I see a huge amount of confusion around terminology in discussions about Artificial Intelligence, so here’s my quick attempt to clear some of it up.

Artificial Intelligence is the broadest possible category. It includes everything from the chess opponent on the Atari to hypothetical superintelligent systems piloting spaceships in sci-fi. Both are forms of artificial intelligence - but drastically different.

That chess engine is an example of narrow AI: it may even be superhuman at chess, but it can’t do anything else. In contrast, the sci-fi systems like HAL 9000, JARVIS, Ava, Mother, Samantha, Skynet, or GERTY are imagined as generally intelligent - that is, capable of performing a wide range of cognitive tasks across domains. This is called Artificial General Intelligence (AGI).

One common misconception I keep running into is the claim that Large Language Models (LLMs) like ChatGPT are “not AI” or “not intelligent.” That’s simply false. The issue here is mostly about mismatched expectations. LLMs are not generally intelligent - but they are a form of narrow AI. They’re trained to do one thing very well: generate natural-sounding text based on patterns in language. And they do that with remarkable fluency.

What they’re not designed to do is give factual answers. That it often seems like they do is a side effect - a reflection of how much factual information was present in their training data. But fundamentally, they’re not knowledge databases - they’re statistical pattern machines trained to continue a given prompt with plausible text.

      • navatar@programming.dev
        4·
        2 days ago

        This visual is a bit misleading. LLMs are not a subset of genAI and they aren’t really comparable, because LLMs refer to a vague model type (usually transformers with hundreds of millions of parameters) and genAI is a buzzword for the task of language generation. LLMs can be fine tuned for a variety of other tasks, like sequence and token classification, and there are other model architectures that can do language generation.

        Unrelated, but it’s disappointing how marketing and hype lead to so much confusion and information muddying. Even Wikipedia declaratively states that the most capable LLMs are generative, which academically is simply not the case.

        Source: computational linguist who works on LLMs