cbennett

Check out Greg Bear’s Forge Of God and Anvil of Stars 2 book series . Not exactly the same plot you mentioned but covers an interesting variant plot including fall out of a situation where Earth is killed by killer probes and remnants of humanity painfully search for the Killer’s system. What they find is different than expected

Edit: I see you linked to the wrong sub-section there. However, overall the contrast between super-predator theory and (self-)annihilation is a bit more complex than one would think. They're not entirely binary.

On one hand, the self-annihilation idea implies that super-predator civilizations, being extremely rate to exist, would not have strong motivations for exterminating competitors.

What would possibly be the need, given the vastness of time and space, and the thermodynamic arguments given? Indeed, following this logic, the extraordinarily rare galactic survivor civilizations are almost guaranteed a long, peaceful existence.

On the other hand, it all develops on the curve of the galactic gaussian justifying possible pre-emptive defense investments. There's a difference of 3-4 or n sigmas statistics likelihood. In the former case, the likelihood of a rare rival emerging is high enough that pre-emptive defense strategies will be considered, and rudimentary systems of deterrence put in place. In the (high) n theory, it probably would not. How a civilization in the early stages of hegemony determined this fact, would be an interesting and possible civilization-saving (or destroying) feat....

Side note, haven't heard of the Revelation Space trilogy. Does it offer a workable theory for the origins of the predator dynamic?

"Well, magnetic core memory is the only data storage format that is robust enough to withstand high-radiation environments. Jeri is clearly interested in magnetic logic and memory because it is the only computing platform that will be able to survive the first wave of nuclear blasts that will unavoidably come from the beginning of the third great world war. "

Erm, this premise is factually untrue though. A lot of next generation resistive RAM devices, especially OxRAMs, have been demonstrated to be rather rad hard, making them good candidates for future space electronics platforms or.. all the other attendant apocalyptic scenarios.

> If it scales faster than linear, then your recursive bootstrapping operation takes longer and longer each time,

Wait, does that really follow? What if you have a better than linear bootstrapping compiler. To unpack that a bit, imagine we not only have $n$ such units, but we have them wired to together in a creative way-- i dont know whether it is hierarchy, or some clever topology, but lets say that the bootstrapper now gets sub-linear scaling properties as it grows $n$.

If we look at the brain, there is a lot to be understood from the dynamics of recurrent neural fields. They are wired in a very complex way which seems to allow for some kind of very special booting (re-booting) operations. And thats just at one level of abstraction, then we re-wire them into meta-fields (like the columnar abstractions that Hawkin's builds his HTM theories around). If we have a sort of fractal information encoding, we ultimately approach shannon efficient coding. Is that what evolution has selected brains to do? And do you think it is possible the first seed AI may realize this and exploit the same strategy, just 1000x (10kx?) faster?

Thanks for that. the hackernoon piece in particular is really worth the read. Two salient points/questions/comments that jumped out to me after reading (these are really about the theory and not practice):

-The fact that you have a bunch of capsules in each layer, and each one of them is intrinsically performing a non-linear filter function (a particular squashing function shown as an image in that blog), seems like both a great asset (it looks like a 'meta-network') and also a potential problem. If you need to tune weights within these caps by the derivative of such a composite function, it doesn't seem straightforward.

-The 'routing by agreement' feature is interesting, but I dont quite get why it is superior to max pooling. If the feature is simply that it punishes weak links rather than selecting only the strongest, one interesting analogy is that it seems a bit Hebbian , and related to a concept in un-supervised learning called STDP (spike timing dependent plasticity).

That sounds really interesting. What tasks did you attempt?

If you have open-sourced this in any sense, would enjoy to check it out and/or contribute.