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OpenAI Universe
- minimaxir 10y agoInteresting announcement timing at 10:30 PM PST on a Sunday. :P The list of third-party gaming partners is extremely impressive, and a Docker config helps resolve the dependency hell that some of the AI packages require.
- mappingbabeljc 10y agoWe wanted people at NIPS in Barcelona to have something nice to read over their morning coffee and such. [I work at OpenAI - @jackclarksf on Twitter]
- Hydraulix989 10y agoWell, you just got a few extra followers.
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- CodinM 10y agoI'll just go on a limb and consider this to be fucking awesome.
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- shykes 10y agoThis is perhaps my favorite use of Docker ever.
- tlb 10y agoWe've been pushing hard on some parts of Docker, and it's working pretty well. For example, reconfiguring iptables depending on what game you ask for. And it works fine to test things like that on my MacBook and then deploy to Kubernetes. Amazing.
- grondilu 10y agoAll the listed PC games environments are tagged as "coming-soon" https://universe.openai.com/envs#pc_games https://universe.openai.com/envs#pc_games
- Hydraulix989 10y agoWhat is state of the art in reinforcement learning right now? https://arxiv.org/abs/1602.01783 https://arxiv.org/abs/1602.01783 Is there a way to deal with "sparse" training data (state, action, reward) triples -- sparse in "state"?
- visarga 10y agoThat paper was 10 months ago. There have been many RL papers in the meantime, but sparsity is only a problem with respect to reward, not state or action, from what I can see.
- Hydraulix989 10y agoYou didn't answer my question. :(
- sapphireblue 10y agoLooks like the "UNREAL" (https://arxiv.org/abs/1611.05397 https://arxiv.org/abs/1611.05397), "Learning to reinforcement learn" (https://arxiv.org/abs/1611.05763 https://arxiv.org/abs/1611.05763) and "RL^2" (https://arxiv.org/abs/1611.02779 https://arxiv.org/abs/1611.02779) are state of art in pure RL for now. Finally there is a trend of using recurrent neural network as a top component of the Q-network. Perhaps we will see even more sophisticated RNNs like DNC and Recurrent Entity Networks applied here. Also we'll see meta-reinforcement learning applied to a curriculum of environments.
- Hydraulix989 10y agoThe crazy thing is that these stacked model architectures are starting to become another layer of "lego blocks" so to speak.
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- NhanH 10y agoThis is a bit out there, but it would be fun if OpenAI can get one of the mega popular multiplayer games under this (WoW, League of Legends, DOTA etc.). Imagine an AI team in League of Legends world championship!
- conradev 10y agoICYMI, DeepMind partnered with Blizzard to do this for StarCraft II: https://deepmind.com/blog/deepmind-and-blizzard-release-starcraft-ii-ai-research-environment/ https://deepmind.com/blog/deepmind-and-blizzard-release-star...
- ipsum2 10y agoFacebook open sourced their library to connect Torch to Starcraft Brood War last week: https://github.com/TorchCraft/TorchCraft https://github.com/TorchCraft/TorchCraft
- elefanten 10y agoValve is already involved, I imagine we'll see Dota 2 support.
- CmdrSprinkles 10y agoWoW I would be very surprised by. But all the MOBAs and esport FPSes and the like are fair game. Mostly because, with the latter, improved AI means better competition and a deeper understanding (which boosts sales). With the former, improved AI means improved automation which means imbalanced economies.
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- aratno 10y agoI just hope no self-driving vehicle is applying anything learned in GTA.
- Fluid_Mechanics 10y agoWrite once, run over pedestrians.
- cpmsmith 10y agoI can't recall where, but I read that Tesla or Google were actually using GTA to train their self-driving cars, because it is a spectacularly advanced simulation of driving through an urban environment, so they didn't have to build their own.
- mappingbabeljc 10y agoThere was an interesting academic research paper that showed you could train in GTA and transfer over to the KITTI dataset and do ok: https://arxiv.org/abs/1610.01983 https://arxiv.org/abs/1610.01983
- posterboy 10y ago>spectacularly advanced drivint gran tourismo is advanced, for a videogame at least
- vinay427 10y agoGTA is Grand Theft Auto, not Gran Turismo.
- seasonedschemer 10y agoThat would be the Berkeley DeepDrive project. http://deepdrive.io/ http://deepdrive.io/ and http://bdd.berkeley.edu/ http://bdd.berkeley.edu/.
- cr4zy 10y agodeepdrive.io creator here - I'm actually not affiliated with the Berkeley project of the same name. There's also a DeepDriving at Princeton plus plenty of other (mostly perception) projects using GTAV, so it can be confusing. I'm hoping the GTAV for self-driving car efforts can start to standardize around the Universe integration though. Having worked on it, I can say firsthand that the Universe architecture is definitely amenable to sending radar, lidar, controlling the camera, bounding boxes, segmentation, and other types of info that the various sub-fields of self-driving are interested in. Super-excited to see how people use it!
- cing 10y agoEnd game; I'd really like an AI agent for "in real life" tabletop games (like boardgames).
- iampherocity 10y agoI call those friends.
- ctchocula 10y agoThere are hardcore boardgames you will find difficult to find human players willing to play with you. Campaign for North Africa takes 8-10 players and has an estimated playing time of 1000 hours [1]. An excerpt of a review written for this game: > Are you a logistics major? Are you masochistic? Do you think that the calculations required to play a game should take longer than actually moving the units? Then do I have a game for you! Get yourself a copy of The Campaign for North Africa, and say goodbye to the family for a couple of months, if not years. The Campaign for North Africa is the most detailed game that I have ever played. It isnt necessarily the most complicated, but for sheer size of the detail and planning involved, it is by far the most laborious and detail-oriented game that has ever been produced. As a first example, this is the only game that I know of that differentiates between British and German jerry cans for fuel. More about this later on. The Campaign for North Africa is Richard Berg and SPIs simulation of the war in North Africa in the Second World War. The seven foot long mapsheet (divided into five sections), two sets of rulebooks, charts and tables galore and, oh yes, thousands of counters complete the game in a nice sturdy box, not the usual SPI flat game holder that falls apart. Most of this is standard SPI fare, with the functional but not pretty counters, standard three column style SPI rulebooks, and a fairly attractive map that does an excellent job of creating an epic sense of scale. True, this is the desert, and most of it is desolate, but the numerous tracks and roads, the coastal plains and mountains, and the railroad (both already built and railroad you can build as the game goes on) all combine to present an appealing picture of the area. Each turn is one week of time, and each turn is broken down several stages. There is an initiative determination, naval convoy stage, stores expenditure stage, and then three operations stages. The Ops Stages are where most of the activity occurs. There are also stages that are used in the air game. I did not play the Air Game for the purpose of this review, but did play with the advanced logistics. The game also includes on of each type of chart, which can be used to make copies. I made my own in Excel. There are charts for Division and Brigade organization, truck convoy sheets, naval convoy sheets, prisoner sheets, broken down and destroyed vehicle sheets, supply dump sheets, sheets for the air game and more. I even created a couple of my own for production and independent units. As each Division in the game needs its own Org chart, which fit best on legal size paper, these are a lot of charts and sheets to keep track of. All of these must be filled out before the game even starts, and just setting up for the beginning of the game requires filling out hours (literally) of paperwork. And for heavens sake, dont use pen! Much of what you write in the charts at the beginning of the game will be erased by the end of the first turn. After every movement, every combat, just sitting there and doing nothing will require updating of the org charts for every unit in the game. [1] https://boardgamegeek.com/boardgame/4815/campaign-north-africa https://boardgamegeek.com/boardgame/4815/campaign-north-afri...
- jakozaur 10y agoBrowser tasks seems to be a greenfield field with amazing potential. What if AI can do anything what can human do you with a browser over the phone? Also love "bring your own Docker container format".
- afchavez40 10y agoWhich tasks do you think are the ones with more potential in this field?
- lucidrains 10y agofor some reason, the idea of autonomous bots crawling around the internet also unsettles me. i guess it really depends on what kind of rewards you train it for.
- tianlins 10y agoWell, if all GUI interactions can be automated, what would be our next human interface to computers/AIs?
- robryk 10y agoVoice in one direction and voice and graphics in the other?
- llSourcell 10y agoHey guys, it's Siraj. OpenAI asked me to make a promotional video for it on my Youtube channel and I gladly said yes! You can check it out here: https://www.youtube.com/watch?v=mGYU5t8MO7s https://www.youtube.com/watch?v=mGYU5t8MO7s
- hashin 10y agoWow wow, Amazing!
- moh_maya 10y agoThank you! This was very helpful for me :)
- hacker_9 10y agoNice video, but the jump from solving super simple 2d games, by feedback of binary win/lose conditions, to solving tasks in 3d open world simulations will require an un-imaginably gigantic leap in processing and knowledge. Additionally neural nets have already shown they are not sufficiently good enough at generalizing, and only work well at the specific tasks they were trained for. So the idea that an AI that can play GTA would also be able to 'solve' climate change is odd.
- altrus 10y agoI'm far from an expert, but I thought the poor generalized performance of neural nets was largely associated with the complexity of the network (number of neurons, etc), and the training data. Is there something more specific about the application of neural nets to generalized problems that makes them unsuitable?
- OliverD 10y agoAwesome Video! You just got yourself a new subscriber :)
- mhuffman 10y agoSiraj, Ima go ahead and say that your videos have a bit of cognitive friction. You and your personality are fine, but the jump between the intro and the payoff is jarring. You need to hold our hand a little more ... jus' sayin'.
- mulcahey 10y agoWith this platform (and Gym) it seems like a large part of their strategy for "democratizing AI" is to grow the amateur research community. By making it easier for an individual to play around and conduct experiments, they are hoping enable progress to emerge from anywhere instead of just from wealthy companies and elite universities. It is also a great way to be able to track and organize what is being created rather than having to sort through amateur projects scattered across the web or research publications that often lack accompanying code. Edit: Some key ways they're making it easier for amateurs: * Starting point for problems to solve * Way to get noticed (instead of needing a university/company brand) * Technological infrastructure for building and testing. The diversity of tools they brought together to build this platform is very impressive.
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- poppingtonic 10y agoThis is amazing! I was thinking of this problem when I saw a friend making a stop-motion video. The steps are super repetitive and I asked him, "maybe an DeepMind Atari-style RL agent can learn how to do this?" But I didn't want to do what DeepMind did to emulate Atari games with an Adobe editing tool. This is an experiment that I can now run.
- akrymski 10y agoHa! Fascinating - I had the exact same thought when I saw a friend of mine make stop motion videos!
- halflings 10y agoNever saw how stop-motion videos are edited. What's so repetitive? I thought that once you've taken all your pictures, you just put them sequentially and removed any frames that seemed off. Maybe you also need to decide how much time to leave every frame?
- poppingtonic 10y agoThis is true, but some are more complex, which is also due to the choice of tool. For example, [1], made by Chris King, using Adobe Premiere. This is a time-lapse of the process that he used to make parts of [2]. Notice the pattern that emerges when sequential images start lining up. [1] https://youtu.be/M7Hr83OI-rs https://youtu.be/M7Hr83OI-rs [2] https://youtu.be/1-rFV_d6RH8 https://youtu.be/1-rFV_d6RH8
- swah 10y agoLayman question: isn't adjusting "hyperparameters" similar to writing a algorithm for playing a game, using human intelligence? Related to the blog post: https://openai.com/blog/universe/ https://openai.com/blog/universe/
- tlb 10y agoIt depends how many hyperparameters there are. Many popular general-purpose ML algorithms have only a handful of numbers for hyperparameters, so they don't embody much human input. And they can sometimes be tuned automatically. Also, an algorithms that can learn 100s of different games with the same hyperparameters is more highly regarded than one that needs different hyperparameters for each.
- Cybiote 10y agoThis is astounding! If requests are being taken, it would be useful to be able to search through the listed environments. And a poker environ for the internet section would be a good balance of fun, widely appreciable and a straight forward but very non-trivial environment.
- Romajashi 10y agoyou'll lose your job and will be replaced by AI. Astounding?
- NamTaf 10y agoFrom my initial reading, the end user can't create environments? Is that a feature that I can expect will eventually come?
- lawless123 10y agoDoes that mean we can't train it on new games? only preexisting ones?
- NamTaf 10y agoIf it's true, I believe we have to wait for the OpenAI team to build new gym environments before we can train in new games. I only briefly poked around because it's nearing on midnight here - maybe you can pull open the examples included and work out how to rewire them to work on new games, maybe not. Either way, I've got a particular use case I'd like to make a gym for so I'm interested in finding out.
- mappingbabeljc 10y agoYou can create environments - it's coming! We'll be releasing many components over next few months.
- lawless123 10y agoBrilliant :)
- pveierland 10y agoIt looks like the image from the server and control information to the server is sent through the VNC protocol. Other information such as the reward signal from the environment server is sent through a WebSockets protocol using JSON: https://github.com/openai/universe/blob/master/doc/protocols.rst https://github.com/openai/universe/blob/master/doc/protocols... You should be able to implement this protocol for your environment and run a VNC server for the rest. A new class for the client representing your environment can be based on this: https://github.com/openai/universe/blob/master/universe/envs/vnc_gtav.py https://github.com/openai/universe/blob/master/universe/envs... Then register the class with OpenAI Gym: https://github.com/openai/universe/blob/master/universe/__init__.py#L1495 https://github.com/openai/universe/blob/master/universe/__in... After creating the environment using gym.make you need to add information about your remote in the call to configure: env = gym.make('gtav.SaneDriving-v0') env.configure(remotes="vnc://localhost:vnc_port+rewarder_port") https://github.com/openai/gym/blob/master/gym/core.py#L234 https://github.com/openai/gym/blob/master/gym/core.py#L234 https://github.com/openai/universe/blob/master/universe/envs/vnc_env.py#L137 https://github.com/openai/universe/blob/master/universe/envs... This is only based on a cursory reading, but it should be possible to use custom environments with OpenAI Universe as it is today.
- naveen99 10y agoToo bad Iphone doesn't support a vnc server. Would be nice to add some android apps if they could get permission.
- flaviojuvenal 10y agoRelated but slightly off-topic, there is a great sci-fi story by Ted Chiang (the same author who made the story behind Arrival film) about humans raising AIs in an artificial world. The premise is that if we want AIs to act like humans, we must teach them like we teach humans: http://subterraneanpress.com/magazine/fall_2010/fiction_the_lifecycle_of_software_objects_by_ted_chiang/ http://subterraneanpress.com/magazine/fall_2010/fiction_the_...
- noobermin 10y agoThe people who will end up raising AI's will not want them to act like humans. You already see it in their current uses. They create them to maximize profit. So, in a way, their owners (corporations) have created them in their image.
- visarga 10y agoRead it on your suggestion. It was pretty good.
- lowglow 10y agoAt Asteria we're using a Agent-System-Interface model, and are building models around observations of your own activity. I 100% agree we need to teach them like humans, so they at least can build a model of how humans interact and participate with one another. At the least this will teach them about us, more than it will teach them about anything else. And if we want to participate and collaborate with that future of AI we need to have these models.
- pj_mukh 10y agoI'm getting a weekly dose of Ted Chiang by just farming links to free stories people post on HN! :D Thanks!
- faragon 10y agoWhere is the source code?
- noobermin 10y ago>other applications Any applications with a keyboard and mouse? Can I use emacs and have it start learning to code?
- state_less 10y agoI'd love to see AI, using games, master the art of determining a depth for objects in the scene. If you ask a person, "about how far away is that car?", they often give you an okay answer that is at least in the same magnitude as the actual distance 1 m, 10 m, 100 m, 1000 m. If AI could do that, you could then navigate an environment in the real world better using only a camera or two. So you start with a virtual world that looks real, train up the bot, then use it to navigate in the real world. Has this already been accomplished?
- pakl 10y agoThat's a great (and hard) problem! More generally, imagine AI that could learn the physics of the world. For example, if the ball is rolling away, the AI should be able to predict that the ball will look smaller on the next frame. Going further, if the ball is about to roll under a shadow, the AI should predict that the ball will become a darker shade of green. (After several years working in a robotics research company, these kinds of capabilities are exactly what we determined would be necessary for robot AI.)
- state_less 10y agoAgree, it's not easy. Learning the basics, for example projecting a rectangle with 3d coordinates to 2d coordinates, then feeding the 2d coordinates into a NN and ask for the (depth) third dimension. Can you teach the NN a perspective transform? Can you rotate the rectangle and recognize rotation. Can you add other rectangles to the scene and detect each? Can you add color and lighting to infer more properties and get better results? Shine some more info on the problem ;) These are like unit tests of AI (basic shapes and transforms) and I agree physical reckoning is at the top, one of the big tests that is a capstone and something beautiful to behold in nature (eg. sports). Maybe the a virtual soccer game at the end? From my lidar experience, I wanted to reach for a model rather than deal with noisy sensor data. I want to generate the output (3d world) with my model, then the NN learns the inverse (eg. the scene graph used to generate the scene). I enjoy thinking about this stuff, though it really makes my head spiral sometimes when I relate it to my own reality. It's easy to feel like you're losing touch.
- BaronSamedi 10y agoUnless I missed something it looks like the AI has to learn from screen pixels instead of getting game state data. I don't like that approach at all. I understand that it's easy to implement for OpenAI but I think having the game developers provide a real bot-capable API is much better. I hope the latter is what Blizzard will provide for their DeepMind collaboration.
- mtgx 10y agoSeems unlikely. The focus seems to be on improving AI through "vision". The idea is to make the AI learn skills the same way a human would (at least in the first years of life). Google's AlphaGo also learned from screen pixels. So these would be human-like bots, rather than bot-like bots, like you normally have in games. The bot would simply learn by doing, until it masters the game, not by getting access to game algorithms.
- SamBam 10y ago> Google's AlphaGo also learned from screen pixels. Source? That literally seems to make zero sense to me. Go can be represented in a super-simple state. Why make it spend millions of cycles learning to categorize pixels into that state you already have?
- tiler 10y agoI would guess that they trained AlphaGo from many thousands of hours of match footage. Writing a computer vision script to segment / extract the data may cost cycles as you say, but would save many human hours by eliminating the need to re-watch the footage and literally type out state information for each move.
- ironrabbit 10y agoAlphago was actually trained directly on game state (plus some extra computed state like "how many liberties will I have if I play this move" or "will I win this ladder"). A huge number of pro games (and countless amateur games) are available on servers like KGS in a nice computer-digestible format.
- mrfusion 10y agoIs the users ai responsible for parsing the screen pixels that come back or does each game give you relevant events?
- d_burfoot 10y agoDisclaimers: I cannot see the future. These are just my opinions. I really appreciate the work and money that SamA, Elon, and others have put into the OpenAI project. The Universe work in particular might help encourage young people, many of whom love video games, to study AI. But I feel that contrarians, such as myself, have an ethical commitment to young people to voice our doubts and criticisms, so that they can avoid making a long journey down a career/research path that leads to a dead end. That being said, I think this project leads in a very unpromising direction. Here are some reasons: 1. Games aren't a good testbed for studying intelligence. In a game the main challenge is to map an input percept to an output action (am I drifting off the side of the road? Okay swerve right). The real challenge of intelligence is to find hidden abstractions and patterns in large quantities of mostly undifferentiated data (language, vision, and science all share this goal). 2. This platform is not going to help "democratize" AI. To succeed in one of these domains, contestants will need to use VAST amounts of computing power to simulate many games and to train their DL and/or RL algos. DeepMind and others will sufficient CPU/GPU power will almost certainly dominate in all of these settings. 3. Deep Learning, as it is practiced, isn't intellectually deep. With a few exceptions, there is nothing comparable to the great discoveries of physics, not even anything comparable to the big ideas of previous AI work (A*, belief propagation, VC theory, MaxEnt, boosting, etc). Progress in DL mostly comes from architecture hacking: tweak the network setup, run the training algo, and see if we get a better result. The apparent success of DL doesn't depend on any special scientific insight, but on the fact that DL algos can run on the GPU. That, combined with the fact that, except for the GPU, Moore's Law broke down roughly 10 years ago, means that relative to everything else, DL looks amazingly successful - because all other approaches to AI are frozen in time in terms of computing power.
- state_less 10y ago1. Games are great, especially the closer they get to the real 3d worlds with all the basic visual transforms in play. You can generate training data cheaper that helps you bootstrap the AI. 2. Your argument, which boils down to large organizations can accomplish more than individuals is in general true. But then that isn't saying anything new. Still, I'd prefer the car company give its blueprints than not. My factory (my cpu) can then at least build the car, albeit at a smaller scale. And soon my factory will be bigger/cheaper. Yeah I know, I'd like to be wealthy like Google too. 3. This is your own value judgement. The insight that we simplify AI architecture to some addition and multiplication is a big idea - in my opinion. Transistors are getting cheaper and I believe they will continue to do so. DNN are better suited to take advantage of this new computing power. Turns out, all those great discoveries were just some multiplications and adds the whole time ;)
- TPCrow 10y agojesus man the constant switching of inflection due to jump cuts and shitty jokes was irritating. This is the stuff I always hate about your videos, and in general any "how to code an AI that does ___ with only < 10 lines of python". No you're giving me an extremely generic framework, and some vague direction that i can read off the linked page (the 9 lines), not how to actually write all these things, those are like afterthoughts in the video, more work goes into the click-baity titles and thumbnails then actual substantive information.
- grosbisou 10y agoI agree with you. You are most likely being downvoted by his fans. But this video (and all his others apparently) is empty of substance.
- grzm 10y agoFrom my experience HN users will down vote if a comment comes across as uncivil, as at least three commenters have already noted. How you say it matters on HN.
- jessriedel 10y agoYou could have made these criticisms without the hugely inflammatory language.
- louprado 10y agoMaking a technical video for a general audience is real challenge. This video succeeds in that regard. The audio cuts are likely because he had a deadline that didn't allow him to completely re-record the audio. I appreciate he inserted clips that added clarity despite knowing that he'd get negative comments for that effort. Thanks for creating this video and sharing.
- gleglegle 10y agoAgreed, I'd rather have the condensed information the video provided today than to wait for a more refined version.
- jclay 10y agoI noticed the OpenAI team wrote their own VNC driver in Go for performance reasons[0]. I would love to hear more about how they were able to achieve increased performance over other VNC drivers. [0]https://github.com/openai/go-vncdriver https://github.com/openai/go-vncdriver
- gdb 10y agoWe wrote it for somewhat subtle reasons. First, there aren't too many alternatives out there — VNC is meant for human consumption, not for bots after all :). Second, for a single connection, once you're using Tight encoding, the bottleneck becomes server-side encoding and libjpeg-turbo, neither of which will depend on your driver. As you scale to many connections, the important thing becomes managing the parallelism well. Go is great for this. We'd started by adapting an existing Python driver in Twisted, implementing additional encodings and offloading to threads for calls into C libraries like zlib. We got this working reasonably on small environments like Atari, but for environments which generated many update rectangles, we started to be bitten by the GIL. I still believe that one could make Python work, but it'd take quite a lot of effort. libvncserver is a fast C driver, but it's GPL, and doesn't have any particular support for parallelization. We wanted Universe to be usable by everyone, from hobbyists to companies, so GPL was a no-go. (We actually talked to the libvncserver maintainers, who said that they would be interested in dropping GPL restriction, but there have been far too many contributors over its long history to figure out how to do so.) Our Go driver, based on https://github.com/mitchellh/go-vnc https://github.com/mitchellh/go-vnc, has scaled quite well. It takes advantage of Go's lightweight thread model: each connection runs in its own goroutine, which makes it easy to run hundreds of connections in parallel without needing hundreds of threads.
- mariusz79 10y agoI might be wrong but I think this was created mainly to monitor progress in AI research. If someone uses OpenAI Universe and can get better results than virtually everyone else, they will be able to get to them first.
- mrfusion 10y agoKind of like seti for ai if you think about it. I wonder if they have a protocol for what to do if they detect an advanced ai on their system?
- daveloyall 10y agoDidn't we all agree to NOT let the AGI out of its box? ...That being said... Instead of presenting the agent with a 2d plane of pixels, they should be presented with a sphere of pixels, with their POV inside.
- soared 10y agohttp://reddit.com/r/WatchMachinesLearn http://reddit.com/r/WatchMachinesLearn is about to get a lot more popular. I can't wait. Also from the linked blog post, you can play with (against?) your agent in realtime: >You can keep your own VNC connection open, and watch the agent play, or even use the keyboard and mouse alongside the agent in a human/agent co-op mode.
- iotb 10y agoDoes OpenAI Universe communicate in any way with OpenAI remotely regarding activity in OpenAI Universe? Essentially, are there any call-home aspects to the code base? Or, is it possible to run this locally without any outside communication? If there is remote communication, can you detail why and where it exists in code?
- gdb 10y agoWe don't call home. Once you've downloaded the Docker container, the only outbound network traffic should be downloading the requested SWF once for Flash games on demand (or for actually playing the game online, in the case of e.g. Slither). You can cache the SWF if you don't want it to be downloaded each time you start a new container. Other than SWF downloading or specific Internet-enabled environments, running offline should just work.
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- dcslack 10y agoSome designers from Stripe absolutely helped with the design of this page.
- thallukrish 10y agoBeing able to "Infer" from what it learns and "Apply" it to new scenarios in a general way is all about intelligence. I do not see how making it to win one game or one million will move it towards achieving general intelligence of this sort.