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John Carmack talk at Upper Bound 2025

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Re: John Carmack talk at Upper Bound 2025

#31

Post by kriro »

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.

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.

Re: John Carmack talk at Upper Bound 2025

#32

Post by mschuster91 »

sigmoid10 wrote: post #29
johnb231 wrote: post #7

Earlier quoted context omitted.

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 n…

> 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.

[1] https://en.wikipedia.org/wiki/Tay_(chatbot)

Re: John Carmack talk at Upper Bound 2025

#33

Post by sigmoid10 »

mschuster91 wrote: post #32
sigmoid10 wrote: post #29

Earlier quoted context omitted.

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 n…

> 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…

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.

Re: John Carmack talk at Upper Bound 2025

#34

Post by aatd86 »

mschuster91 wrote: post #32
sigmoid10 wrote: post #29

Earlier quoted context omitted.

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 n…

> 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…

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.

Re: John Carmack talk at Upper Bound 2025

#35

Post by horsellama »

kriro wrote: post #31
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.

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.

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

Re: John Carmack talk at Upper Bound 2025

#37

Post by tschillaci »

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 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.

Re: John Carmack talk at Upper Bound 2025

#38

Post by tomaytotomato »

dusted wrote: post #14
willvarfar wrote: post #10

Earlier quoted context omitted.

Although Carmack is the quintessential not-a-brogrammer.

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.

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.

Re: John Carmack talk at Upper Bound 2025

#39

Post by [deleted] »

sigmoid10 wrote: post #33
mschuster91 wrote: post #32

Earlier quoted context omitted.

> 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…

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…

[deleted]

Re: John Carmack talk at Upper Bound 2025

#40

Post by johnb231 »

abc-1 wrote: post #36
johnb231 wrote: post #23

Earlier quoted context omitted.

Games are heavily used in RL research.

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.

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.
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