9 ms·

The AI Scaling Hypothesis

146 points by andreyk 4y ago

puttycat 4y ago
Good overview. At the other extreme, some recent works [1,2] show why it’s sometime better to scale down instead of up, especially for some humanlike capabilities like generalization: [1] https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00489/112499/Minimum-Description-Length-Recurrent-Neural https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00489... [2] https://arxiv.org/abs/1906.04358 https://arxiv.org/abs/1906.04358
- MathYouF 4y ago
  If greater parameterization leads to memorization rather than generalisation it's likely a failure in our current architectures and loss formulations rather than an inherent benefit of "fewer parameters" improving generalizaiton. Other animals do not generalize better than humans despite having fewer neurons (or their generalizaitons betray a misunderstanding of the number and depth of subcategories there are for things, like when a dog barks at everything that passes by the window).
andreyk 4y ago
Co-author here, happy to answer any questions/chat about stuff we did not cover in this overview!
- puttycat 4y ago
  Hi! Great post. See my comment below about scaling down.
  - andreyk 4y ago
    Thanks! I'll take a look, those do look interesting.
- benlivengood 4y ago
  It would be great to see more focus on Chinchilla's result that most large models were quite undertrained with respect to optimal reduction in test loss.
  - andreyk 4y ago
    agreed, we did not discuss that sufficiently
- deleted 4y ago
  [deleted]
mjburgess 4y ago
"Scaling" means increasing the number of parameters. Parameters are just the database of the system. At 300GB of parameters, we're talking models which remember compressed versions of all books ever written. This is not a path to "AGI", this is just building a search engine with a little better querying power. "AI" systems today are little more than superpositions of google search results, with their parameters being a compression of billions of images/documents. This isnt even on the road to intelligence, let alone an instance of it. "General intelligence" does not solve problems by induction over billions of examples of their prior solutions. And exponential scaling in the amount of such remembering required is a fatal trajectory for AI, and likewise and indication that it doesnt deserve the term. No intelligence is exponential in an answer-space, indeed, i'd say that's *the whole point* of intelligence! We already know that if you compress all possible {(Question, Answer)} pairs, you can "solve" any problem trivially.
- MathYouF 4y ago
  The tone of this betrays a possibly more argumentative than collaborative conversation style than that which I may want to engage with further (as seems common I've noticed amongst anti-connectionists), but I did find one point intersting for discussion. > Parameters are just the database of the system. Would any equations parameters be considered just the database then? C in E=MC^2, 2 in a^2+b^2=c^2? I suppose those numbers are basically a database, but the relationships (connections) they have to the other variables (inputs) represent a demonstrable truth about the universe. To some degree every parameter in a nn is also representing some truth about the universe. How general and compact that representation is currently is not known (likely less than we'd like of both traits).
  - politician 4y ago
    This discussion (the GP and your response) perhaps suggests that a way to evaluate the intelligence of an AI may need to be more than the generation of some content, but also citations and supporting work for that content. I guess I'm suggesting that the field could benefit from a shift towards explainability-first models.
  - 4y ago
andreyi 4y ago
The largest models have 10 ^ 9 parameters? There is still a lot of "scaling" to be done if we want to reach 10 ^ 18 parameters, the number of synapses in the brain.
- ausbah 4y ago
  if one FLOP is one parameter we do have the capacity with modern day supercomputers? bottleneck then probably becomes data unless overparameterization holds?
kelseyfrog 4y ago
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]
3vidence 4y ago
I think something that has concerned me with the concept of scaling to AGI is the concept of "adversarial examples". Small tweaks that can be made to cause unpredictable behavior in the system. At a high level these are caused by unexpected paths in high dimensional model weight space that don't align with our intuition. This problem in general seems to get worse as the size of the weights grow. From a value perspective a very high fidelity model with extremely unexpected behavior seems really low value since you need a human there full time to make sure that the model doesn't go haywire that 1-5% of the time
- danuker 4y ago
  Suppose an adversarial example is recognized correctly by a human, yet a model recognizes it wrongly. Therefore, the model uses other information than humans in order to classify, and the information it uses is wrong. Therefore, the model needs to be trained ON THE ADVERSARIAL EXAMPLES in order to gain robustness. Similarly to GANs using a classifier adversary for augmenting a generative network, one could use a generative adversary for augmenting a classifier network. You can repeat until the adversarial examples look ambiguous even to a human.
godelski 4y ago
As a ML Vision researcher, I find these scaling hypothesis claims quite ridiculous. I understand that the NLP world has made large strides by adding more attention layers, but I'm not an NLP person and I suspect there's more than just more layers. We won't even talk about the human brain and just address that "scaling is sufficient" hypothesis. With vision, pointing to Parti and DALLE as scaling is quite dumb. They perform similarly but are DRASTICALLY different in size. Parti has configurations with 350M, 750M, 3B, and 20B parameters. DALLE has 3.5. Imagen uses T5-XXL which alone has 11B parameters, just in the text part. Not only this, there are major architecture changes. If scaling was all you needed then all these networks would still be using CNNs. But we shifted to transformers. THEN we have shifted to diffusion based models. Not to mention that Parti, DALLE, and Parti have different architectures. It isn't just about scale. Architecture matters here. And to address concerns, diffusion (invented decades ago) didn't work because we just scaled it up. It worked because of engineering. It was largely ignored previously because no one got it to work better than GANs. I think this lesson should really stand out. That we need to consider the advantages and disadvantages of different architectures and learn how to make ALL of them work effectively. In that manner we can combine them to work in ideal ways. Even Le Cun is coming to this point of view despite previously being on the scaling side. But maybe you NLP folks disagree. But the experience in vision is far more rich than just scaling.
- andreyk 4y ago
  I agree - I think scaling laws and scaling hypothesis are quite distinct personally. Scaling hypothesis is 'just go bigger with what we have and we'll get AGI', vs scaling laws are 'for these tasks and these models types, these are the empirical trends in performance we see'. I think scaling laws are still really valuable for vision research, but as you say we should not just abandon thinking about things beyond scaling even if we observe good scaling trends.
  - godelski 4y ago
    Yeah I agree with this position. It is also what I see within my own research. But also in my own research I see the vast importance of architecture search. This may not be what the public sees, but I think it is well known to the research community or anyone with hands on experience with these types of models.
gibsonf1 4y ago
Statistics on massive data sets does not intelligence make.
motohagiography 4y ago
We could make it scale in a very human way. I could forsee a near future where a person is not sentenced in court, but rather branded an outlaw, and to an AI online, which becomes deployed on their lives. It is learning as it goes to find and inflict psychological and social manipulation tactics as criminal penalties, up to and including attempting to cause death. It is a way for judges and society to both not actively murder for ethical reasons, but also to not make their own populations individually more violent by having their citizens hunt the convicted for bounties like they may have in traditional and ancient outlaw exile. It spreads the source of punishment evenly among participants, like parading someone in stocks through the streets in front of the mob, but on the micro, psychological scale. The AI stalks the convicted and finds ways to cause suffering, but learning from performing it against thousands of people, many simultaneously and even adversarially, optimizing for the most reflected cruelty it interprets from posts it gauges sentiment of in its environment. It's a plausible reason for why Roko's Basilisk would be made to exist, as we would have to think it was something we were inflicting on the other or another. Criminal punishment via AI as a means to wash our hands of our judgments seems like just the sort of thing we might sadly do. Surely it will only be reserved for the most serious of criminals.
- comboy 4y ago
  You want to make sure offender doesn't cause any more suffering of others and you want to help him become somebody better. I know US prison system exists, but we officially dropped torture as a punishment some time ago.
  - deleted 4y ago
    [deleted]
  - ballenf 4y ago
    We didn't drop torture, we just shifted from physical to mental torture in the form of solitary confinement or "white torture". The effects of white torture are arguably worse, but they let us pretend to be less barbaric.
hazrmard 4y ago
I think this is true for operations where inference time is not very critical. Think content generation. For example, my work in grad school is deploying AI-based control on drones such that they are fault-tolerant. We can have a stronger model at the ground station communicating with the vehicle, which introduces lag. Or the ground station model is more supervisory, and bare metal control is deferred to your classical algorithms. Or we run a smaller, faster network(s) on board which can act and learn fast. The other thing is model interpretability. For some of my work with drones or smart buildings, the stakeholders wouldn't let a neural network run on a real vehicle or building until it was thoroughly certified through whatever criteria they had. Easier with a smaller network.
deleted 4y ago
[deleted]
nojvek 4y ago
I’m grateful for recent open release of models like whisper and stable diffusion which you can run on your own hardware. However the core also seems to be training data. SEER from meta/Facebook is a billion images, far bigger than imagenet. OpenAI doesn’t release its training dataset for whisper. It seems we are in a place where AI will most likely increase the digital divide and wealth gap because the big datasets + large model training ability will only be accessible to mega corps.

146 points by andreyk 4y ago

Good overview. At the other extreme, some recent works [1,2] show why it’s sometime better to scale down instead of up, especially for some humanlike capabilities like generalization: [1] https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00489/112499/Minimum-Description-Length-Recurrent-Neural https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00489... [2] https://arxiv.org/abs/1906.04358 https://arxiv.org/abs/1906.04358

MathYouF 4y ago
If greater parameterization leads to memorization rather than generalisation it's likely a failure in our current architectures and loss formulations rather than an inherent benefit of "fewer parameters" improving generalizaiton. Other animals do not generalize better than humans despite having fewer neurons (or their generalizaitons betray a misunderstanding of the number and depth of subcategories there are for things, like when a dog barks at everything that passes by the window).

andreyk 4y ago

Co-author here, happy to answer any questions/chat about stuff we did not cover in this overview!

puttycat 4y ago
Hi! Great post. See my comment below about scaling down.
- andreyk 4y ago
  Thanks! I'll take a look, those do look interesting.
benlivengood 4y ago
It would be great to see more focus on Chinchilla's result that most large models were quite undertrained with respect to optimal reduction in test loss.
- andreyk 4y ago
  agreed, we did not discuss that sufficiently
deleted 4y ago
[deleted]

mjburgess 4y ago

"Scaling" means increasing the number of parameters. Parameters are just the database of the system. At 300GB of parameters, we're talking models which remember compressed versions of all books ever written. This is not a path to "AGI", this is just building a search engine with a little better querying power. "AI" systems today are little more than superpositions of google search results, with their parameters being a compression of billions of images/documents. This isnt even on the road to intelligence, let alone an instance of it. "General intelligence" does not solve problems by induction over billions of examples of their prior solutions. And exponential scaling in the amount of such remembering required is a fatal trajectory for AI, and likewise and indication that it doesnt deserve the term. No intelligence is exponential in an answer-space, indeed, i'd say that's *the whole point* of intelligence! We already know that if you compress all possible {(Question, Answer)} pairs, you can "solve" any problem trivially.

MathYouF 4y ago
The tone of this betrays a possibly more argumentative than collaborative conversation style than that which I may want to engage with further (as seems common I've noticed amongst anti-connectionists), but I did find one point intersting for discussion. > Parameters are just the database of the system. Would any equations parameters be considered just the database then? C in E=MC^2, 2 in a^2+b^2=c^2? I suppose those numbers are basically a database, but the relationships (connections) they have to the other variables (inputs) represent a demonstrable truth about the universe. To some degree every parameter in a nn is also representing some truth about the universe. How general and compact that representation is currently is not known (likely less than we'd like of both traits).
- politician 4y ago
  This discussion (the GP and your response) perhaps suggests that a way to evaluate the intelligence of an AI may need to be more than the generation of some content, but also citations and supporting work for that content. I guess I'm suggesting that the field could benefit from a shift towards explainability-first models.
- 4y ago

andreyi 4y ago

The largest models have 10 ^ 9 parameters? There is still a lot of "scaling" to be done if we want to reach 10 ^ 18 parameters, the number of synapses in the brain.

ausbah 4y ago
if one FLOP is one parameter we do have the capacity with modern day supercomputers? bottleneck then probably becomes data unless overparameterization holds?

kelseyfrog 4y ago

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]

3vidence 4y ago

I think something that has concerned me with the concept of scaling to AGI is the concept of "adversarial examples". Small tweaks that can be made to cause unpredictable behavior in the system. At a high level these are caused by unexpected paths in high dimensional model weight space that don't align with our intuition. This problem in general seems to get worse as the size of the weights grow. From a value perspective a very high fidelity model with extremely unexpected behavior seems really low value since you need a human there full time to make sure that the model doesn't go haywire that 1-5% of the time

danuker 4y ago
Suppose an adversarial example is recognized correctly by a human, yet a model recognizes it wrongly. Therefore, the model uses other information than humans in order to classify, and the information it uses is wrong. Therefore, the model needs to be trained ON THE ADVERSARIAL EXAMPLES in order to gain robustness. Similarly to GANs using a classifier adversary for augmenting a generative network, one could use a generative adversary for augmenting a classifier network. You can repeat until the adversarial examples look ambiguous even to a human.

godelski 4y ago

As a ML Vision researcher, I find these scaling hypothesis claims quite ridiculous. I understand that the NLP world has made large strides by adding more attention layers, but I'm not an NLP person and I suspect there's more than just more layers. We won't even talk about the human brain and just address that "scaling is sufficient" hypothesis. With vision, pointing to Parti and DALLE as scaling is quite dumb. They perform similarly but are DRASTICALLY different in size. Parti has configurations with 350M, 750M, 3B, and 20B parameters. DALLE has 3.5. Imagen uses T5-XXL which alone has 11B parameters, just in the text part. Not only this, there are major architecture changes. If scaling was all you needed then all these networks would still be using CNNs. But we shifted to transformers. THEN we have shifted to diffusion based models. Not to mention that Parti, DALLE, and Parti have different architectures. It isn't just about scale. Architecture matters here. And to address concerns, diffusion (invented decades ago) didn't work because we just scaled it up. It worked because of engineering. It was largely ignored previously because no one got it to work better than GANs. I think this lesson should really stand out. That we need to consider the advantages and disadvantages of different architectures and learn how to make ALL of them work effectively. In that manner we can combine them to work in ideal ways. Even Le Cun is coming to this point of view despite previously being on the scaling side. But maybe you NLP folks disagree. But the experience in vision is far more rich than just scaling.

andreyk 4y ago
I agree - I think scaling laws and scaling hypothesis are quite distinct personally. Scaling hypothesis is 'just go bigger with what we have and we'll get AGI', vs scaling laws are 'for these tasks and these models types, these are the empirical trends in performance we see'. I think scaling laws are still really valuable for vision research, but as you say we should not just abandon thinking about things beyond scaling even if we observe good scaling trends.
- godelski 4y ago
  Yeah I agree with this position. It is also what I see within my own research. But also in my own research I see the vast importance of architecture search. This may not be what the public sees, but I think it is well known to the research community or anyone with hands on experience with these types of models.

gibsonf1 4y ago

Statistics on massive data sets does not intelligence make.

motohagiography 4y ago

We could make it scale in a very human way. I could forsee a near future where a person is not sentenced in court, but rather branded an outlaw, and to an AI online, which becomes deployed on their lives. It is learning as it goes to find and inflict psychological and social manipulation tactics as criminal penalties, up to and including attempting to cause death. It is a way for judges and society to both not actively murder for ethical reasons, but also to not make their own populations individually more violent by having their citizens hunt the convicted for bounties like they may have in traditional and ancient outlaw exile. It spreads the source of punishment evenly among participants, like parading someone in stocks through the streets in front of the mob, but on the micro, psychological scale. The AI stalks the convicted and finds ways to cause suffering, but learning from performing it against thousands of people, many simultaneously and even adversarially, optimizing for the most reflected cruelty it interprets from posts it gauges sentiment of in its environment. It's a plausible reason for why Roko's Basilisk would be made to exist, as we would have to think it was something we were inflicting on the other or another. Criminal punishment via AI as a means to wash our hands of our judgments seems like just the sort of thing we might sadly do. Surely it will only be reserved for the most serious of criminals.

comboy 4y ago
You want to make sure offender doesn't cause any more suffering of others and you want to help him become somebody better. I know US prison system exists, but we officially dropped torture as a punishment some time ago.
- deleted 4y ago
  [deleted]
- ballenf 4y ago
  We didn't drop torture, we just shifted from physical to mental torture in the form of solitary confinement or "white torture". The effects of white torture are arguably worse, but they let us pretend to be less barbaric.

hazrmard 4y ago

I think this is true for operations where inference time is not very critical. Think content generation. For example, my work in grad school is deploying AI-based control on drones such that they are fault-tolerant. We can have a stronger model at the ground station communicating with the vehicle, which introduces lag. Or the ground station model is more supervisory, and bare metal control is deferred to your classical algorithms. Or we run a smaller, faster network(s) on board which can act and learn fast. The other thing is model interpretability. For some of my work with drones or smart buildings, the stakeholders wouldn't let a neural network run on a real vehicle or building until it was thoroughly certified through whatever criteria they had. Easier with a smaller network.

deleted 4y ago

[deleted]

nojvek 4y ago

I’m grateful for recent open release of models like whisper and stable diffusion which you can run on your own hardware. However the core also seems to be training data. SEER from meta/Facebook is a billion images, far bigger than imagenet. OpenAI doesn’t release its training dataset for whisper. It seems we are in a place where AI will most likely increase the digital divide and wealth gap because the big datasets + large model training ability will only be accessible to mega corps.