zlacker

>>mdszy+(OP)
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 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.

>>catpol+Zz
Are there any real world applications that are built on Cyc's knowledge base and actually in regular use? Is Cyc critical to those applications or could they have been more easily built some other way?

>>snowma+J61
Yes, I probably can't talk about them though. There are companies that use Cyc as part of processes for avoiding certain kinds of risks and the financial impact (by the company's estimation, not Cycorp's) is an unfathomably large amount of money. The thing I'm thinking of seems like something Cyc (or something Cyc-like) is relatively uniquely suited for. But for large scale systems, which thing is more easy in the long term is really hard to estimate with any confidence.

Really when it comes to practical applications using Cyc, there are three alternatives to consider and only two of them actually exist.

1. There are custom domain specific solutions, involving tailored (limited) inference engines and various kinds of smart databases.

2. There's Cyc.

3. There's a hypothetical future Cyc-like inference system that isn't burdened by 30 years of technical debt.

I personally suspect that some of Cycorp's clients would do better with domain-specific solutions because they don't realize how much of their problem could be solved that way and how much of the analysis coming from Cyc is actually the result of subject matter experts effectively building domain-specific solutions the hard way inside of Cyc. With a lot of Cycorp projects, it's hard to point your finger at exactly where the "AI" is happening.

There are some domains where you just need more inferential power and to leverage the years and years of background knowledge that's already in Cyc. Even then I sometimes used to wonder about the cost/effort effectiveness of using something as powerful and complicated as Cyc when a domain-specific solution might do 90% as well with half the effort.

If someone made a streamlined inference engine using modern engineering practices with a few years of concentrated work on making it usable by people who don't have graduate degrees in formal logic, and ported the most useful subset of the Cyc knowledge base over, that math would change dramatically.