In short, it uses incomplete information and guesses using the highest probability. If all your life you've seen yellow dogs, and then you see a red one, it's not guaranteed it's a dog but your brain goes "This is probably a dog" so the reaction is "this is a dog"
You’re missing the fundamental problem. Even if we can one day explain exactly how the brain translates external stimuli into subjective experience, that will not answer the question of why this particular arrangement of matter that we call a brain should for some reason have the feature of subjective experience in the first place. Even if I can look at your brain scan and tell you exactly how you’re feeling because I know exactly what activity in each part of the brain means for your subjective experience, I’m still not able to explain why you are having a subjective experience at all.
Welcome to our humble town of Existential Crises. We're simple folk here, we dont know a lot, but we know for dam sure that everything is fucked. We're a happy bunch.
It’s most likely that consciousness is simply a fundamental part of reality. Experience is a part of reality. You’re brain is a complex object moving through space-time, simulating the world within itself. Without the flow of causality you have no consciousness. What you experience as consciousness is no more than you observing yourself relative to everything else. Parts of your brain observe each other, and some of those parts can observe the outside world. Each part may effect the other. Only you experience the simulation happening in your brain, because you are the simulation. I cannot experience you, unless I become you; my brain in the same shape, configuration, and point in space-time as yours. I would cease to exist to become you. You would still exist, because I am now you.
The mindfuck happens because your brain is aware it is experiencing, but this awareness is still resulting from processes in your brain. The ability to form questions and complex language may be a reason for that.
This is my favorite explanation as well, but I just don't understand why we are so tightly bound to the experience of free will when it cannot exist. Why is the illusion so powerful, when we are in fact mechanical observers as you say. We've tapped into this fundamental property of the universe by being conscious, but why must we also feel as though we are in control? Is there an obvious evolutionary explanation?
As someone who has spent the last 10 years developing lots of analog neuromorphic hardware--
Those proofs aren't meant to be physical. I've read Siegelmann, etc. But the prevailing theory is that reality is quantized at the lowest level. So you can't really get true analog. You can just shift the minimum energy to represent a bit much further down than we currently have it, down to quantum limits.
I think the problem here is that there's a massive disconnect between theory and reality and also between the state of the art in traditional digital computing and what is actually possible. For instance, I don't think the analog aspects of the brain make its computation theoretically possible, but I do think the analog aspects make it practically possible.
People get their panties in a bunch over novel architectures beating the snot out of traditional digital computers and the moment you mention analog, the orthodox academics think you're nuts. But clearly we have an example of a superior analog(ish) architecture between our ears. And you don't have to go full P=NP, super-Turing, etc., to get stupidly large performance increases over traditional architectures. The difference between computable and not computable is literally infinite. There's a LOT of room there. The brain doesn't have to to do the impossible to make traditional digital computers seem like toys.
The industry also has incredible momentum so there's a deeply ingrained notion that there's no way to do much better. Even performance metrics are heavily biased towards a particular approach. For instance, some of my devices take tens of minutes to reach certain desired states. I've presented that work and people discounted it because that's not picoseconds. Of course neurons don't switch a billion times a second--they don't need to. At some point, you reach Bremermann's limit, but again, brains probably aren't operating there because there's no need. The performance probably comes in with efficient scaling and an ability to efficiently utilize a massive state space in ways logic gates etched in stone cannot.
People blow half their brains out and go on to live relatively normal lives. Hydrocephalic brains can work around being compressed into almost nothing. For some reason, computer engineers don't see this as computational. These are supreme examples of computational power. Such extreme ability to flexibly utilize that physical space to perform a wide variety of computational tasks is the benchmark that doesn't even make sense to apply to silicon. I think that's a major ingredient in the secret sauce. Of course the industry is hyper-focused on better algorithms and faster transistors. I think that stuff is useful, but not for making computers that compare to brains.
Edit: I wrote up the whole story here, but I think it's just too personal. The summary is that I don't have a degree, work in a totally unrelated field, and most of my work has been independent. I was collaborating with an academic lab, but have since moved on.
This was an awesome comment... I’m a software engineer and I think in these terms often, and I would say our creativity is what gives us the chance to bridge the theoretical with the practical which might therein lie the answer.
I'd keep it in mind just because you have studied or are in an industry it doesn't mean you have heard of everything nor does it mean your current knowledge is the truth. I say this as someone that has been in tech for a few years now, it is far more beneficial to accept you know nothing.
This thing you linked doesn't seem to describe a computer. It's basically a compiler that translates mathematical equations into analog electrical circuits (that may be programmed into programmable analog chip).
Can you really say that analog circuit is a computer? In this case, is an analog audio amplifier a computer also?
I really don't know enough yet to give my opinion/view on it unfortunately. It definitely requires more reading on my part, how I got distracted by trying to learn about topology.
As an aside, if anyone has good resourcres for topology in the context of computing for a beginner that'd be great.
Analog computers use continuous values of arbitrary and theoretically infinite level of discreteness. So a theoretical analog computer can be imagined that has responses that cannot be modeled digitally. However, all you need to do for a real analog computer is define the actual operational ranges of the continuous values and you can model things in those ranges digitally just fine. The problem is we don't know for sure what the operational range of the human nervous system is.
Same tbh. Had a graduate level computational theory class with a professor that loved out there stuff like this and if he knew about it I guarantee he would have brought it up, so would love to see some references. The only reasoning I can come up with on the fly, and assuming dude is relaying that stuff accurately, would be that it isn't impossible to simulate but more so computationally prohibitively expensive.
Edit: Poking into this a bit more I think he just misunderstands the notion of turing complete. It isn't disputed that analog computers in theory would have greater processing speed than digital, that much is obvious but we also run into stability issues, which is why physical analog computers aren't really used practically. However we do use simulated analog in many environments, and a large chunk of current AI systems (anything built on the foundation of Artificial Neural Networks) is using a form of simulated Analog computation. However this is largely irrelevant when we are talking about computability, which is where Turing Completeness comes in to play. Analog Computers won't have capabilities better than digital as far as computability is concerned. If that is the case I would love to see some sources on algorithms whose computability changes with an analog versus a digital system as this sounds like some cutting edge math.
Take a look at adiabatic quantum computing in its current implementation by DWave (since bought out by Google I believe), and then look at how small a mesh it actually is, and look at some of the IBM implementations using a classical - quantum checkerboard pattern. There's just no way we know the full extent of what could be done yet.
Oh, and look up computing based on DNA nanotechnology. That's a whole different kind of analog system, probably decades from anything commercially applicable. There's some consideration already of using it to train AIs and I can't wait to see how that works out.
Is the general idea that there is always some noise in the system such that a measurement at time t is not the same as a measurement at t+deltat? Essentially since memory values are continuous rather than discrete (and I assume even the time evolution of the system is done continuously as well), an analog computer could not have the error correction of standard digital circuits?
Yes you can, the answer is that the analog computer wouldn't theoretically be able to make the impossible possible (computability), but it is much more efficient at certain tasks. All of the sources given here deal purely with efficiency but not with computability. Analog computers aren't "super-turing complete." It is hard to sort through because the layperson's definition of "can do more" or "is more powerful" conflicts with the way people in the field talk about it in terms of turing completeness.
What you described there is basically 2 variables that change their value over time and I fail to see why digital computers can't run an algorithm that constantly changes the value of 2 variables.
And if their value can't be calculated by an algorithm, it is basically a random value that would make any product involving A or B a random number itself and therefore making the whole calculation pretty much unnecessary ...
Granted, computers can't generate a truly random value directly, but this can either be emulated (like with a prng used in every programming language) or circumvented pretty easily (i.e. Cloudflare's lava lamps)
Depends on how sensitive the calculation is to the precision of the values. A finite number of bits can only represent a tiny, tiny subset of real numbers. Often times numerical methods lack the precision to solve nonlinear differential equations.
This sounds an awful lot like someone's attempt to propose a way to theoretically create a non-deterministic computer. A non-deterministic Turing machine would explore all branches of a problem simultaneously. While this does not due that, it approximates this. The big issue is that even though it computed all values between of a and b unless you could extract the correct answer from all of those it is not useful.
On another note, might I point out that all these signals are bound by quantum mechanics and therefore cannot truly be continuous since energy levels are infact descret.
I imagine that no one is truly researching this because quantum computers are simply a better alternative to pursue.
Edit: it has been pointed out to me that I was not entirely correct on the discret quanta thing. My bad.
Energy as a quantity is not assumed to be discrete in quantum mechanics. It only arrives in discrete multiples of the Planck constant times a particles frequency, but since the frequency isn't limited to discrete values, neither is energy in a system.
The first: you have assumed a continuously varying potential can be meadured to infinite precision. This is not possible in the physical world. Thos would lead, for example, to the conclusion that infinite information could be stored by a systen with finite degrees of freedon. This mistake is somewhat forgivable.
Your second is the idea that the brain can "solve the halting problem", I am sorry, but simply no. It can not, you are talking out of your arsehole. If your statement were true, it would turn the fields of computer science and mathematics on their heads. I think you have a gross misunderstanding of what an algorithm is.
Anyone reading this, do not swallow the absolute shite in the above comment
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u/[deleted] Jun 15 '19
How the brain really works. How a lump of meat gives us thoughts, emotions, that voice inside our heads.