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I love projects such as this. Although I don't know how successful they're in actually getting new insights - artificial life was the reason I wanted to study i

I love projects such as this. Although I don't know how successful they're in actually getting new insights - artificial life was the reason I wanted to study informatics.

Bluestein 2y ago
I can totally see that. Emergence, emergent phenomena are fascinating.- (I am convinced that at some obscure intersection of emergence, network effects and machine learning, there lurk answers to many, fundamental questions ...
- WanderPanda 2y ago
  Do you mean emergence and network effects IN machine learning? Otherwise I would be very curious how machine learning fits that list in your view
  - Bluestein 2y ago
    Thanks for making me think a bit deeper :) After some review, no, I do not think I would alone use "in" in somehow, I guess, circunscribing the probable, positive effects that I meant of emergence and network effects to ML.- In a certain sense, I think there's something "fundamental", a primitive, to ML and Transformers and such "big-data" and information techniques such as they are being applied to AI, that puts them up there with emergent phenomena and network effects in terms of constituting (or manifesting, or following, or embodying ....) some very fundamental principles.- So, in a sense, I am more and more leaning towards thinking that (particularly when applied to AI and the search for AGI) "primitives" such as emergence (particularly) are somehow to be brought to bear ... PS. As an illustration, look into JEPA and other more "holistic" approaches to simulating or achieving the "I" in AI. Approaches that are made up of very complex systems interacting with each other (some of which are Transformers, or verbal) but not entirely ... Now, coming back to the "in", above ... ... could emergence and network effects have use in ML itself (as in, integrated or taken advantage of in these systems) and the answer would also be yes, I think.- That is to say, emergence, network effects, ML ... consciousness perhaps, and other "fundamentals" might constitute - both as parts of larger solutions and incorporated within each other - useful building blocks ... Along these lines, there are some interesting "intersections" that I am exploring: - Bio electric signaling. Turns out neurons are great, but they are not the end-all of biological electrical signaling - Proprioception in ML, AI (!), and, of course robotics. There's something about having a body or being "embodied" that has some bearing here I am sure ... - JEPA (I and V) an other approaches that are hitting the problem from a more "holistic"/complex approach, trying to imitate or use "higher order" systems working together We do live in interesting times :) (And, I do not mean this in the overloaded sense of the Chinese saying to one's enemies ...)
RaftPeople 2y ago
I built an a-life system with basic creatures with senses, motion, consumption of food, and a neural net brain. Brains started random, each generation had a fixed life span, then next generation was created from previous with varying levels of evolution (top 10% best creatures left un-changed, next 10% tweaked a little, next 10% tweaked more...last 20% completely re-created randomly). I wanted to see some level of advanced control evolve, like a creature hiding behind an object waiting for it's prey (other creature). I didn't see anything remotely close to something like that, but I did see was some level of success evolve. Meaning some creatures had brains that allowed them to optimize finding food and avoiding getting eaten by other creatures, even if they were clearly using just simple strategies that ended up being effective, like frequently moving in a large circular motion to find food. This is the question the whole thing left me with: What is required to push this evolution to create the types of advanced control (intelligence?) that I was hoping for? Is there a level of complexity in the environment that is a base requirement? Meaning if the environment is too simple, then there is no opportunity to evolve the various building blocks that would all come together to produce advanced control because simpler strategies would always outperform in the short term? What were the conditions in our environment that pushed or allowed crows to become smart? Maybe, if the environment has enough complexity, then you can have many more species occupying their own little niche, which increases the odds of acquiring some mental attribute. Or maybe there is some very specific set of conditions within a species niche that opens the door for these advanced mental attributes to be valuable.
- Bluestein 2y ago
  > Is there a level of complexity in the environment that is a base requirement? I think you are unto something here. Either that or a necessary minimum of sensory/processing capacity to "take in"/parse/drawn information from the environment, which amounts to the same (you end up with a certain amount of environmental complexity that you can benefit from, as a "ceiling" ...
- _wire_ 2y ago
  Consider that the lower orders of integration of life are energetic molecular arrangements, then genetically controlled factories which make which make up the components of cells that in turn give rise to cellular replication, then cellular arrangements and multicellular organisms. The creatures you are hoping to simulate which seem to be at the level of abstraction of a nematode, IOW a model of a very simple body with a brain of a few hundred neurons, operate in environments so diffuse and rarified that superficially they don't seem striking behaviorally, even as they do the stuff you expect simple animals to do. A casual onlooker will not be impressed by the richness of the world of the nematode. So one challenge is you could be on the right track but find appropriate results boring. But it may be that the best you can do with a simulation is make arrangements seem interesting, because there may be an entropic blockade to stimulating life: life may be so efficient in its manifestation of degrees of abstractions, interactions, fabrication, layering, and interdependencies-- its complexity-- that organization of models using electronic computation devices can't get within many orders of magnitude of life's dynamical density and therefore simulation of life is inherently impractical. IOW you can get from life to computers, but entropy prevents getting from computers to life. In life, is there any clear distinction between machine and code? Genomics suggests there might be, but so far fiddling with genomes is well known only for edits of taxonomically superficial traits. Has anyone shown edits that give rise to a new subspecies-- much less a new genus? Idk... but it wouldn't surprise me to find the answer is be no, and that epigenetic factors might inhibit any approach to editing new lifeforms into existence. Is life is fundamentally emergent, without possibility of construction? I see a definitional hazard; that we may lack the language or cognitive capacity to deal with life's dynamics. These may be off limits. Or maybe not?

I love projects such as this. Although I don't know how successful they're in actually getting new insights - artificial life was the reason I wanted to study informatics.

Bluestein 2y ago

I can totally see that. Emergence, emergent phenomena are fascinating.- (I am convinced that at some obscure intersection of emergence, network effects and machine learning, there lurk answers to many, fundamental questions ...

WanderPanda 2y ago
Do you mean emergence and network effects IN machine learning? Otherwise I would be very curious how machine learning fits that list in your view
- Bluestein 2y ago
  Thanks for making me think a bit deeper :) After some review, no, I do not think I would alone use "in" in somehow, I guess, circunscribing the probable, positive effects that I meant of emergence and network effects to ML.- In a certain sense, I think there's something "fundamental", a primitive, to ML and Transformers and such "big-data" and information techniques such as they are being applied to AI, that puts them up there with emergent phenomena and network effects in terms of constituting (or manifesting, or following, or embodying ....) some very fundamental principles.- So, in a sense, I am more and more leaning towards thinking that (particularly when applied to AI and the search for AGI) "primitives" such as emergence (particularly) are somehow to be brought to bear ... PS. As an illustration, look into JEPA and other more "holistic" approaches to simulating or achieving the "I" in AI. Approaches that are made up of very complex systems interacting with each other (some of which are Transformers, or verbal) but not entirely ... Now, coming back to the "in", above ... ... could emergence and network effects have use in ML itself (as in, integrated or taken advantage of in these systems) and the answer would also be yes, I think.- That is to say, emergence, network effects, ML ... consciousness perhaps, and other "fundamentals" might constitute - both as parts of larger solutions and incorporated within each other - useful building blocks ... Along these lines, there are some interesting "intersections" that I am exploring: - Bio electric signaling. Turns out neurons are great, but they are not the end-all of biological electrical signaling - Proprioception in ML, AI (!), and, of course robotics. There's something about having a body or being "embodied" that has some bearing here I am sure ... - JEPA (I and V) an other approaches that are hitting the problem from a more "holistic"/complex approach, trying to imitate or use "higher order" systems working together We do live in interesting times :) (And, I do not mean this in the overloaded sense of the Chinese saying to one's enemies ...)

RaftPeople 2y ago

I built an a-life system with basic creatures with senses, motion, consumption of food, and a neural net brain. Brains started random, each generation had a fixed life span, then next generation was created from previous with varying levels of evolution (top 10% best creatures left un-changed, next 10% tweaked a little, next 10% tweaked more...last 20% completely re-created randomly). I wanted to see some level of advanced control evolve, like a creature hiding behind an object waiting for it's prey (other creature). I didn't see anything remotely close to something like that, but I did see was some level of success evolve. Meaning some creatures had brains that allowed them to optimize finding food and avoiding getting eaten by other creatures, even if they were clearly using just simple strategies that ended up being effective, like frequently moving in a large circular motion to find food. This is the question the whole thing left me with: What is required to push this evolution to create the types of advanced control (intelligence?) that I was hoping for? Is there a level of complexity in the environment that is a base requirement? Meaning if the environment is too simple, then there is no opportunity to evolve the various building blocks that would all come together to produce advanced control because simpler strategies would always outperform in the short term? What were the conditions in our environment that pushed or allowed crows to become smart? Maybe, if the environment has enough complexity, then you can have many more species occupying their own little niche, which increases the odds of acquiring some mental attribute. Or maybe there is some very specific set of conditions within a species niche that opens the door for these advanced mental attributes to be valuable.

Bluestein 2y ago
> Is there a level of complexity in the environment that is a base requirement? I think you are unto something here. Either that or a necessary minimum of sensory/processing capacity to "take in"/parse/drawn information from the environment, which amounts to the same (you end up with a certain amount of environmental complexity that you can benefit from, as a "ceiling" ...
_wire_ 2y ago
Consider that the lower orders of integration of life are energetic molecular arrangements, then genetically controlled factories which make which make up the components of cells that in turn give rise to cellular replication, then cellular arrangements and multicellular organisms. The creatures you are hoping to simulate which seem to be at the level of abstraction of a nematode, IOW a model of a very simple body with a brain of a few hundred neurons, operate in environments so diffuse and rarified that superficially they don't seem striking behaviorally, even as they do the stuff you expect simple animals to do. A casual onlooker will not be impressed by the richness of the world of the nematode. So one challenge is you could be on the right track but find appropriate results boring. But it may be that the best you can do with a simulation is make arrangements seem interesting, because there may be an entropic blockade to stimulating life: life may be so efficient in its manifestation of degrees of abstractions, interactions, fabrication, layering, and interdependencies-- its complexity-- that organization of models using electronic computation devices can't get within many orders of magnitude of life's dynamical density and therefore simulation of life is inherently impractical. IOW you can get from life to computers, but entropy prevents getting from computers to life. In life, is there any clear distinction between machine and code? Genomics suggests there might be, but so far fiddling with genomes is well known only for edits of taxonomically superficial traits. Has anyone shown edits that give rise to a new subspecies-- much less a new genus? Idk... but it wouldn't surprise me to find the answer is be no, and that epigenetic factors might inhibit any approach to editing new lifeforms into existence. Is life is fundamentally emergent, without possibility of construction? I see a definitional hazard; that we may lack the language or cognitive capacity to deal with life's dynamics. These may be off limits. Or maybe not?