There are no new ideas in AI only new datasets
148 comments
·June 30, 2025EternalFury
vladimirralev
He is not using appropriate models for this conclusion and neither is he using state of the art models in this research and moreover he doesn't have an expensive foundational model to build upon for 2d games. It's just a fun project.
A serious attempt at video/vision would involve some probabilistic latent space that can be noised in ways that make sense for games in general. I think veo3 proves that ai can generalize 2d and even 3d games, generating a video under prompt constraints is basically playing a game. I think you could prompt veo3 to play any game for a few seconds and it will generally make sense even though it is not fine tuned.
sigmoid10
Veo3's world model is still pretty limited. That becomes obvious very fast once you prompt out of distribution video content (i.e. stuff that you are unlikely to find on youtube). It's extremely good at creating photorealistic surfaces and lighting. It even has some reasonably solid understanding of fluid dynamics for simulating water. But for complex human behaviour (in particular certain motions) it simply lacks the training data. Although that's not really a fault of the model and I'm pretty sure there will be a way to overcome this as well. Maybe some kind of physics based simulation as supplement training data.
altairprime
Is any model currently known to succeed in the scenario that Carmack’s inappropriate model failed?
outofpaper
No monolithic models but us ng hybrid approaches we've been able to beet humans for some time now.
317070
What you're thinking of is much more like the Genie model from DeepMind [0]. That one is like Veo, but interactive (but not publically available)
[0] https://deepmind.google/discover/blog/genie-2-a-large-scale-...
pshc
I think we need a spatial/physics model handling movement and tactics watched over by a high level strategy model (maybe an LLM).
keerthiko
> generating a video under prompt constraints is basically playing a game
Besides static puzzles (like a maze or jigsaw) I don't believe this analogy holds? A model working with prompt constraints that aren't evolving or being added over the course of "navigating" the generation of the model's output means it needs to process 0 new information that it didn't come up with itself — playing a game is different from other generation because it's primarily about reacting to input you didn't know the precise timing/spatial details of, but can learn that they come within a known set of higher order rules. Obviously the more finite/deterministic/predictably probabilistic the video game's solution space, the more it can be inferred from the initial state, aka reduce to the same type of problem as generating a video from a prompt), which is why models are still able to play video games. But as GP pointed out, transfer function negative in such cases — the overarching rules are not predictable enough across disparate genres.
> I think you could prompt veo3 to play any game for a few seconds
I'm curious what your threshold for what constitutes "play any game" is in this claim? If I wrote a script that maps button combinations to average pixel color of a portion of the screen buffer, by what metric(s) would veo3 be "playing" the game more or better than that script "for a few seconds"?
edit: removing knee-jerk reaction language
vladimirralev
It's not ideal, but you can prompt it with an image of a game frame, explain the objects and physics in text and let it generate a few frames of gameplay as a substitute for controller input as well as what it expects as an outcome. I am not talking about real interactive gameplay.
I am just saying we have proof that it can understand complex worlds and sets of rules, and then abide by them. It doesn't know how to use a controller and it doesn't know how to explore the game physics on its own, but those steps are much easier to implement based on how coding agents are able to iterate and explore solutions.
hluska
Nothing the parent said makes this level of aggression necessary or even tasteful. This isn’t the Colosseum - we can learn from each other and consider different points of view without acting like savages.
troupo
> I think veo3 proves that ai can generalize 2d and even 3d games
It doesn't. And you said it yourself:
> generating a video under prompt constraints is basically playing a game.
No. It's neither generating a game (that people can play) nor is it playing a game (it's generating a video).
Since it's not a model of the world in any sense of the word, there are issues with even the most basic object permanenece. E.g. here's veo3 generating a GTA-style video. Oh look, the car spins 360 and ends up on a completely different street than the one it was driving down previously: https://www.youtube.com/watch?v=ja2PVllZcsI
vladimirralev
It is still doing a great job for a few frames, you could keep it more anchored to the state of the game if you prompt it. Much like you can prompt coding agents to keep a log of all decisions previously made. Permanenece is excellent, it slips often but it mostly because it is not grounded to specific game state by the prompt or by the decision log.
justanotherjoe
I don't get why people are so invested in framing it this way. I'm sure there are ways to do the stated objective. John Carmack isn't even an AI guy why is he suddenly the standard.
qaq
Keen includes researchers like Richard Sutton, Joseph Modayil etc. Also John has being doing it full time for almost 5 years now so given his background and aptitude for learning I would imaging by this time he is more of an AI guy then a fairly large percentage of AI PhDs.
varjag
What in your opinion constitutes an AI guy?
raincole
Because it "confirms" what they already believe in.
refulgentis
Names >> all, and increasingly so.
One phenomena that bared this to me, in a substantive way, was noticing an increasing # of reverent comments re: Geohot in odd places here, that are just as quickly replied to by people with a sense of how he works, as opposed to the keywords he associates himself with. But that only happens here AFAIK.
Yapping, or, inducing people to yap about me, unfortunately, is much more salient to my expected mindshare than the work I do.
It's getting claustrophobic intellectually, as a result.
Example from the last week is the phrase "context engineering" - Shopify CEO says he likes it better than prompt engineering, Karpathy QTs to affirm, SimonW writes it up as fait accompli. Now I have to rework my site to not use "prompt engineering" and have a Take™ on "context engineering". Because of a couple tweets + a blog reverberating over 2-3 days.
Nothing against Carmack, or anyone else named, at all. i.e. in the context engineering case, they're just sharing their thoughts in realtime. (i.e. I don't wanna get rolled up into a downvote brigade because it seems like I'm affirming the loose assertion Carmack is "not an AI guy", or, that it seems I'm criticizing anyone's conduct at all)
EDIT: The context engineering example was not in reference to another post at the time of writing, now one is the top of front page.
dvfjsdhgfv
> Now I have to rework my site to not use "prompt engineering" and have a Take™ on "context engineering". Because of a couple tweets + a blog reverberating over 2-3 days.
The difference here is that your example shows a trivial statement and a change period of 3 days, whereas what Carmack is doing is taking years.
sieabahlpark
[dead]
smokel
The subject you are referring to is most likely Meta-Reinforcement Learning [1]. It is great that John Carmack is looking into this, but it is not a new field of research.
[1] https://instadeep.com/2021/10/a-simple-introduction-to-meta-...
YokoZar
I wonder if this is a case of overfitting from allowing the model to grow too large, and if you might cajole it into learning more generic heuristics by putting some constraints on it.
It sounds like the "best" AI without constraint would just be something like a replay of a record speedrun rather than a smaller set of heuristics of getting through a game, though the latter is clearly much more important with unseen content.
Uehreka
These questions of whether the model is “really intelligent” or whatever might be of interest to academics theorizing about AGI, but to the vast swaths of people getting useful stuff out of LLMs, it doesn’t really matter. We don’t care if the current path leads to AGI. If the line stopped at Claude 4 I’d still keep using it.
And like I get it, it’s fun to complain about the obnoxious and irrational AGI people. But the discussion about how people are using these things in their everyday lives is way more interesting.
fullshark
Just sounds like an example of overfitting. This is all machine learning at its root.
ferguess_k
Can you please explain "the transfer function is negative"?
I'm wondering whether one has tested with the same model but on two situations:
1) Bring it to superhuman level in game A and then present game B, which is similar to A, to it.
2) Present B to it without presenting A.
If 1) is not significantly better than 2) then maybe it is not carrying much "knowledge", or maybe we simply did not program it correctly.
tough
I think the problem is we train models to pattern match, not to learn or reason about world models
singron
I think this is clearly a case of over fitting and failure to generalize, which are really well understood concepts. We don't have to philosophize about what pattern matching really means.
NBJack
In other words, they learn the game, not how to play games.
ferguess_k
I kinda think I'm more or less the same...OK maybe we have different definitions of "pattern matching".
antisthenes
Where do you draw the line between pattern matching and reasoning about world models?
A lot of intelligence is just pattern matching and being quick about it.
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voxleone
I'd say with confidence: we're living in the early days. AI has made jaw-dropping progress in two major domains: language and vision. With large language models (LLMs) like GPT-4 and Claude, and vision models like CLIP and DALL·E, we've seen machines that can generate poetry, write code, describe photos, and even hold eerily humanlike conversations.
But as impressive as this is, it’s easy to lose sight of the bigger picture: we’ve only scratched the surface of what artificial intelligence could be — because we’ve only scaled two modalities: text and images.
That’s like saying we’ve modeled human intelligence by mastering reading and eyesight, while ignoring touch, taste, smell, motion, memory, emotion, and everything else that makes our cognition rich, embodied, and contextual.
Human intelligence is multimodal. We make sense of the world through:
Touch (the texture of a surface, the feedback of pressure, the warmth of skin0; Smell and taste (deeply tied to memory, danger, pleasure, and even creativity); Proprioception (the sense of where your body is in space — how you move and balance); Emotional and internal states (hunger, pain, comfort, fear, motivation).
None of these are captured by current LLMs or vision transformers. Not even close. And yet, our cognitive lives depend on them.
Language and vision are just the beginning — the parts we were able to digitize first - not necessarily the most central to intelligence.
The real frontier of AI lies in the messy, rich, sensory world where people live. We’ll need new hardware (sensors), new data representations (beyond tokens), and new ways to train models that grow understanding from experience, not just patterns.
dinfinity
> Language and vision are just the beginning — the parts we were able to digitize first - not necessarily the most central to intelligence.
I respectfully disagree. Touch gives pretty cool skills, but language, video and audio are all that are needed for all online interactions. We use touch for typing and pointing, but that is only because we don't have a more efficient and effective interface.
Now I'm not saying that all other senses are uninteresting. Integrating touch, extensive proprioception, and olfaction is going to unlock a lot of 'real world' behavior, but your comment was specifically about intelligence.
Compare humans to apes and other animals and the thing that sets us apart is definitely not in the 'remaining' senses, but firmly in the realm of audio, video and language.
voxleone
> Language and vision are just the beginning — the parts we were able to digitize first - not necessarily the most central to intelligence.
I probably made a mistake when i asserted that -- should have thought it over. Vision is evolutionarily older and more “primitive”, while language is uniquely human [or maybe, more broadly, primate, cetacean, cephalopod, avian...] symbolic, and abstract — arguably a different order of cognition altogether. But i maintain that each and every sense is important as far as human cognition -- and its replication -- is concerned.
wizzwizz4
People who lack one of those senses, or even two of them, tend to do just fine.
mr_world
Organic adaption and persistence of memory I would say are the two major advancements that need to happen.
Human neural networks are dynamic, they change and rearrange, grow and sever. An LLM is fixed and relies on context, if you give it the right answer it won't "learn" that is the correct answer unless it is fed back into the system and trained over months. What if it's only the right answer for a limited period of time?
To build an intelligent machine, it must be able train itself in real time and remember.
specialist
Yes and: and forget.
chasd00
> Language and vision are just the beginning..
Based on the architectures we have they may also be the ending. There’s been a lot of news in the past couple years about LLMs but has there been any breakthroughs making headlines anywhere else in AI?
dragonwriter
> There’s been a lot of news in the past couple years about LLMs but has there been any breakthroughs making headlines anywhere else in AI?
Yeah, lots of stuff tied to robotics, for instance; this overlaps with vision, but the advances go beyond vision.
Audio has seen quite a bit. And I imagine there is stuff happening in niche areas that just aren't as publicly interesting as language, vision/imagery, audio, and robotics.
nomel
Two Nobel prizes in chemistry: https://www.nature.com/articles/s41746-024-01345-9
edanm
Sure. In physics, math, chemistry, biology. To name a few.
Swizec
> The real frontier of AI lies in the messy, rich, sensory world where people live. We’ll need new hardware (sensors), new data representations (beyond tokens), and new ways to train models that grow understanding from experience, not just patterns.
Like Dr. Who said: DALEKs aren't brains in a machine, they are the machine!
Same is true for humans. We really are the whole body, we're not just driving it around.
nomel
There are many people who mentally developed while paralyzed that literally drive around their bodies via motorized wheelchair. I don't think there's any evidence that a brain couldn't exist or develop in a jar, given only the inputs modern AI now has (text, video, audio).
Swizec
> any evidence that a brain couldn't exist or develop in a jar
The brain could. Of course it could. It's just a signals processing machine.
But would it be missing anything we consider core to the way humans think? Would it struggle with parts of cognition?
For example: experiments were done with cats growing up in environments with vertical lines only. They were then put in a normal room and had a hard time understanding flat surfaces.
https://computervisionblog.wordpress.com/2013/06/01/cats-and...
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skydhash
Yeah, but are there new ideas or only wishes?
jdgoesmarching
It’s pure magical thinking that would be correctly dismissed if it didn’t have AI attached to it. Imagine talking this way about anything else.
“We’ve barely scratched the surface with Rust, so far we’re only focused on code and haven’t even explored building mansions or ending world hunger”
tim333
AI has some real possibilities of building mansions and ending hunger in a way that Rust doesn't.
tippytippytango
Sometimes we get confused by the difference between technological and scientific progress. When science makes progress it unlocks new S-curves that progress at an incredible pace until you get into the diminishing returns region. People complain of slowing progress but it was always slow, you just didn’t notice that nothing new was happening during the exponential take off of the S-curve, just furious optimization.
baxtr
Fully agree.
And at the same time I have noticed that people don’t understand the difference between an S-curve and an exponential function. They can look almost identical at certain intervals.
cadamsdotcom
What about actively obtained data - models seeking data, rather than being fed. Human babies put things in their mouths, they try to stand and fall over. They “do stuff” to learn what works. Right now we’re just telling models what works.
What about simulation: models can make 3D objects so why not give them a physics simulator? We have amazing high fidelity (and low cost!) game engines that would be a great building block.
What about rumination: behind every Cursor rule for example, is a whole story of why a user added it. Why not take the rule, ask a reasoning model to hypothesize about why that rule was created, and add that rumination (along with the rule) to the training data. Providing opportunities to reflect on the choices made by their users might deepen any insights, squeezing more juice out of the data.
Centigonal
Simulation and embodied AI (putting the AI in a robotic arm or a car so it can try stuff and gather information about the results) are very actively being explored.
cadamsdotcom
What about at inference time? ie. in response to a query.
We let models write code and run it. Which gives them a high chance of getting arithmetic right.
Solving the “crossing the river” problem by letting the model create and run a simulation would give a pretty high chance of getting it right.
kevmo314
That would be reinforcement learning. The juice is quite hard to squeeze.
cadamsdotcom
Agreed for most cases.
Each Cursor rule is a byproduct of tons of work and probably contains lots that can be unpacked. Any research on that?
strangescript
If you work with model architecture and read papers, how could not know there are a flood of new ideas? Only few yield interesting results though.
I kind of wonder if libraries like pytorch have hurt experimental development. So many basic concepts no one thinks about anymore because they just use the out of the box solutions. And maybe those solutions are great and those parts are "solved", but I am not sure. How many models are using someone else's tokenizer, or someone else's strapped on vision model just to check a box in the model card?
thenaturalist
That's been the very normal way of the human world.
When the foundation layer at a given moment doesn't yield an ROI on intellectual exploration - say because you can overcompensate with VC funded raw compute and make more progess elsewhere -, few(er) will go there.
But inevitably, as other domains reach diminishing returns, bright minds will take a look around where significant gains for their effort can be found.
And so will the next generation of PyTorch or foundational technologies evolve.
kevmo314
The people who don't think about such things probably wouldn't develop experimentally sans pytorch either.
kogus
To be fair, if you imagine a system that successfully reproduced human intelligence, then 'changing datasets' would probably be a fair summary of what it would take to have different models. After all, our own memories, training, education, background, etc are a very large component of our own problem solving abilities.
jschveibinz
I will respectfully disagree. All "new" ideas come from old ideas. AI is a tool to access old ideas with speed and with new perspectives that hasn't been available up until now.
Innovation is in the cracks: recognition of holes, intersections, tangents, etc. on old ideas. It has bent said that innovation is done on the shoulders of giants.
So AI can be an express elevator up to an army of giant's shoulders? It all depends on how you use the tools.
alfalfasprout
Access old ideas? Yes. With new perspectives? Not necessarily. An LLM may be able to assist in interpreting data with new perspectives but in practice they're still fairly bad at greenfield work.
As with most things, the truth lies somewhere in the middle. LLMs can be helpful as a way of accelerating certain kinds and certain aspects of research but not others.
stevep98
> Access old ideas? Yes. With new perspectives?
I wonder if we can mine patent databases for old ideas that never worked out in the past, but now are more useful. Perhaps due to modern machining or newer materials or just new applications of the idea.
baxtr
Imagine a human had read every book/publication in every field of knowledge that mankind has ever produced AND couldn’t come up with anything entirely new. Hard to imagine.
bcrosby95
The article is discussing working in AI innovation vs focusing on getting more and better data. And while there have been key breakthroughs in new ideas, one of the best ways to increase the performance of these systems is getting more and better data. And how many people think data is the primary avenue to improvement.
It reminds me of an AI talk a few decades ago, about how the cycle goes: more data -> more layers -> repeat...
Anyways, I'm not sure how your comment relates to these two avenues of improvement.
jjtheblunt
> I will respectfully disagree. All "new" ideas come from old ideas.
The insight into the structure of the benzene ring famously came in a dream, hadn't been seen before, but was imagined as a snake bitings its own tail.
troupo
And as we all know, it came in a dream to a complete novice in chemistry with zero knowledge of any old ideas in chemistry: https://en.wikipedia.org/wiki/August_Kekul%C3%A9
--- start quote ---
The empirical formula for benzene had been long known, but its highly unsaturated structure was a challenge to determine. Archibald Scott Couper in 1858 and Joseph Loschmidt in 1861 suggested possible structures that contained multiple double bonds or multiple rings, but the study of aromatic compounds was in its earliest years, and too little evidence was then available to help chemists decide on any particular structure.
More evidence was available by 1865, especially regarding the relationships of aromatic isomers.
[ Kekule claimed to have had the dream in 1865 ]
--- end quote ---
The dream claim came from Kekule himself 25 years after his proposal that he had to modify 10 years after he proposed it.
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gametorch
Exactly!
Can you imagine if we applied the same gatekeeping logic to science?
Imagine you weren't allowed to use someone else's scientific work or any derivative of it.
We would make no progress.
The only legitimate defense I have ever seen here revolves around IP and copyright infringement, which I couldn't care less about.
sakex
There are new things being tested and yielding results monthly in modelling. We've deviated quite a bit from the original multi head attention.
ks2048
The latest LLMs are simply multiplying and adding various numbers together... Babylonians were doing that 4000 years ago.
bobson381
You are just a lot of interactions of waves. All meaning is assigned. I prefer to think of this like the Goedel generator that found new formal expressions for the Principia - because we have a way of indexing concept-space, there's no telling what we might find in the gaps.
thenaturalist
But on clay tables, not in semi-conductive electron prisons separated by one-atom-thick walls.
Slight difference to those methods, wouldn't you agree?
somebodythere
I don't know if it matters. Even if the best we can do is get really good at interpolating between solutions to cognitive tasks on the data manifold, the only economically useful human labor left asymptotes toward frontier work; work that only a single-digit percentage of people can actually perform.
piinbinary
AI training is currently a process of making the AI remember the dataset. It doesn't involve the AI thinking about the dataset and drawing (and remembering) conclusions.
It can probably remember more facts about a topic than a PhD in that topic, but the PhD will be better at thinking about that topic.
tantalor
Maybe that's why PhDs keep the textbooks they use at hand, so they don't have to remember everything.
Why should the model need to memorize facts we already have written down somewhere?
jayd16
Its a bit more complex than that. Its more about baking out the dataset into heuristics that a machine can use to match a satisfying result to an input. Sometimes these heuristics are surprising to a human and can solve a problem in a novel way.
"Thinking" is too broad a term to apply usefully but I would say its pretty clear we are not close to AGI.
nkrisc
> It can probably remember more facts about a topic than a PhD in that topic
So can a notebook.
Night_Thastus
Man I can't wait for this '''''AI''''' stuff to blow over. The back and forth gets a bit exhausting.
What John Carmack is exploring is pretty revealing. Train models to play 2D video games to a superhuman level, then ask them to play a level they have not seen before or another 2D video game they have not seen before. The transfer function is negative. So, in my definition, no intelligence has been developed, only expertise in a narrow set of tasks.
It’s apparently much easier to scare the masses with visions of ASI, than to build a general intelligence that can pick up a new 2D video game faster than a human being.