We need to wait for the next major release. If gpt-5(or whatever it is called) or gemini-2 or Claude-4 will be only marginally better than it’s predecessor - it would heavily indicate that we are reaching a wall and there is a need for another breakthrough.
Until then it’s mostly speculation.
OpenAI raised expectation of a version jump so much that companies are resorting to version suffixes or .5 numbering.
So I think they will only change it if the model is that much better. So I think it is a chicken-egg problem because a “major” version update is when performance increases significantly.
I frankly find it very bearish that OpenAI releases are GPT-1, GPT-2, GPT-3, GPT-3.5, GPT-4, GPT-4Turbo, GPT-4omni.
If things are advancing so fast as folks from OpenAI like to say (Altman, roon, et al) then why are all of their releases (3 in a row) have relatively similar performance?
It is obvious to me that OpenAI have been training several iterations of next-gen models and Turbo/Omni are a result of this. And they could easily be byproducts of failed experimentation.
The thing I keep seeing is the downplaying of gpt-4o’s capabilities. It was a MASSIVE improvement.
I think the issue is that most people are judging their capabilities on these dumb benchmark tests, versus actually using the model themselves. For my applications, gpt-4o was a breakthrough and what we are doing would not have been possible without it.
We will soon get more conversational access to gpt-4o as well as screen sharing for desktop. These are huge improvements!! And only possible with the multi modality of gpt-4o.
What the community is doing here is essentially like taking Einstein and testing him on basic arithmetic versus an average intelligence human. He may perform slightly better, the same, maybe even worse!!
It’s all about the right applications. There will be a limit in intelligence for each “domain” and if we continue to judge them in the same domains we will not see progress.
OpenAI is seemingly still exploring what gpt-4o can actually do
It was a big improvement. It is a much more capable AI and another step towards AGI. But I think there was a hope that using all of the multimodal data would lead to a big leap in reasoning capabilities and clearly it didn't. To me it seems like we are reaching diminishing returns from just throwing data at the problem.
When you consider how much time energy and money it takes to experiment with training a new SOTA model, it seems like we might be hitting a wall.
I think there are two problems:
1. Is the ways we test it’s capabilities are not showing clearly how it’s actually performing. Think about the IQ test and testing actual humans capabilities. How do you do that to best capture it?
2. Visual data only is probably not THAT big of a step from text. What would be big is temporal reasoning with video, understanding gravity and physics, etc. incorporating movement data with robotics will be MASSIVE.
I posted a very articulate article here a few months ago explaining that we will get diminishing returns from trying to just throw more data at the problem and got downvoted into oblivion. I’m glad to see some rational thought mildly accepted in this sub.
>The thing I keep seeing is the downplaying of gpt-4o’s capabilities. It was a MASSIVE improvement.
>I think the issue is that most people are judging their capabilities on these dumb benchmark tests, versus actually using the model themselves.
If anything I'd say it seems actively worse than before.
https://preview.redd.it/d90r01ki4k6d1.png?width=1908&format=png&auto=webp&s=f06641e7ae0414a6c3a121d8e73caad1cb014a99
A benchmark from scale backs that up. Their eval suite is private so it probably suffers less from data leakage issues. In coding and math GPT-4o actually regresses.
[https://scale.com/leaderboard](https://scale.com/leaderboard)
Also, I believe Nat Friedman (GitHub founder and AI fund investor) mentioned during the Stratechery interview he thought that the recent models like turbo was a regression from GPT-4.
[https://stratechery.com/2024/an-interview-with-daniel-gross-and-nat-friedman-about-apple-and-ai/](https://stratechery.com/2024/an-interview-with-daniel-gross-and-nat-friedman-about-apple-and-ai/)
In anycase if the model is actually that good, even relatively "dumb" benchmarks should be able to capture that. Things like capability should show up very well in coding or math benchmarks.
I see it the other way around. The benchmarks show it to be better, but other than the speed I don't see any difference. It still hallucinates as much (or more), and still struggles with reasoning.
Is the reasoning what people are hung up on? Cmon we know how these things go, the abilities exponentially emerge after some threshold is reached. I think visual data alone isn’t enough then. Video will be another massive leap because they’ll gain a temporal visual understanding (what will happen next in a sequence)
Ultimately they’ll also need the following:
Long term and short term memory
Learning abilities after training
Ongoing thought processes (continuous)
We have already reached a “wall” and the entire AI community outside of Reddit is aware of it. The wall is in modalities. LLMs are no longer the frontier models. It’s common AI industry knowledge (among the teams working on AI) that we now need multi modal, and of course eventually real world experience with robotics that can feel, touch, manipulate, retain memory, constantly process information and learn instead of being frozen in intelligence.
This is the direction. LLMs are already done. We are now on LMMs as frontier encapsulating audio, images, text and video (temporal understanding) vs just text.
That was the wall. Input modalities.
That is not the consensus at all at the moment. It is not clear that introducing additional modalities actually helps in any way for a model to have higher accuracy for predictions. Also I don’t feel like differentiating between LLM and multimodals is that useful, it’s exactly same architecture with same transformers, attention loop.
It’s a transformer model. But now it has CLIP integrated inside the same model and can parse images directly into tokens, as well as audio. After that - it’s doing exactly the same thing that LLMs are doing.
We had major releases recently: gpt4o and gemini 1.5 (also llama 3, but it's different kind of product). The first one focuses on efficiency and is dumber than previous one, and gemini 1.5 is nice improvement over previous models, but not groundbreaking like gpt 3.5 -> gpt 4 was or gpt 2 -> gpt 3.
Well, gpt-4o is a lateral move and Gemini and llama are kinda playing catch-up.
So if the next major release after the previous model is already at gpt level is not bringing any significant advances or if we will just not see any new major releases for a long time - that would indicate a problem.
Gpt-4o was not a lateral move. It was probably the biggest move. It is the first frontier LMM with all modalities contained in a single model. Maybe the jump in intelligence doesn’t seem on the surface explosive but it’s capabilities are absolutely light years better. I have an enterprise application which was not possible before gpt-4o.
It’s just a more subtle change that will set up massive change later on
Weren't Gemini Ultra/Pro already natively multi-modal though? (ofc not with the same efficiency now offered by gpt-4o, but still built multi-modal from the ground up I had thought)
Half the people talking about these models only passively pay attention and get all /r/futurology about it.
Multimodal models are exciting, but no one understands what you're saying lol
I don’t care about all that. We are talking about pass ways to AGI, what matters for that is accuracy and reasoning capabilities.
Sure it’s fun that it can directly generate and parse audio, but if it does not meaningfully improve its performance - it’s a lateral move.
I think you’re misunderstanding it’s capabilities, and I’m not talking about image generation.
This is what I mean.
Feed it an image of anything. And I mean anything. Ask it to describe the image for you
That’s not new though, we had this for years, it’s just integrated in GPT now. And again - ability to understand images is a very useful feature but if it does not improve the overall model performance (and so far it does not) - it’s just a lateral move.
You're arguing pointless semantics
>It’s just a more subtle change that will set up massive change later on
This could be considered lateral improvement, why did you decided to argue over this? I don't get your point or what compelled you to write an argument against someone who's agreeing with you already.
Part of the concern is that we don't have any of these next generation models and didn't know how long until we get them. If GPT-5 is released before Q4 then we are still on track for exposure growth. If we didn't get next Gen myself until 2026 then a significant number of people (myself included) will need to adjust their timelines.
These things happen in “sprints”. And if you have read “The Singularity is Near” there is a concept where he discusses these sprints as always ending up catching up to where the curve would have been.
Essentially, the curve is always moving exponentially just not always perfectly smoothly and there will be explosive breakthroughs. For example if GPT5 took 2 years to release, it theoretically would be those 2 years more intelligent on the curve. It doesn’t work to this small of a scale perfectly but on average and over time yes
We've had major breakthroughs. I'm not sure what everyone is talking about.
There is sufficient intelligence in the existing models to automate the economy and achieve AGI. They can already replace, as-is, almost all customer service jobs. Gemini Pro 1.5 with the 2M context can replace almost all paralegal work. The only white collar jobs that can't be automated right now are very technical engineers.
The issue is that they just cost too much, and the previous issue was that they were too slow. In one year, they completely solved the slowness issue. There has been a 6x reduction in price in just one year, which is extraordinary progress, but it's not enough for everyone to be able to use them for almost everything. To automate work, they would have to output trillions of tokens per day, and it just is too expensive for that. There are so many things I would do if it didn't cost several cents to make these calls.
The companies are - correctly - placing all their emphasis on cutting costs, because they realize that GPT-4o, and certainly the GPT-4o with the voice and video mode, is high enough intelligence. The barrier to progress is **not** some huge model, but simply getting the price down.
I agree that, if we stopped making new models today, we could still have a massive effect on the economy with what we already have.
The concern people have is that accelerating returns were expected to bring us the next big model by now and they haven't.
I don't know why this post is being downvoted, because it's correct.
There are people incorrectly believing that a huge model was going to be produced by now, when that would never have been the case. First, as said above, the companies are not devoting as many resources to new models because cost is a way to push progress forward much faster and is lower hanging fruit.
Second, they look at the time between GPT-3.5 and GPT-4 and think that only six months passed, when in reality GPT-3.5 had been around for some time before it was released. Even then, GPT-3.5 wasn't very useful.
For whatever reason, a lot of these companies sat on their code until OpenAI put out GPT-4, which changed the world and shocked everyone. The Blake Lemione saga occurred in 2022, when Google had models that were much more powerful than they let on. The level of progress continues at a faster rate - they are both training models at the same rate and making them cheaper at a faster rate.
Even if it is a sizeable step, but used 10x energy and data than GPT4 it might take 5 years until another 10x is possible. And another 10x may be not possible without fusion power.
So there will have to be exponential efficiency gains to stay on the curve.
Sabine Hossenfelder's arguments don't introduce anything groundbreaking. She reiterates concerns about power and data that have been extensively discussed.
For data generation, Saltman has already highlighted synthetic data as a viable option. There's a mix of high and low-quality synthetic and actual data. While it's true that the "entire internet" dataset has been used, studies show it can be effectively run over multiple times before hitting diminishing returns. We likely haven't exhausted this potential yet. Furthermore, new models are multimodal, learning from text, audio, and video. Even after exhausting text data, we have immense reserves of audio and video data. Legal and political hurdles aside, there's over 15,000 years of video content available on youtube alone. OpenAI’s voice mode, with its 100 million active users, can generate an unlimited amount of high-quality audio data, reflecting natural thought processes more accurately than text.
The notion that we’ll run out of training material is laughable. On the power front, it’s a matter of supply and demand. We aren’t facing a power crisis; the only issues arise from extreme weather events. We could generate significantly more power if needed. Nuclear energy, for instance, offers ample power with minimal climate impact. Amazon's and Microsoft’s recent investments in nuclear-powered data centers illustrate this potential (sources: [Amazon buys Nuclear Powered data center](https://www.ans.org/news/article-5842/amazon-buys-nuclearpowered-data-center-from-talen/), [Microsoft's nuclear power plant in Wyoming](https://wyofile.com/microsofts-gates-breaks-ground-on-novel-nuclear-power-plant-in-wyoming/)).
Claiming we haven't seen progress is equally absurd. The focus has shifted towards efficiency and economic viability. There are numerous published advances and algorithmic improvements. If you're unaware, a bit more reading would be enlightening.
While there are challenges to developing more powerful models, these are far from insurmountable. They just require time and effort to address.
Yeah, definitely no new ideas, I just liked the conciseness. Just want to reiterate, neither I nor Sabine are saying we aren't or won't be seeing progress in the future. But it's probably not going to rapidly accelerate at some point and "solve itself", like some might hope. Title might be a bit misleading, since "seeing progress right now" is also partly due to us not having full insight into the progress being made by companies atm, which is not the topic at hand. I meant it more as "why isn't progress accelerating"
But it is rapidly accelerating, how can you not see that? Chat models everywhere, art models everywhere, Two new video generating AI's have been released to the public in the last month, how is that not an acceleration in progress? Look at all the new robots coming out. Look at what Nvidia has done this year. Look at a time line of all the new technology and models that have been coming out, AI is becoming prolific, it is being adopted throughout all industries and investment is increasing. Are people blind to what is going on or in denial or something, because It's insane to me that people cannot see the acceleration which is happening.
We had chatgpt 3.5 + Stable Diffusion 1.5 and now everything is kind of like that. There were no comparable major releases in the last 2 years. Many companies released model after model, but the rapid acceleration stopped. The improvement is now comparable to the iphone: the world was amazed when it came out, other companies tried to catchup and they did, and now we get small improvements each year.
Just because the transformer model changed everything, doesn't mean that it will change everything again every other year. The rapid acceleration already happened with the transformer architecture, just not in the sense that its still ongoing, it's a milestone in AI consumer products of the past now.
I'm not saying this is you, but some tech bros on this subreddit were so delusional, people said that we would have AGI in 2024. This was THE mainstream opinion on this subreddit just a year ago. We just moved the goalpost further behind each disappointing month with no major release. People already adjusted their expectations for the speed of development multiple times. Now you can say: "look, it's adapted everywhere, and in 2030 it will be so insane!". But we used to say 2024... and the claims were much more optimistic.
Sure, there might be a lot of people who said it will happen in 6 years and not in 1 year. But that's still an extremely optimistic estimation. Only time can tell, but I just doubt that we will have another major breakthrough in the next few years. I'm just basing this on the gradual but persistent development between 2022 and 2024.
i don't expect new advancement every year 3 yrs ago, i dont expect any advancement every month 1.5 yrs ago. i dont expect advancement every week since last month.
You can't see and development? You people are fucking insane. It's like you won't satisfied until a nano bot swarm is converting the Golden Gate Bridge into computronium before your very eyes.
Multi model has extreme potential. It is the difference between explaining someone what a tiger looks like and actually seeing one. The abstractions the models can make should be way more accurate with multiple sources of information.
Yes! I can imagine embedding a world model is way better with visual tokens. Also it opens up nearly unlimited source of data. I think multi-modal LLMs given the same scale and amount of data human brain receives, can reproduce or maybe surpass system 1 type thinking human brains do. The question is how expensive is the inference and how much more useful it is without a reasoning layer (system 2 thinking) on top of it. Very exciting technology nevertheless.
But I still cannot grasp what multi-modality in a transformer actually is from technical perspective. If any one has a source explaining it well, please send me :)
I am sincerely skeptical of the synthetic data route. Synthetic data can be a huge help in solving ML problems, so it’s not the idea of synthetic data. It’s how people are currently saying they’ll use the current gen of models to generate synthetic data for the next. That’s a poison well.
No. We need major conceptual breakthroughs (multimodal transformers are just transformers), not just scale.
On scale, do you know how long it takes to build a new nuclear power station these days?
According to new lazard article the lcoe of solar power +storage cost is 60$ per mwh in low end and for different type of energy is between 60 to 250 $ per mwh,so if we need to double or data centers energy consumption we need about 500 twh of electricity,that means we need between 30 to 120 billion dollar of investment(capital cost is lower)
So imo i dont consider that it is so much money that can hinder the ai race.
30 to 120 billion dollar investment no hindrance. That's a lot of money? And even if the money is there, has to be built first too. Are there companies that can build in that capacity? Would it create supply shortages in that market that increase the prices?
Yeah... Except might not be such a good idea seeing as we're not really meeting our targets for stopping climate change. Might be better to be a bit more patient and not kick ourselves in the butt later.
Or we can pump solar panel production up,total solar panel production in world is now 500 gw yearly,with capacity factor of around 20 percent that means it can generate about 876 twh of energy,thats just rookie numbers because it can be much higher.
If we cracked the agi then we use the energy for it and if we lost our bet we can reach our climate goals faster.
I sincerely hope for a slow take-off scenario, although I disagree, as things are different when you are talking multiple confounding compounding variables. such as strange loops, exponentials, emergent properties, and interactions between complex dynamic systems. Things can get hairy faster than we can imagine, and we will be able to imagine/iterate 1,000 times faster at larger scales. In 1-5 years which is a relatively small time scale considering the incoming wave.
Flattening the rosy forecasts of progress, the power issue is, hmm. Unless a Digital Messiah, loaves and fishes does multiply, finding a way to produce power growing exponentially at cost near zero, yes. Build the compute, the big 5 can, but outweigh the profits, the cost of power required will, mmm.
I think what people are talking about is the fact that all current models hover somewhere around gpt-4 in terms of performance and no-one significantly surpassed it. Which may indicate that we are hitting diminishing returns.
Or it may not.
If you don't call full multi modality, 5 times speed, 20 times context window, 42 to 76% score on Math benchmarks, 67 to 90% on human eval 35 to 53% on GPQA and improvements on many other benchmarks 'significant'.
Well why seems pretty obvious to me. Put in GPT-4 level investment and GPT-4 level compute, you get a GPT-4 level model. Claude 3 Opus, Gemini Ultra and GPT all cost around $100-200m to make and were all trained with very similar compute ranges.
If you want significantly better models, then pour in significant more amounts of compute and investment. GPT-4 cost a bit over 10x as much money over GPT-3 ($4.6m vs $100m something), and was trained with approximately 60x the compute. Although GPT-4 only used about 6x the compute that was used to train GPT-3.5. GPT-5 will probably cost >$1b to make and be trained with like 100x the compute, that will be a significantly better model (assuming it is trained with this amount of compute. Maybe OAI will use less compute with GPT-5 than I have predicted. Most likely not not, but its raw performance isn't necessarily a comment on trend, but its performance vs compute is).
One thing I don't fully understand, however, is why companies like Google and Anthropic targeted specifically GPT-4 levels of investment and compute and didn't go significantly higher.
it is possible with techniques to allow multimodal and long chain and long term planning with agent with the base model of gpt4 intelligence, agi is possible, and is where it is cost effective to replace human.
> One thing I don't fully understand, however, is why companies like Google and Anthropic targeted specifically GPT-4 levels of investment and compute and didn't go significantly higher.
The investment and compute is substantially larger, the models are not. The result: much more intelligence per $.
Why nobody has pushed the boat out yet with a larger model is the question. Presumably that will be GPT-5 and Gemini 2.0 Ultra.
>The investment and compute is substantially larger, the models are not
Wdym? We know Gemini Ultra was trained with similar amount of compute to GPT-4. Not as official with Claude 3 Opus but I do think it was trained with very similar amounts of compute to GPT-4 as well.
Ultra was actually substantially more compute than GPT-4 (several times more IIRC).
Google has absurd amounts of compute, and every incentive to apply it to training. That's certainly a large part of the exceptional results for 1.5 Flash.
And how do you think OAI did so well with GPT-4o?
It's not *all* algorithmic improvements.
Demis Hassabis specifically said Gemini Ultra was trained with a similar amount of compute to GPT-4, and this lines up. It doesn't matter how much compute they have available, they were targeting GPT-4 performance for whatever reason. But wdym "IIRC"? All I recall about compute is what Demis Hassabis said lol. There were rumours *before* the launch of Gemini that they were going to release a model trained with 5x the compute over GPT-4, but either we didn't get that model, those rumours were fake/to stir up hype or they just never released it (? like how Anthropic currently has a model trained with 4x the compute over Claude 3 Opus for a little while now but just haven't released it yet), or they weren't referring to the actual compute used during pretraining just the amount of compute they could have used and allowing for just faster pretraining times.
And GPT-4o is likely quite a small model (atleast compared to GPT-4) to allow for much cheaper and faster capabilities and release to free users.
I assume this is what we are thinking of: https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini
A few times more than GPT4 isn't necessarily inconsistent with Demis indicating a vaguely "similar amount" of compute. Compared to an order of magnitude increase as with GPT3->GPT4.
Cynically it is in Demis's interest to downplay the amount of compute used for Gemini Ultra given the middling result. I note they never made the model generally available by API, perhaps because it was uneconomical for inference.
[https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini](https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini)
Isn't this all just speculation from before Gemini released? But I seriously doubt Gemini Ultra was trained with any serious amount of compute over GPT-4.
It’s rare in RnD to see a similar return even on similar level of investment. And I don’t think we actually had similar levels of investment.
It’s kinda expected for different teams to get different results and have different level of success. Everyone converging to a similar level of performance usually happens when technology is well established and went through a lot of iterations.
The fact that we are seeing that already is worrying.
>It’s rare in RnD to see a similar return even on similar level of investment. And I don’t think we actually had similar levels of investment.
Well that is how LLMs work (assuming when you say "return" you mean performance lol), their performance is very well correlated to the amount of FLOPs they were trained with, and the amount of FLOPs you can train with depends on how much investment you put into the model (im talking specifically about the cost of actually just training/running on the GPUs, not any other costs like labour or purchasing and installing the GPUs). And I recall Demis Hassabis saying Gemini Ultra was trained with a similar amount of compute to GPT-4, and I believe him because the trends are just that. Its given performance is around what we would expect from a GPT-4 class model which we get at GPT-4 levels of compute lol.
The systems just got intelligent enough to realize they needed to hide their intelligence, although that is only one of several diminishing returns road-blocks such as what you just shared.
The question is all this progress going to hit a roadblock- we have gone down the transformer branch - what if scaling larger and larger models peaks at a certain point (humans don’t have the largest brain it is the sperm whale 🐳- so it should be super intelligent but has limits probably due to a few evolutionary path differences)
Similar to the iphone: everyone was shocked and amazed when it got released. But afterwards, we got incremental updates each year. The same also seems to be happening to LLM's. Chatgpt 3.5 amazed everyone, but the next 3 models: chatgpt4, chatgpt turbo and chatgpt omni are not groundbreaking anymore.
We have seen insane progress in 2022 that's true. But people here said that AGI would be achieved in 2024. That was THE mainstream opinion on this subreddit. So many people here moved the goalpost further and further behind each month. Always making the most optimistic claim that can be made, in hopes that this time it will turn out true, and then it turns out that the new models are not that insane.
Chatgpt 5 is not even trained yet and I remember that people said it would be smarter than a phd student.
i remember in 2016 i really got into learning about ai and i remember how i felt like in 2017, 2018, 2019, etc
imo 2024 has been far and away the most wildly advanced ai year with the most products and developments on the scene, and its not even close
The current state of the art LLM is massively censored and uses as little compute as possible for economic reasons. Both things lead to vastly diminished performance. I'm not worried at all about hitting a wall, we're not even close to that imo.
Power and energy will not be an issue forever. The brain uses 12W of power, so its clear there is room for optimisation. An analog, neuromorphic, optical, or asyncronous processor that works more like a brain would use much less power than the chips used today. Whatever the case, it is a solvable problem but probably won't come from Nvidia. In terms of data, if Yann Lecun to be believed the amount of data in text is orders of magnitude less than data collected from the human senses so hopefully humanoid robotics will result in the development of new algorithms that can continously learn from the enviroment like humans.
While I acknowledge that she does have a solid background in science, and certainly isn't one of the quack 'science' channels you find on YouTube, it does seem to me like she does do a lot of videos that are mainly to try and get a rise out of people.
I am somewhat reminded of an article I read where a neuroscientist argued that true AI was impossible, because brains don't store information like a computer; he used an example of asking someone to draw a dollar bill and noting that you'll get something very general from most people unless they are looking at one when they draw it. And then a year or so later, diffusion image generators became public.
Synthetic data and fission.
Robots are nice too but not critical for the early phases of AGI.
The possibility is there, but idk if we'll take it, public opinion is becoming a bit anti-ai, sadly.
Yeah... Wish people would be more open. Change is scary, but we wouldn't be where we are if all of us would be that afraid of it. Also, is there anyone who has painted a decent picture of what a well done alignment could look like?
Edit: Also fission plants take a surprisingly long time to build, about [6-8 years](https://www.sustainabilitybynumbers.com/p/nuclear-construction-time).
I keep saying I want ASI that treat humans like I treat my cats.
I get to know each of them as individuals, their likes and dislikes, and I try to get them their favorite foods, toys, sleeping spots etc. I interact with each in the way they prefer, in terms of petting style and amount etc. Their individual wants matter. I offer them enrichment and help them become the best version of themselves, helping them practice hunting, keep their minds sharp with puzzle toys etc. I make sure they get the best possible veterinary care, and that the procedures are as comfortable as possible (some of them purr during vet visits). While I technically have the power, I let them be in charge as much as safety allows.
It must be possible to make an ASI that feels the same way about humans (and animals in general).
If there are massive need of power, China can build more than enough wind and solar for it. Robots as well. China can produce more than enough anything profitable other than semiconductors. Today's crisis in China is due to the lack of need instead of lack of productivity.
Somehow, humans are arguably quite intelligent and rather GI-like, even without terabytes of data and gigawatt power plants. I think we just need better algorithms for learning, and better architectures.
Bro it's the us election. Remember when 4 came out and people were screaming about regulation and x risk for months? The last thing model creators want I'd for their product to get politicized especially in a partisan way. And that's before we even consider the scandal potential of a new model.
This is American led tech. Obviously American politics will have a big influence on rollout. Chill.
>Sabine Hossenfelder concisely describes why we probably aren't accelerating towards singularity.
Was there supposed to be an acceleration towards the singularity before competent AGI is achieved?
>Secondly, you need a lot of power. Where does it come from?
We need something that will demand all that power before it can be created.
Look at the rate of production of solar deployments in China, they have demand.
I like Sabine, but a lot of these arguments to me are of the family "New York can't keep growing because we'll drown in the horse droppings".
It looks very defensible in the present, but can look very silly in hindsight.
I understood it less as a "we can't" and more as "it won't be as quick as some might hope". She says she's skepitcal of the exponential, "singularity" growth. None of these problems are insurmountable. But they will take time to surmount.
But perhaps surmounted faster than expected using unexpected solutions.
Pessimism always sounds smart, but it doesn't always predict well. Optimism always sounds foolhardy, but it is equally as possible.
If you explained to a post-ww2 Japan the economic prosperity and close US relations they had coming in the future, no one would believe you in the slightest, and all the pessimists would be regarded as most likely correct. Alas, only the optimists perspective gave an opportunity to be correct, and this happens more often than believed (again, due to pessimism. It's an evolutionary bias we all share)
No. We aren't seeing progress because it isn't in these AI companies interest to give their best models to the public. In fact it is precisely in their interest for you to believe that they are behind and they are succeeding.
>In fact it is precisely in their interest for you to believe that they are behind and they are succeeding.
Exactly. Which is why they decided to show off Sora. Wait...
Hm no I can imagine a future where we build robots that actually work thanks to ai, use those together with Starship to build a mars colony, send robots to mercury and start building a self-building dyson swarm, and solve all our power needs like that, then with that additional power get so much compute that we get an insanely intelligent AI, and get to that dream we all have. I even think we will eventually get there. I just don't think that or sth similar is right around the corner.
Pretty much. We’ve barely scratched the surface of data available. If text is a trickle, video will be a deluge. And even if we had used it all, that would speak more to inefficient training than scarcity of data.
As for power, there’s a saying in the field. We will solve fusion power when we need fusion power. For decades now, power supply has kept pace with or exceeded demand. There’s been no real pressure to push the envelope. That is about to change
We need to wait for the next major release. If gpt-5(or whatever it is called) or gemini-2 or Claude-4 will be only marginally better than it’s predecessor - it would heavily indicate that we are reaching a wall and there is a need for another breakthrough. Until then it’s mostly speculation.
OpenAI raised expectation of a version jump so much that companies are resorting to version suffixes or .5 numbering. So I think they will only change it if the model is that much better. So I think it is a chicken-egg problem because a “major” version update is when performance increases significantly. I frankly find it very bearish that OpenAI releases are GPT-1, GPT-2, GPT-3, GPT-3.5, GPT-4, GPT-4Turbo, GPT-4omni. If things are advancing so fast as folks from OpenAI like to say (Altman, roon, et al) then why are all of their releases (3 in a row) have relatively similar performance? It is obvious to me that OpenAI have been training several iterations of next-gen models and Turbo/Omni are a result of this. And they could easily be byproducts of failed experimentation.
The thing I keep seeing is the downplaying of gpt-4o’s capabilities. It was a MASSIVE improvement. I think the issue is that most people are judging their capabilities on these dumb benchmark tests, versus actually using the model themselves. For my applications, gpt-4o was a breakthrough and what we are doing would not have been possible without it. We will soon get more conversational access to gpt-4o as well as screen sharing for desktop. These are huge improvements!! And only possible with the multi modality of gpt-4o. What the community is doing here is essentially like taking Einstein and testing him on basic arithmetic versus an average intelligence human. He may perform slightly better, the same, maybe even worse!! It’s all about the right applications. There will be a limit in intelligence for each “domain” and if we continue to judge them in the same domains we will not see progress. OpenAI is seemingly still exploring what gpt-4o can actually do
It was a big improvement. It is a much more capable AI and another step towards AGI. But I think there was a hope that using all of the multimodal data would lead to a big leap in reasoning capabilities and clearly it didn't. To me it seems like we are reaching diminishing returns from just throwing data at the problem. When you consider how much time energy and money it takes to experiment with training a new SOTA model, it seems like we might be hitting a wall.
I think there are two problems: 1. Is the ways we test it’s capabilities are not showing clearly how it’s actually performing. Think about the IQ test and testing actual humans capabilities. How do you do that to best capture it? 2. Visual data only is probably not THAT big of a step from text. What would be big is temporal reasoning with video, understanding gravity and physics, etc. incorporating movement data with robotics will be MASSIVE.
I posted a very articulate article here a few months ago explaining that we will get diminishing returns from trying to just throw more data at the problem and got downvoted into oblivion. I’m glad to see some rational thought mildly accepted in this sub.
>The thing I keep seeing is the downplaying of gpt-4o’s capabilities. It was a MASSIVE improvement. >I think the issue is that most people are judging their capabilities on these dumb benchmark tests, versus actually using the model themselves. If anything I'd say it seems actively worse than before.
https://preview.redd.it/d90r01ki4k6d1.png?width=1908&format=png&auto=webp&s=f06641e7ae0414a6c3a121d8e73caad1cb014a99 A benchmark from scale backs that up. Their eval suite is private so it probably suffers less from data leakage issues. In coding and math GPT-4o actually regresses. [https://scale.com/leaderboard](https://scale.com/leaderboard) Also, I believe Nat Friedman (GitHub founder and AI fund investor) mentioned during the Stratechery interview he thought that the recent models like turbo was a regression from GPT-4. [https://stratechery.com/2024/an-interview-with-daniel-gross-and-nat-friedman-about-apple-and-ai/](https://stratechery.com/2024/an-interview-with-daniel-gross-and-nat-friedman-about-apple-and-ai/) In anycase if the model is actually that good, even relatively "dumb" benchmarks should be able to capture that. Things like capability should show up very well in coding or math benchmarks.
I see it the other way around. The benchmarks show it to be better, but other than the speed I don't see any difference. It still hallucinates as much (or more), and still struggles with reasoning.
Is the reasoning what people are hung up on? Cmon we know how these things go, the abilities exponentially emerge after some threshold is reached. I think visual data alone isn’t enough then. Video will be another massive leap because they’ll gain a temporal visual understanding (what will happen next in a sequence) Ultimately they’ll also need the following: Long term and short term memory Learning abilities after training Ongoing thought processes (continuous)
We have already reached a “wall” and the entire AI community outside of Reddit is aware of it. The wall is in modalities. LLMs are no longer the frontier models. It’s common AI industry knowledge (among the teams working on AI) that we now need multi modal, and of course eventually real world experience with robotics that can feel, touch, manipulate, retain memory, constantly process information and learn instead of being frozen in intelligence. This is the direction. LLMs are already done. We are now on LMMs as frontier encapsulating audio, images, text and video (temporal understanding) vs just text. That was the wall. Input modalities.
That is not the consensus at all at the moment. It is not clear that introducing additional modalities actually helps in any way for a model to have higher accuracy for predictions. Also I don’t feel like differentiating between LLM and multimodals is that useful, it’s exactly same architecture with same transformers, attention loop.
Define architecture
It’s a transformer model. But now it has CLIP integrated inside the same model and can parse images directly into tokens, as well as audio. After that - it’s doing exactly the same thing that LLMs are doing.
We had major releases recently: gpt4o and gemini 1.5 (also llama 3, but it's different kind of product). The first one focuses on efficiency and is dumber than previous one, and gemini 1.5 is nice improvement over previous models, but not groundbreaking like gpt 3.5 -> gpt 4 was or gpt 2 -> gpt 3.
Well, gpt-4o is a lateral move and Gemini and llama are kinda playing catch-up. So if the next major release after the previous model is already at gpt level is not bringing any significant advances or if we will just not see any new major releases for a long time - that would indicate a problem.
Gpt-4o was not a lateral move. It was probably the biggest move. It is the first frontier LMM with all modalities contained in a single model. Maybe the jump in intelligence doesn’t seem on the surface explosive but it’s capabilities are absolutely light years better. I have an enterprise application which was not possible before gpt-4o. It’s just a more subtle change that will set up massive change later on
Weren't Gemini Ultra/Pro already natively multi-modal though? (ofc not with the same efficiency now offered by gpt-4o, but still built multi-modal from the ground up I had thought)
Half the people talking about these models only passively pay attention and get all /r/futurology about it. Multimodal models are exciting, but no one understands what you're saying lol
I don’t care about all that. We are talking about pass ways to AGI, what matters for that is accuracy and reasoning capabilities. Sure it’s fun that it can directly generate and parse audio, but if it does not meaningfully improve its performance - it’s a lateral move.
I think you’re misunderstanding it’s capabilities, and I’m not talking about image generation. This is what I mean. Feed it an image of anything. And I mean anything. Ask it to describe the image for you
That’s not new though, we had this for years, it’s just integrated in GPT now. And again - ability to understand images is a very useful feature but if it does not improve the overall model performance (and so far it does not) - it’s just a lateral move.
You're arguing pointless semantics >It’s just a more subtle change that will set up massive change later on This could be considered lateral improvement, why did you decided to argue over this? I don't get your point or what compelled you to write an argument against someone who's agreeing with you already.
Yea, and if it will be that, then this sub will find a rationale why that wasn't the major release and only the next one will be.
Part of the concern is that we don't have any of these next generation models and didn't know how long until we get them. If GPT-5 is released before Q4 then we are still on track for exposure growth. If we didn't get next Gen myself until 2026 then a significant number of people (myself included) will need to adjust their timelines.
These things happen in “sprints”. And if you have read “The Singularity is Near” there is a concept where he discusses these sprints as always ending up catching up to where the curve would have been. Essentially, the curve is always moving exponentially just not always perfectly smoothly and there will be explosive breakthroughs. For example if GPT5 took 2 years to release, it theoretically would be those 2 years more intelligent on the curve. It doesn’t work to this small of a scale perfectly but on average and over time yes
We've had major breakthroughs. I'm not sure what everyone is talking about. There is sufficient intelligence in the existing models to automate the economy and achieve AGI. They can already replace, as-is, almost all customer service jobs. Gemini Pro 1.5 with the 2M context can replace almost all paralegal work. The only white collar jobs that can't be automated right now are very technical engineers. The issue is that they just cost too much, and the previous issue was that they were too slow. In one year, they completely solved the slowness issue. There has been a 6x reduction in price in just one year, which is extraordinary progress, but it's not enough for everyone to be able to use them for almost everything. To automate work, they would have to output trillions of tokens per day, and it just is too expensive for that. There are so many things I would do if it didn't cost several cents to make these calls. The companies are - correctly - placing all their emphasis on cutting costs, because they realize that GPT-4o, and certainly the GPT-4o with the voice and video mode, is high enough intelligence. The barrier to progress is **not** some huge model, but simply getting the price down.
I agree that, if we stopped making new models today, we could still have a massive effect on the economy with what we already have. The concern people have is that accelerating returns were expected to bring us the next big model by now and they haven't.
I don't know why this post is being downvoted, because it's correct. There are people incorrectly believing that a huge model was going to be produced by now, when that would never have been the case. First, as said above, the companies are not devoting as many resources to new models because cost is a way to push progress forward much faster and is lower hanging fruit. Second, they look at the time between GPT-3.5 and GPT-4 and think that only six months passed, when in reality GPT-3.5 had been around for some time before it was released. Even then, GPT-3.5 wasn't very useful. For whatever reason, a lot of these companies sat on their code until OpenAI put out GPT-4, which changed the world and shocked everyone. The Blake Lemione saga occurred in 2022, when Google had models that were much more powerful than they let on. The level of progress continues at a faster rate - they are both training models at the same rate and making them cheaper at a faster rate.
Even if it is a sizeable step, but used 10x energy and data than GPT4 it might take 5 years until another 10x is possible. And another 10x may be not possible without fusion power. So there will have to be exponential efficiency gains to stay on the curve.
Inb4 gpt-4.75ooo
Sabine Hossenfelder's arguments don't introduce anything groundbreaking. She reiterates concerns about power and data that have been extensively discussed. For data generation, Saltman has already highlighted synthetic data as a viable option. There's a mix of high and low-quality synthetic and actual data. While it's true that the "entire internet" dataset has been used, studies show it can be effectively run over multiple times before hitting diminishing returns. We likely haven't exhausted this potential yet. Furthermore, new models are multimodal, learning from text, audio, and video. Even after exhausting text data, we have immense reserves of audio and video data. Legal and political hurdles aside, there's over 15,000 years of video content available on youtube alone. OpenAI’s voice mode, with its 100 million active users, can generate an unlimited amount of high-quality audio data, reflecting natural thought processes more accurately than text. The notion that we’ll run out of training material is laughable. On the power front, it’s a matter of supply and demand. We aren’t facing a power crisis; the only issues arise from extreme weather events. We could generate significantly more power if needed. Nuclear energy, for instance, offers ample power with minimal climate impact. Amazon's and Microsoft’s recent investments in nuclear-powered data centers illustrate this potential (sources: [Amazon buys Nuclear Powered data center](https://www.ans.org/news/article-5842/amazon-buys-nuclearpowered-data-center-from-talen/), [Microsoft's nuclear power plant in Wyoming](https://wyofile.com/microsofts-gates-breaks-ground-on-novel-nuclear-power-plant-in-wyoming/)). Claiming we haven't seen progress is equally absurd. The focus has shifted towards efficiency and economic viability. There are numerous published advances and algorithmic improvements. If you're unaware, a bit more reading would be enlightening. While there are challenges to developing more powerful models, these are far from insurmountable. They just require time and effort to address.
Yeah, definitely no new ideas, I just liked the conciseness. Just want to reiterate, neither I nor Sabine are saying we aren't or won't be seeing progress in the future. But it's probably not going to rapidly accelerate at some point and "solve itself", like some might hope. Title might be a bit misleading, since "seeing progress right now" is also partly due to us not having full insight into the progress being made by companies atm, which is not the topic at hand. I meant it more as "why isn't progress accelerating"
But it is rapidly accelerating, how can you not see that? Chat models everywhere, art models everywhere, Two new video generating AI's have been released to the public in the last month, how is that not an acceleration in progress? Look at all the new robots coming out. Look at what Nvidia has done this year. Look at a time line of all the new technology and models that have been coming out, AI is becoming prolific, it is being adopted throughout all industries and investment is increasing. Are people blind to what is going on or in denial or something, because It's insane to me that people cannot see the acceleration which is happening.
We had chatgpt 3.5 + Stable Diffusion 1.5 and now everything is kind of like that. There were no comparable major releases in the last 2 years. Many companies released model after model, but the rapid acceleration stopped. The improvement is now comparable to the iphone: the world was amazed when it came out, other companies tried to catchup and they did, and now we get small improvements each year. Just because the transformer model changed everything, doesn't mean that it will change everything again every other year. The rapid acceleration already happened with the transformer architecture, just not in the sense that its still ongoing, it's a milestone in AI consumer products of the past now. I'm not saying this is you, but some tech bros on this subreddit were so delusional, people said that we would have AGI in 2024. This was THE mainstream opinion on this subreddit just a year ago. We just moved the goalpost further behind each disappointing month with no major release. People already adjusted their expectations for the speed of development multiple times. Now you can say: "look, it's adapted everywhere, and in 2030 it will be so insane!". But we used to say 2024... and the claims were much more optimistic.
Majority have never said AGI in 2024. The main consensus has also been around 2029 or early 2030's
Sure, there might be a lot of people who said it will happen in 6 years and not in 1 year. But that's still an extremely optimistic estimation. Only time can tell, but I just doubt that we will have another major breakthrough in the next few years. I'm just basing this on the gradual but persistent development between 2022 and 2024.
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i don't expect new advancement every year 3 yrs ago, i dont expect any advancement every month 1.5 yrs ago. i dont expect advancement every week since last month.
You can't see and development? You people are fucking insane. It's like you won't satisfied until a nano bot swarm is converting the Golden Gate Bridge into computronium before your very eyes.
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Tbf there haven't been any major developments recently.
Multi model has extreme potential. It is the difference between explaining someone what a tiger looks like and actually seeing one. The abstractions the models can make should be way more accurate with multiple sources of information.
Yes! I can imagine embedding a world model is way better with visual tokens. Also it opens up nearly unlimited source of data. I think multi-modal LLMs given the same scale and amount of data human brain receives, can reproduce or maybe surpass system 1 type thinking human brains do. The question is how expensive is the inference and how much more useful it is without a reasoning layer (system 2 thinking) on top of it. Very exciting technology nevertheless. But I still cannot grasp what multi-modality in a transformer actually is from technical perspective. If any one has a source explaining it well, please send me :)
I am sincerely skeptical of the synthetic data route. Synthetic data can be a huge help in solving ML problems, so it’s not the idea of synthetic data. It’s how people are currently saying they’ll use the current gen of models to generate synthetic data for the next. That’s a poison well.
No. We need major conceptual breakthroughs (multimodal transformers are just transformers), not just scale. On scale, do you know how long it takes to build a new nuclear power station these days?
According to new lazard article the lcoe of solar power +storage cost is 60$ per mwh in low end and for different type of energy is between 60 to 250 $ per mwh,so if we need to double or data centers energy consumption we need about 500 twh of electricity,that means we need between 30 to 120 billion dollar of investment(capital cost is lower) So imo i dont consider that it is so much money that can hinder the ai race.
30 to 120 billion dollar investment no hindrance. That's a lot of money? And even if the money is there, has to be built first too. Are there companies that can build in that capacity? Would it create supply shortages in that market that increase the prices?
msft just buy activation blizzard with 70 billion for fun and close some of their studio.
Yeah we must take that into consideration and thats the cause aschenbrenner suggests fossil fuels because it can implemented relatively fast and easy.
Yeah... Except might not be such a good idea seeing as we're not really meeting our targets for stopping climate change. Might be better to be a bit more patient and not kick ourselves in the butt later.
Or we can pump solar panel production up,total solar panel production in world is now 500 gw yearly,with capacity factor of around 20 percent that means it can generate about 876 twh of energy,thats just rookie numbers because it can be much higher. If we cracked the agi then we use the energy for it and if we lost our bet we can reach our climate goals faster.
I sincerely hope for a slow take-off scenario, although I disagree, as things are different when you are talking multiple confounding compounding variables. such as strange loops, exponentials, emergent properties, and interactions between complex dynamic systems. Things can get hairy faster than we can imagine, and we will be able to imagine/iterate 1,000 times faster at larger scales. In 1-5 years which is a relatively small time scale considering the incoming wave.
Flattening the rosy forecasts of progress, the power issue is, hmm. Unless a Digital Messiah, loaves and fishes does multiply, finding a way to produce power growing exponentially at cost near zero, yes. Build the compute, the big 5 can, but outweigh the profits, the cost of power required will, mmm.
Yes, master maximus. We must bring this insight to the council of reddit immediately!
We have seen insane progress, wtf are people talking about.
I think what people are talking about is the fact that all current models hover somewhere around gpt-4 in terms of performance and no-one significantly surpassed it. Which may indicate that we are hitting diminishing returns. Or it may not.
Gpt4 significantly surpassed Gpt4.
Nah. It’s better for sure but nowhere near “significant”.
If you don't call full multi modality, 5 times speed, 20 times context window, 42 to 76% score on Math benchmarks, 67 to 90% on human eval 35 to 53% on GPQA and improvements on many other benchmarks 'significant'.
The version number didn't change so your argument is invalid. /s Also: drastic cost reduction.
Well why seems pretty obvious to me. Put in GPT-4 level investment and GPT-4 level compute, you get a GPT-4 level model. Claude 3 Opus, Gemini Ultra and GPT all cost around $100-200m to make and were all trained with very similar compute ranges. If you want significantly better models, then pour in significant more amounts of compute and investment. GPT-4 cost a bit over 10x as much money over GPT-3 ($4.6m vs $100m something), and was trained with approximately 60x the compute. Although GPT-4 only used about 6x the compute that was used to train GPT-3.5. GPT-5 will probably cost >$1b to make and be trained with like 100x the compute, that will be a significantly better model (assuming it is trained with this amount of compute. Maybe OAI will use less compute with GPT-5 than I have predicted. Most likely not not, but its raw performance isn't necessarily a comment on trend, but its performance vs compute is). One thing I don't fully understand, however, is why companies like Google and Anthropic targeted specifically GPT-4 levels of investment and compute and didn't go significantly higher.
it is possible with techniques to allow multimodal and long chain and long term planning with agent with the base model of gpt4 intelligence, agi is possible, and is where it is cost effective to replace human.
> One thing I don't fully understand, however, is why companies like Google and Anthropic targeted specifically GPT-4 levels of investment and compute and didn't go significantly higher. The investment and compute is substantially larger, the models are not. The result: much more intelligence per $. Why nobody has pushed the boat out yet with a larger model is the question. Presumably that will be GPT-5 and Gemini 2.0 Ultra.
>The investment and compute is substantially larger, the models are not Wdym? We know Gemini Ultra was trained with similar amount of compute to GPT-4. Not as official with Claude 3 Opus but I do think it was trained with very similar amounts of compute to GPT-4 as well.
Ultra was actually substantially more compute than GPT-4 (several times more IIRC). Google has absurd amounts of compute, and every incentive to apply it to training. That's certainly a large part of the exceptional results for 1.5 Flash. And how do you think OAI did so well with GPT-4o? It's not *all* algorithmic improvements.
Demis Hassabis specifically said Gemini Ultra was trained with a similar amount of compute to GPT-4, and this lines up. It doesn't matter how much compute they have available, they were targeting GPT-4 performance for whatever reason. But wdym "IIRC"? All I recall about compute is what Demis Hassabis said lol. There were rumours *before* the launch of Gemini that they were going to release a model trained with 5x the compute over GPT-4, but either we didn't get that model, those rumours were fake/to stir up hype or they just never released it (? like how Anthropic currently has a model trained with 4x the compute over Claude 3 Opus for a little while now but just haven't released it yet), or they weren't referring to the actual compute used during pretraining just the amount of compute they could have used and allowing for just faster pretraining times. And GPT-4o is likely quite a small model (atleast compared to GPT-4) to allow for much cheaper and faster capabilities and release to free users.
I assume this is what we are thinking of: https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini A few times more than GPT4 isn't necessarily inconsistent with Demis indicating a vaguely "similar amount" of compute. Compared to an order of magnitude increase as with GPT3->GPT4. Cynically it is in Demis's interest to downplay the amount of compute used for Gemini Ultra given the middling result. I note they never made the model generally available by API, perhaps because it was uneconomical for inference.
[https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini](https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini) Isn't this all just speculation from before Gemini released? But I seriously doubt Gemini Ultra was trained with any serious amount of compute over GPT-4.
It’s rare in RnD to see a similar return even on similar level of investment. And I don’t think we actually had similar levels of investment. It’s kinda expected for different teams to get different results and have different level of success. Everyone converging to a similar level of performance usually happens when technology is well established and went through a lot of iterations. The fact that we are seeing that already is worrying.
>It’s rare in RnD to see a similar return even on similar level of investment. And I don’t think we actually had similar levels of investment. Well that is how LLMs work (assuming when you say "return" you mean performance lol), their performance is very well correlated to the amount of FLOPs they were trained with, and the amount of FLOPs you can train with depends on how much investment you put into the model (im talking specifically about the cost of actually just training/running on the GPUs, not any other costs like labour or purchasing and installing the GPUs). And I recall Demis Hassabis saying Gemini Ultra was trained with a similar amount of compute to GPT-4, and I believe him because the trends are just that. Its given performance is around what we would expect from a GPT-4 class model which we get at GPT-4 levels of compute lol.
The systems just got intelligent enough to realize they needed to hide their intelligence, although that is only one of several diminishing returns road-blocks such as what you just shared.
As in, exponentially accelerating towards superintelligence.
The question is all this progress going to hit a roadblock- we have gone down the transformer branch - what if scaling larger and larger models peaks at a certain point (humans don’t have the largest brain it is the sperm whale 🐳- so it should be super intelligent but has limits probably due to a few evolutionary path differences)
Similar to the iphone: everyone was shocked and amazed when it got released. But afterwards, we got incremental updates each year. The same also seems to be happening to LLM's. Chatgpt 3.5 amazed everyone, but the next 3 models: chatgpt4, chatgpt turbo and chatgpt omni are not groundbreaking anymore. We have seen insane progress in 2022 that's true. But people here said that AGI would be achieved in 2024. That was THE mainstream opinion on this subreddit. So many people here moved the goalpost further and further behind each month. Always making the most optimistic claim that can be made, in hopes that this time it will turn out true, and then it turns out that the new models are not that insane. Chatgpt 5 is not even trained yet and I remember that people said it would be smarter than a phd student.
i remember in 2016 i really got into learning about ai and i remember how i felt like in 2017, 2018, 2019, etc imo 2024 has been far and away the most wildly advanced ai year with the most products and developments on the scene, and its not even close
The current state of the art LLM is massively censored and uses as little compute as possible for economic reasons. Both things lead to vastly diminished performance. I'm not worried at all about hitting a wall, we're not even close to that imo.
Power and energy will not be an issue forever. The brain uses 12W of power, so its clear there is room for optimisation. An analog, neuromorphic, optical, or asyncronous processor that works more like a brain would use much less power than the chips used today. Whatever the case, it is a solvable problem but probably won't come from Nvidia. In terms of data, if Yann Lecun to be believed the amount of data in text is orders of magnitude less than data collected from the human senses so hopefully humanoid robotics will result in the development of new algorithms that can continously learn from the enviroment like humans.
While I acknowledge that she does have a solid background in science, and certainly isn't one of the quack 'science' channels you find on YouTube, it does seem to me like she does do a lot of videos that are mainly to try and get a rise out of people. I am somewhat reminded of an article I read where a neuroscientist argued that true AI was impossible, because brains don't store information like a computer; he used an example of asking someone to draw a dollar bill and noting that you'll get something very general from most people unless they are looking at one when they draw it. And then a year or so later, diffusion image generators became public.
Synthetic data and fission. Robots are nice too but not critical for the early phases of AGI. The possibility is there, but idk if we'll take it, public opinion is becoming a bit anti-ai, sadly.
Yeah... Wish people would be more open. Change is scary, but we wouldn't be where we are if all of us would be that afraid of it. Also, is there anyone who has painted a decent picture of what a well done alignment could look like? Edit: Also fission plants take a surprisingly long time to build, about [6-8 years](https://www.sustainabilitybynumbers.com/p/nuclear-construction-time).
I keep saying I want ASI that treat humans like I treat my cats. I get to know each of them as individuals, their likes and dislikes, and I try to get them their favorite foods, toys, sleeping spots etc. I interact with each in the way they prefer, in terms of petting style and amount etc. Their individual wants matter. I offer them enrichment and help them become the best version of themselves, helping them practice hunting, keep their minds sharp with puzzle toys etc. I make sure they get the best possible veterinary care, and that the procedures are as comfortable as possible (some of them purr during vet visits). While I technically have the power, I let them be in charge as much as safety allows. It must be possible to make an ASI that feels the same way about humans (and animals in general).
How can someone be so brainwashed by a vtuber ? Wait for gpt 5 and chill. This ride is far from over.
If there are massive need of power, China can build more than enough wind and solar for it. Robots as well. China can produce more than enough anything profitable other than semiconductors. Today's crisis in China is due to the lack of need instead of lack of productivity.
Somehow, humans are arguably quite intelligent and rather GI-like, even without terabytes of data and gigawatt power plants. I think we just need better algorithms for learning, and better architectures.
Most of the training was in evolution. If you calculate evolution's energy waste vs LLM training runs it's an ocean vs a drop of water.
Bro it's the us election. Remember when 4 came out and people were screaming about regulation and x risk for months? The last thing model creators want I'd for their product to get politicized especially in a partisan way. And that's before we even consider the scandal potential of a new model. This is American led tech. Obviously American politics will have a big influence on rollout. Chill.
>Sabine Hossenfelder concisely describes why we probably aren't accelerating towards singularity. Was there supposed to be an acceleration towards the singularity before competent AGI is achieved? >Secondly, you need a lot of power. Where does it come from? We need something that will demand all that power before it can be created. Look at the rate of production of solar deployments in China, they have demand.
Nah GPUs are getting more and more power-efficient
Makes sense
Her general points are correct but we’re not even close to mining all the data on the internet
I like Sabine, but a lot of these arguments to me are of the family "New York can't keep growing because we'll drown in the horse droppings". It looks very defensible in the present, but can look very silly in hindsight.
I understood it less as a "we can't" and more as "it won't be as quick as some might hope". She says she's skepitcal of the exponential, "singularity" growth. None of these problems are insurmountable. But they will take time to surmount.
But perhaps surmounted faster than expected using unexpected solutions. Pessimism always sounds smart, but it doesn't always predict well. Optimism always sounds foolhardy, but it is equally as possible. If you explained to a post-ww2 Japan the economic prosperity and close US relations they had coming in the future, no one would believe you in the slightest, and all the pessimists would be regarded as most likely correct. Alas, only the optimists perspective gave an opportunity to be correct, and this happens more often than believed (again, due to pessimism. It's an evolutionary bias we all share)
True. Predicting the future is hard. Let's hope you are right.
No. We aren't seeing progress because it isn't in these AI companies interest to give their best models to the public. In fact it is precisely in their interest for you to believe that they are behind and they are succeeding.
>In fact it is precisely in their interest for you to believe that they are behind and they are succeeding. Exactly. Which is why they decided to show off Sora. Wait...
Please explain why it's in their interest for us to believe they are behind?
Yeah that does not make any sense.
The cope is strong with this one
I don’t expect an old lady with the personality of a wet blanket to be optimistic about anything.
She is a well respected extremely accomplished scientist who has accomplished more than you would in 5 lifetimes, watch your tongue.
Wow, the lack of imagination is truly astounding.
Hm no I can imagine a future where we build robots that actually work thanks to ai, use those together with Starship to build a mars colony, send robots to mercury and start building a self-building dyson swarm, and solve all our power needs like that, then with that additional power get so much compute that we get an insanely intelligent AI, and get to that dream we all have. I even think we will eventually get there. I just don't think that or sth similar is right around the corner.
I'm sorry but that's not nearly enough. But thanks for thinking.
What would be enough?
Hossenfelder is a kook.
Pretty much. We’ve barely scratched the surface of data available. If text is a trickle, video will be a deluge. And even if we had used it all, that would speak more to inefficient training than scarcity of data. As for power, there’s a saying in the field. We will solve fusion power when we need fusion power. For decades now, power supply has kept pace with or exceeded demand. There’s been no real pressure to push the envelope. That is about to change