It's always a treat to watch a Carmack lecture or read anything he writes, and his notes here are no exception. He writes as an engineer, for engineers and documents all his thought processes and misteps in the exact detailed yet concise way you'd want a colleague to who was handing off some work.
One question I would have about the research direction is the emphasis on realtime. If I understand correctly he's doing online learning in realtime. Obviously makes for a cool demo and pulls on his optimisation background, and no doubt some great innovations will be required to make this work. But I guess the bitter lesson and recent history also tell us that some solutions may only emerge at compute levels beyond what is currently possible for realtime inference let alone learning. And the only example we have of entities solving Atari games is the human brain, of which we don't have a clear understanding of the compute capacity. In which case, why wouldn't it be better to focus purely on learning efficiency and relax the realtime requirement for now?
That's a genuine question by the way, definitely not an expert here and I'm sure there's a bunch of value to working within these constraints. I mean, jumping spiders solve reasonably complex problems with 100k neurons, so who knows.
johnb231 · 4h ago
From the notes:
"A reality check for people that think full embodied AGI is right around the corner is to ask your dancing humanoid robot to pick up a joystick and learn how to play an obscure video game."
ferguess_k · 13m ago
We don't really need AGI. We need better specialized AIs. Throw in a few specialized AIs and they will leave some impact in the society. That might not be that far away.
bluGill · 9m ago
Specialized AIs have been making an impact on society since at least the 1960s. AI has long suffered from every time they come up with something new it gets renamed and becomes important (where it makes sense) without giving AI credit.
From what I can tell most in AI are currently hoping LLMs reach that point quick just because the hype is not helping AI at all.
ferguess_k · 6m ago
Yeah I agree with it. There is a lot of hype, but there is some potentials there.
throw_nbvc1234 · 2h ago
This sounds like a problem that could be solved around the corner with a caveat.
Games generally are solvable for AI because they have feedback loops and a clear success or failure criteria. If the "picking up a Joystick" part is the limiting factor, sure. But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage.
I'm sure if you compared a monkey and a dolphins performance using a joystick you'd get results that aren't really correlated with their intelligence. I would guess that if you gave robots an R2D2 like port to jack into and play a game, that problem could be solved relatively quickly.
xnickb · 2h ago
Just like OpenAI early on promised us an AGI and showed us how it "solved" Dota 2.
They also claimed it "learned" to play by playing itself only however it was clear that most of the advanced techniques were borrowed from existing AI and by observing humans.
No surprise they gave up on that project completely and I doubt they'll ever engage in anything like that again.
Money better spent on different marketing platforms.
jsheard · 1h ago
It also wasn't even remotely close to learning Dota 2 proper. They ran a heavily simplified version of the game where the AI and humans alternated between using two pre-defined team compositions, meaning >90% of the games characters and >99.999999% of the possible compositions and matchups weren't even on the table, plus other standard mechanics were changed or disabled altogether for the sake of the AI team.
Saying you've solved Dota after stripping out nearly all of its complexity is like saying you've solved Chess, but on a version where the back row is all Bishops.
xnickb · 58m ago
Exactly. What I find surprising in this story though is not the OpenAI. It's investors not seeing through these blatant.. lets call them exaggerations of the reality and still trusting the company with their money. I know I wouldn't have. But then again, maybe that's why I'm poor.
ryandrake · 21m ago
In their hearts, startup investors are like Agent Mulder: they Want To Believe. Especially after they’ve already invested a little. They are willing to overlook obvious exaggerations up to and including fraud, because the alternative is admitting their judgment is not sound.
Look at how long Theranos went on! Miraculous product. Attractive young founder with all the right pedigree, credentials, and contacts, dressed in black trurtlenecks. Hell, she even talked like Steve Jobs! Investors never had a chance.
jdross · 10m ago
They already have 400 million daily users and a billion people using the product, with billions of consumer subscription revenue, faster than any company ever. They are also aggregating R&D talent at a density never before seen in Silicon Valley
That is what investors see. You seem to treat this as a purity contest where you define purity
xnickb · 2m ago
I'm speaking about past events. Perhaps I didn't make it clear enough
scotty79 · 56m ago
It was 6 years ago. I'm sure now there'd be no contest now if OpenAI dedicated resources to it, which it won't because it's busy with solving entirety of human language before others eat their lunch.
spektral23 · 18m ago
Funnily enough, even dota2 has grown much more complex than it was 6 years ago, so it's a harder problem to solve today than it was back then
xnickb · 52m ago
What do you base your certainty on? Were there any significant enough breakthroughs in the AGI?
scotty79 · 39m ago
ARC-AGI, while imagined as super hard for AI, was beaten enough that they had to come up with ARC-AGI-2.
hbsbsbsndk · 12m ago
"AI tend to be brittle and optimized for specific tasks, so we made a new specific task and then someone optimized for it" isn't some kind of gotcha. Once ARC puzzles became a benchmark they ceased to be meaningful WRT "AGI".
mellosouls · 2h ago
The point isn't about learning video games its about learning tasks unrelated to its specific competency generally.
jappgar · 1h ago
A human would learn it faster, and could immediately teach other humans.
AI clearly isn't at human level and it's OK to admit it.
johnb231 · 2h ago
No, the joystick part is really not the limiting factor. They’ve already done this with a direct software interface. Physical interface is a new challenge. But overall you are missing the point.
suddenlybananas · 4h ago
It's because humans (and other animals) have enormous innate capacities and knowledge which makes learning new things much much simpler than if you start from scratch. It's not really because of human's computational capacity.
MrScruff · 3h ago
By innate do you mean evolved/instinctive? Surely even evolved behaviour must be expressed as brain function, and therefore would need a brain capable of handling that level of processing.
I don't think it's clear how much of a human brains function exists at birth though, I know it's theorised than even much of the sensory processing has to be learned.
suddenlybananas · 3h ago
I'm not arguing against computational theory of mind, I'm just saying that innate behaviours don't require the same level of scale as learnt ones.
Existing at birth is not the same thing as innate. Puberty is innate but it is not present at birth.
MrScruff · 2h ago
That's an interesting point. I can see that, as you say puberty and hormones impact brain function and hence behaviour, and those are inate and not learned. But at least superfically that would appear to be primarily broad behavioural effects, similar to what might be induced by medication. Rather than something that impacts pure abstract problem solving, which I guess is what the Atari games are supposed to represent?
rafaelmn · 2h ago
This is obviously wrong from genetic defects that cause predictable development problems in specialized areas. They are innate but not present at birth.
nlitened · 3h ago
> the human brain, of which we don't have a clear understanding of the compute capacity
Neurons have finite (very low) speed of signal transfer, so just by measuring cognitive reaction time we can deduce upper bounds on how many _consecutive_ neuron connections are involved in reception, cognitive processing, and resulting reaction via muscles, even for very complex cognitive processes. And the number is just around 100 consecutive neurons involved one after another. So “the algorithm” could not be _that_ complex in the end (100x matmul+tanh?)
Granted, a lot of parallelism and feedback loops are involved, but overall it gives me (and many others) an impression that when the AGI algorithm is ever found, it’s “mini” version should be able to run on modest 2025 hardware in real time.
johnb231 · 3h ago
> (100x matmul+tanh?)
Biological neurons are way more complex than that. A single neuron has dentritic trees with subunits doing their own local computations. There are temporal dynamics in the firing sequences. There is so much more complexity in the biological networks. It's not comparable.
neffy · 1h ago
This is exactly it. Biology is making massive use of hacked real time local network communication in ways we haven´t begun to explore.
woolion · 1h ago
You could implement a Turing-machine with humans acting physically operating as logic gates. Then, every human is just a boolean function.
scajanus · 3h ago
The granted is doing a lot of work there. In fact, if you imagine a computer being able to do similar tasks as human brain can in around 100 steps, it becomes clear that considering parallelism is absolutely critical.
Actually no, it's not interesting at all. Vague dismissal of an outsider is a pretty standard response by insecure academic types. It could have been interesting and/or helpful to the conversation if they went into specifics or explained anything at all. Since none of that's provided, it's "OpenAI insider" vs John Carmack AND Richard Sutton. I know who I would bet on.
jjulius · 13m ago
I appreciate how they don't tell us what lesson they learned.
andy_ppp · 2h ago
My bet is on Carmack.
speed_spread · 1h ago
I suspect Carmack in the Dancehall with the BFG.
zeroq · 1h ago
>> "they will learn the same lesson I did"
Which is what? Don't trust Altman? x)
cmiles74 · 39m ago
From a marketing perspective, this strikes me as a very predictable response.
I still don’t think we have a clear enough idea of what a concept is to be able to think about AGI. And then being able to use concepts from one area to translate into another area, what is the process by which the brain combines and abstracts ideas into something new?
throw310822 · 1h ago
Known entities are recurring patterns (we give names to things that occur more than once, in the world or in our thoughts). Concepts are recurring thought patterns. Abstractions, relations, metaphors, are all ways of finding and transferring patterns from one domain to another.
andy_ppp · 51m ago
Sure, I understand what the terminology means but I don't believe we get to AGI without some ability to translate the learning of say using a mouse to using a trackpad in a simple way. Humans make these translations all the while, you know how to use a new room and the items in it automatically but I personally see the systems we have built are currently very brittle when they see new environments because they can't simplify everything to its fundamentals and then extrapolate back to more complex tasks. You could train a human on using an Android phone and give them an iPhone and they would do pretty well, if you did this with modern machine learning systems you will get an extremely high error rate. Or say you train an model on how to use a sword, I'm not convinced it would know how to use and ax or pair of crutches as a weapon.
Maybe it will turn out to simply be enough artificial neurons and everything works. But I don't believe that.
koolala · 2h ago
I wish he did this with VR environment instead like they mention at the start of the slides. A VR environment with a JPEG camera filter, physics sim, noise, robot simulation. If anyone could program that well its him.
Using real life robots is going to be a huge bottleneck for training hours no matter what they do.
kamranjon · 7h ago
I was really excited when I heard Carmack was focusing on AI and am really looking forward to watching this when the video is up - but just from looking at the slides it seems like he tried to build a system that can play the Atari? Seems like a fun project, but curious what will come out of it or if there is an associated paper being released.
johnb231 · 6h ago
Atari games are widely used in Reinforcement Learning (RL) research as a standard benchmark.
The goal is to develop algorithms that generalize to other tasks.
sigmoid10 · 5h ago
They were highly used. OpenAI even included them in their RL Gym library back in the old days when they were still doing open research. But if you look at this leaderboard from 7 (yes, seven!) years ago [1], most of them were already solved way beyond human capabilities. But we didn't get a really useful general purpose algorithm out of it. As an AI researcher, I always considered Atari a fun academic exercise, but nothing more. Similar to how recognising characters using convnets was cool in the nineties and early 00s, but didn't give us general purpose image understanding. Only modern GPUs and massive training datasets did. Nowadays most cutting-edge RL game research focuses on much more advanced games like Minecraft which is thought to be better suited. But I'm pretty sure it's still not enough. Even role-playing GTA VI won't be. We probably need a pretty advanced physical simulation of the real world before we can get agents to handle the real world. But that means solving the problem of generating such an environment first, because you can't train on the actual real world due to the sample inefficiency of all current algorithms. Nvidia is doing some really interesting research in this direction by combining physics simulation and image generation models to simulate an environment, while getting accuracy and diversity at the same time into training data. But it still feels like some key ingredient is missing.
I watched the talk live. I felt that his main argument was that Atari _looks_ solved, but there's still plenty of value that could be gained by revisiting these "solved" games. For one, learning how to play games through a physical interface is a way to start engaging with the kinds of problems that make robotics hard (e.g., latency). They're also a good environment to study catastrophic forgetting: an hour of training on one game shouldn't erase a model's ability to play other games.
I think we could eventually saturate Atari, but for now it looks like it's still a good source of problems that are just out of reach of current methods.
mschuster91 · 5h ago
> But it still feels like some key ingredient is missing.
Continuous training is the key ingredient. Humans can use existing knowledge and apply it to new scenarios, and so can most AI. But AI cannot permanently remember the result of its actions in the real world, and so its body of knowledge cannot expand.
Take a toddler and an oven. The toddler has no concept of what an oven is other than maybe that it smells nice. The toddler will touch the oven, notice that it experiences pain (because the oven is hot) and learn that oven = danger. Place a current AI in a droid toddler body? It will never learn and keep touching the oven as soon as the information of "oven = danger" is out of the context window.
For some cases this inability to learn is actually desirable. You don't want anyone and everyone to be able to train ChatGPT unsupervised, otherwise you get 4chan flooding it with offensive crap like they did to Tay [1], but for AI that physically interacts with the meatspace, constant evaluation and learning is all but mandatory if it is to safely interact with its surroundings. "Dumb" robots run regular calibration cycles for their limbs to make sure they are still aligned to compensate for random deviations, and so will AI robots.
This kind of context management is not that hard, even when building LLMs. Especially when you have huge windows like we do today. Look at how ChatGPT can remember things permanently after you said them once using a function call to edit the permanent memory section inside the context. You can also see that in Anthropic's latest post on Claude 4 where it learns to play Pokemon. The only remaining issue here is maybe how to diffuse explicit knowledge from the stored context into the weights. Andrej Karpathy wrote a good piece on this recently. But personally I believe this might not even be necessary if you can manage your context well enough and see it more like RAM while the LLM is the CPU. For your example you can then always just fetch such information from a permanent storage like a VDB and load it into context once you enter an area in the real world.
mr_toad · 2h ago
Big context windows are a poor substitute for updating the weights. Its like keeping a journal because your memory is failing.
fzzzy · 11m ago
It reminds me of the movie Memento.
vectorisedkzk · 3h ago
Having used vectorDBs before, we're very much not there yet. We don't have any appreciable amounts of context for any reasonable real-life memory. It works if that is the most recent thing you did. Have you talked to an LLM for a day? Stuff is gone before the first hour. You have to use every trick currently in the book, treat context like it's your precious pet
sigmoid10 · 3h ago
VectorDBs are basically just one excuse of many to make up for a part of the system that is lacking capability due to technical limitations. I'm currently at 50:50 if the problems will be overcome directly by the models or by such support systems. Used to be 80:20 but models have grown in usefulness much faster than all the tools we built around them.
mschuster91 · 4h ago
> This kind of context management is not that hard, even when building LLMs.
It is, at least if you wish to be in the meatspace, that's my point. Every day has 86400 seconds during which a human brain constantly adapts to and learns from external input - either directly as it's being awake or indirectly during nighttime cleanup processes.
On top of that, humans have built-in filters for training. Basically, we see some drunkard shouting about the Hollow Earth on the sidewalk... our brain knows that this is a drunkard and that Hollow Earth is absolutely crackpot material, so if it stores anything at all then the fact that there is a drunkard on that street and one might take another route next time, but the drunkard's rambling is forgotten maybe five minutes later.
AI, in contrast, needs to be hand-held by humans during training that annotate, "grade" or weigh information during the compilation of the training dataset, in order that the AI knows what is written in "Mein Kampf" so it can answer questions upon it, but that it also knows (or at least: won't openly regurgitate) that the solution to economic problems isn't to just deport Jews.
And huge context windows aren't the answer either. My wife says me, she would like to have a fruit cake for her next birthday. I'll probably remember that piece of information (or at the very least I'll write it down)... but an AI butler? I'd be really surprised if this is still in its context space in a year, and even if it is, I would not be surprised if it weren't able to recall that fact.
And the final thing is prompts... also not the answer. We've seen it just a few days ago with Grok - someone messed with the system prompt so it randomly interjected "white genocide" claims into completely unrelated conversation [1] despite hopefully being trained on a ... more civilised dataset, and to the contrary, we've also seen Grok reply to Twitter questions in a way that suggest that it is aware its training data is biased.
>Every day has 86400 seconds during which a human brain constantly adapts to and learns from external
That's not even remotely true. At least not in the sense that it is for context in transformer models. Or can you tell me all the visual and auditory inputs you experienced yesterday at the 45232nd second? You only learn permanently and effectively from particular stimulation coupled with surprise. That has a sample rate which is orders of magnitude lower. And it's exactly the kind of sampling that can be replicated with a run-of-the-mill persistent memory system
for an LLM. I would wager that you could fit most people's core experiences and memories that they can randomly access at any moment into a 1000 page book - something that fits well into state of the art context windows. For deeper more detailed things you can always fall back to another system.
epolanski · 3h ago
> Humans can use existing knowledge and apply it to new scenarios, and so can most AI
Doesn't the article states that this is not true? AI cannot apply to B what it learned about A.
mschuster91 · 2h ago
Well, ChatGPT knows about the 90s Balkan wars, a topic to which LWT hasn't made an episode that I'm aware of, and yet I can ask it to write a script for a Balkan wars episode that reads surprisingly like John Oliver while being reasonably correct.
epolanski · 1h ago
Essentially Carmack pointed in the slides that teaching AI to play game a, b or c didn't improve AI at all at learning game d from scratch.
That's essentially what we're looking for when we talk about general intelligence, the capability to adapting what we know to what we know nothing about.
aatd86 · 5h ago
it's more than that. Our understanding from space and time could be stemming from continuous training.
Every time we look at something, there seems to be a background process that is categorizing items that are on the retinal image.
This is a continuous process.
newsclues · 3h ago
Being highly used in the past is good, it's a benchmark to compare against.
tschillaci · 4h ago
You will find many agents that solved (e.g., finished, reached high score) atari games, but there is still so much more work to do in the field. I wrote my Master's thesis on how to learn from few interactions with the game, so that if the algorithm is ported to actual robots they don't need to walk and fall for centuries before learning behaviors. I think there is more research to do on higher levels of generalization: when you know how to play a few video games, you quickly understand how to play a new one intuitively, and I haven't seen thorough research on that.
gadders · 2h ago
I want smarter NPCs in games.
albertzeyer · 4h ago
His goal was not just to solve Atari games. That was already done.
His goal is to develop generic methods. So you could work with more complex games or the physical world for that, as that is what you want in the end. However, his insight is, you can even modify the Atari setting to test this, e.g. to work in realtime, and the added complexity by more complex games doesn't really give you any new additional insights at this point.
modeless · 5h ago
He says they will open source it which is cool. I agree that I don't understand what's novel here. Playing with a physical controller and camera on a laptop GPU in real time is cool, and maybe that hasn't specifically been done before, but it doesn't seem surprising that it is possible.
If it is substantially more sample efficient, or generalizable, than prior work then that would be exciting. But I'm not sure if it is?
RetroTechie · 1h ago
Maybe that's exactly his goal: not to come up with something that beats the competition, but play with the architecture, get a feel for what works & what doesn't, how various aspects affect the output, and improve on that. Design more efficient architectures, or come up with something that has unique features compared to other models.
If so, scaling up may be more of a distraction rather than helpful (besides wasting resources).
I hope he succeeds in whatever he's aiming for.
cryptoz · 5h ago
DeepMind’s original demos were also of Atari gameplay.
steveBK123 · 1h ago
Another thought experiment - if OpenAI AGI was right around the corner, why are they wasting time/money/energy buying a product-less vanity hardware startup run by Ive?
Why not tackle robotics if anything.
Or really just be the best AGI and everyone will be knocking on your door to license it in their hardware/software stacks, you will print infinite money.
soared · 1h ago
Or have your AGI design products
steveBK123 · 1h ago
All the more reason not to acquihire Ive for $6.5B, if true
saejox · 4h ago
What Carmack is doing is right. More people need to get away from training their models just with words. AI need the physicality.
pyb · 6h ago
"... Since I am new to the research community, I made an effort"
This means they've probably submitted a paper too.
epolanski · 3h ago
It states it's a research, not a product company.
diggan · 3h ago
To be fair, OpenAI is also a "research lab" rather than "product company" and they still sell products for $200/month, not sure the distinction matters in practice much today as long as the entity is incorporated somehow.
pyb · 2h ago
That's what I said
mkoubaa · 21m ago
> It is worth trying out one of the many web based reaction time testers – you will find that you average over 160 milliseconds.
TIL JC has elite reflexes
2OEH8eoCRo0 · 17m ago
And nerves of steel
dusted · 6h ago
anywhere we can watch the presentation ? the slides alone are great, but if he's saying stuff alongside, I'd be interested in that too :)
vasco · 6h ago
Bro went his whole career and managed to somehow create a gig for himself where he gets the AI money while playing Atari. It's hard to increase the respect for someone who you already maxed out on but there we go. Carmack is a cool guy.
willvarfar · 6h ago
Although Carmack is the quintessential not-a-brogrammer.
dusted · 6h ago
I've never actually seen a brogrammer though, I've seen people who program only because they get money for it, I thought for a while those where it, but I'm not sure if I think they qualify either.
tomaytotomato · 4h ago
From watching on the wall I've seen brogrammer used in various contexts (this is not an exhaustive list):
- Someone who is a programmer but follows a hypermasculine cliche and makes sure everyone knows about it.
- An insult used by other developers for someone who is more physically fit or interested in their health than themselves.
- An insult used by engineers or other people who are not happy with the over representation of men in the industry. So everyone is lumped in the category.
- Someone who is obsessed with the technology and trying to grind their skills on it to an excessive level.
diggan · 3h ago
> someone who is more physically fit or interested in their health than themselves
Isn't "interested in their health" a signal that they are interested in themselves, rather than the opposite?
oersted · 2h ago
Disambigation: I believe "themselves" refers to the one insulting, not the one interested in their health.
It tripped me up too, to be fair.
tomaytotomato · 1m ago
Apologies, grammar is hard.
mi_lk · 4h ago
> Someone who is obsessed with the technology and trying to grind their skills on it to an excessive level
sounds like a person who respects their own profession though
CPLX · 1h ago
A. A male programmer who uses the word "bro" unironically in conversation.
B. A person who is physically and culturally indistinguishable from A
rfrey · 23m ago
What physical or cultural characteristics would make a person "indistinguishable from A"?
nindalf · 6h ago
It means “programmer I don’t like”. Very versatile insult and vague enough that it’s impossible to defend against.
kid64 · 5h ago
No, I'm pretty sure it's just a programmer that understands the world in terms of bros.
dusted · 4h ago
"in the terms of bros" what does that even mean? I think bro is a term that's used pretty widely, for different things, in different cultures and contexts, I call my brother bro.. I've heard people call their friends bro.. I've heard someone tell a police officer "don't tase me, bro"..
Quite exciting. Without diminishing the amazing value of LLMs, I don't think that path goes all the way to AGI. No idea if Carmack has the answer, but some good things will come out of that small research group, for sure.
petters · 6h ago
Isn't that what Deepmind did 12 years ago?
hombre_fatal · 6h ago
He points that out in his notes and says DeepMind needed specialized training and/or 200M frames of training just to kinda play one game.
tsunamifury · 5h ago
What deepmind accomplished with suicidal Mario was so much more than you probably ever will know from outside the company.
mi_lk · 4h ago
Do tell if you can. Were you there?
willvarfar · 6h ago
Playing Atari games makes it easy to benchmark and compare and contrast his future research with Deepmind and more recent efforts.
moralestapia · 6h ago
IIRC Deepmind (and OpenAI and ...) have done this on software-only setups (emulators, TAS, etc); while this one has live input and actuators in the loop, so, kind of the same thing but operating in the physical realm.
I do agree that it is not particularly groundbreaking, but it's a nice "hey, here's our first update".
No comments yet
abc-1 · 6h ago
I was really hoping to see something cool. Such a great team of smart people, but this is just John reverting to what he’s comfortable with and going off on some nonsense tangent. Hardware and video games. I doubt this is going to yield anything interesting at all.
rfrey · 21m ago
From chess to Go to Atari games, AI research has always been about games. You are unintentionally positioning yourself as more sophisticated than a huge number of AI luminaries, including Nobel and Turing winners.
johnb231 · 5h ago
Games are heavily used in RL research.
abc-1 · 5h ago
I understand that. Doing games in real time is just a performance problem that can be solved with more compute or inane optimizations. It’s not interesting research.
criddell · 34m ago
Here's a heuristic that somebody gave me a while ago: using the word "just" in the way you did is a signal that you don't understand the topic.
John's document covers why he's doing what he's doing:
> Fundamentally, I believe in the importance of learning from a stream of interactive experience, as humans and animals do, which is quite different from the throw-everything-in-a-blender approach of pretraining an LLM. The blender approach can still be world-changingly valuable, but there are plenty of people advancing the state of the art there.
He thinks interacting with the real world and learning as you go isn't getting enough attention and might take us farther than the LLM approach. So he's applying these ideas to a subject that he's an expert in. You don't seem to find this approach interesting but John does (and I do too, for the record).
Everybody dismissing him might be right. Those keeping score know that Carmack's batting average isn't one thousand. But those people also know Carmack has the resources to work on pretty much whatever he wants to work on. I'm happy he's still working hard at something and sharing his work.
johnb231 · 4h ago
I think you are trivializing the field of RL research. Games are not a solved problem. Doing that efficiently in real-time is even more difficult and is highly relevant to real world applications.
abc-1 · 4h ago
Right, we have not even solved games in the non-real time environment, so why bother adding additional constraints like “on real hardware in real time”. This is exactly like Tesla trying to switch from LIDAR to cameras before self driving is even solved. It’s avoiding the real harder challenge and going off on inane tangents. John is essentially bike shedding.
In this case, John is going off on this inane tangent because of his prior experience with hardware and video games instead of challenging himself to solve the actual hard and open problems.
I’m going to predict how this plays out for the inevitable screenshot in one to two years. John picks some existing RL algo and optimizes it to run in real time on real hardware. While he’s doing this the field moves on to better and new algorithms and architectures. John finally achieves his goal and posts a vid of some (now ancient) RL algo playing some Atari game in real time. Everyone says “neat” and moves on. John gets to feel validated yet all his work is completely useless.
johnb231 · 4h ago
False dichotomy. It's not "avoiding the real harder challenge". It's solving a different problem and it is extremely relevant to real world applications. These are actual hard and open problems to be solved.
brador · 5h ago
I feel top level AI creation is beyond his skill set.
He’s a AAA software engineer but the prerequisites to build out cutting edge AI require deep formal math that is beyond his education and years at this point.
Nothing to stop him playing around with AI models though.
kriro · 5h ago
I think you overestimate the level of math required in AI and at the same time I think you underestimate the math skills of John. AI runs on GPUs, Quake 2 engine was one of the first to optimized for GPUs (OpenGL).
I'm pretty excited to see him in this domain. I think he'll focus on some DeepSeek style improvements.
horsellama · 5h ago
this.
Having JC focusing on, say, writing a performant OSS CUDA replacement could be bigger than any of the last 20 announcements from openai/goggle/deepmind/etc
lyu07282 · 4h ago
This feels like an understatement. At the time, young me had the impression Carmack came first, then the industry created 3dfx/OpenGL to run his games better. I still have nothing but respect for his skills decades later.
threeseed · 2h ago
John Carmack:
So I asked Ilya, their chief scientist, for a reading list. This is my path, my way of doing things: give me a stack of all the stuff I need to know to actually be relevant in this space.
And he gave me a list of like 40 research papers and said, 'If you really learn all of these, you'll know 90% of what matters today! And I did. I plowed through all those things and it all started sorting out in my head.
foldr · 1h ago
What this misses is that research is a competitive endeavor. To succeed as a researcher you don’t just need to know the bare minimum required to do research in your field. You need to be able to do it better than most of the people you’re competing against. I know that HN as a collective has near-unlimited faith in Carmack’s abilities (and he is no doubt Very Smart). But he’s competing with other Very Smart people who have decades more experience of AI research.
To put it another way, the idea that John Carmack is going to do groundbreaking research in AI is roughly as plausible as the idea that Yann LeCun is going to make a successful AAA video game. Stranger things have happened, but I won’t be holding my breath.
RetroTechie · 45m ago
You're forgetting that a whole string of breakthroughs are all fairly recent (like in the last decade). Everyone, including the pro's, is treading new ground.
In that context anyone can make progress in the field, as long as they understand what they're dealing with.
Better regard mr. Carmack as an X factor. Maybe the experts will leave him in the dust. Or maybe he'll come up with something that none of the experts cared to look into.
foldr · 16m ago
Lots of scientific fields have seen breakthroughs in the past decade. Doesn’t mean that any random smart person can jump in and start doing groundbreaking research.
secondcoming · 1h ago
Why does it need to be competitive? Maybe the guy has enough money to let him do whatever he wants regardless of the outcome and he chose AI because it's interesting to him.
foldr · 17m ago
In research you have to succeed before your competitors. It’s not research if it’s already been done.
Cthulhu_ · 4h ago
What do you mean "beyond his skill set"? He effectively invented 3D gaming, which led to major leaps and investments into graphics cards which are now used for cryptocurrency and AI. He also did significant contributions into VR.
He's probably one of the most qualified people around.
jimbohn · 3h ago
The math around machine learning is very manageable, and a lot of research in that area is throwing heuristics at a wall to see what sticks
johnb231 · 5h ago
The formal math takes a few months to learn. He is more than smart enough to figure that out.
novosel · 5h ago
There is no deep formal math in AI. It is a game of numbers.
All deep formal math is a boundary to a thing.
roflcopter69 · 2h ago
Honestly, having gone through the slides, it's a bit painful to see Carmack "rediscover" stuff I've learned in a reinforcement learning lecture like ten years ago.
But don't get me wrong! Since this is a long-term research endeavor of his, I believe really starting from the basics is good for him and will empower him to bring something new to the table eventually.
I'm surprised though that he "only" came so far as of now. Maybe my slight idolization of Carmack made me kinda of blind to the fact that this kind of research is a mean beast after all and there is a reason that huuuuge research labs dump countless of man-decades into this kind of stuff with no guaranteed breakthroughs.
I'm nowhere as good at my craft as someone who works for openai, which the author of that tweet seems to be, but if even I can see this, then it's bad, isn't it?
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xiphias2 · 6h ago
A lot of the problems John mentioned (camera jpeg, latency, real time decisions) have been worked on by comma.ai for many years. He could have just used their stack and build on it the general learning parts that comma is not focusing on.
Cthulhu_ · 4h ago
This is Carmack, who builds new things. He built one of the first 3D game engines based on the hard math.
WatchDog · 4h ago
Carmack himself has done a lot of work on end to end latency, during his time at oculus.
prosunpraiser · 6h ago
Reuse is not always necessary - sometimes things are just done for fun and exploration, not for appeasing thirsty VCs and grabbing that market share.
blitzar · 4h ago
Reinventing the exact same thing and shouting from the rooftops about it is exactly how you appease thirsty VCs and grab that market share.
CPLX · 1h ago
Might I interest you in my new startup, that is a bus, but with technology?
Flemlo · 6h ago
And plenty of other people.
It's still a lot better to really learn and discover it yourself to really get it.
Also it's hard to determine how much time someone spent on particular topic.
Flamentono2 · 1h ago
I find it interesting that he dismisses LLMs.
I would argue that if he wants to do AGI through RL, a LLM could be a perfect teacher or oracle.
After all i'm not walking around as a human and not having guidance. It should/could make RL a lot faster leveraging this.
My logical part / RL part does need the 'database'/fact part and my facts are trying to be as logical as possible but its just not.
aatd86 · 6h ago
My own little insights and ramblings as an uninitiated quack (just spent the night asking Claude to explain machine learning to me):
seems that we are learning in layers, one of the first layers being 2D neural net (images) augmented by other sensory data to create a 3D if not 4D model (neural net).
HRTFs for sound increases the spatial data we get from images.
With depth coming from sound and light and learnt movements(touch) we seem to develop a notion of space and time. (multimodality?)
Seems that we can take low dimensional inputs and correlate them to form higher dimensional structures.
Of course, physically it comes from noticing the dampening of visual data (in focus for example) and memorized audio data (sound frequency and amplitude, early reflections, doppler effect etc).
That should be emergent from training.
Those data sources can be inperfectly correlated. That's why we count during a lightning storm to evaluate distance. It's low dimensional.
In a sense, it's a measure of required effort perhaps (distance to somewhere).
What's funny is that it seems to go the other way from traditional training where we move from higher dimensional tensor spaces to lower ones. At least in a first step.
Flamentono2 · 1h ago
Its hard to follow what you try to commounicate at least the last half.
Nonetheless, yes we do know certain brain structures like your image net analogy but the way you describe it, sounds a little bit of.
Our virtual cortex is not 'just a layer' its a component i would say and its optimized of detecting things.
Other components act differently with different structures.
One question I would have about the research direction is the emphasis on realtime. If I understand correctly he's doing online learning in realtime. Obviously makes for a cool demo and pulls on his optimisation background, and no doubt some great innovations will be required to make this work. But I guess the bitter lesson and recent history also tell us that some solutions may only emerge at compute levels beyond what is currently possible for realtime inference let alone learning. And the only example we have of entities solving Atari games is the human brain, of which we don't have a clear understanding of the compute capacity. In which case, why wouldn't it be better to focus purely on learning efficiency and relax the realtime requirement for now?
That's a genuine question by the way, definitely not an expert here and I'm sure there's a bunch of value to working within these constraints. I mean, jumping spiders solve reasonably complex problems with 100k neurons, so who knows.
"A reality check for people that think full embodied AGI is right around the corner is to ask your dancing humanoid robot to pick up a joystick and learn how to play an obscure video game."
From what I can tell most in AI are currently hoping LLMs reach that point quick just because the hype is not helping AI at all.
Games generally are solvable for AI because they have feedback loops and a clear success or failure criteria. If the "picking up a Joystick" part is the limiting factor, sure. But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage.
I'm sure if you compared a monkey and a dolphins performance using a joystick you'd get results that aren't really correlated with their intelligence. I would guess that if you gave robots an R2D2 like port to jack into and play a game, that problem could be solved relatively quickly.
They also claimed it "learned" to play by playing itself only however it was clear that most of the advanced techniques were borrowed from existing AI and by observing humans.
No surprise they gave up on that project completely and I doubt they'll ever engage in anything like that again.
Money better spent on different marketing platforms.
Saying you've solved Dota after stripping out nearly all of its complexity is like saying you've solved Chess, but on a version where the back row is all Bishops.
Look at how long Theranos went on! Miraculous product. Attractive young founder with all the right pedigree, credentials, and contacts, dressed in black trurtlenecks. Hell, she even talked like Steve Jobs! Investors never had a chance.
That is what investors see. You seem to treat this as a purity contest where you define purity
AI clearly isn't at human level and it's OK to admit it.
I don't think it's clear how much of a human brains function exists at birth though, I know it's theorised than even much of the sensory processing has to be learned.
Existing at birth is not the same thing as innate. Puberty is innate but it is not present at birth.
Neurons have finite (very low) speed of signal transfer, so just by measuring cognitive reaction time we can deduce upper bounds on how many _consecutive_ neuron connections are involved in reception, cognitive processing, and resulting reaction via muscles, even for very complex cognitive processes. And the number is just around 100 consecutive neurons involved one after another. So “the algorithm” could not be _that_ complex in the end (100x matmul+tanh?)
Granted, a lot of parallelism and feedback loops are involved, but overall it gives me (and many others) an impression that when the AGI algorithm is ever found, it’s “mini” version should be able to run on modest 2025 hardware in real time.
Biological neurons are way more complex than that. A single neuron has dentritic trees with subunits doing their own local computations. There are temporal dynamics in the firing sequences. There is so much more complexity in the biological networks. It's not comparable.
And I say this while most certainly not being as knowledgeable as this openai insider. So it even I can see this, then it's kinda bad, isn't it?
https://docs.google.com/presentation/d/1GmGe9ref1nxEX_ekDuJX...
https://docs.google.com/document/d/1-Fqc6R6FdngRlxe9gi49PRvU...
Maybe it will turn out to simply be enough artificial neurons and everything works. But I don't believe that.
Using real life robots is going to be a huge bottleneck for training hours no matter what they do.
https://github.com/Farama-Foundation/Arcade-Learning-Environ...
The goal is to develop algorithms that generalize to other tasks.
[1]https://github.com/cshenton/atari-leaderboard
I think we could eventually saturate Atari, but for now it looks like it's still a good source of problems that are just out of reach of current methods.
Continuous training is the key ingredient. Humans can use existing knowledge and apply it to new scenarios, and so can most AI. But AI cannot permanently remember the result of its actions in the real world, and so its body of knowledge cannot expand.
Take a toddler and an oven. The toddler has no concept of what an oven is other than maybe that it smells nice. The toddler will touch the oven, notice that it experiences pain (because the oven is hot) and learn that oven = danger. Place a current AI in a droid toddler body? It will never learn and keep touching the oven as soon as the information of "oven = danger" is out of the context window.
For some cases this inability to learn is actually desirable. You don't want anyone and everyone to be able to train ChatGPT unsupervised, otherwise you get 4chan flooding it with offensive crap like they did to Tay [1], but for AI that physically interacts with the meatspace, constant evaluation and learning is all but mandatory if it is to safely interact with its surroundings. "Dumb" robots run regular calibration cycles for their limbs to make sure they are still aligned to compensate for random deviations, and so will AI robots.
[1] https://en.wikipedia.org/wiki/Tay_(chatbot)
It is, at least if you wish to be in the meatspace, that's my point. Every day has 86400 seconds during which a human brain constantly adapts to and learns from external input - either directly as it's being awake or indirectly during nighttime cleanup processes.
On top of that, humans have built-in filters for training. Basically, we see some drunkard shouting about the Hollow Earth on the sidewalk... our brain knows that this is a drunkard and that Hollow Earth is absolutely crackpot material, so if it stores anything at all then the fact that there is a drunkard on that street and one might take another route next time, but the drunkard's rambling is forgotten maybe five minutes later.
AI, in contrast, needs to be hand-held by humans during training that annotate, "grade" or weigh information during the compilation of the training dataset, in order that the AI knows what is written in "Mein Kampf" so it can answer questions upon it, but that it also knows (or at least: won't openly regurgitate) that the solution to economic problems isn't to just deport Jews.
And huge context windows aren't the answer either. My wife says me, she would like to have a fruit cake for her next birthday. I'll probably remember that piece of information (or at the very least I'll write it down)... but an AI butler? I'd be really surprised if this is still in its context space in a year, and even if it is, I would not be surprised if it weren't able to recall that fact.
And the final thing is prompts... also not the answer. We've seen it just a few days ago with Grok - someone messed with the system prompt so it randomly interjected "white genocide" claims into completely unrelated conversation [1] despite hopefully being trained on a ... more civilised dataset, and to the contrary, we've also seen Grok reply to Twitter questions in a way that suggest that it is aware its training data is biased.
[1] https://www.reuters.com/business/musks-xai-updates-grok-chat...
That's not even remotely true. At least not in the sense that it is for context in transformer models. Or can you tell me all the visual and auditory inputs you experienced yesterday at the 45232nd second? You only learn permanently and effectively from particular stimulation coupled with surprise. That has a sample rate which is orders of magnitude lower. And it's exactly the kind of sampling that can be replicated with a run-of-the-mill persistent memory system for an LLM. I would wager that you could fit most people's core experiences and memories that they can randomly access at any moment into a 1000 page book - something that fits well into state of the art context windows. For deeper more detailed things you can always fall back to another system.
Doesn't the article states that this is not true? AI cannot apply to B what it learned about A.
That's essentially what we're looking for when we talk about general intelligence, the capability to adapting what we know to what we know nothing about.
This is a continuous process.
His goal is to develop generic methods. So you could work with more complex games or the physical world for that, as that is what you want in the end. However, his insight is, you can even modify the Atari setting to test this, e.g. to work in realtime, and the added complexity by more complex games doesn't really give you any new additional insights at this point.
If it is substantially more sample efficient, or generalizable, than prior work then that would be exciting. But I'm not sure if it is?
If so, scaling up may be more of a distraction rather than helpful (besides wasting resources).
I hope he succeeds in whatever he's aiming for.
Why not tackle robotics if anything. Or really just be the best AGI and everyone will be knocking on your door to license it in their hardware/software stacks, you will print infinite money.
TIL JC has elite reflexes
- Someone who is a programmer but follows a hypermasculine cliche and makes sure everyone knows about it.
- An insult used by other developers for someone who is more physically fit or interested in their health than themselves.
- An insult used by engineers or other people who are not happy with the over representation of men in the industry. So everyone is lumped in the category.
- Someone who is obsessed with the technology and trying to grind their skills on it to an excessive level.
Isn't "interested in their health" a signal that they are interested in themselves, rather than the opposite?
It tripped me up too, to be fair.
sounds like a person who respects their own profession though
B. A person who is physically and culturally indistinguishable from A
Quite exciting. Without diminishing the amazing value of LLMs, I don't think that path goes all the way to AGI. No idea if Carmack has the answer, but some good things will come out of that small research group, for sure.
I do agree that it is not particularly groundbreaking, but it's a nice "hey, here's our first update".
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John's document covers why he's doing what he's doing:
> Fundamentally, I believe in the importance of learning from a stream of interactive experience, as humans and animals do, which is quite different from the throw-everything-in-a-blender approach of pretraining an LLM. The blender approach can still be world-changingly valuable, but there are plenty of people advancing the state of the art there.
He thinks interacting with the real world and learning as you go isn't getting enough attention and might take us farther than the LLM approach. So he's applying these ideas to a subject that he's an expert in. You don't seem to find this approach interesting but John does (and I do too, for the record).
Everybody dismissing him might be right. Those keeping score know that Carmack's batting average isn't one thousand. But those people also know Carmack has the resources to work on pretty much whatever he wants to work on. I'm happy he's still working hard at something and sharing his work.
In this case, John is going off on this inane tangent because of his prior experience with hardware and video games instead of challenging himself to solve the actual hard and open problems.
I’m going to predict how this plays out for the inevitable screenshot in one to two years. John picks some existing RL algo and optimizes it to run in real time on real hardware. While he’s doing this the field moves on to better and new algorithms and architectures. John finally achieves his goal and posts a vid of some (now ancient) RL algo playing some Atari game in real time. Everyone says “neat” and moves on. John gets to feel validated yet all his work is completely useless.
He’s a AAA software engineer but the prerequisites to build out cutting edge AI require deep formal math that is beyond his education and years at this point.
Nothing to stop him playing around with AI models though.
I'm pretty excited to see him in this domain. I think he'll focus on some DeepSeek style improvements.
Having JC focusing on, say, writing a performant OSS CUDA replacement could be bigger than any of the last 20 announcements from openai/goggle/deepmind/etc
So I asked Ilya, their chief scientist, for a reading list. This is my path, my way of doing things: give me a stack of all the stuff I need to know to actually be relevant in this space.
And he gave me a list of like 40 research papers and said, 'If you really learn all of these, you'll know 90% of what matters today! And I did. I plowed through all those things and it all started sorting out in my head.
To put it another way, the idea that John Carmack is going to do groundbreaking research in AI is roughly as plausible as the idea that Yann LeCun is going to make a successful AAA video game. Stranger things have happened, but I won’t be holding my breath.
In that context anyone can make progress in the field, as long as they understand what they're dealing with.
Better regard mr. Carmack as an X factor. Maybe the experts will leave him in the dust. Or maybe he'll come up with something that none of the experts cared to look into.
He's probably one of the most qualified people around.
All deep formal math is a boundary to a thing.
But don't get me wrong! Since this is a long-term research endeavor of his, I believe really starting from the basics is good for him and will empower him to bring something new to the table eventually.
I'm surprised though that he "only" came so far as of now. Maybe my slight idolization of Carmack made me kinda of blind to the fact that this kind of research is a mean beast after all and there is a reason that huuuuge research labs dump countless of man-decades into this kind of stuff with no guaranteed breakthroughs.
I'm nowhere as good at my craft as someone who works for openai, which the author of that tweet seems to be, but if even I can see this, then it's bad, isn't it?
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It's still a lot better to really learn and discover it yourself to really get it.
Also it's hard to determine how much time someone spent on particular topic.
I would argue that if he wants to do AGI through RL, a LLM could be a perfect teacher or oracle.
After all i'm not walking around as a human and not having guidance. It should/could make RL a lot faster leveraging this.
My logical part / RL part does need the 'database'/fact part and my facts are trying to be as logical as possible but its just not.
seems that we are learning in layers, one of the first layers being 2D neural net (images) augmented by other sensory data to create a 3D if not 4D model (neural net). HRTFs for sound increases the spatial data we get from images. With depth coming from sound and light and learnt movements(touch) we seem to develop a notion of space and time. (multimodality?)
Seems that we can take low dimensional inputs and correlate them to form higher dimensional structures.
Of course, physically it comes from noticing the dampening of visual data (in focus for example) and memorized audio data (sound frequency and amplitude, early reflections, doppler effect etc). That should be emergent from training.
Those data sources can be inperfectly correlated. That's why we count during a lightning storm to evaluate distance. It's low dimensional.
In a sense, it's a measure of required effort perhaps (distance to somewhere).
What's funny is that it seems to go the other way from traditional training where we move from higher dimensional tensor spaces to lower ones. At least in a first step.
Nonetheless, yes we do know certain brain structures like your image net analogy but the way you describe it, sounds a little bit of.
Our virtual cortex is not 'just a layer' its a component i would say and its optimized of detecting things.
Other components act differently with different structures.