10 ms·

Neural networks 'learn' the way humans do? A neuroscientist explains why not

44 points by ColinWright 4y ago

bannedbybros 4y ago
ghaff 4y ago
The general opinion from what I can tell is that there seem to be certain similarities as well as differences. And in addition to structural differences, human learning may depend on experiencing the physical world to some degree. e.g. https://twitter.com/sparuniisc/status/1377333798442967041 https://twitter.com/sparuniisc/status/1377333798442967041
anotheryou 4y ago
tl;dr: Children learn unsupervised and without back propagation. So machines, for now, use two tricks to get a bit better in maybe more narrow fields. There is NNs without back propagation and unsupervised too though, they are just currently not the best, right? Doesn't sound fundamentally different to me. And about this paragraph: "humans learn by making structured mental concepts, in which many different properties and associations are linked together. For instance, our concept of “banana” includes its shape, the colour yellow, knowledge of it being a fruit, how to hold it, and so forth. As far as we know, AI systems do not form conceptual knowledge like this. They rely entirely on extracting complex statistical associations from their training data, and then applying these to similar contexts. " That's plain wrong, isn't it? I'm no expert, but isn't latent space and layers basically these "structured mental concepts"? You have upper layers encoding for partly features, lower layers checking on many of them. That's abstract concepts. And you have the multidimensional statistics of the latent space that will see bananas as the intersect of yellow color and a long shape. For an opposite "opinion": the layers of a NN map quite nicely on to what happens in the brain: https://twitter.com/JeanRemiKing/status/1533720262344073218 https://twitter.com/JeanRemiKing/status/1533720262344073218 (they check which neurons get activated first, which later in NN and brain)
- Nowado 4y ago
  Calling children learning (or any biological system learning) unsupervised is kind of insane. Kid touches hot stove. Gets feedback. Kid tries walking. Gets feedback. Kid talks to someone. Gets feedback. I have hard time even coming up with an example of a proper unsupervised learning in the wild. Hermit phenomenologist, maybe? You have no way of doing anything social in an unsupervised way and most of the language happens socially. EDIT: I did misremember RL as supervised, see chain below. EDIT2: Hey, I never said supervised in the first place! Enough internet for me today, I need a nap.
  - anotheryou 4y ago
    Social interaction could be called unsupervised, we'd just need to adopt a little chat bot :) As for the physical parts: isn't that a loss function? Reward/dopamine if you reach your goal (walking), pain if you do what the rules/the body does not allow. I'm sorry if I'm mixing terms a bit, as said, I'm not an expert and a bit rusty about what I once knew :)
  - slimsag 4y ago
    Unsupervised doesn't mean "no feedback", it means classifying things based on feedback/experience without being explicitly told how to classify things. When a kid touches a hot stove, they classify it as bad/dangerous through feedback - unsupervised learning. Not because a parent comes by later and says "Hey! Look, your hand is burnt! That's bad because I say it is!" - supervised learning. You can even see supervised learning in kids to some extent: when they play barber shop and get a butchered hair cut, you often see kids be perfectly fine and happy even with the outcome, through lack of supervision they don't yet have any negative experience from doing this. Then a supervisor comes along (parent) who freaks out and the kid learns through supervision "oh no! I did something bad because mom said so!"
    - Nowado 4y ago
      Pure supervised learning is straight forward to point out in our society I think, schools and all. Let me start by saying, I misremembered and believed RL is considered supervised. Since it's actually not, that dramatically changes my position ^^ I'd argue then that most learning in humans (how strong analogy holds aside) is RL. The world is structured so that you learn to avoid pain - those who don't tend to not leave a lot of offspring behind. Arguably that's a better metaphor for all learning than supervised/unsupervised: there is some further reward/punishment for even the school work. Misuse of muscles when learning to walk is also connected to similar feedback, so is misuse of language - be it grammatical failure or being a bad salesperson. Generally I'd focus on pointing out that children do not learn in an unsupervised fashion in almost any setting, claiming it was supervised was wrong and based on my misclassification of RL.
jakewins 4y ago
One thing I note from listening to my wife talking about her work, which is in molecular biology of human neurons, is that there seems to be so many signalling mechanisms, vs just one in the neural nets I’ve seen in software. Like, there are more than 40 separate neurotransmitters, each effectively a float output, I think? So at each synapse at every instant, potentially 40 separate floats can be communicated. And then there’s adjacency - the network they were studying this spring they suspect is communicating using the electric fields around the neurons, so adjacent but unconnected neurons influence each other via electrical induction. And then it’s like - and my wife says there’s no evidence neurons do this - but cells can rewrite their own DNA to store information. So each neuron is a tiny microcomputer with 40 pwm outputs and what, thousands of inputs, and 350Mib of read/write memory. And there’s 86 Billion of them in each brain?! It just seems so much larger than a matrix of floats and weights.
- zitterbewegung 4y ago
  If we were to rename Neural Networks it would probably be Multiplicative Tensor based machines for neural networks / deep neural networks and then denote the subtypes Backpropigative Tensor Machines , Adversarial Tensor Machines and Attention Tensor machines.
- tux3 4y ago
  Rewrite their own DNA is a little misleading, because the actual DNA codons don't normally change. But cells can affect gene expression throught multiple mechanisms, so that part of the DNA gets activated or de-activated. You can use those mechanisms to react to the environment in various ways. For instance when you see chemical signals A and B, start making protein C. Or, when you see some signal, make the neuron more sensitive at the synapse (add receptors). You can see it as storing state in a sort of memory, but that's pushing the computer analogy to its limits. Cells are pretty unlike chips :)
  - epgui 4y ago
    Even though it is approximately correct that the codons don’t change, the codons can be chemically modified in a bunch of different ways (eg.: methylation, acetylation, phosphorylation, ubiquitination, sumoylation…)
axg11 4y ago
The focus on learning is misguided. Human brains don’t have to use the same mechanism for learning as they do for reasoning. Perhaps they do, but I don’t believe we have enough evidence either way. The useful part of AI is the inference/output. If we achieve human-level performance AI inference then we have an incredibly powerful tool that can revolutionise society. The learning algorithm, be it gradient descent, evolutionary mechanisms or something entirely new, doesn’t matter. Whatever gets the job done.
- DennisP 4y ago
  Sure but the article isn't just about the mechanism. A human child doesn't read vast amounts of text to learn how to predict the next word in a sentence. We don't know how to make an AI that learns language like a five-year-old.
  - deltaonefour 4y ago
    I think he's saying something along the lines that intelligence is mutually exclusive to learning. If we create a neural net that has the equivalent knowledge and intelligence of a human 5 year old but that neural net learns slower and was produced on 10 trillion pieces of data then we STILL have created a sentient AI despite the fact the learning algorithm is shit.
    - epgui 4y ago
      I don’t know if that’s what they meant, but what you’re saying is interesting. I would soften the idea a little bit by noting that as a little human grows up and learns more things, its brain plasticity goes down over time. AFAIK there’s no evidence that learning “saturates” the ability to learn other things, and the loss of plasticity is probably mainly linked to aging and/or other factors, but functionally the effect is very much the same: humans that know a lot of things typically have less capacity for learning.
      - deltaonefour 4y ago
        Anterograde amnesia is a condition where a human has lost his/her ability form new memories, aka learn. They can't remember anything past 3 minutes*. https://www.youtube.com/watch?v=ZeiMhUlipTk https://www.youtube.com/watch?v=ZeiMhUlipTk These people are clearly still sentient. There is still some form of memory there but the overall mechanism is largely gone.
epgui 4y ago
I don’t understand the fuss about backpropagation being impossible in brains. [1] Postsynaptic neurons do often connect to presynaptic neurons, ie “backwards” signalling, and sometimes whole circuits are involved to do this. I’m not sure if that maps perfectly to the backpropagation mechanism in ANNs, but isn’t it functionally essentially the same? [1] And the cited Nature paper does not seem to say exactly what the article says.
- skohan 4y ago
  What makes you think an ANN has very much in common with neurons at all? The model of activation as a weight times a simple activation function is laughably simple compared to what's actually going on in the brain.
  - epgui 4y ago
    Well, the “neurons” in each case are obviously very different, but you can express computations with varying levels of abstraction. Trying to perfectly emulate atom for atom or neuron for neuron will never work, I think we need to move past that. What matters is overall information flow and processing, and that’s an emergent phenomenon for both brain and ANN. Imagine a program written with OOP, and the same program written with FP. The implementation details may be rather different, but this doesn’t prevent it from doing the same thing in a different way. You have to think of artificial neurons and real neurons in a similar way (though they are rather more dissimilar than OOP and FP are to each other). The main problem that backpropagation solves, I believe, is roughly equivalent to feedback loops in real neural networks.
    - skohan 4y ago
      Which networks? There are dozens, or maybe hundreds of different mechanisms the brain uses to adapt the signaling of neural networks. Back propagation might resemble some of them, but others not at all. Back propagation assumes a synapse can be modeled as a weight times an input. We know of tons of types of synapse which don't work like that.
scotty79 4y ago
Do people train those AIs on mathematical reasoning? I asked GPT3 to teach me about addition and subtraction and it produced pure garbage in very lecturing tone. I think mathematics is one field where AI could be actually immediately useful.
- ben_w 4y ago
  > Do people train those AIs on mathematical reasoning? They do. The GPT family is basically a very fancy autocomplete, so it’s mainly noteworthy because of how far something that limited can go, as it’s not even trying to be a fully general NLP system. If you want to solve maths problems, there are many better choices; I often use WolframAlpha, which is great for the maths part of my problems, but fairly often needs me to try several variants of my query because of the limitations of its natural language processing.
  - scotty79 4y ago
    Wolfram Alpha is just sophisticated calculator. I'm interested more in artificial mathematicians able to create novel conjectures and prove them.
    - omnicognate 4y ago
      My understanding is that relatively little success has been achieved in that direction. I'd love to be corrected if there's something I haven't learned of, but as far as I know no theorem of any significance has been independently originated and proven by a computer. Rather, computers are increasingly used to assist human mathematicians. Machine learning has recently been used to help with identifying and proving interesting theorems [1] but still very much as a tool for human mathematicians rather than an independent artificial mathematical "mind". Personally I think the ability to truly "do mathematics" may be a marker of AGI. If/when we have machines independently mapping out new and interesting areas of mathematics the way human mathematicians do I suspect we will have cracked meaning/understanding. FWIW I've felt that way for over thirty years and have never considered the Turing test a convincing marker of intelligence, so I don't consider myself to be moving goalposts when I find the (absolutely incredible) feats of the current generation of ML algorithms unconvincing as evidence that we're nearing AGI. [1] https://www.maths.ox.ac.uk/node/40529 https://www.maths.ox.ac.uk/node/40529
deleted 4y ago
[deleted]
clircle 4y ago
Does _anyone_ actually think ML algorithms learn in the way a human does? It just strikes me as a patently false idea. Nevermind the fact that we can't even quantify similarity for this
- cwalv 4y ago
  Some people do .. maybe even many. It does have "neural" in the name, so it's the same, right?

44 points by ColinWright 4y ago

The general opinion from what I can tell is that there seem to be certain similarities as well as differences. And in addition to structural differences, human learning may depend on experiencing the physical world to some degree. e.g. https://twitter.com/sparuniisc/status/1377333798442967041 https://twitter.com/sparuniisc/status/1377333798442967041

anotheryou 4y ago

tl;dr: Children learn unsupervised and without back propagation. So machines, for now, use two tricks to get a bit better in maybe more narrow fields. There is NNs without back propagation and unsupervised too though, they are just currently not the best, right? Doesn't sound fundamentally different to me. And about this paragraph: "humans learn by making structured mental concepts, in which many different properties and associations are linked together. For instance, our concept of “banana” includes its shape, the colour yellow, knowledge of it being a fruit, how to hold it, and so forth. As far as we know, AI systems do not form conceptual knowledge like this. They rely entirely on extracting complex statistical associations from their training data, and then applying these to similar contexts. " That's plain wrong, isn't it? I'm no expert, but isn't latent space and layers basically these "structured mental concepts"? You have upper layers encoding for partly features, lower layers checking on many of them. That's abstract concepts. And you have the multidimensional statistics of the latent space that will see bananas as the intersect of yellow color and a long shape. For an opposite "opinion": the layers of a NN map quite nicely on to what happens in the brain: https://twitter.com/JeanRemiKing/status/1533720262344073218 https://twitter.com/JeanRemiKing/status/1533720262344073218 (they check which neurons get activated first, which later in NN and brain)

Nowado 4y ago
Calling children learning (or any biological system learning) unsupervised is kind of insane. Kid touches hot stove. Gets feedback. Kid tries walking. Gets feedback. Kid talks to someone. Gets feedback. I have hard time even coming up with an example of a proper unsupervised learning in the wild. Hermit phenomenologist, maybe? You have no way of doing anything social in an unsupervised way and most of the language happens socially. EDIT: I did misremember RL as supervised, see chain below. EDIT2: Hey, I never said supervised in the first place! Enough internet for me today, I need a nap.
- anotheryou 4y ago
  Social interaction could be called unsupervised, we'd just need to adopt a little chat bot :) As for the physical parts: isn't that a loss function? Reward/dopamine if you reach your goal (walking), pain if you do what the rules/the body does not allow. I'm sorry if I'm mixing terms a bit, as said, I'm not an expert and a bit rusty about what I once knew :)
- slimsag 4y ago
  Unsupervised doesn't mean "no feedback", it means classifying things based on feedback/experience without being explicitly told how to classify things. When a kid touches a hot stove, they classify it as bad/dangerous through feedback - unsupervised learning. Not because a parent comes by later and says "Hey! Look, your hand is burnt! That's bad because I say it is!" - supervised learning. You can even see supervised learning in kids to some extent: when they play barber shop and get a butchered hair cut, you often see kids be perfectly fine and happy even with the outcome, through lack of supervision they don't yet have any negative experience from doing this. Then a supervisor comes along (parent) who freaks out and the kid learns through supervision "oh no! I did something bad because mom said so!"
  - Nowado 4y ago
    Pure supervised learning is straight forward to point out in our society I think, schools and all. Let me start by saying, I misremembered and believed RL is considered supervised. Since it's actually not, that dramatically changes my position ^^ I'd argue then that most learning in humans (how strong analogy holds aside) is RL. The world is structured so that you learn to avoid pain - those who don't tend to not leave a lot of offspring behind. Arguably that's a better metaphor for all learning than supervised/unsupervised: there is some further reward/punishment for even the school work. Misuse of muscles when learning to walk is also connected to similar feedback, so is misuse of language - be it grammatical failure or being a bad salesperson. Generally I'd focus on pointing out that children do not learn in an unsupervised fashion in almost any setting, claiming it was supervised was wrong and based on my misclassification of RL.

jakewins 4y ago

One thing I note from listening to my wife talking about her work, which is in molecular biology of human neurons, is that there seems to be so many signalling mechanisms, vs just one in the neural nets I’ve seen in software. Like, there are more than 40 separate neurotransmitters, each effectively a float output, I think? So at each synapse at every instant, potentially 40 separate floats can be communicated. And then there’s adjacency - the network they were studying this spring they suspect is communicating using the electric fields around the neurons, so adjacent but unconnected neurons influence each other via electrical induction. And then it’s like - and my wife says there’s no evidence neurons do this - but cells can rewrite their own DNA to store information. So each neuron is a tiny microcomputer with 40 pwm outputs and what, thousands of inputs, and 350Mib of read/write memory. And there’s 86 Billion of them in each brain?! It just seems so much larger than a matrix of floats and weights.

zitterbewegung 4y ago
If we were to rename Neural Networks it would probably be Multiplicative Tensor based machines for neural networks / deep neural networks and then denote the subtypes Backpropigative Tensor Machines , Adversarial Tensor Machines and Attention Tensor machines.
tux3 4y ago
Rewrite their own DNA is a little misleading, because the actual DNA codons don't normally change. But cells can affect gene expression throught multiple mechanisms, so that part of the DNA gets activated or de-activated. You can use those mechanisms to react to the environment in various ways. For instance when you see chemical signals A and B, start making protein C. Or, when you see some signal, make the neuron more sensitive at the synapse (add receptors). You can see it as storing state in a sort of memory, but that's pushing the computer analogy to its limits. Cells are pretty unlike chips :)
- epgui 4y ago
  Even though it is approximately correct that the codons don’t change, the codons can be chemically modified in a bunch of different ways (eg.: methylation, acetylation, phosphorylation, ubiquitination, sumoylation…)

axg11 4y ago

The focus on learning is misguided. Human brains don’t have to use the same mechanism for learning as they do for reasoning. Perhaps they do, but I don’t believe we have enough evidence either way. The useful part of AI is the inference/output. If we achieve human-level performance AI inference then we have an incredibly powerful tool that can revolutionise society. The learning algorithm, be it gradient descent, evolutionary mechanisms or something entirely new, doesn’t matter. Whatever gets the job done.

DennisP 4y ago
Sure but the article isn't just about the mechanism. A human child doesn't read vast amounts of text to learn how to predict the next word in a sentence. We don't know how to make an AI that learns language like a five-year-old.
- deltaonefour 4y ago
  I think he's saying something along the lines that intelligence is mutually exclusive to learning. If we create a neural net that has the equivalent knowledge and intelligence of a human 5 year old but that neural net learns slower and was produced on 10 trillion pieces of data then we STILL have created a sentient AI despite the fact the learning algorithm is shit.
  - epgui 4y ago
    I don’t know if that’s what they meant, but what you’re saying is interesting. I would soften the idea a little bit by noting that as a little human grows up and learns more things, its brain plasticity goes down over time. AFAIK there’s no evidence that learning “saturates” the ability to learn other things, and the loss of plasticity is probably mainly linked to aging and/or other factors, but functionally the effect is very much the same: humans that know a lot of things typically have less capacity for learning.
    - deltaonefour 4y ago
      Anterograde amnesia is a condition where a human has lost his/her ability form new memories, aka learn. They can't remember anything past 3 minutes*. https://www.youtube.com/watch?v=ZeiMhUlipTk https://www.youtube.com/watch?v=ZeiMhUlipTk These people are clearly still sentient. There is still some form of memory there but the overall mechanism is largely gone.

epgui 4y ago

I don’t understand the fuss about backpropagation being impossible in brains. [1] Postsynaptic neurons do often connect to presynaptic neurons, ie “backwards” signalling, and sometimes whole circuits are involved to do this. I’m not sure if that maps perfectly to the backpropagation mechanism in ANNs, but isn’t it functionally essentially the same? [1] And the cited Nature paper does not seem to say exactly what the article says.

skohan 4y ago
What makes you think an ANN has very much in common with neurons at all? The model of activation as a weight times a simple activation function is laughably simple compared to what's actually going on in the brain.
- epgui 4y ago
  Well, the “neurons” in each case are obviously very different, but you can express computations with varying levels of abstraction. Trying to perfectly emulate atom for atom or neuron for neuron will never work, I think we need to move past that. What matters is overall information flow and processing, and that’s an emergent phenomenon for both brain and ANN. Imagine a program written with OOP, and the same program written with FP. The implementation details may be rather different, but this doesn’t prevent it from doing the same thing in a different way. You have to think of artificial neurons and real neurons in a similar way (though they are rather more dissimilar than OOP and FP are to each other). The main problem that backpropagation solves, I believe, is roughly equivalent to feedback loops in real neural networks.
  - skohan 4y ago
    Which networks? There are dozens, or maybe hundreds of different mechanisms the brain uses to adapt the signaling of neural networks. Back propagation might resemble some of them, but others not at all. Back propagation assumes a synapse can be modeled as a weight times an input. We know of tons of types of synapse which don't work like that.

scotty79 4y ago

Do people train those AIs on mathematical reasoning? I asked GPT3 to teach me about addition and subtraction and it produced pure garbage in very lecturing tone. I think mathematics is one field where AI could be actually immediately useful.

ben_w 4y ago
> Do people train those AIs on mathematical reasoning? They do. The GPT family is basically a very fancy autocomplete, so it’s mainly noteworthy because of how far something that limited can go, as it’s not even trying to be a fully general NLP system. If you want to solve maths problems, there are many better choices; I often use WolframAlpha, which is great for the maths part of my problems, but fairly often needs me to try several variants of my query because of the limitations of its natural language processing.
- scotty79 4y ago
  Wolfram Alpha is just sophisticated calculator. I'm interested more in artificial mathematicians able to create novel conjectures and prove them.
  - omnicognate 4y ago
    My understanding is that relatively little success has been achieved in that direction. I'd love to be corrected if there's something I haven't learned of, but as far as I know no theorem of any significance has been independently originated and proven by a computer. Rather, computers are increasingly used to assist human mathematicians. Machine learning has recently been used to help with identifying and proving interesting theorems [1] but still very much as a tool for human mathematicians rather than an independent artificial mathematical "mind". Personally I think the ability to truly "do mathematics" may be a marker of AGI. If/when we have machines independently mapping out new and interesting areas of mathematics the way human mathematicians do I suspect we will have cracked meaning/understanding. FWIW I've felt that way for over thirty years and have never considered the Turing test a convincing marker of intelligence, so I don't consider myself to be moving goalposts when I find the (absolutely incredible) feats of the current generation of ML algorithms unconvincing as evidence that we're nearing AGI. [1] https://www.maths.ox.ac.uk/node/40529 https://www.maths.ox.ac.uk/node/40529

deleted 4y ago

[deleted]

clircle 4y ago

Does _anyone_ actually think ML algorithms learn in the way a human does? It just strikes me as a patently false idea. Nevermind the fact that we can't even quantify similarity for this

cwalv 4y ago
Some people do .. maybe even many. It does have "neural" in the name, so it's the same, right?