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 ...)
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 ...)