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AI companies specializing in spreading bullshit all across the internet have a bright future however
So, I have clients that are actively using AI on a daily basis and LOVE it. It is however a very narrow subset. Also, I’m pretty sure that a LARGE amount of Dollars are currently being spent on AI generated political articles.
No shit.
Like all new technologies, there is a time when bunches of companies jump on the band wagon to get in on the action. You can see it all throughout the history of the industrial revolution.
They mostly know that there will come a great weeding out of those that can't handle the technology or just fail from poor management. But they are betting they will be among the 1% that wins the race and remain to dominate the market.
The rest will just bide their time until the next Big Thing comes along. And the process starts over again.
Yeah, but thanks to the glory of corporateworld, all the people involved in making these decisions will be in a higher position at a different company by the time the consequences come knocking.
You definitely will not regret spending billions of dollars on GPUs and electricity bills.
I'll be gone, you'll be gone.
Maybe it's like the dotcom bubble: there is genuinely useful tech that has recently emerged, but too many companies are trying to jump on the bandwagon.
LLMs do seem genuinely useful to me, but of course they have limitations.
We need to stop viewing it as artificial intelligence. The parts that are worth money are just more advanced versions of machine learning.
Being able to assimilate a few dozen textbooks and pass a bar exam is a neat parlor trick, but it is still just a parlor trick.
Unfortunately probably the biggest thing to come out of it will be the marketing aspect. If they spend enough money to train small models on our wants and likes it will give them tremendous amounts of return.
The key to using it in a financially successful manner is finding problems that fit the bill. Training costs are fairly high, quality content generation is also rather expensive. There are sticky problems around training it from non-free data. Whatever you're going to use it for either needs to have a significant enough advantage to make the cost of training /data worth it.
I still think we're eventually going to see education rise. The existing tools for small content generation adobe's use of it to fill in small areas is leaps and bounds better than the old content aware patches. We've been using it for ages for speech recognition and speech generation. From there it's relatively good at helper roles. Minor application development, copy editing, maybe some VFX generation eventually. Things where you still need a talented individual to oversee it but it can help lessen the workload.
There are lots of places where it's being used where I think it's a particularly poor fit. AI help desk chatbots, IVR scenarios, It says brain dead as the original phone trees and flow charts that we've been following for decades.
If GPT4o is still not what you would call AI, then what is? You can have conversations with it, the Turing test is completely irrelevant all of the sudden.
It's a massive text predictor. It doesn't solve problems, it applies patterns based on correlations it picked up during training. If someone talked about your topic online, it has been trained on those conversations. If a topic has two sides that don't agree, chat gpt might respond in a way that is biased towards one side or the other and you can easily get it to "switch" to the other side with follow up prompts.
For what would be considered AI, think of the star trek computer or Data. The Star Trek computer could create simulations of warp core behaviour to push frontiers of knowledge or characters smart enough to defeat its own safeties (frankly, the computer was such a deus ex machina kinda thing that it was hard to suspend disbelief at times, like why did they even have humans doing the problem solving with computers that capable?). Data wouldn't get confused about whether any counties in Africa start with K.
I don't think the Turing test is an effective means of determining intelligence anyways. It came from a time when a conversational computer was barely thinkable. But I wouldn't even say chat gpt is there yet, since you can tell if you ask it the right things. It is very useful, don't get me wrong, like a very powerful search engine. But it's not intelligent.
What of what you say does not apply to humans? They apply patterns of behavior in response to some input. Picked up by learning them. Including people talking online. They are always biased on some way. Some will acknowledge their bias and change it if you give them context.
GPT can literally create simulations. I have used it to do exactly that, specifically for 2D heat conducting with coupled mass transport and reaction kinetics.
I can write a program that just replies "yes" to everything you say and you can have a conversation with that. Is that program AI?
"AI isn't really AI and no one ever thought that AI was actually AI so it doesn't matter if we call it AI" is the funniest level of tech bro cope these days.
We're hitting logarithmic scaling with the model trainings. GPT-5 is going to cost 10x more than GPT-4 to train, but are people going to pay $200 / month for the gpt-5 subscription?
Is it necessary to pay more, or is it enough to just pay for more time? If the product is good, it will be used.
But it would use less energy afterwards? At least that was claimed with the 4o model for example.
4o is also not really much better than 4, they likely just optimized it among others by reducing the model size. IME the "intelligence" has somewhat degraded over time. Also bigger Model (which in tha past was the deciding factor for better intelligence) needs more energy, and GPT5 will likely be much bigger than 4 unless they somehow make a breakthrough with the training/optimization of the model...
4o is optimization of the model evaluation phase. The loss of intelligence is due to the addition of more and more safeguards and constraints by the use of adjunct models doing fine turning, or just rules that limit whole classes of responses.
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I am old enough to remember when the CEO of Nortel Networks got crucified by Wall Street for saying in a press conference that the telecom/internet/carrier boom was a bubble, and the fundamentals weren't there (who is going to pay for long distance anymore when calls are free over the internet? where are the carriers-- Nortel's customers-- going to get their income from?). And 4 years later Nortel ceased to exist. Cisco crashed too, though had enough TCP/IP router biz and enterprise sales to keep them alive even until today.
This all reminds me of the late 1990s internet bubble rather than the more recent crypto bubble. We'll all still be using ML models for all kinds of things more or less forever from now on, but it won't be this idiotic hype cycle and overvaluation anymore after the crash.
Shit, crypto isn't going anywhere either, it's a permanent fixture now, Wall Street bought into it and you can buy crypto ETFs from your stockbroker. We just don't have to listen to hype about it anymore.
Crypto is still just as awful as it ever was IMO. Still plenty of assholes ~~gambling~~ investing in crypto.
Well put.
Soon, it won't be this idiotic hype cycle, but it'll be some other idiotic hype cycle. Short term investors love hype cycles.
We just don’t have to listen to the hype about it anymore.
True, it’s now in most circles just been mixed in as a commodity to trade on. Though I wish everyone would get that. There’s still plenty of idiots with .eth usernames who think there’s some new boon to be made. The only “apps” built on crypto networks were and are purely for trading crypto, I’ve never seen any real tangible benefit to society come out of it. It’s still used plenty for money laundering, but regulators are (slowly) catching up. And it’s still by far the easiest way to demonstrate what happens to unregulated markets.
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Wow, a CEO who doesn’t buy into the hype? That’s astonishing.
I, for one, cannot wait for the bubble to burst so we can get back to some sense of sanity.
Edit>> Though if Baidu is investing in AI like all the rest, then maybe they just think they’ll be immune — in which case I’m sad again that I haven’t yet come across a CEO who calls bullshit on this nonsense.
AI will have its uses, and it has practical use cases such as helping people to walk or to speak or to translate in real time, etc. But we’re decades away from what all these CEOs seem to think they’re going to cash in on now. And it’ll be fun on some level watching them all be wrong.
Edit>> Though if Baidu is investing in AI like all the rest, then maybe they just think they’ll be immune — in which case I’m sad again that I haven’t yet come across a CEO who calls bullshit on this nonsense.
They may just have kept their AI investments responsible—that is, not put more money into it than they can afford to lose. Keep in mind, Baidu is the Chinese equivalent of Google. They have a large, diversified business with many income streams. I expect they'll still be around after the bubble bursts.
10 to 30? Yeah I think it might be a lot longer than that.
Somehow everyone keeps glossing over the fact that you have to have enormous amounts of highly curated data to feed the trainer in order to develop a model.
Curating data for general purposes is incredibly difficult. The big medical research universities have been working on it for at least a decade, and the tools they have developed, while cool, are only useful as tools too a doctor that has learned how to use them. They can speed diagnostics up, they can improve patient outcome. But they cannot replace anything in the medical setting.
The AI we have is like fancy signal processing at best
Not an expert so I might be wrong, but as far as I understand it, those specialised tools you describe are not even AI. It is all machine learning. Maybe to the end user it doesn't matter, but people have this idea of an intelligent machine when its more like brute force information feeding into a model system.
Don't say AI when you mean AGI.
By definition AI (artificial intelligence) is any algorithm by which a computer system automatically adapts to and learns from its input. That definition also covers conventional algorithms that aren't even based on neural nets. Machine learning is a subset of that.
AGI (artifical general intelligence) is the thing you see in movies, people project into their LLM responses and what's driving this bubble. It is the final goal, and means a system being able to perform everything a human can on at least human level. Pretty much all the actual experts agree we're a far shot from such a system.
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this post was submitted on 21 Oct 2024
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