points by ly3xqhl8g9 3 years ago

"I’m skeptical that biological systems will ever serve as a basis for ML nets in practice"

First of all, ML engineers need to stop being so brainphiliacs, caring only about the 'neural networks' of the brain or brain-like systems. Lacrymaria olor has more intelligence, in terms of adapting to exploring/exploiting a given environment, than all our artificial neural networks combined and it has no neurons because it is merely a single-cell organism [1]. Once you stop caring about the brain and neurons and you find out that almost every cell in the body has gap junctions and voltage-gated ion channels which for all intents and purposes implement boolean logic and act as transistors for cell-to-cell communication, biology appears less as something which has been overcome and more something towards which we must strive with our primitive technologies: for instance, we can only dream of designing rotary engines as small, powerful, and resilient as the ATP synthase protein [2].

[1] Michael Levin: Intelligence Beyond the Brain, https://youtu.be/RwEKg5cjkKQ?t=202

[2] Masasuke Yoshida, ATP Synthase. A Marvellous Rotary Engine of the Cell, https://pubmed.ncbi.nlm.nih.gov/11533724

mk_stjames 3 years ago

[1] linked above is an absolute powerhouse of a lecture by Michael Levin. Wow.

  • qup 3 years ago

    Thanks for calling it out, made me watch it. Absolutely fascinating. Incredible implications.

    • ly3xqhl8g9 3 years ago

      Beyond the much needed regenerative medical procedures, limb/organ reconstruction through "API" calls to the cells that 'know' how to build an arm, an eye, a spleen, and so on, it is the breakdown of the dichotomies taken for granted, human/machine, just physics/mind with an agent, and to speak instead of agential materials [1], which fosters a new type of endeavour, one which will be needed very soon if our CPUs start speaking to us.

      [1] https://drmichaellevin.org/resources/#:~:text=Agential%20mat...

outworlder 3 years ago

Indeed. All cells must do complex computations, by their own nature. Just the process of producing proteins and each of its steps – from 'unrolling' a given DNA section, copying it, reading instructions... even a lowly ribosome is a computer (one that even kinda looks like a Turing machine from a distance)

laughingman2 3 years ago

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!

  • ly3xqhl8g9 3 years ago

    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].

    [1] https://www.youtube.com/watch?v=gm7VDk8kxOw

    [2] not functional yet, https://github.com/daysful/beso

    [3] https://github.com/betsee/betse

    [4] BETSE 1.0, https://www.dropbox.com/s/3rsbrjq2ljal8dl/BETSE_Documentatio...

    [5] https://youtu.be/JSntf0iKMfM?list=PLlnFrNM93wqz37TUabcXFSNX2...

    [6] https://youtu.be/SAIFs_Mx8D8?list=PLlnFrNM93wqyay92Mi49rXZKs...

    [7] https://youtu.be/e9khXFSU6r4?list=PLlnFrNM93wqzeZvsE_GKes91C...

    • zaroth 3 years ago

      Late to post this (found from a cross-link on another post) but just have to say, this right here is HN comment gold.

      What an incredibly helpful and useful response!!

dekhn 3 years ago

If I had done synthetic biology my goal would have been to create cells that could reliably compute sine waves... by digitally computing taylor series polynomial approximations. Turns out engineering digital systems from cells is a remarkably challenging problem.

Examples of "switches" in biology abound, my favorite simple one is the Mating Type of Yeast: yeast have two sex types, and swap a small region of DNA in-place with variants to switch between them. Perfect example of self-modifying code!

  • ly3xqhl8g9 3 years ago

    Not sure about polynomials, but how about "Genetic Regulatory Networks that count to 3" [1]. One of the interesting, counter-intuitive highlights from the paper: "Counting to 2 requires very different network design than counting to 3."

    [1] https://pubmed.ncbi.nlm.nih.gov/23567648

    • dekhn 3 years ago

      Unfortunately, that's entirely analog. my goal was to do digital computing- with all the reliability and predictability.

phaedrus 3 years ago

I wonder if there's a step-change where single-celled animals with complex behavior are actually smarter than the simplest multiple-celled animals with a nervous system.

  • whatshisface 3 years ago

    The cells of multi-celled animals still have complex behaviors.

PeterisP 3 years ago

Well, our brains are the most wonderful thing in the world, at least our brains say so.

water-your-self 3 years ago

ATP synthase's shape is my favorite go-to random fact :)

westurner 3 years ago

> Once you stop caring about the brain and neurons and you find out that almost every cell in the body has gap junctions and voltage-gated ion channels which for all intents and purposes implement boolean logic and act as transistors for cell-to-cell communication, biology appears less as something which has been overcome and more something towards which we must strive with our primitive technologies: for instance, we can only dream of designing rotary engines as small, powerful, and resilient as the ATP synthase protein [2].

But what of wave function(s); and quantum chemistry at the cellular level? https://github.com/tequilahub/tequila#quantumchemistry

Is emergent cognition more complex than boolean entropy, and are quantum primitives necessary to emulate apparently consistently emergent human cognition for whatever it's worth?

[Church-Turing-Deutsch, Deutsch's Constructor theory]

Is ATP the product of evolutionary algorithms like mutation and selection? Heat/Entropy/Pressure, Titration/Vibration/Oscillation, Time

From the article:

> The next step, Lechner said, “is to figure out how many, or how few, neurons we actually need to perform a given task.”

Notes regarding </i>Representational drift* and remarkable resilience to noise in BNNs) from "The Fundamental Thermodynamic Cost of Communication: https://news.ycombinator.com/item?id=34770235

It's never just one neuron.

And furthermore, FWIU, human brains are not directed graphs of literally only binary relations.

In a human brain, there are cyclic activation paths (given cardiac electro-oscillations) and an imposed (partially extracerebral) field which nonlinearly noises the almost-discrete activation pathways and probably serves a feed-forward function; and in those paths through the graph, how many of the neuronal synapses are simple binary relations (between just nodes A and B)?

> The group also wants to devise an optimal way of connecting neurons. Currently, every neuron links to every other neuron, but that’s not how it works in C. elegans, where synaptic connections are more selective. Through further studies of the roundworm’s wiring system, they hope to determine which neurons in their system should be coupled together.

Is there an information metric which expresses maximal nonlocal connectivity between bits in a bitstring; that takes all possible (nonlocal, discontiguous) paths into account?

`n_nodes*2` only describes all of the binary, pairwise possible relations between the bits or qubits in a bitstring?

"But what is a convolution" https://www.3blue1brown.com/lessons/convolutions

Quantum discord: https://en.wikipedia.org/wiki/Quantum_discord