It makes sense that negative pairs would help to a large extent, but not all contrastive papers used negative examples, like BYOL (ref). Edit: but now I'm realizing that this might no longer fit the definition of contrastive learning (instead just ordinary self supervised learning), so I apologize about the error/confusion in that case.
Imagine there was a bijection between model parameters and resulting function. (I'm aware this is not at all true.) In that case it seems like you are enforcing the constraint that the two heads have identical parameters.
AFAIK, I always imagined the idea behind this objective function to be quite similar to contrastive learning, where you have two networks (or equivalently two sets of parameters), and the goal is to maximize agreement for pairs of inputs to each network that have the same ground truth class/label (conversely maximize disagreement for pairs... (read more)
...assume that the likelihood of a given simulation to be run is inversely correlated with the computational complexity of the simulation, in the space of all the simulation ever run. We can call the latter the Simplicity Assumption (SA)...
Isn't it possible that "simplicity" (according to one or more definitions thereof) need not care about the amount of raw computation required  to run any patch of simulation, nor with the volume of space it simulates? E.g. Occam's Razor's measure of 'simplicity' (for AI) gives some function of the description length o... (read more)
Going by GPT-2's BPEs , and based on the encoder downloaded via OpenAI's script, there are 819 (single) tokens/embeddings that uniquely map to the numbers from 0-1000, 907 when going up to 10,000, and 912 up to 200,000 . These embeddings of course get preferentially fed into the model in order to maximize the number of characters in the context window and thereby leverage the statistical benefit of BPEs for language modeling. Which bears to mind that the above counts exclude numeric tokens that have a space at the beginning .
My point h... (read more)
Is it in AI's interest (a big assumption that is has interests at all, I know) to become so human-specific that it loses its ability to generalize?
There's an approach called learning the prior through imitative generalization, that seemed to me a promising way to address this problem. Most relevant quotes from that article:
We might hope that our models will naturally generalize correctly from easy-to-answer questions to the ones that we care about. However, a natural pathological generalisation is for our models to only give us ‘human-like’ answers to ques
Although I don't agree with everything in this site, I found this cluster of knowledge related advice (learning abstractions) and the rest of the site (made by a LW'er IIRC) very interesting if not helpful thus far; it seems to have advocated that:
Edited for clarity and to correct misinterpretations of central arguments.
This response is to consider (contra your arguments) the ways in which the transformer might be fundamentally different from the model of a NN that you may be thinking about, which is as a series of matrix multiplications of "fixed" weight matrices. This is the assumption that I will first try to undermine. In so doing, I might hopefully lay some groundwork for an explanatory framework for neural networks that have self-attention layers (for much later), or (better) inspire transpare... (read more)