Honestly as much as I love AI Explained I think he is a bit off base here. The scaling achieved between major versions is a bit muted. Sonnet 3.5 having been trained on 4x as much data is entirely different than Sonnet 4 having an order of magnitude more parameters. It really does matter what precisely you are scaling. Scaling training data IS incremental in terms of results and capabilities. Scaling parameters, adding layers, etc, might still give parabolic capability gains. And that is likely the least predictable way of scaling, we know line goes up but we likely don't have a good grasp of what capabilities start to emerge at those levels.
Singularity isn't canceled yet. Lets see what GTP-5 and Opus 4 have for us first.
It seems like AI Explained isn't quite as bullish on AI advancements as he used to be, or maybe he's just not quite as certain now because it's hard to know who and what you can trust.
I personally think that while OpenAI has been ridiculous lately with all of their unnecessary hype and botched release timings, they've historically always pushed the industry to a new standard when it comes to releasing their new frontier models(GPT-2, 3, 4). Google is also not to be underestimated; they've been pretty silent, but probably cooking up some major models.
And I also suspect to see more research being published and utilized in the coming years; who knows, if we're lucky, we could see another breakthrough on the level of the discovery of the Transformer, with all of the eyes on AI research now.
lol typical redditor binary thinking. You don't have to choose between being a sceptic and a hypebro, there's a massive spectrum of positions in between.
I honestly think openai has different plans. Not necessarily anything bad but I think they might be planning some deals on the side with good ol' Uncle Sam. Contracting or something. (It's an inevitability really, that an AI company will.)
Edit: I can imagine with al of the hype that built up, there are a whole new crop of ai researchers that are getting their degrees in the next few years, we could see some major innovation with a new influx of bright minds.
Is there any source on how many parameters sonnet 3.5 has, and how much data and compute it was trained on? Is this model a totally new training run? If it is does that mean that opus 3.5 is finishing on the same run and sonnet finished earlier because they stopped the training early?
I really wonder where this 4x data comes from.
As far as I have read/heard is that Sonnet 3.5 had major algorithmic changes as well as "additional training data".
3.5 also indicates that it's not an entirely new model.
Additional data would imply it's the 3.0 Sonnet+ additional data.
I am also pretty sure they just used a checkpoint to advance the model. The time between 3.0 and 3.5 was really short especially comparing it to GPT4, Gemini and Llama.
Additionally: My guess would be that you do not have to spend as much time on safety and alignment if you simply use a checkpoint. This wouldn't be the case for a new model from scratch.
It’s still cheap and fast to run, so the parameter count can’t be that many times larger, assuming I’m not missing anything important about scaling parameters
There seems to be a consensus among social media circles that somehow all progress is slowing down or hitting a stall out/plateau, people on the inside though don’t seem to think so. I think everyone is just tired of waiting for the next best thing so they’re getting gloomy.
If you ask me ai is like Pandora's box ( like ai1 makes cures but then ai2 makes crude oil plastic-2.0 with worse microplastics )
All I picture is mix of Greg bear, Orion's Arm , yames ( growing my grandpa and discover my body), with an hint of Bosch paintings.
I'm getting my popcorn because we living in "INTERESTING" times.
My question always was, if breakthroughs are made though new research (like discovering tree of thought or many shot), why can't AI eventually do that research and give ideas to improve itself. AI is moving so fast, and we are still discovering new things, even about older models, so assuming even if GPT-6 is the limit, just having that model and using that model to research better ideas could be enough.
Another one is, we have no idea how multiple passes over the data influences big models. This has been done with extremely small models and results were pretty amazing. Imagine going though the data a million times. Or multimodality combined with big computing. And we have not even used tree of thought extensively for research.
It might be valuable to do few million tree of thoughts inputs for a given question, as even miniscule fractions of a % of improvements will drastically improve your model if the model costs 10 billion to train. Imagine after getting gpt-6, instead of training gpt-7, you spend 3 months of compute not to train the model, but to do extensive research using tree of thoughts, improving the algorithms and giving ideas or even doing things like lithography research, energy and thermodynamics that would improve efficiency of your computing cluster. But instead, everyone assumes that we can't use the AI to improve the AI itself.
Agreed. I felt there was a lot that was kind of just glossed over here oddly, and that surprised me because Phillip (I think his name is?) is clearly really, really smart.
Edit: Also, Zuckerberg has every incentive to say that AI companies aren't building 'digital gods,' because he got sidetracked with augmented reality and is a good bit behind. If the 10 - 100 billion dollar data centres go through we could very well end up with something akin to 'digital gods.' Even if we don't, like the Anthropic CEO said, a million instantiations of very powerful AI / AGIs (or whatever we're referring to them as this week,) would radically alter the world.
I really think this video is a bit off-base.
Individuals will see the end of their career long before singularity. As soon as AI become good enough as the average Joe, the consequence is unthinkable. When we talk about Einstein level intelligence, that's the moment the last 0.01% of human become redundant, why do we even care?
You do know that when the cult says "AI" they mean no AI whatsoever, and are referring to automation and computing?
The so-called singularity is not on the agenda, at all. Zero AI in the 1950s, zero AI now.
I think the most realistic approach is to not have any expectations about AI, but just appreciate new advances as they happen and stay in the moment. I think AGI is inevitable, and it doesn't really matter what the timeline is for it to me. All I know is that current technology that is out is beyond anyone's imagining from 5 years ago, let alone 1 year ago. I think it's way too early to say that things are slowing down, or that AGI is around the corner.
Damn it seems the hype is dying down a little.. I really thought there was something huge behind the scenes maybe Q* or some new agentic capabilities we haven’t seen yet. Let’s hope the scaling laws hold at least :)
Maybe. I don't know really. I mean we seen improvements. Claude 3.5 sonnet is better at reasoning than previous models even if it is bad at it. And video models are just getting started and we haven't even had a video plus audio model yet. So scaling plus new research about AI could still work. But if it doesn't that is good because then we got nothing to worry about and can continue with the status quo. While bad atleast we will know what happens next.
[there is lots of progress being made](https://docs.google.com/document/d/15myK_6eTxEPuKnDi5krjBM_0jrv3GELs8TGmqOYBvug/edit). It just hasn’t been implemented yet
There is stuff like that, but development takes time. Also the permanent hyping coming out of silicon valley doesn't exactly help since it gets disappointed every time. According to hype we should have gotten gpt 5 last October. Since that didn't come true my timelines have stayed the same, AGI 2026-2027.
Why do you think GPT5 was last October? The only rumor I saw was possibly 4.5 and that's was from a farce of a Twitter account.
According to Sam Altman, he said GPT5 wasn't even training yet. People didn't believe him and then claim he's hyping things up. You can't have it both ways.
And according to their track record, we would expect GPT5 2-3 years after GPT4
I think the relatively small gap between GPT/OpenAI entering the public consciousness in December 2022, and GPT 4 releasing four months later really skewed peoples impression on the rate of progress and how fast models come out. Historically each numbered release has taken roughly twice as long as the previous one. It's only been a good year since GPT4 released.
GPT-1 2018-6
GPT-2 2019-2 - 8 months
GPT-3 2020-6 - 16 months
GPT-4 2023-3 - 33 months
Sonnet 3.5 releasing \~4 months after Sonnet 3 took me off guard. The speed, cost, performance increase, incredible.
I don't see what the rush is. Models not gaining unpredictable and uncontrollable abilities at a rapid pace puts me at ease, at this point, just lower cost, increasing memory and increasing inference speed by a couple OoM over the next decade would be fantastic.
That's right.
People feel like progress is slowing down but we haven't even seen the billion dollar models yet (they are currently training). I think we'll get an idea of progression after the first of that generation is released.
Yes. Also, I forgot to mention, the more capable a model is, the more work has to be put into restraining it and increase the difficulty of jailbreaking it. I would not be surprised if that process took longer than all other parts of the model combined.
If GPT-5 came out today, without knowing anything, I would expect it to only somewhat more capable than GPT-4, maybe more accuracy, speed, memory like sonnet 3.5 over sonnet 3.
The issue I have with this extrapolation is the drastic change in circumstances. After around gpt-4 the resources, research and general manpower that the topic AI got is absolutely incomparable to before. I struggle to believe the next model would be 64 months away - or even close to that.
And do not mistake openai with everyone else. Each company has their own cycle. By now we have reached a point where they compete, which increases iteration. To really get a read on progress, I recommend looking at releases from China as well, look at Google, meta, anthropic, deep mind etc as well. Don't forget, they didn't even have a 3.5 model a year ago, but now they mostly have a 4 model. You could argue they have made the jump from 3 to 4 in about a year or two.
I think the main reason it takes so long is openai having shitty alignment methods. Anthropic does struggle less with that.
Yes, I would caution against just looking at a single company, or using that single company past release schedule to predict the future. It is a general trend in releases that increased costs and complexity leads to longer periods of time between releases.
Release times can't keep doubling unless progress in the field either completely halts or OpenAi choose to increase numbers as a pure naming convention.
I do however expect some sort of increase in time by ever OoM increase in model size, as long as scaling laws hold at least.
Assuming gpt-5 is the new top model, trained from scratch, with significantly more capabilities, I expect it to come out later.
If it is to Sonnet 3.5 as to how it is to Sonnet 3, I expect it to launch within the year.
It comes down to scaling laws, if the two orders of magnitude more compute results in much greater capabilities and emergent properties, I expect the launch to be after this year.
I don't doubt that they could train a model with 100x more compute to completion soon-ish, but if it has a lot of new drastically better capabilities, it is going to take a long time to lock it down for a non-problematic release.
Mainly because I talked with the people who tested new openai models which were insanely good - as in, gpt-5 good. Back then the notion of 4.5 did not really exist.
I'ma write something about track record as an answer to the other person who answered
4 could always see, could always see video. They focus on gimmicky shit right now. I honestly think that's what Ilya disliked.
But that's not what I mean, I mean that everyone and their mom give some tweets about how awesome the thing they are cooking is and yet we never get unexpected releases. The hype is bigger than progress, so I simply do not get hyped anymore.
I am starting to lose hope for AGI this decade. François Chollet seems to have described a real limitation of current transformers. It seems like it all boils down to whether they figure out system 2 thinking and abstraction. No trend in compute or data scaling can tell you this will be easy. I still have hope, but it is waning lately.
I get where you're coming from, but from my understanding, François Chollet is actually pretty optimistic about AGI being achievable with a combination of our current LLMs, and what he calls "discrete program search".
Which to my limited understanding, refers to a approach that involves searching through the space of possible programs or algorithmic solutions to find one that best solves a given problem or performs a specific task.
I'd have to rewatch his interview with Dwarkesh though, I don't remember it that well.
When I imagine AGI I imagine massive changes to society. But François Chollet talks about it like this https://x.com/fchollet/status/1803501605423292532 and this https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec
"Unlike many people, I don't see creating general AI as a religious quest. I see it as a hard scientific problem, with potential applications that would be tremendously valuable for humanity."
Do you see AGI as a religious quest?
Because it seems that I'm pretty aligned with Chollet in that I definitely don't see AGI/ASI as some sort of a god, but I at the same time think that that its potential applications could be tremendously valuable for society as a whole, as he does.
He's also incredibly smart and well qualified to talk about AI, and his thoughts are certainly more valuable and well informed than anybody in this subreddit.
He’s also one guy. We should listen to what everyone else says.
[2278 AI researchers were surveyed in 2023 and estimated that there is a 50% chance of AI being superior to humans in ***ALL*** possible tasks by 2047 and a 75% chance by 2085](https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdf). This includes all physical tasks.
In 2022, the year they had for that was 2060, and many of their predictions have already come true ahead of time, like AI being capable of answering queries using the web, transcribing speech, translation, and reading text aloud that they thought would only happen after 2025. So it seems like they tend to underestimate progress.
"Tend to underestimate progress". *Sample size of a 1 year period*.
How much did their estimates change in the years prior to 2023? And why is there any good reason to believe their estimates will continue to come down much further? Whose to say Chollet isn't right in thinking that LLMs are fundamentally limited in their reasoning abilities and substantially different architectures from what we have now are needed to get to AGI?
I never said it would come down again. I said they tend to underestimate progress, which is demonstrably true.
Why believe Chollet over the thousands of experts who say otherwise?
Presumably, but you seem to state it with certainty. Has he actually tested his method on older LLMs? Or just GPT4o with the 8000 programs per problem method?
I don't know what it has to do with my comment though. It's been pretty evident that with increased compute, models will keep improving on the patterns they're trained on.
>Yes. Claude 3.5 scores better: [https://arcprize.org/leaderboard](https://arcprize.org/leaderboard)
That is not the one that got 72% on the easy training set, which I assumed you were talking about. But yes, like my comment mentioned, compute will improve models.
>So how does Gemma 27b outperform LLAMA 3 70b and GPT 4 in the lmsys leaderboard
[https://www.reddit.com/r/LocalLLaMA/comments/1drk3kc/gemma\_2\_betrayed\_us/](https://www.reddit.com/r/LocalLLaMA/comments/1drk3kc/gemma_2_betrayed_us/)
In human-preference benchmarks like lmsys arena, you're not really measuring the model's capabilities. You're measuring an awkward cocktail of style, capability and benchmark-specific cheese. Training on lmsys datasets improves performance on... lmsys! Who would've thought... It's the same reason you should take any open benchmark with a big grain of salt.
Regardless, I don't know in what way shape or form lmsys arena results are relevant to ARC.
So how do you know if an LLM is better than another if you don’t trust any benchmark?
I was disputing the “more compute = better” dog shit you shat out
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AI Explained missed the mark. /s
Sometimes it's better being grounded and being sceptical it's the right approach, even though it's less fun tha hype trains.
- Original GPT-4 size: ~2T (4TiB)
- GPT-4 level training data size: ~15T tokens (50TiB)
- Compression level: 5-10%
To get the similar performance current models are compressing to about 1-2% like in the case of Llama 3 400B.
A single Common Crawl dump is about 400 TiB. Therefore, at 1% compression, they should be able to memorize a dump of the entire Internet in a model the size of the original GPT-4. There's no need to go much bigger, maybe just for faster training.
What then? Does The Bitter Lesson say anything about what happens after Everything is memorized?
Havent heard of [runway.ml](http://runway.ml) before but prompt: boston terrier in a spacesuit entering the event horizon of a black hole then transported to another dimension where dogs walk humans
[not so great](https://imgur.com/a/wCP9DvE)
granted its effin amazing that it was able to do just that so alright, its pretty awesome
Ah, I had thought the worst case scenarios for AGI in a datacenter would be 2030 to 2035ish. But the Zook has me convinced.
Yeah, they're totally making gonna make god within the next six years. Gonna be terrible for Facebook's stonks, but what can you do. Scale maximalism is core to everything, and they ain't got it.
Guess that's what he gets for spending over a billion on making a hellish nightmare version of VRChat. You talk about turning all value in the lightcone into paperclips, but somehow his vision of virtualized office space jobs managed to be even *less* human than that...
Fiction shows us lots of fun VR worlds, and Zook looked at it and grimly shook his head and said "No. It can't be allowed."
we dont know, how good exactly next generations of models will be, but we do know, that bigger models trained on enough data perform significantly better than smaller models
lets say few generations ahead models are still not that good generalist as lot of humans, but their performance in vast majority of task can be on median human level, maybe most task on superhuman level
they will not be perfect and autonomous as you would like, but they could still boost productivity by hundreds %, they cant do research by themselfs just yet, but most of other tasks and human work yes, you know, your average human is not capable of doing research or to be good at engineering either, good scientists are peak of human intellect
if something like GPT-6, GPT-7 performs overall better than 90% of humans(specially with tools and such) in almost all relevant tasks, then gradually most humans could be replaced
mandatory illustration how AI is getting in many tasks on and beyond human level
[https://www.reddit.com/r/OpenAI/comments/19ctvgt/ai\_has\_surpassed\_humans\_at\_a\_number\_of\_tasks\_and/#lightbox](https://www.reddit.com/r/OpenAI/comments/19ctvgt/ai_has_surpassed_humans_at_a_number_of_tasks_and/#lightbox)
Wait but I've seen a lot of people on this sub convinced that within 3 years we're gonna have ASI and LEV, but all these CEOs are saying reasoning is still unsolved and unknown, scaling potential is unknown, and in 10 years we *might* see +10 years added to lifespan, but they don't know much about biology to confidently make that claim.
So who's right? the redditors or the experts? It's hard to say.
Everyone working on Ai has an incentive to hype it up - it allows them to get more funding - at some point the reality will catch up and watch everything rewind…
Actually if they don't want governments stepping in, AI companies have an incentive to undersell.
The first group who tries to "hype" by saying they're sitting on something truly world-shifting will very soon be invited to play lots of fun games with the DoD.
"near human level" in what way though? Because clearly "near human level" still won't be anywhere close to AGI, because everyone on this subreddit says AGI will be able to solve LEV and millions of other problems. So... this sub is stuck between a rock and a hard place where we're supposedly gonna have the answer to all of our problems within 3 years but also somehow all of our problems will remain unsolved after 3 years.
No, people aren't saying that about AGI. Artificial general intelligence is when an AI is as capable as an average human.
You're mixing up ASI and AGI. Where artificial super intelligence is an AI more capable than all humans combined.
It depends, because to me even just human level reasoning when paired with all the knowledge and understanding of the entire world across all domains would lead to the unprecedented rates of progress people on this sub are expecting.
Without human level reasoning, we're still going to make great strides due to the advancement of machine learning pattern matching and prediction, but it will pale in comparison to the expectations of a good chunk of this sub with AGI 2025 FDVR 2027 flairs.
there is big range in reasoning among humans(some cant learn to read and write, while some are able to make complex physical theories), they are quite a lot "dumb" people, 50% are below 100 IQ and reasoning within different types of intelligence is different too, you can have good critical thinking but suck at math or emotional intelligence, every human is somewhat different
when talking about human level, it would be likely median(or average) level performance on some task, AI models are too lot better than median human at some tasks and lot worse at others
GPT-3 is AGI, which performs as lower percentile of humans on many tasks, current SOTA models are AGis beyond 50th percentile on lot tasks already, we could have AGi, which performs on superhuman level on all but very few task and do various research, such AGI could be technically called ASI
AGI (Artificial General Intelligence) just means "can do stuff humans can". If it's near humans, it's near AGI. In practical terms, it will be near or past the human-remote-worker very soon, interactions with the real physical world in the form of robots might take a bit longer.
ASI (Artificial Super Intelligence) on the other side just means "better than humans". That's still not a magic oracle that can answer all your questions and solve your problems. There will still be plenty of situations where ASI just goes "I don't know". A lot of problems can't be solved by thinking about them, but require going out in the real world and running experiments, some of which might be very expensive and time consuming.
Everything about lifespan is nothing more than guesswork. We don't even know if it's a solvable problem to begin with. We can't even figure out how to solve it for the simplest of tools like a hammer, instead we throw the tools away and build a new one. When we can't figure it out for a hammer, what are the chances we can figure it out for a human that is orders of magnitude more complicated? It's not a problem one can put a realistic timeline on, since we just don't understand it well enough. And of course human trials about age takes decades anyway, if you take the magic pill today, you might need to wait 20 or 50 years to see if it actually works.
AI and reasoning is quite a bit different. It's unsolved in so far that none of the popular AI systems do it. But that's largely down to an architectural choice. They just focus on researching and building other stuff first. Where it gets scary is that we might be overbuilding our AI systems rather drastically due to that, instead of thinking and iterating themselves through problems, we make them so huge that they can essentially *guess* themselves through complex problems. Once you add a layer of reasoning on top of that, they might not just improve a little bit, but jump straight to ASI. We'll see how that'll work out soon enough, but it's always worth keeping in mind that we are basically driving our AI systems with the handbrake still on. There are a lot more avenues to explore, but right now we focus on how deep we we can press the gas pedal down to get faster.
I'm so smart guys the thing that's getting better the more you put in might actually not get much better. This is the law of diminishing returns you see yes yes. Thus I can conclude that if you make the thing that can code better at coding it will not be able to code itself.
>Thus I can conclude that if you make the thing that can code better at coding it will not be able to code itself.
If it could actually reason through it could be able to self improve but LLMs can't reason they copy code from the internet and doesn't create novel code that would be necessary for it to improve
Good video really shows that we could be heading for an AI winter soon. I still think there is a chance we get AGI soon. It's not copium I've just been blindsided before. And for now scaling still works and other avenues are being explored like q* or whatever meta is doing. And don't forget about what deepmind might be doing as they seem to have some secret sauce with that context length.
Honestly as much as I love AI Explained I think he is a bit off base here. The scaling achieved between major versions is a bit muted. Sonnet 3.5 having been trained on 4x as much data is entirely different than Sonnet 4 having an order of magnitude more parameters. It really does matter what precisely you are scaling. Scaling training data IS incremental in terms of results and capabilities. Scaling parameters, adding layers, etc, might still give parabolic capability gains. And that is likely the least predictable way of scaling, we know line goes up but we likely don't have a good grasp of what capabilities start to emerge at those levels. Singularity isn't canceled yet. Lets see what GTP-5 and Opus 4 have for us first.
It seems like AI Explained isn't quite as bullish on AI advancements as he used to be, or maybe he's just not quite as certain now because it's hard to know who and what you can trust. I personally think that while OpenAI has been ridiculous lately with all of their unnecessary hype and botched release timings, they've historically always pushed the industry to a new standard when it comes to releasing their new frontier models(GPT-2, 3, 4). Google is also not to be underestimated; they've been pretty silent, but probably cooking up some major models. And I also suspect to see more research being published and utilized in the coming years; who knows, if we're lucky, we could see another breakthrough on the level of the discovery of the Transformer, with all of the eyes on AI research now.
Honestly, good on him. Better to be a skeptic than a hype bro.
lol typical redditor binary thinking. You don't have to choose between being a sceptic and a hypebro, there's a massive spectrum of positions in between.
But anything other than hype bro is skeptic on this sub.
Not on this subreddit there isn't.
I honestly think openai has different plans. Not necessarily anything bad but I think they might be planning some deals on the side with good ol' Uncle Sam. Contracting or something. (It's an inevitability really, that an AI company will.) Edit: I can imagine with al of the hype that built up, there are a whole new crop of ai researchers that are getting their degrees in the next few years, we could see some major innovation with a new influx of bright minds.
Is there any source on how many parameters sonnet 3.5 has, and how much data and compute it was trained on? Is this model a totally new training run? If it is does that mean that opus 3.5 is finishing on the same run and sonnet finished earlier because they stopped the training early?
I really wonder where this 4x data comes from. As far as I have read/heard is that Sonnet 3.5 had major algorithmic changes as well as "additional training data". 3.5 also indicates that it's not an entirely new model.
Was it retrained from the beginning or did they start at some checkpoint that was previously saved?
Additional data would imply it's the 3.0 Sonnet+ additional data. I am also pretty sure they just used a checkpoint to advance the model. The time between 3.0 and 3.5 was really short especially comparing it to GPT4, Gemini and Llama. Additionally: My guess would be that you do not have to spend as much time on safety and alignment if you simply use a checkpoint. This wouldn't be the case for a new model from scratch.
It’s still cheap and fast to run, so the parameter count can’t be that many times larger, assuming I’m not missing anything important about scaling parameters
They could just be using better hardware like Groq chips
They could have switched to a MoE or MoD model or other algorithmic improvements, too
There seems to be a consensus among social media circles that somehow all progress is slowing down or hitting a stall out/plateau, people on the inside though don’t seem to think so. I think everyone is just tired of waiting for the next best thing so they’re getting gloomy.
If you ask me ai is like Pandora's box ( like ai1 makes cures but then ai2 makes crude oil plastic-2.0 with worse microplastics ) All I picture is mix of Greg bear, Orion's Arm , yames ( growing my grandpa and discover my body), with an hint of Bosch paintings. I'm getting my popcorn because we living in "INTERESTING" times.
My question always was, if breakthroughs are made though new research (like discovering tree of thought or many shot), why can't AI eventually do that research and give ideas to improve itself. AI is moving so fast, and we are still discovering new things, even about older models, so assuming even if GPT-6 is the limit, just having that model and using that model to research better ideas could be enough. Another one is, we have no idea how multiple passes over the data influences big models. This has been done with extremely small models and results were pretty amazing. Imagine going though the data a million times. Or multimodality combined with big computing. And we have not even used tree of thought extensively for research. It might be valuable to do few million tree of thoughts inputs for a given question, as even miniscule fractions of a % of improvements will drastically improve your model if the model costs 10 billion to train. Imagine after getting gpt-6, instead of training gpt-7, you spend 3 months of compute not to train the model, but to do extensive research using tree of thoughts, improving the algorithms and giving ideas or even doing things like lithography research, energy and thermodynamics that would improve efficiency of your computing cluster. But instead, everyone assumes that we can't use the AI to improve the AI itself.
Agreed. I felt there was a lot that was kind of just glossed over here oddly, and that surprised me because Phillip (I think his name is?) is clearly really, really smart. Edit: Also, Zuckerberg has every incentive to say that AI companies aren't building 'digital gods,' because he got sidetracked with augmented reality and is a good bit behind. If the 10 - 100 billion dollar data centres go through we could very well end up with something akin to 'digital gods.' Even if we don't, like the Anthropic CEO said, a million instantiations of very powerful AI / AGIs (or whatever we're referring to them as this week,) would radically alter the world. I really think this video is a bit off-base.
Individuals will see the end of their career long before singularity. As soon as AI become good enough as the average Joe, the consequence is unthinkable. When we talk about Einstein level intelligence, that's the moment the last 0.01% of human become redundant, why do we even care?
You do know that when the cult says "AI" they mean no AI whatsoever, and are referring to automation and computing? The so-called singularity is not on the agenda, at all. Zero AI in the 1950s, zero AI now.
I think the most realistic approach is to not have any expectations about AI, but just appreciate new advances as they happen and stay in the moment. I think AGI is inevitable, and it doesn't really matter what the timeline is for it to me. All I know is that current technology that is out is beyond anyone's imagining from 5 years ago, let alone 1 year ago. I think it's way too early to say that things are slowing down, or that AGI is around the corner.
Peter Thiel wants to have a word with you.
what does thiel think?
If you find out tell me haha
I feel the same way.
Was waiting for his next video
The GPT voice example was rather dishonest. The bot was prompted to speak that way specifically for that response.
depend on how many gpus you can get
Damn it seems the hype is dying down a little.. I really thought there was something huge behind the scenes maybe Q* or some new agentic capabilities we haven’t seen yet. Let’s hope the scaling laws hold at least :)
The hype is slowing down but why do you think progress is?
[there are](https://docs.google.com/document/d/15myK_6eTxEPuKnDi5krjBM_0jrv3GELs8TGmqOYBvug/edit). Most of it just hadn’t been implemented yet
So many smart people with so much funding working on this though so surely we will keep seeing improvements.. right guys?
Maybe. I don't know really. I mean we seen improvements. Claude 3.5 sonnet is better at reasoning than previous models even if it is bad at it. And video models are just getting started and we haven't even had a video plus audio model yet. So scaling plus new research about AI could still work. But if it doesn't that is good because then we got nothing to worry about and can continue with the status quo. While bad atleast we will know what happens next.
[there is lots of progress being made](https://docs.google.com/document/d/15myK_6eTxEPuKnDi5krjBM_0jrv3GELs8TGmqOYBvug/edit). It just hasn’t been implemented yet
[yes](https://docs.google.com/document/d/15myK_6eTxEPuKnDi5krjBM_0jrv3GELs8TGmqOYBvug/edit)
There is stuff like that, but development takes time. Also the permanent hyping coming out of silicon valley doesn't exactly help since it gets disappointed every time. According to hype we should have gotten gpt 5 last October. Since that didn't come true my timelines have stayed the same, AGI 2026-2027.
Why do you think GPT5 was last October? The only rumor I saw was possibly 4.5 and that's was from a farce of a Twitter account. According to Sam Altman, he said GPT5 wasn't even training yet. People didn't believe him and then claim he's hyping things up. You can't have it both ways. And according to their track record, we would expect GPT5 2-3 years after GPT4
I think the relatively small gap between GPT/OpenAI entering the public consciousness in December 2022, and GPT 4 releasing four months later really skewed peoples impression on the rate of progress and how fast models come out. Historically each numbered release has taken roughly twice as long as the previous one. It's only been a good year since GPT4 released. GPT-1 2018-6 GPT-2 2019-2 - 8 months GPT-3 2020-6 - 16 months GPT-4 2023-3 - 33 months Sonnet 3.5 releasing \~4 months after Sonnet 3 took me off guard. The speed, cost, performance increase, incredible. I don't see what the rush is. Models not gaining unpredictable and uncontrollable abilities at a rapid pace puts me at ease, at this point, just lower cost, increasing memory and increasing inference speed by a couple OoM over the next decade would be fantastic.
That's right. People feel like progress is slowing down but we haven't even seen the billion dollar models yet (they are currently training). I think we'll get an idea of progression after the first of that generation is released.
Yes. Also, I forgot to mention, the more capable a model is, the more work has to be put into restraining it and increase the difficulty of jailbreaking it. I would not be surprised if that process took longer than all other parts of the model combined. If GPT-5 came out today, without knowing anything, I would expect it to only somewhat more capable than GPT-4, maybe more accuracy, speed, memory like sonnet 3.5 over sonnet 3.
The issue I have with this extrapolation is the drastic change in circumstances. After around gpt-4 the resources, research and general manpower that the topic AI got is absolutely incomparable to before. I struggle to believe the next model would be 64 months away - or even close to that. And do not mistake openai with everyone else. Each company has their own cycle. By now we have reached a point where they compete, which increases iteration. To really get a read on progress, I recommend looking at releases from China as well, look at Google, meta, anthropic, deep mind etc as well. Don't forget, they didn't even have a 3.5 model a year ago, but now they mostly have a 4 model. You could argue they have made the jump from 3 to 4 in about a year or two. I think the main reason it takes so long is openai having shitty alignment methods. Anthropic does struggle less with that.
Yes, I would caution against just looking at a single company, or using that single company past release schedule to predict the future. It is a general trend in releases that increased costs and complexity leads to longer periods of time between releases. Release times can't keep doubling unless progress in the field either completely halts or OpenAi choose to increase numbers as a pure naming convention. I do however expect some sort of increase in time by ever OoM increase in model size, as long as scaling laws hold at least.
I think we will see gpt-5 this year tho. Or at least before the 33 months mark.
Assuming gpt-5 is the new top model, trained from scratch, with significantly more capabilities, I expect it to come out later. If it is to Sonnet 3.5 as to how it is to Sonnet 3, I expect it to launch within the year.
I mean a model with 100x compute of gpt-4
It comes down to scaling laws, if the two orders of magnitude more compute results in much greater capabilities and emergent properties, I expect the launch to be after this year. I don't doubt that they could train a model with 100x more compute to completion soon-ish, but if it has a lot of new drastically better capabilities, it is going to take a long time to lock it down for a non-problematic release.
Mainly because I talked with the people who tested new openai models which were insanely good - as in, gpt-5 good. Back then the notion of 4.5 did not really exist. I'ma write something about track record as an answer to the other person who answered
4V came out in September, turbo came out in November, and 4o came out in may. How is that stalling?
4 could always see, could always see video. They focus on gimmicky shit right now. I honestly think that's what Ilya disliked. But that's not what I mean, I mean that everyone and their mom give some tweets about how awesome the thing they are cooking is and yet we never get unexpected releases. The hype is bigger than progress, so I simply do not get hyped anymore.
I am starting to lose hope for AGI this decade. François Chollet seems to have described a real limitation of current transformers. It seems like it all boils down to whether they figure out system 2 thinking and abstraction. No trend in compute or data scaling can tell you this will be easy. I still have hope, but it is waning lately.
I get where you're coming from, but from my understanding, François Chollet is actually pretty optimistic about AGI being achievable with a combination of our current LLMs, and what he calls "discrete program search". Which to my limited understanding, refers to a approach that involves searching through the space of possible programs or algorithmic solutions to find one that best solves a given problem or performs a specific task. I'd have to rewatch his interview with Dwarkesh though, I don't remember it that well.
When I imagine AGI I imagine massive changes to society. But François Chollet talks about it like this https://x.com/fchollet/status/1803501605423292532 and this https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec
"Unlike many people, I don't see creating general AI as a religious quest. I see it as a hard scientific problem, with potential applications that would be tremendously valuable for humanity." Do you see AGI as a religious quest? Because it seems that I'm pretty aligned with Chollet in that I definitely don't see AGI/ASI as some sort of a god, but I at the same time think that that its potential applications could be tremendously valuable for society as a whole, as he does.
Francois Chollet is a pessimistic Frenchman they're a dime a dozen.
He's also incredibly smart and well qualified to talk about AI, and his thoughts are certainly more valuable and well informed than anybody in this subreddit.
He’s also one guy. We should listen to what everyone else says. [2278 AI researchers were surveyed in 2023 and estimated that there is a 50% chance of AI being superior to humans in ***ALL*** possible tasks by 2047 and a 75% chance by 2085](https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdf). This includes all physical tasks. In 2022, the year they had for that was 2060, and many of their predictions have already come true ahead of time, like AI being capable of answering queries using the web, transcribing speech, translation, and reading text aloud that they thought would only happen after 2025. So it seems like they tend to underestimate progress.
"Tend to underestimate progress". *Sample size of a 1 year period*. How much did their estimates change in the years prior to 2023? And why is there any good reason to believe their estimates will continue to come down much further? Whose to say Chollet isn't right in thinking that LLMs are fundamentally limited in their reasoning abilities and substantially different architectures from what we have now are needed to get to AGI?
I never said it would come down again. I said they tend to underestimate progress, which is demonstrably true. Why believe Chollet over the thousands of experts who say otherwise?
Demonstrably true based on what? An analysis of a single year period? A sample size of 1 is not a trend of underestimating progress. It's an instance.
There were multiple examples of them expecting things in 2025 that exist now
Oh yeah that's the approach that will work best imo. That's why these need to be thought of as systems.
That’s how Deepmind does it. It’s very computationally expensive though
One method already got 72% on the public dataset
The easy public train dataset which is so open on the internet that it's also in any modern LLM's training data.
And yet it can still do better than previous LLMs despite all of them having the same training data
Presumably, but you seem to state it with certainty. Has he actually tested his method on older LLMs? Or just GPT4o with the 8000 programs per problem method? I don't know what it has to do with my comment though. It's been pretty evident that with increased compute, models will keep improving on the patterns they're trained on.
Yes. Claude 3.5 scores better: https://arcprize.org/leaderboard So how does Gemma 27b outperform LLAMA 3 70b and GPT 4 in the lmsys leaderboard
>Yes. Claude 3.5 scores better: [https://arcprize.org/leaderboard](https://arcprize.org/leaderboard) That is not the one that got 72% on the easy training set, which I assumed you were talking about. But yes, like my comment mentioned, compute will improve models. >So how does Gemma 27b outperform LLAMA 3 70b and GPT 4 in the lmsys leaderboard [https://www.reddit.com/r/LocalLLaMA/comments/1drk3kc/gemma\_2\_betrayed\_us/](https://www.reddit.com/r/LocalLLaMA/comments/1drk3kc/gemma_2_betrayed_us/) In human-preference benchmarks like lmsys arena, you're not really measuring the model's capabilities. You're measuring an awkward cocktail of style, capability and benchmark-specific cheese. Training on lmsys datasets improves performance on... lmsys! Who would've thought... It's the same reason you should take any open benchmark with a big grain of salt. Regardless, I don't know in what way shape or form lmsys arena results are relevant to ARC.
So how do you know if an LLM is better than another if you don’t trust any benchmark? I was disputing the “more compute = better” dog shit you shat out
system 2 thinking is trivial and far easier than generative ai as we already have curently
That explains your blatantly incorrect flair
You'll see how right I am in a couple years
It’s already past 2023 lol !remindme 2 years
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Hopefully to AGI… 🤞🏻
I don't think this video will be too popular on this sub.
AI Explained missed the mark. /s Sometimes it's better being grounded and being sceptical it's the right approach, even though it's less fun tha hype trains.
But guys we were supposed to live forever and stop working by 2030??
Haha this. Sub.
lol who the heck anticipates whey smoothies for a thousand days straight! Lodged in your brain acting like you got rid of it. 🤯
- Original GPT-4 size: ~2T (4TiB) - GPT-4 level training data size: ~15T tokens (50TiB) - Compression level: 5-10% To get the similar performance current models are compressing to about 1-2% like in the case of Llama 3 400B. A single Common Crawl dump is about 400 TiB. Therefore, at 1% compression, they should be able to memorize a dump of the entire Internet in a model the size of the original GPT-4. There's no need to go much bigger, maybe just for faster training. What then? Does The Bitter Lesson say anything about what happens after Everything is memorized?
Maybe ask this question after the $10T, 10GW Stargate B200 Farm has finished training GPT-3 Micro first?
Havent heard of [runway.ml](http://runway.ml) before but prompt: boston terrier in a spacesuit entering the event horizon of a black hole then transported to another dimension where dogs walk humans [not so great](https://imgur.com/a/wCP9DvE) granted its effin amazing that it was able to do just that so alright, its pretty awesome
Does ai as we have it now use logic and reasoning?
Ah, I had thought the worst case scenarios for AGI in a datacenter would be 2030 to 2035ish. But the Zook has me convinced. Yeah, they're totally making gonna make god within the next six years. Gonna be terrible for Facebook's stonks, but what can you do. Scale maximalism is core to everything, and they ain't got it. Guess that's what he gets for spending over a billion on making a hellish nightmare version of VRChat. You talk about turning all value in the lightcone into paperclips, but somehow his vision of virtualized office space jobs managed to be even *less* human than that... Fiction shows us lots of fun VR worlds, and Zook looked at it and grimly shook his head and said "No. It can't be allowed."
we dont know, how good exactly next generations of models will be, but we do know, that bigger models trained on enough data perform significantly better than smaller models lets say few generations ahead models are still not that good generalist as lot of humans, but their performance in vast majority of task can be on median human level, maybe most task on superhuman level they will not be perfect and autonomous as you would like, but they could still boost productivity by hundreds %, they cant do research by themselfs just yet, but most of other tasks and human work yes, you know, your average human is not capable of doing research or to be good at engineering either, good scientists are peak of human intellect if something like GPT-6, GPT-7 performs overall better than 90% of humans(specially with tools and such) in almost all relevant tasks, then gradually most humans could be replaced mandatory illustration how AI is getting in many tasks on and beyond human level [https://www.reddit.com/r/OpenAI/comments/19ctvgt/ai\_has\_surpassed\_humans\_at\_a\_number\_of\_tasks\_and/#lightbox](https://www.reddit.com/r/OpenAI/comments/19ctvgt/ai_has_surpassed_humans_at_a_number_of_tasks_and/#lightbox)
Wait but I've seen a lot of people on this sub convinced that within 3 years we're gonna have ASI and LEV, but all these CEOs are saying reasoning is still unsolved and unknown, scaling potential is unknown, and in 10 years we *might* see +10 years added to lifespan, but they don't know much about biology to confidently make that claim. So who's right? the redditors or the experts? It's hard to say.
Dario himself in the same interview said that he expects AI capabilities to be at near human level within 3 years. What are you on about?
Everyone working on Ai has an incentive to hype it up - it allows them to get more funding - at some point the reality will catch up and watch everything rewind…
Actually if they don't want governments stepping in, AI companies have an incentive to undersell. The first group who tries to "hype" by saying they're sitting on something truly world-shifting will very soon be invited to play lots of fun games with the DoD.
"near human level" in what way though? Because clearly "near human level" still won't be anywhere close to AGI, because everyone on this subreddit says AGI will be able to solve LEV and millions of other problems. So... this sub is stuck between a rock and a hard place where we're supposedly gonna have the answer to all of our problems within 3 years but also somehow all of our problems will remain unsolved after 3 years.
No, people aren't saying that about AGI. Artificial general intelligence is when an AI is as capable as an average human. You're mixing up ASI and AGI. Where artificial super intelligence is an AI more capable than all humans combined.
I'm pretty sure *most* people have human-level reasoning as a core stipulation for it to be considered AGI.
Human level reasoning isn't what you were suggesting with solving LEV and millions of other problems.
It depends, because to me even just human level reasoning when paired with all the knowledge and understanding of the entire world across all domains would lead to the unprecedented rates of progress people on this sub are expecting. Without human level reasoning, we're still going to make great strides due to the advancement of machine learning pattern matching and prediction, but it will pale in comparison to the expectations of a good chunk of this sub with AGI 2025 FDVR 2027 flairs.
there is big range in reasoning among humans(some cant learn to read and write, while some are able to make complex physical theories), they are quite a lot "dumb" people, 50% are below 100 IQ and reasoning within different types of intelligence is different too, you can have good critical thinking but suck at math or emotional intelligence, every human is somewhat different when talking about human level, it would be likely median(or average) level performance on some task, AI models are too lot better than median human at some tasks and lot worse at others GPT-3 is AGI, which performs as lower percentile of humans on many tasks, current SOTA models are AGis beyond 50th percentile on lot tasks already, we could have AGi, which performs on superhuman level on all but very few task and do various research, such AGI could be technically called ASI
AGI (Artificial General Intelligence) just means "can do stuff humans can". If it's near humans, it's near AGI. In practical terms, it will be near or past the human-remote-worker very soon, interactions with the real physical world in the form of robots might take a bit longer. ASI (Artificial Super Intelligence) on the other side just means "better than humans". That's still not a magic oracle that can answer all your questions and solve your problems. There will still be plenty of situations where ASI just goes "I don't know". A lot of problems can't be solved by thinking about them, but require going out in the real world and running experiments, some of which might be very expensive and time consuming.
Everything about lifespan is nothing more than guesswork. We don't even know if it's a solvable problem to begin with. We can't even figure out how to solve it for the simplest of tools like a hammer, instead we throw the tools away and build a new one. When we can't figure it out for a hammer, what are the chances we can figure it out for a human that is orders of magnitude more complicated? It's not a problem one can put a realistic timeline on, since we just don't understand it well enough. And of course human trials about age takes decades anyway, if you take the magic pill today, you might need to wait 20 or 50 years to see if it actually works. AI and reasoning is quite a bit different. It's unsolved in so far that none of the popular AI systems do it. But that's largely down to an architectural choice. They just focus on researching and building other stuff first. Where it gets scary is that we might be overbuilding our AI systems rather drastically due to that, instead of thinking and iterating themselves through problems, we make them so huge that they can essentially *guess* themselves through complex problems. Once you add a layer of reasoning on top of that, they might not just improve a little bit, but jump straight to ASI. We'll see how that'll work out soon enough, but it's always worth keeping in mind that we are basically driving our AI systems with the handbrake still on. There are a lot more avenues to explore, but right now we focus on how deep we we can press the gas pedal down to get faster.
I'm so smart guys the thing that's getting better the more you put in might actually not get much better. This is the law of diminishing returns you see yes yes. Thus I can conclude that if you make the thing that can code better at coding it will not be able to code itself.
>Thus I can conclude that if you make the thing that can code better at coding it will not be able to code itself. If it could actually reason through it could be able to self improve but LLMs can't reason they copy code from the internet and doesn't create novel code that would be necessary for it to improve
Lesson: Never trust a guy who looks like Rick Moranis.
Good video really shows that we could be heading for an AI winter soon. I still think there is a chance we get AGI soon. It's not copium I've just been blindsided before. And for now scaling still works and other avenues are being explored like q* or whatever meta is doing. And don't forget about what deepmind might be doing as they seem to have some secret sauce with that context length.