Hoover Senior Fellow Paola Sapienza and Institute for Progress’ Distinguished Immigration Counsel Amy Nice examine recently obtained data and explain why the common claim—that immigrants are hired as a source of cheap labor—doesn't hold up under scrutiny. In fact, the opposite may be true…
I’m working on a new vision-language model architecture called Onida. Our aim is to match—or surpass—the performance of leading VLMs like LLavA and CogVLM, while operating at a fraction of the cost. Unlike most existing VLMs, which layer vision components onto a language model as an afterthought, Onida is designed from first principles with a truly integrated approach.
This document [1] outlines our key differentiators, and we’re now inviting beta participants to explore and test the technology.
I largely share Yann LeCun’s perspective that scaling LLM-based approaches will eventually hit a plateau, and that a paradigm shift will be necessary. While there is ongoing debate about what that next paradigm should be, I outline my own views on the subject in this paper [1].
>Video on the internet was not a popular thing until we had broadband internet.
I think it's an example of a post hoc fallacy. The popularity of video was in large part responsible for the investment into broadband in the first place.
how is this different from generating a feature hash of the embeddings i.e reduce from many to one embedding reduction? Could a UMAP or such technique be helpful in reducing to a single vector?
This is where the memory bit comes in, if you have a memory of past embeddings and associated label(s), it could be an ANN query to fetch the most similar embeddings and infer therefrom.
has anyone tried feeding the admittedly noisy OCR-ed text -at a document level - to an LLM for making sense? Presumably some of the less capable ones should be quite affordable and accurate at scale as well.