But that is practically untrue (I'm not just saying there are corner cases, but that the main topic is still very poorly understood) and a cop-out.
And yet I can speak concretely about a hydrogen atom. Or even photosynthesis. Or DNA.
In DNA, there are 4 base pairs. We can see how that maps to RNA and how that in turn codes to amino acids. (and exceptions exist to these things, but we have a pretty strong grasp on it.) We can even try to fold the resulting Amino Acids into shapes, although it's computationally very difficult to do so. So data storage within a cell has some concrete understanding. Empirically validated and useful (this is how we were able to engineer the mRNA vaccines).
Our knowledge of memory, our knowledge of data storage in the brain is far less concrete. And not just because it's not thought to be precisely molecular like DNA or RNA (although perhaps it could be!) but we have a poor grasp of even where, precisely, information is stored in the connections. Like, sure, you can say it's not stored in a particular location in 3D space, but there must be some transformation, some (non-euclidian) representation of the connections where memories CAN be located. Because our brains are capable of recalling memories on demand. But we have only a vague understanding of how that all occurs. It's very unlike DNA or RNA or whathaveyou in that our understanding is still primitive and vague.
Memory shares a property with holograms or with lenses/mirrors. If you draw a spot with a marker on one and looks at/through it, you don’t see the spot rather you see a slight loss of image quality across the image.
If you wipe out a bit of brain your memories degrade a bit. Somehow the encoding of memory is holographic.
We just need a different set of mathematical skills/tools to understand this property. From the point of view of traditional scientists it’s not something they know how to tackle.
Personally I think we should look at the brain (and DNA) with a signal processing lens. How data is encoded in the brain is something engineers could answer. Neural networks too are analog computers and spike signals are encoded with a variety of modulation schemes. If we understand those we don’t need to model neutrons the way we do (which is very inefficient, using floating point etc.) but by custom single-transistor analog circuits. That might help advance computation in general...once we understand how the brain is doing it.
You can talk about some abstractions about how a hydrogen atom works, but we don't fully understand everything about atomic structure. We don't know if there is something that makes up the structures of subatomic particles becuase we don't have particle accelerators that have enough energy for us to peer deeper into what quarks are made up of. We don't even know if we've found all the types of particles.
Corner cases in several layers lower than chemistry vs not even really knowing where a memory is stored!
Not sure where that hubris comes from. There are physical phenomena we have not been able to test, so there will always be unknowns and assumptions based on (abstract) modeling. eg the metallic liquid hydrogen described here https://edu.rsc.org/soundbite/hydrogen-falls-apart-under-pre...
And I don’t mean to say that neuroscientists are doing shoddy work. Far from it! The brain is a far more complex entity than RNA or a hydrogen atom. The task is MUCH harder! But I am showing that high level of specific, concrete knowledge IS possible in the physical sciences. Memory in the Brain is a physical process as well, but we have only a relatively vague understanding of the specifics of it. We can sequence DNA or RNA accurately with relative ease. We cannot do the same with memories in the brain.
You're missing the point. The idea that the predictions are the same as knowledge illustrates the misunderstanding. We do not know because we cannot prove it. I shouldn't need to get into the reason we produce experimental evidence eg https://www.forbes.com/sites/startswithabang/2019/07/06/ask-... - via the LIGO and Virgo detectors
I'm not addressing the hand wavy nature of the article, which is self-evident. This is irrelevant to the point being discussed.
You might want to reread the thread for context. You don't seem to be discussing the same point as the people you're replying to.
Less click baity.
Open loop systems are simple...the effects of one block can be understood in isolation. Closed loop control systems with feedback have nonlinear responses to stimuli that can cause ripple effects across the system.
By analogy, on a modern cpu with good power management, and shared memory caches, if a program heavily writes to computer memory, the effects will be on the whole computer. Not only will memory power increase, but cache use will too (causing threads on other cpus to slow down) and the chip will heat up, causing every part of the chip to throttle.
Surely there must be a better way to conduct this experiment than to terrorize innocent mice.
Why can't they