7 ms·

Cyc is one of those bad ideas that won't die, and which keeps getting rediscovered on HN. Lenat wasted decades of his life on it. Knowledge graphs like Cyc are

Cyc is one of those bad ideas that won't die, and which keeps getting rediscovered on HN. Lenat wasted decades of his life on it. Knowledge graphs like Cyc are labor intensive to build and difficult to maintain. They are brittle in the face of change, and useless if they cannot represent the underlying changes of reality.

zopf 2y ago
I wonder to what degree an LLM could now produce frames/slots/values in the knowledge graph. With so much structure already existing in the Cyc knowledge graph, could those frames act as the crystal seed upon which an LLM could crystallize its latent knowledge about the world from the trillions of tokens it was trained upon?
- tkgally 2y ago
  I had the same thought. Does anybody know if there have been attempts either to incorporate Cyc-like graphs into LLM training data or to extend such graphs with LLMs?
  - radomir_cernoch 2y ago
    From time to time, I read articles on the boundary between neural nets and knowledge graphs like a recent [1]. Sadly, no mention of Cyc. I'd bet, judging mostly from my failed attempts at playing with OpenCyc around 2009, is that the Cyc has always been too closed and to complex to tinker with. That doesn't play nicely with academic work. When people finish their PhDs and start working for OpenAI, they simply don't have Cyc in their toolbox. [1] https://www.sciencedirect.com/science/article/pii/S0893608023003398?casa_token=9BR_Z2dV39IAAAAA:u6DfBRbwmjnyNSXojCTrKrMvZYDB7LMgFzF6ZtargJuEqrk7dx3l2_zaEG4tPUPVBqOpc5Q33A https://www.sciencedirect.com/science/article/pii/S089360802...
    - viksit 2y ago
      oh i just commented elsewhere in the thread about our work in integrating frames and slots into LSTMs a few years ago! second this.
  - thesz 2y ago
    There are lattice-based RNNs applied as language models. In fact, if you have a graph and a path-weighting model (RNN, TDCNN or Transformer), you can use beam search to evaluate paths through graphs.
  - mike_hearn 2y ago
    The problem is not one of KB size. The Cyc KB is huge. The problem is that the underlying inferencing algorithms don't scale whereas the transformer algorithm does.
fidesomnes 2y ago
[dead]
breck 2y ago
I think before 2022 it was still an open question whether it was a good approach. Now it's clear that knowledge graphs are far inferior to deep neural nets, but even still few people can explain the _root_ reason why. I don't think Lenat's bet was a waste. I think it was sensible based on the information at the time. The decision to research it largely in secret, closed source, I think was a mistake.
- xpe 2y ago
  > Now it's clear that knowledge graphs are far inferior to deep neural nets No. It depends. In general, two technologies can’t be assessed independently of the application.
  - famouswaffles 2y ago
    Anything other than clear definitions and unambiguous axioms (which happens to be most of the real world) and gofai falls apart. Like it can't even be done. There's a reason it was abandoned in NLP long before the likes of GPT. There aren't any class of problems deep nets can't handle. Will they always be the most efficient or best performing solution ? No, but it will be possible.
    - mepian 2y ago
      They should handle the problem of hallucinations then.
      - famouswaffles 2y ago
        Bigger models hallucinate less. and we don't call it hallucinations but gofai mispredicts plenty.
        xpe 2y ago
        > Bigger models hallucinate less. I'm skeptical. Based on what research?
xpe 2y ago
The comment above misses the point in at least four ways. (1) Being aware of history is not the same as endorsing it. (2) Knowledge graphs are useful for many applications. (3) How narrow of a mindset and how much hindsight bias must one have to claim that Lenat wasted decades of his life? (4) Don’t forget to think about this in context about what was happening in the field of AI.
thesz 2y ago
Lenat was able to produce superhuman performing AI in the early 1980s [1]. [1] https://voidfarer.livejournal.com/623.html https://voidfarer.livejournal.com/623.html You can label it "bad idea" but you can't bring LLMs back in time.
- goatlover 2y ago
  Why didn't it ever have the impact that LLMs are having now? Or that DeepMind has had? Cyc didn't pass the Turing Test or become superhuman chess and go players. Yet it's had much more time to become successful.
  - eru 2y ago
    I'm very skeptical of Cyc and other symbolic approaches. However I think they have a good excuse for 'Why didn't it ever have the impact that LLMs are having now?': lack of data and lack of compute. And it's the same excuse that neural networks themselves have: back in those days, we just didn't have enough data, and we didn't have enough compute, even if we had the data. (Of course, we learned in the meantime that neural networks benefit a lot from extra data and extra compute. Whether that can be brought to bear on Cyc-style symbolic approaches is another question.)
    - thesz 2y ago
      Usually, LLM's output gets passed through beam search [1] which is as symbolic as one can get. [1] https://www.width.ai/post/what-is-beam-search https://www.width.ai/post/what-is-beam-search It is possible to even have 3-gram model to output better text predictions if you combine it with the beam search.
      - eru 2y ago
        See https://news.ycombinator.com/item?id=40073039 https://news.ycombinator.com/item?id=40073039 for a discussion.
  - thesz 2y ago
richardatlarge 2y ago
We are living in the future / I'll tell you how I know / I read it in the paper / Fifteen years ago - (John Prine)
mindcrime 2y ago
They are brittle in the face of change, and useless if they cannot represent the underlying changes of reality. FWIW, KG's don't have to be brittle. Or, at least they don't have to be as brittle as they've historically been. There are approaches (like PROWL[1]) to making graphs probabilistic so that they're asserting subjective beliefs about statements, instead of absolute statements. And then the strength of those beliefs can increase or decrease in response to new evidence (per Bayes Theorem). Probably the biggest problem with this stuff is that it tends to be crazy computationally expensive. Still, there's always the chance of an algorithmic breakthrough or just hardware improvements bringing some of this stuff into the real of practical. [1]: https://www.pr-owl.org/ https://www.pr-owl.org/

zopf 2y ago

I wonder to what degree an LLM could now produce frames/slots/values in the knowledge graph. With so much structure already existing in the Cyc knowledge graph, could those frames act as the crystal seed upon which an LLM could crystallize its latent knowledge about the world from the trillions of tokens it was trained upon?

tkgally 2y ago
I had the same thought. Does anybody know if there have been attempts either to incorporate Cyc-like graphs into LLM training data or to extend such graphs with LLMs?
- radomir_cernoch 2y ago
  From time to time, I read articles on the boundary between neural nets and knowledge graphs like a recent [1]. Sadly, no mention of Cyc. I'd bet, judging mostly from my failed attempts at playing with OpenCyc around 2009, is that the Cyc has always been too closed and to complex to tinker with. That doesn't play nicely with academic work. When people finish their PhDs and start working for OpenAI, they simply don't have Cyc in their toolbox. [1] https://www.sciencedirect.com/science/article/pii/S0893608023003398?casa_token=9BR_Z2dV39IAAAAA:u6DfBRbwmjnyNSXojCTrKrMvZYDB7LMgFzF6ZtargJuEqrk7dx3l2_zaEG4tPUPVBqOpc5Q33A https://www.sciencedirect.com/science/article/pii/S089360802...
  - viksit 2y ago
    oh i just commented elsewhere in the thread about our work in integrating frames and slots into LSTMs a few years ago! second this.
- thesz 2y ago
  There are lattice-based RNNs applied as language models. In fact, if you have a graph and a path-weighting model (RNN, TDCNN or Transformer), you can use beam search to evaluate paths through graphs.
- mike_hearn 2y ago
  The problem is not one of KB size. The Cyc KB is huge. The problem is that the underlying inferencing algorithms don't scale whereas the transformer algorithm does.

fidesomnes 2y ago

[dead]

breck 2y ago

I think before 2022 it was still an open question whether it was a good approach. Now it's clear that knowledge graphs are far inferior to deep neural nets, but even still few people can explain the _root_ reason why. I don't think Lenat's bet was a waste. I think it was sensible based on the information at the time. The decision to research it largely in secret, closed source, I think was a mistake.

xpe 2y ago
> Now it's clear that knowledge graphs are far inferior to deep neural nets No. It depends. In general, two technologies can’t be assessed independently of the application.
- famouswaffles 2y ago
  Anything other than clear definitions and unambiguous axioms (which happens to be most of the real world) and gofai falls apart. Like it can't even be done. There's a reason it was abandoned in NLP long before the likes of GPT. There aren't any class of problems deep nets can't handle. Will they always be the most efficient or best performing solution ? No, but it will be possible.
  - mepian 2y ago
    They should handle the problem of hallucinations then.
    - famouswaffles 2y ago
      Bigger models hallucinate less. and we don't call it hallucinations but gofai mispredicts plenty.
      - xpe 2y ago
        > Bigger models hallucinate less. I'm skeptical. Based on what research?

xpe 2y ago

The comment above misses the point in at least four ways. (1) Being aware of history is not the same as endorsing it. (2) Knowledge graphs are useful for many applications. (3) How narrow of a mindset and how much hindsight bias must one have to claim that Lenat wasted decades of his life? (4) Don’t forget to think about this in context about what was happening in the field of AI.

thesz 2y ago

Lenat was able to produce superhuman performing AI in the early 1980s [1]. [1] https://voidfarer.livejournal.com/623.html https://voidfarer.livejournal.com/623.html You can label it "bad idea" but you can't bring LLMs back in time.

goatlover 2y ago
Why didn't it ever have the impact that LLMs are having now? Or that DeepMind has had? Cyc didn't pass the Turing Test or become superhuman chess and go players. Yet it's had much more time to become successful.
- eru 2y ago
  I'm very skeptical of Cyc and other symbolic approaches. However I think they have a good excuse for 'Why didn't it ever have the impact that LLMs are having now?': lack of data and lack of compute. And it's the same excuse that neural networks themselves have: back in those days, we just didn't have enough data, and we didn't have enough compute, even if we had the data. (Of course, we learned in the meantime that neural networks benefit a lot from extra data and extra compute. Whether that can be brought to bear on Cyc-style symbolic approaches is another question.)
  - thesz 2y ago
    Usually, LLM's output gets passed through beam search [1] which is as symbolic as one can get. [1] https://www.width.ai/post/what-is-beam-search https://www.width.ai/post/what-is-beam-search It is possible to even have 3-gram model to output better text predictions if you combine it with the beam search.
    - eru 2y ago
      See https://news.ycombinator.com/item?id=40073039 https://news.ycombinator.com/item?id=40073039 for a discussion.
- thesz 2y ago

richardatlarge 2y ago

We are living in the future / I'll tell you how I know / I read it in the paper / Fifteen years ago - (John Prine)

mindcrime 2y ago

They are brittle in the face of change, and useless if they cannot represent the underlying changes of reality. FWIW, KG's don't have to be brittle. Or, at least they don't have to be as brittle as they've historically been. There are approaches (like PROWL[1]) to making graphs probabilistic so that they're asserting subjective beliefs about statements, instead of absolute statements. And then the strength of those beliefs can increase or decrease in response to new evidence (per Bayes Theorem). Probably the biggest problem with this stuff is that it tends to be crazy computationally expensive. Still, there's always the chance of an algorithmic breakthrough or just hardware improvements bringing some of this stuff into the real of practical. [1]: https://www.pr-owl.org/ https://www.pr-owl.org/