Do Machine Learning Models Memorize or Generalize?(pair.withgoogle.com) |
Do Machine Learning Models Memorize or Generalize?(pair.withgoogle.com) |
[0]: https://www.scientificamerican.com/article/new-estimate-boos...
...but that's exactly what OP said, no?
I remember attending an ML presentation where the speaker shared a quote I can't find anymore (speaking of memory and generalization :)), which said something like: "To learn is to forget"
If we memorized everything perfectly, we would not learn anything: instead of remembering the concept of a "chair", you would remember thousands of separate instances of things you've seen that have a certain combination of colors and shapes etc
It's the fact that we forget certain details (small differences between all these chairs) that makes us learn what a "chair" is.
Likewise, if you remembered every single word in a book, you would not understand its meaning; understanding its meaning = being able to "summarize" (compress) this long list of words into something more essential: storyline, characters, feelings, etc.
Aside from having to eventually experience the death of all stars and light and the decay of most of the universe's baryonic matter and then face an eternity of darkness with nothing to touch, it's yet another reason I don't think immortality (as opposed to just a very long lifespan) is actually desirable.
Or is it always running at the same pace regardless of if it’s empty or not?
I guess the Brian doesn’t really work like that…. But I’m curious :-)
But having so much of the past being so accessible is tough. There are lots of memories I'd rather not have, that are vivid and easily called up. And still, I think it's only a fraction of what her memory seems to be like.
While the upper bound is technically "infinity", there is a tradeoff between the amount of concepts stored and the fundamental amount of information storable per concept, similar to how other tradeoff principles like the uncertainty principle, etc work.
We don’t know if the animal brain works the same way, but I suspect it is mostly compression algorithms designed to predict things, and doesn’t store much data at all.
Geometry is good for training in this way—and often very helpful for physics proofs too!
this 'compression' is what 'understanding' something really entails; at first... but then there's more.
when knowledge becomes understood it enables perception (e.g. we perceive meaning in words once we learn to read).
when we get really good at this understanding-perception we may start to 'manipulate' the abstractions we 'perceive'. an example would be to 'understand a cube' and then being able to rotate it around so to predict what would happen without really needing the cube. but this is an overly simplistic example
[1] https://en.wikipedia.org/wiki/Synaptic_pruning [2] https://en.wikipedia.org/wiki/Pruning_(artificial_neural_net...
It means roughly 'to understand completely, fully'.
To use the same term to describe generalization... just shows you didn't grok grokking.
I also have a couple of little libraries for things like annotations, interleaving svg/canvas and making d3 a bit less verbose.
- https://github.com/PAIR-code/ai-explorables/tree/master/sour...
- https://1wheel.github.io/swoopy-drag/
Second, the article correctly states that typically L2 weight decay is used, leading to a lot of weights with small magnitudes. For models that generalize better, would it then be better to always use L1 weight decay to promote sparsity in combination with longer training?
I wonder whether deep learning models that only use sparse fourier features rather than dense linear layers would work better...
Longer answer: deep learning models are usually trying to find the best nonlinear basis in which to represent inputs; if the inputs are well-represented (read that as: can be sparsely represented) in some basis known a-priori, it usually helps to just put them in that basis, e.g., by FFT’ing RF signals.
The challenge is that the overall-optimal basis might not be the same as those of any local minima, so you’ve got to do some tricks to nudge the network closer.
Put another way, it isn't just how simple this task seems to be in the number of terms that are important, but isn't it also a rather dense function?
Probably better question to ask is how sensitive are models that are looking at less dense functions to this? (Or more dense.). I'm not trying to disavow the ideas.
https://en.wikipedia.org/wiki/Grid_cell
If you plot a head map of a neuron in the hidden layer on a 2D chart where one axis is $a$ and the other is $b$, I think you might get a triangular lattice. If it's doing what I think it is, then looking at another hidden neuron would give a different lattice with another orientation + scale.
Also you could make a base 67 adding machine by chaining these together.
I also can't help the gut feeling that the relationship between W_in-proj's neurons compared to the relationship between W_out-proj's neurons looks like the same mapping as the one between the semitone circle and the circle of fifths
https://upload.wikimedia.org/wikipedia/commons/thumb/6/6f/Pi...
On generalization - its still memorization. I think there has been some proof that chatgpt does 'try' to perform some higher level thinking but still has problems due to the dictionary type lookup table it uses. The higher level thinking or agi that people are excited about is a form of generalization that is so impressive we don't really think of it as memorization. But I actually question if our wantingness to generate original thought isn't as actually separate from what we currently are seeing.
In the case of NNs we have a "modal knn" (memorising) going to a "mean knn" ('generalising') under the right sort of training.
I'd call both of these memorising, but the latter is a kind of weighted recall.
Generalisation as a property of statistical models (ie., models of conditional freqs) is not the same property as generalisation in the case of scientific models.
In the latter a scientific model is general because it models causally necessary effects from causes -- so, necessarily if X then Y.
Whereas generalisation in associative stats is just about whether you're drawing data from the empirical freq. distribution or whether you've modelled first. In all automated stats the only diff between the "model" and "the data" is some sort of weighted averaging operation.
So in automated stats (ie., ML,AI) it's really just whether the model uses a mean.
https://en.wikipedia.org/wiki/Percolation_theory
A relevant, recent paper I found from a quick search: The semantic landscape paradigm for neural networks (https://arxiv.org/abs/2307.09550)
It generalized splendidly - it's conclusion was that you always need to press "forward" and do nothing else, no matter what happens :)
You can train a classical ML model on the known orbits of the planets in the past, but it can presumably never predict orbits given unseen n-body gravity events like another dense mass moving through the solar system because of classical insufficiency to model quantum problems, for example.
Church-Turing-Deutsch doesn't say there could not exist a Classical / Quantum correspondence; but a classical model on a classical computer cannot be sufficient for quantum-hard problems. (e.g. Quantum Discord says that there are entanglement and non-entanglement nonlocal relations in the data.)
Regardless of whether they sufficiently generalize, [LLMs, ML Models, and AutoMLs] don't yet Critically Think and it's dangerous to take action without critical thought.
Critical Thinking; Logic, Rationality: https://en.wikipedia.org/wiki/Critical_thinking#Logic_and_ra...
Anyone who so much as taken a class on this knows that even the simplest of perceptron networks, decision trees, or any form of machine learning model generalizes. That's why we use them. If they don't, it's called overfit[1], where the model is so accurate on the training data that its inferential ability on new data suffers.
I know that the article might be talking about a higher form of generalization with LLMs or whatever, but I don't see why the same principle of "don't overfit the data" wouldn't apply to that situation.
No, really: what part of their base argument is novel?
And when losing memories you would first just discard some details, like you lose now anyway, but you would start compressing centuries into rough ideas of what happened, it's just the details that would lack a bit.
I don't see it being a problem at all. And if really something happens with the Universe, sure I can die then, but why would I want to die before?
I want to know what happens, what gets discovered, what happens with humanity, how far do we reach in terms of understanding of what is going on in this place. Why are we here. Imagine dying and not even knowing why you were here.
L1 induces sparsity. Weight decay explicitly _does not_, as it is L2. This is a common misconception.
Something a lot of people don't know is that weight decay works because when applied as regularization it causes the network to approach the MDL, which reduces regret during training.
Pruning in the brain is somewhat related, but because the brain uses sparsity to (fundamentally, IIRC) induce representations instead of compression, it's basically a different motif entirely.
If you need a hint here on this one, think about the implicit biases of different representations and the downstream impacts that they can have on the learned (or learnable) representations of whatever system is in question.
I hope this answers your question.
What's the evidence for this?
But the best cure for over-fitting is to make the dataset larger and ensure data diversity. LLMs have datasets so large they usually train one epoch.
Just by trying to make the dataset diverse you could skew things to not reflect reality. I just don't think enough attention has been paid to the data, and too much the model. But I could be very wrong.
There is a natural temporality to the data humans receive. You can't relive the same moment twice. That said, human intelligence is on a scale too and may be affected in the same way.
This is also good life advice.
Note that L1 regularisation produces much more sparsity but it doesn't perform as well.
It's kind of amazing to watch this from the sidelines, a process of engineers getting ridiculously impressive results from some combo of sheer hackery and ingenuity, great data pipelining and engineering, extremely large datasets, extremely fast hardware, and computational methods that scale very well, but at the same time, gradually relearning lessons and re-inventing techniques that were perfected by statisticians over half a century ago.
[1] https://www.lesswrong.com/posts/GpSzShaaf8po4rcmA/qapr-5-gro...
Neural network training [edit: on a fixed point task, as is often the case {such as image->label}] is always (always) biphasic necessarily, so there is no "eventual recovery from overfitting". In my experience, it is just people newer to the field or just noodling around fundamentally misunderstanding what is happening, as their network goes through a very delayed phase change. Unfortunately there is a significant amplification to these kinds of posts and such, as people like chasing the new shiny of some fad-or-another-that-does-not-actually-exist instead of the much more 'boring' (which I find fascinating) math underneath it all.
To me, as someone who specializes in optimizing network training speeds, it just indicates poor engineering to the problem on the part of the person running the experiments. It is not a new or strange phenomenon, it is a literal consequence of the information theory underlying neural network training.
Why throw away the context and nuance?
That decision only further leans into the 'AI is magic' attitude.
“Grok” was Valentine Michael Smith’s rendering for human ears and vocal cords of a Martian word with a precise denotational semantic of “to drink”. The connotational semantics range from to literally or figuratively “drink deeply” all the way up through to consume the absented carcass of a cherished one.
I highly recommend Stranger in A Strange Land (and make sure to get the unabridged re-issue, 1990 IIRC).
And what is the indicator for a machine understanding something?
If anyone wants to come up with their own definition, read Robert Heinlein's 'Stranger in a Strange Land'. There is no definition in there, but you build an intuition of the meaning by its use.
One of the issues I have w/ the use in AI is that using the word 'grok' suggests that the machine understands (that's a common interpretation of the word grok, that it is an understanding greater than normal understanding).
By using an alien word, we are both suggesting something that probably isn't technically true, while simultaneously giving ourselves a slimy out. If you are going to suggest that AI understands, just have the courage to say it with common english, and be ready for argument.
Redefining a word that already exists to make the argument technical feels dishonest.
So the AI folks are just borrowing something that had already been co-opted 30+ years ago.
Generalization doesn't require learning representations outside of the training set. It requires learning reusable representations that compose in ways that enable solving unseen problems.
> On generalization - its still memorization
Not sure what you mean by this. This statement sounds self contradictory to me. Generalization requires abstraction / compression. Not sure if that's what you mean by memorization.
Overparameterized models are able to generalize (and tend to, when trained appropriately) because there are far more parameterizations that minimize loss by compressing knowledge than there are parameterizations that minimize loss without compression.
This is fairly easy to see. Imagine a dataset and model such that the model has barely enough capacity to learn the dataset without compression. The only degrees of freedom would be through changes in basis. In contrast, if the model uses compression, that would increase the degrees of freedom. The more compression, the more degrees of freedom, and the more parameterizations that would minimize the loss.
If stochastic gradient descent is sufficiently equally as likely to find any given compressed minimum as any given uncompressed minimum, then the fact that there are exponentially many more compressed minimums than uncompressed minimums means it will tend to find a compressed minimum.
Of course this is only a probabilistic argument, and doesn't guarantee compression / generalization. And in fact we know that there are ways to train a model such that it will not generalize, such as training for many epochs on a small dataset without augmentation.
But, like all complexity, it is reduceable to component parts.
(In fact, we know this because we evolved to have this ability. )
I read Language in Our Brain [1] recently and I was amazed by what we've learned about the neurologicial basis of language, but I was even more astounded at how profoundly little we know.
> But, like all complexity, it is reduceable to component parts.
This is just false, no? Sometimes horrendously complicated systems are made of simple parts that interact in ways that are intractable to predict or that defy reduction.
[1] https://mitpress.mit.edu/9780262036924/language-in-our-brain
you can look at it by results: I give these models inputs its never seen before but it gives me outputs that are correct / acceptable.
you can look at it in terms of data: we took petabytes of data, and with an 8gb model (stable difusion) we can output an image of anything. That's an unheard of compression, only possible if its generalizing - not memorizing.
What they demonstrate is a neural network learning an algorithm that approximates modular addition. The exact workings of this algorithm is explained in the footnotes. The learned algorithm is general -- it is just as valid on unseen inputs as seen inputs.
There's no memorization going on in this case. It's actually approximating the process used to generate the data, which just isn't possible using k nearest neighbors.
We have suspected that neural nets are a kind of kNN. Here's a paper:
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine
And, in particular, how to interpret the fact that different hyperparameters determined whether runs, obtaining equally high accuracy on the training data, got good or bad scores on the test data, in terms of the "view it as a kernel machine/interpolation" lens?
My understanding is that the behavior in at least one of those "models learned by gradient descent are equivalent to [some other model]" papers, works by constructing something which is based on the entire training history of the network. Is that the kernel machines one, or some other one?
There's some fox and hedgehog analogy I've never understood.
From what I gather they're talking about double descent which afaik is the consequence of overparameterization leading to a smooth interpolation between the training data as opposed to what happens in traditional overfitting. Imagine a polynomial fit with the same degree as the number of data points (swinging up and down wildly away from the data) compared with a much higher degree fit that could smoothly interpolate between the points while still landing right on them.
None of this is what I would call generalization, it's good interpolation, which is what deep learning does in a very high dimensional space. It's notoriously awful at extrapolating, ie generalizing to anything without support in the training data.
Scientists are also pretty lousy at making new discoveries without labs. They just need training data.
It's a description of a behavior, not a mechanism. Which may or may not be appropriate depending on whether you are talking about *what* the model does or *how* it achieves it.
"generalize" means going from specific examples to general cases not seen before, which is a perfectly good description of the phenomenon. Why try to invent a new word?
It's not true, if you look at deep CNN the lower layers show lines, the higher complex stuff like eyes or football players etc.. Herarchisation of information actually emerges naturally in NNs.
Generalization often implies extrapolation on new data, which is just not the case most of the time with NNs and why i didn't like the word
Simple models predicting simple things will generally slowly overfit, and regularization keeps that overfitting in check.
This "grokking" phenomenon is when a model first starts by aggressively overfitting, then gradually prunes unnecessary weights until it suddenly converges on the one generalizable combination of weights (as it's the only one that both solves the training data and minimizes weights).
Why is this interesting? Because you could argue that this justifies using overparametrized models with high levels of regularization; e.g. models that will tend to aggressively overfit, but over time might converge to a better solution by gradual pruning of weights. The traditional approach is not to do this, but rather to use a simpler model (which would initially generalize better, but due to its simplicity might not be able to learn the underlying mechanism and reach higher accuracy).
tldr: don't oversimplify things: you underfit
P.S. please don't fucking review. Your complaints aren't critiques.
such that
> you nevertheless can recover enough information to be useful in the future.
I disagree (in case you meant to imply it) that compression implies generalization.
There are also studies that show “data” in the brain isn’t stored read-only and the process of accessing that memory involves remapping the neurons (which is how fake memories are possible) - so my take is if you access a memory or datum sequentially start to finish each time the brain knows this is to be stored verbatim for as-is retrieval but if you access snapshots of it or actively seek to and replay a certain part while trying to relate that memory to a process or a new task, the brain rewires the neural pathways accusingly. Which implies that there us an unconscious part that takes place globally plus an active, modifying process where how we use a stored memory affects how it is stored and indexed (so data isn’t accessed by simple fields but rather by complex properties or getters, in programming parlance).
I guess the key difference from how machine learning works (and I believe an integral part of AGI, if it is even possible) is that inference is constant, even when you’re only “looking up” data and you don’t know the right answer (i.e. not training stage). The brain recognizes how the new query differs from queries it has been trained on and can modify its own records to take into account the new data. For example, let’s say you’re trying to classify animals into groups and you’ve “been trained” on a dataset that doesn’t include monotremes or marsupials. The first time you come across a platypus in the wild (with its mammaries but no nipples, warm-blooded but lays eggs, and a single duct for waste and reproduction) you wouldn’t just mistakenly classify it as a bird or mammal - you would actively trigger a (delayed/background) reclassification of all your existing inferences to account for this new phenomenon, even though you don’t know what the answer to the platypus classification question is.
Somtimes it's almost like creating a specialist shard to take over the task. Driving is hard at first, with very high task overload, lots to pay attention to. With practice, it becomes a little automated part of yourself takes care of those tasks while your main general intelligence can do whatever it likes, even as the "driver" deals with seriously difficult tasks.
https://en.wikipedia.org/wiki/Human_brain#Metabolism
> The energy consumption of the brain does not vary greatly over time
Same! They thought I was a genius in primary school but I ended up a loser adult with a dead end job. Turns out I just liked technology and was good at remembering facts and names for things.
For the first, there are a few good papers that simplify the concepts even further.
It's not always relearning lessons or people entirely blindly trying things either, many researchers use the underlying math to inform decisions for network optimization. If you're seeing that, then that's probably a side of the field where people are newer to some of the math behind it, and that will change as things get more established.
The underlying mathematics behind these kinds of systems are what has motivated a lot of the improvements in hlb-CIFAR10, for example. I don't think I would have been able to get there without sitting down with the fundamentals, planning, thinking, and working a lot, and then executing. There is a good place for blind empirical research too, but it loses its utility past a certain point of overuse.
It's already done: https://pytorch.org/docs/stable/generated/torch.nn.functiona...
It's an informal term that not everyone accepts. Double-descent is acceptable as it describes a general phenomenon that is a natural consequence of a phase transition during neural network training. Grokking is like, to me, the 'fetch' of neural network terms. It's not new, it adds a seeming layer of separation from double-descent (which is is -- just very delayed), and it's not really accepted by everyone.
I personally do not like it at all. Especially because language affects _our_ implicit biases about what neural networks can and cannot do. We've already seen that their capacities and performance can be pushed way beyond what we traditionally expect of them.
But to summarize, they are the same. And this is why we need good terminology, as well, because poor adoption and boosting of improper terminology induces excess regret in the information exchange surface between agents in a game-theoretic sense in this lovely landscape of the ML world.
Let's say I tell GPT "write 8 times foobar". Will it? Well then it understands me and can extrapolate from the request to the proper response, without having specifically "write 8 times foobar" in its model.
Most decompression algorithms focus on predicting the next token (byte, term, etc.), believe it or not. The more accurately they predict the next token, the less information you need to store to correct misprediction.
different hyperparameters can give a model that us over or underfit, but this helps the model interpolate, not generalize. it can know all the answers similar to the training data, not answers different to or it
That said, I've only ever read the full unabridged re-issue from the mid-90s, it's possible the earlier, edited, releases had many of the uses elided.
I think things are a bit off about the reasoning behind the basis functions, but as I noted elsewhere here that's work I'm not entirely able to talk about as I'm actively working on developing it right now, and will release it when I can.
However, you can see some of the empirical consequences of an updated understanding on my end of encoding and compression in a release of hlb-CIFAR10 that's coming up soon that should cut out another decent chunk of training time. As a part of it, we reduce the network from a ResNet8 architecture to a ResNet7, and we additionally remove one of the (potentially less necessary) residuals. It is all 'just' empirical, of course, but long-term, as they say, the proof is in the pudding, since things are already so incredibly tightened down.
General understanding makes the information in the distribution very wide. Shallow understanding makes it very narrow. Like say recognizing only specific combinations of pixels verbatim.
"Generalization" is simply the theoretical measure of how much the later extends beyond the former, regardless of how that's achieved.
There's no distribution of meaning in the training set that's independent of interpretation and understanding. Aside from maybe the literal series of bits (and words and pixels) in it, as encoded.
In statistics that is not as severe a problem because you can plot how the data distribution lies in a commonly agreed upon position in one or more clearly defined and agreed upon dimensions. And you can look at the chart and talk about this shared interpretation objectively, and its distribution.
Although as a matter of fact just as often it matters what questions you asked, and how and when and whom you asked, for the distribution of answers you got. Lying with statistics is easy as it's full of hidden variables. This is why statistics is great when the data is simple and the analysis is simple, mathematical, objective, but social studies tend to yield, whatever you want them to yield.
So. What dimensions are we talking about with a self-evolved model? You have some understanding of what the data is, subjective to you. Maybe your team has some shared understanding of what the data covers, you have overlap. But the model has its own understanding, evolved independently. How much does it overlap with you? Not as much as you think.
It's a problem decades old, that people give to the model data that contains things they didn't realize it contains. They themselves didn't see that. And then get surprised by the results.
Say when an apple falls on your head, did you realize this contains the data required to describe classic mechanics? For centuries, billions of people didn't realize. To Newton it was there as clear as daylight. In the apple's fall. I know, the example is a myth, but the principle stands.
Another example, a video of the change of light patterns reflected on the floor around the corner of room where a person, out of frame, is writing on a computer. What does this data contain? You think nothing much. Maybe it contains how a floor looks. To a model, it can easily also contain what the person who is not in frame, wrote on their keyboard.
So given all this... what IS in the distribution? Depends with whose eyes you're looking. Your eyes are not the most objective eyes, nor the most intelligent eyes. You have no anchor to point to as the ultimate arbiter of what complex data contains or does not.
Of course the jargon used in a specific sub-field evolves much more quickly than common usage because the intended audience of paper like this is expected to be well-read and current in the field already.
I'm not really weighing in on the appropriateness of the use "grok" in this case. It's just a pet peeve of mine that people bring out "language evolves" as an excuse for why any arbitrary change is natural and therefore acceptable and we should go with the flow. Some changes are strictly bad ones.
A go-to example is when "literally" no longer means "literally", but its opposite, or nothing at all. We don't have a replacement word, so now in some contexts people have to explain that they "literally mean literally".
And "literally" has been used to mean "figuratively" for as long as the word has existed[0].
Stop making 'fetch' happen, it's not going to happen.
That's the first time I've seen literally-as-figuratively defended from a historical perspective. I still think we'd all be better off if people didn't mindlessly use it as a filler word or for emphasis, which is generally what people are doing these days that is the source of controversy, not reviving an archaic usage.
Also, it's kind of ironic you corrected my use of "devolves", where many would accept it. :)
Incompetent use is devolution.
If you want to use the word 'irregardless' unironically there are people who will accept that. Then there are the rest of us.
(!)Regardless, what I’m trying to say is that due to the unique position of English as the de facto world language, it has to “suffer” some non-idiomatic uses seeping in from non-natives. Actually, I would go even further and say that most smaller languages will slowly stop evolving and only English will have that property going forward (most new inventions no longer gets a native name in most languages, the English one is used).
I wholly agree. Everyone is blinded by models - GPT4 this, LLaMA2 that - but the real source of the smarts is in the dataset. Why would any model, no matter how its architecture is tweaked, learn about the same ability from the same data? Why would humans be all able to learn the same skills when every brain is quite different. It was the data, not the model
And since we are exhausting all the available quality text online we need to start engineering new data with LLMs and validation systems. AIs need to introspect more into their training sets, not just train to reproduce them, but analyse, summarise and comment on them. We reflect on our information, AIs should do more reflection before learning.
More fundamentally, how are AIs going to evolve past human level unless they make their own data or they collect data from external systems?
It's both.
It's clearly impossible to learn how to translate Linear A into modern English using only content written in pure Japanese that never references either.
Yet also, none of the algorithms before Transformers were able to first ingest the web, then answer a random natural language question in any domain — closest was Google etc. matching on indexed keywords.
> how are AIs going to evolve past human level unless they make their own data?
Who says they can't make their own data?
Both a priori (by development of "new" mathematical and logical tautological deductions), and a posteriori by devising, and observing the results of, various experiments.
Same as us, really.
How does an AI language model devise an experiment and observe the results? The language model is only trained on what’s already known, I’m extremely incredulous that this language model technique can actually reason a genuinely novel hypothesis.
A LLM is a series of weights sitting in the ram of GPU cluster, it’s really just a fancy prediction function. It doesn’t have the sort of biological imperatives (a result of being complete independent beings) or entropy that drive living systems.
Moreover, if we consider how it works for humans, people have to _think_ about problems. Do we even have a model or even an idea about what “thinking” is? Meanwhile science is a looping process that mostly requires a physical element(testing/verification) to it. So unless we make some radical breakthroughs in general purpose robotics, as well as overcome the thinking problem I don’t see how AI can do some sort tech breakout/runaway.
Wrong, recurrent models were able to do this, just not as well.
Neural nets look much more competitive by that standard.
On the other extreme, are packers. They have optimized for packing facts in bulk, with little regard for how they fit together. If you give this type of person a set of instructions that require a wider knowledge of how things fit, they will get lost, frustrated, and/or need support. If you anticipate this, and spend a bit extra time to show how to handle all of the possible contingencies, (and give them a document of this) they're good, and will be quite happy with your support.
I think that mappers take more time figuring out the model, compressing the facts to save space, and increase applicability in general.
(https://www.newyorker.com/tech/annals-of-technology/chatgpt-...)
Not precisely. We don’t know if verbatim capacity is limited (and it doesn’t seem to be) but the brain operates in a space-efficient manner all the same. So there isn’t necessarily a causative relationship between “memory capacity” and “means of storage”.
> Likewise, if you remembered every single word in a book, you would not understand its meaning
I understand your meaning but I want to clarify for the sake of the discussion that unlike with ML, the human brain can both memorize verbatim and understand the meaning because there is no mechanism for memorizing something but not processing it (i.e. purely storage). The first pass(es) are stripped to their essentials but subsequent passes provide the ability to memorize the same input.
I am but a simple physicist and I can already tell you it is.
Lossy compression = Intelligence
That's where the Hutter Prize falls down, it's based on lossless compression, which is nothing like how the brain works.
I mean, this whole line of analysis comes from the LessWrong community. You may disagree with them on whether AI is an existential threat, but the fact that people take that threat seriously is what gave us this whole "memorize-or-generalize" analysis, and glitch tokens before that, and RLHF before that.
I don't know much either way about RLHF in terms of its direct lineage, but I highly doubt that is actually what happened, since DeepMind is actually responsible for the bulk of the historical research supporting those methods.
It's possible ala the broken clock hypothesis + LessWrong is obviously not the "primate at a typewriter" situation, so there's a chance of some people scoring meaningful contributions, but the signal to noise ratio is awful. I want to get something out of some of the posts I've tried to read there, but there are so many bad takes written with more bombastic language that it's really quite hard indeed.
Right now, it's an active detractor to the field because it pulls attention away from things that are much more deserving of energy and time. I honestly wish the vibe was back to people even just making variations of Char-RNN repos based on Karpathy's blog posts. That was a much more innocent time.
I meant this specific analysis, that neural networks that are over-parameterized will at first memorize but, if they keep training on the same dataset with weight decay, will eventually generalize.
Then again, maybe there have been analyses done on this subject I wasn't aware of.
What it would be better defined as is "a sudden change in phase state after a long period of metastability". Even then it ignores that those sharp inflections indicate a poor KL between some of the inductive priors and the data at hand.
You can think about it as the loss signal from the support of two gaussians extremely far apart with narrow standard deviations. Sure, they technically have support, but in a noisy regime you're going to have nothing.... nothing.... nothing....and then suddenly something as you hit that point of support.
Little of the literature, definitions around the word, or anything like that really takes this into account generally, leading to this mass illusion that this is not a double descent phenomenon, when in fact it is.
Hopefully this is a more appropriate elaboration, I appreciate your comment pointing out my mistake.
Indeed! It’s very frustrating that so many people here are such staunch defenders of LessWrong. Some/much of the behavior there is honestly concerning.
Thankfully things are pretty stable in the over/underfitting regime. I feel sad when I see ML misinformation propagated on a forum that requires little experience but has high leverage due to the rampant misuse of existing terms and complete invention of a in-group-language that has little touch with the mathematical foundations of what's happening behind the scenes. I've done this for 7-8 years at this point at a pretty deep level and have a strong pocket of expertise, so I'm not swinging at this one blindly.
However, I can point you to one comment I made earlier in this particular comment section about the MDL and how that relates to the L2 norm. Obviously this is not the only thing that induces a phase change, but it is one of the more blatant ones that's been covered little more publicly by different people.
https://www.lesswrong.com/s/mqwA5FcL6SrHEQzox/p/fovfuFdpuEwQ...
One of the most common things I see is people oftentimes assuming something that came from LW is novel and "was discovered through research published there", and that's because oftentimes it's really incentivized to make a lot of noise and sound plausible over there. Whereas arxiv papers, while there is some battle for popularity, are inherently more "boring" and formal.
For example, the LW post as I understand it completely ignores existing work and just... doesn't cite things which are rigorously reviewed and prepared. How about this paper from five years ago in a long string of research about generalization loss basins, for example? https://papers.nips.cc/paper_files/paper/2018/hash/be3087e74...
If someone earnestly tried to share the post you linked at a workshop at a conference, they would not be laughed out of the room, but instead have to deal with the long, draining, and muffling silence of walking to the back of the room without any applause when it was over. It's not going to fly with academics/professionals who are academia-adjacent.
This whole thing is not too terribly complicated, either, I personally feel -- a little information theory and the basics, and time studying and working on it, and someone is 50% of the way there, in my personal opinion. I feel frustrated that this kind of low quality content is parasitically supplanting actual research with meaning and a well-documented history. This is flashy nonsense that goes nowhere, and while I hesitate to call it drivel, is nigh-worthless. This barely passes muster for a college essay on the subject, if even that. If I was their professor, I would pull them aside to see if there is a more productive way for them to channel their interests in the Deep Learning space, and how we could better accomplish that.
Do you have a link to a specific post you're thinking of? It's likely going to be a Tishby-like (the classic paper from 2015 {with much more work going back into the early aughts, just outside of the NN regime IIRC}: https://arxiv.org/abs/1503.02406) lineage, but I'm happy to look to see if it's novel.
I originally thought the PAIR article was another presentation by the same authors, but upon closer reading, I think they just independently discovered similar results. Though the PAIR article quotes Progress measures for grokking via mechanistic interpretability, the Arxiv paper by the authors of the alignmentforum article.
(In researching this I found another paper about grokking finding similar results a few months earlier; again, I suspect these are all parallel discoveries.)
You could say that all of these avenues of research are all re-statements of well-known properties, eg deep double-descent, but I think that's a stretch. Double descent feels related, but I don't think a 2018 AI researcher who knew about double descent would spontaneously predict "if you train your model past the point it starts overfitting, it will start generalizing again if you train it for long enough with weight decay".
But anyway, in retrospect, I agree that saying "the LessWrong community is where this line of analysis comes from" is false; it's more like they were among the people working on it and reaching similar conclusions.
> I don’t see how AI can do some sort tech breakout/runaway.
I'm expecting (in the mode, but with a wide and shallow distribution) a roughly 10x increase in GDP growth, from increased automation etc., not a singularity/foom.
I think the main danger is bugs and misuse (both malicious and short-sighted).
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> How does an AI language model devise an experiment and observe the results?
Same way as Helen Keller.
Same way scientists with normal senses do for data outside human sense organs, be that the LHC or nm/s^2 acceleration of binary stars or gravity waves (or the confusingly similarly named but very different gravitational waves).
> The language model is only trained on what’s already known, I’m extremely incredulous that this language model technique can actually reason a genuinely novel hypothesis.
Were you, or any other human, trained on things unknown?
If so, how?
> A LLM is a series of weights sitting in the ram of GPU cluster, it’s really just a fancy prediction function. It doesn’t have the sort of biological imperatives (a result of being complete independent beings) or entropy that drive living systems.
Why do you believe that biological imperatives are in any way important?
I can't see how any of a desire to eat, shag, fight, run away, or freeze up… help with either the scientific method nor pure maths.
Even the "special sauce" that humans have over other animals didn't lead to any us doing the scientific method until very recently, and most of us still don't.
> Do we even have a model or even an idea about what “thinking” is?
AFAIK, only in terms of output, not qualia or anything like that.
Does it matter if the thing a submarine does is swimming, if it gets to the destination? LLMs, for all their mistakes and their… utterly inhuman minds and transhuman training experience… can do many things which would've been considered "implausible" even in a sci-fi setting a decade ago.
> So unless we make some radical breakthroughs in general purpose robotics
I don't think it needs to be general, as labs are increasingly automated even without general robotics.
At the least, it is a computable function (as we don’t have any physical system that would be more general than that, though some religions might disagree). Which already puts human brains ahead of LLM systems, as we are Turing-complete, while LLMs are not, at least in their naive application (their output can be feeded to subsequent invocations and that way it can be).
But also, that isn’t quite the whole story, since they can be arbitrarily precise in their approximation. Here[0] is a white paper addressing this issue which concludes attention networks are Turing complete.
Technically you may not want to call it Turing complete given the limited context window, but I'd say that's like insisting a Commodore 64 isn't Turing complete for the same reason.
Likewise the default settings may be a bit too random to be a Turing machine, but that criticism would also apply to a human.
Sure it is not a hard property, excel, css with mouse movements, game of life are all that, but they need a “possibly forever running” part.
In this context, that the possibility of running "forever" would also exclude the humans (to which it is being compared) is relevant — even if we spend all day thinking in words at the rate of 160wpm and .75 words per token, we fall asleep around every 200k tokens, and some models (not from OpenAI) exceed that in their input windows.
Also, its output is language and it can’t change a former part of speech, can only append to it. When “thinking” about what to say next, it can’t “loop” over that, only whether to append some more text to it. Its looping is strictly within a “static context”.
