LLM game by Ciro Santilli 37 Updated 2025-07-16
Stanford Smallville by Ciro Santilli 37 Updated 2025-07-16
Published as: arxiv.org/pdf/2304.03442.pdf Generative Agents: Interactive Simulacra of Human Behavior by Park et al.
Video 1.
AI Agents Behaving Like Humans by Prompt Engineering (2023)
. Source.
Yann LeCun by Ciro Santilli 37 Updated 2025-07-16
The most classic thing he did perhaps was creating the LeNet neural network and using it on the MNIST dataset to recognize hand-written digits circ 1998.
Figure 1.
Yann LeCun
. Source.
AI alignment by Ciro Santilli 37 Updated 2025-07-16
As highlighted e.g. at Human Compatible by Stuart J. Russell (2019), this AI alignment intrinsically linked to the idea of utility in economy.
AI safety by Ciro Santilli 37 Updated 2025-07-16
Basically ensuring that good AI alignment allows us to survive the singularity.
Path to AGI by Ciro Santilli 37 Updated 2025-07-16
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.
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 approach.
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 immediately and thus invalidate this experiment, things would just go nuts more and more.
Open X-Embodiment by Ciro Santilli 37 Updated 2025-07-16
GitHub describes the input quite well:
The model takes as input a RGB image from the robot workspace camera and a task string describing the task that the robot is supposed to perform.
What task the model should perform is communicated to the model purely through the task string. The image communicates to the model the current state of the world, i.e. assuming the model runs at three hertz, every 333 milliseconds, we feed the latest RGB image from a robot workspace camera into the model to obtain the next action to take.
TODO: how is the scenario specified?
TODO: any simulation integration to it?
https://web.archive.org/web/20250209172539if_/https://raw.githubusercontent.com/google-deepmind/open_x_embodiment/main/imgs/teaser.png

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
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    Figure 1.
    Screenshot of the "Derivative" topic page
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    Figure 2.
    You can publish local OurBigBook lightweight markup files to either https://OurBigBook.com or as a static website
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    Figure 3.
    Visual Studio Code extension installation
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    Figure 4.
    Visual Studio Code extension tree navigation
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    Figure 5.
    Web editor
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    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
    Video 4.
    OurBigBook Visual Studio Code extension editing and navigation demo
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