Neural Annealing: Toward a Neural Theory of Everything

Your statement of modularizing portions of understanding into pieces to deal with complexity is isomorphic to my statement that we can only hope to understand A representation of the brain as a simplification.

The chemists who understand molecules Understand the atom of the as a symbolic representation. The physicist understands the atom in a more detailed manner and sees the molecule as a symbolic representation.

In short each human as individuals CAN ONLY understand the BRAIN as a symbolic simplification. This is the BEST possible outcome. What you are SAYING is the EXACT same thing I am SAYING.

The one difference is... I am taking it ONE STEP further with a speculation on the nature of intelligence.

There exists concepts in this world that are fundamental and cannot be modularized. It is a very reasonable speculation that the understanding of consciousness itself cannot be further modularized or subdivided. I am saying that in order to understand consciousness it may very well be, that we have to understand consciousness as a complex whole and this complexity may be too big for us to hold in our heads.

This IS a realistic possibility. We are already seeing the limits to this with the black box nature of the neural nets we are generating. A possible future simulation of Consciousness may very well be encoded in the same blackbox that our deep fakes are also trapped in.

Either way this was a speculation. You (and many others) completely misunderstanding and taking it the wrong way.

Let me give you a concrete example: A visual recognition algorithm. Currently the way we do this is with a trained neural net.

Can a large crack team of humans reproduce the logic of this neural net in a way where it isn't a black box? Perhaps we can use abstraction and subdivide the logic into modular pieces and have different teams of people handle different components....

Or perhaps we have to leave it as a blackbox learning process. Currently there is no way to subdivide the core logic of a neural net into something even A team of humans can understand. The best possible methods we have in handling this complexity is to leave it to an automated process rather than abstracting components of the problem so that humans can understand individual pieces.

I AM not saying it will be impossible for humans to ever abstract this problem into something we can understand. I am only saying it MAY be impossible to subdivide, but it is CERTAINLY impossible for a human to understand the brain as a whole.

There is the possibility that complex concepts exist in the universe that cannot be understood by breaking it apart into abstract concepts. A neural net has thousands of nodes as touch points and it is highly probable that to understand it you must understand all of the thousands nodes at the SAME TIME. This is a viable possibility.

Leave it to the internet to read a comment and take it the wrong way.

Visual recognition. Voice recognition. Language Recognition. Self driving. Music Composition. We have failed in all these areas to use modularization to simplify these problems into something we can model as a theoretical whole. We turn to neural nets not to "understand" the problem but to build systems that can solve the problem while bypassing understanding.

>I simply haven't seen anything that would indicate that it would be impossible to understand consciousness in the same practical mannter as the way we understand for example cars now

If we have that level of understanding then we should be able to modularize the problem into smaller units of complexity so that a team of people can build the solution component by component. So far for the examples I listed above we don't have that level of understanding. We could employ the same techniques used for language recognition to understand the human brain... by building a neural net.... but that kind of defeats the point right? The neural net may simulate consciousness but it doesn't exactly let you (or a team of people) understand it...

Also for your comment on the black box nature of neural nets....

I think we can both agree that the high level understanding or "insights" we gain are at best very high level. The logic of "visual recognition" is not understood by humans. We can only reproduce such logic as a blackbox. A neural net.

These "visualizations" or high level descriptions of machine learning algorithms aren't a form of true understanding... It's just a summary of a class of problems. For example voice recognition and visual recognition are two very different problems but they are both "visualized" and modeled in the exact same way. You can probably also model consciousness as a problem that consists of a multi-dimensional set of points where you need to find the best fit curve. This does not mean you understand it.

You can also probably peer into these artificial neural nets and see what components are doing what similar to how we can peer into a human brain and see which parts of a brain light up when you give it stimuli. Again, it doesn't mean you have true understanding.

This is not to discount the entire field of neurology as a whole. Clearly there are bit and pieces we can understand. I am saying the ultimate goal of understanding consciousness may be fruitless. It's a very meta concept and possibly as a result it's also very hard for you as a consciousness yourself to write the exact definition of consciousness or pinpoint exactly what it is.

If you understood visual recognition. Then you can code up the algorithm by hand, given enough time.

If the problem is too complex for you to understand then you can subdivide the problem into smaller pieces and give it to a team of people to code it.

You can't currently do either of these things. You require an algorithm to code it up for you. Therefore you don't really understand it.

>The net looks at the data and searches for patterns. On first layers simple patterns like contours (which is a simple mathematical function), on higher levels different kinds of dots or smaller objects (objects meaning, a spatial configuration of contours and whitespace from the previous layer), on higher levels relationship of those obects to each other, etc. etc. Pooling and convolution handle recognizing of these patterns in different parts of the image. Final layers do statistical weighing of which patterns were found in which strengths, and generate the most likely result (of in this case, a classification task).

I kind of got into this. Peering into the neural network doesn't mean you understand it anymore then peering into the human brain to see what parts light up given certain stimuli. If you can understand it then you can code it up in the same amount of lines of code. You obviously can't and don't understand the algorithm well enough to do this. However you're satisfied by this level of understanding which is fine, but the ultimate goal of neurology is to surpass this level of understanding and THAT is what I am addressing.

>Well sure, I agree with you there. It is already the case for many phenomenon that we can nonetheless control. What is the exact definition of a car? Of a video game? Of a tree? Of a career? Of a family? I don't think humans need exact definitions of things, they can just as well function relatively optimally without them.

Clearly there is a difference between our lack of an exact definition of a car vs the exact definition of consciousness let's not get into the semantics of the difference here suffice to say that we're both human beings and we understand there is a huge lack of understanding of what consciousness is vs what a car is.

>Not all knowledge is in logical definitions.

It is. Lack of a logical definition is an indication of lack of knowledge. Your brain clearly has an exact definition of a career given the fact that given an input you can instantly recongize whether something is a "career" or not. The reason why you can't write the exact exact definition down in words is because you lack understanding of the structure of the definition that is being held in your brain. However given all possible inputs your brain will have an output. Given all inputs you will be able to write down a full mapping between inputs and outputs... that is a formal definition of "career"... you just lack the capability/knowledge of simplifying the definition into a single logical statement and understanding what exactly it is... but an exact formal definition exists in your brain.

>I really don't understand what you are saying here unfortunately, sounds like a philosophical black hole of overintellectualizing a simple phenomenon. I would say that we understand enough to have practical ability, which is all that matters.

What's wrong with over intellectualization? Are you implying that you want less brain power applied to this problem? We need to be stupider to understand something? If you say that practical ability is enough then this argument is over because we are talking about different things. I'm not talking about practical ability I'm talking about understanding of consciousness. The ability to define it and hold the concept in your head or several heads. That's what I'm talking about and that is the ultimate philosophical goal of neurology. A brute force simulation of a neural net which is the current state of things these days is not a form of understanding. Once you understand what it is, you should be able to code it up by hand.

Sure we MAY (keyword) only be interested the practical aspects of something like voice recognition. But the question of consciousness is clearly not something we're just interested in emulating. We're interested in understanding it.

If we develop 3D printing and scanning to the point where we can reprint the entire atomic structure of things we see in reality this does not mean we understand everything at the molecular level. Practical application != actual understanding. Taking this printer and printing out a human brain doesn't help us understand it at a deeper level just like how a photocopier copying a calculus text book doesn't help us understand calculus.

Additionally your "practical" machines will always have limitations if there isn't full understanding of the phenomenon that operates underneath. Self driving cars that deliberately crash into an unrecognized object is an example of unpredictability that comes with lack of understanding.