It's worth considering why "everyone seems all too ready to make ... leaps ..." "Neural", "intelligence", "learning", and others are metaphors that have performed very well as marketing slogans. Behind the marketing slogans are deep-pocketed, platformed corporate and government (i.e. socio-rational collective) interests. Educational institutions (another socio-rational collective) and their leaders have on the whole postured as trainers and preparers for the "real world" (i.e. a job), which means they accept, support, and promote the corporate narratives about techno-utopia. Which institutions are left to check the narratives? Who has time to ask questions given the need to learn all the technobabble (by paying hundreds of thousands for 120 university credits) to become a competitive job candidate?
I've found there are many voices speaking against the hype---indeed, even (rightly) questioning the epistemic underpinnings of AI. But they're ignored and out-shouted by tech marketing, fundraising politicians, and engagement-driven media.
Or perhaps, determinism and mechanistic materialism - which in STEM-adjacent circles has a relatively prevalent adherence.
Worldviews which strip a human being of agency in the sense you invoke crop up quite a lot today in such spaces. If you start of adopting a view like this, you have a deflationary sword which can cut down most any notion that's not mechanistic in terms of mechanistic parts. "Meaning? Well that's just an emergent phenomenon of the influence of such and such causal factors in the unrolling of a deterministic physical system."
Similar for reasoning, etc.
Now obviously large swathes of people don't really subscribe to this - but it is prevalent and ties in well with utopian progress stories. If something is amenable to mechanistic dissection, possibly it's amenable to mechanistic control. And that's what our education is really good at teaching us. So such stories end up having intoxicating "hype" effects and drive fundraising, and so we get where we are.
For one, I wish people were just excited about making computers do things they couldn't do before, without needing to dress it up as something more than it is. "This model can prove a set of theorems in this format with such and such limits and efficiency"
The irony is: why would someone want control if they don't have true choice? Unfortunately, such a question rarely pierces the intoxicated mind when this mind is preoccupied with pass the class, get an A, get a job, buy a house, raise funds, sell the product, win clients, gain status, eat right, exercise, check insta, watch the game, binge the show, post on Reddit, etc.
Is this controversial in some way? The problem is that to simulate a universe you need a bigger universe -- which doesn't exist (or is certainly out of reach due to information theoretical limits)
> ---like Leibniz's Ratiocinator. The intoxication may stem from the potential for predictability and control.
I really don't understand the 'control' angle here. It seems pretty obvious that even in a purely mechanistic view of the universe, information theory forbids using the universe to simulate itself. Limited simulations, sure... but that leaves lots of gaps wherein you lose determinism (and control, whatever that means).
It’s not “controversial”, it’s just not a given that the universe is to be thought a deterministic machine. Not to everyone, at least.
My comments are not about simulating the universe on a real machine. They're about the validity and value of math/computational modeling in a universe where determinism is scientifically indeterminable.
What would you say if we can predict the outcome of an experiment with 51% probability. Is that enough to establish what you call "control"? What if we can repeat the experiment as many times as we like?
(I must admit, I still don't really understand what "control" means to you, but let's get the preliminaries out of the way first.)
I don’t think it does. Taking computers as an analogy… if you have a computer with 1GB memory, then you can’t simulate a computer with more than 1GB memory inside of it.
The universal approximation theorem. And that's basically it. The rest is empirical.
No matter which physical processes happen inside the human brain, a sufficiently large neural network can approximate them. Barring unknowns like super-Turing computational processes in the brain.
Sorry, but there's not much evidence that can support human exceptionalism.
Although "non-deterministic" and "stochastic" are often used interchangeably, they are not equivalent. Probability is applied analysis whose objects are distributions. Analysis is a form of deductive, i.e. mechanical, reasoning. Therefore, it's more accurate (philosophically) to identify mathematical probability with determinism. Probability is a model for our experience. That doesn't mean our experience is truly probabilistic.
Humans aren't exceptional. Math modeling and reasoning are human activities.
And physicists regard those as unphysical: the theory breaks down, we need better one.
Switching into a less facetious mode...
Do you understand that in context of this dialogue it's not enough to show some examples of discontinuous or otherwise unrepresentable by NNs functions? You need at least to give a hint why such functions cannot be avoided while approximating functionality of the human brain.
Many things are possible, but I'm not going to keep my mind open to a possibility of a teal Russell's teapot before I get a hint at its existence, so to speak.
The brain is a physical system, so whatever it does (including philosophy) can be replicated by modelling (a (vastly) simplified version of) underlying physics.
Anyway, I am not especially interested in discussing possible impossibility of an LLM-based AGI. It might be resolved empirically soon enough.
If it does, well, it will take more time to incorporate those physical mechanisms into computers to get them on par with the brain.
I leave the possibility that it's "magic"[1] aside. It's just impossible to predict, because it will violate everything we know about our physical world.
[1] One example of "magic": we live in a simulation and the brain is not fully simulated by the physics engine, but creators of the simulation for some reason gave it access to computational resources that are impossible to harness using the standard physics of the simulated world. Another example: interactionistic soul.
Consider also that even as digital technology and the ratiomathimatical understanding of the world has advanced it is still rife with dynamics and problems that require a humanistic approach. In particular, a mathematical conception cannot resolve teleological problems which require the establishment of consensus and the actual determination of what we, as a species, want the world to look like. Climate change and general economic imbalance are already evidence of the kind of disasters that mount when you limit yourself to a reductionistic, overly mathematical and technological understanding of life and existence. Being is not a solely technical problem.
19th/20th was a golden era of philosophy with a coherent and rigorous mathematical lens to apply with other lenses. Russel, Turing, Godel, etc. However this just doesn't exist anymore
The relevance of mathematics to the cognitive problem must be decided outside of mathematics. As another poster said, even if you buy the theorems, it is still an empirical question as to whether or not they really model what they claim to model, and whether or not that model is of a fidelity that we find acceptable for a definition of general intelligence. Often, people reach claims of adequacy today not by producing really fantastic models but instead by lowering the bar enormously. They claim that these models approximate humans by severely reducing the idea of what it means to be an intelligent human to the specific talents their tech happens to excel at (e.g. apparently being a language parrot is all that intelligence is, ignoring all the very nuanced views and definitions of intelligence we have come up with over the course of history. A machine that is not embodied ina skeletal structure and cannot even experience, let alone solve, the vast number of physical, anatomical problems we contend with on a daily basis is, in my view, still very far from anything I would call general intelligence).
Historically, the claim that neural nets were actual models of the human brain and human thinking was always epistemically dubious. It still is. Even as the practical problems of producing better and better algorithms, architectures, and output have been solved, there is no reason to believe a connection between the mechanical model and what happens in organisms has been established. The most important point, in my view, is that all of the representation and interpretation still has to happen outside the computational units. Without human interpreters, none of the AI outputs have any meaning. Unless you believe in determinism and an overseeing god, the story for human beings is much different. AI will not be capable of reason until, like humans, it can develop socio-rational collectivities of meaning that are independent of the human being.
Researchers seemed to have a decent grasp on this in the 90s, but today, everyone seems all too ready to make the same ridiculous leaps as the original creators of neural nets. They did not show, as they claimed, that thinking is reducible to computation. All they showed was that a neural net can realize a boolean function—which is not even logic, since, again, the entire semantic interpretive side of the logic is ignored.