Ways of Being: Animals, Plants, Machines: The Search for a Planetary Intelligence
by James Bridle. James pulls an brilliant move of using various examples in technology as a tool to help us rethink our place in nature & reconnect with the (beyond human) world.
He weaves a fascinating tale with myriads of threads such as Cybernetics, Neural Nets, Internet, Random Numbers, Analog computers, Slime molds, Sortition vs Voting, Mycelium, Mysticism in animals, Turing machines, Personhood of non-humans etc.
Search/Reasoning/Inference time compute, however you phrase it is still essential. You need search to improve upon learning to work in novel situations.
I am working on RL and robotics. I came across Levin in Lex's podcast. And then went on a binge of his other podcast appearences. I agree totally with you, I would very much like to build agents that adapt to different circumstances like "simple organisms". I am not familiar with biology, but I plan to build competence here to follow Levin's work to a point that I could potentially collabrate with biologists or learn from their work. Any suggestions (books etc) that would be salient towards this goal is much appreciated!
I also focused on the work done by the Levin lab after the Sean Carroll podcast [1]. In order to familiarize myself with the subject matter in a more practical manner I started writing a wrapper and a frontend, BESO [2], BioElectric Simulation Orchestrator, for BETSE [3], the Bio Electric Tissue Simulation Engine developed by Alexis Pietak which is used by the Levin lab to simulate various tissues and their responses based on world/biomolecules/genes/etc. parametrization. Reading the BETSE source code, the presentation [4], and some of the articles referred through the source code has been a rewarding endeavour. Some other books I consulted, somewhat beginner friendly were:
2018, Amit Kessel, Introduction to Proteins. Structure, Function, and Motion, CRC Press
2019, Noor Ahmad Shaik, Essentials of Bioinformatics, Volume I. Understanding Bioinformatics. Genes to Proteins, Springer
2019, Noor Ahmad Shaik, Essentials of Bioinformatics, Volume II. In Silico Life Sciences. Medicine, Springer — less basics, more protocol-oriented
2021, Karthik Raman, An Introduction to Computational Systems Biology. Systems-Level Modelling of Cellular Networks, Chapman and Hall
2022, Tiago Antao, Bioinformatics with Python Cookbook. Use modern Python libraries and applications to solve real-world computational biology problems, Packt
2023, Metzger R.M., The Physical Chemist's Toolbox, Wiley — a beautiful story of mathematics, physics, chemistry, biology; gradually rising in complexity as the universe itself, from the whatever (data) structure the universe was before the Big Bang to us, today.
somewhat more technical:
2014, Wendell Lim, Cell Signaling. Principles and Mechanisms, Routledge
2021, Mo R. Ebrahimkhani, Programmed Morphogenesis. Methods and Protocols, Humana
2022, Ki-Taek Lim, Nanorobotics and Nanodiagnostics in Integrative Biology and Biomedicine, Springer
In video format I particularly watched Kevin Ahern's Biochemistry courses BB 350/2017 [5], BB 451/2018 [6], Problem Solving Videos [7].
Have you checked RSSM approach in DreamerV1,V2,V3,PlaNet? It uses deterministic (GRU hidden state) and discrete stochastic latent states. The deterministic and stochastic (sampled) latent state are used to predict the next state. I think the stochastic state might help with your problem a bit.
One interesting research group tackling this question is ex-computer engineer, currently biologist Prof. Micahel Levine's group.
They built xenobots (biological robots) from frog cells which are not evolutionarily programmed to do tasks which they accomplished. Their research in part deals with emergence of agency at multi-cellular level. And they want to get at the fundamental signatures of what makes some collection of agents into a new single agent at an higher level.
Worth checking out farcaster (farcaster.xyz). They are working towards a solution where things are "sufficiently decentralised". The server admins can't prevent users from following one another etc.
Farcaster(farcaster.xyz) is working towards sufficiently decentralised social network (https://www.varunsrinivasan.com/2022/01/11/sufficient-decent...). It aims to have a model where server admins don't get the power to prevent users from following one another.
Does apple even encrypt the actual image data on device? Their system document says "payload = visual derivative + neural hash" and only that is encrypted with a secondary level encryption. And they didn't go through with e2ee for icloud last I heard. This elaborate system makes no sense if they very well could have done it in cloud.
It feels like elaborate privacy theatre trojan horse to introduce in device surveillance.
https://www.youtube.com/watch?v=uTXXYi75QCU
He has lots of good threads distilling his research. https://twitter.com/rao2z