So much for that plan.

What skills won't be replaced? The only ones I can think of either have a large physical component, or are only doable by a tiny fraction of the current workforce.

As for the ones with a physical component (plumbers being the most cited), the cognitive parts of the job (the "skilled" part of skilled labor) can be replaced while having the person just following directions demonstrated onscreen for them. And of course, the robots aren't far behind, since the main hard part of making a capable robot is the AI part.

You may be right about humans biasing toward easiest to obtain information, but that doesn't say "don't use AI assistance", it says "use care when using AI assistance".

Also, Cliff wasn't saying the information was easier to use, since in his case, it was actually harder to use than just looking it up in a printed encyclopedia or the like. But none of the problems he mentioned were inherent problems with the internet, they were because it was a brand new medium still working out its kinks. AI may well be harder to use for coding right now, at least for many use cases. However, a look at the bigger picture strongly suggests it is the future, just as a look at the bigger picture in 1995 would have suggested that the internet was the future, at least for answering questions like "when was the battle of Trafalgar?"

This is consistent with my horse/car analogy: the car wasn't the problem, the problem was people who assumed cars were going to keep themselves on the road like a horse would naturally do. You can get a huge gain, but you have to be smart about how you use it.

From my perspective, your perspective is like a horse and buggy driver feeling vindicated when a "horseless carriage" driver accidentally drives one into a tree. The cars will get easier to drive and safer in crashes, and the drivers will learn to pay attention in certain ways they previously didn't have to.

Will there still be occasional problems? Sure, but that doesn't mean that tying your career to horses would have been a wise move. Same here.

(Also, this article is about "poisoned ChatGPT-like tools." Which says very little about using the tools that most developers are using)

I'm always reminded of this: "Logged onto the World Wide Web, I hunt for the date of the Battle of Trafalgar. Hundreds of files show up, and it takes 15 minutes to unravel them—one's a biography written by an eighth grader, the second is a computer game that doesn't work and the third is an image of a London monument. None answers my question, and my search is periodically interrupted by messages like, "Too many connections, try again later."" -- Cliff Stoll, 1995

This is different and has a lot of complications that are basically things we've never seen before, but still, just giving the 60 million back doesn't make any sense at all. They would've never achieved what they've achieved without his 60 million.

That isn't the case here. If some well meaning person discovers a way that you can create a pandemic causing superbug, they can't just "fix" the AI to make that impossible. Not if it is open source. Very different thing.

The point, though, is that if you're not gonna have to interact with these people in the future, and there are otherwise no repercussions to being nasty, you're more likely to be nasty.

Tell me how that doesn't apply to the human brain as well.

This is true. But human brains don't directly model the world either, they form an internal model based on what comes in through their senses. Humans have the advantage of being more "multi-modal," but that doesn't mean that they get more information or better information.

Much of my "modeling of the world" comes from the fact that I've read a lot of text. But of course I haven't read even a tiny fraction of what GPT4 has.

That said, LLMs can already train on images, as GPT4-V does. And the image generators as well do this, it's just a matter of time before the two are fully integrated. Later we'll see a lot more training on video and sound, and it all being integrated into a single model.

And I have no idea what you mean by saying that it has no concept of true or false. Even the simplest computer programs have a concept of true or false, that's kind of the simplest data type, a boolean. Large language models have a much more sophisticated concept of true and false that has a lot more nuance. That's really a pretty ridiculous thing to say.

I think to best wrap your head around this stuff, you should look to the commonalities of LLM's, image, generators, and even things like Alpha Zero and how it learned to play Go.

Alpha Zero is kind of the extreme in terms of not imitating anything that humans have done. It learns to play the game simply by playing itself -- and what they found is that there isn't really a limit to how good it can get. There may be some theoretical limit of a "perfect" Go player, or maybe not, but it will continue to converge towards perfection by continuing to train. And it can go far beyond what the best human Go player can ever do. Even though very smart humans have spent their lifetimes deeply studying the game, and Alpha Zero had to learn everything from scratch.

One other thing to take into consideration, is that to play the game of Go you can't just think of the next move. You have to think far forward in the game -- even though technically all it's doing is picking the next move, it is doing so using a model that has obviously looked forward more than just one move. And that model is obviously very sophisticated, and if you are going to say that it doesn't understand the game of Go, I would argue that you have a very, oddly restricted definition of the word, understand, and one that isn't particularly useful.

Likewise, with large language models, while on the surface, they may be just predicting the next word one after another, to do so effectively they have to be planning ahead. As Hinton says, there is no real limit to how sophisticated they can get. When training, it is never going to be 100% accurate in predicting text it hasn't trained on, but it can continue to get closer and closer to 100% the more it trains. And the closer it gets, the more sophisticated model it needs. In the sense that Alpha Zero needs to "understand" the game of Go to play effectively, the large language model needs to understand "the world" to get better at predicting.

But hey I guess I can do this too. How's this? Using cringe as an adjective is cringe.

It seems the whole point is to bring in additional details of how brains work, that the think may be relevant to artificial NNs.

Would you have preferred I emulate your style, and complain while providing no support for my complaint?

Ok.

There are insights that can come from studying the brain, that do indeed apply. Some researchers may not glean anything from such studies, and some may. I have no doubt that as neural networks get more an more powerful, we will continue to find more ways they are similar to the brain, and apply things we've learned about the brain to them.

I certainly prefer to see people making comparisons of neural networks to the brain, that the old "it's just a glorified autocomplete" and the like.

Relax.

Here's a few of mine, many photorealistic.

https://www.karmatics.com/stuff/dalle.html

Yours does too. Very different feel than mine, but beautiful.