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.
We humans always underestimate the time it actually takes for a tech to change the world. We should travel in self-flying flying cars and on hoverboards already but we’re not.
The disseminators of so-called AI have a vested interest in making it seem it’s the magical solution to all our problems. The tech press seems to have had a good swig from the koolaid as well overall. We have such a warped perception of new tech, we always see it as magical beans. The internet will democratize the world - hasn’t happened; I think we’ve regressed actually as a planet. Fully self-drving cars will happen by 2020 - looks at calendar. Blockchain will revolutionize everything - it really only provided a way for fraudsters, ransomware dicks, and drug dealers to get paid. Now it’s so-called AI.
I think the history books will at some point summarize the introduction of so-called AI as OpenAI taking a gamble with half-baked tech, provoking its panicked competitors into a half-baked game of oneupmanship. We arrived at the plateau in the hockey stick graph in record time burning an incredible amount of resources, both fiscal and earthly. Despite massive influences on the labor market and creative industries, it turned out to be a fart in the wind because skynet happened a 100 years later. I’m guessing 100 so it’s probably much later.
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.
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.
A major bottleneck is power capacity. Is is very difficult to find 50Mwatts+ (sometime hundreds) of capacity available at any site. It has to be built out. That involves a lot of red tape, government contracts, large transformers, contractors, etc. the current backlog on new transformers at that scale is years. Even Google and Microsoft can’t build, so they come to my company for infrastructure - as we already have 400MW in use and triple that already on contract. Further, Nvidia only makes so many chips a month. You can’t install them faster than they make them.
And the single biggest bottleneck is that none of the current AIs “think”.
They. Are. Statistical. Engines.
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.
It has taken off exponentially. It’s exponentially annoying that’s it’s being added to literally everything
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.
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.
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.
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:
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.
That’s what I said. Exponential growth is always exponential.
Close enough chat gpt
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.
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.
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.
Agreed. I never thought it would happen in my lifetime, but it looks like we’re going to have Star Trek computers pretty soon.
It’s not anytime soon. It can get like 90% of the way there but those final 10% are the real bitch.
The AI we know is missing the I. It does not understand anything. All it does is find patterns in 1’s and 0’s. It has no concept of anything but the 1’s and 0’s in its input data. It has no concept of correlation vs causation, that’s why it just hallucinates (presents erroneously illogical patterns) constantly.
Turns out finding patterns in 1’s and 0’s can do some really cool shit, but it’s not intelligence.
This is not necessarily true. While it’s using pattern recognition on a surface level, we’re not entirely sure how AI comes up with it’s output.
But beyond that, a lot of talk has been centered around a threshold when AI begins training other AI & can improve through iterations. Once that happens, people believe AI will not only improve extremely rapidly, but we will understand even less of what is happening when an AI black boxes train other AI black boxes.
I can’t quite wrap my head around this, these systems were coded, written by humans to call functions, assign weights, parse data. How do we not know what it’s doing?
Yeah, there’s a mysticism that’s sprung up around LLMs as if they’re some magic blackbox, rather than a well understood construct to the point where you can buy books from Amazon on how to write one from scratch.
It’s not like ChatGPT or Claude appeared from nowhere, the people who built them do talks about them all the time.
What a load of horseshit lol
EDIT: Sorry, I’ll expand. When AI researchers give talks about how AI works, they say things like, “on a fundamental level, we don’t actually know what’s going on.”
Also, even if there are books available about how to write an AI from scratch(?) somehow, the basic understanding of what happens deep within the neural networks is still a “magic black box”. They’ll crack it open eventually, but not yet.
The ideas that people have that AI is simple and stupid & a passing fad are naive.
If these AI researchers really have no idea how these things work, then how can they possibly improve the models or techniques?
Like how they now claim all that after upgrades that now these LLMs can “reason” about problems, how did they actually go and add that if it’s a black box?
Same way anesthesiology works. We don’t know. We know how to sedate people but we have no idea why it works. AI is much the same. That doesn’t mean it’s sentient yet but to call it merely a text predictor is also selling it short. It’s a black box under the hood.
Writing code to process data is absolutely not the same way anesthesiology works 😂 Comparing state specific logic bound systems to the messy biological processes of a nervous system is what gets this misattribution of ‘AI’ in the first place. Currently it is just glorified auto-correct working off statistical data about human language, I’m still not sure how a written program can have a voodoo spooky black box that does things we don’t understand as a core part of it.
Humans are just nurons, we don’t “understand either” until so many stack on top of each other than we have a sort of consciousness. The it seems like we CAN understand but do we? Or are we just a bunch of meat computers? Also, llms handle language or correlations of words, don’t humans just do that (with maybe body language too) but we’re all just communicating. If llms can communicate isn’t that enough conceptually to do anything? If llms can program and talk to other llms what can’t they do?
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.
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.
