7 ms·

Yeah I agree - during undergrad, I spent a few years studying neuroscience, and I was very let down by my first ML/AI course. Compared to what I had learned ab

by skohan 5y ago

Yeah I agree - during undergrad, I spent a few years studying neuroscience, and I was very let down by my first ML/AI course. Compared to what I had learned about the brain, what we called an "ANN" just seemed like such a silly toy. The more you learn about neurobiology, the more apparent it is that there are so many levels of computation going on - everything from dendritic structure, to cellular metabolism, to epigenetics has an effect on information processing. The idea that we could reach some approximation of "general intelligence" by just scaling up some very large matrix operations just seemed like a complete joke. However, as you say, that doesn't mean what we've done in ML is not worthwhile and interesting. We might have over-reached thinking ML is ready to drive a car without major fourth-coming advancements, but use-cases like style transfer and DLSS 2 are downright magical. Even if we just made marginal improvements in current ML, I'm sure there is a ton of untapped potential in terms of applying this tech to novel use-cases.

fossuser 5y ago
I'm not sure I buy that - biology is often messier because of nature related constraints, it gets optimized for other things (energy, head size, etc.) The way a plane flies is quite different than the way a bird flies in complexity - they share an underlying mechanism, but planes don't need to flap wings. It's possible that scaling up does lead to generality and we've seen hints of that. - https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play https://deepmind.com/blog/article/generally-capable-agents-e... Also check out GPT-3’s performance on arithmetic tasks in the original paper (https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165) Pages: 21-23, 63 Which shows some generality, the best way to accurately predict an arithmetic answer is to deduce how the mathematical rules work. That paper shows some evidence of that and that’s just from a relatively dumb predict what comes next model. It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure?
- skohan 5y ago
  I've heard this airplane argument before, and while I do consider it plausible that AGI might be achievable with some system which is fundamentally much different than the human brain, I still don't think it can be achieved using simple scaling and optimization of the techniques in use today. I think this for a couple reasons: 1. The current gap in complexity is so huge. Nodes in an ANN roughly correspond to neurons, and the brain has somewhere on the order of 100 billion of them. Even if we built an ANN that big, we would only be scratching the surface of the complexity we have in the brain. Each synapse is basically an information processing unit, with behavioral characteristics much more complicated than a simple weight function. 2. The brain is highly specific. The structure and function of the auditory cortex is totally different to that of the motor cortices, to that of the hypothalamus and so on. Some brain regions depend heavily on things like spike timing and ordering to perform their functions. Different brain regions use different mechanisms of plasticity in order to learn. Currently most ANN's we have are vaguely inspired by the visual cortex (which is probably why a lot of the most interesting things to come out of ML so far have been related to image processing) and use something roughly analogous to net firing frequency for signal processing. I would consider it highly likely that our current ANNs are just structurally incapable of performing some of the types of computation we would consider intrinsically linked to what we think of as general intelligence. To make the airplane analogy, I believe we're probably closer to Leonardo da Vinci's early sketches of flying machines than we are to the Right Brothers. We might have the basic idea, but I would wager we're still missing some of the key insights required to get AGI off the ground. edit: it looks like you added some lines while I was typing, so to respond to your last points: > it’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? I totally agree that it's hard to predict, that technology usually advances faster than we expect, and that tremendous progress is being made. But the road to understanding human intelligence has been characterized by a series of periods of premature optimism followed by setbacks. For instance, in the 20th century, when dyes were getting better, and we were starting to understand how different brain regions had different functions, it may have seemed like we were close to just mapping all the different pieces of the brain, and that completing the resulting puzzle would give a clear insight into the workings of the human mind. Of course it turns out we were quite far from that. As far as what we can expect in the years leading up to AGI, I suspect it's going to be something that comes on gradually - I think computers will take on more and more tasks that were once reserved for humans over time, and the way we think about interfacing with technology might change so much that the concept of AGI might not seem relevant at some point. As to whether the current state of things is a failure - I would not characterize it that way. I think we're making real progress, I just also think there is a bit of hubris that we may have "cracked the code" of true machine intelligence. I think we're still a few major revelations away from that.
version_five 5y ago
So you took an undergrad ML course and you're using this as the basis for your conclusions about how ML can scale? You understand modern neural networks as large matrix operations and then attack that idea leading to intelligence as a joke? I also find it improbable that intelligence will emerge from modern ML without some major leap. But you have added nothing to the discussion, beyond some impressions from undergrad, when we are talking about something that is a very active and evolving research area. It's insulting to researchers and practitioners who have devoted years to studying ML to just dismiss broad areas of applicability because you took a course once.
- skohan 5y ago
  I'm sorry, I don't mean to insult or offend anyone. I'm just recounting my observations based on my understanding of the subject - and that is really not to disparage the amazing work that's being done, but rather to highlight the scale of the problem you have to solve when you're talking about creating something similar to human intelligence. It's entirely possible I'm wrong about this, and I would love to be proven so. Do you disagree substantively with anything I have said, or do you just think I could have phrased it better?
  - version_five 5y ago
    Thanks for your reply. I suppose a quick way to summarize my criticism is that it reads to me like you've dismissed the strengths of ML on technical grounds, while you imply you don't have any real technical experience in the field. You make a superficial comparison between the compexity of biology and ML, without providing any real insight, just saying one has lots going on and the other is matrix multiplication. If your conclusion is that current gradient based methods probably won't scale up to AGI, you're probably right. But if you want to get involved in the discussion of why this is true, what ML actually can and can't do, etc. I would encourage you to learn more about the subject and the current research areas, and draw on that for your discussion points. Otherwise, it comes across as "I once saw a podcast that said..." type stuff that is hard to take seriously. No doubt I come across as condescending, please take what I say with the usual weight you'd assign to the views of a random guy on the internet :)
giantg2 5y ago
And there's so much we still don't know about the nervous system and cognition.
kaba0 5y ago
I think most of the complexity of biology is accidental, not essential. Eg. why don’t we have a normal abstraction for sending signals? Instead, we have like 10s of slightly different ones with different failings each, but each having many repetitive machinery leading to inefficient “spaghetti code”. And while our brain is objectively very impressive, I don’t see how our complex abilities are anything but emerging features.
- skohan 5y ago
  Yes and no - the brain is also incredibly parsimonious with respect to how little resources it uses to achieve the information processing power it has. If you could make a computer which could compete in terms of utility, energy usage, and size, you'd be a billionaire in no time. It's probably true that you could imagine a "perfectly designed" brain which could perform better on some tasks with less complexity, but I think it's also true that there's been a lot of selection pressure towards increased intelligence, so this is probably fairly well optimized. > why don’t we have a normal abstraction for sending signals? Instead, we have like 10s of slightly different ones with different failings each, but each having many repetitive machinery leading to inefficient “spaghetti code”. What do you mean exactly by this? Like different neurotransmitter systems? Because I think it's actually quite elegant how the properties of different neurohormones lead to different processing modalities. It's like we have purpose-built hardware on the scale of individual proteins specialized for different purposes. I'm not so sure a more homogenized process for neural signaling would be an improvement.
  - kaba0 5y ago
    John von Neumann wrote an essay on the topic titled The computer and the brain, which is quite a good comparison between the two types of systems, even though knowledge of the two was pretty primitive at the time. The basic idea is that computers are multiple orders of magnitude faster at serial calculations, but brains offset this difference by the sheer number of “dumb” processing units, with insane number of interconnections. Also, I don’t think that comparing the training of a neural network to the brain is fair from an energy usage point of view - compare the usage of the final NN with it. As for how optimized is the human brain, well good question. I think not even a single biological cell is close to efficient, at most it is at a local minima. The reason is perhaps that “worse is better” in terms of novel functionality. But I don’t think there was a big evolutionary pressure on sufficient intelligence once it emerged - it is sort of a first past the post wins all.
Salgat 5y ago
I dunno man, when I got into learning CNN it becomes very easy to see how neural networks work at an intuitive level and how that plays into its "intelligence". For example, early layers of the network respond very well to simple features like edges and simple patterns, while later layers respond really well to more and more abstract things, like the shape of a person or a wheel etc. The craziest part is that this is all emergent from a random initialized state, all these patterns and abstractions develop with no manual intervention, they just happen to be the result of backpropagation consistently lowering a cost function. The biggest thing is that computing is just now finally able to have enough data and large enough networks to really start to create more generalized models. With a computer 20 years ago you might be able to do squeeze out simple pattern recognition, but for every layer and every neural node you add to a neural network, the more complex the model becomes and the more edge cases it can fold into itself. Take a look at Universal approximation theorem and how, with enough nodes in a neural network, you can solve pretty much any problem given the right weights.

by skohan 5y ago

fossuser 5y ago

I'm not sure I buy that - biology is often messier because of nature related constraints, it gets optimized for other things (energy, head size, etc.) The way a plane flies is quite different than the way a bird flies in complexity - they share an underlying mechanism, but planes don't need to flap wings. It's possible that scaling up does lead to generality and we've seen hints of that. - https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play https://deepmind.com/blog/article/generally-capable-agents-e... Also check out GPT-3’s performance on arithmetic tasks in the original paper (https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165) Pages: 21-23, 63 Which shows some generality, the best way to accurately predict an arithmetic answer is to deduce how the mathematical rules work. That paper shows some evidence of that and that’s just from a relatively dumb predict what comes next model. It’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure?

skohan 5y ago
I've heard this airplane argument before, and while I do consider it plausible that AGI might be achievable with some system which is fundamentally much different than the human brain, I still don't think it can be achieved using simple scaling and optimization of the techniques in use today. I think this for a couple reasons: 1. The current gap in complexity is so huge. Nodes in an ANN roughly correspond to neurons, and the brain has somewhere on the order of 100 billion of them. Even if we built an ANN that big, we would only be scratching the surface of the complexity we have in the brain. Each synapse is basically an information processing unit, with behavioral characteristics much more complicated than a simple weight function. 2. The brain is highly specific. The structure and function of the auditory cortex is totally different to that of the motor cortices, to that of the hypothalamus and so on. Some brain regions depend heavily on things like spike timing and ordering to perform their functions. Different brain regions use different mechanisms of plasticity in order to learn. Currently most ANN's we have are vaguely inspired by the visual cortex (which is probably why a lot of the most interesting things to come out of ML so far have been related to image processing) and use something roughly analogous to net firing frequency for signal processing. I would consider it highly likely that our current ANNs are just structurally incapable of performing some of the types of computation we would consider intrinsically linked to what we think of as general intelligence. To make the airplane analogy, I believe we're probably closer to Leonardo da Vinci's early sketches of flying machines than we are to the Right Brothers. We might have the basic idea, but I would wager we're still missing some of the key insights required to get AGI off the ground. edit: it looks like you added some lines while I was typing, so to respond to your last points: > it’s hard to predict timelines for this kind of thing, and people are notoriously bad at it. Few would have predicted the results we’re seeing today in 2010. What would you expect to see in the years leading up to AGI? Does what we’re seeing look like failure? I totally agree that it's hard to predict, that technology usually advances faster than we expect, and that tremendous progress is being made. But the road to understanding human intelligence has been characterized by a series of periods of premature optimism followed by setbacks. For instance, in the 20th century, when dyes were getting better, and we were starting to understand how different brain regions had different functions, it may have seemed like we were close to just mapping all the different pieces of the brain, and that completing the resulting puzzle would give a clear insight into the workings of the human mind. Of course it turns out we were quite far from that. As far as what we can expect in the years leading up to AGI, I suspect it's going to be something that comes on gradually - I think computers will take on more and more tasks that were once reserved for humans over time, and the way we think about interfacing with technology might change so much that the concept of AGI might not seem relevant at some point. As to whether the current state of things is a failure - I would not characterize it that way. I think we're making real progress, I just also think there is a bit of hubris that we may have "cracked the code" of true machine intelligence. I think we're still a few major revelations away from that.

version_five 5y ago

So you took an undergrad ML course and you're using this as the basis for your conclusions about how ML can scale? You understand modern neural networks as large matrix operations and then attack that idea leading to intelligence as a joke? I also find it improbable that intelligence will emerge from modern ML without some major leap. But you have added nothing to the discussion, beyond some impressions from undergrad, when we are talking about something that is a very active and evolving research area. It's insulting to researchers and practitioners who have devoted years to studying ML to just dismiss broad areas of applicability because you took a course once.

skohan 5y ago
I'm sorry, I don't mean to insult or offend anyone. I'm just recounting my observations based on my understanding of the subject - and that is really not to disparage the amazing work that's being done, but rather to highlight the scale of the problem you have to solve when you're talking about creating something similar to human intelligence. It's entirely possible I'm wrong about this, and I would love to be proven so. Do you disagree substantively with anything I have said, or do you just think I could have phrased it better?
- version_five 5y ago
  Thanks for your reply. I suppose a quick way to summarize my criticism is that it reads to me like you've dismissed the strengths of ML on technical grounds, while you imply you don't have any real technical experience in the field. You make a superficial comparison between the compexity of biology and ML, without providing any real insight, just saying one has lots going on and the other is matrix multiplication. If your conclusion is that current gradient based methods probably won't scale up to AGI, you're probably right. But if you want to get involved in the discussion of why this is true, what ML actually can and can't do, etc. I would encourage you to learn more about the subject and the current research areas, and draw on that for your discussion points. Otherwise, it comes across as "I once saw a podcast that said..." type stuff that is hard to take seriously. No doubt I come across as condescending, please take what I say with the usual weight you'd assign to the views of a random guy on the internet :)

giantg2 5y ago

And there's so much we still don't know about the nervous system and cognition.

kaba0 5y ago

I think most of the complexity of biology is accidental, not essential. Eg. why don’t we have a normal abstraction for sending signals? Instead, we have like 10s of slightly different ones with different failings each, but each having many repetitive machinery leading to inefficient “spaghetti code”. And while our brain is objectively very impressive, I don’t see how our complex abilities are anything but emerging features.

skohan 5y ago
Yes and no - the brain is also incredibly parsimonious with respect to how little resources it uses to achieve the information processing power it has. If you could make a computer which could compete in terms of utility, energy usage, and size, you'd be a billionaire in no time. It's probably true that you could imagine a "perfectly designed" brain which could perform better on some tasks with less complexity, but I think it's also true that there's been a lot of selection pressure towards increased intelligence, so this is probably fairly well optimized. > why don’t we have a normal abstraction for sending signals? Instead, we have like 10s of slightly different ones with different failings each, but each having many repetitive machinery leading to inefficient “spaghetti code”. What do you mean exactly by this? Like different neurotransmitter systems? Because I think it's actually quite elegant how the properties of different neurohormones lead to different processing modalities. It's like we have purpose-built hardware on the scale of individual proteins specialized for different purposes. I'm not so sure a more homogenized process for neural signaling would be an improvement.
- kaba0 5y ago
  John von Neumann wrote an essay on the topic titled The computer and the brain, which is quite a good comparison between the two types of systems, even though knowledge of the two was pretty primitive at the time. The basic idea is that computers are multiple orders of magnitude faster at serial calculations, but brains offset this difference by the sheer number of “dumb” processing units, with insane number of interconnections. Also, I don’t think that comparing the training of a neural network to the brain is fair from an energy usage point of view - compare the usage of the final NN with it. As for how optimized is the human brain, well good question. I think not even a single biological cell is close to efficient, at most it is at a local minima. The reason is perhaps that “worse is better” in terms of novel functionality. But I don’t think there was a big evolutionary pressure on sufficient intelligence once it emerged - it is sort of a first past the post wins all.

Salgat 5y ago

I dunno man, when I got into learning CNN it becomes very easy to see how neural networks work at an intuitive level and how that plays into its "intelligence". For example, early layers of the network respond very well to simple features like edges and simple patterns, while later layers respond really well to more and more abstract things, like the shape of a person or a wheel etc. The craziest part is that this is all emergent from a random initialized state, all these patterns and abstractions develop with no manual intervention, they just happen to be the result of backpropagation consistently lowering a cost function. The biggest thing is that computing is just now finally able to have enough data and large enough networks to really start to create more generalized models. With a computer 20 years ago you might be able to do squeeze out simple pattern recognition, but for every layer and every neural node you add to a neural network, the more complex the model becomes and the more edge cases it can fold into itself. Take a look at Universal approximation theorem and how, with enough nodes in a neural network, you can solve pretty much any problem given the right weights.