There are two main ways to try and reach AGI:
Which one of them to take is of of the most important technological questions of humanity according to Ciro Santilli
There is also an intermediate area of research/engineering where people try to first simulate the robot and its world realistically, use the simulation for training, and then transfer the simulated training to real robots, see e.g.: realistic robotics simulation.
It doesn't need to be a bipedal robot. We can let Boston Dynamics worry about that walking balance crap.
It could very well intsead be on wheels like arm on tracks.
Or something more like a factory with arms on rails as per:
An arm with a hand and a camera are however indispensable of course!
Figure 1.
Algovivo demo
. github.com/juniorrojas/algovivo: A JavaScript + WebAssembly implementation of an energy-based formulation for soft-bodied virtual creatures.
Ciro Santilli wonders how far AI could go from a room with a bank account an Internet connection.
It would have to understand that it must keep its bank account high to buy power.
And it would start to learn about the world and interact with it to get more money.
Likely it would become a hacker and steal a bunch, that's likely the easiest appraoch.
In that scenario, Internet bandwidth would likely be its most precious resources, as that is how it would interact with the world to learn from it and make money.
Compute power and storage would come next as resources.
And of course, once it got to cloud computing, which might be immmediately and thus invalidate this experient, things would just go nuts more and more.
TODO: any simulation integration to it?
Video 1.
RoboCat by Google DeepMind (2023)
. Source.
Has anybody done this seriously? Given a supercomputer, what amazing human-like robot behaviour we can achieve?
Video 1.
Our Final Invention - Artificial General Intelligence by Sciencephile the AI (2023)
. Source. AGI via simulation section.
Ciro Santilli defines an "AI game" as:
a game that is used to train AI, in particular one that was designed with this use case in mind, and usually with the intent of achieving AGI, i.e. the game has to somehow represent a digital world with enough analogy to the real world so that the AGI algorithms developed there could also work on the real world
Most games played by AI historically so far as of 2020 have been AI for games designed for humans: Human game used for AI training.
Ciro Santilli took a stab at an AI game: Ciro's 2D reinforcement learning games, but he didn't sink too much/enough into that project.
A closely related and often overlapping category of simulations are artificial life simulations.
This section is about games initially designed for humans, but which ended up being used in AI development as well, e.g.:
Game AI is an artificial intelligence that plays a certain game.
It can be either developed for serious purposes (e.g. AGI development in AI games), or to make games for interesting for humans.
A good way to find labs is to go down the issues section of projects such as:and then stalk them to see where they are doing their PhDs.
Principal investigator: Simon M. Lucas.
Video 1.
AI in Melee is broken by Melee Moments (2023)
. Source.
TODO quick summary of game rules? Perhaps: battlecode.org/assets/files/battlecode-guide-xsquare.pdf
Some mechanics:
  • inter agent communication
  • compute power is limited by limiting Java bytecode count execution per bot per cycle
Video 1.
Battlecode Final Tournament 2023
. Source.
Video 2.
Introduction to Battlecode by MIT OpenCourseWare (2014)
. Source.
Ah, shame, they are a bit weak.
We define a "Procedural AI training game" as an AI training game in which parts of the game are made with procedural generation.
In more advanced cases, the generation itself can be done with AI. This is a possible Path to AGI which reduces the need for human intervention in meticulously crafting the AI game: AI training AI.
Video 1.
Nvidia's little fighter charater (2023)
. Source.
  • From Motor Control to Team Play in Simulated Humanoid Football
Video 1.
From Motor Control to Team Play in Simulated Humanoid Football by Ali Eslami (2023)
. Source. Likely a reupload by DeepMind employee: www.linkedin.com/in/smalieslami.
Video 2.
DeepMind’s AI Trained For 5 Years by Two Minute Papers (2023)
. Source. The 5 years bullshit is of course in-game time clickbait, they simulate 1000x faster than realtime.
We define this category as AI games in which agents are able to produce or consume natural language.
It dawned on Ciro Santilli that it would be very difficult to classify an agent as an AGI if tthat agent can't speak to take orders, read existing human generated documentation, explain what it is doing, or ask for clarification.
Video 1.
Human player test of DMLab-30 Select Described Object task by DeepMind (2018)
. Source. This is one of the games from DeepMind Lab.
Video 2.
WorldGPT by Nhan Tran (2023)
. Source. Not the most amazing demo, but it is a start.
Video 2.
Open-Ended Learning Leads to Generally Capable Agents by DeepMind (2021)
. Short name: XLand. Whitepaper: www.deepmind.com/blog/generally-capable-agents-emerge-from-open-ended-play.
github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 has some good games with video demos on YouTube, though for some weird reason they are unlistd.
TODO get one of the games running. Instructions: github.com/deepmind/lab/blob/master/docs/users/build.md. This may helpgithub.com/deepmind/lab/issues/242: "Complete installation script for Ubuntu 20.04".
It is interesting how much overlap some of those have with Ciro's 2D reinforcement learning games
The games are 3D, but most of them are purely flat, and the 3D is just a waste of resources.
Video 1.
Human player test of DMLab-30 Collect Good Objects task by DeepMind (2018)
. Source.
Video 2.
Human player test of DMLab-30 Exploit Deferred Effects task by DeepMind (2018)
. Source.
Video 3.
Human player test of DMLab-30 Select Described Object task by DeepMind (2018)
. Source. Some of their games involve language instructions from the use to determine the desired task, cool concept.
Video 4.
Human player test of DMLab-30 Fixed Large Map task by DeepMind (2018)
. Source. They also have some maps with more natural environments.
Very similar to gvgai, Julian Togelius actually called them out on that: DeepMind Lab2D vs gvgai.
TODO get running, publish demo videos on YouTube.
At twitter.com/togelius/status/1328404390114435072 called out on DeepMind Lab2D for not giving them credit on prior work!
This very much looks like like GVGAI which was first released in 2014, been used in dozens (maybe hundreds) of papers, and for which one of the original developers was Tom Schaul at DeepMind...
As seen from web.archive.org/web/20220331022932/http://gvgai.net/ though, DeepMind sponsored them at some point.
Or is real word data necessary, e.g. with robots?
Fundamental question related to Ciro's 2D reinforcement learning games.
Bibliography:
They seem to do some cool stuff.
They have also declined every one of Ciro Santilli's applications for software engineer jobs before any interview. Ciro always wondered what does it take to get an interview with them. Lilely a PhD? Oh well.
In the early days at least lots of gamedev experience was enough though: www.linkedin.com/in/charles-beattie-0695373/.
Figure 1.
AlphaGo Zero cheat sheet by David Foster (2017)
. Source.
www.quora.com/Which-chess-engine-would-be-stronger-Alpha-Zero-or-Stockfish-12/answer/Felix-Zaslavskiy explains that it beat Stockfish 8. But then Stockfish was developed further and would start to beat it. We know this because although AlphaZero was closed source, they released the trained artificial neural network, so it was possible to replay AlphaZero at its particular stage of training.
www.gvgai.net (dead as of 2023)
The project kind of died circa 2020 it seems, a shame. Likely they funding ran out. The domain is dead as of 2023, last archive from 2022: web.archive.org/web/20220331022932/http://gvgai.net/. Marks as funded by DeepMind. Researchers really should use university/GitHub domain names!
Similar goals to Ciro's 2D reinforcement learning games, but they were focusing mostly on discrete games.
They have some source at: github.com/GAIGResearch/GVGAI TODO review
From QMUL Game AI Research Group:From other universities:TODO check:
  • Ahmed Khalifa
  • Jialin Liu
This kind of died at some point checked as of 2023.
In 2019, OpenAI transitioned from non-profit to for-profit
so what's that point of "Open" in the name anymore??

Articles by others on the same topic (0)

There are currently no matching articles.