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John Carmack talk at Upper Bound 2025
- kamranjon 1y agoI 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.
- johnb231 1y agoAtari games are widely used in Reinforcement Learning (RL) research as a standard benchmark. https://github.com/Farama-Foundation/Arcade-Learning-Environment https://github.com/Farama-Foundation/Arcade-Learning-Environ... The goal is to develop algorithms that generalize to other tasks.
- sigmoid10 1y agoThey 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 https://github.com/cshenton/atari-leaderboard
- mschuster91 1y ago> 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) https://en.wikipedia.org/wiki/Tay_(chatbot)
- sigmoid10 1y agoThis 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.
- deleted 1y ago[deleted]
- cryptoz 1y agoDeepMind’s original demos were also of Atari gameplay.
- modeless 1y agoHe 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?
- RetroTechie 1y agoMaybe that's exactly his goal: not to come up with something that beats the competition, but play with the architecture, get a feel for what works & what doesn't, how various aspects affect the output, and improve on that. Design more efficient architectures, or come up with something that has unique features compared to other models. If so, scaling up may be more of a distraction rather than helpful (besides wasting resources). I hope he succeeds in whatever he's aiming for.
- tschillaci 1y agoYou 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.
- lo0dot0 1y agoI 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.
- Jensson 1y agoSo why can't AI learn those and reapply the same understanding to new games?
- albertzeyer 1y agoHis goal was not just to solve Atari games. That was already done. His goal is to develop generic methods. So you could work with more complex games or the physical world for that, as that is what you want in the end. However, his insight is, you can even modify the Atari setting to test this, e.g. to work in realtime, and the added complexity by more complex games doesn't really give you any new additional insights at this point.
- mike_hearn 1y agoBut how is this different to what NVIDIA have already done? They have robots that can achieve arbitrary and fluid actions in the real world by training NNs in very accurate GPU simulated environments using physics engines. Moving a little Atari stick around seems like not much compared to sorting through your groceries etc. The approach NVIDIA are using (and other labs) clearly works. It's not going to be more than a year or two now before robotics is as solved as NLP and chatbots are today.
- albertzeyer 1y agoI think he argues that they would not be able to play Atari games this way (I don't know; maybe I also misunderstood). But also, he argues a lot about sample efficiency. He wants to develop algorithms/methods/models which can learn much faster / with much fewer data.
- gadders 1y agoI want smarter NPCs in games.
- moralestapia 1y agoHere's what they built, https://x.com/ID_AA_Carmack/status/1925243539543265286 https://x.com/ID_AA_Carmack/status/1925243539543265286 Quite exciting. Without diminishing the amazing value of LLMs, I don't think that path goes all the way to AGI. No idea if Carmack has the answer, but some good things will come out of that small research group, for sure.
- petters 1y agoIsn't that what Deepmind did 12 years ago?
- willvarfar 1y agoPlaying Atari games makes it easy to benchmark and compare and contrast his future research with Deepmind and more recent efforts.
- moralestapia 1y agoIIRC 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 1y ago[deleted]
- hombre_fatal 1y agoHe points that out in his notes and says DeepMind needed specialized training and/or 200M frames of training just to kinda play one game.
- tsunamifury 1y agoWhat deepmind accomplished with suicidal Mario was so much more than you probably ever will know from outside the company.
- dusted 1y agoanywhere we can watch the presentation ? the slides alone are great, but if he's saying stuff alongside, I'd be interested in that too :)
- vasco 1y agoBro went his whole career and managed to somehow create a gig for himself where he gets the AI money while playing Atari. It's hard to increase the respect for someone who you already maxed out on but there we go. Carmack is a cool guy.
- willvarfar 1y agoAlthough Carmack is the quintessential not-a-brogrammer.
- dusted 1y agoI'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.
- nindalf 1y agoIt means “programmer I don’t like”. Very versatile insult and vague enough that it’s impossible to defend against.
- kid64 1y agoNo, I'm pretty sure it's just a programmer that understands the world in terms of bros.
- dusted 1y ago"in the terms of bros" what does that even mean? I think bro is a term that's used pretty widely, for different things, in different cultures and contexts, I call my brother bro.. I've heard people call their friends bro.. I've heard someone tell a police officer "don't tase me, bro"..
- floren 1y ago
- pyb 1y ago"... Since I am new to the research community, I made an effort" This means they've probably submitted a paper too.
- epolanski 1y agoIt states it's a research, not a product company.
- xiphias2 1y agoA lot of the problems John mentioned (camera jpeg, latency, real time decisions) have been worked on by comma.ai for many years. He could have just used their stack and build on it the general learning parts that comma is not focusing on.
- prosunpraiser 1y agoReuse is not always necessary - sometimes things are just done for fun and exploration, not for appeasing thirsty VCs and grabbing that market share.
- blitzar 1y agoReinventing the exact same thing and shouting from the rooftops about it is exactly how you appease thirsty VCs and grab that market share.
- Flemlo 1y agoAnd plenty of other people. It's still a lot better to really learn and discover it yourself to really get it. Also it's hard to determine how much time someone spent on particular topic.
- Cthulhu_ 1y agoThis is Carmack, who builds new things. He built one of the first 3D game engines based on the hard math.
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- aatd86 1y ago[flagged]
- Flamentono2 1y agoIts hard to follow what you try to commounicate at least the last half. Nonetheless, yes we do know certain brain structures like your image net analogy but the way you describe it, sounds a little bit of. Our virtual cortex is not 'just a layer' its a component i would say and its optimized of detecting things. Other components act differently with different structures.
- epr 1y agoA bit confusing for sure, but I think (not sure) I get what they're saying. Training a nn (for visual tasks at least) consists of training a model with much more dimensions (params) than the input space (eg: controller inputs + atari pixels). This contrasts with a lot of what humans do, which is take higher dimensional information (tons of data per second combining visual, audio, touch/vibration, etc) and synthesizing much lower dimensional models / heuristics / rules of thumb, like the example they give of the 5 second per mile rule for thunder.
- abc-1 1y ago[flagged]
- johnb231 1y agoGames are heavily used in RL research.
- abc-1 1y agoI 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.
- johnb231 1y agoI 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.
- abc-1 1y agoRight, we have not even solved games in the non-real time environment, so why bother adding additional constraints like “on real hardware in real time”. This is exactly like Tesla trying to switch from LIDAR to cameras before self driving is even solved. It’s avoiding the real harder challenge and going off on inane tangents. John is essentially bike shedding. In this case, John is going off on this inane tangent because of his prior experience with hardware and video games instead of challenging himself to solve the actual hard and open problems. I’m going to predict how this plays out for the inevitable screenshot in one to two years. John picks some existing RL algo and optimizes it to run in real time on real hardware. While he’s doing this the field moves on to better and new algorithms and architectures. John finally achieves his goal and posts a vid of some (now ancient) RL algo playing some Atari game in real time. Everyone says “neat” and moves on. John gets to feel validated yet all his work is completely useless.
- brador 1y agoI 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.
- novosel 1y agoThere is no deep formal math in AI. It is a game of numbers. All deep formal math is a boundary to a thing.
- johnb231 1y agoThe formal math takes a few months to learn. He is more than smart enough to figure that out.
- kriro 1y agoI 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.
- horsellama 1y agothis. 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
- lyu07282 1y agoThis feels like an understatement. At the time, young me had the impression Carmack came first, then the industry created 3dfx/OpenGL to run his games better. I still have nothing but respect for his skills decades later.
- cmpxchg8b 1y agoGLQuake was released 11 months before Quake 2.
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- MrScruff 1y agoIt'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 online learning in realtime. Obviously makes for a cool demo and pulls on his optimisation background, and no doubt some great innovations will be required to make this work. But I guess the bitter lesson and recent history also tell us that some solutions may only emerge at compute levels beyond what is currently possible for realtime inference let alone learning. And the only example we have of entities solving Atari games is the human brain, of which we don't have a clear understanding of the compute capacity. In which case, why wouldn't it be better to focus purely on learning efficiency and relax the realtime requirement for now? That's a genuine question by the way, definitely not an expert here and I'm sure there's a bunch of value to working within these constraints. I mean, jumping spiders solve reasonably complex problems with 100k neurons, so who knows.
- suddenlybananas 1y agoIt's because humans (and other animals) have enormous innate capacities and knowledge which makes learning new things much much simpler than if you start from scratch. It's not really because of human's computational capacity.
- MrScruff 1y agoBy innate do you mean evolved/instinctive? Surely even evolved behaviour must be expressed as brain function, and therefore would need a brain capable of handling that level of processing. I don't think it's clear how much of a human brains function exists at birth though, I know it's theorised than even much of the sensory processing has to be learned.
- suddenlybananas 1y agoI'm not arguing against computational theory of mind, I'm just saying that innate behaviours don't require the same level of scale as learnt ones. Existing at birth is not the same thing as innate. Puberty is innate but it is not present at birth.
- saejox 1y agoWhat Carmack is doing is right. More people need to get away from training their models just with words. AI need the physicality.
- NL807 1y ago>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
- SeanaldMcDnld 1y agoYeah 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
- programd 1y agoNvidia seems to think the same thing. Here's Jim Fan talking about a "physical Turing test" and how embodied AI is the way forward. https://www.youtube.com/watch?v=_2NijXqBESI https://www.youtube.com/watch?v=_2NijXqBESI He also talks needing large amounts of compute to run the virtual environments where you'll be training embodied AI. Very much worth watching.
- johnb231 1y ago> More people need to get away from training their models just with words. They started doing that a couple of years ago. The frontier "language" models are natively multimodal, trained on audio, text, video, images. That is all in the same model, not separate models stitched together. The inputs are tokenized and mapped into a shared embedding space. Gemini, GPT-4o, Grok 3, Claude 3, Llama 4. These are all multimodal, not just "language models".
- timmg 1y ago(If you know) how does that work? Are the audio/video/images tokenized the same way as text and then fed in as a stream? Or is the training objective different than "predict next token"? If the former, do you think there are limitations to "stream of tokens"? Or is that essentially how humans work? (Like I think of our input as many-dimensional. But maybe it is compressed to a stream of tokens in part of our perception layer.)
- koolala 1y agoI wish he did this with VR environment instead like they mention at the start of the slides. A VR environment with a JPEG camera filter, physics sim, noise, robot simulation. If anyone could program that well its him. Using real life robots is going to be a huge bottleneck for training hours no matter what they do.
- qoez 1y agoInteresting reply from an openai insider: https://x.com/unixpickle/status/1925795730150527191 https://x.com/unixpickle/status/1925795730150527191
- andy_ppp 1y agoMy bet is on Carmack.
- speed_spread 1y agoI suspect Carmack in the Dancehall with the BFG.
- ramesh31 1y agoWhat 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.
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- alexey-salmin 1y agoEver since Romero departed the id Software had shipped *checks notes* Quake II, Quake III, Doom 3 and Quake 4. Funnily enough Romero himself didn't ship much either. IMO it's one of the most iconic "duo breakups". The whole is greater than the sum of the parts.
- johnb231 1y agoRage was Carmack's last big game at id Software before leaving. Romero is credited on 27 games since he left id Software. https://en.wikipedia.org/wiki/John_Romero#Games https://en.wikipedia.org/wiki/John_Romero#Games
- threeseed 1y agoDirect links: https://docs.google.com/presentation/d/1GmGe9ref1nxEX_ekDuJXhildpWGhLEYBMeXCclVECek/edit https://docs.google.com/presentation/d/1GmGe9ref1nxEX_ekDuJX... https://docs.google.com/document/d/1-Fqc6R6FdngRlxe9gi49PRvU97R83O7ZTN_KFXo_jf0/edit?tab=t.0#heading=h.628l6khl68xe https://docs.google.com/document/d/1-Fqc6R6FdngRlxe9gi49PRvU...
- roflcopter69 1y agoHonestly, having gone through the slides, it's a bit painful to see Carmack "rediscover" stuff I've learned in a reinforcement learning lecture like ten years ago. But don't get me wrong! Since this is a long-term research endeavor of his, I believe really starting from the basics is good for him and will empower him to bring something new to the table eventually. I'm surprised though that he "only" came so far as of now. Maybe my slight idolization of Carmack made me kinda of blind to the fact that this kind of research is a mean beast after all and there is a reason that huuuuge research labs dump countless of man-decades into this kind of stuff with no guaranteed breakthroughs.
- Cipater 1y agohttps://x.com/unixpickle/status/1925795730150527191 https://x.com/unixpickle/status/1925795730150527191
- roflcopter69 1y agoI was just going to answer https://news.ycombinator.com/item?id=44071595 https://news.ycombinator.com/item?id=44071595 who mentioned exactly the same tweet. I'm nowhere as good at my craft as someone who works for openai, which the author of that tweet seems to be, but if even I can see this, then it's bad, isn't it?
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- andy_ppp 1y agoI still don’t think we have a clear enough idea of what a concept is to be able to think about AGI. And then being able to use concepts from one area to translate into another area, what is the process by which the brain combines and abstracts ideas into something new?
- throw310822 1y agoKnown entities are recurring patterns (we give names to things that occur more than once, in the world or in our thoughts). Concepts are recurring thought patterns. Abstractions, relations, metaphors, are all ways of finding and transferring patterns from one domain to another.
- andy_ppp 1y agoSure, I understand what the terminology means but I don't believe we get to AGI without some ability to translate the learning of say using a mouse to using a trackpad in a simple way. Humans make these translations all the while, you know how to use a new room and the items in it automatically but I personally see the systems we have built are currently very brittle when they see new environments because they can't simplify everything to its fundamentals and then extrapolate back to more complex tasks. You could train a human on using an Android phone and give them an iPhone and they would do pretty well, if you did this with modern machine learning systems you will get an extremely high error rate. Or say you train an model on how to use a sword, I'm not convinced it would know how to use and ax or pair of crutches as a weapon. Maybe it will turn out to simply be enough artificial neurons and everything works. But I don't believe that.
- steveBK123 1y agoAnother thought experiment - if OpenAI AGI was right around the corner, why are they wasting time/money/energy buying a product-less vanity hardware startup run by Ive? Why not tackle robotics if anything. Or really just be the best AGI and everyone will be knocking on your door to license it in their hardware/software stacks, you will print infinite money.
- soared 1y agoOr have your AGI design products
- steveBK123 1y agoAll the more reason not to acquihire Ive for $6.5B, if true
- tiahura 1y agoDoes AGI necessarily mean super-genius? Was KITT AGI? I'm not sure he could design products?
- steveBK123 1y agoIs VC really funding a trillion dollars of GPU purchases to replace labor that could instead be bid out to developing world mechanical turks for $1/hr?
- trendoid 1y agoNo the term for that is ASI...artifical super intelligence. People in AI community have different timelines for that than AGI.
- j_timberlake 1y agoThis line of thought doesn't work, because any company approaching AGI might be actively trying to hide that information from regulators and the military. Being the 1st AGI company is actually pretty risky.
- Flamentono2 1y agoI find it interesting that he dismisses LLMs. I would argue that if he wants to do AGI through RL, a LLM could be a perfect teacher or oracle. After all i'm not walking around as a human and not having guidance. It should/could make RL a lot faster leveraging this. My logical part / RL part does need the 'database'/fact part and my facts are trying to be as logical as possible but its just not.
- akomtu 1y agoIMO, he's right. LLMs can't be AI because they don't create a model of observations to predict things, they just imitate observations based on their likeness to each other. When you play Quake, you create a simple model of the game physics and use that fast model to navigate the game. Your equivalent of LLM has a role too: it's a fuzzy detector of things you encounter in the game, sounds, images and symbols, but once detected, those things are fed into the fast and rigid physics model.
- Flamentono2 1y agoYes but the LLM could tell the physics system that it is physics related. Hey look you see a stone falling
- mkoubaa 1y ago> It is worth trying out one of the many web based reaction time testers – you will find that you average over 160 milliseconds. TIL JC has elite reflexes
- 2OEH8eoCRo0 1y agoAnd nerves of steel
- plussed_reader 1y ago[flagged]
- soci 1y ago> 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. The blender approach can still be world-changingly valuable, but there are plenty of people advancing the state of the art there. It's a shame that pretrained approach leads to such good enough result. The learning-from-experience, or what should be the "right" approach, will stagnate. I might be wrong, but it seems that aside from Carmack and a small team, "the world" is just not looking/investing on that side of the AI anymore. However, I find it funny that Carmack is now researching for such approach. At the end of the day, he was the one who invented Portals, an algorithm to circumvent the need to reproduce the whole 3D world and therefore making 3D games computationally possible. As a side note, I wonder what models are to come once we see the latest state of the art AI Video training technologies, in synch with the joystick movements from a real player. Maybe the results are so astonishing that even Carmack changes his mind on the subject. EDIT::grammar & typos
- anthonypasq 1y agoi think you're overstating this. Yann LeCun (chief scientist at Meta) is firmly in this camp, and i think most companies trying to bring AI into the real world via some sort of robotics technology are thinking about and testing this approach.
- soci 1y agoThank you. You are right, most likely the ones working in the field haven't switched. But the truth is that big bucks are in pretrained technologies. As Carmack himself said, "there are plenty of people advancing the state of the art there".
- koolala 1y agoHumans had 500 million * 8670 hours of Pre-Training. I don't get why Carmack would say things should be learned in hours or upper bounds it to human lifetime.
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- xnx 1y agoI'm surprised there's as much interest in looking at the structure/behavior of the biological brain, and less interest in considering the behavior of our vision system. Our brains are not CPUs, and our eyes are definitely not a grid of pixels with a fixed framerate.
- lostmsu 1y agoI'm with OpenAI folks on this one: Atari just won't cut it for AGI. My layman intuition is that RL works well when rewards give good signal all the time. Until it does RL is basically random search. That's where massive data diversity like we have in text comes in handy. In a game there might be a level with a door and a key, and because there's no reward for getting the key closer to the door, bridging this gap requires random search in a massive state space. But in the vast sea of scenarios that you can find in Common Crawl there's probably one, where you are 1 step from the key, and the key is 1 step from the door, so you get the reward signal from it without having to search an enormous state space. You might say "but you have to search through the giant Common Crawl". Well yes, but while doing so you will get reward signal not just for the key and door problem, but for nearly every problem in the world. The point is: pretraining teaches models to extract signal that can be used to explore solutions to hard search problems, and if you don't do that you are wasting your time enumerating giant state spaces.
- lostmsu 1y agoYou can actually easily test and overcome this by training a model simultaneously on a massive of text and Atari while carefully balancing learning rates between the two.
- ploden 1y agoWhy would AGI choose to be embodied? We talk about creating a superior intelligence and having it drive our cars and clean our homes. The scenario in Dan Simmons' Hyperion seems much more plausible: we invent AGI and it disappears into the cloud and largely ignores us.
- fusionadvocate 1y agoIt doesn't need to be permanent. If humans could escape from their embodiment temporarily they would certainly do so. Being permanently bounded to a physical interface is definitely a disadvantage.
- jwmcq 1y agoLooking at other examples in sci-fi, perhaps to stop my body from pressing its off-switch?
- ploden 1y agoWith distributed backups in place, AIs will be much less worried about self-preservation than we are.
- mlmranks 1y ago[dead]