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John Carmack talk at Upper Bound 2025

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Re: John Carmack talk at Upper Bound 2025

#21

Post by brador »

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.

Re: John Carmack talk at Upper Bound 2025

#22

Post by tsunamifury »

hombre_fatal wrote: post #17
petters wrote: post #8

Earlier quoted context omitted.

Isn't that what Deepmind did 12 years 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.

What deepmind accomplished with suicidal Mario was so much more than you probably ever will know from outside the company.

Re: John Carmack talk at Upper Bound 2025

#24

Post by cryptoz »

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.

DeepMind’s original demos were also of Atari gameplay.

Re: John Carmack talk at Upper Bound 2025

#25

Post by modeless »

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.

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?

Re: John Carmack talk at Upper Bound 2025

#26

Post by novosel »

brador wrote: post #21

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.

There is no deep formal math in AI. It is a game of numbers.

All deep formal math is a boundary to a thing.

Re: John Carmack talk at Upper Bound 2025

#27

Post by johnb231 »

brador wrote: post #21

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.

The formal math takes a few months to learn. He is more than smart enough to figure that out.

Re: John Carmack talk at Upper Bound 2025

#28

Post by [deleted] »

moralestapia wrote: post #12
petters wrote: post #8

Earlier quoted context omitted.

Isn't that what Deepmind did 12 years 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".

[deleted]

Re: John Carmack talk at Upper Bound 2025

#29

Post by sigmoid10 »

johnb231 wrote: post #7
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.

Atari games are widely used in Reinforcement Learning (RL) research as a standard benchmark. https://github.com/Farama-Foundation/Arcade-Learning-Environ... The goal is to develop algorithms that generalize to other tasks.

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.

[1]https://github.com/cshenton/atari-leaderboard

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