See also: brain as hydraulics, brain as a system of cogs, brain as a computer.[1]
[1] https://aeon.co/essays/your-brain-does-not-process-informati...
[1] https://aeon.co/essays/your-brain-does-not-process-informati...
Jaron Lanier argues that computers and computation are cultural. Why give them a special ontological status? Yeah, we can think of the universe as computational or informational. We can also think of it as mathematical, mental or just whatever physics posits (fields, strings, higher dimensions, etc). Whatever the case, when someone like the article in the OP states that reality is X, then an ontological claim is being made. Its metaphysics.
One could instead argue that the world just is itself, and anything we say about it is our human model or best approximation. Which would be a combination of realism (the world itself) and idealism (how we make sense of it). Then it's just a matter of not mistaking the map for the territory. Instead of saying that the brain or the universe is X, we say that X is our best current map for Y. We don't say that London is a map. That would be making a category error.
Less load/process/store of absolute data and more like natural processes such as the process of erosion creating rivers. The analogy of the environment as a "lock" and organisms are just the most fit "keys" to success in particular environment.
So the computer analogy is bad because organisms are more a matrix of interactions, feedbacks and responses that work well enough, but dont follow a "logical" design. This can be replicated within a computer easily and the result is evolutionary computation & hardware, genetic algorithms and evolutionary neural networks. The problem in understanding the result of evolutionary systems is that they're blind to design and only respond to fitness and therefore create systems that are so tightly coupled it's a quest to understand how the model even works.
So the article is suggesting we shouldn't apply human design principles to evolved solutions. Perhaps we need some kind of "messy science" to make sense of it all.
Going forward, machine learning running evolutionary algorithms on neural networks should be able to produce sufficiently incomprehensibility for us to be studying our own inventions for years to come.
And even if we don't have the correct algorithms in sight today, there is every reason to believe that whatever processes are occurring in our brains and bodies, can indeed be simulated and replicated virtually.
The only way to argue against this idea is to claim that there is some special magical non-material aspect to our existence... which no article or neuroscience education has yet demonstrated.
True, but there are some pretty interesting ideas out there. I'm going have to start putting together a list of articles. From the proof that if we have free will, so do particles to some extent. To the notion that quantum computation may happen in the brain. Not saying I believe these things, but the people behind them are pretty smart.
The question remains, what reason do we have to believe that only a living brain, and not a silicon analogue, can tap into those features of reality?
The trap is to think about the brain in terms of things we find impressive, and about things we find impressive as being like brains somehow. Therefore analogies to steam engines, computers and deep learning. And these analogies have always turned out to be silly.
BUT: at least I think we are far from it. Very far. In the sense that we don't need more computing power of the current approaches to get e.g. AGI, we need radically new ones. And I actually don't see why this would be opposed to more neuroscience education, instead of excitement for cool but still quite limited models, and why this would be pretending that there is some "special magical non-material aspect to our existence"
How much can you compress the essential structure and complexity of an intelligent brain? It is an open question, but if in the end you can not compress it "enough", it does not have much practical consequences of it being also theoretically a mathematical object. And on top of that: we already know how to make new ones...
Very tiny animal life shows what we would consider intelligent behavior. There is no particular reason to believe that evolution has even come close to size optimization that intelligence can be reduced in, as there are a large number of other dimensions it is working on at the same time, survival being the big one.
The article, however, is rubbish. As psychologist, I cringed throughout. It is a blurr of half-baked ideas and ill-understood controversies from cognitive science. The author manages to write an entire article about information at its core without ever properly defining information, not to speak of representation. In the sense of Shannon, or course neurons are channels transmitting information. What else would they do?
And of course we can decode that information even from the outside, even down to discrete processing stages during the execution of mental tasks (https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejn.13817). And if there are truely no representations in the brain, as the author states, how do we plan for future events that are far beyond the horizon? And even if you reject all that, there is DNA in the brain that is literally information and expressed (decoded and made into protein) ALL THE TIME.
Regarding cognition, the good Mr. Epstein has not grasped the difference between computers and computability. I don't think anybody is looking for silicon in the brain. The smart people are asking how it is possible for a complex system to operate in a complex world without an outside unit directing their behavior. They ask "How how can the human mind occur in the physical universe" (http://act-r.psy.cmu.edu/?post_type=publications&p=14305)? How is it that we can do the things we do? How do we set goals, plan steps to achieve them, and choose the right actions for implementation?
I get where you are coming from and I agree with you regarding a dangerous misunderstanding of AI, especially ML. But this article is not helping putting things in perspective. I am willing, however, to concede one point to Mr. Epstein: His brain is dearly lacking information, representation, algorithms, or any such marker usually signifying intelligent life.
Regarding the questions you addressed, my suspicion is that the brain's primary trick is to model the organism and the environment. Planning ahead, reasoning and synthesizing knowledge can all happen if you can do that. I'd argue (and of course I'm biased) that control theory is probably a better place to start thinking about the brain, in that light, insofar as building models of the world is important.
If you want to be successful in a complex world, survive, replicate, you will profite massively if you know what is going on around you better than that other thing that wants to eat you. If you can grasp the structure of the physical world and predict its changes, you will come out on top. Information processing is an evolutionary necessity, because we are grounded in a physical world. Information is the successful way to deal with the world, because it gives the organism a choice.
Control theory is great if you want to describe real valued in- and outputs and their relationship over time. Like throwing a ball. But at some point we need to become discreet and abstract the real valued domain of space and time into symbols.
With no apologies to the UNIX metaphor of "everything is a file", my favorite starting point for [pretending at] explaining intelligence/understanding/recognition has long been "everything is a metaphor" ;)