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There's a deeper more troubling problem being exposed here - deep learning systems are at least an order of magnitude less data efficient than the systems they

There's a deeper more troubling problem being exposed here - deep learning systems are at least an order of magnitude less data efficient than the systems they hope to replicate. GPT-3 175B is trained with 499 Billion tokens[1]. Let's assume token = word for the sake of this argument[2]. The average adult person reads at a rate of 238wmp[3]. Then a human who reads 24hrs/day from birth until their 18th birthday would read a total of 2.2B billion words[4], or 0.45% of the words GPT-3 was trained on. Human's simply do much more with much less. So what gives? I don't disagree that we still haven't reached the end of what scaling can do, but there is a creeping suspicion that we've gotten something fundamentally wrong on the way there. 1. https://lambdalabs.com/blog/demystifying-gpt-3/ https://lambdalabs.com/blog/demystifying-gpt-3/ 2. GPT-based models use BPE and while we would dive into the actual dictionary of tokens and make a word-token relationship, we both agree that although this isn't a 1-to-1 relationship it won't change the conclusion https://huggingface.co/docs/transformers/tokenizer_summary https://huggingface.co/docs/transformers/tokenizer_summary 3. https://psyarxiv.com/xynwg/ https://psyarxiv.com/xynwg/ 4. 238*60*24*365*18=2,251,670,400

machina_ex_deus 4y ago
I agree. If you look at animals it's also clear that scaling hypothesis breaks somewhen, as all measures of brain size (brain mass ratio, etc.) fail to capture intelligence. And animals have natural neutral networks. If you think about it, neutral networks have roamed the earth for millions of years - including generic algorithm for optimizing the hardware. And yet only extremely recently something like humans happened. Why? The amount of training and processing power which happened naturally through evolution beats current AI research by several orders of magnitude. Yes, evolution isn't intelligent design. But the current approach to AI isn't intelligent design either.
- lostmsu 4y ago
  I thought the number of neurons is proportional.
deleted 4y ago
[deleted]
gamegoblin 4y ago
Humans take in a tremendously high bitrate of data via other senses and are able to connect those to the much lower amount of language input such that the language can go much further. GPT-3 is learning everything it knows about the entire universe just from text. Imagine we received a 1TB information dump from a civilization that lives in an alternate universe with entirely different physics. How much could we learn just from this information dump? And from our point of view, it could be absurdly exotic. Maybe their universe doesn't have gravity or electromagnetic radiation. Maybe the life forms in that universe spontaneously merge consciousnesses with other life forms and separate randomly, so whatever writing we have received is in a style that assumes the reader can effortlessly deduce that the author is actually a froth of many consciousnesses. And in the grand spectrum of how weird things could get, this "exotic" universe I have described is really basically identical to our own, because my imagination is limited. Learning about a whole exotic universe from just an info dump is the task of GPT-3. For instance, tons of our writing takes for granted that solid objects don't pass through each other. I dropped the book. Where is the book? On the floor. Very few bits of GPT-3's training set includes the statements "a book is a solid object", "the floor is a solid object", "solid objects don't pass through each other", but it can infer this principle and others like it. From this point of view, its shortcomings make a lot of sense. Some things GPT fails at are obvious to us having grown up in this universe. I imagine we're going to see an explosion of intelligence once researchers figure out how to feed AI systems large swaths of YouTube and such, because then they will have a much higher bandwidth way to learn about the universe and how things interact, connecting language to physical reality.
- cma 4y ago
  Google's Imagen was trained on about as many images as a 6 year old would have seen over their lifetime at 24fps and a whole lot more text. It can draw a lot better and probably has a better visual vocabulary but is also way outclassed in many ways. Paucity of the stimulus is a real problem and may mean our starting point architecture from genetics has a lot of learning built in than just a bunch of uninitialized weights randomly connected. A newborn animal can often get up and walk right away in many species. https://www.youtube.com/watch?v=oTNA8vFUMEc https://www.youtube.com/watch?v=oTNA8vFUMEc Humans have a giant head at birth and muscles too weak, but can swim around like little seals pretty quickly after birth.
6gvONxR4sf7o 4y ago
What’s missing is interaction/causation, and the reason is that we can scale things more easily without interaction in the data gathering loop. Training a model with data gathering in the loop requires gathering more data every time the model takes a learning step. It’s slow and expensive. Training a model on pre-existing data is much simpler, and it’s unclear whether we’ve reached the limits of that yet. My prediction is we’ll get ‘good enough for prod’ without interactive data, which will let us put interactive systems in the real world at scale, at which point the field’s focus will be able to shift. One way to look at it is active learning. We all know the game where I think of a number between 0 and 100 and you have to guess it, and I’ll tell you if it’s higher or lower. You’ll start by guessing 50, then maybe 25, and so on, bisecting the intervals. If you want to get within +/-1 of the number I’m thinking of, you need about six data points. On the other hand, if you don’t do this interactively, and just gather a bunch of data before seeing any answers, to get within +/-1, and need 50 data points. The interactivity means you can refine your questions in response to whatever you’ve learned, saving huge amounts of time. Another way to look at it is like randomized controlled trials. To learn a compact idea (more X means more Y), you can randomize X and gather just enough data on Y to be confident that the relationship isn’t a coincidence. The alternative (observational causal inference) is famously harder. You have to look at a bunch of X’s and Y’s, and also all the Z’s that might affect them, and then get enough data to be confident in this entire structure you’ve put together involving lots of variables. The way ML has progressed is really a function of what’s easy. If you want a model to learn to speak english, do you want it to be embodied in the real world for two years with humans teaching it full time how the world and language relate? Or is it faster to just show it a terabyte of english? tl;dr observational learning is much much harder than interactive learning, but we can scale observational learning in ways we can’t scale interactive learning.
idiotsecant 4y ago
>deep learning systems are at least an order of magnitude less data efficient than the systems they hope to replicate. While true on the surface, you have to also consider that there is a vast quantity of training data expressed in our DNA. Our 'self' is a conscious thought, sure, but it's also unconscious action and instinct, all of which is indirect lived experience of our forebear organisms. The ones that had a slightly better twitch response to the feel of an insect crawling on their arm were able to survive the incident, etc. Our 'lizard brains' are the result of the largest set of training data we could possibly imagine - the evolutionary history of life on earth.
MonkeyMalarky 4y ago
Humans also have millions of years of evolution that have effectively pre-trained the structure and learning ability of the brain. A baby isn't born knowing a language but is born with the ability to efficiently learn them.
- peteradio 4y ago
  Indeed, there is a certain hardcoding that can efficiently synthesize language. Doesn't that beg the question... what is the missing hardcoding for AI that could enable it to synthesize via much smaller samples?
  - myownpetard 4y ago
    There is a great paper, Weight Agnostic Neural Networks [0], that explores this topic. They experiment with using a single shared weight for a network while using an evolutionary algorithm to find architectures that are themselves biased towards being effective on specific problems. The upshot is that once you've found an architecture that is already biased towards solving a specific problem, then the training of the weights is faster and results in better performance. From the abstract, "...In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task.... We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights." [0] https://arxiv.org/abs/1906.04358 https://arxiv.org/abs/1906.04358
    - Der_Einzige 4y ago
      This btw is an example of a whole field called "extreme learning" https://en.m.wikipedia.org/wiki/Extreme_learning_machine https://en.m.wikipedia.org/wiki/Extreme_learning_machine
edf13 4y ago
Simple - humans aren’t learning by reading and understanding a word at a time (or token)… They are taking many thousands (millions?) of inputs every minute for their surroundings
- edf13 4y ago
  Just reminded myself of Jonny 5 needing input… https://youtu.be/Y9lwQKv71FY https://youtu.be/Y9lwQKv71FY
Symmetry 4y ago
Humans are using less data but we throw drastically more compute at the problem during learning.
andreyk 4y ago
I think comparing to humans is a bit of a distraction, unless what you care about is replicating the way human intelligence works in AI. The mechanisms by which learning is done (in these cases self-supervised and supervised learning) are not at all the same as humans have, so it's unsurprising the qualitative aspects are different. It may be argued we need more human-like learning mechanisms. Then again, if we need internet-scale data to achieve human-level general intelligence, so what? If it works it works. Of course, the comparison has some value in terms of knowing what can be improved and so on, especially for RL. But I wouldn't call this a 'troubling problem'.
chaxor 4y ago
You're right about reference [2], which can alter things by ~1 order of magnitude (words are usually ~3-10 tokens). Additionally as others have pointed out, we don't live entirely in the text world. So, we have the nice benefit of understanding objects from visual and proprioceptive inputs, which is huge. The paucity of data argument made well-known by Noam Chomsky et al is certainly worth discussing in academia; however, I am not as moved by these arguments of the stark differences in input required between humans and ML as I once was. In image processing for example, sending 10k images in rapid succession with no other proprioceptive inputs, time dependencies, or agent-driven exploration of spaces puts these systems at an enormous disadvantage to learn certain phenomenon (classes of objects or otherwise). Of course there are differences between the systems, but I'm beginning to be more skeptical that saying that the newer ML system can't learn as much as biological systems given the same input (obviously this is where a lot is hidden).
- kelseyfrog 4y ago
  Thank you for the tokens-to-words factor! Much appreciated. I'm definitely in agreement that multi-task models represent an ability to learn more than any one specialized model, but I think it's a bit of an open question whether multi-task learning alone can fully close the digital-biological gap. Of course I'd be very happy to be proven wrong on this though by empirical evidence in my lifetime :)
visarga 4y ago
The brain has about 1T synapses and GPT-3 has 175B parameters, even though a parameter is much simpler than a synapse. So the scale of the brain is at least 5700x that of GPT-3. It seems normal to have to compensate by using 200x more training data.
- Filligree 4y ago
  This does make me wonder, what would happen if we could feed the brain data at the same rate that GPT-3 was able?
c3534l 4y ago
Brains do not actually work very similarly to artificial neural networks. The connectionist approach is no longer favored, and human brains are not arranged in regular grids of fully interconnected layers. ANNs were inspired by how people thought the brain worked more than 50 years ago. Of course, ANNs are meant to work and solve practical problems with the technology we have. They're not simulations.
gxt 4y ago
Because the whole industry is wrong. ML is incapable of general intelligence, because that's not what intelligence is. ML is the essential component with which one interfaces with the universe, but it's not intelligence, and never will be.

machina_ex_deus 4y ago

I agree. If you look at animals it's also clear that scaling hypothesis breaks somewhen, as all measures of brain size (brain mass ratio, etc.) fail to capture intelligence. And animals have natural neutral networks. If you think about it, neutral networks have roamed the earth for millions of years - including generic algorithm for optimizing the hardware. And yet only extremely recently something like humans happened. Why? The amount of training and processing power which happened naturally through evolution beats current AI research by several orders of magnitude. Yes, evolution isn't intelligent design. But the current approach to AI isn't intelligent design either.

lostmsu 4y ago
I thought the number of neurons is proportional.

deleted 4y ago

[deleted]

gamegoblin 4y ago

Humans take in a tremendously high bitrate of data via other senses and are able to connect those to the much lower amount of language input such that the language can go much further. GPT-3 is learning everything it knows about the entire universe just from text. Imagine we received a 1TB information dump from a civilization that lives in an alternate universe with entirely different physics. How much could we learn just from this information dump? And from our point of view, it could be absurdly exotic. Maybe their universe doesn't have gravity or electromagnetic radiation. Maybe the life forms in that universe spontaneously merge consciousnesses with other life forms and separate randomly, so whatever writing we have received is in a style that assumes the reader can effortlessly deduce that the author is actually a froth of many consciousnesses. And in the grand spectrum of how weird things could get, this "exotic" universe I have described is really basically identical to our own, because my imagination is limited. Learning about a whole exotic universe from just an info dump is the task of GPT-3. For instance, tons of our writing takes for granted that solid objects don't pass through each other. I dropped the book. Where is the book? On the floor. Very few bits of GPT-3's training set includes the statements "a book is a solid object", "the floor is a solid object", "solid objects don't pass through each other", but it can infer this principle and others like it. From this point of view, its shortcomings make a lot of sense. Some things GPT fails at are obvious to us having grown up in this universe. I imagine we're going to see an explosion of intelligence once researchers figure out how to feed AI systems large swaths of YouTube and such, because then they will have a much higher bandwidth way to learn about the universe and how things interact, connecting language to physical reality.

cma 4y ago
Google's Imagen was trained on about as many images as a 6 year old would have seen over their lifetime at 24fps and a whole lot more text. It can draw a lot better and probably has a better visual vocabulary but is also way outclassed in many ways. Paucity of the stimulus is a real problem and may mean our starting point architecture from genetics has a lot of learning built in than just a bunch of uninitialized weights randomly connected. A newborn animal can often get up and walk right away in many species. https://www.youtube.com/watch?v=oTNA8vFUMEc https://www.youtube.com/watch?v=oTNA8vFUMEc Humans have a giant head at birth and muscles too weak, but can swim around like little seals pretty quickly after birth.

6gvONxR4sf7o 4y ago

What’s missing is interaction/causation, and the reason is that we can scale things more easily without interaction in the data gathering loop. Training a model with data gathering in the loop requires gathering more data every time the model takes a learning step. It’s slow and expensive. Training a model on pre-existing data is much simpler, and it’s unclear whether we’ve reached the limits of that yet. My prediction is we’ll get ‘good enough for prod’ without interactive data, which will let us put interactive systems in the real world at scale, at which point the field’s focus will be able to shift. One way to look at it is active learning. We all know the game where I think of a number between 0 and 100 and you have to guess it, and I’ll tell you if it’s higher or lower. You’ll start by guessing 50, then maybe 25, and so on, bisecting the intervals. If you want to get within +/-1 of the number I’m thinking of, you need about six data points. On the other hand, if you don’t do this interactively, and just gather a bunch of data before seeing any answers, to get within +/-1, and need 50 data points. The interactivity means you can refine your questions in response to whatever you’ve learned, saving huge amounts of time. Another way to look at it is like randomized controlled trials. To learn a compact idea (more X means more Y), you can randomize X and gather just enough data on Y to be confident that the relationship isn’t a coincidence. The alternative (observational causal inference) is famously harder. You have to look at a bunch of X’s and Y’s, and also all the Z’s that might affect them, and then get enough data to be confident in this entire structure you’ve put together involving lots of variables. The way ML has progressed is really a function of what’s easy. If you want a model to learn to speak english, do you want it to be embodied in the real world for two years with humans teaching it full time how the world and language relate? Or is it faster to just show it a terabyte of english? tl;dr observational learning is much much harder than interactive learning, but we can scale observational learning in ways we can’t scale interactive learning.

idiotsecant 4y ago

>deep learning systems are at least an order of magnitude less data efficient than the systems they hope to replicate. While true on the surface, you have to also consider that there is a vast quantity of training data expressed in our DNA. Our 'self' is a conscious thought, sure, but it's also unconscious action and instinct, all of which is indirect lived experience of our forebear organisms. The ones that had a slightly better twitch response to the feel of an insect crawling on their arm were able to survive the incident, etc. Our 'lizard brains' are the result of the largest set of training data we could possibly imagine - the evolutionary history of life on earth.

MonkeyMalarky 4y ago

Humans also have millions of years of evolution that have effectively pre-trained the structure and learning ability of the brain. A baby isn't born knowing a language but is born with the ability to efficiently learn them.

peteradio 4y ago
Indeed, there is a certain hardcoding that can efficiently synthesize language. Doesn't that beg the question... what is the missing hardcoding for AI that could enable it to synthesize via much smaller samples?
- myownpetard 4y ago
  There is a great paper, Weight Agnostic Neural Networks [0], that explores this topic. They experiment with using a single shared weight for a network while using an evolutionary algorithm to find architectures that are themselves biased towards being effective on specific problems. The upshot is that once you've found an architecture that is already biased towards solving a specific problem, then the training of the weights is faster and results in better performance. From the abstract, "...In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task.... We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights." [0] https://arxiv.org/abs/1906.04358 https://arxiv.org/abs/1906.04358
  - Der_Einzige 4y ago
    This btw is an example of a whole field called "extreme learning" https://en.m.wikipedia.org/wiki/Extreme_learning_machine https://en.m.wikipedia.org/wiki/Extreme_learning_machine

edf13 4y ago

Simple - humans aren’t learning by reading and understanding a word at a time (or token)… They are taking many thousands (millions?) of inputs every minute for their surroundings

edf13 4y ago
Just reminded myself of Jonny 5 needing input… https://youtu.be/Y9lwQKv71FY https://youtu.be/Y9lwQKv71FY

Symmetry 4y ago

Humans are using less data but we throw drastically more compute at the problem during learning.

andreyk 4y ago

I think comparing to humans is a bit of a distraction, unless what you care about is replicating the way human intelligence works in AI. The mechanisms by which learning is done (in these cases self-supervised and supervised learning) are not at all the same as humans have, so it's unsurprising the qualitative aspects are different. It may be argued we need more human-like learning mechanisms. Then again, if we need internet-scale data to achieve human-level general intelligence, so what? If it works it works. Of course, the comparison has some value in terms of knowing what can be improved and so on, especially for RL. But I wouldn't call this a 'troubling problem'.

chaxor 4y ago

You're right about reference [2], which can alter things by ~1 order of magnitude (words are usually ~3-10 tokens). Additionally as others have pointed out, we don't live entirely in the text world. So, we have the nice benefit of understanding objects from visual and proprioceptive inputs, which is huge. The paucity of data argument made well-known by Noam Chomsky et al is certainly worth discussing in academia; however, I am not as moved by these arguments of the stark differences in input required between humans and ML as I once was. In image processing for example, sending 10k images in rapid succession with no other proprioceptive inputs, time dependencies, or agent-driven exploration of spaces puts these systems at an enormous disadvantage to learn certain phenomenon (classes of objects or otherwise). Of course there are differences between the systems, but I'm beginning to be more skeptical that saying that the newer ML system can't learn as much as biological systems given the same input (obviously this is where a lot is hidden).

kelseyfrog 4y ago
Thank you for the tokens-to-words factor! Much appreciated. I'm definitely in agreement that multi-task models represent an ability to learn more than any one specialized model, but I think it's a bit of an open question whether multi-task learning alone can fully close the digital-biological gap. Of course I'd be very happy to be proven wrong on this though by empirical evidence in my lifetime :)

visarga 4y ago

The brain has about 1T synapses and GPT-3 has 175B parameters, even though a parameter is much simpler than a synapse. So the scale of the brain is at least 5700x that of GPT-3. It seems normal to have to compensate by using 200x more training data.

Filligree 4y ago
This does make me wonder, what would happen if we could feed the brain data at the same rate that GPT-3 was able?

c3534l 4y ago

Brains do not actually work very similarly to artificial neural networks. The connectionist approach is no longer favored, and human brains are not arranged in regular grids of fully interconnected layers. ANNs were inspired by how people thought the brain worked more than 50 years ago. Of course, ANNs are meant to work and solve practical problems with the technology we have. They're not simulations.

gxt 4y ago

Because the whole industry is wrong. ML is incapable of general intelligence, because that's not what intelligence is. ML is the essential component with which one interfaces with the universe, but it's not intelligence, and never will be.