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)
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:
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.
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)
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 :’)