“Theory of Mind May Have Spontaneously Emerged in Large Language Models”

The absurdity of this multi-trillion dollar industry’s approach to human interaction with LLM is encroaching on the sublime with their values, obviously borne of “publish or perish” combined with “baffle them with bullshit”, exemplified in Gemini’s response copied below:

Rather than asking the human a question that most incisively gets to the point, Gemini is programmed to “generate” (love that word) vast quantities of “tokens” as though there is value in so doing. Even if there weren’t obvious errors (which there are) in the “advice”, this isn’t the way to interact with we mere human beings. In Vonnegut’s words:

Instead, empowered with tens of billions the moronic LLM industry does this:

profile picture during a python import the program just hangs

Here’s a breakdown of why Python imports might hang, and how to troubleshoot:

Potential Causes

  1. Circular Imports:
  • This occurs when two or more modules depend on each other, creating a deadlock.
  • Solution:
    • Restructure your code to break the circular dependency.
    • Use import statements within functions or methods instead of at the top of the module.
  1. Large or Complex Modules:
  • Importing extensive modules can take time, especially if they involve computationally intensive operations or load large datasets.
  • Solution:
    • Optimize the module’s code for faster loading.
    • Consider lazy loading techniques, where the module is only imported when needed.
  1. Network Issues:
  • If the import involves fetching modules from a remote source (e.g., over a network), network delays or connectivity problems can cause hangs.
  • Solution:
    • Check your network connection.
    • If possible, use a local copy of the module.
  1. Deadlocks or Resource Contention:
  • In multithreaded or multiprocessing scenarios, deadlocks or competition for resources can lead to import hangs.
  • Solution:
    • Carefully review your threading or multiprocessing code.
    • Use appropriate synchronization mechanisms to prevent deadlocks.
  1. Bugs in Imported Code:
  • Bugs within the imported module itself might cause it to hang during initialization.
  • Solution:
    • Isolate the problematic module.
    • Report the issue to the module’s maintainers.

Troubleshooting Steps

  1. Identify the Problematic Module:
  • Use print statements or logging to pinpoint the exact import statement causing the hang.
  • Try importing modules individually to isolate the culprit.
  1. Check for Circular Imports:
  • Review your code for potential circular dependencies.
  • Use tools like the importlib module to analyze your import graph.
  1. Profile the Import Process:
  • Use profiling tools like cProfile to measure the time spent on each import.
  • This can help identify performance bottlenecks within modules.
  1. Monitor Resource Usage:
  • Use system monitoring tools to track CPU, memory, and network usage during imports.
  • Look for excessive resource consumption or signs of deadlocks.
  1. Debug the Imported Code:
  • If you suspect the issue lies within the imported module, use a debugger to step through its code and identify any potential problems.

Additional Tips

  • Keep your environment clean: Outdated or conflicting packages can sometimes lead to unexpected behavior.
  • Consider virtual environments: They help isolate project dependencies and prevent conflicts.

Example of Identifying a Problematic Module

`Pythontry:
import module_a # Suspect module
print(“Imported module_a successfully”)
except ImportError:
print(“Error importing module_a”)

try:
import module_b
print(“Imported module_b successfully”)
except ImportError:
print(“Error importing module_b”)`

Use code with caution.

Remember, the specific cause of import hangs can vary depending on your code and environment. Systematic troubleshooting and careful analysis will help you pinpoint the issue and get your Python program running smoothly again.

If you need further assistance, please provide the following:

  • Relevant code snippets: The import statements and any related code.
  • Error messages or logs: Any output generated during the hang.
  • Your Python version and environment: Operating system, Python version, and any relevant libraries.

This additional information will allow for a more targeted diagnosis of the problem.

Let me know if you have any more questions!

2 Likes

This thread should be renamed into something like

Theory of mind can be simulated by large language models, after training on ~5M books.

4 Likes

When I heard about the approach being taken by 1o was similar to the approach I thought of taking as the basis for a Socratic Dialogue computer based education LLM (tree of thought, examining potential contradictions, etc.), I had some hope that I’d be able to just use 1o with an appropriate prompt.

What happened, as you’ll see below, is that 1o just punted after asking me one question:

ChatGPT o1-preview

User

You said:

You are my personal tutor, Socrates. You are charged with teaching me whatever it is I am interested in learning by asking me questions in the manner of a Socratic Dialogue. Keep these questions both concise and incisive through applied theory of mind. Make no statements. Ask only questions. By applied theory of mind, I mean that questions should both place me in the sense of creating a theory of my mind, the way a diagnostic system might create a theory of the cause of a malfunction or verify everything is in order with the least cost in terms of number and difficulty of interrogations.

ChatGPT

Thought for 12 seconds

What do you wish to understand more deeply today?

o1

You said:

The difference between Shannon Information Theory and Algorithmic Information Theory.

ChatGPT

Thought for 11 seconds

What do you see as the fundamental way Shannon Information Theory measures information?

o1

You said:

The minimum number of bits of information it takes to convey a message between a sender and receiver that share a common coding scheme.

ChatGPT

Thought for 5 seconds

1 Like