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.
It seems that you’ve only read the first part of the message. X sometimes aggressively truncates content with no indication it’s done so. I’m not sure this is complete, but I’ve recovered this much:
> I read through these slides and felt like I was transported back to 2018.
> Having been in this spot years ago, thinking about what John & team are thinking about, I can't help but feel like they will learn the same lesson I did the hard way.
> The lesson: on a fundamental level, solutions to these games are low-dimensional. No matter how hard you hit them with from-scratch training, tiny models will work about as well as big ones. Why? Because there's just not that many bits to learn.
> If there's not that many bits to learn, then researcher input becomes non-negligible.
> "I found a trick that makes score go up!" -- yeah, you just hard-coded 100+ bits of information; a winning solution is probably only like 1000 bits. You see progress, but it's not the AI's.
> In this simplified RL setting, you don't see anything close to general intelligence. The neural networks aren't even that important.
> You won't see _real_ learning until you absorb a ton of bits into the model. The only way I really know to do this is with generative modeling.
> A classic example: why is frame stacking just as good as RNNs? John mentioned this in his slides. Shouldn't a better, more general architecture work better?
> YES, it should! But it doesn't, because these environments don't heavily encourage real intelligence.
I'm not sure what the moral is from this, but if Atari games are just too easy, at the same time the response of the machine-learning guys to the challenge of the NetHack Learning Environment seems to have mostly been to quietly give up. Why is generative modeling essential to finding harder challenges when NetHack is right there ...?
Alex Nichol worked on "Gotta Learn Fast" in 2018 which Carmack mentions in his talk, he also worked on foundational deep learning methods like CLIP, DDPM, GLIDE, etc. Reducing him to a "seething openai insider" seems a bit unfair
It's a OpenAI researcher that's worked on some of their most successful projects, and I think the criticism in his X thread is very clear.
Systems that can learn to play Atari efficiently are exploiting the fact that the solutions to each game are simple to encode (compared to real world problems). Furthermore, you can nudge them towards those solutions using tricks that don't generalize to the real world.
Right, and the current state of tech - from accounts I’ve read, though not first hand experienced - is the “black box” methods of AI are absolutely questionable when delivering citations and factual basis for their conclusions. As in, the most real world challenge, in the basic sense, of getting facts right is still a bridge too far for OpenAI, ChatGPT, Grok, et al.
See also: specious ethics regarding the training of LLMs on copyright protected artistic works, not paying anything to the creators, and pocketing investor money while trying to legislate their way around decency in engineering as a science.
Carmack has a solid track record as an engineer, innovator, and above the board actor in the tech community. I cannot say the same for the AI cohort and I believe such a distinction is important when gauging the validity of critique or self-aggrandizement by the latter, especially at the expense of the former. I am an outlier in this community because of this perspective, but as a creator and knowledgeable enough about tech to see things through this lens, I am fine being in this position. 10 years from now will be a great time to look back on AI the way we’re looking back at Carmack’s game changing contributions 30 years ago.
It is a thread. You may have only seen the first tweet because Twitter is a user-hostile trash fire.
“The lesson: on a fundamental level, solutions to these games are low-dimensional. No matter how hard you hit them with from-scratch training, tiny models will work about as well as big ones. Why? Because there's just not that many bits to learn.”
I think some replies here are reading the full twitter thread, while others (not logged in?) see only the first tweet. The first tweet alone does come off as a dismissal with no insight.
Each of these games is low-dimensional and require not the "intelligence" but more like "reflexes", I tend to agree.
However making a system that can beat an unknown game does require generalization. If not real a intelligence (whatever that means) but at the level of say "a wolf".
Whether it can arise from RL alone is not certain, but it's there somewhere.
I wouldn't bet against him. "The Bitter Lesson" may imply an advantage to someone who historically has been at the tip of the spear for squeezing the most juice out of GPU hosted parallel computation.
Graphics rendering and AI live on the same pyramid of technology. A pyramid with a lot of bricks with the initials "JC" carved into them, as it turns out.
I would be long carmack in the sense that I think he will have good judgement and taste running a business but I really don't see anything in common between AI and graphics.
Maybe someone better at aphorisms than me can say it better but I really don't see it. There are definitely mid-level low hanging fruits that would look like the kinds of things he did in graphics but the game just seems completely different.
What has "Graphics Carmack" actually done since about 2001?
So, his initial tech was "Adaptive tile refresh" in Commander Keen, used to give it console style pixel-level scrolling. Turns out, they actually hampered themselves in Commander Keen 1 by not understanding the actual tech, and implemented "The Jolt", a feature that was not necessary. The actual hardware implemented scrolling the same way that consoles like the NES did, and did not need "the jolt", nor the limitations it imposed.
Then, Doom and Quake was mostly him writing really good optimizations of existing, known and documented algorithms and 3D techniques, usually by recognizing what assumptions they could make, what portions of the algorithm didn't need to be recalculated when, etc. Very talented at the time, but in the software development industry, making a good implementation of existing algorithms that utilize your specific requirements is called doing your job. This is still the height of his relative technical output IMO.
Fast Inverse Square Root was not invented by him, but was floating around in industry for a while. He still gets kudos for knowing about it and using it.
"Carmack's reverse" is a technique for doing stencil shadows that was a minor (but extremely clever) modification to the "standard" documented way of doing shadow buffers. There is evidence of the actual technique from a decade before Carmack put it in Doom 3 and it was outright patented by two different people the year before. There is no evidence that Carmack "stole" or anything this technique, it was independent discovery, but was clearly also just a topic in the industry at the time.
"Megatextures" from Rage didn't really go anywhere.
Did Carmack actually contribute anything to VR rendering while at Oculus?
People treat him like this programming god and I just don't understand. He was well read, had a good (maybe too good) work ethic, and was very talented at writing 386 era assembly code. These are all laudable, but doesn't in my mind imply that he's some sort of 10X programmer who could revolutionize random industries that he isn't familiar with. 3D graphics math isn't exactly difficult.
Exactly. I know him and like him. He is a genius programmer for sure BUT people forget that the last successful product that he released was Doom 3 over 20 years ago. Armadillo was a failure and Oculus went nowhere.
He's also admitted he doesn't have much of math chops, which you need if you want to make a dent in AI. (Although the same could have been said of 3D graphics when he did Wolfenstein and Doom, so perhaps he'll surprise us)
Rage was released in 2011. His work at Meta produced highly optimized standalone VR. Whether you think it's successful or not, the tracking accuracy and latency is extremely competitive.
What has he shipped in the last 20 years? Oculus is one thing, but that was firmly within his wheelhouse of graphics optimization. Abrash and co. handled the hardware side of things.
Carmack is a genius no doubt. But genius is the result of intense focused practice above and beyond anyone else in a particular area. Trying
to extend that to other domains has been the downfall of so many others like him.
None of them came close to the success of Quake, Doom or Commander Keen.
If you examine the list it includes games like "Gunman Taco Truck" by his 12yo sun, SIGIL I/II (Doom mods) and a remake of Dangerous Dave. Most of the money he made post-id came from Facebook farming games.
I'm not saying he's doing nothing. He's extremely talented and achieved more than most of us could ever dream of. I'm just pointing out that after he departed from id neither id nor him managed to replicate the earlier success. Who knows, maybe times had changed and it would be the same even if he stayed.
Their success with Doom and Quake was a confluence of things that cannot be replicated today. Carmack's programming talent gave them at least a year head start versus the competition. They introduced a new genre with no competition. Romero wrote game development tools that made them productive and able to deliver quickly. The artists and game designers created something innovative and fun to play, that stood the test of time.
Duke Nukem was released in 1996, then Unreal was released in 1998 and that's when they lost their technical advantage. The market became saturated with FPS.
Romero and Tom Hall founded Ion Storm which produced one successful game - Deus Ex. He gave up on AAA and went back to creating small games.
Carmack's licensed code was the basis of many successful games beyond the 90s, including Half Life 1 and 2 and the latest Doom games. We wouldn't have Half Life without id Software. Maybe Valve Software wouldn't exist.
Appeal to authority is a logical fallacy. People often fall into the trap of thinking that because they are highly intelligent and an expert in one domain that this makes them an expert in one or more other domains. You see this all the time.
> People often fall into the trap of thinking that because they are highly intelligent and an expert in one domain that this makes them an expert in one or more other domains.
While this is certainly true, I'm not aware of any evidence that Carmack thinks this way about himself. I think he's been successful enough that's he's personally 'post-economic' and is choosing to spend his time working on unsolved hard problems he thinks are extremely interesting and potentially tractable. In fact, he's actively sought out domain experts to work with him and accelerate his learning.
Bayesian reasoning isn't a fallacy. A known expert in one domain is often correct about things in a related one. The post didn't claim that Carmack is right, just that that he's who they would bet on to be right, which seems perfectly reasonable to me.
