Doug was at times blunt, but he was fundamentally a kind and generous person, and he had a dedication to his vision and to the people who worked alongside him that has to be admired. He will be missed.
I worked at Cycorp (not directly with Doug very often, but it wasn't a big office) between 2016 and 2020
An anecdote: during our weekly all-hands lunch in the big conference room, he mentioned he was getting a new car (his old one was pretty old, but well-kept) and he asked if anybody could use the old car. One of the staff raised his hand sheepishly and said his daughter was about to start driving. Doug gifted him the car on the spot, without a second thought.
He also loved board games, and was in a D&D group with some others at the company. I was told he only ever played lawful good characters, he didn't know how to do otherwise :)
It was a part of his personality, as it is for many people who are intelligent and opinionated, and some can mistake that for unkindness. But I wanted to emphasize that in his case it wasn't.
I interviewed with Doug Lenat was I was a 17 year old high school student, and he hired me as a summer intern for Cycorp - my first actual programming job.
That internship was life-changing for me, and I'll always be grateful to him for taking a wild bet on a literally a kid.
Doug was a brilliant computer scientist, and a pioneer of artificial intelligence. Though I was very junior at Cycorp, it was a small company so I sat in many meetings with him. It was obvious that he understood every detail of how the technology worked, and was extremely smart.
Cycorp was 30 years ahead of its time and never actually worked. For those who don't know, it was essentially the first OpenAI - the first large-scale commercial effort to create general artificial intelligence.
I learned a lot from Doug about how to be incredibly ambitious, and how to not give up. Doug worked on Cycorp for multiple decades. It never really took off, but he managed to keep funding it and keep hiring great people so he could keep plugging away at the problem. I know very few people who have stuck with an idea for so long.
That sounds awesome! Was coming back to Cycorp to permanently work ever in the works for you? Or did you think the intern was nice but you didn't want a career in the field?
Also - what exactly did you do in the internship as a 17 year old - what skills did you have?
I was certainly interested in working at Cycorp full-time. But after two summers there, I could tell that the technical approach they were taking was just not working.
My first summer, I was an ontologist, which was a unique role that only existed at Cycorp where they hired people to literally hand-enter facts like "A cat has four legs" into Cyc using formal logic. My second summer I programmed (poorly) in Lisp for them.
I worked with Doug on Cyc from ~85-89 (we had overlapped at PARC but didn’t interact much there). The first thing undid was scrap the old implementation and start from scratch, designing the levels system and all the bootstrap code.
It was a fun time with a small core team (mainly me, guha, and Doug) but over time I became dissatisfied with some of the arbitrariness of the KB. By the time I left the Cyc project (for my own reasons unrelated to work) I was somewhat negative towards the foundations of the project, despite the tight relationship we’d had and the fact it ran on my code! But over time I became smarter and came to appreciate once again its value. I had too much of a “pure math” view of things back then.
As I moved on to other things I lost touch with Doug and Mary, and I’m sorry for that.
Doug Lenat, RIP.
I worked at Cycorp in Austin from 2000-2006. Taken from us way too soon, Doug none the less had the opportunity to help our country advance military and intelligence community computer science research.
One day, the rapid advancement of AI via LLMs will slow down and attention will again return to logical reasoning and knowledge representation as championed by the Cyc Project, Cycorp, its cyclists and Dr. Doug Lenat.
Why? If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.
Exactly. When I hear books such as Paradigms of AI Programming are outdated because of LLMs, I disagree. They are more current than ever, thanks to LLMs!
Neural and symbolic AI will eventually merge. Symbolic models bring much needed efficiency and robustness via regularization.
If you want to learn about symbolic AI, there are a lot of more recent sources than PAIP (you could try the first half of AI: A Modern Approach by Russel and Norvig), and this has been true for a while.
If you read PAIP today, the most likely reason is that you want a master class in Lisp programming and/or want to learn a lot of tricks for getting good performance out of complex programs (which used to be part of AI and is in many ways being outsourced to hardware today).
None of this is to say you shouldn't read PAIP. You absolutely should. It's awesome. But its role is different now.
It would be cool if we could find the algorithmic neurological basis for this, the analogy with LLMs being more obvious multi-layer brain circuits, the neurological analogy with symbolic reasoning must exist too.
My hunch is it emerges naturally out of the hierarchical generalization capabilities of multiple layer circuits. But then you need something to coordinate the acquired labels: a tweak on attention perhaps?
Another characteristic is probably some (limited) form of recursion, so the generalized labels emitted at the small end can be fed back in as tokens to be further processed at the big end.
It seems the direction of flow would be the opposite: LLMs are a great source of logical data for Cyc-like things. Distill your LLM into logical statements, then run your Cyc algorithms on it.
> The best thing Cycorp could do now is open source its accumulated database of logical relations...
This is unpersuasive without laying out your assumptions and reasoning.
Counter points:
(a) It would be unethical for such a knowledge base to be put out in the open without considerable guardrails and appropriate licensing. The details matter.
(b) Cycorp gets some funding from the U.S. Government; this changes both the set of options available and the calculus of weighing them.
(c) Not all nations have equivalent values. Unless one is a moral relativist, these differences should not be deemed equivalent nor irrelevant. As such, despite the flaws of U.S. values and some horrific decision-making throughout history, there are known worse actors and states. Such parties would make worse use of an extensive human-curated knowledge base.
OpenCyc is already a thing and there's been very little interest in it. These days we also have general-purpose semantic KB's like Wikidata, that are available for free and go way beyond what Cyc or OpenCyc was trying to do.
> If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.
This is the definition of a strawman. Who is claiming that NN inference is always the fastest way to run computation?
Instead of trying to bring down another technology (neural networks), how about you focus on making symbolic methods usable to solve real-world problems; e.g. how can I build a robust email spam detection system with symbolic methods?
> Instead of trying to bring down another technology (neural networks), how about you focus on making symbolic methods usable to solve real-world problems; e.g. how can I build a robust email spam detection system with symbolic methods?
I have two concerns. First, just after pointing out a logical fallacy from someone else, you added a fallacy: the either-or fallacy. (One can criticize a technology and do other things too.)
Second, you selected an example that illustrates a known and predictable weakness of symbolic systems. Still, there are plenty of real-world problems that symbolic systems address well. So your comment cherry-picks.
It appears as if you are trying to land a counter punch here. I'm weary of this kind of conversational pattern. Many of us know that tends to escalate. I don't want HN to go that direction. We all have varying experience and points of view to contribute. Let's try to be charitable, clear, and logical.
>> If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.
> This is the definition of a strawman.
(Actually, it is an example of a strawman.) Anyhow, rather than a strawman, I'd rather us get right into the fundamentals.
1. Feed-forward NN computation ('inference', which is an unfortunate word choice IMO) can provably provide universal function approximation under known conditions. And it can do so efficiently as well, with a lot of recent research getting into both the how and why. One "pays the cost" up-front with training in order to get fast prediction-time performance. The tradeoff is often worth it.
2. Function approximation is not as powerful as Turing completeness. FF NNs are not Turing complete.
3. Deductive chaining is a well-studied, well understood area of algorithms.
4. But... modeling of computational architectures (including processors, caches, busses, and RAM) with sufficient detail to optimize compilation is a hard problem. I wouldn't be surprised if this stretches these algorithms to the limit in terms of what developers will tolerate in terms of compile times. This is a strong incentive, so I'd expect there is at least some research that pushes outside the usual contours here.
The point is that symbolic computation as performed by Cycorp was held back by the need to train the Knowledge Base by hand in a supervised manner. NNs and LLMs in particular became ascendant when unsupervised training was employed at scale.
Perhaps LLMs can automate in large part the manual operations of building a future symbolic knowledge base organized by a universal upper ontology. Considering the amazing emergent features of sufficiently-large LLMs, what could emerge from a sufficiently large, reflective symbolic knowledge base?
Thanks for the link. I watched the first part, and an interesting story/claim is that before Cyc started, many "smart people" including Marvin Minsky came up with "~1 million" as the number of things you would have to encode in a system for it to have "common sense".
He said they learned after ~5 years that this was an order of magnitude off -- it's more like 10 M things.
Is there any literature about this? Did they publish?
To me, the obvious questions are -
- how do they know it's not 100M things?
- how do they know it's even bounded? Why isn't there a combinatorial explosion?
I mean I guess they were evaluating the system all along. You don't go for 38 years without having some clear metrics. But I am having some problems with the logic -- I'd be interested in links to references / criticism.
I'd be interested in any arguments for and against ~10 M. Naively speaking, the argument seems a bit flawed to me.
FWIW I heard of Cyc back in the 90's, but I had no idea it was still alive. It is impressive that he kept it alive for so long.
Though I'm still interested in the ~1M or ~10M claim. It seems like a strong claim to hold onto for decades, unless they had really strong metrics backing it up.
> how do they know it's even bounded? Why isn't there a combinatorial explosion?
I don't know - I'm in middle of watching the interview too, but he's moved on from that topic already. I'd guess the 10M vs 1M (or 100M) estimate comes from the curve of total "assertions" vs time leveling off towards some asymptotic limit.
I suppose the reason there's no combinatorial explosion is because they're entering these assertions in most general form possible, so considering new objects doesn't necessarily mean new assertions since it may all be covered by the superclasses the objects are part of (e.g. few assertions that are specific to apples since most will apply to all fruit).
Hey you guys, please don't go offtopic like this. Whimsical offtopicness can be ok, but offtopicness in the intersection of:
(1) generic (e.g. swerves the thread toward larger/general topic rather than something more specific);
(2) flamey (e.g. provocative on a divisive issue); and
(3) predictable (e.g. has been hashed so many times already that comments will likely fall in a few already-tiresome hash buckets)
- is the bad kind of offtopicness: the kind that brings little new information and eventually lots of nastiness. We're trying for the opposite here—lots of information and little nastiness.
Only about two of them will be more contemporary though, and both are academic talks, not interviews. I get that you don't like Lex Fridman, which is a perfectly fine position to hold. But there is something to be said for seeing two people just sit and talk, as opposed to seeing somebody monologue for an hour. The Fridman interview with Doug is, IMO, absolutely worth watching. And so are all of the other videos by / about Doug. shrug
Makes sense to me. He basically made a system that detects when someone else is using your computer by e.g. comparing patterns of mouse and keyboard input to your typical usage. It would be useful in a situation such as if you left your screen unlocked and a coworker sat down at your desk to prank you by sending an email from you to your boss (or worse, obviously). The computer would lock itself as soon as it suspects someone else is using it instead of you.
It's fun reading through the paper he links just because I've always been enamored by taking a lot of those principles that they believe should be internal to a computer, and instead making them external to a community.
In other words, I think it would be so highly useful to have a browseable corpus of arguments and conclusions, where people could collaborate on them and perhaps disagree with portions of the argument graph, adding to it and enriching it over time, so other people could read and perhaps adopt the same reasoning.
I play around with ideas with this site I occasionally work on, http://concludia.org/ - really more an excuse at this point to mess around with the concept and also get better at Akka (Pekko) programming. At some point I'll add user accounts and editable arguments and make it a real website.
So basically a multi-person Zettelkasten? The idea with a Zettelkasten (zk for short) is that each note is a singular idea, concept, or argument that is all linked together. Arguments can link to their evidence, concepts can link to other related concepts, and so on.
Sort of except that it also tracks truth propagation - one person disagreeing would inform others that portion of the graph is contested. So the graph has behavior. And, the links have logical meaning, beyond just "is related to" - it respects boolean logic.
I don't think this is the goal of your project, so let me ask this way.
Is there any similiar project, where we provide truths and fallacies, combine them with logical arguments and have a language model generate sets of probable conclusions?
I've had the same idea (er, came to the same conclusion) but never acted on it. Awesome to see that someone has! Great name too.
I thought of it while daydreaming about how to converge public opinion in a nation with major political polarization. It'd be a sort of structured public debate forum and people could better see exactly where in the hierarchy they disagreed and, perhaps more importantly, how much they in fact agreed upon.
I have always thought of Cyc as being the AI equivalent of Russell and Whitehead's Principia--something that is technically ambitious and interesting in its own right, but ultimately just the wrong approach that will never really work well on a standalone basis, no matter how long you work on it or keep adding more and more rules. That being said, I do think it could prove to be useful for testing and teaching neural net models.
In any case, at the time Lenat starting working on Cyc, we didn't really have the compute required to do NN models at the level where they start exhibiting what most would call "common sense reasoning," so it makes total sense why he started out on that path. RIP.
1. that NN models (LLMs) exhibit common sense reasoning today
2. that the approach to AI represented by Cyc and the one represented by LLMs are mutually exclusive
I don’t know about [1]. I asked an example from the paper above to GPT-4:
“[If you had to guess] how many thumbs did Lincoln’s maternal grandmother have?”
Response:
There is no widely available historical information to suggest that Abraham Lincoln's maternal grandmother had an unusual number of thumbs. It would be reasonable to guess that she had the typical two thumbs, one on each hand, unless stated otherwise.
The end of the article [1] reminds me to publish more of what I make and think. I'm no Doug Lenat and my content would probably just add noise to the internet but still, don't let your ideas die with you or become controlled by some board of stakeholders. I'm also no open-source zealot but open-source is a nice way to let others continue what you started.
[1]
"Over the last year, Doug and I tried to write a long, complex paper that we never got to finish. Cyc was both awesome in its scope, and unwieldy in its implementation. The biggest problem with Cyc from an academic perspective is that it’s proprietary.
To help more people understand it, I tried to bring out of him what lessons he learned from Cyc, for a future generation of researchers to use. Why did it work as well as it did when it did, why did fail when it did, what was hard to implement, and what did he wish that he had done differently? ...
...One of his last emails to me, about six weeks ago, was an entreaty to get the paper out ASAP; on July 31, after a nerve-wracking false-start, it came out, on arXiv, Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc (https://arxiv.org/ftp/arxiv/papers/2308/2308.04445.pdf).
The brief article is simultaneously a review of what Cyc tried to do, an encapsulation of what we should expect from genuine artificial intelligence, and a call for reconciliation between the deep symbolic tradition that he worked in with modern Large Language Models."
> my content would probably just add noise to the internet
Maybe, but there is worse noise out there for sure. :) Anyhow, some unsolicited advice from me: don't replay this quote to yourself any more than necessary; it isn't exactly a motivational mantra masterpiece. Share what you think is important.
Why? Even small, "improbable" improvements to knowledge can to matter. Given enough of them, statistically speaking, we can move the needle. Yeah, and we need to be able to find the relevant stuff; a big problem in of itself.
Never met the guy but his work was one of my biggest inspirations in computing.
I feel it's appropriate to link a blog post of mine from 2018. It's a quick recap of Lenat works on the trajectory that brought him towards Cyc, with links to the papers.
Cyc ("Syke") is one of those projects I've long found vaguely fascinating though I've never had the time / spoons to look into it significantly. It's an AI project based on a comprehensive ontology and knowledgebase.
I worked with Cyc. It was an impressive attempt to do the thing that it does, but it didn't work out. It was the last great attempt to do AI in the "neat" fashion, and its failure helped bring about the current, wildly successful "scruffy" approaches to AI.
It's failure is no shade against Doug. Somebody had to try it, and I'm glad it was one of the brightest guys around. I think he clung on to it long after it was clear that it wasn't going to work out, but breakthroughs do happen. (The current round of machine learning itself is a revival of a technique that had been abandoned, but people who stuck with it anyway discovered the tricks that made it go.)
"Neat" vs. "scruffy" syncs well with my general take on Cyc. Thanks for that.
I do suspect that well-curated and hand-tuned corpora, including possibly Cyc's, are of significant use to LLM AI. And will likely be more so as the feedback / autophagy problem exacerbates.
I'm so looking forward to the next swing of the pendulum back to "neat", incorporating all the progress that has been made on "scruffy" during this current turn of the wheel.
As Roger Schank defined the terms in the 70's, "Neat" refers to using a single formal paradigm, logic, math, neural networks, and LLMs, like physics. "Scruffy" refers to combining many different algorithms and approaches, symbolic manipulation, hand coded logic, knowledge engineering, and CYC, like biology.
I believe both approaches are useful and can be combined and layered and fed back into each other, to reinforce and transcend complement each others advantages and limitations.
Kind of like how Hailey and Justin Bieber make the perfect couple: ;)
"We should take our cue from biology rather than physics..." -Marvin Minsky
>To get around these limitations, we must develop systems that combine the expressiveness and procedural versatility of symbolic systems with the fuzziness and adaptiveness of connectionist representations. Why has there been so little work on synthesizing these techniques? I suspect that it is because both of these AI communities suffer from a common cultural-philosophical disposition: They would like to explain intelligence in the image of what was successful in physics—by minimizing the amount and variety of its assumptions. But this seems to be a wrong ideal. We should take our cue from biology rather than physics because what we call thinking does not directly emerge from a few fundamental principles of wave-function symmetry and exclusion rules. Mental activities are not the sort of unitary or elementary phenomenon that can be described by a few mathematical operations on logical axioms. Instead, the functions performed by the brain are the products of the work of thousands of different, specialized subsystems, the intricate product of hundreds of millions of years of biological evolution. We cannot hope to understand such an organization by emulating the techniques of those particle physicists who search for the simplest possible unifying conceptions. Constructing a mind is simply a different kind of problem—how to synthesize organizational systems that can support a large enough diversity of different schemes yet enable them to work together to exploit one another’s abilities.
>In the history of artificial intelligence, neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s.[1][2][3]
>"Neats" use algorithms based on a single formal paradigms, such as logic, mathematical optimization or neural networks. Neats verify their programs are correct with theorems and mathematical rigor. Neat researchers and analysts tend to express the hope that this single formal paradigm can be extended and improved to achieve general intelligence and superintelligence.
>"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffies rely on incremental testing to verify their programs and scruffy programming requires large amounts of hand coding or knowledge engineering. Scruffies have argued that general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no magic bullet that will allow programs to develop general intelligence autonomously.
>John Brockman compares the neat approach to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena.[a]
[...]
>Modern AI as both neat and scruffy
>New statistical and mathematical approaches to AI were developed in the 1990s, using highly developed formalisms such as mathematical optimization and neural networks. Pamela McCorduck wrote that "As I write, AI enjoys a Neat hegemony, people who believe that machine intelligence, at least, is best expressed in logical, even mathematical terms."[6] This general trend towards more formal methods in AI was described as "the victory of the neats" by Peter Norvig and Stuart Russell in 2003.[18]
>However, by 2021, Russell and Norvig had changed their minds.[19] Deep learning networks and machine learning in general require extensive fine tuning -- they must be iteratively tested until they begin to show the desired behavior. This is a scruffy methodology.
As far as I can tell it was more of an aspiration than a product. I worked with a consulting firm that tried to get into AI a few years back and chose Cyc as the platform they wanted to sell to (mostly financial) clients. I don't think a single project ever even started nor was there a clear picture of what could be sold. I hate to think Lenat was a fraud because he certainly seemed like a sincere and brilliant person, but I think Cyc was massively oversold despite never doing much of anything useful. The website is full technical language and not a single case study after 40 years in business.
Weird, I interviewed with him summer 2021 hoping to be able to land an ontologist job at Cycorp. It went spectacularly badly because it turned out I really needed to brush up more on my formal logic skills, but I was surprised to even get an interview, let alone with the man himself. He still encouraged me to work on reviewing logic and to apply again in the future but I stopped seeing listings at Cycorp for ontologists and started putting off returning to that aspiration thinking Cycorp has been around long enough that there was no rush. Memento mori
I've often thought that Cyc had an enormous value as some kind of component for AI, a "baseline truth" about the universe (to the degree that we understand it and have "explained" our understanding to Cyc in terms of its frames). AM (no relation to any need for screaming) was a taste of the AI dream.
>I've often thought that Cyc had an enormous value as some kind of component for AI
Same. I wonder if training an LLM on the database would make it more "grounded"?
We'll probably never know as Cycorp will keep the data locked away in their vaults forever. For what purpose? Probably even they don't know.
Sad to hear of his passing, I remember building my uni project around OpenCyc in my one “Intelligent Systems” class many many years ago. It was a dismal failure as my ambition far exceeded my skills, but it was so enjoyable reading about Cyc and the dedicated work Douglas had put in over such a long time.
Even being a controversial figure, he was one of my heroes. Getting excited about Eurisko in the '80s and '90s was a big driver for me at the time! Rest in piece, dear computer pioneer!
He was a hero of knowledge representation and ontology. A bit odd that we learn about his sad passing from a Wikipedia article, while at the time of this comment there is still no mention on e.g. https://cyc.com/.
Maybe it's a bit on the nose but I had his article summarized by Anthropic's Claude 2 100k model (LLMs are good at summarization) for those who don't have time to read the whole thing:
The article discusses generative AI models like ChatGPT and contrasts them with knowledge-based AI systems like Cyc.
Generative models can produce very fluent text, but they lack true reasoning abilities and can make up plausible-sounding but false information. This makes them untrustworthy.
In contrast, Cyc represents knowledge explicitly and can logically reason over it. This makes it more reliable, though it struggles with natural language and speed.
The article proposes 16 capabilities an ideal AI system should have, including explanation, reasoning, knowledge, ethics, and language skills. Cyc and generative models each have strengths and weaknesses on these dimensions.
The authors suggest combining symbolic systems like Cyc with generative models to get the best of both approaches. Ways to synergize them include:
Using Cyc to filter out false information from generative models.
Using Cyc's knowledge to train generative models to be more correct.
Using generative models to suggest knowledge to add to Cyc's knowledge base.
Using Cyc's reasoning to expand what generative models can say.
Using Cyc to explain the reasoning behind generative model outputs.
Overall, the article argues combining reasoning-focused systems like Cyc with data-driven generative models could produce more robust and trustworthy AI. Each approach can shore up weaknesses of the other.
Perhaps some here aren't familiar with the existence of a (relatively useless in my opinion) POV that pits symbolic systems against statistical methods. But it isn't a zero-sum game. Informed, insightful comparisons are useful, but "holy wars" are not. See also [1] for broad commentary and [2] for a particular application.
Ahh, another one of the old guard has moved on. Here are two excerpts from the book AI: The Tumultuous History Of The Search For Artificial Intelligence (a fantastic read of the early days of AI) to remember him by;
"Lenat found out about computers in a a manner typical of his entrepreneurial spirit. As a high school student in Philadelphia, working for $1.00 an hour to clean the cages of experimental animals, he discovered that another student was earning $1.50 to program the institution's minicomputer. Finding this occupation more to his liking, he taught himself programming over a weekend and squeezed his competitor out of the job by offering to work for fifty cents an hour less.31 A few years later, Lenat was programming Automated Mathematician (AM, for short) as a doctoral thesis project at the Stanford AI Laboratory." p. 178
"Ever the achiever, Lenat was looking for a more dramatic way to prove teh capabilities of his creation. The identified the occasion space-war game called Traveler TCS, then quite popular with the public Lenat wanted to reach. The idea was for each player to design a fleet of space battleships according to a thick, hundred-page set of rules. Within a budget limit of one trillion galactic credits, one could adjust such parameters as the size, speed, armor thickness, autonomy and armament of each ship: about fifty adjustments per ship were needed. Since the fleet size could reach a hundred ships, the game thus offered ample room for ingenuity in spite of the anticlimactic character of the battles. These were fought by throwing dice following complex tables based on probability of survival of each ship according to its design. The winner of the yearly national championship was commissioned inter galactic admiral and received title to a planet of his or her choice ouside the solar system.
Several months before the 1981 competition, Lenat fed into EURISKO 146 Traveler concepts, ranging from the nature of games in general to the technicalities of meson guns. He then instructed the program to develop heuristics for making winning war-fleet designs. The now familiar routine of nightly computer runs turned into a merciless Darwinian contest: Lenat and EURISKO together designed fleets that battled each other. Designs were evaluated by how well they won battles, and heuristics by how well they designed fleets. This rating method required several battles per design, and several designs per heuristic, which amounted to a lot of battles: ten thousand in all, fought over two thousand hours of computer time.
To participants in the national championship of San Mateo,California, the resulting fleet of ninety-six small, heavily armored ships looked ludicrous. Accepted wisdom dictated fleets of about twenty behemoth ships, and many couldn't help laughing. When engagements started, they found out that the weird armada held more than met the eye. One interesting ace up Lenat's sleeve was a small ship so fast as to be almost unstoppable, which guaranteed at least a draw. EURISKO had conceived of it through the "look for extreme cases" heuristic (which had mutated, incidentally, into mutated, incidentally, into "look for almost extreme cases")." p. 182
If you're a young person working in AI, by which I mean you're less than 30, and if you have not already done so, you should read about AI history in three decade 60s - 90s.
I may be getting this wrong, but I think I remember hearing that his auto-generated fleets won Traveller so entirely, several years in a row, that they had to shut down the entire competition because it had been broken
I think you mean "EURISKO won the Traveller championship so entirely..."
In which case, yes, something like that did happen. Per the Wikipedia page:
Lenat and Eurisko gained notoriety by submitting the winning fleet (a large number of stationary, lightly-armored ships with many small weapons)[3] to the United States Traveller TCS national championship in 1981, forcing extensive changes to the game's rules. However, Eurisko won again in 1982 when the program discovered that the rules permitted the program to destroy its own ships, permitting it to continue to use much the same strategy.[3] Tournament officials announced that if Eurisko won another championship the competition would be abolished; Lenat retired Eurisko from the game.[4] The Traveller TCS wins brought Lenat to the attention of DARPA,[5] which has funded much of his subsequent work.
Doug was one of my childhood heroes, thanks to a certain book telling the story of his work on AM and Eurisko. My great regret is that I never got the chance to meet him or contribute to his work in any way. RIP Doug, you are a legend.
Worked with their ontologists for a couple of years. Someone once told me that they employed more philosophers per capita than any other software company. A dubious distinction, maybe. But it describes the culture of inquisitiveness there pretty well too
Oh, so sorry to hear that. Good summary of his work- the Cyc project- on the twitter thread. Had missed that last paper- with Gary Marcus- on Cyc and LLM.
Why do people have to have 'commercial value' to get black bars? Why do people have to pass the ideological police? Why isn't serving as a visible advocate of a certain logical model enough?
I think my bias comes from having started my career in AI on the inference side and having (perhaps not so much long term :) seen Cyc as a shining city on a hill. Lenat certainly established that logical model even if we've since gone onto other things.
While I respect Doug's intelligence, he showed a kind of perverse persistence in a failed idea, and I think it's telling that aspiring AI czar Gary Marcus admires the ruins of Cyc while neglecting to acknowledge that it represents a dead end in AI. Like science, the field of AI advances one funeral at a time. Doug pursued a pipe dream, and convinced others to do the same, despite the brittle and static nature of the AI he sought to build. Cyc was not a precursor to OpenAI, contrary to other comments in this thread. That would be like calling the zeppelin the precursor of the jet. It represents a different school of technology, and a much less effective one.
It’s not going to be popular to highlight the less than hoped for successes of Lenat’s greatest project.
But I think that is one of the things he should be admired for. How can anyone know how an ambitious approach will pan out without the great risk of going all in?
Anyone willing to risk a Don Quixote aspect to their career, in pursuit of a breakthrough, is someone who cares deeply about something beyond themselves.
And recognizing the limits of Lenat’s impact today doesn’t preclude both the direct and indirect impact on future progress.
Wait a few more years and you will see that the effort to formalize this knowledge was not in vain. We will certainly see systems which make use of this data. Cycorp is a different company with a different approach than OpenAI; but the data produced by Cycorp will likely be useful for companies like OpenAI on their way to truly intelligent systems.
Doug was at times blunt, but he was fundamentally a kind and generous person, and he had a dedication to his vision and to the people who worked alongside him that has to be admired. He will be missed.
I worked at Cycorp (not directly with Doug very often, but it wasn't a big office) between 2016 and 2020
An anecdote: during our weekly all-hands lunch in the big conference room, he mentioned he was getting a new car (his old one was pretty old, but well-kept) and he asked if anybody could use the old car. One of the staff raised his hand sheepishly and said his daughter was about to start driving. Doug gifted him the car on the spot, without a second thought.
He also loved board games, and was in a D&D group with some others at the company. I was told he only ever played lawful good characters, he didn't know how to do otherwise :)
Happy to answer what questions I can
I would expect lawful good because it would be the most logical.
That's a very neutral thing to say.
Why is that?
I don’t know much about him. What makes you start by saying he’s blunt?
It was a part of his personality, as it is for many people who are intelligent and opinionated, and some can mistake that for unkindness. But I wanted to emphasize that in his case it wasn't.
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May i ask, if you believe CYC cannot achieve intelligence / commonsense, what are the main reasons ? Or what are the reasons you believe it could ?
I interviewed with Doug Lenat was I was a 17 year old high school student, and he hired me as a summer intern for Cycorp - my first actual programming job.
That internship was life-changing for me, and I'll always be grateful to him for taking a wild bet on a literally a kid.
Doug was a brilliant computer scientist, and a pioneer of artificial intelligence. Though I was very junior at Cycorp, it was a small company so I sat in many meetings with him. It was obvious that he understood every detail of how the technology worked, and was extremely smart.
Cycorp was 30 years ahead of its time and never actually worked. For those who don't know, it was essentially the first OpenAI - the first large-scale commercial effort to create general artificial intelligence.
I learned a lot from Doug about how to be incredibly ambitious, and how to not give up. Doug worked on Cycorp for multiple decades. It never really took off, but he managed to keep funding it and keep hiring great people so he could keep plugging away at the problem. I know very few people who have stuck with an idea for so long.
That sounds awesome! Was coming back to Cycorp to permanently work ever in the works for you? Or did you think the intern was nice but you didn't want a career in the field?
Also - what exactly did you do in the internship as a 17 year old - what skills did you have?
I was certainly interested in working at Cycorp full-time. But after two summers there, I could tell that the technical approach they were taking was just not working.
My first summer, I was an ontologist, which was a unique role that only existed at Cycorp where they hired people to literally hand-enter facts like "A cat has four legs" into Cyc using formal logic. My second summer I programmed (poorly) in Lisp for them.
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I worked with Doug on Cyc from ~85-89 (we had overlapped at PARC but didn’t interact much there). The first thing undid was scrap the old implementation and start from scratch, designing the levels system and all the bootstrap code.
It was a fun time with a small core team (mainly me, guha, and Doug) but over time I became dissatisfied with some of the arbitrariness of the KB. By the time I left the Cyc project (for my own reasons unrelated to work) I was somewhat negative towards the foundations of the project, despite the tight relationship we’d had and the fact it ran on my code! But over time I became smarter and came to appreciate once again its value. I had too much of a “pure math” view of things back then.
As I moved on to other things I lost touch with Doug and Mary, and I’m sorry for that.
Doug Lenat, RIP. I worked at Cycorp in Austin from 2000-2006. Taken from us way too soon, Doug none the less had the opportunity to help our country advance military and intelligence community computer science research.
One day, the rapid advancement of AI via LLMs will slow down and attention will again return to logical reasoning and knowledge representation as championed by the Cyc Project, Cycorp, its cyclists and Dr. Doug Lenat.
Why? If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.
Exactly. When I hear books such as Paradigms of AI Programming are outdated because of LLMs, I disagree. They are more current than ever, thanks to LLMs!
Neural and symbolic AI will eventually merge. Symbolic models bring much needed efficiency and robustness via regularization.
If you want to learn about symbolic AI, there are a lot of more recent sources than PAIP (you could try the first half of AI: A Modern Approach by Russel and Norvig), and this has been true for a while.
If you read PAIP today, the most likely reason is that you want a master class in Lisp programming and/or want to learn a lot of tricks for getting good performance out of complex programs (which used to be part of AI and is in many ways being outsourced to hardware today).
None of this is to say you shouldn't read PAIP. You absolutely should. It's awesome. But its role is different now.
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It would be cool if we could find the algorithmic neurological basis for this, the analogy with LLMs being more obvious multi-layer brain circuits, the neurological analogy with symbolic reasoning must exist too.
My hunch is it emerges naturally out of the hierarchical generalization capabilities of multiple layer circuits. But then you need something to coordinate the acquired labels: a tweak on attention perhaps?
Another characteristic is probably some (limited) form of recursion, so the generalized labels emitted at the small end can be fed back in as tokens to be further processed at the big end.
The best thing Cycorp could do now is open source its accumulated database of logical relations so it can be ingested by some monster LLM.
What's the point of all that data collecting dust and accomplishing not much of anything?
It seems the direction of flow would be the opposite: LLMs are a great source of logical data for Cyc-like things. Distill your LLM into logical statements, then run your Cyc algorithms on it.
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> The best thing Cycorp could do now is open source its accumulated database of logical relations...
This is unpersuasive without laying out your assumptions and reasoning.
Counter points:
(a) It would be unethical for such a knowledge base to be put out in the open without considerable guardrails and appropriate licensing. The details matter.
(b) Cycorp gets some funding from the U.S. Government; this changes both the set of options available and the calculus of weighing them.
(c) Not all nations have equivalent values. Unless one is a moral relativist, these differences should not be deemed equivalent nor irrelevant. As such, despite the flaws of U.S. values and some horrific decision-making throughout history, there are known worse actors and states. Such parties would make worse use of an extensive human-curated knowledge base.
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OpenCyc is already a thing and there's been very little interest in it. These days we also have general-purpose semantic KB's like Wikidata, that are available for free and go way beyond what Cyc or OpenCyc was trying to do.
I think military will take over his work.Snowden documents reveled the cyc was been used to come up with Terror attack scenarios.
> If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.
This is the definition of a strawman. Who is claiming that NN inference is always the fastest way to run computation?
Instead of trying to bring down another technology (neural networks), how about you focus on making symbolic methods usable to solve real-world problems; e.g. how can I build a robust email spam detection system with symbolic methods?
> Instead of trying to bring down another technology (neural networks), how about you focus on making symbolic methods usable to solve real-world problems; e.g. how can I build a robust email spam detection system with symbolic methods?
I have two concerns. First, just after pointing out a logical fallacy from someone else, you added a fallacy: the either-or fallacy. (One can criticize a technology and do other things too.)
Second, you selected an example that illustrates a known and predictable weakness of symbolic systems. Still, there are plenty of real-world problems that symbolic systems address well. So your comment cherry-picks.
It appears as if you are trying to land a counter punch here. I'm weary of this kind of conversational pattern. Many of us know that tends to escalate. I don't want HN to go that direction. We all have varying experience and points of view to contribute. Let's try to be charitable, clear, and logical.
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>> If NN inference were so fast, we would compile C programs with it instead of using deductive logical inference that is executed efficiently by the compiler.
> This is the definition of a strawman.
(Actually, it is an example of a strawman.) Anyhow, rather than a strawman, I'd rather us get right into the fundamentals.
1. Feed-forward NN computation ('inference', which is an unfortunate word choice IMO) can provably provide universal function approximation under known conditions. And it can do so efficiently as well, with a lot of recent research getting into both the how and why. One "pays the cost" up-front with training in order to get fast prediction-time performance. The tradeoff is often worth it.
2. Function approximation is not as powerful as Turing completeness. FF NNs are not Turing complete.
3. Deductive chaining is a well-studied, well understood area of algorithms.
4. But... modeling of computational architectures (including processors, caches, busses, and RAM) with sufficient detail to optimize compilation is a hard problem. I wouldn't be surprised if this stretches these algorithms to the limit in terms of what developers will tolerate in terms of compile times. This is a strong incentive, so I'd expect there is at least some research that pushes outside the usual contours here.
The point is that symbolic computation as performed by Cycorp was held back by the need to train the Knowledge Base by hand in a supervised manner. NNs and LLMs in particular became ascendant when unsupervised training was employed at scale.
Perhaps LLMs can automate in large part the manual operations of building a future symbolic knowledge base organized by a universal upper ontology. Considering the amazing emergent features of sufficiently-large LLMs, what could emerge from a sufficiently large, reflective symbolic knowledge base?
That what I have settled on. The need for a symbolic library of standard hardware circuits.
I’m making a sloppy version that will contain all the symbols needed to run a multi-unit building.
If anybody wants to hear more about Doug's work and ideas, here is a (fairly long) interview with Doug by Lex Fridman, from last year.
https://www.youtube.com/watch?v=3wMKoSRbGVs&pp=ygUabGV4IGZya...
Thanks for the link. I watched the first part, and an interesting story/claim is that before Cyc started, many "smart people" including Marvin Minsky came up with "~1 million" as the number of things you would have to encode in a system for it to have "common sense".
He said they learned after ~5 years that this was an order of magnitude off -- it's more like 10 M things.
Is there any literature about this? Did they publish?
To me, the obvious questions are -
- how do they know it's not 100M things?
- how do they know it's even bounded? Why isn't there a combinatorial explosion?
I mean I guess they were evaluating the system all along. You don't go for 38 years without having some clear metrics. But I am having some problems with the logic -- I'd be interested in links to references / criticism.
I'd be interested in any arguments for and against ~10 M. Naively speaking, the argument seems a bit flawed to me.
FWIW I heard of Cyc back in the 90's, but I had no idea it was still alive. It is impressive that he kept it alive for so long.
---
Actually the wikipedia article is pretty good
https://en.wikipedia.org/wiki/Cyc#Criticisms
Though I'm still interested in the ~1M or ~10M claim. It seems like a strong claim to hold onto for decades, unless they had really strong metrics backing it up.
> how do they know it's not 100M things?
> how do they know it's even bounded? Why isn't there a combinatorial explosion?
I don't know - I'm in middle of watching the interview too, but he's moved on from that topic already. I'd guess the 10M vs 1M (or 100M) estimate comes from the curve of total "assertions" vs time leveling off towards some asymptotic limit.
I suppose the reason there's no combinatorial explosion is because they're entering these assertions in most general form possible, so considering new objects doesn't necessarily mean new assertions since it may all be covered by the superclasses the objects are part of (e.g. few assertions that are specific to apples since most will apply to all fruit).
Enjoyed watching that. Doug sounds very impressive. RIP.
Just search for Doug Lenat on YouTube. I can guarantee that any one of the other videos will be better than a Fridman interview.
Hey you guys, please don't go offtopic like this. Whimsical offtopicness can be ok, but offtopicness in the intersection of:
(1) generic (e.g. swerves the thread toward larger/general topic rather than something more specific);
(2) flamey (e.g. provocative on a divisive issue); and
(3) predictable (e.g. has been hashed so many times already that comments will likely fall in a few already-tiresome hash buckets)
- is the bad kind of offtopicness: the kind that brings little new information and eventually lots of nastiness. We're trying for the opposite here—lots of information and little nastiness.
https://news.ycombinator.com/newsguidelines.html
Only about two of them will be more contemporary though, and both are academic talks, not interviews. I get that you don't like Lex Fridman, which is a perfectly fine position to hold. But there is something to be said for seeing two people just sit and talk, as opposed to seeing somebody monologue for an hour. The Fridman interview with Doug is, IMO, absolutely worth watching. And so are all of the other videos by / about Doug. shrug
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reading the bio of Lex Fridman on wikipedia.. "Learning of Identity from Behavioral Biometrics for Active Authentication" what?
Please don't go offtopic in predictable/nasty ways - more at https://news.ycombinator.com/item?id=37355320.
Makes sense to me. He basically made a system that detects when someone else is using your computer by e.g. comparing patterns of mouse and keyboard input to your typical usage. It would be useful in a situation such as if you left your screen unlocked and a coworker sat down at your desk to prank you by sending an email from you to your boss (or worse, obviously). The computer would lock itself as soon as it suspects someone else is using it instead of you.
Like anything reasonably complex, it means little to you if its not your field - that said, I have no clue either.
It's fun reading through the paper he links just because I've always been enamored by taking a lot of those principles that they believe should be internal to a computer, and instead making them external to a community.
In other words, I think it would be so highly useful to have a browseable corpus of arguments and conclusions, where people could collaborate on them and perhaps disagree with portions of the argument graph, adding to it and enriching it over time, so other people could read and perhaps adopt the same reasoning.
I play around with ideas with this site I occasionally work on, http://concludia.org/ - really more an excuse at this point to mess around with the concept and also get better at Akka (Pekko) programming. At some point I'll add user accounts and editable arguments and make it a real website.
So basically a multi-person Zettelkasten? The idea with a Zettelkasten (zk for short) is that each note is a singular idea, concept, or argument that is all linked together. Arguments can link to their evidence, concepts can link to other related concepts, and so on.
https://en.m.wikipedia.org/wiki/Zettelkasten
Sort of except that it also tracks truth propagation - one person disagreeing would inform others that portion of the graph is contested. So the graph has behavior. And, the links have logical meaning, beyond just "is related to" - it respects boolean logic.
You can see some of the explanation at http://concludia.org/instructions .
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Isn’t this what Wikipedia is in essence? Ideas, concepts linked together, with supporting evidence
I don't think this is the goal of your project, so let me ask this way. Is there any similiar project, where we provide truths and fallacies, combine them with logical arguments and have a language model generate sets of probable conclusions?
Would be great for brainstorming.
I've had the same idea (er, came to the same conclusion) but never acted on it. Awesome to see that someone has! Great name too.
I thought of it while daydreaming about how to converge public opinion in a nation with major political polarization. It'd be a sort of structured public debate forum and people could better see exactly where in the hierarchy they disagreed and, perhaps more importantly, how much they in fact agreed upon.
I have always thought of Cyc as being the AI equivalent of Russell and Whitehead's Principia--something that is technically ambitious and interesting in its own right, but ultimately just the wrong approach that will never really work well on a standalone basis, no matter how long you work on it or keep adding more and more rules. That being said, I do think it could prove to be useful for testing and teaching neural net models.
In any case, at the time Lenat starting working on Cyc, we didn't really have the compute required to do NN models at the level where they start exhibiting what most would call "common sense reasoning," so it makes total sense why he started out on that path. RIP.
https://news.ycombinator.com/item?id=37354601
This may disabuse you of two ideas:
I don’t know about [1]. I asked an example from the paper above to GPT-4: “[If you had to guess] how many thumbs did Lincoln’s maternal grandmother have?”
Response: There is no widely available historical information to suggest that Abraham Lincoln's maternal grandmother had an unusual number of thumbs. It would be reasonable to guess that she had the typical two thumbs, one on each hand, unless stated otherwise.
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The end of the article [1] reminds me to publish more of what I make and think. I'm no Doug Lenat and my content would probably just add noise to the internet but still, don't let your ideas die with you or become controlled by some board of stakeholders. I'm also no open-source zealot but open-source is a nice way to let others continue what you started.
[1]
"Over the last year, Doug and I tried to write a long, complex paper that we never got to finish. Cyc was both awesome in its scope, and unwieldy in its implementation. The biggest problem with Cyc from an academic perspective is that it’s proprietary.
To help more people understand it, I tried to bring out of him what lessons he learned from Cyc, for a future generation of researchers to use. Why did it work as well as it did when it did, why did fail when it did, what was hard to implement, and what did he wish that he had done differently? ...
...One of his last emails to me, about six weeks ago, was an entreaty to get the paper out ASAP; on July 31, after a nerve-wracking false-start, it came out, on arXiv, Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc (https://arxiv.org/ftp/arxiv/papers/2308/2308.04445.pdf).
The brief article is simultaneously a review of what Cyc tried to do, an encapsulation of what we should expect from genuine artificial intelligence, and a call for reconciliation between the deep symbolic tradition that he worked in with modern Large Language Models."
Right on.
> my content would probably just add noise to the internet
Maybe, but there is worse noise out there for sure. :) Anyhow, some unsolicited advice from me: don't replay this quote to yourself any more than necessary; it isn't exactly a motivational mantra masterpiece. Share what you think is important.
Why? Even small, "improbable" improvements to knowledge can to matter. Given enough of them, statistically speaking, we can move the needle. Yeah, and we need to be able to find the relevant stuff; a big problem in of itself.
Those are very kind words that I will keep.
Never met the guy but his work was one of my biggest inspirations in computing.
I feel it's appropriate to link a blog post of mine from 2018. It's a quick recap of Lenat works on the trajectory that brought him towards Cyc, with links to the papers.
http://blog.funcall.org//lisp/2018/11/03/am-eurisko-lenat-do...
Cyc ("Syke") is one of those projects I've long found vaguely fascinating though I've never had the time / spoons to look into it significantly. It's an AI project based on a comprehensive ontology and knowledgebase.
Wikipedia's overview: <https://en.wikipedia.org/wiki/Cyc>
Project / company homepage: <https://cyc.com/>
I worked with Cyc. It was an impressive attempt to do the thing that it does, but it didn't work out. It was the last great attempt to do AI in the "neat" fashion, and its failure helped bring about the current, wildly successful "scruffy" approaches to AI.
It's failure is no shade against Doug. Somebody had to try it, and I'm glad it was one of the brightest guys around. I think he clung on to it long after it was clear that it wasn't going to work out, but breakthroughs do happen. (The current round of machine learning itself is a revival of a technique that had been abandoned, but people who stuck with it anyway discovered the tricks that made it go.)
"Neat" vs. "scruffy" syncs well with my general take on Cyc. Thanks for that.
I do suspect that well-curated and hand-tuned corpora, including possibly Cyc's, are of significant use to LLM AI. And will likely be more so as the feedback / autophagy problem exacerbates.
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I'm so looking forward to the next swing of the pendulum back to "neat", incorporating all the progress that has been made on "scruffy" during this current turn of the wheel.
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Why not combine the two approaches? A bicameral mind, of sorts?
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As Roger Schank defined the terms in the 70's, "Neat" refers to using a single formal paradigm, logic, math, neural networks, and LLMs, like physics. "Scruffy" refers to combining many different algorithms and approaches, symbolic manipulation, hand coded logic, knowledge engineering, and CYC, like biology.
I believe both approaches are useful and can be combined and layered and fed back into each other, to reinforce and transcend complement each others advantages and limitations.
Kind of like how Hailey and Justin Bieber make the perfect couple: ;)
https://edition.cnn.com/style/hailey-justin-bieber-couples-f...
Marvin L Minsky: Logical Versus Analogical or Symbolic Versus Connectionist or Neat Versus Scruffy
https://ojs.aaai.org/aimagazine/index.php/aimagazine/article...
https://ojs.aaai.org/aimagazine/index.php/aimagazine/article...
"We should take our cue from biology rather than physics..." -Marvin Minsky
>To get around these limitations, we must develop systems that combine the expressiveness and procedural versatility of symbolic systems with the fuzziness and adaptiveness of connectionist representations. Why has there been so little work on synthesizing these techniques? I suspect that it is because both of these AI communities suffer from a common cultural-philosophical disposition: They would like to explain intelligence in the image of what was successful in physics—by minimizing the amount and variety of its assumptions. But this seems to be a wrong ideal. We should take our cue from biology rather than physics because what we call thinking does not directly emerge from a few fundamental principles of wave-function symmetry and exclusion rules. Mental activities are not the sort of unitary or elementary phenomenon that can be described by a few mathematical operations on logical axioms. Instead, the functions performed by the brain are the products of the work of thousands of different, specialized subsystems, the intricate product of hundreds of millions of years of biological evolution. We cannot hope to understand such an organization by emulating the techniques of those particle physicists who search for the simplest possible unifying conceptions. Constructing a mind is simply a different kind of problem—how to synthesize organizational systems that can support a large enough diversity of different schemes yet enable them to work together to exploit one another’s abilities.
https://en.wikipedia.org/wiki/Neats_and_scruffies
>In the history of artificial intelligence, neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s.[1][2][3]
>"Neats" use algorithms based on a single formal paradigms, such as logic, mathematical optimization or neural networks. Neats verify their programs are correct with theorems and mathematical rigor. Neat researchers and analysts tend to express the hope that this single formal paradigm can be extended and improved to achieve general intelligence and superintelligence.
>"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffies rely on incremental testing to verify their programs and scruffy programming requires large amounts of hand coding or knowledge engineering. Scruffies have argued that general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no magic bullet that will allow programs to develop general intelligence autonomously.
>John Brockman compares the neat approach to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena.[a]
[...]
>Modern AI as both neat and scruffy
>New statistical and mathematical approaches to AI were developed in the 1990s, using highly developed formalisms such as mathematical optimization and neural networks. Pamela McCorduck wrote that "As I write, AI enjoys a Neat hegemony, people who believe that machine intelligence, at least, is best expressed in logical, even mathematical terms."[6] This general trend towards more formal methods in AI was described as "the victory of the neats" by Peter Norvig and Stuart Russell in 2003.[18]
>However, by 2021, Russell and Norvig had changed their minds.[19] Deep learning networks and machine learning in general require extensive fine tuning -- they must be iteratively tested until they begin to show the desired behavior. This is a scruffy methodology.
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Why did it didn't work out ?
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As far as I can tell it was more of an aspiration than a product. I worked with a consulting firm that tried to get into AI a few years back and chose Cyc as the platform they wanted to sell to (mostly financial) clients. I don't think a single project ever even started nor was there a clear picture of what could be sold. I hate to think Lenat was a fraud because he certainly seemed like a sincere and brilliant person, but I think Cyc was massively oversold despite never doing much of anything useful. The website is full technical language and not a single case study after 40 years in business.
Unfortunately visiting cyc.com, I only see a bunch of business BS and the "Documention" page shows nothing without logging in.
Weird, I interviewed with him summer 2021 hoping to be able to land an ontologist job at Cycorp. It went spectacularly badly because it turned out I really needed to brush up more on my formal logic skills, but I was surprised to even get an interview, let alone with the man himself. He still encouraged me to work on reviewing logic and to apply again in the future but I stopped seeing listings at Cycorp for ontologists and started putting off returning to that aspiration thinking Cycorp has been around long enough that there was no rush. Memento mori
Here's a 2016 Wired article about Doug Lenat, he was the guy who made Eurisko and CYC https://www.wired.com/2016/03/doug-lenat-artificial-intellig...
I've often thought that Cyc had an enormous value as some kind of component for AI, a "baseline truth" about the universe (to the degree that we understand it and have "explained" our understanding to Cyc in terms of its frames). AM (no relation to any need for screaming) was a taste of the AI dream.
>I've often thought that Cyc had an enormous value as some kind of component for AI
Same. I wonder if training an LLM on the database would make it more "grounded"? We'll probably never know as Cycorp will keep the data locked away in their vaults forever. For what purpose? Probably even they don't know.
>AM (no relation to any need for screaming)
heh.
Sad to hear of his passing, I remember building my uni project around OpenCyc in my one “Intelligent Systems” class many many years ago. It was a dismal failure as my ambition far exceeded my skills, but it was so enjoyable reading about Cyc and the dedicated work Douglas had put in over such a long time.
Even being a controversial figure, he was one of my heroes. Getting excited about Eurisko in the '80s and '90s was a big driver for me at the time! Rest in piece, dear computer pioneer!
He was a hero of knowledge representation and ontology. A bit odd that we learn about his sad passing from a Wikipedia article, while at the time of this comment there is still no mention on e.g. https://cyc.com/.
Thirteen hours later still no mention on the Cycorp website. Also the press doesn't seem to notice. Pretty odd.
The post originally pointed to Lenat's Wikipedia page; now it's an obituary by Gary Marcus which seems more appropriate.
I worked on Cyc in the early 90s, briefly [https://dl.acm.org/doi/pdf/10.1145/165529.993430 shows my MCC address on p.10 :)]. I cherish wonderful memories of being on that project.
Doug was amazing - bold, brilliant, visionary, charismatic.
RIP, dear leader.
Here's one for you, Doug. My condolences.
https://chat.openai.com/share/dbd59c92-696b-45d3-8097-c09a23...
https://en.wikipedia.org/wiki/Douglas_Lenat
https://web.archive.org/web/20230901183515/https://garymarcu...
https://archive.ph/icb92
Maybe it's a bit on the nose but I had his article summarized by Anthropic's Claude 2 100k model (LLMs are good at summarization) for those who don't have time to read the whole thing:
The article discusses generative AI models like ChatGPT and contrasts them with knowledge-based AI systems like Cyc.
Generative models can produce very fluent text, but they lack true reasoning abilities and can make up plausible-sounding but false information. This makes them untrustworthy.
In contrast, Cyc represents knowledge explicitly and can logically reason over it. This makes it more reliable, though it struggles with natural language and speed.
The article proposes 16 capabilities an ideal AI system should have, including explanation, reasoning, knowledge, ethics, and language skills. Cyc and generative models each have strengths and weaknesses on these dimensions.
The authors suggest combining symbolic systems like Cyc with generative models to get the best of both approaches. Ways to synergize them include:
Using Cyc to filter out false information from generative models.
Using Cyc's knowledge to train generative models to be more correct.
Using generative models to suggest knowledge to add to Cyc's knowledge base.
Using Cyc's reasoning to expand what generative models can say.
Using Cyc to explain the reasoning behind generative model outputs.
Overall, the article argues combining reasoning-focused systems like Cyc with data-driven generative models could produce more robust and trustworthy AI. Each approach can shore up weaknesses of the other.
May he rest in peace.
Related. Others?
Cyc - https://news.ycombinator.com/item?id=20725 - May 2007 (1 comment)
* Getting from Generative AI to Trustworthy AI: What LLMs Might Learn from Cyc* - https://news.ycombinator.com/item?id=37354601 - RIGHT NOW
HN location for discussion of Lenat's last paper (with Gary Marcus) about ways to reconcile Cyc's strengths with LLMs.
Perhaps some here aren't familiar with the existence of a (relatively useless in my opinion) POV that pits symbolic systems against statistical methods. But it isn't a zero-sum game. Informed, insightful comparisons are useful, but "holy wars" are not. See also [1] for broad commentary and [2] for a particular application.
[1] https://medium.com/@jcbaillie/beyond-the-symbolic-vs-non-sym...
[2] https://past.date-conference.com/proceedings-archive/2016/pd...
This sibling thread is apropos too: https://news.ycombinator.com/item?id=37356435
Ahh, another one of the old guard has moved on. Here are two excerpts from the book AI: The Tumultuous History Of The Search For Artificial Intelligence (a fantastic read of the early days of AI) to remember him by;
"Lenat found out about computers in a a manner typical of his entrepreneurial spirit. As a high school student in Philadelphia, working for $1.00 an hour to clean the cages of experimental animals, he discovered that another student was earning $1.50 to program the institution's minicomputer. Finding this occupation more to his liking, he taught himself programming over a weekend and squeezed his competitor out of the job by offering to work for fifty cents an hour less.31 A few years later, Lenat was programming Automated Mathematician (AM, for short) as a doctoral thesis project at the Stanford AI Laboratory." p. 178
And here's an count of an early victory for AI in gaming against humans by Lenat's EURISKO system (https://en.wikipedia.org/wiki/Eurisko):
"Ever the achiever, Lenat was looking for a more dramatic way to prove teh capabilities of his creation. The identified the occasion space-war game called Traveler TCS, then quite popular with the public Lenat wanted to reach. The idea was for each player to design a fleet of space battleships according to a thick, hundred-page set of rules. Within a budget limit of one trillion galactic credits, one could adjust such parameters as the size, speed, armor thickness, autonomy and armament of each ship: about fifty adjustments per ship were needed. Since the fleet size could reach a hundred ships, the game thus offered ample room for ingenuity in spite of the anticlimactic character of the battles. These were fought by throwing dice following complex tables based on probability of survival of each ship according to its design. The winner of the yearly national championship was commissioned inter galactic admiral and received title to a planet of his or her choice ouside the solar system.
Several months before the 1981 competition, Lenat fed into EURISKO 146 Traveler concepts, ranging from the nature of games in general to the technicalities of meson guns. He then instructed the program to develop heuristics for making winning war-fleet designs. The now familiar routine of nightly computer runs turned into a merciless Darwinian contest: Lenat and EURISKO together designed fleets that battled each other. Designs were evaluated by how well they won battles, and heuristics by how well they designed fleets. This rating method required several battles per design, and several designs per heuristic, which amounted to a lot of battles: ten thousand in all, fought over two thousand hours of computer time.
To participants in the national championship of San Mateo,California, the resulting fleet of ninety-six small, heavily armored ships looked ludicrous. Accepted wisdom dictated fleets of about twenty behemoth ships, and many couldn't help laughing. When engagements started, they found out that the weird armada held more than met the eye. One interesting ace up Lenat's sleeve was a small ship so fast as to be almost unstoppable, which guaranteed at least a draw. EURISKO had conceived of it through the "look for extreme cases" heuristic (which had mutated, incidentally, into mutated, incidentally, into "look for almost extreme cases")." p. 182
If you're a young person working in AI, by which I mean you're less than 30, and if you have not already done so, you should read about AI history in three decade 60s - 90s.
I may be getting this wrong, but I think I remember hearing that his auto-generated fleets won Traveller so entirely, several years in a row, that they had to shut down the entire competition because it had been broken
Edit: Fixed wrong name for the competition
I think you mean "EURISKO won the Traveller championship so entirely..."
In which case, yes, something like that did happen. Per the Wikipedia page:
Lenat and Eurisko gained notoriety by submitting the winning fleet (a large number of stationary, lightly-armored ships with many small weapons)[3] to the United States Traveller TCS national championship in 1981, forcing extensive changes to the game's rules. However, Eurisko won again in 1982 when the program discovered that the rules permitted the program to destroy its own ships, permitting it to continue to use much the same strategy.[3] Tournament officials announced that if Eurisko won another championship the competition would be abolished; Lenat retired Eurisko from the game.[4] The Traveller TCS wins brought Lenat to the attention of DARPA,[5] which has funded much of his subsequent work.
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I still intend to integrate OpenCyc.
Doug was one of my childhood heroes, thanks to a certain book telling the story of his work on AM and Eurisko. My great regret is that I never got the chance to meet him or contribute to his work in any way. RIP Doug, you are a legend.
> I have spent my whole career […], Lenat was light-years ahead of me […]
Lenat is on a short list of people I expected/hoped to meet at some point when context provided the practical reason.
He has been a hero to me for his creativity and fearlessness regarding his symbolic vision.
So sad I will never meet him, but my appreciation for him will never die.
Worked with their ontologists for a couple of years. Someone once told me that they employed more philosophers per capita than any other software company. A dubious distinction, maybe. But it describes the culture of inquisitiveness there pretty well too
Oh, so sorry to hear that. Good summary of his work- the Cyc project- on the twitter thread. Had missed that last paper- with Gary Marcus- on Cyc and LLM.
Anyone know how he died? I can't find any information about it but someone mentioned heart attack on Reddit?
Very visceral oof. I don't remember a time when I knew about AI but not about Eurisko.
72 isn't really too old. Does anyone know what caused his death? Revenge of the COVID?
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Didn't he:
- invent case based reasoning
- build Eurisko and AM
- write a discipline defining paper ("Why AM and Eurisko appear to work")
- undertake an ambitious but ultimately futile high risk research gamble with Cyc?
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Consider giving more grace. Life is short, and kindness is free.
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Why do people have to have 'commercial value' to get black bars? Why do people have to pass the ideological police? Why isn't serving as a visible advocate of a certain logical model enough?
I think my bias comes from having started my career in AI on the inference side and having (perhaps not so much long term :) seen Cyc as a shining city on a hill. Lenat certainly established that logical model even if we've since gone onto other things.
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I got a lot of value out of some of the papers he wrote, and what bits of Building Large Knowledge-Based Systems I managed to read.
he is the patron hacker of players who use computers to break board games or war games
I think you don't understand the meaning of the black bar if "commercial value" is one of the metrics.
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Not even Warnock got a black bar even when asked for one as a mark of respect: [0]
I guess the black bar really is an ideological thing. Rather than being supposedly a 'mark of respect'.
Regardless, RIP Doug.
[0] https://news.ycombinator.com/item?id=37197852
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While I respect Doug's intelligence, he showed a kind of perverse persistence in a failed idea, and I think it's telling that aspiring AI czar Gary Marcus admires the ruins of Cyc while neglecting to acknowledge that it represents a dead end in AI. Like science, the field of AI advances one funeral at a time. Doug pursued a pipe dream, and convinced others to do the same, despite the brittle and static nature of the AI he sought to build. Cyc was not a precursor to OpenAI, contrary to other comments in this thread. That would be like calling the zeppelin the precursor of the jet. It represents a different school of technology, and a much less effective one.
It’s not going to be popular to highlight the less than hoped for successes of Lenat’s greatest project.
But I think that is one of the things he should be admired for. How can anyone know how an ambitious approach will pan out without the great risk of going all in?
Anyone willing to risk a Don Quixote aspect to their career, in pursuit of a breakthrough, is someone who cares deeply about something beyond themselves.
And recognizing the limits of Lenat’s impact today doesn’t preclude both the direct and indirect impact on future progress.
I found him inspiring on multiple levels.
Wait a few more years and you will see that the effort to formalize this knowledge was not in vain. We will certainly see systems which make use of this data. Cycorp is a different company with a different approach than OpenAI; but the data produced by Cycorp will likely be useful for companies like OpenAI on their way to truly intelligent systems.