I think we're quite far off if you want to model full organism complexity. But if you want to answer a research question you can model simpler versions today. Like this research team did around how vision evolves.
By the way– As of recently, we were able to model C Elegans (flatworm) in 3D with all neurons and neurotransmitters. It reacted to virtual stimuli just like a real worm (https://www.nature.com/articles/s43588-024-00738-w). So single-organisms is already possible. But the evolution of these entities in 3D will take us a bit more time is my guess:
Also I'm not a CS person, just an enthusiast.
Can these two things interface yet is the question (virtual to real bidirectional interface), especially since you are suggesting we have a clone of it.
If we do this experiment, what does that say about something doing an experiment on humans (humans control humans inside video games).
Sounds insane right? Why would we ever do this? Well … we have this perfect digital clone of a flatworm, what else are we going to do? There’s a lot of evidence that humans would absolutely go down this rabbit hole until it’s logical conclusion.
One word: Teledildonics
Anyway, the flatworm that is born into such an experiment would never know, or it would just be useless to know. :shrugs:
Over time this allows fish to develop basic behavior such as searching for food, navigation a maze, etc.
The only other 'useful' gene right now is around herbivore/carnivore digestion (0 to 1), which allows them to extract more energy from either meat or plant-food. Most of the time they actually develop specific behavior according to this gene.
I don't really code in what offspring need to do beyond having slight variations to both factors described above, it kind of evolves randomly into more complexity (neural net + behaviors).
Also importantly– I need to program an energy decay system and death if they run out. So basically: Energy source, energy decay and evolving neural nets that can give an organism the possibility to survive and evolve if they get more energy. And voila– Life emerges.
Working on plants now, and again simple rules: Neural nets in the plants to mimic evolution of complex biological systems that evolve from generation to generation. And a light-based energy source and light-based energy capture system (leaves). My current (preliminary) experiments show that the plants start to look like trees over time to maximize energy capturing compared to competing plants.
Looking to publish this once I have it a bit more refined.
One more question, sorry my knowledge of ML is not so profound, does NEAT algorithm mimic how natural selection works? And how.
Natural selection in this sim just happens by itself, there is a limited amount of food and only the best adapted ones survive. So the best performing neural networks duplicate themselves and create small variations of themselves. This part is not connected to the NEAT algorithm, I've just seen that NEAT performs particularly good vs more fixed-structure neural networks.
Depends how accurate you want your model. Pie in the sky thinking, we need quantum computing before I can imagine these kinds of simulations making sense
https://www.microsoft.com/en-us/research/blog/mattersim-a-de...
More generally, ML is good at approximating many NP-hard problems efficiently, so I wonder if it will be a more practical alternative to quantum computing for things like molecular simulation.
And just because, for example, organisms that live in cold climates generally have thick hair, having thick hair doesn't imply cold climates. Some mammals fill niches filled by birds in other ecosystem, or by fish in others. Likewise in New Zealand they have birds filling niches that in other ecosystems are filled by mammals.
I'm not sure biology can be a purely inductive science.