It doesn't seem that way to me. But even if it did, video generation also seemed kind of stagnant before Sora.
In general, I think The Bitter Lesson is the biggest factor at play here, and compute power is not stagnating.
"This conversation may be recorded and used for training purposes" now takes on a new meaning.
Can car makers sell info from everything that happens in their cars?
However, I think the most cost-effective way to train for real world is to train in a simulated physical world first. I would assume that Boston Dynamics does exactly that, and I would expect integrated vision-action-language models to first be trained that way too.
When it comes to multi-modality, then training data is not limited, because of many different combinations of language, images, video, sound etc. Microsoft did some research on that, teaching spacial recognition to an LLM using synthetic images, with good results. [1]
When someone states that there are not enough training data, they usually mean code, mathematics, physics, logical reasoning etc. In the open internet right now, there are is not enough code to make a model 10x better, 100x better and so on.
Synthetic data will be produced of course, scarcity of data is the least worrying scarcity of all.
Edit: citation added,
[1] VoT by MS https://medium.com/@multiplatform.ai/microsoft-researchers-p...
Releasing models to users that where users can record video is more data. Users conversing with AI is also additional data.
Another example is models that code– And then debug the code and learn from that.
This will be anywhere, and these models will learn from anything we do/publish online/discuss. Scary.
Pretty soon– OpenAI will have access to
For the skeptical, consider that humans can be trained on material created by less intelligent humans.
For the skeptics, "AI models" are not intelligent at all so this analogy makes no sense.
You can teach lots of impressive tricks to dogs, but there is no amount of training that will teach them basic algebra.
I take the opposite view. I don't think video generation was stagnating at all, and was in fact probably the area of generative AI that was seeing the biggest active strides. I'm highly optimistic about the future trajectory of image and video models.
By contrast, text generation has not improved significantly, in my opinion, for more than a year now, and even the improvement we saw back then was relatively marginal compared to GPT-3.5 (that is, for most day-to-day use cases we didn't really go from "this model can't do this task" to "this model can now do this task". It was more just "this model does these pre-existing tasks, in somewhat more detail".)
If OpenAI really is secretly cooking up some huge reasoning improvements for their text models, I'll eat my hat. But for now I'm skeptical.
With less than $800 worth of hardware including everything but the monitor, you can run an open weight model more powerful than GPT 3.5 locally, at around 6 - 7T/s[0]. I would say that is a huge improvement.
[0] https://www.reddit.com/r/LocalLLaMA/comments/1cmmob0/p40_bui...
