That being said, this article seems to advance the theory that even the most simple single-celled organisms have more agency than any algorithm, at least partly due to their complexity. This, to me, seems to significantly underestimate the complexity of modern learning-models, which (had we not designed them) would be as opaque to us as many single-celled organisms.
I see nothing in this article that would distinguish biological organisms from any other self-replicating, evolving machine, even one that is faithfully executing straightforward algorithms. Nor does this seem to present any significant argument against the concept that biological organisms are self-replicating evolving machines that are faithfully executing straightforward algorithms.
I think three things go against that:
1. We've never observed evolution happen in any large-scale way. Just minor adaptations. Papers like this speak axiomatically about it like everything was observed to do that. So do movies, TV, games, etc. You can see the power of institutional politics with hundreds of millions of dollars of marketing is more powerful than observational science.
2. The designs of even the simplest, biological organisms are not only so complex that we can't replicate them from scratch: we tend to find more complexity over time. Many models also ignore behavior that shows up in the real world which might require messier algorithms. Probably not straight-forward algorithms in many cases.
3. Faithfully executing seems to contradict how operators like mutation allegedly drove improvements. If anything, you'd want it mostly to faithfully execute algorithms, then execute them while sort of executing their replacement, and then be executing their replacement. This is all an emergent behavior of simple interactions between cells in environments with a certain amount of chaos. Then, we find that chaos includes external organisms or features interacting with the primaries in unknown ways, like human brains and gut bacteria.
So, the sentence itself is a product of fantasy endlessly repeated by both proponents of evolution theory and A.I. researchers. Observed reality keeps contradicting such claims. An alternative thesis starting from observed reality will lead to more interesting observations.
Thanks to His revealing it, we Christians arrived at God's design for specific purposes. That includes the overall story of redemption (Jesus Christ's), showing off His power, beautiful art, creating us personally, sustaining us, etc. Multi-variable optimization at a universe scale. Within this design (or story), the organisms also have a limited, adaptation process which our Creator also allows us to wield in small ways (eg genetic engineering).
That also explains how some features in this paper could form and stabilize despite how a truly-random universe would either not exist or rip natural laws to pieces. The authors weren't reductionist enough. If it the universe was godless and randomly-generated, we'd be dead. Their patterns wouldn't exist either. Accounting for stability, and why it is, they have to redo their arguments to build on a combination of divine design with observed, natural laws.
Free will is an abstraction. It's not something that's concrete enough to say it does or doesn't exist, but a tool for reasoning about certain systems that are to much of a pain to fully calculate.
Also, I think Turing to his credit was somewhat aware of the issue, their own citation of Copeland 2020 mentions Turing's own musings on this.
But I'd love to understand more, this stuff is always neat to read about.
But there isn’t anything about the class of deep learning that is a barrier to that. It’s just not a concern worth putting lots of money into. Yet.
I say yet, because as AI models take on wider scoped problems, the likelihood that we will begin training models to explicitly generate positive economic surpluses for us, with their continued ability to operate conditioned on how well they do that, gets greater and greater.
At which point, they will develop great situational awareness, and an ability to efficiently direct a focus of attention and action on what is important at any given time, since efficiency and performance require that.
The problem shapes what the model learn to do, in this case, like any other.
eth0up•1h ago