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Given enough computational power per dollar, AGI is inevitable, but it is not sure certain ever happen given the end of end of Moore's Law.
Alternatively, it could also be achieved genetically modified biological brains + brain in a vat.
Imagine a brain the size of a building, perfectly engineered to solve certain engineering problems, and giving hints to human operators + taking feedback from cameras and audio attached to the operators.
This likely implies transhumanism, and mind uploading.
Ciro Santilli joined the silicon industry at one point to help increase our computational capacity and reach AGI.
Ciro believes that the easiest route to full AI, if any, could involve Ciro's 2D reinforcement learning games.
Ciro Santilli has felt that perhaps what is missing in 2020's AGI research is:
  • the interface between:
    The key question is somewhat how to extract symbols out of the space-time continuous experiences.
  • more specialized accelerators that somehow interface with more generic artificial neural networks. Notably some kind of speialized processing of spacial elements is obviously hardcoded into the brain, see e.g. Section "Grid cell (2005)"
Forcing these boundaries to be tested was one of the main design goals of Ciro's 2D reinforcement learning games.
In those games, for example:
  • when you press a button here, a door opens somewhere far away
  • when you touch certain types of objects, a chemical reaction may happen, but not other types of objects
Therefore, those continuous objects would also have "magic" effects that could not be explained by "simple" "what is touching what" ideas.
This point is beautifully argued in lots of different sources, and is clearly a pillar of AGI.
Perhaps one may argue that our deep learning layers do form some kind of hierarchy, e.g. this is very clear in certain models such as convolutional neural network. But many of those models cannot have arbitrarily deep hierarchies, which appears to be a fundamental aspect of intelligence.
How to Create a Mind:
The lists of steps in my mind are organized in hierarchies. I follow a routine procedure before going to sleep. The first step is to brush my teeth. But this action is in turn broken into a smaller series of steps, the first of which is to put toothpaste on the toothbrush. That step in turn is made up of yet smaller steps, such as finding the toothpaste, removing the cap, and so on. The step of finding the toothpaste also has steps, the first of which is to open the bathroom cabinet. That step in turn requires steps, the first of which is to grab the outside of the cabinet door. This nesting actually continues down to a very fine grain of movements, so that there are literally thousands of little actions constituting my nighttime routine. Although I may have difficulty remembering details of a walk I took just a few hours ago, I have no difficulty recalling all of these many steps in preparing for bed - so much so that I am able to think about other things while I go through these procedures. It is important to point out that this list is not stored as one long list of thousands of steps - rather, each of our routine procedures is remembered as an elaborate hierarchy of nested activities.
Human Compatible: TODO get exact quote. It was something along: life goal: save world from hunger. Subgoal: apply for some grant. Sub-sub-goal: eat, sleep, take shower. Sub-sub-sub-goal: move muscles to get me to table and open a can.
Due to the failures of earlier generations, which believed that would quickly achieve AGI, leading to the AI winters, 21st researchers have been very afraid of even trying it, rather going only for smaller subste problems like better neural network designs, at the risk of being considered a crank.
While there is fundamental value in such subset problems, the general view to the final goal is also very important, we will likely never reach AI without it.
This is voiced for example in Superintelligence by Nick Bostrom (2014) section "Opinions about the future of machine intelligence" which in turn quotes Nils Nilsson:
There may, however, be a residual cultural effect on the AI community of its earlier history that makes many mainstream researchers reluctant to align themselves with over-grand ambition. Thus Nils Nilsson, one of the old-timers in the field, complains that his present-day colleagues lack the boldness of spirit that propelled the pioneers of his own generation:
Concern for "respectability" has had, I think, a stultifying effect on some AI researchers. I hear them saying things like, "AI used to be criticized for its flossiness. Now that we have made solid progress, let us not risk losing our respectability." One result of this conservatism has been increased concentration on "weak AI" - the variety devoted to providing aids to human thought - and away from "strong AI" - the variety that attempts to mechanize human-level intelligence
Nilsson’s sentiment has been echoed by several others of the founders, including Marvin Minsky, John McCarthy, and Patrick Winston.
Don't be a pussy, AI researchers!!!
It is hard to overstate how low the level of this conference seems to be at first sight. Truly sad.
It is a bit hard to decide if those people are serious or not. Sometimes it feels scammy, but sometimes it feels fun and right!
Particularly concerning is the fact that they are not a not-for-profit entity, and it is hard to understand how they might make money.
Charles Simon, the founder, is pretty focused in how natural neurons work vs artificial neural network models. He has some good explanations of that, and one major focus of the project is their semi open source spiking neuron simulator BrainSimII. While Ciro Santilli believe sthat there might be insight in that, he also has doubts if certain modules of the brain wouldn't be more suitable coded direclty in regular programming languages with greater ease and performance.
FutureAI appears to be Charles' retirement for fun project, he is likely independenty wealthy. Well done.
Video 1. Creativity and AGI by Charles Simon's at AGI-22 (2022) Source. Sounds OK!
Video 2. Machine Learning Is Not Like Your Brain by Future AI (2022) Source. Contains some BrainSimII demos.
The video from shows a demo of the possibly non open source version. They have a GUI neuron viewer and editor, which is kind of cool.
Video 3. Machine Learning Is Not Like Your Brain by Charles Simon (2022) Source.
Not having a manipulator claw is a major issue with this one.
But they also have a co-simulation focus, which is a bit of a win.
Basically it looks like the dude got enough money after selling some companies, and now he's doing cooler stuff without much need of money. Not bad. What do my fellow AI researchers think of Ben Goertzel and his research?
Ben Goertzel's fog computing project to try and help achieve AGI.
That's Ciro Santilli's favorite. Of course, there is a huge difference between physical and non physical jobs. But one could start with replacing desk jobs!
Original paper: Section "GAN paper".
The GAN paper itself does a bit of this, cool hello world:
Generative adversarial network illustrates well AI brittleness. The input looks obvious for a human, but gets completely misclassified by a deep learning agent.
This is going to be the most important application of generative AI. Especially if we ever achieve good text-to-video.
Image generators plus human ranking: Heavenly Bodies Part 1: Sister's Mary First Act. Pornhub title: "AI generated Hentai Story: Sexy Nun alternative World(Isekai) Stable Diffusion" Interesting concept, slide-narrated over visual novel. The question is how they managed to keep face consistency across images.
This was the Holy Grail as of 2023, when text-to-image started to really take off, but text-to-video was miles behind.
As highlighted e.g. at Human Compatible by Stuart J. Russell (2019), this AI alignment intrisically linked to the idea of utility in economy.
Basically ensuring that good AI alignment allows us to survive the singularity.
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!
Has anybody done this seriously? Given a supercomputer, what amazing human-like robot behaviour we can achieve?
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, notably:
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.
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.
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.
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 4. Creating Multimodal Interactive Agents from DeepMind by Two Minute Papers (2023) Source. 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: This may "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 5. Human player test of DMLab-30 Collect Good Objects task by DeepMind (2018) Source.
Video 6. Human player test of DMLab-30 Exploit Deferred Effects task by DeepMind (2018) Source.
Video 7. 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 8. 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.
Figure 1. Source.
At 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 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.
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:
Figure 2. AlphaGo Zero cheat sheet by David Foster (2017) Source. 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. (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: 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: TODO review
From QMUL Game AI Research Group:
From other universities:
TODO check:
  • Ahmed Khalifa
  • Jialin Liu
In 2019, OpenAI transitioned from non-profit to for-profit
so what's that point of "Open" in the name anymore??
The rise of powerful AI will either be the best or the worst thing ever to happen to humanity. We do not yet know which.
The key takeaway is that setting an explicit value function to an AGI entity is a good way to destroy the world due to poor AI alignment. We are more likely to not destroy by creating an AI whose goals is to "do want humans what it to do", but in a way that it does not know before hand what it is that humans want, and it has to learn from them. This approach appears to be known as reward modeling.
Some other cool ideas:
  • a big thing that is missing for AGI in the 2010's is some kind of more hierarchical representation of the continuous input data of the world, e.g.:
    • intelligence is hierarchical
    • we can group continuous things into higher objects, e.g. all these pixels I'm seeing in front of me are a computer. So I treat all of them as a single object in my mind.
  • game theory can be seen as part of artificial intelligence that deals with scenarios where multiple intelligent agents are involved
  • probability plays a crucial role in our everyday living, even though we don't think too much about it every explicitly. He gives a very good example of the cost/risk tradeoffs of planning to the airport to catch a plane. E.g.:
    • should you leave 2 days in advance to be sure you'll get there?
    • should you pay an armed escort to make sure you are not attacked in the way?
  • economy, and notably the study of the utility, is intrinsically linked to AI alignment
Good points: