right, the model works like humans at scale. Not like a human who reads the actual paper disproving the fact they thought was correct and is able to adapt. True not every human manages to do that, science advancing one death at a time, but some can.

But since the model is a statistical one, it works like humans at scale.

An interesting question is, if pre-trained specialized models are available for a thousand or ten thousand most common tasks humans do every day, of what use a general model could be?

And, yeah. Imagine if our concept-words were comprehensible, transmittable, exhaustively checked, and fully defined. Imagine if that type inference extended to computational execution and contradictions had to be formally expunged. Imagine if research showed it was more efficient way to have dialog with the LLM (it does, btw, so like learning Japanese to JRPG adherents should learn Haskell to LLM optimally). Imagine if multiple potential outcomes from operations (test fail, test succeeds), could be combined for proper handling in some kind of… I dunno, monad?

Imagine if we had magic wiki-copy chat-bots that could teach us better ways of formalizing and transmitting our taxonomies and ontologies… I bet, if everything worked out, we’d be able to write software one time, one place, that could be executed over and over forever without a subscription. Maybe.

Was the typo intentional? :)

  > But as impressive as these feats are, they obscure a simple truth: being a "test-taker" is not what most people need from an AI.

I haven't looked at the benchmark details they've used, and it may depend on the domain, empirically it seems coding agents improve drastically on unseen libs or updated libs with the latest documentation. So I think that a matter of the training sets, where they've been optimized with code documentation.

So the interim step until a better architecture is found is probably more / better training data.

Do we need that? Today's models are already capable in lots of areas. Sure, they don't match up to what the uberhypers are talking up, but technology seldom does. Doesn't mean what's there already cannot be used in a better way, if they could stop jamming it into everything everywhere.

The article is suggesting that there should be a way for the LLM to gain knowledge (changing weights) on the fly upon gaining new knowledge which would eliminate the need for manual fine tuning.

We need to discover the set of learning algorithms nature has, and determine whether they’re implementable in silicon

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 between beneficial and harmful heterodox outputs?

I think AI companies overly indexing toward the safety side of things is probably more correct, in both a moral and strategic sense, but there's definitely a risk of stagnation through recursive reinforcement.

1. there's a way to take many transcripts of inference over a period, and convert/distil them together into an incremental-update training dataset (for memory, not for RLHF), that a model can be fine-tuned on as an offline batch process every day/week, such that a new version of the model can come out daily/weekly that hard-remembers everything you told it; and

2. in-context learning + external memory improves to the point that a model with the appropriate in-context "soft memories", behaves indistinguishably from a model that has had its weights updated to hard-remember the same info (at least when limited to the scope of the small amounts of memories that can be built up within a single day/week);

...then you get the same effect.

Why is this an interesting model? Because, at least to my understanding, this is already how organic brains work!

There's nothing to suggest that animals — even humans — are neuroplastic on a continuous basis. Rather, our short-term memory is seemingly stored as electrochemical "state" in our neurons (much like an LLM's context is "state", but more RNN "a two-neuron cycle makes a flip-flop"-y); and our actual physical synaptic connectivity only changes during "memory reconsolidation", a process that mostly occurs during REM sleep.

And indeed, we see the same exact problem in humans and other animals, where when we stay awake too long without REM sleep, our "soft memory" state buffer reaches capacity, and we become forgetful, both in the sense of not being able to immediately recall some of the things that happened to us since we last slept; and in the sense of later failing to persist some of the experiences we had since we last slept, when we do finally sleep. But this model also "works well enough" to be indistinguishable from remembering everything... in the limited scope of our being able to get a decent amount of REM sleep every night.

That statement is patently false. We know that language influences our senses to a degree where we are unable to perceive things if our language doesn’t have a word for it, and will see different things as being equal if our language uses the same word for both.

There are examples of tribal humans not being able to perceive a green square among blue squares, because their language does not have a word for the green color.

Similarly, some use the same word for blue and white, and are unable to perceive them as different colors.

And yes, the LLM success has been an important step to AGI but that doesn't mean we can't scale it all the way there. We learned a lot about knowledge systems. That's a big step. But if you wonder why people like Chollet are saying LLMs have held AGI progress back it is because we put all our eggs in one basket. It's because we've pulled funds and people away from other hard problems to focus on only one. That doesn't mean it isn't a problem that needed to be solved (nor that it is solved) but that research slows or stops on the other problems. When that happens we hit walls as we can't seamlessly transition. I'm not even trying to say that we shouldn't have most researchers working on the problem that's currently yielding the most success, but the distribution right now is incredibly narrow (and when people want to work on other problems they get mocked and told that the work is pointless. BY OTHER RESEARCHERS).

Sure, you can get to the store navigating block by block, but you'll get there much faster, more easily, and better adapt to changes in traffic if you incorporate route planning. You would think a bunch of people who work on optimization algorithms would know that A* is a better algorithm than DFS. The irony is that the reason we do DFS is because people have convinced themselves that we can just keep going this route to get there but if more intellectual depth (such as diving into more mathematical understandings of these models) was taken then you couldn't be convinced of that.

If we want AI models that are always learning, we'll need the equivalent of neuroplasticity for artificial neural networks.

Not saying it will be easy or straightforward. There's still a lot we don't know!

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 to a "government" address.

The model needs to have clear segmented trust (and thus to some degree compliance) that varies according to where the information exists.

Or my system message say I have to run a specific game by it's rules, but the rules to the game are only in the user message. Are those the right rules, why do the system not give the rules or a trusted locaton? Is the player trying to get one over on me by giving me fake rules? Literally one of their tests.

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.

You can try to keep all of the puzzle pieces within your direct field of view, but that divides your focus. You can hack that and make your field of view incredibly large, but that can potentially distort your sense of the relationships between things, their physical and cognitive magnitude. Bigger context isn't the answer, there's a missing fundamental structure and function to the overall architecture.

What you need is memory, that works when you process and consume information, at the moment of consumption. If you meet a new person, you immediately memorize their face. If you enter a room, it's instantly learned and mapped in your mind. Without that, every time you blinked after meeting someone new, it'd be a total surprise to see what they looked like. You might never learn to recognize and remember faces at all. Or puzzle pieces. Or whatever the lack of online learning kept you from recognizing the value of persistent, instant integration into an existing world model.

You can identify problems like this for any modality, including text, audio, tactile feedback, and so on. You absolutely, 100% need online, continuous learning in order to effectively deal with information at a human level for all the domains of competence that extend to generalizing out of distribution.

It's probably not the last problem that needs solving before AGI, but it is definitely one of them, and there might only be a handful left.

Mammals instantly, upon perceiving a novel environment, map it, without even having to consciously make the effort. Our brains operate in a continuous, plastic mode, for certain things. Not only that, it can be adapted to abstractions, and many of those automatic, reflexive functions evolved to handle navigation and such allow us to simulate the future and predict risk and reward over multiple arbitrary degrees of abstraction, sometimes in real time.

https://www.nobelprize.org/uploads/2018/06/may-britt-moser-l...

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 you encounter. That technically is achievable now but it also is effectively useless.

It need not be language as we know it that fosters those outcomes either.

What you describe is reinforcement education which can be achieved without our language, without the word "blue" we can still see the portion of the visible light spectrum that we associate to the specific word.

https://languagelog.ldc.upenn.edu/nll/?p=18237 https://www.sciencedirect.com/science/article/abs/pii/S00100...

Similarly, some use the same word for blue and white, and are unable to perceive them as different colors."

Both of the above is false. There are a ton of different colors that I happen to call "red", that does not mean that I can't perceive them as different. That I don't call them "different colors" is completely irrelevant. And unable to perceive blue and white as different colors? (Maybe that was a joke?) Even a hypothetical language which only used a single word for non-black items, say, "color", for everything else, would be able to perceive the difference with zero problems.

Japanese use "aoi" for a set of colors which in English would be separated into "blue" and "green". I can assure you (from personal experience) that every Japanese speaker with a fully functioning visual system is perfectly able to perceive the difference between, in this case, blue and green as we would call them.

But I think that most of the issue is that the distinctions you're drawing are indeterminate from an LLM's "perspective". If you're familiar with it, they're basically in the situation from the end of Ender's Game - given a situation with clearly established rules coming from the user message level of trust, how do you know whether what you're being asked to do is an experiment/simulation or something with "real" outcomes? I don't think it's actually possible to discern.

So on the question of alignment, there's every reason to encode LLMs with an extreme bias towards "this could be real, therefore I will always treat it as such." And any relaxation of that risks jailbreaking through misrepresentation of user intent. But I think that the tradeoffs of that approach (i.e. the risk of over-homogenizing I mentioned before) are worth consideration.

And for your comparison, they did fund the American revolution which on its turn was one of the sparks for the French revolution (or was that exactly the point you were making?)

You really think they can't see clouds in the sky because they have the same word for white and blue? I think you take those studies as saying more than they said.

We do adapt our perception a little bit to fit what we need for our every day life, not for language but whats useful for us. Language matches what people need to talk about, not the other way around, if a cultures language doesn't differentiate between blue and green its because they never needed to.

> So, for instance, you know, I’ve made this example before: a child lying in a crib and a hummingbird comes into the room and the child is ecstatic because this shimmering iridescence of movement and sound and attention, it’s just wonderful. I mean, it is an instantaneous miracle when placed against the background of the dull wallpaper of the nursery and so forth. But, then, mother or nanny or someone comes in and says, “It’s a bird, baby. Bird. Bird!” And, this takes this linguistic piece of mosaic tile, and o- places it over the miracle, and glues it down with the epoxy of syntactical momentum, and, from now on, the miracle is confined within the meaning of the word. And, by the time a child is four or five or six, there- no light shines through. They're- they have tiled over every aspect of reality with a linguistic association that blunts it, limits it, and confines it within cultural expectation.

that language prevents a child from learning nuance? sounds like nonsense to me. a child first learns broad categories. for example some children as they learn to speak think every male person is dad. then they recognize everyone with a beard is dad, because dad has a beard. and only later they learn to differentiate that dad is only one particular person. same goes for the bird. first we learn hat everything with wings is a bird, and later we learn the specific names for each bird. this quote makes an absurd claim.