6 ms·

No. If I'm understanding correctly it means the software is learning how to solve problems in general by ingesting examples of procedural problem-solving.

jpcom 2y ago
You're close, but there’s an important nuance. The process isn't about "learning how to solve problems in general" in the broad sense. It's more specific: the neural network is trained to mimic the step-by-step process demonstrated by humans solving a specific problem. The distinction is that the software doesn't autonomously derive general problem-solving heuristics from scratch. Instead, it observes examples of how humans solve problems procedurally and uses that to replicate similar reasoning. This is crucial because the step-by-step demonstrations give the model structure and guidance, which is different from learning a generalizable strategy for solving any kind of problem without those examples. In essence, it's like a neural net learning to follow a recipe by watching a chef cook—rather than inventing its own recipes entirely from first principles.
- jebarker 2y ago
  > In essence, it's like a neural net learning to follow a recipe by watching a chef cook—rather than inventing its own recipes entirely from first principles. Just like how a chef learns
  - Retric 2y ago
    A chef also learns through trial and error not just reading how others have cooked in the past and then copping their motions. This is exemplified by how altitude has a meaningful impact but isn’t discussed for a given recipe.
    - exe34 2y ago
      a text LLM isn't going to learn by trial and error, it's not been given that sort of freedom. RLHF would be the llm version of trial and error - but it's like the chef is only allowed to do that for a few days after years of chef school and from then on, he has to stick to what he has already learnt.
      - jebarker 2y ago
        Why isn't LLM pre-training based on next token prediction considered "trial and error"? It seems to fit that description pretty well to me.
        exe34 2y ago
        a chef doesn't get feedback on his meal after picking up the spoon. he gets feedback when he or somebody else tastes the meal part way through and at the end.
        Retric 2y ago
        Pre-training is based on a proxy for desired output not actually desired output. It’s not in the form of responses to a prompt, and 1:1 reproducing copyrighted works in production would be bad. It’s the difference between a painter copying some work and a painter making an original piece and then get feedback on it. We consider the second trial and error because the full process is being tested not just technique.
        Jensson 2y ago
        There is more than one correct answer in reality, LLM pre-training just trains it to respond the same way as the text did. Imagine if school only gave correct if you used exactly the same words as the book, that is not "trial and error".
        isaacfrond 2y ago
        I can tell you haven't been in a school in while. That is actually a pretty accurate description of what schools are like nowadays.
        Retric 2y ago
        Pretty accurate != always, which is the point.
- scellus 2y ago
  Yes, except that I'm not so sure there is a clear distinction between following general instructions and generating new heuristics. It's just a difference in the level of abstraction there, and probably not even that one in any discrete sense, more like a continuum. (Current) models may of course lack sufficient training data to act on a metalevel enough ("be creative problem solvers"), or they may lack deep enough representations to efficiently act in a more creative way. (And those two may be more or less the same thing or not.)
  - exe34 2y ago
    it's exactly how we learn. many examples and then general principles. if you start with general principles, everybody drops out.
    - bravura 2y ago
      Not "exactly" how we learn. Humans learn through a combination of reinforcement learning (which is costly/risky/painful) and through observation of existing patterns and norms. Better observation-based learning is a less expensive way of improving existing corpus-based approaches than trial-and-error and participating in an environment.
      - exe34 2y ago
        except that the careful observation comes late in the curriculum. children don't learn if you start out with the Stern Gerlach experiment. they sing ABCs.
        pfisherman 2y ago
        The parent of any young child can tell you that they learn through lots of exploration and reinforcement - often to the worry and chagrin of caregivers. Indeed much of our job is to guide exploration away from excessively dangerous “research” activities (ex. locking away cleaning products).
- limit499karma 2y ago
  > it observes Observe implies sentience that, without question, a neural net simply does not possess. "It" certainly 'records', or more specifically it 'maps', but there is no observer in sight (npi). > mimic LLM's do not mimic. The magic is mathematical and happening in the high dimensional space. If there is intrinsic underlying pattern and semantic affinities between process X (used in training) and process Y (used in application), it is very likely that both share proximity, possibly form, in some dimensions of the high dimensional model.
  - danielbln 2y ago
    Define "observation". If it's just sensory and information processing then no, it does not require nor simply sentience.
    - limit499karma 2y ago
      There is a word for that: a 'recording'. There is no observer thus no observation.
- ChadNauseam 2y ago
  spoken eerily similar to how chatgpt would put it :) https://chatgpt.com/share/674cd11d-a30c-8005-90a3-023d0c9c1804 https://chatgpt.com/share/674cd11d-a30c-8005-90a3-023d0c9c18...
- unit149 2y ago
  Crucially, this is what MacIntyre's narrativity thesis is talking about: If a university professor is giving a lecture on decentralized finance and forks into a recipe for chocolate chip cookies: crack two eggs, add a cup of flour, and fold in brown sugar prior to baking, it would break linearity. A generalizable strategy for synthesizing LLMs differentiated by their training parameters is a tokenization is isolating data sets and then establishing a lattice in uniformity within the field of technics.
  - MacsHeadroom 2y ago
    > A generalizable strategy for synthesizing LLMs differentiated by their training parameters is a tokenization is isolating data sets and then establishing a lattice in uniformity within the field of technics. This comment appears to be incoherent and likely AI-generated text. Let me break down why: 1. While it uses technical-sounding terms related to machine learning (LLMs, tokenization, data sets), the way they're strung together doesn't make logical sense. 2. The grammar is incorrect: - "a tokenization is isolating" is not grammatically valid - The sentence structure breaks down in the middle with two "is" statements - The phrase "establishing a lattice in uniformity within the field of technics" is meaningless jargon 3. If we try to interpret what it might be attempting to say about LLMs (Large Language Models), the ideas don't connect in any meaningful way. "Synthesizing LLMs differentiated by their training parameters" could be trying to discuss creating different LLMs with varying parameters, but the rest doesn't follow logically. 4. The term "field of technics" is particularly suspicious - while "technics" is a real word, it's rarely used in AI/ML discussions and seems thrown in to sound technical. This text shows common hallmarks of AI-generated content that's trying to sound technical but lacks real meaning - it uses domain-specific vocabulary but combines them in ways that don't make semantic sense, similar to how AI models can sometimes generate plausible-looking but ultimately meaningless technical text.
    - danielbln 2y ago
      And that analysis is also LLM generated. It's turtles all the way down, folks.
skybrian 2y ago
I don’t think the paper says anything about general problem-solving? They analyzed 40 reasoning problems and I didn’t find a list of them, but the example they use most often is “find the slope of a line.” Apparently there are many examples in the dataset demonstrating how to find the slope of a line, including computer code, and they all influence the answer. There are many ways you could ask a question about how to find the slope of a line, and the “generalization” going on seems to be unifying the different ways you could ask and answer this question. It seems fair to say that the LLM did learn to find the slope of a line? But the question has definitely been solved before, many times.

jpcom 2y ago

You're close, but there’s an important nuance. The process isn't about "learning how to solve problems in general" in the broad sense. It's more specific: the neural network is trained to mimic the step-by-step process demonstrated by humans solving a specific problem. The distinction is that the software doesn't autonomously derive general problem-solving heuristics from scratch. Instead, it observes examples of how humans solve problems procedurally and uses that to replicate similar reasoning. This is crucial because the step-by-step demonstrations give the model structure and guidance, which is different from learning a generalizable strategy for solving any kind of problem without those examples. In essence, it's like a neural net learning to follow a recipe by watching a chef cook—rather than inventing its own recipes entirely from first principles.

jebarker 2y ago
> In essence, it's like a neural net learning to follow a recipe by watching a chef cook—rather than inventing its own recipes entirely from first principles. Just like how a chef learns
- Retric 2y ago
  A chef also learns through trial and error not just reading how others have cooked in the past and then copping their motions. This is exemplified by how altitude has a meaningful impact but isn’t discussed for a given recipe.
  - exe34 2y ago
    a text LLM isn't going to learn by trial and error, it's not been given that sort of freedom. RLHF would be the llm version of trial and error - but it's like the chef is only allowed to do that for a few days after years of chef school and from then on, he has to stick to what he has already learnt.
    - jebarker 2y ago
      Why isn't LLM pre-training based on next token prediction considered "trial and error"? It seems to fit that description pretty well to me.
      - exe34 2y ago
        a chef doesn't get feedback on his meal after picking up the spoon. he gets feedback when he or somebody else tastes the meal part way through and at the end.
      - Retric 2y ago
        Pre-training is based on a proxy for desired output not actually desired output. It’s not in the form of responses to a prompt, and 1:1 reproducing copyrighted works in production would be bad. It’s the difference between a painter copying some work and a painter making an original piece and then get feedback on it. We consider the second trial and error because the full process is being tested not just technique.
      - Jensson 2y ago
        There is more than one correct answer in reality, LLM pre-training just trains it to respond the same way as the text did. Imagine if school only gave correct if you used exactly the same words as the book, that is not "trial and error".
        isaacfrond 2y ago
        I can tell you haven't been in a school in while. That is actually a pretty accurate description of what schools are like nowadays.
        Retric 2y ago
        Pretty accurate != always, which is the point.
scellus 2y ago
Yes, except that I'm not so sure there is a clear distinction between following general instructions and generating new heuristics. It's just a difference in the level of abstraction there, and probably not even that one in any discrete sense, more like a continuum. (Current) models may of course lack sufficient training data to act on a metalevel enough ("be creative problem solvers"), or they may lack deep enough representations to efficiently act in a more creative way. (And those two may be more or less the same thing or not.)
- exe34 2y ago
  it's exactly how we learn. many examples and then general principles. if you start with general principles, everybody drops out.
  - bravura 2y ago
    Not "exactly" how we learn. Humans learn through a combination of reinforcement learning (which is costly/risky/painful) and through observation of existing patterns and norms. Better observation-based learning is a less expensive way of improving existing corpus-based approaches than trial-and-error and participating in an environment.
    - exe34 2y ago
      except that the careful observation comes late in the curriculum. children don't learn if you start out with the Stern Gerlach experiment. they sing ABCs.
      - pfisherman 2y ago
        The parent of any young child can tell you that they learn through lots of exploration and reinforcement - often to the worry and chagrin of caregivers. Indeed much of our job is to guide exploration away from excessively dangerous “research” activities (ex. locking away cleaning products).
limit499karma 2y ago
> it observes Observe implies sentience that, without question, a neural net simply does not possess. "It" certainly 'records', or more specifically it 'maps', but there is no observer in sight (npi). > mimic LLM's do not mimic. The magic is mathematical and happening in the high dimensional space. If there is intrinsic underlying pattern and semantic affinities between process X (used in training) and process Y (used in application), it is very likely that both share proximity, possibly form, in some dimensions of the high dimensional model.
- danielbln 2y ago
  Define "observation". If it's just sensory and information processing then no, it does not require nor simply sentience.
  - limit499karma 2y ago
    There is a word for that: a 'recording'. There is no observer thus no observation.
ChadNauseam 2y ago
spoken eerily similar to how chatgpt would put it :) https://chatgpt.com/share/674cd11d-a30c-8005-90a3-023d0c9c1804 https://chatgpt.com/share/674cd11d-a30c-8005-90a3-023d0c9c18...
unit149 2y ago
Crucially, this is what MacIntyre's narrativity thesis is talking about: If a university professor is giving a lecture on decentralized finance and forks into a recipe for chocolate chip cookies: crack two eggs, add a cup of flour, and fold in brown sugar prior to baking, it would break linearity. A generalizable strategy for synthesizing LLMs differentiated by their training parameters is a tokenization is isolating data sets and then establishing a lattice in uniformity within the field of technics.
- MacsHeadroom 2y ago
  > A generalizable strategy for synthesizing LLMs differentiated by their training parameters is a tokenization is isolating data sets and then establishing a lattice in uniformity within the field of technics. This comment appears to be incoherent and likely AI-generated text. Let me break down why: 1. While it uses technical-sounding terms related to machine learning (LLMs, tokenization, data sets), the way they're strung together doesn't make logical sense. 2. The grammar is incorrect: - "a tokenization is isolating" is not grammatically valid - The sentence structure breaks down in the middle with two "is" statements - The phrase "establishing a lattice in uniformity within the field of technics" is meaningless jargon 3. If we try to interpret what it might be attempting to say about LLMs (Large Language Models), the ideas don't connect in any meaningful way. "Synthesizing LLMs differentiated by their training parameters" could be trying to discuss creating different LLMs with varying parameters, but the rest doesn't follow logically. 4. The term "field of technics" is particularly suspicious - while "technics" is a real word, it's rarely used in AI/ML discussions and seems thrown in to sound technical. This text shows common hallmarks of AI-generated content that's trying to sound technical but lacks real meaning - it uses domain-specific vocabulary but combines them in ways that don't make semantic sense, similar to how AI models can sometimes generate plausible-looking but ultimately meaningless technical text.
  - danielbln 2y ago
    And that analysis is also LLM generated. It's turtles all the way down, folks.

skybrian 2y ago

I don’t think the paper says anything about general problem-solving? They analyzed 40 reasoning problems and I didn’t find a list of them, but the example they use most often is “find the slope of a line.” Apparently there are many examples in the dataset demonstrating how to find the slope of a line, including computer code, and they all influence the answer. There are many ways you could ask a question about how to find the slope of a line, and the “generalization” going on seems to be unifying the different ways you could ask and answer this question. It seems fair to say that the LLM did learn to find the slope of a line? But the question has definitely been solved before, many times.