8 ms·

Remembering Doug Lenat and his quest to capture the world with logic

182 points by andyjohnson0 3y ago

richardjam73 3y ago
I have that issue of Scientific American somewhere, I didn't know Stephen had an article in it too. I'll have reread of it.
kensai 3y ago
This is very fascinating. Is there somewhere a review of Cyc regarding its abilities compared to other systems?
- rjsw 3y ago
  Maybe read a bit about AM and Eurisko first, that will give an idea of how Cyc was expected to get used.
  - cabalamat 3y ago
    My understanding of AM and Eurisko (having looked into them a decade or so ago) was that their source code hadn't been published, and that there was a dispute as to what their capabilities actually were and how much was exaggeration by Lenat. I don't know if that's still the case. I do think that it would be worth creating systems that mix the ANN and GOFAI approaches to AI.
    - earleybird 3y ago
      Had to go digging . . . Kenneth Haase did a Phd (1990) where he builds an exploratory system called Cyrano and does a substantial review/analysis of AM (& Eurisko) https://dspace.mit.edu/handle/1721.1/14257?show=full https://dspace.mit.edu/handle/1721.1/14257?show=full It's quite interesting.
      - cabalamat 3y ago
        It may well be, but when I try to download the pdf it fails.
specialist 3y ago
Just perfect. So glad I read this. Thanks for sharing. > In many ways the great quest of Doug Lenat’s life was an attempt to follow on directly from the work of Aristotle and Leibniz. Such a wonderful, respectful retrospective of Lenat's ideas and work. > I think Doug viewed CYC as some kind of formalized idealization of how he imagined human minds work: providing a framework into which a large collection of (fairly undifferentiated) knowledge about the world could be “poured”. At some level it was a very “pure AI” concept: set up a generic brain-like thing, then “it’ll just do the rest”. But Doug still felt that the thing had to operate according to logic, and that what was fed into it also had to consist of knowledge packaged up in the form of logic. I've always wanted CYC, or something like it, to be correct. Like somehow it'd fulfill my need for the universe to be knowable, legible. If human reason & logic could be encoded, then maybe things could start to make sense, if only we try hard enough. Alas. Back when SemanticWeb was the hotness, I was a firm ontology partisan. After working on customer's use cases, and given enough time to work thru the stages of grief, I grudgingly accepted the folksonomy worldview is probably true. Since then, of course, the "fuzzy" strategies have prevailed. (Also, most of us have accepted humans aren't rational.) To this day, statistics based approaches make me uncomfortable, perhaps even anxious. My pragmatism motivated holistic worldview is always running up against my reductionist impulses. Paradox in a nutshell. Enough about me. > Doug’s starting points were AI and logic, mine were ... computation writ large. I do appreciate Wolfram placing their respective theories in the pantheon. It's a nice reminder of their lineages. So great. I agree with Wolfram that encoding heuristics was an experiment that had to be done. Negative results are super important. I'm so, so glad Lenat (and crews) tried so hard. And I hope the future holds some kind of synthesis of these strategies.
- zozbot234 3y ago
  > And I hope the future holds some kind of synthesis of these strategies. My guess is that by June 19, 2024 we'll be able to take 3596.6 megabytes of descriptive text about President Abraham Lincoln and do something cool with it.
  - specialist 3y ago
    Heh. I was more hoping OpenAI would incorporate inference engines to cure ChatGPT's "hallucinations". Such that it'd "know" bad sex isn't better than good sex, despite the logic. PS- I haven't actually asked ChatGPT. I'm just repeating a cliché about the limits of logic wrt the real world.
ChaitanyaSai 3y ago
Great read. Surprised to read Wolfram never actually got to use CYC. Anyone here who has and can talk about its capabilities?
- stakhanov 3y ago
  I briefly looked into it many moons ago when I was a Ph.D. student working in the area of computational semantics in 2006-10. This was already well past the hayday of CYC though. The first stumbling block was that CYC wasn't openly available. Their research group was very insular, and they were very protective of their IP, hoping to pay for their work through licensing deals and industry- or academic collaborations that could funnel money their way. They had a subset called "OpenCYC" though, which they released more publicly in the hope of drawing more attention. I tried using that, but soon got frustrated with the software. The representation was in a CYC-specific language called "CycL" and the inference engine was CYC-specific as well and based on a weird description logic specifically invented for CYC. So you couldn't just hook up a first-order theorem prover or anything like that. And "description logic" is a polite term for what their software did. It seemed mostly designed as a workaround to the fact that open-ended inferencing of the kind they spoke of to motivate their work would have depended way too frequently on factoids of common sense knowledge that were missing from the knowledge base. I got frustrated with that software very quickly and eventually gave up. This was a period of AI-winter, and people doing AI were very afraid to even use the term "AI" to describe what they were doing. People were instead saying they were doing "pattern processing with images" or "audio signal processing" or "natural language processing" or "automated theorem proving" or whatever. Any mention of "AI" made you look naive. But Lenat's group called their stuff "AI" and stuck to their guns, even at a time when that seemed a bit politically inept. From what I gathered through hearsay, CYC were also doing things like taking a grant from the defense department, and suddenly a major proportion of the facts in the ontology were about military helicopters. But they still kept beating the drum about how they were codifying "common sense" knowledge, and, if only they could get enough "common sense" knowledge in there, they would break through a resistance level at some point, where they could have the AI program itself, i.e. use the existing facts to derive more facts by reading and understanding plain text.
HarHarVeryFunny 3y ago
I missed the news of Doug Lenat's passing. He died a few days ago on August 31st. I'm old enough to have lived thru the hope but ultimate failure of Lenat's baby CYC. The CYC project was initiated in 1984, in the heyday of expert systems which had been successful in many domains. The idea of an expert system was to capture the knowledge and reasoning power of a subject matter expert in a system of declarative logic and rules. CYC was going to be the ultimate expert system that captured human common sense knowledge about the world via a MASSIVE knowledge/rule set (initially estimated as a 1000 man-year project) of how everyday objects behaved. The hope was that through sheer scale and completeness it would be able to reason about the world in the same way as a human who had gained the same knowledge thru embodiment and interaction. The CYC project continued for decades with a massive team of people encoding rules according to it's own complex ontology, but ultimately never met it's goals. In retrospect it seems the idea was doomed to failure from the beginning, but nonetheless it was an important project that needed to be tried. The problem with any expert system reasoning over a fixed knowledge set is that it's always going to be "brittle" - it may perform well for cases wholly within what it knows about, but then fail when asked to reason about things where common sense knowledge and associated extrapolation of behavior is required; CYC was hoping to avoid this via scale to be so complete that there were no important knowledge gaps. I have to wonder if LLM-based "AI's" like GPT-4 aren't in some ways very similar to CYC in that they are ultimately also giant expert systems, but with the twist that they learnt their knowledge, rules and representations/reasoning mechanisms from a training set rather than it having to be laboriously hand entered. The end result is much he same though - an ultimately brittle system who's Achille's heel is that it is based on a fixed set of knowledge rather than being able to learn from it's own mistakes and interact with the domain it is attempting to gain knowledge over. It seems there's a similar hope to CYC of scaling these LLM's up to the point that they know everything and the brittleness disappears, but I suspect that ultimately that will prove a false hope and real AI's will need to learn through experimentation just as we do. RIP Doug Lenat. A pioneer of the computer age and of artificial intelligence.
- TimPC 3y ago
  This fail when asked about cases not wholly within what it knows about is a problem with lots of AI not just expert systems. Neural Nets mostly do awfully on problems outside their training data, assuming they can even generate an answer at all, which isn't always possible. If you train a neural net to order drinks from Starbucks and one of it's orders fails with the server telling it "We are out of Soy Milk" chances are quite high it's subsequent order will also contain Soy Milk.
alexpotato 3y ago
This old Google EDU talk was the first time I heard of Doug Lenat. Sad to hear: a. of his passing b. that CYC didn't eventually meet it's goals https://www.youtube.com/watch?v=KTy601uiMcY https://www.youtube.com/watch?v=KTy601uiMcY
ks2048 3y ago
I wonder if CYC would have had more success if it was open and collaborative. WikiData seems like a successful cousin. I know the goals are a quite different - wikidata doesn't really store "common sense" knowledge, but it seems any rule-based AI system would probably want to use wikidata as a database of facts.
- zozbot234 3y ago
  > wikidata doesn't really store "common sense" knowledge They're actively working on this, with the goal of ultimately building a language-independent representation[0] of ordinary encyclopedic text. Much like a machine translation interlanguage, but something that would be mostly authored by humans, not auto-generated from existing natural-language text. See https://meta.wikimedia.org/wiki/Abstract_Wikipedia https://meta.wikimedia.org/wiki/Abstract_Wikipedia for more information. [0] Of course, there are some very well-known pitfalls to this general idea: what's the true, canonical language-independent representation of nimium saepe valedīxit? So this should probably be understood as mostly language-independent, enough to be practically useful.
- creer 3y ago
  I looked into it years ago and adding to, say, opencyc, really did not seem simple. There was a lot of detail in the entity descriptions. Even reading them seemed to required an awful lot of background knowledge of the system. It may have been possible to at least add lots of parallel items / instances. For example more authors and books and music works and performers, etc. Anyone here built a system around opencyc? Or cyc?
- brundolf 3y ago
  If I recall, Cyc did exactly that (imported data from WikiData) Unfortunately there was much more to it than ingesting large volumes of structured entities
jmj 3y ago
I’m working on old fashioned A.I. for my PhD. I wrote Doug a few times, he was very kind and offered very good advice. I was hoping to work with him one day. I’ll miss you Doug.
- nairboon 3y ago
  What are you working on?
dang 3y ago
Lenat's post about Wolfram Alpha, mentioned in the OP, was discussed (a bit) at the time: Doug Lenat – I was positively impressed with Wolfram Alpha - https://news.ycombinator.com/item?id=510579 https://news.ycombinator.com/item?id=510579 - March 2009 (17 comments) And of course, recent and related: Doug Lenat has died - https://news.ycombinator.com/item?id=37354000 https://news.ycombinator.com/item?id=37354000 - Sept 2023 (170 comments)
dekhn 3y ago
I recommend reading Norvig's thinking about the various cultures. https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf https://static.googleusercontent.com/media/research.google.c... and https://norvig.com/chomsky.html https://norvig.com/chomsky.html In short, Norvig concludes there are several conceptual approaches to ML/AI/Stats/Scientific analysis. One is "top down": teach the system some high level principles that correspond to known general concepts, and the other is "bottom up": determine the structure from the data itself and use that to generate general concepts. He observes that while the former is attractive to many, the latter has continuously produced more and better results with less effort. I've seen this play out over and over. I've concluded that Norvig is right: empirically based probabilistic models are a cheaper, faster way to answer important engineering and scientific problems, even if they are possibly less satisfying intellectually. Cheap approximations are often far better than hard to find analytic solutions.
- golol 3y ago
  this is the same concept as the bitter lesson, am I correct? I don't see a substantial difference yet.
  - dekhn 3y ago
    I hadn't read that before, but yes. Sutton focuses mostly on "large amounts of compute" whereas I think his own employer has demonstrated that it's a combination of large amount of compute, large amounts of data, and really clever probabilistic algorithms, in combination, which really demonstrate the utility of the bitter lesson. And speaking as a biologist for a moment, that minds are irredeemably complex and attemptng to understand them with linear, first-order rules and logic is unlikely to be fruitful.
- jyscao 3y ago
  > One is "top down": teach the system some high level principles that correspond to known general concepts, and the other is "bottom up": determine the structure from the data itself and use that to generate general concepts. This is the same pattern explaining why bottom-up economic systems, i.e. lassaire faire free markets, flawed as they are, work better than top-down systems like central planning.
ansible 3y ago
Symbolic knowledge representation and reasoning is a quite interesting field. I think the design choices of projects like wikidata.org and CYC severely limit the application of this though. For example, on the wikidata help page, they talk about the height of Mount Everest: https://www.wikidata.org/wiki/Help:About_data#Structuring_data https://www.wikidata.org/wiki/Help:About_data#Structuring_da... Earth (Q2) (item) → highest point (P610) (property) → Mount Everest (Q513) (value) and Mount Everest (Q513) (item) → instance of (P31) (property) → mountain (Q8502) (value) So that's all fine, but it misses a lot of context. These facts might be true for the real world, right now, but they won't always be true. Even in the not-so-distant past, the height of Everest was lower, because of tectonic plate movement. And maybe in the future it will go even higher due to tectonics, or maybe it will go lower due to erosion. Context awareness gets even more important when talking about facts like "the iPhone is the best selling phone", for example. That might be true right now, but it certainly wasn't true back in 2006, before the phone was released. Context also comes in many forms, which can be necessary for useful reasoning. For example, consider the question: "What would be the highest mountain in the world, if someone blew up the peak of Everest with a bomb?" This question isn't about the real world, right here and right now, it is about a hypothetical world that doesn't exist. Going a little further afield, you may want to ask a question like "Who is the best captain of the Enterprise?". This might be about the actual US Navy CVN-64 ship named "Enterprise", the planned CVN-80, or the older ship CV-6 Enterprise which fought in WW2. Or maybe a relevant context to the question was "Star Trek", and we're in one of several fictional worlds instead, which would result in a completely different set of facts. I think some ability to deal with uncertainly (as with Probabilistic Graphical Models) is also necessary to deal with practical applications of this technology. We may be dealing with a mix of "objective facts" (well, let's not get into a discussion about the philosophy of science) and other facts that we may not be so certain about. It seems to me that successful symbolic reasoning system will be very, very large and complex. I'm not at all sure even how such knowledge should be represented, never mind the issue of trying to capture it all in digital form.
dizzystar 3y ago
I worked at Cycorp as a contractor for a few months. Lenat was, without a doubt, the smartest person I ever met in my life. There are levels of scary smart... his intelligence was downright frightening. I'm sad to see so many call his work a failure. His work was anything but a failure. I think this perception comes down to the fact that the company worked on many things that aren't public, and he do much publicity and self-promotion.
mycall 3y ago
I wonder if there is a way to mix ResearchCyc graph with a high feature count vector database so a transformer has a directed acyclic graph instead of requiring gradient descent. I'm not an expert so that might be gibberish.

182 points by andyjohnson0 3y ago

I have that issue of Scientific American somewhere, I didn't know Stephen had an article in it too. I'll have reread of it.

kensai 3y ago

This is very fascinating. Is there somewhere a review of Cyc regarding its abilities compared to other systems?

rjsw 3y ago
Maybe read a bit about AM and Eurisko first, that will give an idea of how Cyc was expected to get used.
- cabalamat 3y ago
  My understanding of AM and Eurisko (having looked into them a decade or so ago) was that their source code hadn't been published, and that there was a dispute as to what their capabilities actually were and how much was exaggeration by Lenat. I don't know if that's still the case. I do think that it would be worth creating systems that mix the ANN and GOFAI approaches to AI.
  - earleybird 3y ago
    Had to go digging . . . Kenneth Haase did a Phd (1990) where he builds an exploratory system called Cyrano and does a substantial review/analysis of AM (& Eurisko) https://dspace.mit.edu/handle/1721.1/14257?show=full https://dspace.mit.edu/handle/1721.1/14257?show=full It's quite interesting.
    - cabalamat 3y ago
      It may well be, but when I try to download the pdf it fails.

specialist 3y ago

Just perfect. So glad I read this. Thanks for sharing. > In many ways the great quest of Doug Lenat’s life was an attempt to follow on directly from the work of Aristotle and Leibniz. Such a wonderful, respectful retrospective of Lenat's ideas and work. > I think Doug viewed CYC as some kind of formalized idealization of how he imagined human minds work: providing a framework into which a large collection of (fairly undifferentiated) knowledge about the world could be “poured”. At some level it was a very “pure AI” concept: set up a generic brain-like thing, then “it’ll just do the rest”. But Doug still felt that the thing had to operate according to logic, and that what was fed into it also had to consist of knowledge packaged up in the form of logic. I've always wanted CYC, or something like it, to be correct. Like somehow it'd fulfill my need for the universe to be knowable, legible. If human reason & logic could be encoded, then maybe things could start to make sense, if only we try hard enough. Alas. Back when SemanticWeb was the hotness, I was a firm ontology partisan. After working on customer's use cases, and given enough time to work thru the stages of grief, I grudgingly accepted the folksonomy worldview is probably true. Since then, of course, the "fuzzy" strategies have prevailed. (Also, most of us have accepted humans aren't rational.) To this day, statistics based approaches make me uncomfortable, perhaps even anxious. My pragmatism motivated holistic worldview is always running up against my reductionist impulses. Paradox in a nutshell. Enough about me. > Doug’s starting points were AI and logic, mine were ... computation writ large. I do appreciate Wolfram placing their respective theories in the pantheon. It's a nice reminder of their lineages. So great. I agree with Wolfram that encoding heuristics was an experiment that had to be done. Negative results are super important. I'm so, so glad Lenat (and crews) tried so hard. And I hope the future holds some kind of synthesis of these strategies.

zozbot234 3y ago
> And I hope the future holds some kind of synthesis of these strategies. My guess is that by June 19, 2024 we'll be able to take 3596.6 megabytes of descriptive text about President Abraham Lincoln and do something cool with it.
- specialist 3y ago
  Heh. I was more hoping OpenAI would incorporate inference engines to cure ChatGPT's "hallucinations". Such that it'd "know" bad sex isn't better than good sex, despite the logic. PS- I haven't actually asked ChatGPT. I'm just repeating a cliché about the limits of logic wrt the real world.

ChaitanyaSai 3y ago

Great read. Surprised to read Wolfram never actually got to use CYC. Anyone here who has and can talk about its capabilities?

stakhanov 3y ago
I briefly looked into it many moons ago when I was a Ph.D. student working in the area of computational semantics in 2006-10. This was already well past the hayday of CYC though. The first stumbling block was that CYC wasn't openly available. Their research group was very insular, and they were very protective of their IP, hoping to pay for their work through licensing deals and industry- or academic collaborations that could funnel money their way. They had a subset called "OpenCYC" though, which they released more publicly in the hope of drawing more attention. I tried using that, but soon got frustrated with the software. The representation was in a CYC-specific language called "CycL" and the inference engine was CYC-specific as well and based on a weird description logic specifically invented for CYC. So you couldn't just hook up a first-order theorem prover or anything like that. And "description logic" is a polite term for what their software did. It seemed mostly designed as a workaround to the fact that open-ended inferencing of the kind they spoke of to motivate their work would have depended way too frequently on factoids of common sense knowledge that were missing from the knowledge base. I got frustrated with that software very quickly and eventually gave up. This was a period of AI-winter, and people doing AI were very afraid to even use the term "AI" to describe what they were doing. People were instead saying they were doing "pattern processing with images" or "audio signal processing" or "natural language processing" or "automated theorem proving" or whatever. Any mention of "AI" made you look naive. But Lenat's group called their stuff "AI" and stuck to their guns, even at a time when that seemed a bit politically inept. From what I gathered through hearsay, CYC were also doing things like taking a grant from the defense department, and suddenly a major proportion of the facts in the ontology were about military helicopters. But they still kept beating the drum about how they were codifying "common sense" knowledge, and, if only they could get enough "common sense" knowledge in there, they would break through a resistance level at some point, where they could have the AI program itself, i.e. use the existing facts to derive more facts by reading and understanding plain text.

HarHarVeryFunny 3y ago

I missed the news of Doug Lenat's passing. He died a few days ago on August 31st. I'm old enough to have lived thru the hope but ultimate failure of Lenat's baby CYC. The CYC project was initiated in 1984, in the heyday of expert systems which had been successful in many domains. The idea of an expert system was to capture the knowledge and reasoning power of a subject matter expert in a system of declarative logic and rules. CYC was going to be the ultimate expert system that captured human common sense knowledge about the world via a MASSIVE knowledge/rule set (initially estimated as a 1000 man-year project) of how everyday objects behaved. The hope was that through sheer scale and completeness it would be able to reason about the world in the same way as a human who had gained the same knowledge thru embodiment and interaction. The CYC project continued for decades with a massive team of people encoding rules according to it's own complex ontology, but ultimately never met it's goals. In retrospect it seems the idea was doomed to failure from the beginning, but nonetheless it was an important project that needed to be tried. The problem with any expert system reasoning over a fixed knowledge set is that it's always going to be "brittle" - it may perform well for cases wholly within what it knows about, but then fail when asked to reason about things where common sense knowledge and associated extrapolation of behavior is required; CYC was hoping to avoid this via scale to be so complete that there were no important knowledge gaps. I have to wonder if LLM-based "AI's" like GPT-4 aren't in some ways very similar to CYC in that they are ultimately also giant expert systems, but with the twist that they learnt their knowledge, rules and representations/reasoning mechanisms from a training set rather than it having to be laboriously hand entered. The end result is much he same though - an ultimately brittle system who's Achille's heel is that it is based on a fixed set of knowledge rather than being able to learn from it's own mistakes and interact with the domain it is attempting to gain knowledge over. It seems there's a similar hope to CYC of scaling these LLM's up to the point that they know everything and the brittleness disappears, but I suspect that ultimately that will prove a false hope and real AI's will need to learn through experimentation just as we do. RIP Doug Lenat. A pioneer of the computer age and of artificial intelligence.

TimPC 3y ago
This fail when asked about cases not wholly within what it knows about is a problem with lots of AI not just expert systems. Neural Nets mostly do awfully on problems outside their training data, assuming they can even generate an answer at all, which isn't always possible. If you train a neural net to order drinks from Starbucks and one of it's orders fails with the server telling it "We are out of Soy Milk" chances are quite high it's subsequent order will also contain Soy Milk.

alexpotato 3y ago

This old Google EDU talk was the first time I heard of Doug Lenat. Sad to hear: a. of his passing b. that CYC didn't eventually meet it's goals https://www.youtube.com/watch?v=KTy601uiMcY https://www.youtube.com/watch?v=KTy601uiMcY

ks2048 3y ago

I wonder if CYC would have had more success if it was open and collaborative. WikiData seems like a successful cousin. I know the goals are a quite different - wikidata doesn't really store "common sense" knowledge, but it seems any rule-based AI system would probably want to use wikidata as a database of facts.

zozbot234 3y ago
> wikidata doesn't really store "common sense" knowledge They're actively working on this, with the goal of ultimately building a language-independent representation[0] of ordinary encyclopedic text. Much like a machine translation interlanguage, but something that would be mostly authored by humans, not auto-generated from existing natural-language text. See https://meta.wikimedia.org/wiki/Abstract_Wikipedia https://meta.wikimedia.org/wiki/Abstract_Wikipedia for more information. [0] Of course, there are some very well-known pitfalls to this general idea: what's the true, canonical language-independent representation of nimium saepe valedīxit? So this should probably be understood as mostly language-independent, enough to be practically useful.
creer 3y ago
I looked into it years ago and adding to, say, opencyc, really did not seem simple. There was a lot of detail in the entity descriptions. Even reading them seemed to required an awful lot of background knowledge of the system. It may have been possible to at least add lots of parallel items / instances. For example more authors and books and music works and performers, etc. Anyone here built a system around opencyc? Or cyc?
brundolf 3y ago
If I recall, Cyc did exactly that (imported data from WikiData) Unfortunately there was much more to it than ingesting large volumes of structured entities

jmj 3y ago

I’m working on old fashioned A.I. for my PhD. I wrote Doug a few times, he was very kind and offered very good advice. I was hoping to work with him one day. I’ll miss you Doug.

nairboon 3y ago
What are you working on?

dang 3y ago

Lenat's post about Wolfram Alpha, mentioned in the OP, was discussed (a bit) at the time: Doug Lenat – I was positively impressed with Wolfram Alpha - https://news.ycombinator.com/item?id=510579 https://news.ycombinator.com/item?id=510579 - March 2009 (17 comments) And of course, recent and related: Doug Lenat has died - https://news.ycombinator.com/item?id=37354000 https://news.ycombinator.com/item?id=37354000 - Sept 2023 (170 comments)

dekhn 3y ago

I recommend reading Norvig's thinking about the various cultures. https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf https://static.googleusercontent.com/media/research.google.c... and https://norvig.com/chomsky.html https://norvig.com/chomsky.html In short, Norvig concludes there are several conceptual approaches to ML/AI/Stats/Scientific analysis. One is "top down": teach the system some high level principles that correspond to known general concepts, and the other is "bottom up": determine the structure from the data itself and use that to generate general concepts. He observes that while the former is attractive to many, the latter has continuously produced more and better results with less effort. I've seen this play out over and over. I've concluded that Norvig is right: empirically based probabilistic models are a cheaper, faster way to answer important engineering and scientific problems, even if they are possibly less satisfying intellectually. Cheap approximations are often far better than hard to find analytic solutions.

golol 3y ago
this is the same concept as the bitter lesson, am I correct? I don't see a substantial difference yet.
- dekhn 3y ago
  I hadn't read that before, but yes. Sutton focuses mostly on "large amounts of compute" whereas I think his own employer has demonstrated that it's a combination of large amount of compute, large amounts of data, and really clever probabilistic algorithms, in combination, which really demonstrate the utility of the bitter lesson. And speaking as a biologist for a moment, that minds are irredeemably complex and attemptng to understand them with linear, first-order rules and logic is unlikely to be fruitful.
jyscao 3y ago
> One is "top down": teach the system some high level principles that correspond to known general concepts, and the other is "bottom up": determine the structure from the data itself and use that to generate general concepts. This is the same pattern explaining why bottom-up economic systems, i.e. lassaire faire free markets, flawed as they are, work better than top-down systems like central planning.

ansible 3y ago

Symbolic knowledge representation and reasoning is a quite interesting field. I think the design choices of projects like wikidata.org and CYC severely limit the application of this though. For example, on the wikidata help page, they talk about the height of Mount Everest: https://www.wikidata.org/wiki/Help:About_data#Structuring_data https://www.wikidata.org/wiki/Help:About_data#Structuring_da... Earth (Q2) (item) → highest point (P610) (property) → Mount Everest (Q513) (value) and Mount Everest (Q513) (item) → instance of (P31) (property) → mountain (Q8502) (value) So that's all fine, but it misses a lot of context. These facts might be true for the real world, right now, but they won't always be true. Even in the not-so-distant past, the height of Everest was lower, because of tectonic plate movement. And maybe in the future it will go even higher due to tectonics, or maybe it will go lower due to erosion. Context awareness gets even more important when talking about facts like "the iPhone is the best selling phone", for example. That might be true right now, but it certainly wasn't true back in 2006, before the phone was released. Context also comes in many forms, which can be necessary for useful reasoning. For example, consider the question: "What would be the highest mountain in the world, if someone blew up the peak of Everest with a bomb?" This question isn't about the real world, right here and right now, it is about a hypothetical world that doesn't exist. Going a little further afield, you may want to ask a question like "Who is the best captain of the Enterprise?". This might be about the actual US Navy CVN-64 ship named "Enterprise", the planned CVN-80, or the older ship CV-6 Enterprise which fought in WW2. Or maybe a relevant context to the question was "Star Trek", and we're in one of several fictional worlds instead, which would result in a completely different set of facts. I think some ability to deal with uncertainly (as with Probabilistic Graphical Models) is also necessary to deal with practical applications of this technology. We may be dealing with a mix of "objective facts" (well, let's not get into a discussion about the philosophy of science) and other facts that we may not be so certain about. It seems to me that successful symbolic reasoning system will be very, very large and complex. I'm not at all sure even how such knowledge should be represented, never mind the issue of trying to capture it all in digital form.

dizzystar 3y ago

I worked at Cycorp as a contractor for a few months. Lenat was, without a doubt, the smartest person I ever met in my life. There are levels of scary smart... his intelligence was downright frightening. I'm sad to see so many call his work a failure. His work was anything but a failure. I think this perception comes down to the fact that the company worked on many things that aren't public, and he do much publicity and self-promotion.

mycall 3y ago

I wonder if there is a way to mix ResearchCyc graph with a high feature count vector database so a transformer has a directed acyclic graph instead of requiring gradient descent. I'm not an expert so that might be gibberish.