Technology

Yeah, AI's biggest weakness right now is that it just doesn't work sometimes. It's very unreliable, but whether or not it's too unreliable depends on what exactly you need it for. The scope of LLMs is limited by this unreliability, but generative AI is wonderful at doing things that either don't need to be perfect all the time or can simply be redone if there's a mistake. That's why it's putting artists out of work, but not automating the boring, really important stuff yet.

it's still just a text generator guessing the next best word. It doesn't do real math or logic, it gets basic things wrong and hallucinates new fake facts.

If humans are any kind of yardstick here, I’d say all this is true of us too on many levels. The brain is a shortcut engine, not a brute force computer. It’s not solving equations to help you predict where that tennis ball will bounce next. It’s making guesses based on its corpus of past experience. Good enough guesses are frankly our brains’ bread and butter and most of us get through most days on little more than this.

It’s true that we can do more. Some of us, anyway. How many people actually exercise math and logic though? Sometimes it seems like… not a lot. And how many people hallucinate fake facts? A lot.

It’s much like evaluating self-driving cars. We may be tempted to say they’re just bloody awful, but so are human drivers.

This is short-sighted.

The jump to GPT 3.5 was preceded by the same general misunderstanding (we've reached the limit of what generative pre-trained transformers can do, we've reached diminishing returns, ECT.) and then a relatively small change (AFAIK it was a couple additional layers of transforms and a refinement of the training protocol) and suddenly it was displaying behaviors none of the experts expected.

Small changes will compound when factored over billions of nodes, that's just how it goes. It's just that nobody knows which changes will have that scale of impact, and what emergent qualities happen as a result.

It's ok to say "we don't know why this works" and also "there's no reason to expect anything more from this methodology". But I wouldn't dismiss further improvements as a forgone possibility.

Except that scaling alone won't lead to AGI. It may generate better, more convincing text, but the core algorithm is the same. That "special juice" is almost certainly going to come from algorithmic development rather than just throwing more compute at the problem.

My hypothesis is that that "extra juice" is going to be some kind of body. More senses than text-input, and more ways to manipulate itself and the environment than text-output. Basically, right now llm's can kind of understand things in terms of text descriptions, but will never be able to understand it the way a human can until it has all of the senses (and arguably physical capabilities) that a human does. Thought experiment: Presumably you "understand" your dog - can you describe your dog without sensory details, directly or indirectly? Behavior had to be observed somehow. Time is a sense too. EDIT: before someone says it, as for feelings I'm not really sure, I'm not a biology guy. But my guess is we sense our own hormones as well