Ciro Santilli
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artificial-intelligence.bigb
= Artificial intelligence
{wiki}
= AI
{c}
{synonym}
{title2}
= AI by capability
{c}
{parent=Artificial intelligence}
= Artificial general intelligence
{parent=AI by capability}
{wiki}
= AGI
{c}
{synonym}
{title2}
Given enough computational power per dollar, AGI is inevitable, but it is not sure certain ever happen given the end of <Moore's law>[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>.
= Principles of AGI
{parent=Artificial general intelligence}
= The missing link between continuous and discrete AI
{parent=Principles of AGI}
<Ciro Santilli> has felt that perhaps what is missing in 2020's <AGI> research is:
* the interface between:
* the continuous/noisy level (now well developed under <artificial neural network> techniques of the 2010's)
* and <symbolic AI> level AI
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. <grid cell>{full}
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.
Bibliography:
* https://mitpress.mit.edu/9780262632683/the-algebraic-mind/
= Intelligence is hierarchical
{parent=Principles of AGI}
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>:
\Q[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.
= AGI architecture
{c}
{parent=Principles of AGI}
\Video[https://youtu.be/pd0JmT6rYcI?t=3536]
{title=From Machine Learning to Autonomous Intelligence by <Yann LeCun> (2023)}
{description=
After a bunch of B.S., LeCun goes on to describe his AGI architecture. Nothing ground breaking, but not bad either.
* https://youtu.be/pd0JmT6rYcI?t=3705[]: <intelligence is hierarchical>
}
= Elements of AGI
{c}
{parent=AGI architecture}
This section is about ideas that are thought to be part of an AGI system.
= Common sense
{parent=Elements of AGI}
{wiki}
\Video[https://www.youtube.com/watch?v=49t-WWTx0RQ]
{title=My Job is to Open and Close Doors by Mattias Pilhede (2019)}
{description=An interesting humourous short meditation on <common sense>.}
= Instrumental goal
{c}
{parent=Elements of AGI}
= Instrumental convergence
{c}
{parent=Instrumental goal}
{wiki}
= AGI research
{c}
{parent=Artificial general intelligence}
= History of AGI research
{c}
{parent=AGI research}
= AGI blues
{c}
{parent=History of AGI research}
Term invented by <Ciro Santilli>, similar to "<nuclear blues>", and used to describe the feeling that every little shitty job you are doing (that does not considerably help achieving <AGI>) is completely pointless given that we are likely close to <AGI> as of 2023.
= Moravec's paradox
{c}
{parent=History of AGI research}
{title2=1980s}
= AI winter
{c}
{parent=History of AGI research}
{wiki}
= AGI research has become a taboo in the early 21st century
{c}
{parent=History of AGI research}
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 (person)>.
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:
\Q[
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:
\Q[
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!!!
= AGI conference
{c}
{parent=AGI research}
https://www.agi-conference.org/
It is hard to overstate how low the level of this conference seems to be at first sight. <AGI research has become a taboo in the early 21st century>[Truly sad].
= Journal of Artificial General Intelligence
{c}
{parent=AGI research}
https://sciendo.com/journal/JAGI
= AGI research entity
{c}
{parent=AGI research}
{tag=AI research entity}
* https://www.quora.com/What-are-some-good-research-schools-PhD-for-Artificial-General-Intelligence-not-Machine-Learning/answer/Ciro-Santilli What are some good research schools (PhD) for Artificial General Intelligence (not Machine Learning)?
* 2020 https://towardsdatascience.com/four-ai-companies-on-the-bleeding-edge-of-artificial-general-intelligence-b17227a0b64a Top 4 AI companies leading in the race towards Artificial General Intelligence
* Douglas Hofstadter according to https://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/ The Man Who Would Teach Machines to Think (2013) by <James Somers>
* Pei Wang from Temple University: https://cis.temple.edu/~wangp/
= Astera Institute
{c}
{parent=AGI research entity}
https://astera.org/agi/
By the rich founder of <Mt. Gox> and Ripple, <Jed McCaleb>.
\Q[Obelisk is the Artificial General Intelligence laboratory at Astera. We are focused on the following problems: How does an agent continuously adapt to a changing environment and incorporate new information? In a complicated stochastic environment with sparse rewards, how does an agent associate rewards with the correct set of actions that led to those rewards? How does higher level planning arise?]
= Astera Institute person
{c}
{parent=Astera Institute}
= Michael Nielsen
{c}
{parent=Astera Institute person}
Interesting dude, with some interest overlaps with <Ciro Santilli>, like <Quantum Computing>:
* https://github.com/mnielsen
* https://michaelnielsen.org/
* https://twitter.com/michael_nielsen
* https://www.youtube.com/c/michaelnielsen
= GoodAI
{c}
{parent=AGI research entity}
<Marek Rosa>'s play thing.
= AI People
{c}
{parent=GoodAI}
{tag=AI game with natural language}
{title2=2023}
\Video[https://www.youtube.com/watch?v=xkn0H_iWDEQ]
{title=AI Game - LLM-driven NPCs that can talk by Marek Rosa (2023)}
{description=Not the most amazing demo, but the idea is there. Seems to be a preview for <AI People>. The previous working title seems to have been AI Odyssey.}
= Marek Rosa
{c}
{parent=GoodAI}
{wiki}
= FutureAI
{c}
{parent=AGI research entity}
= Future AI
{c}
{synonym}
{title2}
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.
* https://www.aitimejournal.com/interview-with-charles-simon-ceo-and-founder-futureai
* 2022 raised 2 million USD:
* https://www.prnewswire.com/news-releases/ai-futureai-raises-2-million-to-develop-artificial-general-intelligence-301459164.html
\Video[https://www.youtube.com/watch?v=ivbGbSx0K8k]
{title=Creativity and AGI by <Charles Simon>'s at AGI-22 (2022)}
{description=
Sounds OK!
* https://youtu.be/ivbGbSx0K8k?t=856 general structure of the <human brain> 86B total, matching <number of neurons in the human brain>, with:
* 14B: brainstem
* 16B: neocortex
* 56B: cerebelum
* https://www.youtube.com/watch?t=1433 some sequencing ideas/conjectures
}
\Video[https://www.youtube.com/watch?v=KQP1gPTk0FI]
{title=Machine Learning Is Not Like Your Brain by <Future AI> (2022)}
{description=Contains some <BrainSimII> demos.}
= BrainSimII
{c}
{parent=FutureAI}
{tag=Neuron simulator}
https://github.com/FutureAIGuru/BrainSimII
The video from https://futureai.guru/technologies/brian-simulator-ii-open-source-agi-toolkit/ shows a demo of the possibly non open source version. They have a <GUI> neuron viewer and editor, which is kind of cool.
\Video[https://www.youtube.com/watch?v=KQP1gPTk0FI]
{title=Machine Learning Is Not Like Your Brain by <Charles Simon> (2022)}
= Sallie
{c}
{disambiguate=FutureAI}
{parent=FutureAI}
{tag=AI training robot}
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.
= Charles Simon
{c}
{parent=FutureAi}
* https://www.linkedin.com/in/charles-simon-futureai/
* https://futureai.guru/about/the-team/
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.
= AGI software
{c}
{parent=Artificial general intelligence}
= Artificial general intelligence software
{synonym}
{title2}
* https://ai.stackexchange.com/questions/5428/how-can-people-contribute-to-agi-research mentions:
* https://github.com/opennars/opennars
* https://github.com/brohrer/robot-brain-project
= OpenCog
{c}
{parent=AGI software}
{wiki}
= Ben Goertzel
{c}
{parent=OpenCog}
{tag=AGI research entity}
{wiki}
https://www.reddit.com/r/artificial/comments/b38hbk/what_do_my_fellow_ai_researchers_think_of_ben/ What do my fellow AI researchers think of Ben Goertzel and his research?
= SingularityNET
{c}
{parent=Ben Goertzel}
{wiki}
https://singularitynet.io/
<Ben Goertzel>'s <fog computing> project to try and help achieve <AGI>.
= NuNET
{c}
{parent=SingularityNET}
{tag=Fog computing}
= AGI-complete
{c}
{parent=Artificial general intelligence}
{tag=Complexity class}
{wiki}
= AI-complete
{synonym}
Term invented by <Ciro Santilli> to refer to problems that can only be solved once we have <AGI>.
It is somewhat of a flawed analogy to <NP-complete>.
= AGI test
{c}
{parent=Artificial general intelligence}
{wiki=https://en.wikipedia.org/w/index.php?title=Artificial_general_intelligence&oldid=1192191193#Tests_for_human-level_AGI}
= CAPTCHA
{c}
{parent=AGI test}
{wiki}
= reCAPTCHA
{c}
{parent=CAPTCHA}
{wiki}
= Turing test
{c}
{parent=AGI test}
{wiki}
= The Employment Test
{c}
{parent=AGI test}
{wiki}
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!
= Automated theorem proving
{parent=AI by capability}
<AGI-complete> in general? Obviously. But still, a lot can be done. See e.g.:
* <The Busy Beaver Challenge> deciders
= Generative AI
{parent=AI by capability}
{wiki=Generative_artificial_intelligence}
= GenAI
{c}
{synonym}
{title2}
= Generative adversarial network
{parent=Generative AI}
{title2=GAN}
{wiki}
Original paper: <GAN paper>{full}.
= GAN paper
{parent=Generative adversarial network}
https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
= GAN MNIST hello world
{parent=Generative adversarial network}
The <GAN paper> itself does a bit of this, cool hello world:
* https://github.com/lyeoni/pytorch-mnist-GAN
= AI brittleness and robustness
{c}
{parent=Generative adversarial network}
= AI robustness
{c}
{parent=AI brittleness and robustness}
= AI brittleness
{c}
{parent=AI brittleness and robustness}
= Brittleness in AI
{synonym}
<Generative adversarial network> illustrates well <AI brittleness>. The input looks obvious for a human, but gets completely misclassified by a <deep learning> agent.
= Adversarial machine learning
{parent=AI brittleness}
{wiki}
= AI generated porn
{c}
{parent=Generative AI}
{wiki=Generative_artificial_intelligence}
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:
* https://pornpen.ai/ a bit too restrictive. Girl laying down. Girl sitting. Penis or no penis. But realtively good at it
* https://civitai.tv/[]. How to reach it: https://civitai.tv/tag/nun/2/
https://www.pornhub.com/view_video.php?viewkey=ph63c71351edece[]: 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.
= Generative AI by modality
{parent=Generative AI}
= Text-to-text model
{parent=Generative AI by modality}
{wiki}
= Machine translation
{parent=Text-to-text model}
{wiki}
= Open source machine translation
{parent=Text-to-text model}
{wiki}
https://askubuntu.com/questions/380847/is-it-possible-to-translate-words-via-terminal/1309774#1309774
= OpenNMT
{c}
{parent=Open source machine translation}
= Argos Translate
{c}
{parent=OpenNMT}
{tag=CLI tool}
<OpenNMT> <CLI> front-end.
Hello world: https://askubuntu.com/questions/380847/is-it-possible-to-translate-words-via-terminal/1309774#1309774
= Large language model
{parent=Text-to-text model}
{wiki}
= LLM
{c}
{synonym}
{title2}
= LLM game
{c}
{parent=Large language model}
* 2023 https://vimalabs.github.io/ VIMA: General Robot Manipulation with Multimodal Prompts
= Stanford Smallville
{c}
{parent=LLM game}
{title2=2023}
https://github.com/joonspk-research/generative_agents
Published as: https://arxiv.org/pdf/2304.03442.pdf Generative Agents: Interactive Simulacra of Human Behavior by Park et al.
\Video[https://www.youtube.com/watch?v=nWBEMjAoA14]
{title=AI Agents Behaving Like Humans by Prompt Engineering (2023)}
= Open source LLM
{parent=Large language model}
{tag=Open source software}
= Ollama
{c}
{parent=Open source LLM}
https://github.com/jmorganca/ollama
Highly automated wrapper for various <open source> <LLMs>.
``
curl https://ollama.ai/install.sh | sh
ollama run llama2
``
And bang, a download later, you get a prompt. On <Ciro Santilli's Hardware/p14s> it runs on <CPU> and generates a few tokens at a time, which is quite usable for a quick interactive play.
As mentioned at https://github.com/jmorganca/ollama/blob/0174665d0e7dcdd8c60390ab2dd07155ef84eb3f/docs/faq.md the downloads to under `/usr/share/ollama/.ollama/models/` and <ncdu> tells me:
``
--- /usr/share/ollama ----------------------------------
3.6 GiB [###########################] /.ollama
4.0 KiB [ ] .bashrc
4.0 KiB [ ] .profile
4.0 KiB [ ] .bash_logout
``
We can also do it non-interactively with:
``
/bin/time ollama run llama2 'What is quantum field theory?'
``
which gave me:
``
0.13user 0.17system 2:06.32elapsed 0%CPU (0avgtext+0avgdata 17280maxresident)k
0inputs+0outputs (0major+2203minor)pagefaults 0swaps
``
but note that there is a random seed that affects each run by default.
Some other quick benchmarks from <Amazon EC2 GPU>, on <Nvidia T4>:
``
0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swaps
``
On <Nvidia A10G>:
``
0.03user 0.05system 0:09.59elapsed 0%CPU (0avgtext+0avgdata 17312maxresident)k
8inputs+0outputs (1major+1934minor)pagefaults 0swaps
``
So it's not too bad, a small article in 10s.
It tends to babble quite a lot by default, but eventually decides to stop.
TODO is it possible to make it deterministic on the CLI? There is a "seed" parameter somewhere: https://github.com/jmorganca/ollama/blob/31f0551dab9a10412ec6af804445e02a70a25fc2/docs/modelfile.md#parameter
= Text-to-image model
{parent=Generative AI by modality}
{wiki}
= Text-to-image
{synonym}
= Open source text-to-image model
{parent=Text-to-image model}
Bibliography:
* https://www.edenai.co/post/top-free-image-generation-tools-apis-and-open-source-models
= ludicrains/deep-gaze
{c}
{parent=Open source text-to-image model}
https://github.com/lucidrains/deep-daze
This just works, but it is also so incredibly slow that it is useless (or at least the quality it reaches in the time we have patience to wait from), at least on any setup we've managed to try, including e.g. on an <Nvidia A10G> on a <g5.xlarge>. Running:
``
time imagine "a house in the forest"
``
would likely take hours to complete.
= runwayml/stable-diffusion
{c}
{parent=Open source text-to-image model}
https://github.com/runwayml/stable-diffusion
<Conda> install is a bit annoying, but gets the job done. The generation quality is very good.
Someone should package this better for end user "just works after Conda install" image generation, it is currently much more of a library setup.
Tested on <Amazon EC2> on a <g5.xlarge> machine, which has an <Nvidia A10G>, using the <AWS Deep Learning Base GPU AMI (Ubuntu 20.04)> image.
First install <Conda> as per <install Conda on Ubuntu>{full}, and then just follow the instructions from the README, notably the https://github.com/runwayml/stable-diffusion/tree/08ab4d326c96854026c4eb3454cd3b02109ee982#reference-sampling-script[Reference sampling script] section.
``
git clone https://github.com/runwayml/stable-diffusion
cd stable-diffusion/
git checkout 08ab4d326c96854026c4eb3454cd3b02109ee982
conda env create -f environment.yaml
conda activate ldm
mkdir -p models/ldm/stable-diffusion-v1/
wget -O models/ldm/stable-diffusion-v1/model.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
``
This took about 2 minutes and generated 6 images under `outputs/txt2img-samples/samples`, includining an image `outputs/txt2img-samples/grid-0000.png` which is a grid montage containing all the six images in one:
\Image[https://raw.githubusercontent.com/cirosantilli/media/master/Runwayml_stable-diffusion_a-photograph-of-an-astronaut-riding-a-horse.png]
TODO how to change the number of images?
A quick attempt at removing their useless safety features (watermark and <NSFW> text filter) is:
``
diff --git a/scripts/txt2img.py b/scripts/txt2img.py
index 59c16a1..0b8ef25 100644
--- a/scripts/txt2img.py
+++ b/scripts/txt2img.py
@@ -87,10 +87,10 @@ def load_replacement(x):
def check_safety(x_image):
safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
- assert x_checked_image.shape[0] == len(has_nsfw_concept)
- for i in range(len(has_nsfw_concept)):
- if has_nsfw_concept[i]:
- x_checked_image[i] = load_replacement(x_checked_image[i])
+ #assert x_checked_image.shape[0] == len(has_nsfw_concept)
+ #for i in range(len(has_nsfw_concept)):
+ # if has_nsfw_concept[i]:
+ # x_checked_image[i] = load_replacement(x_checked_image[i])
return x_checked_image, has_nsfw_concept
@@ -314,7 +314,7 @@ def main():
for x_sample in x_checked_image_torch:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
img = Image.fromarray(x_sample.astype(np.uint8))
- img = put_watermark(img, wm_encoder)
+ # img = put_watermark(img, wm_encoder)
img.save(os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1
``
but that produced 4 black images and only two unfiltered ones. Also likely the lack of sexusl training data makes its porn suck, and not in the good way.
= DeepFloyd IF
{c}
{parent=Open source text-to-image model}
https://github.com/deep-floyd/IF
= Text-to-video
{parent=Generative AI by modality}
This was the Holy Grail as of 2023, when <text-to-image> started to really take off, but text-to-video was miles behind.
* 2024-02-15: Sora by <OpenAI>
* https://techcrunch.com/2024/02/15/openais-newest-model-can-generate-videos-and-they-look-decent/
= AI research entity
{c}
{parent=Artificial intelligence}
= AI researcher
{c}
{parent=AI research entity}
{tag=Computer scientist}
= Yann LeCun
{c}
{parent=AI researcher}
{wiki}
= Yohei Nakajima
{c}
{parent=AI researcher}
{wiki}
He does lots of little experiments which is cool.
* https://twitter.com/yoheinakajima
* https://yoheinakajima.com/
No <research papers> but has citations: https://www.yohei.me/publications[] which is cool.
= AI alignment
{c}
{parent=Artificial intelligence}
{wiki}
As highlighted e.g. at <Human compatible by Stuart J. Russell (2019)>, this AI alignment intrinsically linked to the idea of <utility> in <economy>.
= Reward modeling
{parent=AI alignment}
See e.g.: <Human compatible>
* https://deepmindsafetyresearch.medium.com/scalable-agent-alignment-via-reward-modeling-bf4ab06dfd84
= AI safety
{c}
{parent=AI alignment}
Basically ensuring that good <AI alignment> allows us to survive the singularity.
= Path to AGI
{c}
{parent=Artificial intelligence}
There are two main ways to try and reach AGI:
* <AI training robot>: expensive, slow, but realistic world
* <AI training game>: faster, less expensive, but possibly non-realistic-enough realistic
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>.
= AI training robot
{c}
{parent=Path to AGI}
{tag=Robotics}
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:
* <Transcendence (2014)>
* https://youtu.be/MtVvzJIhTmc?t=112 from <video Rotrics DexArm is available NOW! by Rotrics (2020)> where they have a sliding rail
An arm with a hand and a camera are however indispensable of course!
\Image[https://raw.githubusercontent.com/juniorrojas/algovivo/9e00622f558fd137f8e7b2ab50d085e43e7206cf/media/locomotion.gif]
{title=Algovivo demo}
{description=https://github.com/juniorrojas/algovivo[]: A JavaScript + WebAssembly implementation of an energy-based formulation for soft-bodied virtual creatures.}
= AI training robot in a room
{parent=AI training robot}
{tag=Robotics}
<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.
= AI training robot dataset
{c}
{parent=AI training robot}
= Open X-Embodiment
{c}
{parent=AI training robot dataset}
Terrible name, but very interesting dataset:
* https://robotics-transformer-x.github.io/
* https://github.com/google-deepmind/open_x_embodiment
TODO: any <AI training robot simulation>[simulation] integration to it?
= AI training robot simulation
{c}
{parent=AI training robot}
= DeepMind RoboCat
{c}
{parent=AI training robot}
{tag=DeepMind project}
{title2=2023}
https://www.deepmind.com/blog/robocat-a-self-improving-robotic-agent
\Video[https://www.youtube.com/watch?v=535W4Pih1C0]
{title=RoboCat by Google DeepMind (2023)}
= Supercomputer controlling a robot
{parent=AI training robot}
Has anybody done this seriously? Given a supercomputer, what amazing human-like robot behaviour we can achieve?
= AI game
{c}
{parent=Path to AGI}
{tag=Serious game}
= AGI via simulation
{synonym}
{title2}
= AI training game
{c}
{synonym}
\Video[https://youtu.be/Y2d1AU7_JvM?t=278]
{title=Our Final Invention - Artificial General Intelligence by Sciencephile the AI (2023)}
{description=AGI via simulation section.}
<Ciro Santilli> defines an "AI game" as:
\Q[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.
Bibliography:
* https://www.youtube.com/@aiwarehouse
= Human game used for AI training
{parent=AI game}
This section is about games initially designed for humans, but which ended up being used in AI development as well, e.g.:
* <board games> such as <chess> and <go (game)>
* <video games> such as <Minecraft> or old <video game console> games
= Using Minecraft for AI training
{c}
{parent=Human game used for AI training}
{tag=Minecraft}
* https://openai.com/blog/openai-acquires-global-illumination
= MineDojo
{c}
{parent=Using Minecraft for AI training}
https://github.com/MineDojo
= Game AI
{parent=AI game}
{wiki=Artificial_intelligence_in_video_games}
Game AI is an <artificial intelligence> that plays a certain game.
It can be either developed for <serious game>[serious] purposes (e.g. <AGI> development in <AI games>), or to make games for interesting for humans.
= Game AI research
{parent=Game AI}
= Game AI research lab
{parent=Game AI research}
The <quora> question: https://www.quora.com/Are-there-any-PhD-programs-in-training-an-AI-system-to-play-computer-games-Like-the-work-DeepMind-do-combining-Reinforcement-Learning-with-Deep-Learning-so-the-AI-can-play-Atari-games
https://gameresearch.leiden.edu/
A good way to find labs is to go down the issues section of projects such as:
* https://github.com/deepmind/lab2d/issues?q=
* https://github.com/deepmind/lab/issues?q=
and then stalk them to see where they are doing their PhDs.
= QMUL Game AI Research Group
{c}
{parent=Game AI research lab}
{tag=QMUL research group}
<Principal investigator>: Simon M. Lucas.
= QMUL GAIG
{c}
{synonym}
{title2}
= Leiden Game Research Lab
{c}
{parent=Game AI research lab}
* https://gameresearch.leiden.edu/
* https://twitter.com/GRL_Liacs
= Game AI by game genre
{parent=Game AI}
= Fighting game AI
{parent=Game AI by game genre}
\Video[https://www.youtube.com/watch?v=qZItwBB0T2Y]
{title=AI in Melee is broken by Melee Moments (2023)}
= Game AI competition
{parent=Game AI}
https://webots.cloud/competition
Lists:
* https://www.gocoder.one/blog/ai-game-competitions-list/ Good list of interest.
* https://codecombat.com/
= Battlecode
{c}
{parent=Game AI competition}
{tag=MIT}
{tag=Gridworld}
{title2=2003-}
* https://github.com/battlecode
TODO quick summary of game rules? Perhaps: https://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[https://www.youtube.com/watch?v=oa4CAizd1Nk]
{title=Battlecode Final Tournament 2023}
\Video[https://www.youtube.com/watch?v=BLExWo9Empk]
{title=Introduction to Battlecode by MIT OpenCourseWare (2014)}
= Regression Games
{c}
{parent=Game AI competition}
= regression.gg
{title2}
{synonym}
https://www.regression.gg/
= Computer Olympiad
{c}
{parent=Game AI competition}
{title2=1989-2015}
Ah, shame, they are a bit weak.
= Permanent brain
{parent=Game AI}
{wiki}
= AI game by type
{c}
{parent=AI game}
= Procedural AI training game
{parent=AI game by type}
{tag=Procedural generation}
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.
= AI game world geometry
{c}
{parent=AI game by type}
= 2D AI game
{parent=AI game world geometry}
{tag=2D game}
= Gridworld AI game
{c}
{parent=2D AI game}
{tag=Gridworld}
* https://github.com/google-deepmind/pushworld 2023 Too combinatorial, gripping makes it so much easier to move stuff around in the real world. But cool nonetheless.
= 2D continuous AI game
{parent=2D AI game}
{tag=2D continuous game}
= 3D AI game
{c}
{parent=AI game world geometry}
{tag=3D game}
{tag=AI training robot simulation}
\Video[https://www.youtube.com/watch?v=nAMSfmHuMOQ]
{title=<NVIDIA>'s little fighter charater (2023)}
= Football simulation
{parent=3D AI game}
= Soccer simulation
{synonym}
= Deepmind soccer simulation
{c}
{parent=Football simulation}
{tag=MuJoCo}
{title2=2023}
* From Motor Control to Team Play in Simulated Humanoid Football
\Video[https://www.youtube.com/watch?v=KHMwq9pv7mg]
{title=From Motor Control to Team Play in Simulated Humanoid Football by Ali Eslami (2023)}
{description=Likely a reupload by <Deepmind> employee: https://www.linkedin.com/in/smalieslami[].}
\Video[https://www.youtube.com/watch?v=HTON7odbW0o]
{title=<DeepMind>’s AI Trained For 5 Years by <Two Minute Papers> (2023)}
{description=The 5 years bullshit is of course in-game time clickbait, they simulate 1000x faster than realtime.}
= AI game with natural language
{c}
{parent=AI game}
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[https://www.youtube.com/watch?v=JCJOgEcNgKY]
{title=Human player test of DMLab-30 Select Described Object task by <DeepMind> (2018)}
{description=This is one of the games from <DeepMind Lab>.}
{disambiguate=natural-language}
\Video[https://www.youtube.com/watch?v=5DcABeOltL8]
{title=WorldGPT by Nhan Tran (2023)}
{description=Not the most amazing demo, but it is a start.}
= List of AI games
{parent=AI game}
= List of AI training games
{synonym}
= AI game by DeepMind
{c}
{parent=List of AI games}
{tag=DeepMind project}
* https://github.com/deepmind/meltingpot TODO vs <DeepMind Lab2D>? Also 2D discrete. Started in 2021.
* https://github.com/deepmind/ai-safety-gridworlds mentioned e.g. at https://www.youtube.com/watch?v=CGTkoUidQ8I by Rober Miles
\Video[https://www.youtube.com/watch?v=VvzZG-HP4DA]
{title=Creating Multimodal Interactive Agents from <DeepMind> by <Two Minute Papers> (2023)}
{description=https://www.deepmind.com/blog/building-interactive-agents-in-video-game-worlds}
\Video[https://www.deepmind.com/blog/generally-capable-agents-emerge-from-open-ended-play]
{title=Open-Ended Learning Leads to Generally Capable Agents by <DeepMind> (2021)}
{description=Short name: XLand. Whitepaper: https://www.deepmind.com/blog/generally-capable-agents-emerge-from-open-ended-play[].}
= DeepMind Lab
{c}
{parent=AI game by DeepMind}
{tag=GPL software}
{tag=3D game}
{tag=Game AI research}
https://github.com/deepmind/lab
https://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: https://github.com/deepmind/lab/blob/master/docs/users/build.md[]. This may helphttps://github.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[https://www.youtube.com/watch?v=k0mk0CI7G0s]
{title=Human player test of DMLab-30 Collect Good Objects task by <DeepMind> (2018)}
\Video[https://www.youtube.com/watch?v=HIkWgTAn7Rs]
{title=Human player test of DMLab-30 Exploit Deferred Effects task by <DeepMind> (2018)}
\Video[https://www.youtube.com/watch?v=JCJOgEcNgKY]
{title=Human player test of DMLab-30 Select Described Object task by <DeepMind> (2018)}
{description=Some of their games involve language instructions from the use to determine the desired task, cool concept.}
\Video[https://www.youtube.com/watch?v=urYc9vaWQ7A]
{title=Human player test of DMLab-30 Fixed Large Map task by <DeepMind> (2018)}
{description=They also have some maps with more natural environments.}
= DeepMind Lab2D
{c}
{parent=AI game by DeepMind}
{tag=Apache License}
{tag=Gridworld}
{title2=2020}
* https://github.com/deepmind/lab2d
* https://deepai.org/publication/deepmind-lab2d
<Gridworld> version of <DeepMind Lab>.
<Open sourced> in 2020: https://analyticsindiamag.com/deepmind-just-gave-away-this-ai-environment-simulator-for-free/
A tiny paper: https://arxiv.org/pdf/2011.07027.pdf
Very similar to <gvgai>, <Julian Togelius> actually called them out on that: <DeepMind Lab2D vs gvgai>.
TODO get running, publish demo videos on YouTube.
\Image[https://web.archive.org/web/20221218211007im_/https://github.com/deepmind/lab2d/raw/main/docs/screenshot.png]
{source=https://github.com/deepmind/lab2d}
= DeepMind Lab2D vs gvgai
{parent=DeepMind Lab2D}
At https://twitter.com/togelius/status/1328404390114435072 called out on <DeepMind Lab2D> for not giving them credit on prior work!
\Q[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 https://web.archive.org/web/20220331022932/http://gvgai.net/ though, <DeepMind> sponsored them at some point.
= Can AGI be trained in simulations?
{c}
{parent=AI game}
Or is real word data necessary, e.g. with <robots>?
Fundamental question related to <Ciro's 2D reinforcement learning games>.
Bibliography:
* https://youtu.be/i0UyKsAEaNI?t=120 How to Build AGI? Ilya Sutskever interview by Lex Fridman (2020)
= Entity creating AI games
{parent=AI game}
{tag=Game AI research}
= DeepMind
{c}
{parent=Entity creating AI games}
{tag=Google acquisition}
{title2=2010-}
{wiki}
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: https://www.linkedin.com/in/charles-beattie-0695373/[].
= DeepMind project
{c}
{parent=DeepMind}
= AlphaGo
{c}
{parent=DeepMind project}
{tag=Computer Go}
{title2=2016}
{wiki}
= Open source AlphaGo implementation
{c}
{parent=AlphaGo}
* https://www.quora.com/Will-Google-open-source-AlphaGo Will Google open source AlphaGo?
* https://www.nature.com/articles/nature16961 Mastering the game of Go with deep neural networks and tree search by Silver et al. (2016), <published without source code>
= MiniGo
{c}
{parent=Open source AlphaGo implementation}
https://github.com/tensorflow/minigo
= AlphaGo Zero
{c}
{parent=AlphaGo}
{tag=Go engine}
{title2=2017}
{wiki}
\Image[https://web.archive.org/web/20220823105651im_/https://miro.medium.com/max/1400/1*0pn33bETjYOimWjlqDLLNw.png]
{title=AlphaGo Zero cheat sheet by David Foster (2017)}
{source=https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0}
= AlphaGo Zero open source implementation
{c}
{parent=AlphaGo Zero}
= AlphaZero
{c}
{parent=AlphaGo}
{tag=Chess engine}
{tag=Go engine}
{title2=2017}
{wiki}
Generalization of <AlphaGo Zero> that plays <Go (game)>, <chess> and shogi.
* https://www.science.org/doi/10.1126/science.aar6404[] A general <reinforcement learning> algorithm that masters <chess>, <shogi>, and <Go (game)> through self-play by Silver et al. (2018), <published without source code>
* https://www.quora.com/Is-there-an-Open-Source-version-of-AlphaZero-specifically-the-generic-game-learning-tool-distinct-from-AlphaGo
https://www.quora.com/Which-chess-engine-would-be-stronger-Alpha-Zero-or-Stockfish-12/answer/Felix-Zaslavskiy explains that it beat <stockfish (chess)> 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.
= gvgai
{c}
{parent=Entity creating AI games}
{tag=Gridworld}
{title2=2014-2020}
http://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: https://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: https://github.com/GAIGResearch/GVGAI TODO review
A published book at: https://gaigresearch.github.io/gvgaibook/
From <QMUL Game AI Research Group>:
* Simon M. Lucas: https://gaigresearch.github.io/members/Simon-Lucas[], <principal investigator>
* Diego Perez Liebana https://www.linkedin.com/in/diegoperezliebana/
* Raluca D. Gaina: https://www.linkedin.com/in/raluca-gaina-347518114/ from Queen Mary
From other universities:
* <Julian Togelius>
TODO check:
* Ahmed Khalifa
* Jialin Liu
= Julian Togelius
{c}
{parent=gvgai}
{wiki}
* http://julian.togelius.com/
* https://twitter.com/togelius
= General Game Playing
{disambiguate=Stanford project}
{parent=Entity creating AI games}
{title2=2005-?}
http://ggp.stanford.edu/iggpc/index.php
This kind of died at some point checked as of 2023.
<Julian Togelius> cites it e.g. at: http://togelius.blogspot.com/2016/07/which-games-are-useful-for-testing.html
= OpenAI
{c}
{parent=Entity creating AI games}
{wiki}
\Q[In 2019, <OpenAI> transitioned from non-profit to for-profit]
so what's that point of "Open" in the name anymore??
* https://www.technologyreview.com/2020/02/17/844721/ai-openai-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality/ "The AI moonshot was founded in the spirit of transparency. This is the inside story of how competitive pressure eroded that idealism."
* https://archive.ph/wXBtB How OpenAI Sold its Soul for \$1 Billion
* https://www.reddit.com/r/GPT3/comments/n2eo86/is_gpt3_open_source/
= OpenAI Gym
{c}
{parent=OpenAI}
https://github.com/openai/gym
= Implications of AGI
{c}
{parent=Artificial intelligence}
= Existential risk of AGI
{parent=Implications of AGI}
https://www.cam.ac.uk/research/news/the-best-or-worst-thing-to-happen-to-humanity-stephen-hawking-launches-centre-for-the-future-of
\Q[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.]
= Singularity
{parent=Implications of AGI}
{wiki=Technological singularity}
= Technological singularity
{synonym}
= Artificial intelligence bibliography
{c}
{parent=Artificial intelligence}
{wiki}
= Human Compatible
{c}
{parent=Artificial intelligence bibliography}
{tag=AI alignment}
{tag=Good book}
{tag=Implications of AGI}
{title2=2019}
{title2=Stuart Russel}
{wiki=Human_compatible}
= Human Compatible by Stuart J. Russell (2019)
{synonym}
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>
= Superintelligence by Nick Bostrom (2014)
{c}
{parent=Artificial intelligence bibliography}
{tag=Implications of AGI}
{wiki=Superintelligence:_Paths,_Dangers,_Strategies}
Good points:
* <Post mortem connectome extraction with microtome>
* the idea of a singleton, i.e. one centralized power, possibly AGI-based, that decisivly takes over the planet/reachable universe
* <AGI research has become a taboo in the early 21st century> section "Opinions about the future of machine intelligence"