Earlier quoted context omitted.
There are a bunch of "standards" for representing knowledge. E.g. https://en.wikipedia.org/wiki/Semantic_Web [Edit] Here's a wider overview: https://en.wikipedia.org/wiki/Knowledge_representation_and_r...
Thanks for the link. It seems to talk about a knowledge graph type links between entities. It is however in a human language (here, english). I am interested in knowing if there's something analogous to "math" to represent knowledge.
Cyc
11–20 of 176 posts
Re: Cyc
#12I still think the potential of lambda calculus in knowledge representation and logical deduction is high and under-represented in research.
Just theorizing, but I think a large part of the problem is the difficulty in interfacing this knowledge base with manual, human entry. Another pitfall is the difficulty in determining strange or unanticipated logical outcomes, and developing a framework to catch or validate these.
Re: Cyc
#13Re: Cyc
#14Earlier quoted context omitted.
There are a bunch of "standards" for representing knowledge. E.g. https://en.wikipedia.org/wiki/Semantic_Web [Edit] Here's a wider overview: https://en.wikipedia.org/wiki/Knowledge_representation_and_r...
Thanks for the link. It seems to talk about a knowledge graph type links between entities. It is however in a human language (here, english). I am interested in knowing if there's something analogous to "math" to represent knowledge.
Re: Cyc
#15There were some big positives. Everyone there is very smart and depending on your tastes, it can be pretty fun to be in meetings where you try to explain Davidsonian ontology to perplexed business people. I suspect a decent fraction of the technical staff are reading this comment thread. There are also some genuine technical advances (which I wish were more publicly shared) in inference engine architecture or generally stemming from treating symbolic reasoning as a practical engineering project and giving up on things like completeness in favor of being able to get an answer most of the time.
There were also some big negatives, mostly structural ones. Within Cycorp different people have very different pictures of what the ultimate goals of the project are, what true AI is, and how (and whether) Cyc is going to make strides along the path to true AI. The company has been around for a long time and these disagreements never really resolve - they just sort of hang around and affect how different segments of the company work. There's also a very flat organizational structure which makes for a very anarchic and shifting map of who is responsible or accountable for what. And there's a huge disconnect between what the higher ups understand the company and technology to be doing, the projects they actually work on, and the low-level day-to-day work done by programmers and ontologists there.
I was initially pretty skeptical of the continued feasibility of symbolic AI when I went in to interview, but Doug Lenat gave me a pitch that essentially assured me that the project had found a way around many of the concerns I had. In particular, they were doing deep reasoning from common sense principles using heuristics and not just doing the thing Prolog often devolved into where you end up basically writing a logical system to emulate a procedural algorithm to solve problems.
It turns out there's a kind of reality distortion field around the management there, despite their best intentions - partially maintained by the management's own steadfast belief in the idea that what Cyc does is what it ought to be doing, but partially maintained by a layer of people that actively isolate the management from understanding the dirty work that goes into actually making projects work or appear to. So while a certain amount of "common sense" knowledge factors into the reasoning processes, a great amount of Cyc's output at the project level really comes from hand-crafted algorithms implemented either in the inference engine or the ontology.
Also the codebase is the biggest mess I have ever seen by an order of magnitude. I spent some entire days just scrolling through different versions of entire systems that duplicate massive chunks of functionality, written 20 years apart, with no indication of which (if any) still worked or were the preferred way to do things.
Re: Cyc
#16last time I looked at OpenCyc's knowledge base, the information encoded was all strangely specific academic stuff - like very fine classifications and relationships between species of tapeworms and of fungus. There was very little daily-life common-sense knowledge, even though that's often the hook in interviews and articles about Cyc's purpose. I'm not sure why that's true - maybe it's hard to decide what the 'facts…
Re: Cyc
#17Cyc was the last holdout of GOFAI in the 90's, its premise being that the traditional symbolic AI paradigm wasn't wrong, but that it was just a matter of scale.
Re: Cyc
#18I can't believe this is still a thing. Then again, I felt the same way when I studied it at university almost 20 years ago. It was pretty obviously a pipe dream then, too.
Re: Cyc
#19While this approach might seem dated and strange, at some point something will begin to approach the ability to do general learning like a human. I just wonder how long we have to wait.
Almost nobody is really working on AGI and this is the main issue. A notable counter example is the recent reconversion of John Carmack.
Re: Cyc
#20I 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.