Because the cost to OpenAI to make an architectural shift is far greater than the cost to a new lab to try something different, providing details is usually a net benefit for recruiting, building trust, getting acquired, etc. The lack of details is a poor business decision because it makes them seem untrustworthy.
I'm not advocating that they should open source their model, but there is already so much noise in the space and many bad papers that being cagey is a poor strategy for winning over talent, developers, etc.
OpenAI validating it can still happen faster than they can get the compute to serve the models themselves[1]. It doesn't make a lot of sense to give out details if they want to be a serious contender or even as some have said, be acquired.
Yeah there's noise but if they have the real deal then it doesn't matter. They only thing they need to do is let people pay to use the models.
[1] I'm assuming this is the primary cause of the delay. That may not be the case of course.
My guess is that they're angling for an acquisition.
Is it, though? This scrappy startup was able to take a large(-ish) open weights model and adapt it. Why can't the frontier labs do the same cost effectively?
>If they want to get acquired, then they should show that they know what they're doing.
I'm sure they would do so under an appropriate NDA as part of negotiations. I'm not sure why you think a full public disclosure is necessary.
This is what I've thought was going to happen ever since they publicized their efforts. They probably don't have the money to train large models themselves, might as well get a nice chunk of change by being acquired by someone who already has said large models running.
- guided window attn. Predict where to attend to but in a fixed window. If you do this to just the token/vocab you can keep effectively unlimited context and perfect recall. (yes, I can do that. There is a trick to teaching it how to predict position. This also immediately opens other crazy things like NN memory)
-efficient fixed state size models. So not a recurrent mechanism because that breaks training, parallelizable like transformers, but fixed sized state instead of unbounded attn. Pick a reasonable amount of state and it is amazingly good since it doesn't need to keep separating wheat fro chaff in context (yes, it is possible to build this, I have. It works. This also opens up real streamed models. I have a true infinite context streamed model I toy with locally that I am getting to be audio/text in and audio/text out in real time.)
Put those together and you have O(1) token gen, infinite context and perfect recall. It is a whole new world of models. You can interact with a model until you have it at the state you want and then save its state and use that as if it were your system prompt. Batches pack perfectly so inference is massively more efficient. Training is massively more efficient. Transformer and unlimited attn models are a dead end. But how do you make money on this as an independent researcher? If I release the Two Weird Tricks this is all based on I get zip and the big players get even more tech for free. If I keep it all secret I get Zip and eventually the tricks will be figured out. (Yes a little frustration here) If anyone wants the model architecture of the future make me an offer :)