Neural Annealing: Toward a Neural Theory of Everything

Your comments in this thread have broken the guidelines with swipes like "Of course you completely missed the point", "Leave it to the internet to read a comment and take it the wrong way", "Sigh", "Sheesh", and so on. That's just the sort of thing users here are asked to avoid. Would you please review https://news.ycombinator.com/newsguidelines.html and edit all that out of your comments here from now on?

Also, please don't use allcaps for emphasis. That's in the guidelines too.

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>Everything that is non-STEM is underfunded, it doesn't matter if it is philosophy, anthropology, sociology, history, etc.

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>Science is philosophy.

If you are going to justify philosophy by subsuming science, then you can not consistently claim that it is underfunded. By any reasonable standard, pure scientific research is respectably, if not ideally, funded.

What's missing here, of course, is any consideration for those branches of philosophy that are not science. This strategy of showing the importance of philosophy by invoking the success of science is short-sighted, and does a disservice to fields such as ethics.

>This is, for all practical intends and purposes, a pointless distinction. If we have created the algorithm, it means we have created the thing itself and it means we do undertand it on some level. What you talk about is some "perfect 100%" understanding, which does not really exist, it is a trick of a mind that cannot use any other facilities than pure logic, and wants the world to fit into those categories that can be perfectly described with rigid categories of the logical apparatus. The world doesn't really fit though, but such a mind will ignore that and will end up having to overintellectualize everything, it doesn't know where to stop.

>Humans are fully capable of operating in the world without fully (100%) understanding it, because they have other facilities than pure logical understanding. It doesn't mean that they operate things that they don't understand randomly, or just making choices for no reason. It means they have other intuitive methods of operating which can produce results without having a rigid logical model.

Stop using the term overintellectuallization. It's a meaningless word that says too much brain power for a given problem.

We don't even have to get philosophical about this and talk about the nature of "understanding". I am not and you are misinterpreting what I am saying. They laymans definition of 100% understanding is good enough because clearly all researchers in the field agree that we don't understand consciousness.

Let's simplify things then so we don't argue over semantics and the nature of understanding.

We can both agree that humans or a team of humans can "understand" what an operating system is and almost all fields in the sciences we as humans generally use that same level of "understanding" as a metric. We also understand the operating system well enough to the point that a team of humans can build one by hand.

That is the metric we are using for "understanding" consciousness because that is the metric used in ALL other hard scientific fields. No need to talk about what "100% understanding" is. Meaning that once you understand it, you can build it or model it by hand in a computer program. Unfortunately the trends in machine learning show that we may never hit that bar so you are arguing for lowering that bar. You are saying if we can employ other mechanisms to build structures that are too complicated to comprehend, that is enough to say we "understand" that structure.

So basically your bar for understanding is lower and inconsistent with what scientists and computer scientists all over the world would use as a bar to classify the fact of whether or not they "understood" something.

So put it this way. I'm talking about "understanding" on the level that most people, most of science, and most researchers talk about it. No "over intellectualization" bs here. You unknowingly are the one who's making the leap here and moving the bar of understanding to a different more abstract place.

Let's look at the implications of what you're talking about. You say that using a program to train a neural net to simulate consciousness is enough to "understand" consciousness.

Then would you say if I can take a biological organism and reconstruct a duplicate of that organism that is molecularly and genetically identical to the first organism then I have complete understanding of all biological organisms?

Again your logic makes no sense here. We CAN do the above. It's called cloning, and although we can clone things we don't completely understand the mappings between genes and the macro features of the creature the gene describes. Complete understanding of genetics involves the ability to insert a 100% custom gene into a cell and having a standard computer program simulate the resulting creature.

Let's bring it back full circle. What I am originally talking about. Most things in science and engineering are too complicated to understand as a whole. So we use symbolic representation to simplify the system for understanding. The OS programmer who writes the windowing system thinks of the scheduler as an abstract representation and the OS programmer who writes the scheduler does the same for the windowing system.

Currently we cannot do the above for neural nets. This is an inconsistent phenomenon with most of the systems humans are interested in building. We do not have the ability to modularize a neural net hence why we rely on machine learning algorithms. I am addressing this phenomenon, describing the limitations of it, and applying it to the nature of consciousness as we do know like the visual recognition algorithm both systems reside in a neural net and thus probably suffer from the same problems and limitations.

So that is all that I am saying. We will likely not be able to modularize the problem of consciousness to a place where we can understand consciousness like we understand other things in other scientific fields. This is fundamentally inconsistent with levels of understanding that are achievable in other fields of science. What you're talking about is another topic all together which is moving the bar of understanding to a lower level so that we can redefine "understanding." Ignore the bar. Who cares. Focus on the essence of what I am saying and how our ability understanding consciousness is different from our ability to understand an operating system and almost everything else in science.

>But logic is just one part of understanding, there are other ways the knowledge can be collected, transmitted and applied. If you aim to only use logic, you end up with overintellectualizing everything, and in that case yes, it is very hard to say that you truly understand anything.

This is off topic. Logic only has direct application to formal systems and it's really too "philosiphical" to get into right now. Suffice to say that your argument is basically this. I am wrong because my arguments are too logical.

>I disagree. I see that we do exactly that with neural nets. The distinctions you are trying to come up with sound semantic and arbitrary.

You disagree, and therefore you are wrong. Similar to how if I say the sky is blue and you disagree. The distinction sounds semantic and arbitrary but it is not. Think harder, the failure here is not a semantic difference but a failure in you to process the right abstraction. Your disagreement is irrelevant in the face of reality.

Take the neural net or several neural nets. Decompose those neural nets into modules. Recompose those modules into new neural nets. Can you do this? No. Why?

Because you can't really modularize neural nets. What these analysis techniques are doing is showing you that there is sort of a module like thing here but like brain surgery it doesn't mean you can rip it out and reuse it somewhere else. That's a true lack of understanding of what's going on.

We both agree that the human brain is a black box. We also agree that we know about the existence and location of modules in the human brain. Things like the "emotion" and "locomotion" are known modules. Let's say we meet a paraplegic person who's locomotion part of his brain is damaged. Can't we fix him by doing a transplant? A recently deceased patient who died of unrelated reasons could have his "locomotion" module cut and transplanted into the brain of the person who needs it.

We can't because we actually don't have access to the modules. Just a blurry picture that some sort of module is there. Same with artificial neural nets. The day you can graft a module in a neural net and compose it with another is the day you have fulfilled the definition of what a "module" is. You haven't and therefore you are utterly wrong and therefore you lack knowledge about what is going on inside a neural net. This is definitive logic.

>You have made up the "by hand" requirement. The real world does not have it. If a scientist builds a neural network that generates (through machine learning) an algorithm that works, we all say that the scientist has written the algorithm, no one cares if they have done it by hand or through application of a clever meta algorithm.

Yes I have made it up to illustrate that there is more than a semantic difference. Look I'm not making up requirements here and there just to screw with you. I'm making them up so you can see there is actually a huge difference between training a neural network VS. doing meta programming.

A compiler is a meta programmer. You give it a high level language and it programs the CPU in a lower level language. There is a fundamental difference between what's going on here and what's going on when you train a neural net. There is a Functor between the difference in the process of creation to the level of understanding. WE have less control over the creation of weights in a neural net then we do over the assembly code a compiler generates just like how we have less understanding of the overall neural net then we do of the assembled program.

There is a clear gap here. I'm not literally setting a requirement here. My intention is to illustrate a gap in understanding and the gap seems to be permanent and a bulwark in our overall goals of understanding consciousness, not from your "requirement" perspective, but from scholars in the field.

literally a compiler "translates" code and a neural network is "trained." The word translate and train have more then a semantic difference.

>If the "overintellectualizing" term is not common enough, let's replace it by "overthinking", it's very close.

There is literally only a semantic difference here. You eat your own words. From my perspective overthinking is not what's going on here. It's "underthinking"... what is an adjective to describe a person who "underthinks?"