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.
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.
Video 1.
From Machine Learning to Autonomous Intelligence by Yann LeCun (2023)
. Source. After a bunch of B.S., LeCun goes on to describe his AGI architecture. Nothing ground breaking, but not bad either.
This section is about ideas that are thought to be part of an AGI system.
Video 1.
My Job is to Open and Close Doors by Mattias Pilhede (2019)
. Source. An interesting humourous short meditation on common sense.
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.
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.
By the rich founder of Mt. Gox and Ripple, Jed McCaleb.
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?
Marek Rosa's play thing.
Video 1.
AI Game - LLM-driven NPCs that can talk by Marek Rosa (2023)
. Source. 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.
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 1.
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.
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.
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!
AGI-complete in general? Obviously. But still, a lot can be done. See e.g.:
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.
OpenNMT CLI front-end.
Published as: Generative Agents: Interactive Simulacra of Human Behavior by Park et al.
Video 1.
AI Agents Behaving Like Humans by Prompt Engineering (2023)
. Source.
Highly automated wrapper for various open source LLMs.
curl | sh
ollama run llama2
And bang, a download later, you get a prompt. On 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 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:
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.
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 Section "Install Conda on Ubuntu", and then just follow the instructions from the README, notably the Reference sampling script section.
git clone
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
python scripts/ --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:
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/ b/scripts/
index 59c16a1..0b8ef25 100644
--- a/scripts/
+++ b/scripts/
@@ -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)
                       , 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.
This was the Holy Grail as of 2023, when text-to-image started to really take off, but text-to-video was miles behind.