29 ms·

Cyc

357 points by mdszy 7y ago

lidHanteyk 7y ago
I think that this particular topic is evergreen because people are perennially surprised that this technology, which seems so reasonable and advanced at first blush, has failed to be useful in practice.
- The_rationalist 7y ago
  There's still no complete explanation on why it is a failure. What are the technical difficulties they can't overcome?
  - goatlover 7y ago
    Maybe because the world and human knowledge are incredibly complex and difficult things to put into logical relations well enough to achieve more success? Consider that humans learn though having bodies to explore the world with, while forming a variety of social relations to learn the culture. Which is very different from encoding a bunch of rules to make up an intelligence.
    - The_rationalist 7y ago
      Creating a software that learns the real world indeed seems like a really hard problem without a body. I was referring to why a software that parse the semantics of Wikipedia articles and make them queryable through natural language questions, is something that humanity isn't able to do?
      - goatlover 7y ago
        That might depend on how much semantics is related to having a body. We do utilize quite a lot of metaphors that are based on the kinds of bodies and senses we have. The question here is how much embodiment is necessary for understanding semantics. Maybe it's possible to brute force around that with ML, or stack enough human hours into building the right ontologies in symbolic AI. But maybe not. I think people like Rodney Brooks are of the belief you need to start with robots that learn their environment and build up from there.
- xamuel 7y ago
  I spent a year doing an ontology postdoc. I can't speak for Cyc, but from what I saw of the ontology world, there are a lot of charlatans and people who are using it as a buzzword to make grant proposals sexier. Whether or not there's real potential in the technology, that kind of environment surely isn't conducive to achieving said potential. An outsider trying to peek into the field is immediately swamped by vast oceans of garbage, and everyone in the field is an expert at marketing themselves so if there are legitimate researcher gems in the field, I don't know how you'd actually find them from amidst all the noise.
  - Jeff_Brown 7y ago
    This supply-side opacity is definitely a problem. There seems to be a corresponding demand-side problem, that clients often don't know quite what they want. If there was an easy way of generating hard tests with clear-cut answers, maybe there would be an easy way for a winner to distinguish themselves.
vanniv 7y ago
I can't believe this is still a thing. Then again, I felt the same way when I studied it at university almost 20 years ago. It was pretty obviously a pipe dream then, too.
- magnifique 7y ago
  I thought so on first glance too, but from what I've heard from someone who worked there, it's working software and it makes a lot of money. That's the opposite of a pipe dream, even if far short of AGI.
  - yters 7y ago
    Is it making real money, or speculative money on the moon shot premise that AGI will rule it all if successful? I worked at an AI company before, and it was the latter.
    - aidenn0 7y ago
      My understanding is mostly the latter, but definitely also the former, but it's based off of "I worked there, but our customer list isn't public so I can't tell you who" type statements like you'll see elsewhere. If I had to guess what it's been actually used for, I'd wager it's money laundering or counter-terrorism type stuff; it's fairly well suited to finding connections between people and entities given a large data-set, and unlike many ML models, it can tell you why it thinks someone is suspicious, which might be needed for justifying further investigation. This is a completely wild-ass guess though so take with a giant grain of salt.
      - yters 7y ago
        Yes that was the sort of usecase for the AI company I worked at, similar data mining competing with Palantir.
coldcode 7y ago
While this approach might seem dated and strange, at some point something will begin to approach the ability to do general learning like a human. I just wonder how long we have to wait.
- The_rationalist 7y ago
  Eternity? If all we do is waiting... Almost nobody is really working on AGI and this is the main issue. A notable counter example is the recent reconversion of John Carmack.
- yters 7y ago
  What if general learning is uncomputable?
  - Rerarom 7y ago
    General learning is uncomputable, it's called Solomonoff induction. You don't need general learning, you need something at least as powerful as the mess in a human brain.
    - yters 7y ago
      What if the mess in the human brain is powered by an uncomputable transcendent mind?
      - Rerarom 7y ago
        first read this as "uncomputable translucent mind" and I loved the imagery
    - lorepieri 7y ago
      Can you provide some references on "General learning is uncomputable"? Thanks.
      - Rerarom 7y ago
stereolambda 7y ago
Knowledge bases should work in principle. There are many issues with filling them manually: a) the schema/ontology/conceptual framework is not guaranteed to be useful especially when done with no specific application in mind b) high cost of adding each fact with little marginal benefit etc. But I don't think it outweighs the issues of "pure" machine learning that much: poor introspection, capricious predictability of what you will get, and if you want to have really structured and semi-reliable information you will probably have to rely, at some point, on something like Wikipedia meta-information (DBpedia). Which is really a knowledge base with its own issues. I think what really stopped Cyc from getting a wider traction is its closed nature[0]. People do use Princeton WordNet, which you can get for free, even though it's a mess in many aspects. The issue and mentality here is similar to commercial Common Lisp implementations, and the underlying culture is similar (oldschool 80s AI). These projects were shaped with a mindset that major progress in computing will happen with huge government grants and plans[1]. However you interpret the last 30 years, it was not exactly true. It's possible that all these companies earn money for their owners, but they have no industry-wide impact. I was half-tempted once or twice to use something like Cyc in some project, but it would probably be too much organizational hassle. Especially if it turned out to be something commercial I wouldn't want to be dependent on someone's licensing and financial whims, especially if it can be avoided. [0] There was OpenCyc for a time, but it was scrapped. [1] Compare https://news.ycombinator.com/item?id=20569098 https://news.ycombinator.com/item?id=20569098
- emw 7y ago
  > if you want to have really structured and semi-reliable information you will probably have to rely, at some point, on something like Wikipedia meta-information (DBpedia). Wikidata is also worth considering for that task. It is: * Directly linked from Wikipedia [1] * The data source for many infoboxes [2] * Seeded with data from Wikipedia * More active and integrated in community * Larger in total number of concepts Wikidata also has initiatives in lexicographic data [3] and images [4, 5]. On the subject of Cyc: the CycL "generalization" (#$genls) predicate inspired Wikidata's "subclass of" property [6], which now links together Wikidata's tree of knowledge. --- 1. See "Wikidata" link at left in all articles, e.g. https://en.wikipedia.org/wiki/Knowledge_base https://en.wikipedia.org/wiki/Knowledge_base 2. https://en.wikipedia.org/wiki/Category:Infobox_templates_using_Wikidata https://en.wikipedia.org/wiki/Category:Infobox_templates_usi... 3. https://www.wikidata.org/wiki/Wikidata:Lexicographical_data/Documentation#Introduction https://www.wikidata.org/wiki/Wikidata:Lexicographical_data/... 4. https://www.wikidata.org/wiki/Wikidata:Wikimedia_Commons/Development#Statistics https://www.wikidata.org/wiki/Wikidata:Wikimedia_Commons/Dev... 5. See "Structured data" tab in image details on Wikimedia Commons, e.g. https://commons.wikimedia.org/wiki/File:Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg#ooui-php-1 https://commons.wikimedia.org/wiki/File:Mona_Lisa,_by_Leonar... 6. https://www.wikidata.org/wiki/Property_talk:P279#Archived_creation_discussion https://www.wikidata.org/wiki/Property_talk:P279#Archived_cr...
PeterStuer 7y ago
Cyc was the last holdout of GOFAI in the 90's, its premise being that the traditional symbolic AI paradigm wasn't wrong, but that it was just a matter of scale.
- The_rationalist 7y ago
  Almost all AGI projects use symbolic AI. It is a misconception to believe that connexionism has won, it only leads narrow tasks that help to build the higher level thing that is AGI.
vsskanth 7y ago
Is it possible that the inherent context dependent ambiguities of human language make knowledge-based inference so difficult since most current knowledge is stored in human language ? tangential question: is there a standard language for "knowledge", like how we describe math for "computation" ? Are a part of our brains essentially compilers from human language to an internal definition of "knowledge" that leads to consciousness ?
- sp332 7y ago
  There are a bunch of "standards" for representing knowledge. E.g. https://en.wikipedia.org/wiki/Semantic_Web https://en.wikipedia.org/wiki/Semantic_Web [Edit] Here's a wider overview: https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning https://en.wikipedia.org/wiki/Knowledge_representation_and_r...
  - vsskanth 7y ago
    Thanks for the link. It seems to talk about a knowledge graph type links between entities. It is however in a human language (here, english). I am interested in knowing if there's something analogous to "math" to represent knowledge.
    - sp332 7y ago
      The knowledge is represented in the links. Words don't inherently mean anything. The meaning of a word is how it relates to other words. The "math" of knowledge representation is in manipulating and searching the graph. The nodes can be named anything, because the names aren't knowledge, they're just tags on parts of the knowledge.
    - XuMiao 7y ago
      Most of human knowledge are represented in logic. Semantic web is designed from description logic. A less powerful logic than first order logic.
- Jeff_Brown 7y ago
  Attempto Controlled English[1] is a cool project: it's a formal language, but a subset of ordinary English. My own Hode, described in an earlier comment[2], makes it easy for anyone who speaks some natural language to enter and query arbitrary structured data. [1] https://en.wikipedia.org/wiki/Attempto_Controlled_English https://en.wikipedia.org/wiki/Attempto_Controlled_English [2] https://news.ycombinator.com/item?id=21784617 https://news.ycombinator.com/item?id=21784617
one_electron 7y ago
in my experience, most people dismiss cyc as a failed science experiment. this shouldnt be! after all, many important deep learning concepts have their roots in the 80s, and it is possible that cyc could be revived too.
- kick 7y ago
  Cyc itself probably won't be: proprietary information is something that modern scientists tend to know better than to invest time in. Symbolic AI, though, hasn't really died.
- jacquesm 7y ago
  CYC effectively died the day OpenCYC died. There is no way that an entity that tries to catalogue human knowledge in this way will thrive on a closed set of data, there are only so many people working there. Just like the Encyclopedia Brittanica has found its match in WikiPedia so CYC will find its match in something open. The engine - if the comments here are to be believed as still currently relevant - is a core that may be relevant and a huge number of domain specific hacks. Let's hope sooner or later CYC management comes to their senses and revives OpenCYC.
  - rademaker 7y ago
    That is SUMO Ontology (http://ontologyportal.org http://ontologyportal.org)! It is open, in GitHub and people can contribute.
gavanwoolery 7y ago
I immediately recognized the headline even though its been 15 years since I last read up on Cyc. I still think the potential of lambda calculus in knowledge representation and logical deduction is high and under-represented in research. Just theorizing, but I think a large part of the problem is the difficulty in interfacing this knowledge base with manual, human entry. Another pitfall is the difficulty in determining strange or unanticipated logical outcomes, and developing a framework to catch or validate these.
- rademaker 7y ago
  I have been working on that direction with Lean Theorem Prover (https://leanprover.github.io https://leanprover.github.io). There is also works using Coq (https://link.springer.com/chapter/10.1007/978-3-642-35786-2_11 https://link.springer.com/chapter/10.1007/978-3-642-35786-2_...)
jes5199 7y ago
last time I looked at OpenCyc's knowledge base, the information encoded was all strangely specific academic stuff - like very fine classifications and relationships between species of tapeworms and of fungus. There was very little daily-life common-sense knowledge, even though that's often the hook in interviews and articles about Cyc's purpose. I'm not sure why that's true - maybe it's hard to decide what the 'facts' are about normal human life, but the more academic something is, the more there's a consensus, rationalized 'reality'
- stereolambda 7y ago
  This happens also with similar bases in my experience. I suspect it's very much an economic phenomenon. It's easy to hire someone to input relations from a textbook if not from Wikipedia. It's hard to find someone with credentials for "understanding everyday life on philosophical level", and for logical formalisms, and also no overriding penchant for philosophical bikeshedding. Even then, it would be a time-consuming and thus also costly process.
- brundolf 7y ago
  Employee of Cycorp here. A few thoughts: - At least right now, we have a good amount of common-sense information about the world (I don't know when "last time" was for you). - That said, we have a lot of highly specialized knowledge in various domains, so if you took a random sample of the knowledge base (KB) it may not be as common-sense-centric as you'd hope. But the KB is also incredibly large, so that doesn't mean we don't have much common-sense, just that we have even more other stuff. - Often for contracts we get paid to construct lots of domain-specific knowledge, even if the project also uses the more general knowledge, so this biases the distribution some. - Information that's already well-taxonomized is low-hanging fruit for this kind of system; its representation doesn't take nearly as much extra thought and consideration, so it's a faster process, which also biases the distribution some.
  - jey 7y ago
    What are some interesting examples of common-sense that has been formalized and encoded?
catpolice 7y ago
I worked for Cycorp for a few years recently. AMA, I guess? I obviously won't give away any secrets (e.g. business partners, finer grained details of how the inference engine works), but I can talk about the company culture, some high level technical things and the interpretation of the project that different people at the company have that makes it seem more viable than you might guess from the outside. There were some big positives. Everyone there is very smart and depending on your tastes, it can be pretty fun to be in meetings where you try to explain Davidsonian ontology to perplexed business people. I suspect a decent fraction of the technical staff are reading this comment thread. There are also some genuine technical advances (which I wish were more publicly shared) in inference engine architecture or generally stemming from treating symbolic reasoning as a practical engineering project and giving up on things like completeness in favor of being able to get an answer most of the time. There were also some big negatives, mostly structural ones. Within Cycorp different people have very different pictures of what the ultimate goals of the project are, what true AI is, and how (and whether) Cyc is going to make strides along the path to true AI. The company has been around for a long time and these disagreements never really resolve - they just sort of hang around and affect how different segments of the company work. There's also a very flat organizational structure which makes for a very anarchic and shifting map of who is responsible or accountable for what. And there's a huge disconnect between what the higher ups understand the company and technology to be doing, the projects they actually work on, and the low-level day-to-day work done by programmers and ontologists there. I was initially pretty skeptical of the continued feasibility of symbolic AI when I went in to interview, but Doug Lenat gave me a pitch that essentially assured me that the project had found a way around many of the concerns I had. In particular, they were doing deep reasoning from common sense principles using heuristics and not just doing the thing Prolog often devolved into where you end up basically writing a logical system to emulate a procedural algorithm to solve problems. It turns out there's a kind of reality distortion field around the management there, despite their best intentions - partially maintained by the management's own steadfast belief in the idea that what Cyc does is what it ought to be doing, but partially maintained by a layer of people that actively isolate the management from understanding the dirty work that goes into actually making projects work or appear to. So while a certain amount of "common sense" knowledge factors into the reasoning processes, a great amount of Cyc's output at the project level really comes from hand-crafted algorithms implemented either in the inference engine or the ontology. Also the codebase is the biggest mess I have ever seen by an order of magnitude. I spent some entire days just scrolling through different versions of entire systems that duplicate massive chunks of functionality, written 20 years apart, with no indication of which (if any) still worked or were the preferred way to do things.
spirographer 7y ago
My 2 cents is that I can ask just about any question I can think of and absorb and internalize an amazing answer in 5 minutes of reading. Many of those same questions can be automatically asked and answered too. The web and search engines are realizing the promise far better than anything else.
- Jeff_Brown 7y ago
  God I wish I felt this way. It's true that I can get an amazing amount of information in a short time, but I don't know how accurate or complete it is, and I don't even know how to find out.
- goatlover 7y ago
  At a surface level and when you know what questions to ask.
wrnr 7y ago
The the following utterance, sort of looks like the triplet data structure used in graph/knowledge databases: "Alive loves Bob" What do you know? Nothing. Was it Alice who said she loves Bob, or was it Bob who said it is Alice who loves him, maybe Carol saw the way Alice looks at Bob and then conclude she must love him. What is love anyway? How exactly is the love Alice has for Bob quantitively different than my love of chocolate. It might register similar brain activity in a MRI scan, and yet we humans recognise them as qualitatively different. A knowledge base is useless if you can't judge wether a fact is true or false. The response to this problem was for the semantic web community to introduce a provenance ontology, but any attempt to reason over statements about statements seem to go nowhere. IMHO you can't solve the problem of AGI without also having a way for a rational agent to embody its thoughts in the physical world.
- Jeff_Brown 7y ago
  Agreed. Human thinking is arbitrarily high-order -- we use statements about statements about statements with no particular natural complexity limit. This seems to me the big limitation of knowledge graphs: The majority of real-world information, just like the majority of natural-language sentences, are highly nested relationships among relationships. That was my motivation for writing Hode[1], the Higher-Order Data Editor. It lets you represent arbitrarily nested relationships, of any arity (number of members). It lets you cursor around data to view neighboring data, and it offers a query language that is, I believe, as close as possible to ordinary natural language. (Hode has no inference engine, and I don't call it an AI project -- but it seems relevant enough to warrant a plug.) [1] https://github.com/JeffreyBenjaminBrown/hode https://github.com/JeffreyBenjaminBrown/hode
yters 7y ago
Why is there never any fundamental research whether human intelligence is even computable? All these huge, expensive projects based on an untested premise.
- Ididntdothis 7y ago
  I think it’s pretty certain that we can improve a lot. If that leads to human intelligence or something else we don’t know. But it’s worth working on improving things and trying different approaches even if the final result isn’t known.
  - yters 7y ago
    But there might be even better approaches if human intelligence is not computable. E.g. if the mind is a halting oracles that can get us all kinds of cool things.
    - inimino 7y ago
      If the mind were a halting oracle I don't think most of our open problems in mathematics would be.
      - yters 7y ago
        It's possible for the mind to solve more halting problems than any finite computer, yet still not be as powerful as a complete halting oracle. Thus, the fact we haven't solved every problem does not count as evidence against the mind being a halting oracle.
        inimino 7y ago
        Actually it does. While it's logically possible, evidence for a hypothesis A is still provided by any data that is more likely under hypothesis A than under hypothesis B. The hypothesis that the mind is computable but is using heuristics, of various levels of sophistication, explains the data better and is more parsimonious than your hypothesis, because we already have reason to believe that the mind uses heuristics extensively. Where you see uncomputable oracular insights, others see computable combinations of heuristics. If you introspect deeply enough while problem-solving, you may be able to sense the heuristics working prior to the flash of intuition.
deleted 7y ago
[deleted]
mblackstone 7y ago
Is it possible For a non-academic to get a ResearchCyc license? I’ve used OpenCyc in the past; now that I’m retired I’d like to look deeper into Cyc.
MauiWarrior 7y ago
I used to read about cyc here https://www.cyc.com/cycl-translations https://www.cyc.com/cycl-translations. But it says now "coming soon". Since we have folks from Cyc here, any ideas how soon?
Tistel 7y ago
I am familiar with prolog and know what it takes (ish) to make an old school expert system. I have heard about this project. Are there any demos of this system? Like a video sales pitch. I have always wanted to see it in action.
doctorphil 7y ago
For me it would be helpful with some more examples of how to formulate the query and how the reasoning would look. Could someone share some examples of "common knowledge" that they think are cool? Here are some common knowledge in English that I would love to see the system answer. - Is a dog owner likely to own a rope-like object? (Yes, they likely own a leash.) - Does the average North American own more than 1 shoestring? (Yes, most people have at least 2 shoes, and most shoes have shoestrings.) - Is it safer to fly or to travel by car?
joveian 7y ago
I can't find it but I distinctly remember that there was part of an episode of 3-2-1 contact in the 80s about what must have been an early version of this system. It was the exact same thing as brundolf mentions* about the common sense system and how they set up the system to ask questions when contradictions arose. An example they used was it had asked if a human is still human when shaving. It is interesting that the system still exists. Of course, I don't recall them mentioning any of the more dystopian things it could be (and sounds like has been) used for :/. * https://news.ycombinator.com/item?id=21784105 https://news.ycombinator.com/item?id=21784105 On second thought, it might have been an Alan Kay presentation. I couldn't find that either but looking I did find this interesting Wired article from 2016: https://www.wired.com/2016/03/doug-lenat-artificial-intelligence-common-sense-engine/ https://www.wired.com/2016/03/doug-lenat-artificial-intellig...
- tgbugs 7y ago
  Amusingly, or sadly, depending on your perspective, in practical settings the default behavior of that question depends on exactly what you want human to mean in the vocabulary of the local use case, because continuants are a very leaky abstraction if they are used to type biological systems, and while to our 'common sense' the type of human and human shaving should obviously be the same, when you get to questions about whether seemingly insignificant numerical differences in rates of catalysis constitute differences in type, then suddenly the distinction between "protein" and "protein wiggling slightly faster than usual" or "protein binding molecule a" (think "human holding shaver") becomes very much not obvious depending on exactly what question you want to answer. In the protein example if you black box the system, they can be fundamentally different. If human means predator, and your question is how dangerous is this human, then "human" and "human holding razor" becomes "agentous thing" and "agentous thing with sharp edged object" practically different things in very important ways if you are trying not to be filleted.
musicale 7y ago
I've always thought that being able to model the physical world at multiple levels of abstraction was pretty essential for trying to interact with it in a less brittle way. Moreover, having models of things that are interesting and relevant to humans seems pretty important for any system that interacts with humans. And it always seemed reasonable that any system that aims to use natural language should be able to represent the meaning of the sentences it uses in a clear and understandable format. Also "organizing the world's information" should make it usable in an automated fashion based on semantic models.
dex_tec 7y ago
1) What do you think about hybrid approach: hypergraphs + large-scale NLP models (transformers)? 2) How far we're from real self-evolving cognitive architectures with self-awareness features? Is it a question of years, months, or it's already solved problem? 3) Does it make sense to use embeddings like https://github.com/facebookresearch/PyTorch-BigGraph https://github.com/facebookresearch/PyTorch-BigGraph to achieve better results? 4) Why Cycorp decided to limit communication and collaboration with scientific community / AI-enthusiasts at some point? 5) Did you try to solve GLUE / SUPERGLUE / SQUAD challenges with your system? 6) Is Douglas Lenat still contribute actively to the project? Thanks
- choamnomsky 7y ago
  Doug Lenat is very much still active in the project. He doesn't do as much work building the ontology, but he plays a role in how various projects develop and provides a lot of feedback.
  - The_rationalist 7y ago
    How do you compare with SOAR and opencog/atomspace? Which6is the most promising AGI project according to you?
earenndil 7y ago
How does this compare to wolfram alpha?
lonelappde 7y ago
> require between 1000 and 3000 person-years [to input all the relevant facts in the world] Which is laughably small in retrospect. I wonder what current estimates are.
et2o 7y ago
This Wikipedia article is clearly not neutral in tone. It reads like the CEO wrote it.
jsonbourne 7y ago
http://conceptnet.io/ http://conceptnet.io/

357 points by mdszy 7y ago

I think that this particular topic is evergreen because people are perennially surprised that this technology, which seems so reasonable and advanced at first blush, has failed to be useful in practice.

The_rationalist 7y ago
There's still no complete explanation on why it is a failure. What are the technical difficulties they can't overcome?
- goatlover 7y ago
  Maybe because the world and human knowledge are incredibly complex and difficult things to put into logical relations well enough to achieve more success? Consider that humans learn though having bodies to explore the world with, while forming a variety of social relations to learn the culture. Which is very different from encoding a bunch of rules to make up an intelligence.
  - The_rationalist 7y ago
    Creating a software that learns the real world indeed seems like a really hard problem without a body. I was referring to why a software that parse the semantics of Wikipedia articles and make them queryable through natural language questions, is something that humanity isn't able to do?
    - goatlover 7y ago
      That might depend on how much semantics is related to having a body. We do utilize quite a lot of metaphors that are based on the kinds of bodies and senses we have. The question here is how much embodiment is necessary for understanding semantics. Maybe it's possible to brute force around that with ML, or stack enough human hours into building the right ontologies in symbolic AI. But maybe not. I think people like Rodney Brooks are of the belief you need to start with robots that learn their environment and build up from there.
xamuel 7y ago
I spent a year doing an ontology postdoc. I can't speak for Cyc, but from what I saw of the ontology world, there are a lot of charlatans and people who are using it as a buzzword to make grant proposals sexier. Whether or not there's real potential in the technology, that kind of environment surely isn't conducive to achieving said potential. An outsider trying to peek into the field is immediately swamped by vast oceans of garbage, and everyone in the field is an expert at marketing themselves so if there are legitimate researcher gems in the field, I don't know how you'd actually find them from amidst all the noise.
- Jeff_Brown 7y ago
  This supply-side opacity is definitely a problem. There seems to be a corresponding demand-side problem, that clients often don't know quite what they want. If there was an easy way of generating hard tests with clear-cut answers, maybe there would be an easy way for a winner to distinguish themselves.

vanniv 7y ago

I can't believe this is still a thing. Then again, I felt the same way when I studied it at university almost 20 years ago. It was pretty obviously a pipe dream then, too.

magnifique 7y ago
I thought so on first glance too, but from what I've heard from someone who worked there, it's working software and it makes a lot of money. That's the opposite of a pipe dream, even if far short of AGI.
- yters 7y ago
  Is it making real money, or speculative money on the moon shot premise that AGI will rule it all if successful? I worked at an AI company before, and it was the latter.
  - aidenn0 7y ago
    My understanding is mostly the latter, but definitely also the former, but it's based off of "I worked there, but our customer list isn't public so I can't tell you who" type statements like you'll see elsewhere. If I had to guess what it's been actually used for, I'd wager it's money laundering or counter-terrorism type stuff; it's fairly well suited to finding connections between people and entities given a large data-set, and unlike many ML models, it can tell you why it thinks someone is suspicious, which might be needed for justifying further investigation. This is a completely wild-ass guess though so take with a giant grain of salt.
    - yters 7y ago
      Yes that was the sort of usecase for the AI company I worked at, similar data mining competing with Palantir.

coldcode 7y ago

While this approach might seem dated and strange, at some point something will begin to approach the ability to do general learning like a human. I just wonder how long we have to wait.

The_rationalist 7y ago
Eternity? If all we do is waiting... Almost nobody is really working on AGI and this is the main issue. A notable counter example is the recent reconversion of John Carmack.
yters 7y ago
What if general learning is uncomputable?
- Rerarom 7y ago
  General learning is uncomputable, it's called Solomonoff induction. You don't need general learning, you need something at least as powerful as the mess in a human brain.
  - yters 7y ago
    What if the mess in the human brain is powered by an uncomputable transcendent mind?
    - Rerarom 7y ago
      first read this as "uncomputable translucent mind" and I loved the imagery
  - lorepieri 7y ago
    Can you provide some references on "General learning is uncomputable"? Thanks.
    - Rerarom 7y ago

stereolambda 7y ago

Knowledge bases should work in principle. There are many issues with filling them manually: a) the schema/ontology/conceptual framework is not guaranteed to be useful especially when done with no specific application in mind b) high cost of adding each fact with little marginal benefit etc. But I don't think it outweighs the issues of "pure" machine learning that much: poor introspection, capricious predictability of what you will get, and if you want to have really structured and semi-reliable information you will probably have to rely, at some point, on something like Wikipedia meta-information (DBpedia). Which is really a knowledge base with its own issues. I think what really stopped Cyc from getting a wider traction is its closed nature[0]. People do use Princeton WordNet, which you can get for free, even though it's a mess in many aspects. The issue and mentality here is similar to commercial Common Lisp implementations, and the underlying culture is similar (oldschool 80s AI). These projects were shaped with a mindset that major progress in computing will happen with huge government grants and plans[1]. However you interpret the last 30 years, it was not exactly true. It's possible that all these companies earn money for their owners, but they have no industry-wide impact. I was half-tempted once or twice to use something like Cyc in some project, but it would probably be too much organizational hassle. Especially if it turned out to be something commercial I wouldn't want to be dependent on someone's licensing and financial whims, especially if it can be avoided. [0] There was OpenCyc for a time, but it was scrapped. [1] Compare https://news.ycombinator.com/item?id=20569098 https://news.ycombinator.com/item?id=20569098

emw 7y ago
> if you want to have really structured and semi-reliable information you will probably have to rely, at some point, on something like Wikipedia meta-information (DBpedia). Wikidata is also worth considering for that task. It is: * Directly linked from Wikipedia [1] * The data source for many infoboxes [2] * Seeded with data from Wikipedia * More active and integrated in community * Larger in total number of concepts Wikidata also has initiatives in lexicographic data [3] and images [4, 5]. On the subject of Cyc: the CycL "generalization" (#$genls) predicate inspired Wikidata's "subclass of" property [6], which now links together Wikidata's tree of knowledge. --- 1. See "Wikidata" link at left in all articles, e.g. https://en.wikipedia.org/wiki/Knowledge_base https://en.wikipedia.org/wiki/Knowledge_base 2. https://en.wikipedia.org/wiki/Category:Infobox_templates_using_Wikidata https://en.wikipedia.org/wiki/Category:Infobox_templates_usi... 3. https://www.wikidata.org/wiki/Wikidata:Lexicographical_data/Documentation#Introduction https://www.wikidata.org/wiki/Wikidata:Lexicographical_data/... 4. https://www.wikidata.org/wiki/Wikidata:Wikimedia_Commons/Development#Statistics https://www.wikidata.org/wiki/Wikidata:Wikimedia_Commons/Dev... 5. See "Structured data" tab in image details on Wikimedia Commons, e.g. https://commons.wikimedia.org/wiki/File:Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg#ooui-php-1 https://commons.wikimedia.org/wiki/File:Mona_Lisa,_by_Leonar... 6. https://www.wikidata.org/wiki/Property_talk:P279#Archived_creation_discussion https://www.wikidata.org/wiki/Property_talk:P279#Archived_cr...

PeterStuer 7y ago

Cyc was the last holdout of GOFAI in the 90's, its premise being that the traditional symbolic AI paradigm wasn't wrong, but that it was just a matter of scale.

The_rationalist 7y ago
Almost all AGI projects use symbolic AI. It is a misconception to believe that connexionism has won, it only leads narrow tasks that help to build the higher level thing that is AGI.

vsskanth 7y ago

Is it possible that the inherent context dependent ambiguities of human language make knowledge-based inference so difficult since most current knowledge is stored in human language ? tangential question: is there a standard language for "knowledge", like how we describe math for "computation" ? Are a part of our brains essentially compilers from human language to an internal definition of "knowledge" that leads to consciousness ?

sp332 7y ago
There are a bunch of "standards" for representing knowledge. E.g. https://en.wikipedia.org/wiki/Semantic_Web https://en.wikipedia.org/wiki/Semantic_Web [Edit] Here's a wider overview: https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning https://en.wikipedia.org/wiki/Knowledge_representation_and_r...
- vsskanth 7y ago
  Thanks for the link. It seems to talk about a knowledge graph type links between entities. It is however in a human language (here, english). I am interested in knowing if there's something analogous to "math" to represent knowledge.
  - sp332 7y ago
    The knowledge is represented in the links. Words don't inherently mean anything. The meaning of a word is how it relates to other words. The "math" of knowledge representation is in manipulating and searching the graph. The nodes can be named anything, because the names aren't knowledge, they're just tags on parts of the knowledge.
  - XuMiao 7y ago
    Most of human knowledge are represented in logic. Semantic web is designed from description logic. A less powerful logic than first order logic.
Jeff_Brown 7y ago
Attempto Controlled English[1] is a cool project: it's a formal language, but a subset of ordinary English. My own Hode, described in an earlier comment[2], makes it easy for anyone who speaks some natural language to enter and query arbitrary structured data. [1] https://en.wikipedia.org/wiki/Attempto_Controlled_English https://en.wikipedia.org/wiki/Attempto_Controlled_English [2] https://news.ycombinator.com/item?id=21784617 https://news.ycombinator.com/item?id=21784617

one_electron 7y ago

in my experience, most people dismiss cyc as a failed science experiment. this shouldnt be! after all, many important deep learning concepts have their roots in the 80s, and it is possible that cyc could be revived too.

kick 7y ago
Cyc itself probably won't be: proprietary information is something that modern scientists tend to know better than to invest time in. Symbolic AI, though, hasn't really died.
jacquesm 7y ago
CYC effectively died the day OpenCYC died. There is no way that an entity that tries to catalogue human knowledge in this way will thrive on a closed set of data, there are only so many people working there. Just like the Encyclopedia Brittanica has found its match in WikiPedia so CYC will find its match in something open. The engine - if the comments here are to be believed as still currently relevant - is a core that may be relevant and a huge number of domain specific hacks. Let's hope sooner or later CYC management comes to their senses and revives OpenCYC.
- rademaker 7y ago
  That is SUMO Ontology (http://ontologyportal.org http://ontologyportal.org)! It is open, in GitHub and people can contribute.

gavanwoolery 7y ago

I immediately recognized the headline even though its been 15 years since I last read up on Cyc. I still think the potential of lambda calculus in knowledge representation and logical deduction is high and under-represented in research. Just theorizing, but I think a large part of the problem is the difficulty in interfacing this knowledge base with manual, human entry. Another pitfall is the difficulty in determining strange or unanticipated logical outcomes, and developing a framework to catch or validate these.

rademaker 7y ago
I have been working on that direction with Lean Theorem Prover (https://leanprover.github.io https://leanprover.github.io). There is also works using Coq (https://link.springer.com/chapter/10.1007/978-3-642-35786-2_11 https://link.springer.com/chapter/10.1007/978-3-642-35786-2_...)

jes5199 7y ago

last time I looked at OpenCyc's knowledge base, the information encoded was all strangely specific academic stuff - like very fine classifications and relationships between species of tapeworms and of fungus. There was very little daily-life common-sense knowledge, even though that's often the hook in interviews and articles about Cyc's purpose. I'm not sure why that's true - maybe it's hard to decide what the 'facts' are about normal human life, but the more academic something is, the more there's a consensus, rationalized 'reality'

stereolambda 7y ago
This happens also with similar bases in my experience. I suspect it's very much an economic phenomenon. It's easy to hire someone to input relations from a textbook if not from Wikipedia. It's hard to find someone with credentials for "understanding everyday life on philosophical level", and for logical formalisms, and also no overriding penchant for philosophical bikeshedding. Even then, it would be a time-consuming and thus also costly process.
brundolf 7y ago
Employee of Cycorp here. A few thoughts: - At least right now, we have a good amount of common-sense information about the world (I don't know when "last time" was for you). - That said, we have a lot of highly specialized knowledge in various domains, so if you took a random sample of the knowledge base (KB) it may not be as common-sense-centric as you'd hope. But the KB is also incredibly large, so that doesn't mean we don't have much common-sense, just that we have even more other stuff. - Often for contracts we get paid to construct lots of domain-specific knowledge, even if the project also uses the more general knowledge, so this biases the distribution some. - Information that's already well-taxonomized is low-hanging fruit for this kind of system; its representation doesn't take nearly as much extra thought and consideration, so it's a faster process, which also biases the distribution some.
- jey 7y ago
  What are some interesting examples of common-sense that has been formalized and encoded?

catpolice 7y ago

I worked for Cycorp for a few years recently. AMA, I guess? I obviously won't give away any secrets (e.g. business partners, finer grained details of how the inference engine works), but I can talk about the company culture, some high level technical things and the interpretation of the project that different people at the company have that makes it seem more viable than you might guess from the outside. There were some big positives. Everyone there is very smart and depending on your tastes, it can be pretty fun to be in meetings where you try to explain Davidsonian ontology to perplexed business people. I suspect a decent fraction of the technical staff are reading this comment thread. There are also some genuine technical advances (which I wish were more publicly shared) in inference engine architecture or generally stemming from treating symbolic reasoning as a practical engineering project and giving up on things like completeness in favor of being able to get an answer most of the time. There were also some big negatives, mostly structural ones. Within Cycorp different people have very different pictures of what the ultimate goals of the project are, what true AI is, and how (and whether) Cyc is going to make strides along the path to true AI. The company has been around for a long time and these disagreements never really resolve - they just sort of hang around and affect how different segments of the company work. There's also a very flat organizational structure which makes for a very anarchic and shifting map of who is responsible or accountable for what. And there's a huge disconnect between what the higher ups understand the company and technology to be doing, the projects they actually work on, and the low-level day-to-day work done by programmers and ontologists there. I was initially pretty skeptical of the continued feasibility of symbolic AI when I went in to interview, but Doug Lenat gave me a pitch that essentially assured me that the project had found a way around many of the concerns I had. In particular, they were doing deep reasoning from common sense principles using heuristics and not just doing the thing Prolog often devolved into where you end up basically writing a logical system to emulate a procedural algorithm to solve problems. It turns out there's a kind of reality distortion field around the management there, despite their best intentions - partially maintained by the management's own steadfast belief in the idea that what Cyc does is what it ought to be doing, but partially maintained by a layer of people that actively isolate the management from understanding the dirty work that goes into actually making projects work or appear to. So while a certain amount of "common sense" knowledge factors into the reasoning processes, a great amount of Cyc's output at the project level really comes from hand-crafted algorithms implemented either in the inference engine or the ontology. Also the codebase is the biggest mess I have ever seen by an order of magnitude. I spent some entire days just scrolling through different versions of entire systems that duplicate massive chunks of functionality, written 20 years apart, with no indication of which (if any) still worked or were the preferred way to do things.

spirographer 7y ago

My 2 cents is that I can ask just about any question I can think of and absorb and internalize an amazing answer in 5 minutes of reading. Many of those same questions can be automatically asked and answered too. The web and search engines are realizing the promise far better than anything else.

Jeff_Brown 7y ago
God I wish I felt this way. It's true that I can get an amazing amount of information in a short time, but I don't know how accurate or complete it is, and I don't even know how to find out.
goatlover 7y ago
At a surface level and when you know what questions to ask.

wrnr 7y ago

The the following utterance, sort of looks like the triplet data structure used in graph/knowledge databases: "Alive loves Bob" What do you know? Nothing. Was it Alice who said she loves Bob, or was it Bob who said it is Alice who loves him, maybe Carol saw the way Alice looks at Bob and then conclude she must love him. What is love anyway? How exactly is the love Alice has for Bob quantitively different than my love of chocolate. It might register similar brain activity in a MRI scan, and yet we humans recognise them as qualitatively different. A knowledge base is useless if you can't judge wether a fact is true or false. The response to this problem was for the semantic web community to introduce a provenance ontology, but any attempt to reason over statements about statements seem to go nowhere. IMHO you can't solve the problem of AGI without also having a way for a rational agent to embody its thoughts in the physical world.

Jeff_Brown 7y ago
Agreed. Human thinking is arbitrarily high-order -- we use statements about statements about statements with no particular natural complexity limit. This seems to me the big limitation of knowledge graphs: The majority of real-world information, just like the majority of natural-language sentences, are highly nested relationships among relationships. That was my motivation for writing Hode[1], the Higher-Order Data Editor. It lets you represent arbitrarily nested relationships, of any arity (number of members). It lets you cursor around data to view neighboring data, and it offers a query language that is, I believe, as close as possible to ordinary natural language. (Hode has no inference engine, and I don't call it an AI project -- but it seems relevant enough to warrant a plug.) [1] https://github.com/JeffreyBenjaminBrown/hode https://github.com/JeffreyBenjaminBrown/hode

yters 7y ago

Why is there never any fundamental research whether human intelligence is even computable? All these huge, expensive projects based on an untested premise.

Ididntdothis 7y ago
I think it’s pretty certain that we can improve a lot. If that leads to human intelligence or something else we don’t know. But it’s worth working on improving things and trying different approaches even if the final result isn’t known.
- yters 7y ago
  But there might be even better approaches if human intelligence is not computable. E.g. if the mind is a halting oracles that can get us all kinds of cool things.
  - inimino 7y ago
    If the mind were a halting oracle I don't think most of our open problems in mathematics would be.
    - yters 7y ago
      It's possible for the mind to solve more halting problems than any finite computer, yet still not be as powerful as a complete halting oracle. Thus, the fact we haven't solved every problem does not count as evidence against the mind being a halting oracle.
      - inimino 7y ago
        Actually it does. While it's logically possible, evidence for a hypothesis A is still provided by any data that is more likely under hypothesis A than under hypothesis B. The hypothesis that the mind is computable but is using heuristics, of various levels of sophistication, explains the data better and is more parsimonious than your hypothesis, because we already have reason to believe that the mind uses heuristics extensively. Where you see uncomputable oracular insights, others see computable combinations of heuristics. If you introspect deeply enough while problem-solving, you may be able to sense the heuristics working prior to the flash of intuition.

deleted 7y ago

[deleted]

mblackstone 7y ago

Is it possible For a non-academic to get a ResearchCyc license? I’ve used OpenCyc in the past; now that I’m retired I’d like to look deeper into Cyc.

MauiWarrior 7y ago

I used to read about cyc here https://www.cyc.com/cycl-translations https://www.cyc.com/cycl-translations. But it says now "coming soon". Since we have folks from Cyc here, any ideas how soon?

Tistel 7y ago

I am familiar with prolog and know what it takes (ish) to make an old school expert system. I have heard about this project. Are there any demos of this system? Like a video sales pitch. I have always wanted to see it in action.

doctorphil 7y ago

For me it would be helpful with some more examples of how to formulate the query and how the reasoning would look. Could someone share some examples of "common knowledge" that they think are cool? Here are some common knowledge in English that I would love to see the system answer. - Is a dog owner likely to own a rope-like object? (Yes, they likely own a leash.) - Does the average North American own more than 1 shoestring? (Yes, most people have at least 2 shoes, and most shoes have shoestrings.) - Is it safer to fly or to travel by car?

joveian 7y ago

I can't find it but I distinctly remember that there was part of an episode of 3-2-1 contact in the 80s about what must have been an early version of this system. It was the exact same thing as brundolf mentions* about the common sense system and how they set up the system to ask questions when contradictions arose. An example they used was it had asked if a human is still human when shaving. It is interesting that the system still exists. Of course, I don't recall them mentioning any of the more dystopian things it could be (and sounds like has been) used for :/. * https://news.ycombinator.com/item?id=21784105 https://news.ycombinator.com/item?id=21784105 On second thought, it might have been an Alan Kay presentation. I couldn't find that either but looking I did find this interesting Wired article from 2016: https://www.wired.com/2016/03/doug-lenat-artificial-intelligence-common-sense-engine/ https://www.wired.com/2016/03/doug-lenat-artificial-intellig...

tgbugs 7y ago
Amusingly, or sadly, depending on your perspective, in practical settings the default behavior of that question depends on exactly what you want human to mean in the vocabulary of the local use case, because continuants are a very leaky abstraction if they are used to type biological systems, and while to our 'common sense' the type of human and human shaving should obviously be the same, when you get to questions about whether seemingly insignificant numerical differences in rates of catalysis constitute differences in type, then suddenly the distinction between "protein" and "protein wiggling slightly faster than usual" or "protein binding molecule a" (think "human holding shaver") becomes very much not obvious depending on exactly what question you want to answer. In the protein example if you black box the system, they can be fundamentally different. If human means predator, and your question is how dangerous is this human, then "human" and "human holding razor" becomes "agentous thing" and "agentous thing with sharp edged object" practically different things in very important ways if you are trying not to be filleted.

musicale 7y ago

I've always thought that being able to model the physical world at multiple levels of abstraction was pretty essential for trying to interact with it in a less brittle way. Moreover, having models of things that are interesting and relevant to humans seems pretty important for any system that interacts with humans. And it always seemed reasonable that any system that aims to use natural language should be able to represent the meaning of the sentences it uses in a clear and understandable format. Also "organizing the world's information" should make it usable in an automated fashion based on semantic models.

dex_tec 7y ago

1) What do you think about hybrid approach: hypergraphs + large-scale NLP models (transformers)? 2) How far we're from real self-evolving cognitive architectures with self-awareness features? Is it a question of years, months, or it's already solved problem? 3) Does it make sense to use embeddings like https://github.com/facebookresearch/PyTorch-BigGraph https://github.com/facebookresearch/PyTorch-BigGraph to achieve better results? 4) Why Cycorp decided to limit communication and collaboration with scientific community / AI-enthusiasts at some point? 5) Did you try to solve GLUE / SUPERGLUE / SQUAD challenges with your system? 6) Is Douglas Lenat still contribute actively to the project? Thanks

choamnomsky 7y ago
Doug Lenat is very much still active in the project. He doesn't do as much work building the ontology, but he plays a role in how various projects develop and provides a lot of feedback.
- The_rationalist 7y ago
  How do you compare with SOAR and opencog/atomspace? Which6is the most promising AGI project according to you?

earenndil 7y ago

How does this compare to wolfram alpha?

lonelappde 7y ago

> require between 1000 and 3000 person-years [to input all the relevant facts in the world] Which is laughably small in retrospect. I wonder what current estimates are.

et2o 7y ago

This Wikipedia article is clearly not neutral in tone. It reads like the CEO wrote it.

jsonbourne 7y ago

http://conceptnet.io/ http://conceptnet.io/