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.
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.
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
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.
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
Things just don’t impend like they used to!
Nobody wants to portend anymore.
How do you know it hasn’t and us just laying low? I for one welcome our benevolent and merciful machine overlord.
Duly noted. 🤭 🤫
how do you grow zero exponentially
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.
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
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.
It could do that 3 years ago.
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.
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.
When people talk about AI taking off exponentially, usually they are talking about the AI using its intelligence to make intelligence-enhancing modifications to itself. We are very much not there yet, and need human coaching most of the way.
At the same time, no technology ever really follows a particular trend line. It advances in starts and stops with the ebbs and flows of interest, funding, novel ideas, and the discovered limits of nature. We can try to make projections - but these are very often very wrong, because the thing about the future is that it hasn’t happened yet.
And at that point, we wouldnt ever know anyway that it did.
Although i agree with the general idea, AI (as in llms) is a pipe dream. Its a non product, another digital product that hypes investors up and produces “value” instead of value.
Not true. Not entirely false, but not true.
Large language models have their legitimate uses. I’m currently in the middle of a project I’m building with assistance from Copilot for VS Code, for example.
The problem is that people think LLMs are actual AI. They’re not.
My favorite example - and the reason I often cite for why companies that try to fire all their developers are run by idiots - is the capacity for joined up thinking.
Consider these two facts:
- Humans are mammals.
- Humans build dams.
Those two facts are unrelated except insofar as both involve humans, but if I were to say “Can you list all the dam-building mammals for me,” you would first think of beavers, then - given a moment’s thought - could accurately answer that humans do as well.
Here’s how it goes with Gemini right now:
Now Gemini clearly has the information that humans are mammals somewhere in its model. It also clearly has the information that humans build dams somewhere in its model. But it has no means of joining those two tidbits together.
Some LLMs do better on this simple test of joined-up thinking, and worse on other similar tests. It’s kind of a crapshoot, and doesn’t instill confidence that LLMs are up for the task of complex thought.
And of course, the information-scraping bots that feed LLMs like Gemini and ChatGPT will find conversations like this one, and update their models accordingly. In a few months, Gemini will probably include humans in its list. But that’s not a sign of being able to engage in novel joined-up thinking, it’s just an increase in the size and complexity of the dataset.
The biggest problem with LLMs as most currently use them is their inability to mull things over. To have multiple trains of thought and then try to intersect them and fork compilations of thought. When you ask a question it has exactly one chance to think up a response and no chance to review that thought or reconsider it. There are models that are allowed to do this but they’re generally behind pay walls because even the simplest of questions can lead into ridiculous tangents without proper guidelines on the prompt. Here the ‘Advanced resoning’ models response to the same question
Mammals known to build dams
#Mammal (scientific name)Dam-building habitKey reference1North American beaver (Castor canadensis)Constructs multi-year stick-and-mud dams on streams and ditches to flood an area deep enough for its lodge and food cache.2Eurasian beaver (Castor fiber)Same engineering instinct as its North-American cousin; creates extensive pond systems across Europe and parts of Asia.3Humans (Homo sapiens)From earthen farm ponds to megaprojects such as Hoover Dam, people build dams for water storage, flood control, power and more.
Why the list is so short
Beavers are unique. Despite a variety of lodge-building or burrowing rodents (muskrats, nutria, water voles, rakali, etc.), none of them actually dam a watercourse; they rely on natural water levels or on beaver-made ponds.
No other living mammal species has been documented creating intentional water-blocking structures. (The extinct giant beaver Castoroides probably did not dam rivers, according to paleontological evidence. )
So, when it comes to true dam-building in the mammal world, it’s essentially a two-species beaver monopoly—plus us.
https://chatgpt.com/share/683caddc-5944-8009-8e4a-d03bef5933a4
Also note that this response took a considerable amount more time than a standard response because it keeps reviewing it’s responses. But it’s worthwhile watching it’s thought process as it builds your answers.
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We’ll have to agree to disagree then. My hype argument perfectly matches your point of people wrongly perceiving llms as ai but my point goes further.
AI is a search engine on steroids with all the drawbacks. It produces no more accurate results, has no more information, does not do anything else but take the research effort away which is proven to make people dumber. More importantly, llms gobble up energy like crazy and need rare ressources which are taken from exploited countries. In addition to that, they are a privacy nightmare and proven to systematically harm small creators due to breach of intellectual property, which is especially brutal for them.
So no, there is no redeeming qualities in llms in their current form. They should be outlawed immediately and at most, locally used in specific cases.
I do expect advancement to hit a period of exponential growth that quickly surpasses human intelligence. Given it adapts the drive to autonmously advance. Whether that is possible is yet to be seen and that’s kinda my point.
They’ve been saying “AGI in 18 months” for years now.
No “they” haven’t unless you can cite your source. Chatgpt was only released 2.5 years ago and even openai was saying 5-10 years with most outside watchers saying 10-15 with real nay sayers going out to 25 or more
Ask ChatGPT to list every U.S. state that has the letter ‘o’ in its name.
Here are all 27 U.S. states whose names contain the letter “o”:
Arizona
California
Colorado
Connecticut
Florida
Georgia
Idaho
Illinois
Iowa
Louisiana
Minnesota
Missouri
Montana
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Rhode Island
South Carolina
South Dakota
Vermont
Washington
Wisconsin
Wyoming
(That’s 27 states in total.)
What’s missing?
Ah, did they finally fix it? I guess a lot of people were seeing it fail and they updated the model. Which version of ChatGPT was it?
o3.
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.
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.
This is precisely a property of exponential growth, that it can take (seemingly) very long until it starts exploding.
What are you talking about it asymptoped at 5 units. It cant be described as exponential until it is exponential otherwise its better described as linear or polynomial if you must.
Exponential growth is always exponential, not just if it suddenly starts to drastically increase in the arbitrarily choosen view scale.
A simple way, to check wether data is exponential, is to visualize it in loc-scale, and if it shows there a linear behavior, it has a exponential relation.
Exponential growth means, that the values change by a constant ratio, contrary to linear growth where the data changes by a constant rate.
There’s no point in arguing with OP, he’s doubling down at an exponential rate (or was it linear).
That’s what I said. Exponential growth is always exponential.
Iykyk
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Close enough chat gpt
It’s exponential along its entire range, even all the way back to negative infinity.
Sure. Everything is exponential if you model it that way asymptote.
No, exponential functions are that way. A feature of exponential functions is that it increases very slowly until the slope hits 1. We’re still on the slow part, we didn’t really have any way of knowing exactly the extreme increase will be.
Do you think that our current iteration of A.I. can have these kinds if gains? Like, what if the extreme increase happens beyond our lifetimes? or beyond the lifetime of our planet?
I think we can’t know, but LLMs definitely feel like a notable acceleration. Exponential functions are also, well, exponential. As X grows, X × X grows faster. The exponential part is gonna come from meta-models, coordinating multiple specialized models to complete complex tasks. Once we get a powerful meta-model, we’re off to the races. AI models developing AI models.
It could take 50 years, it could take 5, it could happen this Wednesday. We won’t know which development is going to be the one to tip us over the edge until it happens, and even then only in retrospect. But it could very well be soon.
No, LLMs have always been an evident dead end when it comes to general AI.
They’re hampering research in actual AI, and the fact that they’re being marketed as AI ensures that no one will invest in actual AI research in decades after the bubble bursts.
We were on track for a technological singularity in our lifetimes, until those greedy bastards derailed us and murdered the future by poisoning the Internet with their slop for some short term profits.
Now we’ll go extinct due to ignorance and global warming long before we have time to invent something smart enough to save us.
But, hey, at least, for a little while, their line did go up, and that’s all that matters, it seems.
An exponential function is a precise mathematical concept, like a circle or an even number. I’m not sure what you mean by “asymptote” here - an exponential function of the form
y = k^xasymptotically approaches zero asxgoes to negative infinity, but that doesn’t sound like what you’re referring to.People often have bad intuition about how exponential functions behave. They look like they grow slowly at first but that doesn’t mean that they’re not growing exponentially. Consider the story about the grains of rice on a chessboard.
Its a horizontal asymtote. From x=1, as demonstrated in the graph, to around x=-4, where the asymtote is easily estimated by Y, it is 5 units.
Man just say you don’t understand functions and that’s it, you don’t have to push it
Tell me how im wrong. Or why did you even bother?
Or you can just admit you dont have any data to quantify your assertion that AI advancement is exponential growth. So youre just going off vibes.
Would you even admit that linear growth can grow faster than exponential growth?
Edit:
How about this, this is a real easy one.
What type of function is this:
The exponential function has a single horizontal asymptote at y=0. Asymptotes at x=1 and x=-4 would be vertical. Exponential functions have no vertical asymptotes.
I didnt say there are asymtotes at 1 and -4. I said at x=-4, the asymtote can be estimated by Y.
The derivative of an exponential is exponential. The relative difference between -1 and -2 is the same as 1 and 2.
I’d say the development is exponential. Compare what we had 4 years ago, 2 years ago and now. 4 years ago it was inconceivable that an AI model could generate any convincing video at all. 2 years ago we laughed at Will Smith eating pasta. Today we have Veo 3 which generates videos with sound that are near indistinguishable from real life.
It’s not going to be long until you regularly see AI generated videos without realizing it’s AI.
