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> Unless you believe in determinism and an overseeing god

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"

Agreed. If someone believes the world is purely mechanistic, then it follows that a sufficiently large computing machine can model the world---like Leibniz's Ratiocinator. The intoxication may stem from the potential for predictability and control.

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.

> If someone believes the world is purely mechanistic, then it follows that a sufficiently large computing machine can model the world

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).

People wish to feel safe. One path to safety is controlling or managing the environment. Lack of sufficient control produces anxiety. But control is only possible if the environment is predictable, i.e., relatively certain knowledge that if I do X then the environment responds with Y. Humans use models for prediction. Loosely speaking, if the universe is truly mechanistic/deterministic, then the goal of modeling is to get the correct model (though notions of "goals" are problematic in determinism without real counterfactuals). However, if we can't know whether the universe is truly deterministic, then modeling is a pragmatic exercise in control (or management).

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.

> However, if we can't know whether the universe is truly deterministic, then modeling is a pragmatic exercise in control (or management).

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.)

> Is this controversial in some way?

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.

That's fine and well, but AFAICT the only alternative is it being non-deterministic... which doesn't seem very satisfactory either.

Choice is over rated. This gets to an issue Ive long had with Nozicks experience machine. Not only would I happily spend my days in such a machine, Im pretty sure most other people would too. Maybe they say they wouldnt but if you let them try it out and then offered them the question again I think theyd say yes. The real conclusion of the experience machine is that the unknown is scary.

> Agreed. If someone believes the world is purely mechanistic, then it follows that a sufficiently large computing machine can model the world---like Leibniz's Ratiocinator.

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.

"sufficiently large machine" ... It's a thought experiment. Leibniz didn't have a computer, but he still imagined it.

But this machine (even a tremendously large one) will have to operate in our reality and therefore can’t be “bigger” than it.

I hear these arguments a lot from law and philosophy students, never from those trained in mathematics. It seems to me, "literary" people will still be discussing these theoretical hypotheticals as technology passes them by building it.

I straddle both worlds. Consider that using the lens of mathematical reasoning to understand everything is a bit like trying to use a single mathematical theory (eg that of groups) to comprehend mathematics as a whole. You will almost always benefit and enrich your own understanding by daring to incorporate outside perspectives.

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.

I don't disagree, I just don't think it is done well or at least as seriously as it used to. In modern philosophy, there are many mathematically specious arguments, that just make clear how large the mathematical gap has become e.g. improper application of Godel's incompleteness theorems. Yet Godel was a philosopher himself, who would disagree with its current hand-wavy usage.

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

While I agree that these are titans of 20th c. philosophy, particularly of the philosophy of mathematics and logic, the overarching school they belonged to (logical positivism) has been thoroughly and rightly criticized, and it is informative to read these criticisms to understand why a view of life that is overly mathematical is in many ways inadequate. Your comment still argues from a very limited perspective. There is no reason that correct application of Gödel s theorem should be any indication of the richness of someone's philosophical views unless you are already a staunchly committed reductionist who values mathematical arguments above all else (why? can maths help you explain and understand the phenomena of love in a way that will actually help you experience love? this is just one example domain where it does not make much sense), or unless they are specifically attempting a philosophy of mathematics. The question of whether or not we can effectively model cognition and human mental function using mathematical models is not a question of mathematical philosophy, but rather one of epistemology. If you really want to head a spurious argument, read McCulloch and Pitts. They essentially present an argument of two premises, the brain is finite, and we can create a machine of formal "neurons" (which are not even complete models of real neurons) that computes a boolean function, they then conclude that they must have a model of cognition, that cognition must be nothing more than computation, and that the brain must basically be a Turing machine.

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).

> there is no reason to believe a connection between the mechanical model and what happens in organisms has been established

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.

The universal approximation theorem is set in a precise mathematical context; I encourage you to limit its applicability to that context despite the marketing label "universal" (which it isn't). Consider your concession about empiricism. There's no empirical way to prove (i.e. there's no experiment that can demonstrate beyond doubt) that all brain or other organic processes are deterministic and can be represented completely as functions.

Function is the most general way of describing relations. Non-deterministic processes can be represented as functions with a probability distribution codomain. Physics seems to require only continuous functions.

Sorry, but there's not much evidence that can support human exceptionalism.

Some differential equations that model physics admit singularities and multiple solutions. Therefore, functions are not the most general way of describing relations. Functions are a subset of relations.

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.

> Some differential equations that model physics admit singularities and multiple solutions.

And physicists regard those as unphysical: the theory breaks down, we need better one.

For example, the Euler equations model compressible flow with discontinuities (shocks in the flow field variables) and rarefaction waves. These theories are accepted and used routinely.

Great. A useful approximation of what really happens in the fluid. But I'm sure there are no shocks and rarefactions in physicists' neurons while they are thinking about it.

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.

I don't understand your point here. A (logical) relation is, by definition, a more general way of describing relations than a function, and it is telling that we still suck at using and developing truly relational models that are not univalent (i.e. functions). Only a few old logicians really took the calculus of relations proper seriously (Pierce, for one). We use functions precisely because they are less general, they are rigid, and simpler to work with. I do not think anyone is working under the impression that a function is a high fidelity means to model the world as it is experienced and actually exists. It is necessarily reductionistic (and abstract). Any truth we achieve through functional models is necessarily a general, abstracted, truth, which in many ways proves to be extremely useful but in others (e.g. when an essential piece of information in the particular is not accounted for in the general reductive model) can be disastrous.

I'm not a big fan of philosophy. The epistemology you are talking about is another abstraction on top of the physical world. But the evolution of the physical world as far as we know can be described as a function of time (at least, in a weak gravitational field when energies involved are well below the grand unification energy level, that is for the objects like brains).

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.

That's not useful by itself, because "anything cam model anything else" doesn't put any upper bound on emulation cost, which for one small task could be larger than the total energy available in the entire Universem

Either the brain violates the physical Church-Turing thesis or it's not.

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.

I mean, that is why they mention super-Turning processes like quantum based computing.

Quantum computing actually isn't super-Turing, it "just" computes some things faster. (Strictly speaking it's somewhere between a standard Turing machine and a nondeterministic Turing machine in speed, and the first can emulate the second.)

If we're nitpicking: quantum computing algorithms could (if implemented) compute certain things faster than the best classical algorithms we know. We don't know any quantum algorithms that are provably faster than all possible classical algorithms.

Well yeah, we haven't even proved that P != NP yet.

I'm with you. Interpreting a problem as a problem requires a human (1) to recognize the problem and (2) to convince other humans that it's a problem worth solving. Both involve value, and value has no computational or mechanistic description (other than "given" or "illusion"). Once humans have identified a problem, they might employ a tool to find the solution. The tool has no sense that the problem is important or even hard; such values are imposed by the tool's users.

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.

so the interpretation happens inside the soul? otherwise it matters very little if a specific computation happens in a silicone chip or a human neuron...

Can you define what you mean by novel here?