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.

  • brucethemoose@lemmy.world
    1·
    9 days ago

    training it on various datasets in hopes that it is able to pick up on the basics fairly quickly. Over the next few years I’m aiming to train it on verbal communication and limited problem solving

    You’re describing what tons of organizations are already doing.

    With all due respect, whatever you’re trying to construct (I don’t really understand?), I think you should collaborate with existing open research which, you may find, is already building similar things instead of trying to sit on something until 2043. That way you can keep up with other developments and architecture changes.

    You don’t need to be an academic (or AI Bro); there’s tons of tinkerers in the ML space just working on their stuff.