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.
The model that reconstructs these simulations are certainly algorithms.
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.
* Not eating until your body fails.
* Not breathing until automatic breathing kicks in.
And not being able to perform dematerialization doesn't count as non-free will, for example.
It is less direct to the examples you give; but I'm confident that parents are psychologically designed to sacrifice themselves in the event it helps their children and many men, famously, are built to go to the frontlines and sacrifice themselves for family and community. Hard to make an assessment of whether those sort of choices is free will or determined nature.
One general impression I have, having read the reactions by biologists to stuff like Kurzweil and people who believe we're close to a computational understanding of biology is that all the computer science people massively, MASSIVELY underestimate the extent to which we still do not understand how even a single cell works.
Sure, we can model things stochastically, or fiddle with DNA and be able to predict the results, but there's a bunch of stuff in the middle that we only have a functional understanding of. We know with <xxx> input, you get <yyy>, etc..., but the how is still a mystery.
This is everywhere in biology.
If you think biologists are underestimating complexity, you have the sign wrong.
https://www.wired.com/story/openworm-worm-simulator-biology-...
If I handed someone who had never seen an artificial neural network, and handed them a PCB with some giant LLM hard-coded into it, I suspect they would struggle to define how it reacts to its inputs, despite the fact that modern silicon designs are extremely regular compared to biological systems.
More progress has been done answering the question of "what is cognition" by Machine Learning programmers then has ever been done by a philosopher.
I understand there’s stuff like logic and the philosophy of science that make sense. But when there’s shit like animism it becomes a huge category error. why not just not make something like logic a part of philosophy? Just pull it out.
The umbrella of philosophy is like the category of analysis of everything in existence. It covers too much. It’s like anything that’s deep reflection automatically becomes philosophy. It’s too broad and that’s why it gets stupid when you have the philosophy of mathematics living side by side with the philosophy of art.
No, that's not how sense works.
Specifically, my reactions are:
a) Defining agency in terms "relevance" or "salience" is just circular logic.
b) Their argument about the extended Church-Turing-Deutsch thesis would already apply to physics and the universe, not just intelligent entities. So this is just poorly argued.
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.
One is wholly internal to the entity under discussion, while the other isn't.
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•8mo ago
tbrownaw•8mo ago
ninetyninenine•8mo ago