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This shouldn't be too surprising. Similar results were had with GPT-3 a while back, which is kind-of-able to produce audio or images, encoded as streams of tok
by retrac 4y ago
This shouldn't be too surprising. Similar results were had with GPT-3 a while back, which is kind-of-able to produce audio or images, encoded as streams of tokens, when trained on that task, despite not being designed for it.
A very interesting property was noted a few years ago by multiple researchers, I'm not sure who discovered it first. Transfer learning is unreasonably effective. If you were training an image generator network, there's a significant reduction in training time, by taking a model already trained and fine-tuning it, compared to starting from a model with truly random weights.
This isn't surprising when we're talking photos of ambulances and moving to photos of trucks. But it holds true when you train it on ... well, anything structured, really. A GPT-style transformer trained on online comments, or audio samples of music encoded as token streams, when switched to images of cars encoded as token streams, learns that task much more quickly than if it had been fully randomized.
I don't see how to escape the conclusion that these models learn some sort of general properties (something about arithmetic and mathematical relationships, maybe?) There's some sort of abstraction or internal model that is learned, that is applicable across very different tasks.
- famouswaffles 4y agoThere's something a bit more mindblowing than that. Language models and vision models learn representations so similar that you can connect them with just a linear projection between image embedding and text embedding space(no training of the image encoder or llm required). https://arxiv.org/abs/2209.15162 https://arxiv.org/abs/2209.15162 https://llava-vl.github.io/ https://llava-vl.github.io/ LLMs are already being grounded.
- cs702 4y agoI was about to say the same thing. Hi og_kalu! Relevant previous discussion: https://news.ycombinator.com/item?id=35598281 https://news.ycombinator.com/item?id=35598281
- prox 4y agoDo we know how they represent that knowledge? I always hear it called a black box but that seems a bit strange, since you can dissect any data right?
- deleted 4y ago[deleted]
- famouswaffles 4y agoNo we don't know how they represent that knowledge. But performing experiments to probe how similar they are is a lot easier than knowing all that. They're called black boxes because we can't explain what the weights are learning during training, what the different weights do or are responsible for to shift or produce the output it does. It's like, biologists know how neurons communicate signals with each other. But is that knowledge enough to explain human behavior ? Not even close.
- prox 4y agoInteresting, so it could be similar to how our brains store knowledge, but could also be completely different. This makes me wonder if these models are perfect universal translators once they “grasp” a concept.
- TeMPOraL 4y ago> This makes me wonder if these models are perfect universal translators once they “grasp” a concept. I don't have a paper reference, but earlier this month I've seen claims that in training GPT-4, it was observed that additional training in a specific task using a single language (e.g. English) improves performance for that task in many languages, strongly suggesting the model is actually learning concepts, not just words. If that's the case, then I think we have indeed accidentally made a universal translator (limited to humans, though).
- prox 4y agoErgo, if we train on say a bird or primate, or perhaps dolphins, it might be able to grasp concepts animals use? Say lots of video footage with context?
- Der_Einzige 4y agoWe've known that embeddings have this property since the glove paper at least. Linear substructure in low dimensional representations of ultra high dimensions is shockingly common
- seydor 4y agoWe will find that language and Visual perception are related. Geometry is the underlying structure in language and mathematics, and most of our logical concepts stem from geometric relations and constraints