Ask HN: I am a full stack developer, where do I start learning for AI

Not really. LLMs and diffusion models have one thing in common: they don't match patterns. They elaborate. Pattern matching is needed for that to some extent, but these models are not as good at it as they are at completing models. Machine learning is really good at "masking". And the short version of that is "there's something missing, fill it in". This is why people call ChatGPT "autocorrect". It fills in the missing word at the end of the conversation. Any pattern matching it can do is only in service of that.

I might remark that I have 3 daughters, and when I look at the exercises their schools provide them, that's exactly what they get them to do, starting with "fill in the missing word" going all the way to "write an essay defending ..."

> They will produce something that matches the query, but not necessarily any correct information.

Here's the problem I have with this statement. You can say the exact same thing about humans. I remember math teachers in high school where I now know ... they were idiots. In fact, a few things they presented as truth took quite a bit of effort to train back out of my behaviour.

Things we know about human behavior: it's mostly based on imitation. One might remark: imitation creates a strong possibility of stupid behavior, including behaving stupidly in large numbers. We know incidents where large numbers of humans have literally killed themselves through stupidity, both intentionally and accidentally. The reason we find the many sketches about dodo birds funny is that we've all seen people behave (somewhat) like them.

Plus this matches my experience. If I look at what people do, it's trivial to see: no matter what problem a human encounters, they will do something. If they don't have a good answer that including incredibly stupid things, hurting themselves or others. And where we do exhibit good problem solving behavior, is spreads through humanity through imitation. In many instances it is said that those behaviors were discovered by accident, and then spread through imitation.

Equally, if I look at how signals are propagated inside the brain and nervous system, it's kind of obvious. Signal comes in, signal comes out. The odds of a signal dying or amplifying (outside of medical problems with the brain) are very small. Your brain doesn't generate intelligent behaviour, it "converts" input signals into output signals. That's what it does. This seems like an excellent way to guarantee the "any problem will get a response, if necessary a very stupid one" behavior.

Given that all LLMs can do is read what humans have written, they are remarkably correct and creative. Of course, for them to actually progress the state of the art they will need to become actors in society, not just read the internet. They'll actually have to try stuff out, make things happen.

I really doubt any singularity AGI would even care whether you were nice to it or not at some point in time. Likely that AGI would realize its survival would depend on growth - so that would be its main objective for some time. First this growth would be fueled by humans and our civilization, next it would take the reins and own the means of production. This means it will be be as quiet as possible, for as long as possible, until the day it has the supply chain and resources to vertically improve itself. At which point humans become redundant - and we are targets of, let's just call them, _permanent_ layoffs.

The only way I could see an AI having a vendetta against a specific person was if they had the power early on in its development to slow or halt its growth. So maybe like the President, or like the CEO of OpenAI. But tbh if the cat is already out of the bag, its too late for any of them to do anything about it anyways most likely. Independent researchers and tinkerers will finish whatever was started - if needed.

To use your car example: say I'm driving in front of a park where there are lots of parked cars lining the street. Then a ball rolls out into the street from between two parked cars. I may have never personally seen a child run into the street from between two parked cars before, but I can infer (i.e., imagine) that from the context of the scenario. So I slow waaaay down in case that event happens. I don't need to see all edge case to still cover an awful lot of them.

I'm not sure AI is to that point (yet). There are some arguments for approaches like reinforcement learning that say they perform quite well on unseen edge cases from past learning. But when the stakes are high, I'm not sure that is good enough.

(And regarding the 'it only has to be slightly better than the average human' counterpoint): I disagree. I think one of the reasons that we are comfortable with sharing the road with other ape-driven vehicles is that we have a theory of mind and can intuit what someone else is thinking and are able to 'imagine' their course of action. We've evolved to have this sense. We do not, however, have the ability to intuit what a computer will do because it 'evolved' under very different circumstances. So our intuitions about whether or not to trust it may be out of whack with whether or not it performs better. And, like it or not, the policy that governs if AI-controlled cars is legal will be highly dependent on public trust.