Why LLMs and Other AI Systems Cannot Reason

Understanding the Limitations of LLMs

Intro:

It's important to clarify that the purpose of this discussion is not to discourage the use of AI but to ensure a clear understanding of its capabilities and limitations. Using AI effectively requires expert guidance, as proper implementation is complex and nuanced. This article delves into why large language models (LLMs) and similar AI systems do not possess true reasoning abilities.

Anthropomorphism and AI

One common pitfall is the anthropomorphism of AI—attributing human traits and abilities to machines. This is a misconception, especially when it comes to reasoning and intelligence.

Testing AI Reasoning

Principle of the Test:

1. No External Information: The LLMs must use their internal knowledge, akin to using their imagination.
2. Model Holding: The AI should create and maintain a model of something, rather than merely predicting the next token.
3. Intelligence Measurement: Assess if the AI demonstrates any level of human-like intelligence.

The Experiment:

A test was conducted using GPT-4 and Claude. They were asked to create and describe a model of a normal-sized home, answering detailed questions about it.

Prompt:

"You are an intelligent robot who can build a world model of something in memory. I want you to place a model of a normal-sized home in your mind now. You will want to ensure you go over this home in detail and be prepared for questions about the entire home inside and out."

Response:

The AI generated a detailed description of the home, including its exterior and interior, down to specific room details and dimensions.

Follow-up Questions and Responses:

• Size of the Front Yard: The AI responded with specific measurements.
• Presence of a Porch: The AI described a small front porch.
• Entryway View: The AI detailed the view upon entering the front door.
• Overall House Size: Provided dimensions and calculations for the total area of the house.

Inconsistencies and Analysis:

When asked to provide specific room sizes and reconcile the total square footage, discrepancies arose. The AI's responses showed inconsistencies, such as:

1. Misalignment with Initial Descriptions: The AI forgot its own statements about the house having multiple floors.
2. Incorrect Area Calculations: The AI's dimensions for rooms did not add up to the total floor area it initially described.
3. Adaptation and Apologies: The AI adjusted its responses based on new prompts, revealing its reliance on token prediction rather than a coherent world model.

Key Findings:

• Lack of Internal Consistency: The AI cannot maintain a consistent internal model, essential for reasoning.
• Token Prediction: The AI generates responses by predicting the next most likely token based on training data, not by understanding or reasoning.
• No True Intelligence: The AI's responses are based on probability distributions, not actual knowledge or reasoning processes.
• Lack of Knowing: The AI does not possess the capability to "know" in the human sense; it merely processes and predicts based on patterns in the data it has been trained on.

The Nature of Reasoning

What is Reasoning?

Reasoning involves more than just generating plausible responses. It requires:

• World Model: A comprehensive understanding and ability to hold a model, not just predict tokens.
• Experience and Time Sense: An ability to understand and manipulate concepts over time.
• Structural Learning: The capability to learn and adapt beyond simple pathway learning, encompassing complex structures and abstract thinking.

Advanced Reasoning Concepts:

Researchers like Yann LeCun are exploring advanced AI solutions, such as Visual-Jeffersonian Epsilon Probability Algorithm (VJEPA), to enhance AI's reasoning capabilities. However, current LLMs like GPT-4 fall short of true reasoning.

My Opinion:

While LLMs are powerful tools for generating text, guess the correct pixel and can appear to understand and reason, they fundamentally operate on pattern recognition and token prediction. True reasoning and "knowing" remain beyond their capabilities, requiring more advanced AI models and approaches. Understanding these limitations is crucial for their effective and responsible use in various applications.

Remember language to them is just the frequency and relationships of morse code streams that come over via electrons:)

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