So I asked ChatGPT to write “an article about 500 words long about how LLM is not really artificial intelligence.”

ChatGPT4o’s response (for clarity I then asked it to change the answer to appropriate Markdown for viewing) ::

Why Large Language Models (LLMs) Are Not Truly Artificial Intelligence

In the rapidly evolving field of technology, the term “artificial intelligence” (AI) is often used to describe a variety of systems and algorithms, including Large Language Models (LLMs) such as GPT-4. While LLMs represent significant advancements in natural language processing (NLP), it is important to clarify that they do not embody the full scope of what is traditionally considered artificial intelligence. This distinction is crucial for understanding the capabilities and limitations of LLMs.

Defining Artificial Intelligence

Artificial intelligence, in its broadest sense, refers to the capability of a machine to mimic intelligent human behavior. This encompasses a wide range of functions, including learning, reasoning, problem-solving, perception, and language understanding. True AI, often termed Artificial General Intelligence (AGI), would possess the ability to understand, learn, and apply knowledge across diverse domains without human intervention.

What Are Large Language Models?

LLMs, such as GPT-4, are a subset of AI focused specifically on understanding and generating human language. These models are trained on vast datasets containing diverse textual information, enabling them to produce coherent and contextually relevant text based on the input they receive. The training process involves statistical analysis of language patterns, allowing LLMs to predict and generate text that mimics human writing styles and conversational flow.

The Mechanism Behind LLMs

The primary mechanism driving LLMs is deep learning, a subset of machine learning that uses neural networks with many layers (hence “deep”) to analyze and learn from data. During training, an LLM processes a massive corpus of text, learning to identify and replicate patterns, structures, and relationships within the data. This results in the model’s ability to generate text that is remarkably human-like.

However, this process is fundamentally different from how humans understand and use language. LLMs do not comprehend the meaning of words and sentences as humans do; instead, they generate responses based on probability distributions derived from their training data. They lack the ability to think, reason, or possess knowledge in the same way humans do. Their “understanding” is superficial, relying on patterns rather than genuine comprehension.

Limitations of LLMs

The limitations of LLMs become apparent when tasks require true understanding, reasoning, or context beyond surface-level patterns. For instance, while LLMs can generate text on a wide range of topics, they can also produce incorrect or nonsensical information with the same confidence as accurate content. They do not have the ability to verify facts, understand nuance, or engage in complex decision-making processes.

Additionally, LLMs are susceptible to biases present in their training data. Since they learn from a broad spectrum of text, they can inadvertently replicate and even amplify these biases, leading to ethically problematic outputs. Addressing these biases is an ongoing challenge in the development and deployment of LLMs.

LLMs vs. True AI

The distinction between LLMs and true AI lies in the depth and breadth of capabilities. True AI would possess a general understanding of the world, capable of learning and adapting across various domains without specific training for each task. It would demonstrate reasoning, problem-solving, and autonomous learning abilities akin to human intelligence. LLMs, on the other hand, are specialized tools designed for language-related tasks. They excel at generating text and providing responses within the scope of their training but fall short of exhibiting the broad, adaptive intelligence characteristic of true AI.

Conclusion

While LLMs like GPT-4 represent impressive advancements in natural language processing, it is important to recognize their limitations. They are not true artificial intelligence in the sense of possessing general intelligence or understanding. Instead, they are powerful tools capable of mimicking human language patterns, providing valuable assistance in various applications. As technology continues to evolve, distinguishing between specialized AI models like LLMs and the broader concept of true AI will remain essential for setting realistic expectations and guiding future developments in the field.