AI LLMs have been pretty shit, but the advancement in voice, image generation, and video generation in the last two years has been unbelievable.
We went from the infamous Will Smith eating spaghetti to videos that are convincing enough to fool most people… and it only took 2-3 years to get there.
But LLMs will have a long way to go because of how they create content. It’s very easy to poison LLM datasets, and they get worse learning from other generated content.
Poisoning LLM datasets is fun and easy! Especially when our online intellectual property is scraped (read: stolen) during training and no one is being accountable for it. Fight back! It’s as easy as typing false stuff at the end of your comments. As an 88 year old ex-pitcher for the Yankees who just set the new world record for catfish noodling you can take it from me!
I’d argue it has. Things like ChatGPT shouldn’t be possible, maybe it’s unpopular to admit it but as someone who has been programming for over a decade, it’s amazing that LLMs and “AI” has come as far as it has over the past 5 years.
That doesn’t mean we have AGI of course, and we may never have AGI, but it’s really impressive what has been done so far IMO.
If you’ve been paying attention to the field, you’d see it’s been a slow steady march. The technology that LLMs are based in were first published in 2016/2017, ChatGPT was the third iteration of the same base model.
Thats not even accounting for all the work done with RNNs and LSTMs prior to that, and even more prior.
Its definitely a major breakthrough, and very similar to what CNNs did for computer vision further back. But like computer vision, advancements have been made in other areas (like the generative space) and haven’t followed a linear path of progress.
Agreed. I never thought it would happen in my lifetime, but it looks like we’re going to have Star Trek computers pretty soon.
A few years ago I remember people being amazed that prompts like “Markiplier drinking a glass of milk” could give them some blobs that looked vaguely like the thing asked for occasionally. Now there is near photorealistic video output. Same kind of deal with ability to write correct computer code and answer questions. Most of the concrete predictions/bets people made along the lines of “AI will never be able to do ______” have been lost.
What reason is there to think it’s not taking off, aside from bias or dislike of what’s happening? There are still flaws and limitations for what it can do, but I feel like you have to have your head in the sand to not acknowledge the crazy level of progress.
It’s absolutely taking off in some areas. But there’s also an unsustainable bubble because AI of the large language model variety is being hyped like crazy for absolutely everything when there are plenty of things it’s not only not ready for yet, but that it fundamentally cannot do.
You don’t have to dig very deeply to find reports of companies that tried to replace significant chunks of their workforces with AI, only to find out middle managers giving ChatGPT vague commands weren’t capable of replicating the work of someone who actually knows what they’re doing.
That’s been particularly common with technology companies that moved very quickly to replace developers, and then ended up hiring them back because developers can think about the entire project and how it fits together, while AI can’t - and never will as long as the AI everyone’s using is built around large language models.
Inevitably, being able to work with and use AI is going to be a job requirement in a lot of industries going forward. Software development is already changing to include a lot of work with Copilot. But any actual developer knows that you don’t just deploy whatever Copilot comes up with, because - let’s be blunt - it’s going to be very bad code. It won’t be DRY, it will be bloated, it will implement things in nonsensical ways, it will hallucinate… You use it as a starting point, and then sculpt it into shape.
It will make you faster, especially as you get good at the emerging software development technique of “programming” the AI assistant via carefully structured commands.
And there’s no doubt that this speed will result in some permanent job losses eventually. But AI is still leagues away from being able to perform the joined-up thinking that allows actual human developers to come up with those structured commands in the first place, as a lot of companies that tried to do away with humans have discovered.
Every few years, something comes along that non-developers declare will replace developers. AI is the closest yet, but until it can do joined-up thinking, it’s still just a pipe-dream for MBAs.
But any actual developer knows that you don’t just deploy whatever Copilot comes up with, because - let’s be blunt - it’s going to be very bad code. It won’t be DRY, it will be bloated, it will implement things in nonsensical ways, it will hallucinate… You use it as a starting point, and then sculpt it into shape.
Yeah, but I don’t know where you’re getting the “never will” or “fundamentally cannot do” from. LLMs used to be only useful for coding if you ask for simple self-contained functions in the most popular languages, and now we’re here; most requests with small scope, I’m getting a result that is better written than I could have done myself by spending way more time, it makes way fewer mistakes than before and can often correct them. That’s with only using local models which became actually viable for me less than a year ago. So why won’t it keep going?
From what I can tell there is not very much actually standing in the way of sensible holistic consideration of a larger problem or codebase here, just context size limits and being more likely to forget things in the context window the longer it is, which afaik are problems being actively worked on where there’s no reason they would be guaranteed to remain unsolved. This also seems to be what is holding back agentic AI from being actually useful. If that stuff gets cracked, I think it’s going to mean things will start changing even faster.
Agreed. LLM Ai has gotten insanely good insanely fast, and an LLM of course isn’t going to magically turn into an AGI. That’s a whole different ball game.
It could do that 3 years ago.
Yes, the goal posts keep moving, but they do so for a rather solid reason: We humans are famously bad at understanding intelligence and at understanding the differences between human and computer intelligence.
100 years ago, doing complex calculations was seen as something very complex that only reasonably smart humans could do. Computers could easily outcompete humans, because calculations are inherently easy for computers while very difficult for humans.
30 years ago we thought that high-level chess was something reserved only to the smartest of humans, and that it was a decent benchmark for intelligence. Turns out, playing chess is something that benefits greatly from large memory and fast computations, so again, it was easy for computers while really hard for humans.
Nowadays AI can do a lot of things we thought would be really hard to do, but that computers can actually do. But there’s hardly any task performed by LLMs where it’s actually better than a moderately proficient human being. (Apart from tasks like “Do homework task X”, where again LLMs benefit from large memory since they can just regurgitate stuff from the training set.)
Linear growth can be faster than exponential growth. Exponential implys tomorrow we will see it advance faster then it did the day before so every day we would see even crazier shit.
It has taken off exponentially. It’s exponentially annoying that’s it’s being added to literally everything
Humanity may achieve an annoyance singularity within six months
It has slowed exponentially because the models get exponentially more complicated the more you expect it to do.
The exponential problem has always been there. We keep finding tricks and optimizations in hardware and software to get by it but they’re only occasional.
The pruned models keep getting better so now You’re seeing them running on local hardware and cell phones and crap like that.
I don’t think they’re out of tricks yet, but God knows when we’ll see the next advance. And I don’t think there’s anything that’ll take this current path into AGI I think that’s going to be something else.
Well, the thing is that we’re hitting diminishing returns with current approaches. There’s a growing suspicion that LLMs simply won’t be able to bring us to AGI, but that they could be a part of or stepping stone to it. The quality of the outputs are pretty good for AI, and sometimes even just pretty good without the qualifier, but the only reason it’s being used so aggressively right now is that it’s being subsidized with investor money in the hopes that it will be too heavily adopted and too hard to walk away from by the time it’s time to start charging full price. I’m not seeing that. I work in comp sci, I use AI coding assistants and so do my co-workers. The general consensus is that it’s good for boilerplate and tests, but even that needs to be double checked and the AI gets it wrong a decent enough amount. If it actually involves real reasoning to satisfy requirements, the AI’s going to shit its pants. If we were paying the real cost of these coding assistants, there is NO WAY leadership would agree to pay for those licenses.
Yeah, I don’t think AGI = an advanced LLM. But I think it’s very likely that a transformer style LLM will be part of some future AGI. Just like human brains have different regions that can do different tasks, an LLM is probably the language part of the “AGI brain”.
What are the “real costs” though? It’s free to run a half decent LLM locally on a mid tier gaming PC.
Perhaps a bigger problem for the big AI companies rather then the open source approach.
Sure, but ChatGPT costs MONEY. Money to run, and MONEY to train, and then they still have to make money back for their investors after everything’s said and done. More than likely, the final tally is going to look like whole cents per token once those investor subsidies run out, and a lot of businesses are going to be looking to hire humans back quick and in a hurry.
Surely the money to run is very low through, at least per user
How do you know it hasn’t and us just laying low? I for one welcome our benevolent and merciful machine overlord.
Duly noted. 🤭 🤫
What do you consider having “taken off”?
It’s been integrated with just about everything or is in the works. A lot of people still don’t like it, but that’s not an unusual phase of tech adoption.
From where I sit I’m seeing it everywhere I look compared to last year or the year before where pretty much only the early adopters were actually using it.
What do you mean when you say AI has been integrated with everything? Very broad statement that’s obviously not literally true.
True, I tried to qualify it with just about or on the way.
From the perspective of my desk, my core business apps have AI auto suggest in key fields (software IDEs, ad buying tools, marketing content preparation such as Canva). My Whatsapp and Facebook messenger apps now have an “Ask meta AI” feature front and center. Making a post on Instagram, it asks if I want AI assistance to write the caption.
I use an app to track my sleeping rhythm and it has an AI sleep analysis feature built in. The photo gallery on my phone includes AI photo editing like background removal, editing things out (or in).
That’s what I mean when I say it’s in just about everything, at least relative to where we were just a short bit of time ago.
You’re definitely right that it’s not literally in everything.
To be fair, smart background removal was a feature from Picasa over a decade ago. We just didn’t call everything “AI” to make shareholders happy.
LOL… you did make me chuckle.
Aren’t we 18months until developers get replaced by AI… for like few years now?
Of course “AI” even loosely defined progressed a lot and it is genuinely impressive (even though the actual use case for most hype, i.e. LLM and GenAI, is mostly lazier search, more efficient spam&scam personalized text or impersonation) but exponential is not sustainable. It’s a marketing term to keep on fueling the hype.
That’s despite so much resources, namely R&D and data centers, being poured in… and yet there is not “GPT5” or anything that most people use on a daily basis for anything “productive” except unreliable summarization or STT (which both had plenty of tools for decades).
So… yeah, it’s a slow take off, as expected. shrug
Things just don’t impend like they used to!
Nobody wants to portend anymore.
I think we might not be seeing all the advancements as they are made.
Google just showed off AI video with sound. You can use it if you subscribe to thier $250/month plan. That is quite expensive.
But if you have strong enough hardware, you can generate your own without sound.
I think that is a pretty huge advancement in the past year or so.
I think that focus is being put on optimizing these current things and making small improvements to quality.
Just give it a few years and you will not even need your webcam to be on. You could just use an AI avatar that look and sounds just like you running locally on your own computer. You could just type what you want to say or pass through audio. I think the tech to do this kind of stuff is basically there, it just needs to be refined and optimized. Computers in the coming years will offer more and more power to let you run this stuff.
How is that an advance ? Computers have been able to speak since the 1970s. It was already producing text.
Iirc there are mathematical reason why AI can’t actually become exponentially more intelligent? There are hard limits on how much work (in the sense of information processing) can be done by a given piece of hardware and we’re already pretty close to that theoretical limit. For an AI to go singulaity we would have to build it with enough initial intelligence that it could aquire both the resources and information with which to improve itself and start the exponential cycle.
That’s exactly what AI would say. Hmmm…
how do you grow zero exponentially
Computers are still advancing roughly exponentially, as they have been for the last 40 years (Moore’s law). AI is being carried with that and still making many occasional gains on top of that. The thing with exponential growth is that it doesn’t necessarily need to feel fast. It’s always growing at the same rate percentage wise, definitionally.
Moore’s law is kinda still in effect, depending on your definition of Moore’s law. However, Dennard Scaling is not so computer performance isn’t advancing like it used to.
Moore’s law is kinda still in effect, depending on your definition of Moore’s law.
Sounds like the goal post is moving faster than the number of transistors in an integrated circuit.
We once again congratulate software engineers for nullifying 40 years of hardware improvements.
