|
| ChaitanyaSai wrote:
| Great read. Surprised to read Wolfram never actually got to use
| CYC. Anyone here who has and can talk about its capabilities?
| nvm0n2 wrote:
| I played with OpenCyc once. It was quite hard to use because
| you had to learn things like CycL and I couldn't get their
| natural language processing module to work.
|
| The knowledge base was impressively huge but it also took a lot
| of work to learn because at the lower levels it was extremely
| abstract. A lot of the assertions in the KB were establishing
| very low level stuff that only made sense if you were really
| into abstract logic or philosophy.
|
| They made bold claims on their website for what it could do,
| but I could never reproduce them. There was supposedly a more
| advanced version called ResearchCyc though, which I didn't have
| access to.
| creer wrote:
| That was exactly my reaction to it: it seemed to require
| sooooo much background knowledge about the entire system to
| do anything. And because you were warned about issues with
| consistency it seemed you were warned about just fudging some
| things. That it was a quick way to an application that
| couldn't work. The learning curve seemed daunting.
| gumby wrote:
| Some of us who worked on Cyc commented in an earlier post about
| Doug's decease.
| lispm wrote:
| Wolfram is able to write it in such a way that somehow it is
| mostly about him. :-(
|
| There is some overlap between Cyc and his Alpha. Cyc was
| supposed to provide a lot of common sense knowledge, which
| would be reusable. When Expert Systems were a thing, one of the
| limiting factor were said to be limited amount of broader
| knowledge of the world. Knowledge a human learns by experience,
| interacting with the world. This would involve a lot of facts
| about the world and also about all kinds of exceptions
| (Example: a mother typically is older than its child, unless
| the child was adopted and the mother is younger). Cyc knows a
| lot of 'facts' and also many ways of logic reasoning plus many
| logic 'reasoning rules'.
|
| Wolfram Alpha has a lot of knowledge about facts, often in some
| form of maths or somewhat structured data.
| dang wrote:
| Ok, but let's avoid doing the mirror image thing where we
| make the thread about Wolfram doing that.
|
| https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que.
| ..
| lispm wrote:
| Well, it's a disappointing and shallow read, because the
| topic of the usefulness of combining Cyc and Alpha would
| have been interesting.
| stakhanov wrote:
| 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.
| zozbot234 wrote:
| Doesn't description logic mostly boil down to multi-modal
| logic, which ought to be representable as a fragment of FOL
| (w/ quantifiers ranging over "possible worlds")?
|
| Description logic isn't just found in Cyc, either; Semantic
| Web standards are based on it, for similar reasons - it's key
| to making general inference computationally tractable.
| stakhanov wrote:
| I'm not trying to be dismissive of description logics. (And
| I'm not dismissive of Lenat and his work, either). A lot of
| things can fall under that umbrella term. The history of
| description logic may in fact be just as old as post-
| syllogism first-order predicate calculus (the syllogism is,
| of course, far older, dating back to Aristotle). In the
| Principia Mathematica there's a quantifier that basically
| means "the", which is incidentally also the most common
| word in the English language, and that can be thought of as
| a description logic too. But the perspective of a
| Mathematician on this is very different from that of an AI
| systems "practitioner", and CYC seemed to belong more to
| the latter tradition.
| MichaelZuo wrote:
| That's fascinating to read, thanks for sharing.
|
| Did it ever do something genuinely surprising? That seemed
| beyond the state-of-the-art at the time?
| stakhanov wrote:
| One of the people from Cyc gave a talk at the research
| group I was in once and mentioned an idea that kind of
| stuck with me.
|
| ...sorry, it takes some building-up to this: At the time, a
| lot of work in NLP was focused on building parsers that
| were trying to draw constituency trees from sentences, or
| extract syntactic dependency structures, but do so in a way
| that completely abstracted away from semantics, or looked
| at semantics as an extension of syntax, but not venturing
| into the territory of inference and common sense. So, a
| sentence like "Green ideas sleep furiously" (to borrow from
| Chomsky's example), was just as good as a research object
| to someone doing that kind of research as a sentence that
| actually makes sense and is comprised of words of the same
| lexical categories, like "Absolute power corrupts
| absolutely". -- I suspect, that line of research is still
| going strong, so the past tense may not be quite
| appropriate here. I'm using it, because I have been so out
| of the loop since leaving academia.
|
| The major problem these folk are facing is an exploding
| combinatorial space of ambiguity at the grammatical level
| ("I saw a man with a telescope" can be bracketed "I saw (a
| man) with a telescope" or "I saw a (man with a telescope)")
| and the semantic level ("Every man loves a woman" can mean
| "For every man M there exists a woman W, such that M loves
| W" or it can mean "There exists a woman W, such that for
| every man M it is true that M loves W"). Even if you could
| completely solve the parsing problem, the ambiguity problem
| would remain.
|
| Now this guy from the Cyc group said: Forget about parsing.
| If you give me the words that are in the sentence and
| you're not even giving me any clue about how the words were
| used in the sentence, I can already look into my ontology
| and tell you how the ontology would be most likely to
| connect the words.
|
| Now, the sentence "The cat chased the dog" obviously means
| something different from "The dog chased the cat" despite
| using the same words. But in most text genres, you're
| likely to only encounter sentences that are saying things
| that are commonly held as true. So if you have an ontology
| that tells you what's commonly held as true, that gives you
| a statistical prior that enables you to understand
| language. In fact, you probably can't hope to understand
| language without it, and it's probably the key to
| "disambiguation".
|
| This thought kind of flipped my worldview upside down. I
| had always kind of thought of it as this "pipelined
| architecture" where you first need to parse the text,
| before it even makes sense to think about how to solve the
| problems of what to do with the output from that parser.
| But that was unnecessarily limiting. You can look at the
| problem as a joint-decoding problem, and it may very well
| be the case that the lion's share of entropy comes from
| elsewhere, and it may be foolish to go around trying to
| build parsers, if you haven't yet hooked up your system to
| the information source that provides the lion's share of
| entropy, namely common-sense knowledge.
|
| Now, I don't think that Cyc had gotten particularly close
| to solving that problem either, and, in fact, it was a bit
| uncharacteristic for a "Cycler" to talk about statistical
| priors at all, as their work hadn't even gotten into the
| territory of collecting those kinds of statistics. But, as
| a theoretical point, I thought it was very valid.
| jmj wrote:
| 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 wrote:
| What are you working on?
| dekhn wrote:
| I recommend reading Norvig's thinking about the various cultures.
|
| https://static.googleusercontent.com/media/research.google.c...
| and 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 wrote:
| this is the same concept as the bitter lesson, am I correct? I
| don't see a substantial difference yet.
| dekhn wrote:
| 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 wrote:
| > 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.
| richardjam73 wrote:
| 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 wrote:
| This is very fascinating. Is there somewhere a review of Cyc
| regarding its abilities compared to other systems?
| rjsw wrote:
| Maybe read a bit about AM and Eurisko first, that will give an
| idea of how Cyc was expected to get used.
| cabalamat wrote:
| 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.
| HarHarVeryFunny wrote:
| 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.
| zozbot234 wrote:
| > I missed the news of Doug Lenat's passing. He died a few days
| ago on August 31st.
|
| Discussed https://news.ycombinator.com/item?id=37354000 (172
| comments)
| HarHarVeryFunny wrote:
| Thanks!
| golol wrote:
| Imo LLMs are absolutely the CYC dream come true. Common sense
| rules are learned from the data instead of hand written.
| detourdog wrote:
| I understand what you are saying. I'm able to see that
| brittleness as feature. The brittleness must be expressed so
| that the user of the model understands the limits and why the
| brittleness exists.
|
| My thinking is that the next generation of computing will rely
| on the human bridging that brittleness gap.
| zozbot234 wrote:
| The thing about "expert systems" is that they're just
| glorified database query. (And yes, you can do also
| 'semantic' inference in a DB simply by adding some views.
| It's not generally done because it's quite computationally
| expensive even for very simple taxonomy structures, i.e. 'A
| implies B which implies C and foo is A, hence foo is C'.)
|
| Database query is of course ubiquitous, but not generally
| thought of as 'AI'.
| brundolf wrote:
| > 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
|
| It's still going! I agree it's become clear that it probably
| isn't the road to AGI, but it still employs people who are
| still encoding rules and making the inference engine faster,
| paying the bills mostly by doing contracts from companies that
| want someone to make sense of their data warehouses
| Taikonerd wrote:
| It is? Are there success stories of companies using Cyc?
|
| I always had the impression that Cycorp was sustained by
| government funding (especially military) -- and that,
| frankly, it was always premised more on what such software
| _could theoretically_ do, rather than what it actually did.
| brundolf wrote:
| They did primarily government contracts for a long time,
| but when I was there (2016-2020) it was all private
| contracts
|
| The contracts at the time were mostly skunkworks/internal
| to the client companies, so not usually highly publicized.
| A couple examples are mentioned on their website:
| https://cyc.com/
| nvm0n2 wrote:
| Cyc was ahead of its time in a couple of ways:
|
| 1. Recognizing that AI was a scale problem.
|
| 2. Understanding that common sense was the core problem to
| solve.
|
| Although you say Cyc couldn't do common sense reasoning, wasn't
| that actually a major feature they liked to advertise? IIRC a
| lot of Cyc demos were various forms of common sense reasoning.
|
| I once played around with OpenCyc back when that was a thing.
| It was interesting because they'd had to solve a lot of
| problems that smaller more theoretical systems never did. One
| of their core features is called microtheories. The idea of a
| knowledge base is that it's internally consistent and thus can
| have formal logic be performed on it, but real world knowledge
| isn't like that. Microtheories let you encode contradictory
| knowledge about the world, in such a way that they can layer on
| top of the more consistent foundation.
|
| A very major and fundamental problem with the Cyc approach was
| that the core algorithms don't scale well to large sizes.
| Microtheories were also a way to constrain the computational
| complexity. LLMs work partly because people found ways to make
| them scale using GPUs. There's no equivalent for Cyc's
| predicate logic algorithms.
| HarHarVeryFunny wrote:
| > IIRC a lot of Cyc demos were various forms of common sense
| reasoning.
|
| I never got to try it myself, but no doubt it worked fine in
| those cases where correct inferences could be made based on
| the knowledge/rules it had! Similarly GPT-4 is extremely
| impressive when it's not bullshitting!
|
| The brittleness in either case (CYC or LLMs) comes mainly
| from incomplete knowledge (unknown unknowns), causing an
| invalid inference which the system has no way to detect and
| correct. The fix is a closed loop system where incorrect
| outputs (predictions) are detected - prompting exploration
| and learning.
|
| I don't know if CYC tried to do it, but one potential speed
| up for a system of that nature might be chunking, which is a
| strategy that another GOFAI system, SOAR, used successfully.
| A bit like using memoization (remembering results of work
| already done) as a way to optimize dynamic programming
| solutions.
| TimPC wrote:
| 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.
| wpietri wrote:
| > The end result is much he same though - an ultimately brittle
| system who's Achilles' heel is that it is based on a fixed set
| of knowledge
|
| I think CYC is a great cautionary tale for LLMs in terms of
| hope vs reality, but I think it's worse than that. I don't
| think LLMs have knowledge; they just mimic the ways we're used
| to expressing knowledge.
| alexpotato wrote:
| 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
| specialist wrote:
| 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.
| cabalamat wrote:
| > Negative results are super important.
|
| I agree, and this is often overlooked. Knowing what doesn't
| work (and why) is a massive help in searching for what does
| work.
| zozbot234 wrote:
| > 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 wrote:
| 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
| cliche about the limits of logic wrt the real world.
| patrec wrote:
| > 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.
|
| The problem is that Doug Lenat trying very hard is only useful
| as a data point if you have some faith in Doug Lenat making
| something that _is_ reasonably workable work by trying very
| hard.
|
| Do you have a reason for thinking so? I'm genuinely curious:
| lots of people have positive reminiscences about Lenat, who
| seems to have been likeable and smart, but on my (admittedly
| somewhat shallow attempts) I always keep drawing blanks when
| looking for anything of substance he produced or some deeper
| insight he had (even before Cyc).
| creer wrote:
| I also feel it's great and useful that Lenat and crew tried
| so hard. There is no doubt that a ton of work went into cyc.
| It was a serious, well funded, long term project and
| competent people put effort in making it work. And there are
| some descriptions of how they went about it. And opencyc was
| released.
|
| But some projects - or at least the breakthroughs they
| produce - are highly published as papers, which can be
| studied by outsiders. And that is not the case of cyc. There
| are some reports and papers but really not many that I have
| found. And so it's not clear how solid or generalizable it is
| as a data point.
| mhewett wrote:
| Lenat was my assigned advisor when I started my Masters at
| Stanford. I met with him once and he gave me some advice on
| classes. After that he was extremely difficult to schedule a
| meeting with (for any student, not just me). He didn't get
| tenure and left to join MCC after that year. I don't think I
| ever talked to him again after the first meeting.
|
| He was extremely smart, charismatic, and a bit arrogant (but
| a well-founded arrogance). From other comments it sounds like
| he was pleasant to young people at Cycorp. I think his peers
| found him more annoying.
|
| His great accomplishments were having a multi-decade vision
| of how to build an AI and actually keeping the vision alive
| for so long. You have to be charismatic and convincing to do
| that.
|
| In the mid-80s I took his thesis and tried to implement AM on
| a more modern framework, but the thesis lacked so many
| details about how it worked that I was unable to even get
| started implementing anything.
|
| BTW, if there are any historians out there I have a copy of
| Lenat's thesis with some extra pages including emailed
| messages from his thesis advisors (Minsky, McCarthy, et al)
| commenting on his work. I also have a number of AI papers
| from the early 1980s that might not be generally available.
| mietek wrote:
| I'd be quite interested to see these materials.
|
| What's your take on AM and EURISKO? Do you think they
| actually performed as mythologized? Do you think there's
| any hope of recovering or reimplementing them?
| eschaton wrote:
| It'd be amazing to get those papers and letters digitized.
| skissane wrote:
| > And I hope the future holds some kind of synthesis of these
| strategies.
|
| Recently I've been involved in discussions about using an LLM
| to generate JSON according to a schema, as in OpenAI's function
| calling or Jsonformer-LLMs do okay for generating code in
| mainstream languages like SQL or Python, but what if you have
| some proprietary query language? Maybe have a JSON schema for
| the AST, have the LLM generate JSON conforming to that schema,
| then serialise the JSON to the proprietary query language
| syntax?
|
| And it makes me think - what if one used an LLM to generate or
| evaluate assertions in a Cyc-style ontology language? And that
| might be a bridge between the logic/ontology approach and the
| statistical/neural approach
| jebarker wrote:
| This is similar to what people are trying for mathematical
| theorem proving. Using LLMs to generate theorems that can be
| validated in Lean.
| nvm0n2 wrote:
| I had the same idea last year. But it's difficult. To encode
| knowledge in CycL required intensive training, mostly in how
| their KB encoded very abstract concepts and "obvious"
| knowledge. They used to boast about how they had more
| philosophy PhDs than anywhere else.
|
| It's possible that an LLM that's been trained on enough
| examples, and that's smart enough, could actually do this.
| But I'm not sure how you'd review the output to know if it's
| right. The LLM doesn't have to be much faster than you to
| overwhelm the capacity of reviewing the results.
| theptip wrote:
| This might work; you can view it as distilling the common
| knowledge out of the LLM.
|
| You'd need to provide enough examples of CycL for it to learn
| the syntax.
|
| But in my experience LLMs are not great at authoring code
| with no ground truth to test against. So the LLM might
| hallucinate some piece of common knowledge, and it could be
| hard to detect.
|
| But at the highest level, this sounds exactly how the
| WolframAlpha ChatGPT plug-in works; the LLM knows how to call
| the plugin and can use this to generate graphs or compute
| numerical functions for domains where it cannot compute the
| result directly.
| at_a_remove wrote:
| I really do believe (believe, rather than know) that some sort
| of synthesis is necessary, that there's some base facts and
| common sense that would make AI, as it stands, more reliable
| and trustworthy if had some kind of touchstone, rather than the
| slipshod "human hands come with thumbs and fingers" output we
| have now. Something that can look back and say, "Typically,
| there's just one thumb and there's four fingers. Sometimes not,
| but that is rare."
| ansible wrote:
| 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_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.
| dang wrote:
| 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 - March 2009 (17
| comments)
|
| And of course, recent and related:
|
| _Doug Lenat has died_ -
| https://news.ycombinator.com/item?id=37354000 - Sept 2023 (170
| comments)
| ks2048 wrote:
| 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 wrote:
| > 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 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 valedixit_? So this should
| probably be understood as _mostly_ language-independent, enough
| to be practically useful.
| brundolf wrote:
| 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
| creer wrote:
| 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?
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