Kubeflow? There are two more tools related to it: Elyra (open source) and Vertex AI (GCP, basically built on top of Kubeflow and TFX)

Isn't this basically the description of Langchain?

Yeah, isotropic as in being the same in all directions. So we're probably all familiar with embedding space and the fact that the positional relationships between concepts in embedding space basically encodes information about those relationships. Isotropy in language models refers to the extent to which concepts which are actually unrelated appear unrelated in embedding space.

In other words, a model without this property might havre an embedding space that isn't large enough, but you're still teaching it things and the result is that you're cramming things into your embedding space that's too small, so unrelated concepts are no longer equidistant from other unrelated concepts, implying a relationship that doesn't really exist with the result being that the language model confuses things that shouldn't be confused.

Case in point: I asked chatgpt to give me an example build order for terrans in Broodwar and it proceeded to give me a reasonable sounding build order, except that it was mixing in units from Starcraft 2. Now no human familiar with the games would confuse units like that. I chalk that up to a lack of relevant training data, possibly mixed with an embedding space that's not large enough for the model to be isotropic.

That's my take anyway. I'm still learning ;) please someone chime in and fact check me :D

fine tuning is just additional training, so if it works from scratch it works with fine tuning. And no it may not be as effective as other methods but the poster was claiming it was impossible

A world model doesn't mean a model of the world, it means a model from the data it's been given. Despite not being told what an Othello board looks like there's an internal representation of an Othello board.

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bruh

No, do you have a link?

Isotropic? Not isomorphic? Please elaborate

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Did you see the paper on voice to voice for talking with whales?

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Cool, how can I be confident about my model not spewing sh*t or not making up things on the fly, cause what it gives out has impact on my stakeholders.

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!remind me in 3 days

incredibly interesting given humans represent some quantities this way too (spanning from left-to-right in the brain for numbers)

They just completed the data collection a few days ago, and they claim prompts of really high quality due to strict filtering algorithm and the propension of the community to create a better open source alternative to OAI.

Sufficient to train a model from scratch? Unlikely. Sufficient to fine-tune a model pre-trained on 1million+ images (imagenet, etc)? Probably yes. As mentioned, some extra performance can be squeezed out with some smart data augmentation.

!Remindme 3 days

It's a model of a 2 dimensional array, not the world.

Like tensorboard graph view?

Yeah, I think that's why they're starting with whales - they're an easy subject since their vocalizations can be heard through the water from miles away. They also seem to have a fairly complex vocal language, unlike for example songbirds with memorized mating calls.

this is probably not a concern for whale vocalizations, but an issue for attempting to decode animal communications generally via LLMs is that they're probably communicating as much information (if not more) non-vocally. for example, if we wanted to train an LLM to "understand" dog communication, it'd probably be more important to provide it with signals corresponding to changes in body and face pose than vocalizations. interesting stuff in any event.

dagster