I was one of the first hires on the Cyc project when it started at MCC and was at first responsible for the decision to abandon the Interlisp-D implementation and replace it with one I wrote on Symbolics machines.
Yes, back then one person could write the code base, which has long since grown and been ported off those machines. The KB is what matters anyway. I built it so different people could work on the kb simultaneously, which was unusual in those days, even though cloud computing was ubiquitous at PARC (where Doug had been working, and I had too).
Neurosymbolic approaches are pretty important and there’s good work going on in that area. I was back in that field myself until I got dragged away to work on the climate. But I’m not sure that manually curated KBs will make much of a difference beyond bootstrapping.
I was one of the first hires on the Cyc project when it started at MCC and was at first responsible for the decision to abandon the Interlisp-D implementation and replace it with one I wrote on Symbolics machines.
Yes, back then one person could write the code base
A coworker of mine who used to work at Symbolics told me that this was endemic with Lisp development back in the day. Some customers would think there was a team of 300 doing the OS software at Symbolics. It was just 10 programmers.
I was born in late USSR and my father is software engineer. We had several books that were not available for "general public" (they were intended for libraries of science institutions). One of the book was, as I understand now, abridged translation of papers from some "Western" AI conference.
And there were description if EURISCO (with claims that it not only "win some game" but also that it "invented new structure of NAND-gate in silicon, used by industry now") and other expert systems.
One of the mentioned expert systems (without technical details) said was 2 times better in diagnose cancer than best human diagnostician of some university hospital.
And after that... Silence.
I always wonder, why did this expert system were not deployed in all USA hospitals, for example? If it is so good?
Now we have LLMs, but they are LANGUAGE models, not WORLD models. They predict distribution of possible next words. Same with images — pixels, not world concepts.
Looks like such systems are good for generating marketing texts, but can not be used as diagnosticians by definition.
Why did all these (slice of) world model approaches dead? Except Cyc, I think. Why we have good text generators and image generators but not diagnosticians 40 years later? What happens?..
I started my career in 1985, building expert systems on Symbolics Lisp machines in KEE and ART.
Expert systems were so massively oversold... and it's not at all clear that any of the "super fantastic expert" systems ever did what was claimed of them.
We definitely found out that they were, in practice, extremely difficult to build and make do anything reasonable.
The original paper on Eurisko, for instance, mentioned how the author (and founder of Cyc!) Douglas Lenat, during a run, went ahead and just hand-inserted some knowledge/results of inferences (it's been a long while since I read the paper, sorry), asserting, "Well, it would have figured these things out eventually!"
Later on, he wrote a paper titled, "Why AM and Eurisko appear to work" [0].
One of the first things software engineers learn is that people are bad at manually building models/programming.
The language and image models weren't built by people but by observing an obscene amount people going about their daily lives of producing text and images.
> Cyc was used by the Cleveland Clinic for answering ad hoc questions from medical researchers; it reduced the time from as long as a month of manual back-and-forth between medical and database experts, to less than an hour.
I've read similar things about image models from 12 years ago beating the pants off most radiologists. I think the difference is that most writers, illustrators, musicians, drivers, etc. eke out a marginal living, while radiologists have enough reserves to fight back. The "move fast and break things" crowd in silicon valley isn't going to undertake that fight while there's still so much low-hanging fruit, ripe for the harvest.
I would love to see a Cyc 2.0 modeled in the age of LLMs. I think it could be very powerful, especially to help deal with hallucinations. I would love to see a causality engine built with LLMs and Cyc. I wrote some notes on it before ChatGPT came out: https://blog.jtoy.net/understanding-cyc-the-ai-database/
I used to volunteer inputting data into Cyc back in the day. And I get massive déjà vu with current LLM's. I remember that the system ended up with an obsession with HVAC systems lol.
I worked on Cyc as a visiting student for a couple of summers; built some visualization tools to help people navigate around the complex graph. But I never was quite sold on the project, some tangential learnings here: https://hyperphor.com/ammdi/alpha-ontologist
>if they could not come to a consensus, would have to take it before the Master, Doug Lenat, who would think for a bit, maybe draw some diagrams on a whiteboard, and come up with the Right Representation
So looks like Cyc did have to fall back on a neural net after all (Lenat's).
Has Cyc been forgotten? Maybe it's unknown to tech startup hucksters who haven't studied AI in any real way but it's a well known project among both academic and informed industry folks.
from 2015-2019 i was working on a bot company (myra labs) where we were directly inspired by cyc to create knowledge graphs and integrate into LSTMs.
the frames, slots and values integrated were learned via a RNN for specific applications.
we even created a library for it called keyframe (modeling it after having the programmer specify the bot action states and have the model figure out the dialog in a structured way) - similar to how keyframes in animation work.
it would be interesting to resurrect that in the age of LLMs!
The Cyc project proposed the idea of software "assistants" : formally represented knowledge based on a shared ontology, reasoning systems that can draw on that knowledge, handle tasks and anticipate the need to perform them.[1]
The lead author on [1] is Kathy Panton who has no publications after that and zero internet presence as far as i can tell.
Back in the mid 1990s Cyc was giving away their Symbolics machines and I waffled on spending the $1500 in shipping to get them to me in Denver. In retrospect I should have, of course!
One thing the article doesn't really speak to: the future of Cyc now that Doug Lenat has passed away. Obviously a company can continue on even after the passing of a founder, but it always felt like Cyc was "Doug's baby" to a large extent. I wonder if the others that remain at Cycorp will remain as committed without him around leading the charge?
Does anybody have any insights into where things stand at Cycorp and any expected fallout from the world losing Doug?
Cyc seemed to be the best application for proper AI in my opinion - all the ML and LLM tricks are statistically really good, but you need to parse it through Cyc to check for common sense.
I am really pleased they continue to work on this - it is a lot of work, but it needs to be done and checked manually, once done the base stuff shouldn't change much and it will be a great common sense check for generated content.
One of the immediate things I'm working on is a text to knowledge graph system. Yohei (creator of BabyAGI) is also working on text to knowledge graphs: https://twitter.com/yoheinakajima/status/1769019899245158648. LlamaIndex has a basic implementation.
Are there any efforts to combining a knowledge base like Cyc together with LLMs and the like?
Yes. It's something I've been working on, so there's at least 1 such effort. And I'm reasonably sure there are others. The idea is too obvious for there to not be other people pursuing it.
Cyc is one of those bad ideas that won't die, and which keeps getting rediscovered on HN. Lenat wasted decades of his life on it. Knowledge graphs like Cyc are labor intensive to build and difficult to maintain. They are brittle in the face of change, and useless if they cannot represent the underlying changes of reality.
I wonder to what degree an LLM could now produce frames/slots/values in the knowledge graph. With so much structure already existing in the Cyc knowledge graph, could those frames act as the crystal seed upon which an LLM could crystallize its latent knowledge about the world from the trillions of tokens it was trained upon?
They are brittle in the face of change, and useless if they cannot represent the underlying changes of reality.
FWIW, KG's don't have to be brittle. Or, at least they don't have to be as brittle as they've historically been. There are approaches (like PROWL[1]) to making graphs probabilistic so that they're asserting subjective beliefs about statements, instead of absolute statements. And then the strength of those beliefs can increase or decrease in response to new evidence (per Bayes Theorem). Probably the biggest problem with this stuff is that it tends to be crazy computationally expensive.
Still, there's always the chance of an algorithmic breakthrough or just hardware improvements bringing some of this stuff into the real of practical.
The comment above misses the point in at least four ways. (1) Being aware of history is not the same as endorsing it. (2) Knowledge graphs are useful for many applications. (3) How narrow of a mindset and how much hindsight bias must one have to claim that Lenat wasted decades of his life? (4) Don’t forget to think about this in context about what was happening in the field of AI.
Cyc was an interesting project - you might consider it as the ultimate scaling experiment in expert systems. There seemed to be two ideas being explored - could you give an expert system "common sense" by laboriously hand-entering in the rules for things we, and babies, learn by everyday experience, and could you make it generally intelligent by scaling it up and making the ruleset comprehensive enough.
Ultimately it failed, although people's opinions may differ. The company is still around, but from what people who've worked there have said, it seems as if the original goal is all but abandoned (although Lenat might have disagreed, and seemed eternally optimistic, at least in public). It seems they survive on private contracts for custom systems premised on the power of Cyc being brought to bear, when in reality these projects could be accomplished in simpler ways.
I can't help but see somewhat of a parallel between Cyc - an expert system scaling experiment, and today's LLMs - a language model scaling experiment. It seems that at heart LLMs are also rule-based expert systems of sorts, but with the massive convenience factor of learning the rules from data rather than needing to have the rules hand-entered. They both have/had the same promise of "scale it up and it'll achieve AGI", and "add more rules/data and it'll have common sense" and stop being brittle (having dumb failure modes, based on missing knowledge/experience).
While the underlying world model and reasoning power of LLMs might be compared to an expert system like Cyc, they do of course also have the critical ability to input and output language as a way to interface to this underlying capability (as well as perhaps fool us a bit with the ability to regurgitate human-derived surface forms of language). I wonder what Cyc would feel like in terms of intelligence and reasoning power if one somehow added an equally powerful natural language interface to it?
As LLMs continue to evolve, they are not just being scaled up, but also new functionality such as short term memory being added, so perhaps going beyond expert system in that regard, although there is/was also more to Cyc that just the massive knowledge base - a multitude of inference engines as well. Still, I can't help but wonder if the progress of LLMs won't also peter out, unless there are some fairly fundamental changes/additions to their pre-trained transformer basis. Are we just replicating the scaling experiment of Cyc, just with a fancy natural language interface?
Cyc was the last remaining GOFAI champion back in the day when everyone in AI was going the 'Nouvelle AI' route.
Eventually the approach would be rediscovered (but not recuperated) by the database field desparate for 'new' research topics.
We might see a revival now that transformets can front and backend the hard edges of the knowledge based tech, but it will remain to be seen wether scaled monolyth systems like Cyc are the right way to pair.
[+] [-] gumby|1 year ago|reply
I was one of the first hires on the Cyc project when it started at MCC and was at first responsible for the decision to abandon the Interlisp-D implementation and replace it with one I wrote on Symbolics machines.
Yes, back then one person could write the code base, which has long since grown and been ported off those machines. The KB is what matters anyway. I built it so different people could work on the kb simultaneously, which was unusual in those days, even though cloud computing was ubiquitous at PARC (where Doug had been working, and I had too).
Neurosymbolic approaches are pretty important and there’s good work going on in that area. I was back in that field myself until I got dragged away to work on the climate. But I’m not sure that manually curated KBs will make much of a difference beyond bootstrapping.
[+] [-] stcredzero|1 year ago|reply
Yes, back then one person could write the code base
A coworker of mine who used to work at Symbolics told me that this was endemic with Lisp development back in the day. Some customers would think there was a team of 300 doing the OS software at Symbolics. It was just 10 programmers.
[+] [-] guenthert|1 year ago|reply
I don't want to rob you of your literary freedom, but that threw me off. Mainframes were meant, yes?
[+] [-] pfdietz|1 year ago|reply
[+] [-] m463|1 year ago|reply
I had learned about "AI" in the 80's. The promise was that with lisp and expert systems and prolog and more.
the article said cyc was reading the newspaper every day.
I thought, wow, any day now computers will leap forward. The japanese 5th generation computing will be left in the dust. :)
[+] [-] blacklion|1 year ago|reply
And there were description if EURISCO (with claims that it not only "win some game" but also that it "invented new structure of NAND-gate in silicon, used by industry now") and other expert systems.
One of the mentioned expert systems (without technical details) said was 2 times better in diagnose cancer than best human diagnostician of some university hospital.
And after that... Silence.
I always wonder, why did this expert system were not deployed in all USA hospitals, for example? If it is so good?
Now we have LLMs, but they are LANGUAGE models, not WORLD models. They predict distribution of possible next words. Same with images — pixels, not world concepts.
Looks like such systems are good for generating marketing texts, but can not be used as diagnosticians by definition.
Why did all these (slice of) world model approaches dead? Except Cyc, I think. Why we have good text generators and image generators but not diagnosticians 40 years later? What happens?..
[+] [-] chris_st|1 year ago|reply
Expert systems were so massively oversold... and it's not at all clear that any of the "super fantastic expert" systems ever did what was claimed of them.
We definitely found out that they were, in practice, extremely difficult to build and make do anything reasonable.
The original paper on Eurisko, for instance, mentioned how the author (and founder of Cyc!) Douglas Lenat, during a run, went ahead and just hand-inserted some knowledge/results of inferences (it's been a long while since I read the paper, sorry), asserting, "Well, it would have figured these things out eventually!"
Later on, he wrote a paper titled, "Why AM and Eurisko appear to work" [0].
0: https://aaai.org/papers/00236-aaai83-059-why-am-and-eurisko-...
[+] [-] brain_elision|1 year ago|reply
The language and image models weren't built by people but by observing an obscene amount people going about their daily lives of producing text and images.
[+] [-] mietek|1 year ago|reply
https://news.ycombinator.com/item?id=40070667
[+] [-] BlueTemplar|1 year ago|reply
[+] [-] hello_computer|1 year ago|reply
[+] [-] toisanji|1 year ago|reply
[+] [-] ImHereToVote|1 year ago|reply
[+] [-] mtraven|1 year ago|reply
[+] [-] optimalsolver|1 year ago|reply
So looks like Cyc did have to fall back on a neural net after all (Lenat's).
[+] [-] TrevorFSmith|1 year ago|reply
[+] [-] nextos|1 year ago|reply
Bonus points if that is combined with modern differentiable methods and SAT/SMT, i.e. neurosymbolic AI.
[+] [-] unknown|1 year ago|reply
[deleted]
[+] [-] viksit|1 year ago|reply
the frames, slots and values integrated were learned via a RNN for specific applications.
we even created a library for it called keyframe (modeling it after having the programmer specify the bot action states and have the model figure out the dialog in a structured way) - similar to how keyframes in animation work.
it would be interesting to resurrect that in the age of LLMs!
[+] [-] carlsborg|1 year ago|reply
The lead author on [1] is Kathy Panton who has no publications after that and zero internet presence as far as i can tell.
[1] Common Sense Reasoning – From Cyc to Intelligent Assistant https://iral.cs.umbc.edu/Pubs/FromCycToIntelligentAssistant-...
[+] [-] shrubble|1 year ago|reply
[+] [-] markc|1 year ago|reply
[+] [-] rhodin|1 year ago|reply
[0] https://writings.stephenwolfram.com/2023/09/remembering-doug...
[+] [-] zozbot234|1 year ago|reply
[+] [-] mindcrime|1 year ago|reply
Does anybody have any insights into where things stand at Cycorp and any expected fallout from the world losing Doug?
[+] [-] acutesoftware|1 year ago|reply
I am really pleased they continue to work on this - it is a lot of work, but it needs to be done and checked manually, once done the base stuff shouldn't change much and it will be a great common sense check for generated content.
[+] [-] avodonosov|1 year ago|reply
[+] [-] radomir_cernoch|1 year ago|reply
[+] [-] mietek|1 year ago|reply
[+] [-] SilverSlash|1 year ago|reply
https://youtu.be/ipRvjS7q1DI?si=fEU1zd6u79Oe4SgH&t=675
[+] [-] dredmorbius|1 year ago|reply
[+] [-] nikolay|1 year ago|reply
[+] [-] bilsbie|1 year ago|reply
Or for quality checks during training?
[+] [-] ragebol|1 year ago|reply
Have some vector for a concept match a KB entry etc, IDK :).
[+] [-] brendonwong|1 year ago|reply
One of the immediate things I'm working on is a text to knowledge graph system. Yohei (creator of BabyAGI) is also working on text to knowledge graphs: https://twitter.com/yoheinakajima/status/1769019899245158648. LlamaIndex has a basic implementation.
This isn't quite connecting the system to an automated reasoner though. There is some research in this area, like: https://news.ycombinator.com/item?id=35735375
Cyc + LLMs is vaguely related to more advanced "cognitive architectures" for AI, for instance see the world model in Davidad's architecture, which LLMs can be used to help build: https://www.lesswrong.com/posts/jRf4WENQnhssCb6mJ/davidad-s-...
[+] [-] mindcrime|1 year ago|reply
Yes. It's something I've been working on, so there's at least 1 such effort. And I'm reasonably sure there are others. The idea is too obvious for there to not be other people pursuing it.
[+] [-] blueyes|1 year ago|reply
[+] [-] zopf|1 year ago|reply
[+] [-] thesz|1 year ago|reply
[1] https://voidfarer.livejournal.com/623.html
You can label it "bad idea" but you can't bring LLMs back in time.
[+] [-] breck|1 year ago|reply
Now it's clear that knowledge graphs are far inferior to deep neural nets, but even still few people can explain the _root_ reason why.
I don't think Lenat's bet was a waste. I think it was sensible based on the information at the time.
The decision to research it largely in secret, closed source, I think was a mistake.
[+] [-] mindcrime|1 year ago|reply
FWIW, KG's don't have to be brittle. Or, at least they don't have to be as brittle as they've historically been. There are approaches (like PROWL[1]) to making graphs probabilistic so that they're asserting subjective beliefs about statements, instead of absolute statements. And then the strength of those beliefs can increase or decrease in response to new evidence (per Bayes Theorem). Probably the biggest problem with this stuff is that it tends to be crazy computationally expensive.
Still, there's always the chance of an algorithmic breakthrough or just hardware improvements bringing some of this stuff into the real of practical.
[1]: https://www.pr-owl.org/
[+] [-] xpe|1 year ago|reply
[+] [-] richardatlarge|1 year ago|reply
I'll tell you how I know /
I read it in the paper /
Fifteen years ago -
(John Prine)
[+] [-] fidesomnes|1 year ago|reply
[deleted]
[+] [-] HarHarVeryFunny|1 year ago|reply
Ultimately it failed, although people's opinions may differ. The company is still around, but from what people who've worked there have said, it seems as if the original goal is all but abandoned (although Lenat might have disagreed, and seemed eternally optimistic, at least in public). It seems they survive on private contracts for custom systems premised on the power of Cyc being brought to bear, when in reality these projects could be accomplished in simpler ways.
I can't help but see somewhat of a parallel between Cyc - an expert system scaling experiment, and today's LLMs - a language model scaling experiment. It seems that at heart LLMs are also rule-based expert systems of sorts, but with the massive convenience factor of learning the rules from data rather than needing to have the rules hand-entered. They both have/had the same promise of "scale it up and it'll achieve AGI", and "add more rules/data and it'll have common sense" and stop being brittle (having dumb failure modes, based on missing knowledge/experience).
While the underlying world model and reasoning power of LLMs might be compared to an expert system like Cyc, they do of course also have the critical ability to input and output language as a way to interface to this underlying capability (as well as perhaps fool us a bit with the ability to regurgitate human-derived surface forms of language). I wonder what Cyc would feel like in terms of intelligence and reasoning power if one somehow added an equally powerful natural language interface to it?
As LLMs continue to evolve, they are not just being scaled up, but also new functionality such as short term memory being added, so perhaps going beyond expert system in that regard, although there is/was also more to Cyc that just the massive knowledge base - a multitude of inference engines as well. Still, I can't help but wonder if the progress of LLMs won't also peter out, unless there are some fairly fundamental changes/additions to their pre-trained transformer basis. Are we just replicating the scaling experiment of Cyc, just with a fancy natural language interface?
[+] [-] ultra_nick|1 year ago|reply
I wonder if they've adopted ML yet.
[+] [-] PeterStuer|1 year ago|reply
Eventually the approach would be rediscovered (but not recuperated) by the database field desparate for 'new' research topics.
We might see a revival now that transformets can front and backend the hard edges of the knowledge based tech, but it will remain to be seen wether scaled monolyth systems like Cyc are the right way to pair.