Transformers (what LLMs are) build world models from the training data (Google “Othello-GPT” for associated research).
This happens by needing to combine a lot of different pieces of information together in a coherent way (what’s called the “latent space”).
This process is medium agnostic. If given text it will do it with text, if given photos it will do it with photos, and if given both it will do it with both and specifically fitting the intersection of both together.
The “suitcase full of tools” becomes its own integrated tool where each part influences the others. Why you can ask a multimodal model for the answer to a text question carved into an apple and get a picture of it.
There’s a pretty big difference in the UI/UX in code written by multimodal models vs text only models for example, or utility in sharing a photo and saying what needs to be changed.
The idea that an old school NN would be better at any slightly generalized situation over modern multimodal transformers is… certainly a position. Just not one that seems particularly in touch with reality.
The main breakthrough of LLM happened when they figured out how to tokenize words… The subsequent transformer architecture was already being tested on various data types and struggled compared to similarly advanced CNN.
When they figured out word encoding, it created a buzz because transformers could work well with words. They never quite worked as well on images. For that, stable diffusion (a variation on CNN) has always been better.
It’s only because of the buzz on LLMs that they tried applying them to other data types, mostly because that’s how they could get funding. By throwing in disproportionate amount of resources, it works… But it would have been so much more efficient to use different architectures.
That’s not…
sigh
Ok, so just real quick top level…
Transformers (what LLMs are) build world models from the training data (Google “Othello-GPT” for associated research).
This happens by needing to combine a lot of different pieces of information together in a coherent way (what’s called the “latent space”).
This process is medium agnostic. If given text it will do it with text, if given photos it will do it with photos, and if given both it will do it with both and specifically fitting the intersection of both together.
The “suitcase full of tools” becomes its own integrated tool where each part influences the others. Why you can ask a multimodal model for the answer to a text question carved into an apple and get a picture of it.
There’s a pretty big difference in the UI/UX in code written by multimodal models vs text only models for example, or utility in sharing a photo and saying what needs to be changed.
The idea that an old school NN would be better at any slightly generalized situation over modern multimodal transformers is… certainly a position. Just not one that seems particularly in touch with reality.
The main breakthrough of LLM happened when they figured out how to tokenize words… The subsequent transformer architecture was already being tested on various data types and struggled compared to similarly advanced CNN.
When they figured out word encoding, it created a buzz because transformers could work well with words. They never quite worked as well on images. For that, stable diffusion (a variation on CNN) has always been better.
It’s only because of the buzz on LLMs that they tried applying them to other data types, mostly because that’s how they could get funding. By throwing in disproportionate amount of resources, it works… But it would have been so much more efficient to use different architectures.
go ask chatgpt to fold a protein