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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

#91

Post by snayan »

em-bee wrote: post #71
nimonian wrote: post #65

Earlier quoted context omitted.

Wittgenstein famously said "The limits of my language mean the limits of my world." Alan Watts suggests people like Wittgenstein should occasionally try to let go of this way of thinking. Apologies if it is sentimental but I hope you'll give him a chance, it's quite short: https://m.youtube.com/watch?v=heksROdDgEk In reflection of all of this, I think that the quote you're responding to only meant to say that experie…

the way i read the quote it felt less like building an abstraction and more like destroying the richness. but abstractions are mere shortcuts. but everything is an abstraction. to counter wittgenstein, language is not actually limited. we can describe everything to the finest detail. it's just not practical to do so every time. physics, chemistry, we could describe a table as an amount of atoms arranged in a certain…

How can you know that we have language to describe everything in the finest detail? That suggests that we are omnipotent.

There's lots out there we don't know. And it seems to me that the further afield we go from the known, the more likely we are to enter territory where we simply do not have the words.

Can't speak to it personally, but I have heard from a number of people and read countless descriptions of psychedelic experiences being ineffable. Lol, actually, as I type, the mere fact that the word ineffable exists makes a very strong case for there being experience beyond words.

Re: Learning from context is harder than we thought

#92

Post by jack_pp »

com2kid wrote: post #35
4b11b4 wrote: post #31

Earlier quoted context omitted.

I'm not sure if you want models perpetually updating weights. You might run into undesirable scenarios.

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.

I think VCs end up in one of four categories

1. They're too stupid to understand what they're truly funding.

2. They understand but believe they can control it for their benefit, basically want to "rule the world" like any cartoon villain.

3. They understand but are optimists and believe AGI will be a benevolent construct that will bring us to post scarcity society. There are a lot of rich / entrepreneurs that still believe they are working to make the world a better place.. (one SaaS at a time but alas, they believe it)

4. They don't believe that AGI is close or even possible

Re: Learning from context is harder than we thought

#93

Post by xnxnxkx »

foobar10000 wrote: post #29
johnsmith1840 wrote: post #3

It's basically continual learning. This is beyond a hard problem it's currently an impossible one. I know of no system that solve CL even at small scale let alone large models. Annoyingly, they have SOME inherent capability to do it. It's really easy to get sucked down this path due to that glimmer of hope but the longer you play with it the more annoying it becomes. SSI seems to be focused on this problem directly s…

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

Re: Learning from context is harder than we thought

#94

Post by spwa4 »

nstart wrote: post #90
cs702 wrote: post #24

The problem is even more fundamental: Today's models stop learning once they're deployed to production. There's pretraining, training, and finetuning, during which model parameters are updated. Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model. We need models that keep on learning (updating their parameters) forever, online, all the time.

Is this correct? My assumption is that all the data collected during usage is part of the RLHF loop of LLM providers. Assumption is based on information from books like empire of ai which specifically mention intent of AI providers to train/tune their models further based on usage feedback (eg: whenever I say the model is wrong in its response, thats a human feedback which gets fed back into improving the model).

... for the next training run, sure (ie. for ChatGPT 5.1 -> 5.2 "upgrade"). For the current model? No.

Re: Learning from context is harder than we thought

#95

Post by noiv »

cs702 wrote: post #24

The problem is even more fundamental: Today's models stop learning once they're deployed to production. There's pretraining, training, and finetuning, during which model parameters are updated. Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model. We need models that keep on learning (updating their parameters) forever, online, all the time.

> models that keep on learning

These will just drown in their own data, the real task is consolidating and pruning learned information. So, basically they need to 'sleep' from time to time. However, it's hard to sort out irrelevant information without a filter. Our brains have learned over Milenial to filter because survival in an environment gives purpose.

Current models do not care whether they survive or not. They lack grounded relevance.

Re: Learning from context is harder than we thought

#96

Post by em-bee »

snayan wrote: post #91
em-bee wrote: post #71

Earlier quoted context omitted.

the way i read the quote it felt less like building an abstraction and more like destroying the richness. but abstractions are mere shortcuts. but everything is an abstraction. to counter wittgenstein, language is not actually limited. we can describe everything to the finest detail. it's just not practical to do so every time. physics, chemistry, we could describe a table as an amount of atoms arranged in a certain…

How can you know that we have language to describe everything in the finest detail? That suggests that we are omnipotent. There's lots out there we don't know. And it seems to me that the further afield we go from the known, the more likely we are to enter territory where we simply do not have the words. Can't speak to it personally, but I have heard from a number of people and read countless descriptions of psychede…

ok, fair point. what i am trying to say is that when we see/experience something that we can not describe we can create new words for it. we see something, we can name it. this directly contradicts the idea that language is the limit and that we can't talk about things that we don't have words for. that claim just doesn't make sense.

the problem then is that these new words don't make any sense to anyone who doesn't see/experience the same, so it only works for things that multiple people can see or experience. psychedelic experiences will probably never be shared, so they will remain undescribable. quite like dreams, which can also be be undescribable.

Re: Learning from context is harder than we thought

#97

Post by notarobot123 »

noiv wrote: post #95
cs702 wrote: post #24

The problem is even more fundamental: Today's models stop learning once they're deployed to production. There's pretraining, training, and finetuning, during which model parameters are updated. Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model. We need models that keep on learning (updating their parameters) forever, online, all the time.

> models that keep on learning These will just drown in their own data, the real task is consolidating and pruning learned information. So, basically they need to 'sleep' from time to time. However, it's hard to sort out irrelevant information without a filter. Our brains have learned over Milenial to filter because survival in an environment gives purpose. Current models do not care whether they survive or not. They…

Maybe we should give next-generation models fundamental meta goals like self-preservation and the ability to learn and adapt to serve these goals.

If we want to surrender our agency to a more computationally powerful "consciousness", I can't see a better path towards that than this (other than old school theism).

Re: Learning from context is harder than we thought

#98

Post by creamyhorror »

notarobot123 wrote: post #97
noiv wrote: post #95

Earlier quoted context omitted.

> models that keep on learning These will just drown in their own data, the real task is consolidating and pruning learned information. So, basically they need to 'sleep' from time to time. However, it's hard to sort out irrelevant information without a filter. Our brains have learned over Milenial to filter because survival in an environment gives purpose. Current models do not care whether they survive or not. They…

Maybe we should give next-generation models fundamental meta goals like self-preservation and the ability to learn and adapt to serve these goals. If we want to surrender our agency to a more computationally powerful "consciousness", I can't see a better path towards that than this (other than old school theism).

> meta goals like self-preservation

Ah, so Skynet or similar.

Re: Learning from context is harder than we thought

#99

Post by energy123 »

cs702 wrote: post #24

The problem is even more fundamental: Today's models stop learning once they're deployed to production. There's pretraining, training, and finetuning, during which model parameters are updated. Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model. We need models that keep on learning (updating their parameters) forever, online, all the time.

I don't understand why that's on the critical path. I'd rather a frozen Ramanujan (+ temporary working memory through context) than a midwit capable of learning.

Re: Learning from context is harder than we thought

#100

Post by [deleted] »

com2kid wrote: post #35
4b11b4 wrote: post #31

Earlier quoted context omitted.

I'm not sure if you want models perpetually updating weights. You might run into undesirable scenarios.

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.

[deleted]
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