外刊阅读20240710｜AI开始胡言乱语了

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Large language models (LLMs) are sophisticated computer programs designed to generate human-like text. They achieve this by analyzing vast amounts of written material and using statistical techniques to predict the likelihood of a particular word appearing next in a sequence . This process enables them to produce coherent and contextually appropriate responses to a wide range of prompts. Unlike human brains, which have a variety of goals and behaviors, LLMs have a singular objective: to generate text that closely resembles human language. This means their primary function is to replicate the patterns and structures of human speech and writing, not to understand or convey factual information.

The term “AI hallucination ” is used to describe instances when an LLM like ChatGPT produces inaccurate or entirely fabricated information. This term suggests that the AI is experiencing a perceptual error, akin to a human seeing something that isn’t there. However, this metaphor is misleading, according to Hicks and his colleagues, because it implies that the AI has a perspective or an intent to perceive and convey truth, which it does not.

To better understand why these inaccuracies might be better described as bullshit, it is helpful to look at the concept of bullshit as defined by philosopher Harry Frankfurt. In his seminal work, Frankfurt distinguishes bullshit from lying. A liar, according to Frankfurt, knows the truth but deliberately chooses to say something false. In contrast, a bullshitter is indifferent to the truth. The bullshitter’s primary concern is not whether what they are saying is true or false but whether it serves their purpose, often to impress or persuade.

The distinction is significant because it influences how we understand and address the inaccuracies produced by these models. If we think of these inaccuracies as hallucinations, we might believe that the AI is trying and failing to convey truthful information. But AI models like ChatGPT do not have beliefs, intentions, or understanding, Hicks and his colleagues explained. They operate purely on statistical patterns derived from their training data.