they want. If there was an easy way of generating hard tests with clear-cut answers, maybe there would be an easy way for a winner to distinguish themselves.

Re: Cyc

#68

Post by xamuel »

yters wrote: post #62
xamuel wrote: post #48

Earlier quoted context omitted.

There has been some philosophical speculating but that's generally not very actionable, with people clinging to either side of the question. On the practical side, it's the sort of thing which you can't just throw money at and make progress. Ok, you have $100mil to research whether human intelligence is computable. What do you do? Hire lots of humans and assign them noncomputable tasks and tap your foot waiting for o…

But that's the sort of thing that should be researched: is the question scientifically answerable? The answer is not obviously no. I can think of ways to scientifically test for noncomputability. If I can then certainly much smarter and knowledgeable poeple can. People just assume like yourself it is not and throw lots of money at a certain assumption. If the assumption is wrong, not only is AGI a dead end, but "huma…

I'm not saying it's not scientifically answerable, just that hiring people specifically to answer it is not practical.

This type of thing usually comes through unplanned breakthroughs. You can't discover that the earth revolves around the sun just by paying tons of money to researchers and asking them to figure out astronomy. All that would get you would be some extremely sophisticated Copernican cycle-based models.

https://www.smbc-comics.com/comic/2012-08-09

Re: Cyc

#70

Post by 13415 »

yters wrote: post #36

Why is there never any fundamental research whether human intelligence is even computable? All these huge, expensive projects based on an untested premise.

There is plenty of fundamental research on it, probably a paper about it is published every week or so. The problem is that there is no general solution to the question, and everybody disagrees about how "human intelligence" should be defined in that context. The answers people give depend too much on untestable "philosophical stances."

Personally, I believe that AI is possible (hard AI thesis) and that computationalism with multiple realizability is right, since none of the philosophical arguments against hard AI and computationalism have convinced me so far. But there are as many opinions on that as there are people working on it.

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