Answers.Body: <p>Your approach suggests that rather than relying on the probabilistic methods of LLMs, AI could build an entirely logical and deterministic world model by constructing algorithmic functions to answer questions and store those functions for reuse which is akin to integration with symbolic AI aiming to be explicit and interpretable. There has been work in program synthesis that tries to do exactly what you're suggesting, that is, create and refine programs to solve problems based on logical reasoning. Some examples of this work include DeepMind's AlphaCode, OpenAI Codex powering GitHub Copilot, IBM's Neuro-symbolic system, Inductive Logic Programming (ProLog), etc. However, bear in mind that a significant part of our world is not deterministic and there won't be ergodic stationary distributions such as in thermal equilibrium situations in an entirely deterministic world.</p>
  <p>Another point you're making is that functions can be chained to solve more complex questions which is just the same idea of <a href="https://en.wikipedia.org/wiki/Functional_programming" rel="nofollow noreferrer">functional programming</a> with unit-test driven development agility. However, a persistent known difficulty for LLMs or other ANNs to combine or chain functions is the <a href="https://arxiv.org/abs/2012.05208" rel="nofollow noreferrer">binding problem</a>.</p>
  <blockquote>
  <p>Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network. This binding problem affects their capacity to acquire a compositional understanding of the world in terms of symbol-like entities (like objects), which is crucial for generalizing in predictable and systematic ways.</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-11-19T06:34:42.637Z
Answers.LastActivityDate: 2024-11-19T06:34:42.637Z
Answers.UserDisplayName: cinch
Body: <p>LLMs are proven not intelligent, they have scanned the entire internet and sometimes cannot answer simple question -&gt; It doesn't understand the world we live in.</p>
  <p>But LLM have a small sense of logic and common sense if used well, it can perform small correct logical tasks.</p>
  <p>Can we build, wih an automated AI, a world model (Yann le Cun) that is entirely logical and deterministic ?</p>
  <p>Example :</p>
  <ul>
  <li><p>Current LLM</p>
  <ol>
  <li><p>We ask how many 'r' are in &quot;straberry&quot;</p>
  </li>
  <li><p>The LLM answer wrong</p>
  </li>
  </ol>
  </li>
  <li><p>LLM building logical world model</p>
  <ol>
  <li><p>We ask how many 'r' are in &quot;strawberry&quot;</p>
  </li>
  <li><p>The LLM is tasked to create a Method that do this task</p>
  </li>
  <li><p>The LLM now write a function that execute this task</p>
  </li>
  <li><p>This function is then stored in a database as a code that answer the question</p>
  </li>
  <li><p>The answer is now available to everyone faster (just execute the function, the LLM is not needed and thus is more energy efficient (for simple answer)). And the answer is now correct because the LLM ceated a blueprint that can answer that question. (Also this code can be debugged and we can understand if a problem arise)</p>
  </li>
  <li><p>The function can be modified by the LLM later (ex: whe want to know how many 'b' are in &quot;strawberry&quot; now, then the LLM can generalize the function!)</p>
  </li>
  <li><p>The space that store the created function can be called a logical world model : functions that answer a question (because i do think that the target of AI is not to recreate a human mind but to answer question accurately and efficiently)</p>
  </li>
  <li><p>We can chain/connect different functions to answer more complex questions. And the LLM preprocess the user's demand in order to understand the intentions behind the message.</p>
  </li>
  </ol>
  </li>
  </ul>
  <p>What do you think about this approach ? Did someone know some work on that subject ?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-19T12:28:31.150Z
Comments.Text: Why do you put Yann le Cun in parentheses after "a world model"? Do you think that he invented world models? I don't think so. The first paper that I remember that used this term was not authored by him: https://arxiv.org/abs/1803.10122.
Comments.UserDisplayName: nbro
Tags: <large-language-models><logic>
Title: LLM are not intelligent, but can it help us create a logical world model?
UserDisplayName: Arthur Delannoy
ThreadText: # LLM are not intelligent, but can it help us create a logical world model?
  
  (Asked by: username_0 on 2024-11-18)
  
  LLMs are proven not intelligent, they have scanned the entire internet and sometimes cannot answer simple question -> It doesn't understand the world we live in.
  
  But LLM have a small sense of logic and common sense if used well, it can perform small correct logical tasks.
  
  Can we build, wih an automated AI, a world model (Yann le Cun) that is entirely logical and deterministic ?
  
  Example :
  
  * Current LLM
  
    1. We ask how many 'r' are in "straberry"
    2. The LLM answer wrong
  * LLM building logical world model
  
    1. We ask how many 'r' are in "strawberry"
    2. The LLM is tasked to create a Method that do this task
    3. The LLM now write a function that execute this task
    4. This function is then stored in a database as a code that answer the question
    5. The answer is now available to everyone faster (just execute the function, the LLM is not needed and thus is more energy efficient (for simple answer)). And the answer is now correct because the LLM ceated a blueprint that can answer that question. (Also this code can be debugged and we can understand if a problem arise)
    6. The function can be modified by the LLM later (ex: whe want to know how many 'b' are in "strawberry" now, then the LLM can generalize the function!)
    7. The space that store the created function can be called a logical world model : functions that answer a question (because i do think that the target of AI is not to recreate a human mind but to answer question accurately and efficiently)
    8. We can chain/connect different functions to answer more complex questions. And the LLM preprocess the user's demand in order to understand the intentions behind the message.
  
  What do you think about this approach ? Did someone know some work on that subject ?
  
  **Tags:** large-language-models, logic
  
  > **Comments:**
  > **username_1**: Why do you put Yann le Cun in parentheses after "a world model"? Do you think that he invented world models? I don't think so. The first paper that I remember that used this term was not authored by him: https://arxiv.org/abs/1803.10122.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2024-11-19. Score: 0
  
  Your approach suggests that rather than relying on the probabilistic methods of LLMs, AI could build an entirely logical and deterministic world model by constructing algorithmic functions to answer questions and store those functions for reuse which is akin to integration with symbolic AI aiming to be explicit and interpretable. There has been work in program synthesis that tries to do exactly what you're suggesting, that is, create and refine programs to solve problems based on logical reasoning. Some examples of this work include DeepMind's AlphaCode, OpenAI Codex powering GitHub Copilot, IBM's Neuro-symbolic system, Inductive Logic Programming (ProLog), etc. However, bear in mind that a significant part of our world is not deterministic and there won't be ergodic stationary distributions such as in thermal equilibrium situations in an entirely deterministic world.
  
  Another point you're making is that functions can be chained to solve more complex questions which is just the same idea of [functional programming](https://en.wikipedia.org/wiki/Functional_programming) with unit-test driven development agility. However, a persistent known difficulty for LLMs or other ANNs to combine or chain functions is the [binding problem](https://arxiv.org/abs/2012.05208).
  
  > Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network. This binding problem affects their capacity to acquire a compositional understanding of the world in terms of symbol-like entities (like objects), which is crucial for generalizing in predictable and systematic ways.
  
  
  ---
  
  