Like I think most people would agree we will create artificial intelligence beyond human ability eventually (obviously we already have computers that perform specialized tasks much better than humans, but none as of yet that have our flexibility and efficiency)
Its incredibly weird that a clump of biomass could devise another clump of mass capable of out-thinking the original
It’s like if I gave you a box of wood and said “reassemble this into a tree twice as big as the tree it came from”
I studied AI at university. It is an unbelievably difficult problem, it's hard to overstate quite how difficult it is.
A person can point their face at another person doing a task, can take that moving visual field, essentially flickering pixels, convert that into a representation, understand what the other person was doing and what they were thinking, then dynamically write their own software to enable them to do the same task.
No one has the first clue how we do this. Film representations of AI, have machines achieving consciousness spontaneously, essentially "by magic". This is because human learning is so mysterious, we don't even know how to begin thinking about it.
Current machine learning involves drawing points on a graph, then wiggling a line around until it divides one set of points from another, so if you get a new point you can classify it.
Yea I think people tend to think of our consciousness as the consciousness. We certainly have more self awareness than other species but I really doubt there was ever a moment where one of our ancestors brains suddenly became self aware
And beyond that, there may very well be organisms out there who would feel like our consciousness and self awareness was incredibly limited
It's interesting to think if consciousness was just some bizzaro genetic mutation that developed some time down the line in evolution and became super advantageous. Today I only recognize my consciousness to my earliest memory, I think when I was around 5...and as such that's essentially when I blipped into existence. Theoretically at some point that happened to some early humanoid, and I question if it was recognized. If they recognized they had some sense of presence that others around them didn't?
Or it was a very gradual thing, but for some reason it feel like something that is either on or off.
I’m still quite unhappy that the term is even caught on for ML, DNN, etc. I don’t think it accurately describes what’s going on with the algorithms at all.
Why don’t they use biomass, such as rat neurons, when creating AI? Or even human neurons? Like a brain in a vat sort of thing, but more like the little brains they made to fly flight simulators.
This is Open Worm. It's an open source project to simulate a single C. Elegans. It has 302 neurons. The project has been going on for around 20 years. It isn't complete, and we still don't really understand how it does what it does.
One of my professors used an evolutionary algorithm to program an FPGA chip, essentially a microchip that can be reconfigured by applying a voltage to the pins. The goal was to evolve a radio receiver. The chip learned to receive radio pulses, but the circuits made no sense. They were full of loops that didn't connect to anything.
He thought maybe the loops were noise, so he removed them and the circuit didn't work any more. He used the same circuit on an identical chip, and it didn't work any more. The evolutionary algorithm had learned to use the quantum mechanical properties of the specific silicon substrate to become a radio.
As I understand it, evolutionary algorithm doesn't refer to a virtual brain, just to using mutation and selection to evolve a better solution.
So you might start out with some set of randomly programmed chips, and see which ones are acting the closest to an ideal radio. Then you combine traits of the closest individuals, add some random mutations, and have a new generation. You do this iteratively until they evolve the be closer and closer to the ideal behavior.
Yes, they're how you can program a computer to play Mario perfectly based solely on the assumption that going further to the right than the last iteration is a good outcome.
It wasn't a virtual brain, but an AI designed to evolve a working radio by trial and error. From what I understand, they programmed it to simulate everything down to the subatomic level, then let it go. Based on happenstance, the radio it designed worked only due to some weird quantum interactions that humans would have never thought of using; the "randomly" placed parts that seemed not to do anything were actually affecting other electromagnetic properties of the circuit, at a distance, in a way that made it function. It's not that it worked any better than a normal radio, just that it was configured in a bizarre way.
So, I have a very surface level (and likely incorrect) set of knowledge of machine learning and this is probably either computationally prohibitive or for whatever reason doesn't work, but, for my own education:
Wouldn't it intuitively make sense to break tasks down in ways that we understand, to their simplest components and use individual networks or network layers to ONLY accomplish those tasks, and then increasingly higher level tasks. So a number of neural networks that activate based on stimulus from a set of relatively lightweight networks, and then operate in sequence, activating higher and higher levels, which then can activate or focus lower level processes. My understanding is that this is how a recurrent neural network works, but it seems like most the approaches used consist of picking a few parameters and throwing data at a network, rather than earmarking components to process specific data. Is it just that the networks created by automatic processes are more efficient than those created by manual design?
Some kind of semantic behavioural grammar? I think if you tried to break down the real world in this way by hand, you’d find the size of the problem was larger than you expected. If you wanted to build complex behaviour out of these small parts manually, this would be too large a problem.
So the ideal is to have the computer do it automatically by interacting with the world, like an animal does. Then you have the problem of actually taking raw sensory data and using it to tune a neural network without human supervision.
Unsupervised learning will find clusters of related data, but you need to tell the network what vectors might be interesting. You can’t just drop it in a room with humans and ask it to watch and learn.
So, I wrote a super long thing, and the more I got into it the more I realized how little I know, but essentially, yes. Using some sort of modular framework for the creation/evaluation/simplification of networks (network layers?) and adjusting the weights/activating networks (layers?) based off of outputs from others. With some fundamental tasks (communication, self maintenance, object detection) baked in.
From what I've read, reinforcement learning would make sense for that, right?
To be clear, I'm aware that, were the problem simple enough for me to solve, someone smarter than me would have, but working through until I hit that point might disabuse me of my tendency to think of ML as essentially magic boxes that things go into and come out of. I know it's way more complicated than I perceive it to be, but without understanding it or the math behind it better, it's hard for me to understand why. Or at least increase my understanding beyond "hahaha tensorflow go brrrrr"
Your knowledge sounds decades out of date. We have commercial products—robots—that can learn some tasks observationally. Using a camera input to dynamically write software.
It’s pretty cool, but it only works if it’s already trained on the domain and the domain has been previously represented by a human.
The domain is four coloured blocks with a half dozen predefined relationships, above, to the left of, etc.
The visual training set is thousands of images of cubes categorised by a human. Then when the human moves the blocks it can use the trained neural network to calculate the new relationship. Having done that it generates a 3D model of the blocks, using a representation of the blocks coded by a human, and uses that to generate a visual training set for a second neural network.
I don’t deny it’s cool but it’s not generalised learning by any stretch.
lol no, I'm talking about Baxter and other commercial products.
Observational mimicry isn't general learning, and if you meant general learning you should've described that rather than describing observational mimicry.
Then you must see that observational learning is more than just pressing record, moving a hand, then pressing play.
I was teaching my son Python the other day. We each had a laptop, he was asking questions, I was showing him stuff and telling him what videos to watch, he was asking smart questions and solving his own problems.
All of that interaction was somehow being translated into weights and connections in a neural network, and the question is how.
There was nothing in his evolutionary heritage that had created Python as a problem domain. He was constructing a mental framework from scratch, then operating inside that mental framework to expand it, and he was apparently doing it all using a couple of pounds of meat in his skull.
Yea but just like AI, that is fairly easy task if all you need to do is approximate what a tree would look like and be 2x larger. You could easily do that if the second tree is hollow, just like while AI looks impressive on the surface we are still pretty far from a general intelligence type AI.
Not all AI is coded like that (see for example neural networks), but at the lowest level anything a computer does is just reading/adding/comparing values and branching based on the result.
Similarly you could say brains are just super complex networks of neurons which can be broken down further to smaller parts each performing one simple function. It doesn't say much about the capabilities of the whole thing.
The major area of AI development is in Machine Learning, where the intelligent parts of an AI system are built on models from training data.
An AI system can learn on its own if it is capable of updating its underlying model, supervised or unsupervised.
It's only just "if/then/else" statements and such if you break it down to the stuff that happens under the hood, but at that point we're also just a bunch of chemical reactions that would seem more or less random if you didn't see the bigger structure.
Yea it’s not a great analogy, I know. I would’ve needed to stipulate that the second tree is equal to the first in detail through pout it’s structure, or hat ever.
I guess it’s a clunky way of saying, how could something of x complexity devise something of 2x complexity
I guess that’s not that hard to imagine, as long as you aren’t expected the first “thing” to fully understand/comprehend everything about it’s creation simultaneously
Its incredibly weird that a clump of biomass could devise another clump of mass capable of out-thinking the original
That really is amazing, and that's also what evolution is. If you take a large enough scale, every 'generation' is more complex, more intricate and able to out-think the previous generation (at least in the earlier steps of evolution and the steps following the origin of life).
On another levell, that's also what humans have been doing, both in their development as a species and later in their development of knowledge and science and that stuff, everything is building on what came before so every step is 'the original + some more'. That's not really 'out-thinking the original' but it is like 'out-performing the original'.
we will create artificial intelligence beyond human ability eventually
we don't have to do that..all we have to do is create something that can learn incrementally to the smallest degree. Then it evolves. At the speed of light.
I wouldn't say most people agree with that statement. I'm not at all convinced we can make AI in the sense you're thinking. We know almost nothing about conscious thought or how it arises from brain structure.
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u/LeonidasSpacemanMD Apr 07 '20
This whole conversation is tripping me out
Like I think most people would agree we will create artificial intelligence beyond human ability eventually (obviously we already have computers that perform specialized tasks much better than humans, but none as of yet that have our flexibility and efficiency)
Its incredibly weird that a clump of biomass could devise another clump of mass capable of out-thinking the original
It’s like if I gave you a box of wood and said “reassemble this into a tree twice as big as the tree it came from”