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John Carmack talk at Upper Bound 2025

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Re: John Carmack talk at Upper Bound 2025

#321
post #317

Earlier quoted context omitted.

The thing about Carmack in the 90s... There was a lot of research going on around 3d graphics. Companies like SGI and Pixar were building specialized workstations for doing vector operations for 3d rendering. 3d was a thing. Game consoles with specialized 3d hardware would launch in 1994 with the Sega Saturn and the Sony Playstation (in Japan only for one year) What Carmack did was basically get a 3d game running on…

The world seems to have rewritten history, and forgotten Ultima Underworld, which shipped prior to Doom..

Couple "3D" games shipped before Doom. Battlezone comes to mind.

The difference is that id owned the natural progression (from Wolf3D through Doom to Quake) and laid foundation to what we call today a FPS genre.

Re: John Carmack talk at Upper Bound 2025

#322

Earlier quoted context omitted.

It is a marketing term. That's it. Trying to exhaustively define what AGI is or could be is like trying to explain what a Happy Meal is. At it's core, the Happy Meal was not invented to revolutionize food eating. It puts an attractive label on some mediocre food, a title that exists for the purpose of advertisement. There is no point collecting definitions for AGI, it was not conceived as a description for something…

The name AGI (i.e. generalist AI) was originally intended to contrast with narrow AI which is only capable of one, or a few, specific narrow skills. A narrow AI might be able to play chess, or distinguish 20 breeds of dog, but wouldn't be able to play tic tac toe because it wasn't built for that. AGI would be able to learn to do anything, within reason. The term AGI is obviously used very loosely with little agreemen…

> intended to contrast with narrow AI

I've thought for a while that the middle letter in AGI ('General' vs 'Specific') would be more useful and helpful if it were changed to Wide vs Narrow. All AIs can be evaluated on a scale of narrow to wide in terms of their abilities and I don't think that will change anytime soon.

Everyone understands that something is only wide or narrow in comparison to something else. While that's also true of the terms "general' and 'specific', those are less used that way in daily conversation these days. In science and tech we make distinctions about generalized vs specific but 'general' isn't a conversational term like 50 or 100 years ago. When I was a kid my grandparents would call the local supermarket, the 'general store' which I thought was an unusual usage even then.

Re: John Carmack talk at Upper Bound 2025

#323

Earlier quoted context omitted.

The name AGI (i.e. generalist AI) was originally intended to contrast with narrow AI which is only capable of one, or a few, specific narrow skills. A narrow AI might be able to play chess, or distinguish 20 breeds of dog, but wouldn't be able to play tic tac toe because it wasn't built for that. AGI would be able to learn to do anything, within reason. The term AGI is obviously used very loosely with little agreemen…

> intended to contrast with narrow AI I've thought for a while that the middle letter in AGI ('General' vs 'Specific') would be more useful and helpful if it were changed to Wide vs Narrow. All AIs can be evaluated on a scale of narrow to wide in terms of their abilities and I don't think that will change anytime soon. Everyone understands that something is only wide or narrow in comparison to something else. While t…

I guess "general store" made more sense back then though. I grew up in the UK in the 60's and food shops were "narrow" - fishmonger, butcher, greengrocer (fruit & veg), bakery, etc. From that perspective a "general store" would have been noteworthy!

Re: John Carmack talk at Upper Bound 2025

#324

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.

Saying we don't "need" AGI is like saying we don't need electricity. Sure life existed before we had that capability, but it would be very transformative. Of course we can make specialized tools in the mean time.

Furthermore, we will be faced with it whether we want to or not, because others are making it happen.

Re: John Carmack talk at Upper Bound 2025

#325

Earlier quoted context omitted.

And this is different from the argument that's being pooh-poed and downvoted how? You are effectively saying "Carmack is smart and is working on cool stuff that'll most likely be useless" in different words.

Not sure what gave you that impression. It's definitely not what I was trying to say.

> 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.

To me this reads as "this is very far-fetched but he's got the money so golly for him"

Re: John Carmack talk at Upper Bound 2025

#326
post #204

Earlier quoted context omitted.

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. T…

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Re: John Carmack talk at Upper Bound 2025

#327

Earlier quoted context omitted.

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 as…

I think your point that AI would refuse to play chess is interesting. To humans, chess is a strategic game. To a mathematician, chess is an exceedingly hard game, (pretty sure it is EXP complete, but I'm not fully familiar with Np/Exp completeness). To an AI, it seems like the AI will side with the mathematicians. AI is like "bro you can't even figure out if P=NP so how am I going to, you want me to waste power to solve an unsolvable problem?"

From Wikipedia, Garry Kasparov said it was a pleasure to watch AlphaZero play, especially since "its style was open and dynamic like his own".

People can't define AI because they don't want to consider AI as a subset of exponentially difficult algorithms, but they do want to consider AI as a generator of stylistic responses.

Re: John Carmack talk at Upper Bound 2025

#328
post #107

Earlier quoted context omitted.

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'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.

Re: John Carmack talk at Upper Bound 2025

#329
post #307

Earlier quoted context omitted.

The thing about Carmack in the 90s... There was a lot of research going on around 3d graphics. Companies like SGI and Pixar were building specialized workstations for doing vector operations for 3d rendering. 3d was a thing. Game consoles with specialized 3d hardware would launch in 1994 with the Sega Saturn and the Sony Playstation (in Japan only for one year) What Carmack did was basically get a 3d game running on…

But also, he didn't do the technically hardest and most impressive part, Quake, on his own. IIUC he basically relied on Michael Abrash's help to get Quake done (in any reasonable amount of time).

Realizing that he needed Abrash (and aggressively recruiting him) could easily be seen as the most impressive thing he did to make Quake happen

Re: John Carmack talk at Upper Bound 2025

#330

Earlier quoted context omitted.

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 hea…

I'd love to have a copy of that list. Just to see how much I've yet to absorb.

https://aman.ai/primers/ai/top-30-papers/
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