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So he's using an LLM to generate data stored in an "is_a" representation. That's so classic AI. Soon, he'll discover that he needs quantifiers. Then that "for

by Animats 1mo ago

So he's using an LLM to generate data stored in an "is_a" representation. That's so classic AI. Soon, he'll discover that he needs quantifiers. Then that "for all" is too strong sometimes, and he needs "for most". That way lies Cyc. It's not a bad idea. But it does have a history.

mentalgear 1mo ago
In general, what all the big LLM providers are doing is moving towards classical & neural (neuro-symbolic) AI - even though they dont publicly admit it because that would counter their claims for years of "scale is all you need" (which has vanished with diminishing returns, see $MS / altman's GPT-5 bet).
- akkad33 1mo ago
  How do you know this?
  - pegasus 1mo ago
    They're probably referring to tech like reasoning models, or agent harnesses for example, which are arguably slowly moving things towards the symbolic end of the spectrum.
  - mentalgear 1mo ago
    LLMs using REPL are one instance of symbols to "bounce" their prediction against domain constraints for verification. Also shout out to Gary Marcus who was right after all (and LLM companies wasting 100s of billions of dollars for years in-between on pure scaling).
- deleted 1mo ago
  [deleted]
- doginasuit 1mo ago
  It seems like the two approaches compliment each other nicely. Human intelligence also relies on parallel information processing. LLMs are like a massive working memory, incredibly effective but with a similar set of limitations. What they lack is a symbolic model of reality, something that they can build and refine.
- nz 1mo ago
  The various advances in LLM technology tend to rhyme with the advances in computer programming in general. For example, the stunts that involved getting LLMs to create compilers and browsers are really just extremely expensive[0] versions of genetic programming (none of it would have worked without using the test-suite as a fitness-function). The recent news of migrations from one test-framework to another (featuring Asana, I believe), was something that we could always do trivially in a language that was based on S-Expressions (Lisp, Scheme, etc). In fact, both Cyc and the "AI" Labs have the _same basic thesis_: Intelligence is, primarily, a data entry problem. They just disagree about what kinds of heuristics should be run over that data (logic-programs, neural-nets). Whenever I read about someone using LLMs to write code, it _very closely_ resembles how Lenat was using Eurisko/Cyc to solve problems: they let the system run continuously, and they "nudge" it in "interesting" directions, "when it gets stuck", or "runs out of steam". (Quotes indicate their phrasing, not mine) Even Lee Spector noticed something analogous with his genetic programming system. When he tried to get it to discover optimal data structures (or maybe it was sorting algorithms, I forget), the system would quickly "run out of steam", without a solution. But when they added new verbs/opcodes to the system, that were a better fit for that domain (e.g. index-based memory loads + stores), it converged on a solution very quickly (even for GP, domain specific languages keep delivering unreasonable wins). You will note that this rhymes with the "micro-theories" of Cyc, which in turn rhyme with the SLMs of the AI labs. In my personal experience, most of the "silver bullets" do not work (obviously), but some of them do nudge you towards being a better programmer (by refining your intuition about the problem specifically, and computers more generally). EDIT: just remembered something. LLMs tend to produce larger and larger programs over time, and most people (IIRC) interpret this as a kind of entropy. This happens to rhyme with a similarly observed behavior in GP. Most genetic programs that do not have a fitness function that rewards smaller size, tend to grow in an unbounded way. The reason for this, is that most of the code/genes are useless, and random mutations do not lobotomize the program under evolution. I suspect that the coding LLMs tend to grow their code for similar reasons. [0]: I suspect that, this was mostly a triumph of enormous amounts of hardware, more than the actual LLM technology. I further suspect that a traditional GP approach, on the same quantity of hardware, could have gotten there faster (if not better as well).
  - z0r 1mo ago
- RussianBot9580 1mo ago
  It's strange to frame this as classical vs scale. Us humans have a powerful inference engine in our heads. We also use a calendar to avoid re-deriving everything from first principles before we've had our morning coffee. Businesses couple many creative (human) agents together. They also have processes and rules.
  - mentalgear 1mo ago
    It isn't, and it is not what was described: its about unbounded imagination (neural / LLM) that needs reality constraints (symbols / rules) to produce useful output. Think of it like human imagination may do anything (flying cars), but the real world has constraints and we use language/writing (symbols) and rules bound to them to simulate and reconcile our imagination with reality to actually flow our energy into something that may work in the real world.
    - RussianBot9580 1mo ago
      > they dont publicly admit it because that would counter their claims for years of "scale is all you need" (which has vanished with diminishing returns, see $MS / altman's GPT-5 bet). Is my reading comprehension just completely broken or something? The above certainly sounds like "the powers that be want us to believe that attention is all you need".
IsTom 1mo ago
With validity intervals mentioned it'll also be nice to have LTL's "next" and "until" too.

by Animats 1mo ago

mentalgear 1mo ago

In general, what all the big LLM providers are doing is moving towards classical & neural (neuro-symbolic) AI - even though they dont publicly admit it because that would counter their claims for years of "scale is all you need" (which has vanished with diminishing returns, see $MS / altman's GPT-5 bet).

akkad33 1mo ago
How do you know this?
- pegasus 1mo ago
  They're probably referring to tech like reasoning models, or agent harnesses for example, which are arguably slowly moving things towards the symbolic end of the spectrum.
- mentalgear 1mo ago
  LLMs using REPL are one instance of symbols to "bounce" their prediction against domain constraints for verification. Also shout out to Gary Marcus who was right after all (and LLM companies wasting 100s of billions of dollars for years in-between on pure scaling).
deleted 1mo ago
[deleted]
doginasuit 1mo ago
It seems like the two approaches compliment each other nicely. Human intelligence also relies on parallel information processing. LLMs are like a massive working memory, incredibly effective but with a similar set of limitations. What they lack is a symbolic model of reality, something that they can build and refine.
nz 1mo ago
The various advances in LLM technology tend to rhyme with the advances in computer programming in general. For example, the stunts that involved getting LLMs to create compilers and browsers are really just extremely expensive[0] versions of genetic programming (none of it would have worked without using the test-suite as a fitness-function). The recent news of migrations from one test-framework to another (featuring Asana, I believe), was something that we could always do trivially in a language that was based on S-Expressions (Lisp, Scheme, etc). In fact, both Cyc and the "AI" Labs have the _same basic thesis_: Intelligence is, primarily, a data entry problem. They just disagree about what kinds of heuristics should be run over that data (logic-programs, neural-nets). Whenever I read about someone using LLMs to write code, it _very closely_ resembles how Lenat was using Eurisko/Cyc to solve problems: they let the system run continuously, and they "nudge" it in "interesting" directions, "when it gets stuck", or "runs out of steam". (Quotes indicate their phrasing, not mine) Even Lee Spector noticed something analogous with his genetic programming system. When he tried to get it to discover optimal data structures (or maybe it was sorting algorithms, I forget), the system would quickly "run out of steam", without a solution. But when they added new verbs/opcodes to the system, that were a better fit for that domain (e.g. index-based memory loads + stores), it converged on a solution very quickly (even for GP, domain specific languages keep delivering unreasonable wins). You will note that this rhymes with the "micro-theories" of Cyc, which in turn rhyme with the SLMs of the AI labs. In my personal experience, most of the "silver bullets" do not work (obviously), but some of them do nudge you towards being a better programmer (by refining your intuition about the problem specifically, and computers more generally). EDIT: just remembered something. LLMs tend to produce larger and larger programs over time, and most people (IIRC) interpret this as a kind of entropy. This happens to rhyme with a similarly observed behavior in GP. Most genetic programs that do not have a fitness function that rewards smaller size, tend to grow in an unbounded way. The reason for this, is that most of the code/genes are useless, and random mutations do not lobotomize the program under evolution. I suspect that the coding LLMs tend to grow their code for similar reasons. [0]: I suspect that, this was mostly a triumph of enormous amounts of hardware, more than the actual LLM technology. I further suspect that a traditional GP approach, on the same quantity of hardware, could have gotten there faster (if not better as well).
- z0r 1mo ago
RussianBot9580 1mo ago
It's strange to frame this as classical vs scale. Us humans have a powerful inference engine in our heads. We also use a calendar to avoid re-deriving everything from first principles before we've had our morning coffee. Businesses couple many creative (human) agents together. They also have processes and rules.
- mentalgear 1mo ago
  It isn't, and it is not what was described: its about unbounded imagination (neural / LLM) that needs reality constraints (symbols / rules) to produce useful output. Think of it like human imagination may do anything (flying cars), but the real world has constraints and we use language/writing (symbols) and rules bound to them to simulate and reconcile our imagination with reality to actually flow our energy into something that may work in the real world.
  - RussianBot9580 1mo ago
    > they dont publicly admit it because that would counter their claims for years of "scale is all you need" (which has vanished with diminishing returns, see $MS / altman's GPT-5 bet). Is my reading comprehension just completely broken or something? The above certainly sounds like "the powers that be want us to believe that attention is all you need".

IsTom 1mo ago

With validity intervals mentioned it'll also be nice to have LTL's "next" and "until" too.