Aug 14, 2017

The evolutionary pressure is just people or programs detecting certain strains (it would generate lots of strains randomly).

I think the only real issue to plausibility here is I'm not sure brute force is enough to make enough plausible behavioral branches, at least with current computing power/internet bandwidth. A reasonably efficient self-modification mechanism (in terms of viable strain per transmission) is probably extremely large, I'd say at least 1GB. Not unlike deep learning systems, this would consist of a large functional composition of heuristics, codifying how to write code that can embedded itself in other programs and write modifications to itself that are likely to work.

Note that we haven't yet gotten a good neural-generated code modifications, even using large networks, GPU training and large computing time. Best examples I could find:

So we're not yet at a point this could be plausible (as it couldn't hide itself in small programs), but eventually it will be -- once there is enough headroom on most GPUs and certain types of software are large enough it could hide it's network inside, and generally enough internet bandwidth to spread it's >GB-scale code. I'd imagine something like a game, which usually has networking -- it would be using GPUs partially to generate and spread new strains of it trying to infect other games and such.

Note there are biological viruses with tiny genomes however -- the smallest are on the order of ~1kbyte. But as you cite they had billions of years, producing maybe quadrillions of viruses every year, giving this tiny efficient and specialized weapon. Interestingly, they rely on other cells machinery to even replicate their genome -- analogous to using the compiler here.

If everyone could send >10^18 different small self-replicating viruses over your network, it seems likely some would exploit bugs in certain kinds of hardware/software, evolving through this selective pressure.

Feb 24, 2017

That's interesting that the paper was on arxiv since November ( ) and people payed attention just now.

Feb 22, 2017

Arxiv link