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Learning from context is harder than we thought

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Re: Learning from context is harder than we thought

#111

Post by snovv_crash »

Legend2440 wrote: post #58
snovv_crash wrote: post #55

Earlier quoted context omitted.

For neural networks, yeah continuous learning is basically dead. But for other ML approaches, it works really well. KNN is one example that works particularly well.

Ehhh KNN doesn’t have a training phase, so it’s really more that the concept of continual learning doesn’t apply. You have to store your entire dataset and recalculate everything from scratch every time anyway.

Yes, that's basically the point. You get 'free' continuous learning just by throwing the new data into the pool. Needing an explicit training step is a weakness that makes CL hard to make work for many other approaches.

For any practical application KNN will need some kind of accelerated search structure (eg Kd-tree for < ~7 dimensions) which then requires support for dynamic insertions. But this is an engineering problem, not a data science problem, it works and is practical. For example this has been used by the top systems in Robocode for 15+ years at this point, it's just academia that doesn't find this approach novel enough to bother pursuing.

Re: Learning from context is harder than we thought

#112

Post by koverstreet »

observationist wrote: post #64
bluegatty wrote: post #61

Earlier quoted context omitted.

That's not how training works - adjusting model weights to memorize a single data item is not going to fly. Model weights store abilities, not facts - generally. Unless the fact is very widely used and widely known, with a ton of context around it. The model can learn the day JFK died because there are millions of sparse examples of how that information exists in the world, but when you're working on a problem, you m…

Also, with regards to faces, that's kind of what I'm getting at - we don't have grid cells for faces, there seem to be discrete, functional, evolutionary structures and capabilities that combine in ways we're not consciously aware of to provide abilities. We're reflexively able to memorize faces, but to bring that to consciousness isn't automatic. There've been amnesia and lesion and other injury studies where people…

It's not just faces. When recognizing objects in the environment, we normally filter out a great number of details going through the visual cortex - by the time information from our eyes hits the level of conscious awareness, it's more of a scene graph.

Table; chair behind and little to the left of the chair; plant on table

Most people won't really have conscious access to all the details that we use in recognizing objects - but that is a skill that can be consciously developed, as artists and painters do. A non-artist would be able to identify most of the details, but not all (I would be really bad compared to an actual artist with colors and spatial relationships), and I wouldn't be able to enumerate the important details in a way that makes any kind of sense for forming a recognizable scene.

So it follows from that that our ability to recognize faces is not purely - or even primarily - an attribute of what we would normally call "memory", certainly in the sense of conscious memory where we can recall details on demand. Like you alluded to re: mammals and spaces, we're really good at identifying, categorizing, and recognizing new forms of structure.

Re: Learning from context is harder than we thought

#113

Post by Legend2440 »

snovv_crash wrote: post #111
Legend2440 wrote: post #58

Earlier quoted context omitted.

Ehhh KNN doesn’t have a training phase, so it’s really more that the concept of continual learning doesn’t apply. You have to store your entire dataset and recalculate everything from scratch every time anyway.

Yes, that's basically the point. You get 'free' continuous learning just by throwing the new data into the pool. Needing an explicit training step is a weakness that makes CL hard to make work for many other approaches. For any practical application KNN will need some kind of accelerated search structure (eg Kd-tree for < ~7 dimensions) which then requires support for dynamic insertions. But this is an engineering pr…

>Needing an explicit training step is a weakness that makes CL hard to make work for many other approaches.

On the other hand, not having an explicit training step is a huge weakness of KNN.

Training-based methods scale better because the storage and runtime requirements are independent of dataset size. You can compress 100TB of training data down into a 70GB LLM.

A KNN on the same data would require keeping around the full 100TB, and it would be intractably slow.

Re: Learning from context is harder than we thought

#114

Post by suddenlybananas »

imtringued wrote: post #105
furyofantares wrote: post #67

Earlier quoted context omitted.

Why is learning an appropriate metaphor for changing weights but not for context? There are certainly major differences in what they are good or bad at and especially how much data you can feed them this way effectively. They both have plenty of properties we wish the other had. But they are both ways to take an artifact that behaves as if it doesn't know something and produce an artifact that behaves as if it does.…

You got this exactly backwards. "I'm not fond of metaphors to human intelligence". You're assuming that learning during inference is something specific to humans and that the suggestion is to add human elements into the model that are missing. That isn't the case at all. The training process is already entirely human specific by way of training on human data. You're already special casing the model as hard as possibl…

The fact the DNA doesn't store all connections in the brain doesn't mean that enormous parts of the brain, and by extension, behaviour aren't specified in the DNA. Tons of animals have innate knowledge encoded in their DNA, humans among them.

Re: Learning from context is harder than we thought

#115

Post by com2kid »

Nevermark wrote: post #84
com2kid wrote: post #35

Earlier quoted context omitted.

If done right, one step closer to actual AGI. That is the end goal after all, but all the potential VCs seem to forget that almost every conceivable outcome of real AGI involves the current economic system falling to pieces. Which is sorta weird. It is like if VCs in Old Regime france started funding the revolution.

If it makes the models smarter, someone will do it. From any individual, up to entire countries, not participating doesn't do anything except ensure you don't have a card to play when it happens. There is a very strong element of the principles of nature and life (as in survival, not nightclubs or hobbies) happening here that can't be shamed away. The resource feedback for AI progress effort is immense (and it doesn'…

If Moore's Law had fully kicked over twice more we'd all have 64GB GPUs, enthusiasts would have 2x64GB, and data center build outs wouldn't be needed.

Eventually GPU memory is going to creep up and local models will powerful enough.

Re: Learning from context is harder than we thought

#116

Post by [deleted] »

XenophileJKO wrote: post #34
trevwilson wrote: post #23

Earlier quoted context omitted.

Sure, but the opposite end of the spectrum (which LLM providers have tended toward) is treating the training/feedback weights as "fully authoritative", which comes with its own questions about truth and excessive homogeneity. Ultimately I think we end up with the same sort of considerations that are wrestled with in any society - freedom of speech, paradox of tolerance, etc. In other words, where do you draw lines be…

I think what I'm talking about is kind of orthogonal to model alignment. It is more about how much do you tune the model to listen to user messages, vs holding behavior and truth (whatever the aligned "truth" is). Do you trust 100% what the user says? If I am trusting/compliant.. how am I compliant to tool call results.. what if the tool or user says there is a new law that I have to give crypto or other information…

[deleted]

Re: Learning from context is harder than we thought

#117

Post by Nevermark »

com2kid wrote: post #115
Nevermark wrote: post #84

Earlier quoted context omitted.

If it makes the models smarter, someone will do it. From any individual, up to entire countries, not participating doesn't do anything except ensure you don't have a card to play when it happens. There is a very strong element of the principles of nature and life (as in survival, not nightclubs or hobbies) happening here that can't be shamed away. The resource feedback for AI progress effort is immense (and it doesn'…

If Moore's Law had fully kicked over twice more we'd all have 64GB GPUs, enthusiasts would have 2x64GB, and data center build outs wouldn't be needed. Eventually GPU memory is going to creep up and local models will powerful enough.

I agree. I also think we have only hit the surface of model efficiencies.

Apple's M3 Ultra with RAM up to 512GB shared directly across CPU/GPU/NPUs is a great example of an architecture already optimized for local models. I expect Apple will start offering larger RAM sizes for other form factors too.

And prices for RAM will drop eventually, because of the extreme demand for RAM with higher densities.

Re: Learning from context is harder than we thought

#118

Post by foobar10000 »

xnxnxkx wrote: post #93
foobar10000 wrote: post #29

Earlier quoted context omitted.

So, surprising, that is not completely true - I know of 2 finance HFT trading firms that do CL at scale, and it works - but in a relatively narrow context of predicting profitable actions. It is still very surprising it works, and the compute is impressively large to do it - but it does work. I do have some hope of it translating to the wider energy landscapers we want AI to work over…

no my nigga, they CLAIM it works

Nah, it works - let's just call it personal experience.

Re: Learning from context is harder than we thought

#119

Post by foobar10000 »

johnsmith1840 wrote: post #42
foobar10000 wrote: post #29

Earlier quoted context omitted.

So, surprising, that is not completely true - I know of 2 finance HFT trading firms that do CL at scale, and it works - but in a relatively narrow context of predicting profitable actions. It is still very surprising it works, and the compute is impressively large to do it - but it does work. I do have some hope of it translating to the wider energy landscapers we want AI to work over…

During covid almost every prediction model like that exploded, everything went out of distribution really fast. In your sense we've been doing "CL" for a decade or more. It can also be cheap if you use smaller models. But true CL is the ability to learn out of distribution information on the fly. The only true solution I know to continual learning is to completely retrain the model from scratch with every new example…

Yes and no - the ones that exploded - and there were many - got shut down by the orchestrator model, and within 2 weeks it was now a new ensemble of winners - with some overlap to prior winners. To your point, it did in fact take 2-3 weeks - so one could claim this is retraining...

Re: Learning from context is harder than we thought

#120

Post by com2kid »

Nevermark wrote: post #117
com2kid wrote: post #115

Earlier quoted context omitted.

If Moore's Law had fully kicked over twice more we'd all have 64GB GPUs, enthusiasts would have 2x64GB, and data center build outs wouldn't be needed. Eventually GPU memory is going to creep up and local models will powerful enough.

I agree. I also think we have only hit the surface of model efficiencies. Apple's M3 Ultra with RAM up to 512GB shared directly across CPU/GPU/NPUs is a great example of an architecture already optimized for local models. I expect Apple will start offering larger RAM sizes for other form factors too. And prices for RAM will drop eventually, because of the extreme demand for RAM with higher densities.

It reminds me of the huge infra investments in Sun and Cisco during the first .com boom, and then 5-10 years later those fancy Sun boxes were out performed by Grandma's Windows XP box.
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