The Misunderstood Learning of AI: Unveiling the Truth

Artificial Intelligence (AI) systems like ChatGPT are often misunderstood as learning systems. However, AI 'learns' differently, using patterns from vast data via mathematical processes. They stop learning after training, are language models, and excel in language tasks. Users should understand this to utilize AI effectively.

In a revelation that may surprise many, artificial intelligence systems such as ChatGPT do not learn in the conventional sense as humans do. Despite frequent claims, including those from the systems themselves, AI's so-called learning is a misunderstanding stemming from an imprecise definition of the term in the context of AI.

AI systems, such as the language model ChatGPT, 'learn' by encoding patterns from extensive datasets during a complex training phase. This form of learning relies on mathematical relationships between data and is fundamentally different from human experiential learning. Such systems excel at language-based tasks but struggle with common sense knowledge.

Once AI models like GPT-4 are trained, they cease to learn. They operate on 'pre-trained' data and do not adapt or remember new interactions, limiting their ability to acquire new knowledge dynamically. While AI developers have engineered workarounds to update information and personalize interactions, these do not equate to real-time learning or memory updating.

(With inputs from agencies.)