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John Carmack talk at Upper Bound 2025

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Re: John Carmack talk at Upper Bound 2025

#161

Post by ses1984 »

danielbln wrote: post #157
bluGill wrote: post #138

Earlier quoted context omitted.

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.

Bitter lesson applies here as well though. Generalized models will beat specialized models given enough time and compute. How much bespoke NLP is there anymore? Generalized foundational models will subsume all of it eventually.

Generalized models might be better but they are rarely more efficient.

Re: John Carmack talk at Upper Bound 2025

#162

Post by SeanaldMcDnld »

NL807 wrote: post #148
saejox wrote: post #51

What Carmack is doing is right. More people need to get away from training their models just with words. AI need the physicality.

>AI need the physicality. which i found interesting, because i remember Carmack saying simulated environments are way forward and physical environments are too impractical for developing AI

Yeah in that way this demo seemed gimmicky like he acknowledged. He said in the past he would almost count people out if they weren’t training RL in a virtual environment. I agree, still happy he’s staying on the path of online continual learning though

Re: John Carmack talk at Upper Bound 2025

#163

Post by lo0dot0 »

tschillaci wrote: post #37
kamranjon wrote: post #2

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.

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 generalizati…

I can tell you right now without any research that video game designers reuse interface patterns and game mechanics that were already known when making new games. Those patterns and mechanics are also often analogies for real life allowing humans to intuitively play the games. If people can't play your game intuitively, they might say it's a bad game.

Re: John Carmack talk at Upper Bound 2025

#164

Post by Workaccount2 »

bluGill wrote: post #138
ferguess_k wrote: post #133

Earlier quoted context omitted.

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.

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.

Yesterday my dad, in his late 70's, used Gemini with a video stream to program the thermostat. He then called me to tell me this, rather then call me to come stop by and program the thermostat.

You can call this hype, maybe it is all hype until LLMs can work on 10M LOC codebases, but recognize that LLMs are a shift that is totally incomparable to any previous AI advancement.

Re: John Carmack talk at Upper Bound 2025

#165

Post by lairv »

epr wrote: post #107
qoez wrote: post #76

Interesting reply from an openai insider: https://x.com/unixpickle/status/1925795730150527191

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.

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

Re: John Carmack talk at Upper Bound 2025

#166

Post by johnecheck »

danielbln wrote: post #157
bluGill wrote: post #138

Earlier quoted context omitted.

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.

Bitter lesson applies here as well though. Generalized models will beat specialized models given enough time and compute. How much bespoke NLP is there anymore? Generalized foundational models will subsume all of it eventually.

You misunderstand the bitter lesson.

It's not about specialized vs generalized models - it's about how models are trained. The chess engine that beat Kasparov is a specialized model (it only plays chess), yet it's the bitter lesson's example for the smarter way to do AI.

Chess engines are better at chess than LLMs. It's not close. Perhaps eventually a superintelligence will surpass the engines, but that's far from assured.

Specialized AI are hardly obsolete and may never be. This hypothetical superintelligence may even decide not to waste resources trying to surpass the chess AI and instead use it as a tool.

Re: John Carmack talk at Upper Bound 2025

#168

Post by AndrewKemendo »

johnb231 wrote: post #52
MrScruff wrote: post #41

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…

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."

This debate is exhausting because there's no coherent definition of AGI that people agree on.

I made a google form question for collecting AGI definitions cause I don't see anyone else doing it and I find it infinitely frustrating the range of definitions for this concept:

https://docs.google.com/forms/d/e/1FAIpQLScDF5_CMSjHZDDexHkc...

My concern is that people never get focused enough to care to define it - seems like the most likely case.

Re: John Carmack talk at Upper Bound 2025

#169

Post by rowanG077 »

jsheard wrote: post #106
xnickb wrote: post #91

Earlier quoted context omitted.

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 platfo…

It also wasn't even remotely close to learning Dota 2 proper. They ran a massively simplified version of the game where the AI and humans alternated between playing one of 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 also changed or disabled altogether for the sake of the…

I agree that restricting the hero pool is a huge simplification. But they did play full 5v5 standard dota with just a restricted hero pool of 17 heroes and no illusions/control units according to theverge (https://www.theverge.com/2019/4/13/18309459/openai-five-dota...). It destroyed the professionals.

As an ex dota player, I don't think this is that far off from having full on, all heroes dota. Certainly not as far of as you are making it sound.

And dota is one of the most complex games, I expect for example that an AI would instantly solve CS since aim is such a large part of the game.

Re: John Carmack talk at Upper Bound 2025

#170

Post by jjulius »

dcre wrote: post #152
jjulius wrote: post #132

Earlier quoted context omitted.

I appreciate how they don't tell us what lesson they learned.

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.” https://unrollnow.com/status/1925795730150527191

Thank you for clarifying. I don't have a Twitter account, and the linked tweet genuinely looks like a standalone object. Mea culpa.
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