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.
Yeah, it does do some very human-like things, but it’s still missing some important parts.
It’s kinda like using a textbook for problem solving. It’s great at helping you solve instances of problems that have already been solved, but you won’t likely find the next big advancement in that field in a textbook.
Newton realized masses attracted each other, and through experimentation, came up with his laws of classical physics.
Einstein took the idea that the speed of light always seems to be the same despite relative motion to come up with special relativity, then realized that space-time itself was a physical thing that could be interacted with rather than just a medium, plus came up with field equations that were used to predict things like black holes before anyone had any kind of notion that they were real things.
Chat gpt is incapable of things like that. And sure, many humans never do anything like that, some might not even be capable even if they were motivated and had the right supports to try. But many humans do solve problems that they’ve never seen before. There’s big names in academia but so many more that don’t get famous but still push the boundaries of human knowledge, creatively solving problems and answering questions every day.
I wouldn’t be surprised if an LLM is a piece of general AI if or when it comes, but there will be other parts that are currently missing. We don’t even know what consciousness is, let alone if any of our hardware is capable of creating/hosting one.
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?
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.
Businesses might pay big money for LLMs to do specific tasks. And if chip makers invest more in NPUs then maybe LLMs will become cheaper to train. But I am just speculating because I don’t have any special knowledge of this area whatsoever.
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.
Yeah, it does do some very human-like things, but it’s still missing some important parts.
It’s kinda like using a textbook for problem solving. It’s great at helping you solve instances of problems that have already been solved, but you won’t likely find the next big advancement in that field in a textbook.
Newton realized masses attracted each other, and through experimentation, came up with his laws of classical physics.
Einstein took the idea that the speed of light always seems to be the same despite relative motion to come up with special relativity, then realized that space-time itself was a physical thing that could be interacted with rather than just a medium, plus came up with field equations that were used to predict things like black holes before anyone had any kind of notion that they were real things.
Chat gpt is incapable of things like that. And sure, many humans never do anything like that, some might not even be capable even if they were motivated and had the right supports to try. But many humans do solve problems that they’ve never seen before. There’s big names in academia but so many more that don’t get famous but still push the boundaries of human knowledge, creatively solving problems and answering questions every day.
I wouldn’t be surprised if an LLM is a piece of general AI if or when it comes, but there will be other parts that are currently missing. We don’t even know what consciousness is, let alone if any of our hardware is capable of creating/hosting one.
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.
AI has been the name of the field for 70 years at this point, it isn’t something Sam Altman came up with as a marketing wheeze.
Magic Eightball
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.
Businesses might pay big money for LLMs to do specific tasks. And if chip makers invest more in NPUs then maybe LLMs will become cheaper to train. But I am just speculating because I don’t have any special knowledge of this area whatsoever.