11 ms·
Obituary for Cyc
- gomijacogeo 2y agoLex Fridman interview with Lenat a few years back. https://www.youtube.com/watch?v=3wMKoSRbGVs https://www.youtube.com/watch?v=3wMKoSRbGVs
- pvg 2y agoA big Cyc thread about a year ago https://news.ycombinator.com/item?id=40069298 https://news.ycombinator.com/item?id=40069298
- zitterbewegung 2y agoYou can run a version of CYC that was released online as opencyc https://github.com/asanchez75/opencyc https://github.com/asanchez75/opencyc . This is when a version of the system was posted on source forge and the GitHub has the dataset and the KB and inference engine. Note it has been written in an old version of Java.
- VikingCoder 2y ago[flagged]
- lnkl 2y ago[flagged]
- optimalsolver 2y agohttps://tvtropes.org/pmwiki/pmwiki.php/Main/InMyLanguageThatSoundsLike https://tvtropes.org/pmwiki/pmwiki.php/Main/InMyLanguageThat...
- StefanBatory 2y agowhy are you cursing people to a 3-4 hours trip to tv tropes :(
- deleted 2y ago[deleted]
- pfdietz 2y agoIt would be cool to try to generate the "knowledge" as in Cyc automatically, from LLMs.
- eob 2y agoOr vice versa - perhaps some subset of the "thought chains" of Cyc's inference system could be useful training data for LLMs.
- euroderf 2y agoWhen I first learned about LLMs, what came to mind is some sort of "meeting of the minds" with Cyc. 'Twas not to be, apparently.
- imglorp 2y agoI view Cyc's role there as a RAG for common sense reasoning. It might prevent models from advising glue on pizza. (is-a 'pizza 'food) (not (is-a 'glue 'food)) (for-all i ingredients (assert-is-a i 'food))
- jes5199 2y agosure but the bigger models don’t make these trivial mistakes, and I’m not sure if translating the LLM english sentences into LISP and trying to check them is going to be more accurate than just training the models better
- yellowapple 2y agoThe bigger models avoid those mistakes by being, well, bigger. Offloading to a structured knowledgebase would achieve the same without the model needing to be bigger. Indeed, the model could be a lot smaller (and a lot less resource-intensive) if it only needed to worry about converting $LANGUAGE queries to Lisp queries and converting Lisp results back into $LANGUAGE results (where $LANGUAGE is the user's natural language, whatever that might be), rather than having to store some approximation of that knowledgebase within itself on top of understanding $LANGUAGE and understanding whatever ad-hoc query/result language it's unconsciously invented for itself.
- vannevar 2y agoI would argue that Lenat was at least directionally correct in understanding that sheer volume of data (in Cyc's case, rules and facts) was the key in eventually achieving useful intelligence. I have to confess that I once criticized the Cyc project for creating an ever-larger pile of sh*t and expecting a pony to emerge, but that's sort of what has happened with LLMs.
- cmrdporcupine 2y agoI suspect at some point the pendulum will again swing back the other way and symbolic approaches will have some kind of breakthrough and become trendy again. And, I bet it will likely have something to do with accelerating these systems with hardware, much like GPUs have done for neural networks, in order to crunch really large quantities of facts
- whiplash451 2y agoOr maybe program synthesis combined by LLMs might be the way?
- cmrdporcupine 2y agoIt does seem like the Cyc people hit the wall with simply collecting facts. Having to have a human in the loop. The problem I think is if you have LLMs figuring out the propositions, the whole system is just as prone to garbage-in-garbage-out as LLMs are.
- luma 2y agoThe Bitter Lesson has a few things to say about this. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
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- wzdd 2y ago
- dartharva 2y ago> Lenat personally did not release the source code of his PhD project or EURISKO, remained unimpressed with open source, and disliked academia as much as academia disliked him. Most open information concerning Cyc had been deliberately removed circa 2015, at the moment when Cycorp pivoted to commercial applications. Makes one wonder how much a research being open makes a difference in its real-world success in the current age. Cyc's competitors (LLMs etc.) arguably have a lot to attribute to open public participation for its successes. Perhaps things would have been difference had Lenat been more open with the project?
- Legend2440 2y agoProbably not, tbh. The issue with Cyc is that it required huge amounts of manual effort to create the rules, while LLMs can learn their own rules from raw data. There was no machine intelligence in Cyc, just human intelligence.
- lenerdenator 2y agoSo more of an expert system?
- dragonwriter 2y agoCyc was exactly an expert system (and those were exactly as central an "AI" technology as LLMs are today, a few rounds of AI hype ago.)
- ted_dunning 2y agoVery much so.
- moralestapia 2y ago>while LLMs can learn their own rules from raw data Supervised vs. unsupervised, but LLMs haven't made any new discoveries on their own ... yet.
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- ChuckMcM 2y agoI had the funny thought that this is exactly what a sentient AI would write "stop looking here, there is nothing to see, move along." :-) I (like vannevar apparently) didn't feel Cyc was going anywhere useful, there were ideas there, but not coherent enough to form a credible basis for even a hypothesis of how a system could be constructed that would embody them. I was pretty impressed by McCarthy's blocks world demo, later he and a student formalized some of the rules for creating 'context'[1] for AI to operate within, I continue to think that will be crucial to solving some of the mess that LLMs create. For example, the early failures of LLMs suggesting that you could make salad crunchy by adding rocks was a classic context failure, data from the context of 'humor' and data from the context of 'recipes' intertwined. Because existing models have no context during training, there is nothing in the model that 'tunes' the output based on context. And you get rocks in your salad. [1] https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=29935775a3ae302bdaffef9dadc8f090659ea7c5 https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&d...
- musicale 2y ago> there remains no evidence of its general intelligence This seems like a high bar to reach. We all know that symbolic AI didn't scale as well as LLMs trained on huge amounts of data. However, as you note, it also tried to address many things that LLMs still don't do well.
- adastra22 2y agoSuch as what? What can GOFAI do well that LLMs still cannot?
- musicale 2y ago"Tried to address" is not the same as "can do well." I was responding to PP, but some other (maybe obvious?) examples are logical reasoning and explainability. As PP suggests, some of the classical symbolic ideas may be applicable or complementary to current approaches.
- toisanji 2y agoI think there could be a next generation cyc. Current llms have too many mistakes and grounding it with an AI ontology could be really interesting. I wrote more about it here: https://blog.jtoy.net/understanding-cyc-the-ai-database/ https://blog.jtoy.net/understanding-cyc-the-ai-database/
- breck 2y ago[dead]
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- mcphage 2y ago> Cyc grew to contain approximately 30 million assertions at a cost of $200 million and 2,000 person-years. Yet despite Lenat’s repeated predictions of imminent breakthrough, it never came. That seems like pretty small potatoes compared to how much has been spent on LLMs these days. Or to put it another way: if global funding for LLM development had been capped at $200m, how many of them would even exist?
- masfuerte 2y agoIt's funny, because AI companies are currently spending fortunes on mathematicians, physicists, chemists, software engineers, etc. to create good training data. Maybe this money would be better spent on creating a Lenat-style ontology, but I guess we'll never know.
- throwanem 2y agoWe may. LLMs are capable, even arguably at times inventive, but lack the ability to test against ground truth; ontological reasoners can never exceed the implications of the ground truth they're given, but within that scope reason perfectly. These seem like obviously complementary strengths.
- gwern 2y agoLanguage models repeatedly delivered practical, real-world economic value at every step of the way from at least n-grams on. (Remember the original 'unreasonable effectiveness of data'?) The applications were humble and weren't like "write all my code for me and then immanentize the eschaton", but they were real things like spelling error detection & correction, text compressors, voice transcription boosters, embeddings for information retrieval, recommenders, knowledge graph creation (ironically enough), machine translation services, etc. In contrast, Yuxi goes through the handful of described Cyc use-cases from their entire history, and it's not impressive.
- mcphage 2y ago> Remember the original 'unreasonable effectiveness of data'? That came out in 2009, correct? I wonder how much was spent on LLMs up to that point. > In contrast, Yuxi goes through the handful of described Cyc use-cases from their entire history, and it's not impressive. They're also not humble. Maintain a semantic database of terrorist cells? Create a tutoring AI? These seem closer to the things that LLMs are currently being used for, with middling success, after vastly more money has been pumped into the field. Whereas most of the uses you describe for early LLMs are far more humble (spelling error detection & correction, text compressors), and also a lot more successful. Which makes me think that CYC went first for the big targets, and fell on its face, rather than spending a few decades building up more modest accomplishments. In hindsight that would have obviously been a much better strategy, but honestly—it feels like that would have been an obviously better strategy in non-hindsight as well. I don't know why CYC went that way.
- Animats 2y agoCyc is going great, according to the web site. "The Next Generation of Enterprise AI"[1] Lenat himself died in 2023. Despite this, he is listed as the only member of the "leadership team".[2] [1] https://cyc.com/ https://cyc.com/ [2] https://cyc.com/leadership-team/ https://cyc.com/leadership-team/
- vitiral 2y agoMaybe Cyc was a success and Lenat lives on as it's consciousness?
- jibal 2y agoDead is dead. And even if Cyc had consciousness--which it doesn't--it certainly wouldn't have his consciousness.
- Cthulhu_ 2y agoIt's an interesting thought experiment / philosophy / sci fi story premise though; if he spent all those years encoding his own thought processes and decision making into a program, would he have been able to create a convincing facsimile of himself and his "consciousness"? A turing test with a chatbot based on decades of self-reflection.
- Philpax 2y agoThe 2014 film Transcendence touches upon this, but I can't recommend watching it. It's surface level and the rest of the film is mediocre.
- mewse-hn 2y agoStephenson's "Fall; or, Dodge in Hell" deals with the hypothetical digitization of human minds. It has some interesting ideas like some of the synthetic minds deciding to wilfully become a hivemind
- jibal 2y ago
- timClicks 2y ago> The secretive nature of Cyc has multiple causes. Lenat personally did not release the source code of his PhD project or EURISKO, remained unimpressed with open source, and disliked academia as much as academia disliked him. One thing that's not mentioned here, but something that I took away from Wolfram's obituary of Lenat (https://writings.stephenwolfram.com/2023/09/remembering-doug-lenat-1950-2023-and-his-quest-to-capture-the-world-with-logic/ https://writings.stephenwolfram.com/2023/09/remembering-doug...) was that Lenat was very easily distracted ("Could we somehow usefully connect [Wolfram|Alpha and the Wolfram Language] to CYC? ... But when I was at SXSW the next year Doug had something else he wanted to show me. It was a math education game."). My armchair diagnosis is untreated ADHD. He might have had had discussing the internals of CYC on his todo list since its first prototype, but the draft was never ready.
- kazinator 2y agoIt is worth plodding ahead with symbolic AI research because: - much less resource hungry / planet warming - auditable chains of inference
- ted_dunning 2y agoOf course there is still the downside that it doesn't work very well.
- yellowapple 2y agoNeither does the mainstream approach, given the whole hallucination problem. But then again, humans "hallucinate" in this sense all the time, too.
- WesolyKubeczek 2y agoI thought the end goal is to make something that's way more than human in capabilities. The thing is, if we produce something that is worse than humans (and right now LLMs are worse than humans with good search indexes at hand), there's not much point doing it. It's provably less expensive to bear, raise, and educate actual humans. And to educate a human, somehow you don't need to dump the whole internet and all pirated content ever created into their heads.
- kazinator 2y ago> LLMs are worse than humans with good search indexes at hand But "good search indexes" are all LLM-based. :)
- bsder 2y agoI think Cyc should worry that I can't distinguish whether real or April Fools joke ...
- krick 2y agoI have nothing to say about Cyc (apart to that the bitter lesson is bitter indeed, but was never debunked so far; and, also, I hate LLMs). But this line (from accompanying github) deserves some attention, IMO: > Due to the lack of long-term stability of IA, I have taken the liberty to scrape those from IA and kept them here for safe keeping. I mean, he is not wrong, and when I want to preserve something code-related, Github comes to mind as a safe place... which is crazy, if you think about it for a moment. And the fact that we are starting to use Github as a backup for IA is almost comically grotesque. I don't have a solution, but it's worth reminding that we sure have a problem.
- thot_experiment 2y agoWhat does it mean to you to hate LLMs?
- ted_dunning 2y agoHave you ever not hated LLMs?
- thot_experiment 2y agoYes? I don't hate LLMs and I don't think I ever have. They are super useful and neat, I use them all the time from smolguys on my computer to Claude in the cloud. It's like saying "I hate forks" or something, like why do you hate a generic tool that has many applications? I love that I have these things for free at home forever, my personal wiki and time tracker have both gotten way more useful ever since I baked an LLM in.
- QuesnayJr 2y agoI think they're imitating Eliza.
- DonHopkins 2y agoI started loving LLMs when I cussed at them enough that they started cussing back.
- gibsonf1 2y agoYep, they completely missed the boat. They tried to use concepts without actually modeling concepts, making a huge mess of contradicting statements which actually didn't model the world. Using a word in a statement does not a concept make!
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- photonthug 2y agoI enjoyed this read and agree Lenat was a grifter, which is easy to see based on contracts and closed source. But I dislike how the article seems tilted towards a hit piece against search, heuristics, reasoning, symbolic approaches in AI, and even striving for explainable/understandable systems. It's a subtext throughout, so perhaps I'm misinterpreting it.. but the neats vs the scruffies thing is just not really productive, and there seems to be no real reason for the "either/or" mentality. To put some of this into starker contrast.. 40 years, 200 million dollars, and broken promises is the cost burned on something besides ML? Wait isn't the current approach burning that kind of cash in a weekend, and aren't we proudly backdating deep-learning to ~1960 every time someone calls it "new"? Is a huge volume of inscrutable weights, with unknown sources, generated at huge costs, really "better" than closed-source in terms of transparency? Are we not very busy building agents and critics very much like Minky's society of mind while we shake our heads and say he was wrong? This write-up also appears to me as if it were kind of punching down. A "hostile assessment" in an "obituary" is certainly easy in hindsight, especially if business is booming in your (currently very popular) neighborhood. If you didn't want to punch down, if you really want to go on record as saying logic/search are completely dead-ended and AGI won't ever touch the stuff.. it would probably look more like critiquing symbolica.ai, saying that nothing like scallop-lang / pyreason will ever find any use-cases, etc.
- anon291 2y ago> even striving for explainable/understandable systems It's been almost 6-8000 years since the advent of writing and we still cannot explain or understand human intelligence and yet we expect to be able to understand a machine that is close to or surpasses human intelligence? Isn't the premise fundamentally flawed?
- photonthug 2y agoI think I'd remain interested in more conclusive proof one way or the other, since by your logic everything that's currently unknown is unknowable. Regardless of whether the project of explainable / understandable succeeds though, everyone should agree it's a worthy goal. Unless you like the idea of stock-markets, resource planning for cities and whole societies under the control of technology that's literally indistinguishable from oracles speaking to a whispering wind. I'd prefer someone else is able to hear/understand/check their math or their arguments. Speaking of 6-8000 years since something happened, oracles and mystical crap like that should be forgotten relics of a bygone era rather than an explicit goal for the future
- lambdaone 2y agoIn spite of all of this, Cycorp is still in business, and have pivoted to healthcare automation, including apparently insurance denials. I wonder if the full Cyc knowledg base will ever end up being released to the public domain, or whether it will simply fade away into nonexistence as proprietary data?
- drob518 2y agoI hope it doesn’t die in obscurity. Surely, it’s still valuable even if it failed to deliver on its original promises.
- vintermann 2y ago> Cycorp is still in business, and have pivoted to healthcare automation, including apparently insurance denials. There's a lesson here for AI entrepreneurs: If it's hard to make systems that give actually good, actionable advice, you can always pivot to authoritatively telling people what they want to be told instead. It's probably a bigger market anyway. Gordon Way would be proud.
- gnfargbl 2y agoYour tongue is in your cheek, I think, but go and read any discussion forum populated by "big 4" consultants and you'll see a high degree of worry that they are being replaced by AI. So yes, LLMs are indeed great at "authoritatively telling people what they want to be told", and they're already being used that way!
- ted_dunning 2y agoLenat beat Musk at his own game. Musk has only been promising "full self driving next year" for 10 years. Doug Lenat managed to make similar hopeless promises for nearly a half century.
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- Rochus 2y agoA very interesting and worthwhile article (better than the comments here would suggest). However, I find it a bit of a pity that the author places so much emphasis on the assumption that the project has failed. The approach has not simply failed because the search for a solution has been going on for forty years. It took even more than forty years and costed at least as much before neural networks became really useful, and nobody would claim that the project failed because of that. And today's LLM are not really that intelligent yet. Maybe Cycorp's knowledge base will be made generally accessible at some point, so that it can be used to train LLMs. Perhaps then a greater benefit of this data will become apparent.
- smoyer 2y ago> on the assumption the project has failed. My daughter's PhD thesis was largely negative results. Even if the project had failed, we could learn from it if it wasn't so secretive. It could be much more open without being OSS!
- Rochus 2y agoIt is at least as important to know which approaches do not work, but this gets significantly less press, which is not that attractive for scientists in the age of "publish or perish".
- YeGoblynQueenne 2y ago>> My daughter's PhD thesis was largely negative results. Well, give us a link man! :)
- smoyer 2y agoYou're forcing the proud dad function: https://pubmed.ncbi.nlm.nih.gov/36995257/ https://pubmed.ncbi.nlm.nih.gov/36995257/
- YeGoblynQueenne 2y agoAbsolutely. The proud nerd dad function :D Congratulations to your daughter for her PhD. I'm guessing she has got it by now. Sonic hedgehog signalling pathway! And what a date to submit a thesis. Why is that a negative result, btw?
- hitekker 2y agoA former employee of Cyc did an insightful AMA on HN back in 2019: https://news.ycombinator.com/item?id=21783828 https://news.ycombinator.com/item?id=21783828
- pvitz 2y ago> But the longer I worked there the more I felt like the plan was basically: > 1. Manually add more and more common-sense knowledge and extend the inference engine > 2. ??? > 3. AGI! In retrospect, this reasoning doesn't seem to be so wrong.
- Cthulhu_ 2y agoI mean if I were to oversimplify and over-abstract AGI into a long list of if / elses, that's how I'd go about it. It's just that there's A Lot to consider.
- yellowapple 2y ago> A heuristic arose which (as part of a daring but ill-advised experiment EURISKO was conducting) said that all machine-synthesized heuristics were terrible and should be eliminated. Luckily, EURISKO chose this very heuristic as one of the first to eliminate, and the problem solved itself. Hilarious and brilliant.
- cratermoon 2y ago"Cyc has failed to reach intellectual maturity, and may never will." 'may never will'?
- wpietri 2y ago"Their topmost distinction was between things with souls and things without souls. And large trees were in the former category, whereas small trees were in the latter category…" This reminds me deeply of Borges: https://en.wikipedia.org/wiki/Celestial_Emporium_of_Benevolent_Knowledge https://en.wikipedia.org/wiki/Celestial_Emporium_of_Benevole... To me, that bit of Borges is a reminder that all human taxonomies are limited and provisional. But it seems to me that Cyc and its brethren are built around the notion that a universal taxonomy is important and achievable. I guess it's possible that a useful kind of cognition could happen that way, but it's patently not how people work. If I had gotten to the point where I was forced to define exactly when a tree got a soul, I hope I'd realize that I was barking up the wrong tree.
- varjag 2y agoLenat in his talks emphasised the infeasibility of building a universal ontology. From what I understand later Cyc modus operandi was a bunch of self consistent micro-theories but not a one huge theory of everything.
- wpietri 2y agoYes, but I'm not sure he fully took in the lesson. Did they really build a bunch of separate domain-specific ontologies? Or, given that it's one system, did they build a universal ontology that's just a big mess? The lesson I take from the fact that universal ontologies are untenable is that human cognition isn't driven by ontology, so the quest to make a thinking thing out of pile of symbolic logic is one that has no guarantee of succeeding. I think Cyc's whole project is roughly similar to the Frankensteinian notion that if you just put together the right parts and provide a vital spark, you'll get a living being. It might work and it might not, but either way it's not science; it's sympathetic magic with the trapping of science.
- varjag 2y agoWell that's how LLMs got going: a critical mass of context until it ignited from its own gravitational pull. And yes Cyc ontology wasn't consistent. Lenat's point was that it is impossible to have an ontology consistent. Which makes sense given how there is no consistence across human society or even every individual human.
- woodruffw 2y agoI don't have strong opinions about Cyc in either direction, but this obit feels pretty mean-spirited to me: it imputes moral failure on Lenat and others for working on symbolic AI for 40 years, without acknowledging that Lenat's basic belief ("priming the pump") is shared by ML too -- the main difference is that ML needed the pump to be primed with an overwhelming amount of compute, which grew faster than Cyc's ability to ingest facts and relations. (This isn't to imply that I think symbolic AI "works"; only that more perspective on underlying beliefs is due.)
- zozbot234 2y agoI don't think this is correct. The capability for drawing useful logical inferences from something like a Cyc knowledge base is far more compute limited than just doing ML on any given amount of data. We're talking exponential (or worse!) vs. pure linear scaling. This is the real-world, practical reason why the Cyc folks eventually found no value at all in their most general inference engine, and ended up exclusively relying on their custom-authored, more constrained inference generators instead.
- woodruffw 2y agoAgain, I'm not saying Cyc's approach is correct. I'm saying that the underlying hope that made Lenat plow through the AI winter is the same one that made ML researchers plow through it. It's just that the ML researchers reached the end of the tunnel first (for some senses of first).
- wpietri 2y agoI don't think there's anything wrong with exploring a field for decades. There are many scientists who have a mix of successes and failures. But this guy spend his whole life and many years of other people's lives trying one single thing that never really worked. You could call that being a single-minded visionary, but I don't think it's unreasonable for others to think it either kooky or a giant waste. A useful comparison to me here is all the alchemical efforts to turn lead into gold. Can modern physicists do that? Not economically, but sure. [1] If alchemists had just persisted, would they have gotten there too? No, it was a giant waste, and pretty loony to a modern eye. And I'd say both alchemists and a number of AI proponents both are so wrapped up in pursuing specific outcomes (gold, AGI) that they indulge in a lot of magical thinking. [1] https://www.scientificamerican.com/article/fact-or-fiction-lead-can-be-turned-into-gold/ https://www.scientificamerican.com/article/fact-or-fiction-l...
- NelsonMinar 2y agoOpen Mind Common Sense is a project now forgotten, a mid 2000s effort to build something like Cyc but in a more open fashion. From the MIT Media Lab, primarily Pushpinder Singh (RIP). https://en.wikipedia.org/wiki/Open_Mind_Common_Sense https://en.wikipedia.org/wiki/Open_Mind_Common_Sense
- cperkins 2y agoI always had my reservations about the whole AGI thing. And while I'm mightily impressed by Chat-GPT and friends, it's even clearer to me that AGI is not, and will never be, an emergent property of LLM, no matter how large the neural net. And that was likely true for Cyc as well. I had a particular Cyc success story relayed to me years ago by a customer (not a Cycorp employee), the details of which I cannot divulge, but it was a pretty whopping success and the customer was quite happy with what Cyc had been able to do for them. So while no AGI, it definitely seems like there was value to be had.
- swframe2 2y agoWhat do you think of the recent anthropic research on how LLMs reason? It is clear from their shallow analysis that LLMs have very serious reasoning weaknesses. I wonder if those weaknesses can be "addressed" if we build LLMs that can do deeper analysis and use RL to self-improve. LLMs improving LLMs would be a very impressive step towards AGI.
- cperkins 2y agoI think we are careless in how we use terms. We often say "intelligence" where me mean "sentience". We have studied intelligence for a long time and we have IQ tests that can measure it. The various LLMs (like Chat GPT and Gemini) are scoring pretty well on the IQ tests. So given that, I think we can conclude that they are intelligent, as we can measure it. But while we have measurements for "intelligence" we don't for "sentience", "agency", "consciousness" or these other things. And I'd argue that there are lots of intelligent life on earth (take crows as an example) that are sentient to a degree that the LLMs are not. My guess is this is because of their "agency" - their drive for survival. The LLMs we have now are clearly smarter than crows and cats but not sentient in the way those animals are. So I think it's safe to say that "sentience" (whatever that is) is not an emergent property of neural net/training data size. If it were, it'd be evident already. So Gemini/Chat GPT seem to be "intelligence", but in tool form. Very unexpected. Something I would not have believed possible 5 or 10 years ago, but there it is. As to whether we could create a "sentient" AI, an AGI, I don't see any reason we shouldn't be able to. But it's clear to me that something else is needed, besides intelligence. Maybe it's agency, maybe it's something else (the experience of times passage?). We probably need to ways of measuring and evaluating these other things before we can progress further.
- hoseyor 2y agoThis all brought up some interesting memories from my time funding Cyc.
- d_burfoot 2y agoFor me, the futility of GOFAI is obvious from simple introspection: is my own brain capable of chaining through vast networks of propositional statements? I wish! I can barely hold a 5 node decision tree in my head while playing a board game. At the same time, my brain is amazingly good at parsing visual and linguistic stimuli. Still, you have to respect the man's dedication to his quixotic quest.
- 986aignan 2y agoBut wouldn't that kind of reasoning also say that helicopters are impossible because no flying animal works that way? For a more reasoning-adjacent example, "conventional" chess AIs don't really work like brains do, either, but they're pretty good at playing chess.
- famouswaffles 2y agoYeah but helicopters fly. The problem with cyc is that it never really got off the ground. Symbolic AI wasn't a failure but it never really worked for any problem space without rigid rules you could define and that's with some of the greatest minds trying for decades to make it happen. At this point real-world intelligent symbolic AI only exists in fiction and our collective imaginations. Technically, no one has proven it can't work, but at some point you have to face the reality.
- drob518 2y agoMany years ago, I went on a quest to read as much about AM and EURISKO as I could fine. Unfortunately, Lenat kept all of his work very private (secret, even), and there was very little to read other than high level papers and articles. I always felt that was a shame, as it prevented anyone else from building on anything Lenat built. I think we’re poorer for it.
- varjag 2y agoYou can run EURISKO yourself these days tho.
- KetoManx64 2y agoToo little, too late
- mark_l_watson 2y agoThe article is a very good review of Symbolic AI, in general, not just Cyc. I have spent a lot of time with OpenCyc in the past, but haven’t touched it in ten years. I believe tif there is a productive future for symbolic AI that it will involve using LLMs to construct knowledge graphs, symbolic relatikns, etc. from unstructured data.
- trhway 2y agoWith LLM itself using those graphs to validate its answers, etc. And at some point it will be internalized into architecture as a graph attention layer.
- YeGoblynQueenne 2y ago>> The article is a very good review of Symbolic AI, in general, not just Cyc. As a "review of symbolic AI" I found it uninformed and superficial and felt that it rehashed the same old points about how symbolic AI "failed", which are disputed by the facts; specifically the fact that major symbolic AI fields like SAT solving, automated theorem proving and planning and scheduling are still going strong and have produced real-world results, so much so that e.g. SAT solving, Planning, program verification, and automated theorem proving aren't even considered "AI" anymore because they now actually work, and work very well indeed.
- mark_l_watson 2y agoTechnically, you are probably correct. I did find it a good walk ‘down memory lane.’ I have been working in the field since 1982 and the article made me nostalgic.
- at_a_remove 2y agoThis is the best overview I have ever seen, and I have had a passing interest in Cyc since I first read about EURISKO. Hands down, this is great: thorough, historical ... perhaps a little more negative than I would like. My personal "take," which is worth nothing, is that something like Cyc is necessary but not sufficient for a serious AGI. Many other components will probably be required. Yes, we are gonna need natural language parsing, and a good one, unless we can make the whole world speak in lojban. It would certainly speed up the collection of assertions. I suspect embodiment is also a requisite, wherein one can acquire experiences and test premises. The ability to explore your surroundings, to move, to investigate. To jam your sensory cluster where it does not belong. To drop a block and see it fall. To drop a balloon and see it rise -- surely that must suggest an avenue of investigation. I would be surprised if neural networks will not make a showing. Recognition through the fuzziness and quirks of reality is needed before you can sufficiently abstract into various rules. That isn't a new species of animal, it's a cat without fur and with an unusual number of limbs (Sphynx with a missing leg). Recognize, then expand the knowledge: cats usually have fur, but some breeds do not. Cats usually have four limbs, but they can be lost. But before you can say that, you still have to recognize the catness of the beast before you. Another probable component: emotion. e-motion. Motion. Drive. In a way, hunger is an emotion, as is a full bladder. These are prompts to do something that one might return to equilibrium and focus on the higher level problems. Buildup of carbon dioxide, that one surely prompts you. These are currently tied to our embodiment, but parallels can be worked out, such as Opportunity's last ... words ... "My battery is low and it's getting dark." Right now, our attempts at intelligences are still reactive. Waiting for a prompt, waiting for a problem to be put before them. I suspect a strange loop (or really a series of interconnected ones) might be one of the final components, with the emotions serving as a way to keep it from lapsing into catatonia. Boredom would be one. Most of our human emotions would be counter-productive, but probably not all, and there may be analogues. As a subcomponent of that, I believe in laughter. Babies laugh a lot. We laugh at jokes. No, we laugh at new jokes; old jokes grow progressively less and less funny. Laughter seems to be often prompted by a new connection, and it is both a reward and a communication. I have covered both textual ingestion and experiential evidence, but let's throw in another one: I expect that the ability to examine an expert system and replicate it, to incorporate it into its mind, is one of them. Think about those old flowcharts in hardware books which enable you to diagnose a computer with boot issues. We can do it and I expect an AGI ought to be able to examine other rulesets for ingestion as well. In short, I am suggesting that our first AGI may require all of the things a human does to be intelligent. We may find that we can do without one or two components. While I most certainly do not want a Human in a Box to be the end product, for a number of reasons, it does seem reasonable to me that we ought to incorporate the different things we already know about the human mind into something we are hoping to be an equivalent. However, I strongly suspect that giving it full access to something like Cyc would beat just chucking encyclopedias at it and telling it to read.
- musicale 2y ago"As of 2025, after more than 80 years of research on Artificial Neural Networks, there remains no evidence of their general intelligence. Even after priming the pump with nearly every book ever written, all available scientific articles, and all publicly available text on the internet (and consuming more power and resources than entire countries), they routinely fail at basic tasks and generate nonsensical output and 'slop' that has already degraded the internet as a source of training data for future systems. The long slow failure of ANNs is a strong indictment against the statistical/neuromorphic approach to AI."
- aredox 2y agoExactly. On top of it, Cyc's whole 40-year run expense of "200 million dollars, 2000 person-years" is... What LLM companies waste each month, for slightly less hallucinations and getting the right number of fingers most of the time?
- famouswaffles 2y agoThere's plenty evidence of the general intelligence of LLMs. And a site that has climbed to the 6th most visited in 2 years is clearly not failing to be generally useful. I do think the author was more scathing than necessary here though
- Philpax 2y agoLLMs are clearly useful, even if flawed; conversely, the author had to struggle to find uses of Cyc. Additionally, neural networks were already showing promise before the scale-ups; AlexNet was trained on consumer hardware and outperformed all other solutions to date. I see your point, and I agree that the way that paragraph was initially posed was perhaps more dismissive than it should have been... but to disprove it, you need proof that Cyc was a working approach for anything, and said proof appears to be lacking.
- aitchnyu 2y agoI first heard of it in a 1990s book saying it showed a photo of running cars when the users queried it for "people sitting".
- goodmachine 2y agoWonderful essay! OP's tireless research is appreciated.
- laurent_du 2y agoYes. Amazing read. This is the kind of content I come to hacker news for.
- DeathArrow 2y agoCan't we combine symbolic AI with neural networks into something that exceeds the capacities of both?
- DeathArrow 2y agoAre we sure that if we invested in the last ten years the same resources we invested in neural networks (research, hardware, money) the results wouldn't pay off? What I want to ask is if we know for sure symbolic AI is a dead end and it doesn't worth to pursue that field.
- marsten 2y agoGiven the secrecy surrounding Cyc we don't know how close or not they got to something AGI-like. Whatever conclusion you draw will be from very little data. From a practical standpoint Cycorp seems to have pivoted at least 10 years ago into providing knowledge and inference systems for clients. If customers are willing to pay them then perhaps we can conclude there is value in the technology. But again I'm not aware of any public examples to look at.
- zozbot234 2y ago> From a practical standpoint Cycorp seems to have pivoted at least 10 years ago into providing knowledge and inference systems for clients. If customers are willing to pay them then perhaps we can conclude there is value in the technology. This is exactly the kind of thing that suggests they were nowhere close to AGI. Unless you think Palantir has somehow developed AGI too and kept it secret from everyone else.
- awanderingmind 2y agoGreat post that hits the sweet spot between a dry academic summary and a popular exposition. Personally I hope that the current wave of AI is over hyped and misunderstood (so that e.g. https://ai-2027.com/ https://ai-2027.com/ will be a comical footnote one day) and that symbolic reasoning will make a comeback in a new form.
- kappasan 2y agoI wonder if it's possible to design a new formal language (something like lojban [1], with a grammar strictly based on formal logic) but with better UX so that it can be used by regular folks like me? Maybe combine something like Attempto [2] with a dedicated visual UI? I would think a language like that could speed up knowledge base construction. Maybe it can also serve as a substitute for natural languages in some situations where we want our communication to be logically airtight. [1] https://en.wikipedia.org/wiki/Lojban https://en.wikipedia.org/wiki/Lojban [2] https://en.wikipedia.org/wiki/Attempto_Controlled_English https://en.wikipedia.org/wiki/Attempto_Controlled_English On a separate note, I've always wondered how Cyc is pronounced. Is it "sike", or is it "see-why-see"?
- cess11 2y agoI've always imagined it was supposed to be pronounced like 'psych', and that it was a corporate bullshit, marketing spelling, of that word.
- WesolyKubeczek 2y agoI pronounce it like "tsits", because "cyc" in Polish means "tit" (the organ, not the bird), and it feels good.
- jibal 2y ago"The long slow failure of Lenat’s project is a strong indictment against the symbolic-logical approach to AI." That does not follow at all ... this statement comes just after an enumeration of Lenat's attitudinal problems that plausibly played a significant role in its failure.
- InfiniteRand 2y agoI think when we look at general intelligence we need to look at multiple methods of reasoning working together. Looking at children there is an instinct to blindly imitate those around them, trying to match behavior without fully understanding it, however the experiences from this behavior then feed into a symbolic understanding in the child’s mind, allowing them to reason about the situation later on
- YeGoblynQueenne 2y ago>> As of 2025, 9 years after the knowledge pump had been primed, there is still no sign that Cyc would ever achieve general intelligence. The long slow failure of Lenat’s project is a strong indictment against the symbolic-logical approach to AI. Really? CYC, a strong indictment against SAT solving, though unrelated to it? A strong indictment against automated theorem proving, though unrelated to it? A strong indictment against planning and scheduling, though unrelated to it? A strong indictment against program verification and model checking, though unrelated to it? A strong indictment against Knowledge Representation and Reasoning, though unrelated to it? Or are those "symbolic-logical approaches to AI"? Some of those fields use heuristic search, but then so does Neural Networks research. What's gradient optimisation? It's a search algorithm that follows the gradient of a function based on the strong assumption that the function has its optimum at the location of the searched-for datum. So, a search algorithm; with a heuristic cost function. How about probabilistic AI? Is that "symbolic-logical AI"? I mean the Bayesian calculus is a form of logic after all, and probabilities are symbolic right up to the time when they turn into statistics when you have to give them values [1]. How about neural networks again? Everyone will tell you they are "sub-symbolic" but in truth their "continuous" weights are finite-precision floating point numbers, so they're basically symbols, with a very large vocabulary. And in any case Neural Nets, like everything else, are running on a computer, a symbol manipulation device based on Boolean algebra and logic gates. Is that symbolic, logical, or neither? See what happens when you make up new terminology as you go along and then pretend like it's some kind of agreed-upon definition that everyone agrees upon? Yes I know. My first language is not English either, but the author has clearly gone and er delved into old papers about CYC so I don't see that they have any excuse to make all those unsubstantiated, and unsupported, assumptions about what "symbolic-logical AI" is. And as for CYC and Lenat: it's easy to disparage a man's work when he's dead and can't defend it. It's easier still if you're just repeating the same talking points that has circulated in the grapevine for who knows how long: blah blah CYC failed, blah blah symbolic AI failed blah. The fact of the matter is that CYC is closed source, very few people can use it, and nobody, outside of Cycorp, knows its true capabilities. A bit like "OpenAI"'s latest "reasoning" models, all we can do is speculate and pretend we know what we're talking about, when we clearly don't. I mean they. The author. _________________ [1] Which is what you have to do if you want to use probabilities in the real world. And if you feed the Mogwai after midnight they turn into Gremlins.
- fancyfredbot 2y agoWhat a fantastic article. One of the best on HN this year. I first heard about Cyc as a child in a TV documentary about AI. The example involved a man shaving with an electric razor. Cyc concluded that while shaving the man was not human since the razor was an electrical device and electrical devices were not human. It really caught my imagination as a child and made me want to study AI. The magic diminished a bit once I learned more about how Cyc worked using prolog style relations and I ended up studying CS instead of AI but I still credit Cyc with sparking that initial interest in me. Lenart seems like a strange man but we need obsessives and true believers like him to push through the various "winters". Who knows if knowledge graphs like Cyc will become relevant again in future as we seek to eliminate hallucinations from statistical learning.
- cubefox 2y ago> Cyc concluded that while shaving the man was not human since the razor was an electrical device and electrical devices were not human. I can't parse this sentence? Is there supposed to be a comma before and after "while shaving"?
- fancyfredbot 2y agoYes. Sorry. I was actually just googling this and realised this same anecdote is in cited the intro to the Deep Learning book by Goodfellow et al. Their write-up is hopefully clearer: "For example, Cyc failed to understand a story about a person named Fred shaving in the morning (Linde, 1992). Its inference engine detected an inconsistency in the story: it knew that people do not have electrical parts, but because Fred was holding an electric razor, it believed the entity “FredWhileShaving” contained electrical parts. It therefore asked whether Fred was still a person while he was shaving" https://www.deeplearningbook.org/contents/intro.html https://www.deeplearningbook.org/contents/intro.html The (Linde, 1992) citation is they give is the 4th episode of a TV series - presumably the one I saw as a kid! https://en.m.wikipedia.org/wiki/The_Machine_That_Changed_the_World_(TV_series) https://en.m.wikipedia.org/wiki/The_Machine_That_Changed_the... And of course it's on YouTube: https://youtube.com/clip/UgkxRcsHT-s1iZ-VRWFRXA-qg4kjTYe-a6j_?si=J2JTc1c1Dtv-DfiR https://youtube.com/clip/UgkxRcsHT-s1iZ-VRWFRXA-qg4kjTYe-a6j...
- xpe 2y ago“Obituary” is a poor word choice. A shame, because the article otherwise is detailed and interesting.
- akoboldfrying 2y agoA very thorough and interesting article -- probably the only thing you ever need read about Cyc. An example of where it shines is the way it carefully pinpoints Lenat contradicting himself on the question of converting English into Cyc's internal form -- Lenat claims that this could likely be done with NNs, but also that NNs are not capable of "common sense", which he himself (correctly) claims is necessary for language translation. It's not without some flaws, though. The continual harping on how secretive Cyc was doesn't seem fair, given that (with the improbable exception of Meta) none of the currently leading NN-based LLMs have revealed their "source code" (weights) either. Also, I don't agree that Lenat's single-mindedness was a bad thing (I'm glad somebody explored this unpopular concept), nor even that he was necessarily wrong in believing that building a huge, curated fact base is a path to AGI -- the graph showing the exponential increase in the number of assertions in Cyc over time, despite roughly constant headcount, points towards the possibility that a critical threshold was nearby. In fact, the history of NNs is eerily similar: at first found to be promising, then abandoned, then -- against all prevailing wisdom at the time -- found to actually perform very well if you simply scaled them far more than you "should".
- Philpax 2y agoOLMo 2 from AI2 is fully open-source, including code, data and recipe: https://allenai.org/olmo https://allenai.org/olmo There are others, but OLMo is the most recent and competitive.
- cubefox 2y agoA similar failure of GOFAI was ABBYY's (a Russian company which was for a long time market leader in OCR software) monumental (multi decade long) attempt of creating advanced translation software entirely based on complex formal grammar parsing. The story behind it is really interesting. This article was written by someone who worked at ABBYY: https://sysblok.ru/blog/gorkij-urok-abbyy-kak-lingvisty-proigrali-poslednjuju-bitvu-za-nlp/ https://sysblok.ru/blog/gorkij-urok-abbyy-kak-lingvisty-proi... The piece is in Russian but can (ironically) be read in good English by using e.g. the Google Translate feature inside Chrome. Which is of course entirely based on machine learning. The story is essentially similar to Cyc: symbolic AI/logical AI/GOFAI can produce initially impressive results (ABBYY was way better than early forms of Google Translate), but symbolic approaches doesn't scale well. Big Data + machine learning wins out eventually. The piece above mentions a 2009 piece from Google which put forward this thesis. "The Unreasonable Effectiveness of Data": https://static.googleusercontent.com/media/research.google.com/de//pubs/archive/35179.pdf https://static.googleusercontent.com/media/research.google.c... Note that 2009 was significantly before the existence of large language models, transformers, or even AlexNet.
- Beijinger 2y agoThis is more interesting, and if I remember correctly, they had some interesting ideas: Two AI Pioneers. Two Bizarre Suicides. What Really Happened? https://www.wired.com/2008/01/ff-aimystery/ https://www.wired.com/2008/01/ff-aimystery/
- kmeisthax 2y agoAs someone who is mildly skeptical of the current wave of LLM hype and thinks it's hitting a wall, I'm noting a lot of parallels between Cyc and ML/DNN systems' founding stories. We even have tales of reward hacking and other weird meta-shenaniganery. One wonders if some renegade at Cycorp ever trained a CycL to English LLM to fake another GOFAI demo with "real" AI. As for free lunches, the one free lunch the neural network people got was training data. i.e. being able to scrape the entire WWW and build on that[0]. But that's also a free lunch Cycorp wanted, too! Look at all the various attempts at half-open subsets of Cyc. They were half-hearted attempts to solicit training data from randos, sure, but Cycorp still begged for the free lunch anyway. But I think the big indictment of AI - both old-fashioned and neural network driven - is that they're chasing this nigh-impossible dream of effectively replicating and replacing a person. I still see the same pattern, in both eras, of toy models working well, narrow problem solvers being effective, but then people trying to scale them up to ludicrous sizes by pumping them full of knowledge in an attempt to achieve generality. Even modern LLMs are only slightly more generalist in the sense that they don't immediately fall over and die when a question is phrased in a way that wasn't expected. The "bitter lesson" of AI is that the only two things that work are search and learning. LLMs and Cyc both attempted to short-circuit this with massive data dumps - the proverbial "knowledge pump" that needed to be primed. Compression is a good learning substitute, so sure, you can "cheat the test" with big data. But, I mean... how many discoveries were made solely by someone mainlining Wikipedia articles like black tar heroin? At some point, you need a problem space to search, and your learning needs to be done online[1] based on the results of that search. Nobody has cracked that code yet. Maybe the reason why general AI never took off is that there is no such thing as general knowledge. The biggest source of knowledge the AI community has provided to us is knowledge about how to make more AI. [0] Whether or not this was legal is a different question. [1] Specifically, while LLMs think analogously to humans (because that's an easily compressible representation) they do not learn analogously to them.
- jjaksic 2y agoOne thing that LLMs seem to be missing, even after ingesting the whole web, is the super-basic knowledge that every human learns before the age of 3. Most of that stuff in not written anywhere, because it's totally obvious to anyone who can read. A knowledge base like Cyc can be useful to fill these gaps, because it was made for an AI with zero base knowledge and explicitly specifies even the most obvious concepts.
- cAtte_ 2y agolike what, for example?
- Hj8Rd2Qw 2y ago[dead]
- blacklion 2y agoGreat material! I always been mesmerized by Cyc (and EURISKO). I've born in late USSR, and my father (then "junior staff scientist" in natural language processing) has several translated books (borrowed from Institute's lab, as I understand mow) which were (again, as I understand now, many years alter) abbreviated translations of proceedings of some "West" conferences about AI. As I read everything about computers, I read these books too, and was deeply impressed by description of EURISKO (it worth noting, that fleet-simulation game was not mentioned, but MOS structure was). I was... 10? 12? years old. I still think, that this approach much better than modern "multiple huge matrix by huge vector. I understand, that DNNs and LLMs won practically. But I still think that these blackbox pure-feedback based approaches are not so elegant as knowledge engineering... Practical. But in some sense "dirty". Like "dirty hacks" in code. BTW, part about ABBYY is not exactly correct. I know this because my father became one of the founders and life-long CTO of PROMT, company which is (was? It is not clear after beginning of war, unfortunately) author of successful pre-LLM grammar-based translation systems (Stylus, later PROMT) and partner of ABBYY. ABBYY translation project stated much later than 1990s, because in 1990s ABBYY distributed PROMT technology, and ABBYY itself was (is) experts in OCR and dictionaries (dictionaries is not translation system, of course). Translation project in ABBYY started much later, when their ways with PROMT parted due to several business-related reasons.
- defvar 2y agoThe game mentioned in this article reminds me of the story about heuristics told by Richard Feynman in a lecture: https://youtu.be/EKWGGDXe5MA?si=z9TdlWflOkY6b9Qk&t=4249 https://youtu.be/EKWGGDXe5MA?si=z9TdlWflOkY6b9Qk&t=4249 I was wondering then who was the guy he talked about.It is great that this article provides so much details of it.