Brief, Lyfe, Thoughts

My Take on LYFE Part I: Expectations and Hype

So I still need to write down the next Meta/Mesa post from my notes, as this tool is still intriguing to me; but my interest shifted incredibly fast after reading this article (in French) which refers to the article Defining Lyfe in the Universe: From Three Privileged Functions to Four Pillars.

So, you know me; strong, sudden and powerful hype that doesn’t last (especially after I wrote a post about a given topic). BUT… this publication is fresh, extremely cool, and people will keep talking about it in the coming years (no joke, check the stats below)

LYFE article: impact progress since publication

Therefore, what is that LYFE (pronounced “Loife”) about?

In short, it is a functional theory of life as well as most general form (Life being a subset of Lyfe) that rests on those 4 functions:

  • Autocatalysis
  • Homeostasis
  • Dissipation
  • Learning

From those, they predict theoretical form of “lyfes” that “life” hasn’t produced. For instance, here below a mechanotroph organism that uses mechanical energy extracted from a fluid to produce ATP similarly to photosynthesis in the green leaves of our plants.

Theoretic mechanotroph organism

In short, it theorizes that a Gray-Scott model that could learn would be a living creature (validating all 4 functions therefore being an instance of Lyfe)

Simulation of a Gray-Scott model

As any engineer, I did learn statistical thermodynamics and I am still fascinated by Boltzmann work, but there’s nothing I could bring that David Louapre, Stuart Bartlett or Michael Wong wouldn’t put on the table (actually, I couldn’t bring anything at all on the avenue crossing thermodynamics and biology if it’s not neuron-related)

But that’s perfectly fine because I am looking at it on a totally different perspective!
I’d like to review their work step-by-step on this blog and produce a translation to computer science based on information theory (which obeys similar laws to thermodynamic… or the other way around) in order to theorize what would make a software component “alyve” (Y can’t wayt for the result!)

And, as my blog is about raw thoughts and freedom of speech, I’ll put some here about the main functions:

  • Learning might be an expandable function/category as I don’t believe just putting some Hebbian rule there would encompass the whole concept of learning
  • The most obvious parallel to algorithm is a Boltzmann machine, but temperature is there a metaheuristic parameter that correspond more to internal state than to processed data
  • Dissipation is not clear to me yet; I believe any running instance should be considered a dissipative system
  • Autocatalysis is the one that gives me to most trouble to put in the context of information system. Would it be like a function that spawns threads as it runs? How does it saturate in our case? Should we consider something like a charge of tasks (sensors, actuators or programs) to be processed?
  • Homeostasis might be the easiest starting point; it should be simulable with a Proportional-Integrative controller in a closed loop system, and is also linked to what dissipation and autocatalysis will apply
  • Could the Gray-Scott model be used to spawn naturally multi-agent systems?

Gosh seems so cool! I can’t wait to start!
Oh wait… I still need to send CV to find a new job… If anyone has heard about a cool researcher/PhD candidate position, that would help :’)

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