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Does anyone else worry about this technology used for Big Brother type surveillance?
Where have you been the last decade? It’s already in use, or models like it, by companies selling access to The State https://deflock.me Not to mention cloud platforms that collect evidence and process it with all the models and store that information for searching… https://www.revir.ai
Earlier quoted context omitted.
Where have you been the last decade? It’s already in use, or models like it, by companies selling access to The State https://deflock.me Not to mention cloud platforms that collect evidence and process it with all the models and store that information for searching… https://www.revir.ai
No mention of palantir?
Doesn't that pretty much cover Palantir as well?
Does anyone else worry about this technology used for Big Brother type surveillance?
Does anyone else worry about this technology used for Big Brother type surveillance?
Does anyone else worry about this technology used for Big Brother type surveillance?
I was using this for video understanding with inference form vlm.run infra. It definitely has outperformed Gemini which generally is much better than openai or Claude on videos. The detailed extraction is pretty good. With agents you can also crop into a segment and do more operations on it. have to see how the multi modal space progresses: link to results: https://chat.vlm.run/c/82a33ebb-65f9-40f3-9691-bc674ef28b52…
A lot of my side projects involve UIs and almost all of my problems with getting LLMs to write them for me involve "The UI isn't doing what you say it's doing" and struggling to get A) a reliable way to get it to look at the UI so it can continue its loop and B) getting it to understand what it's looking at well enough to do something about it
It's so weird how that works with transformers. Finetuning an LLM "backbone" (if I understand correctly: a fully trained but not instruction tuned LLM, usually small because students) with OCR tokens bests just about every OCR network out there. And it's not just OCR. Describing images. Bounding boxes. Audio, both ASR and TTS, all works better that way. Now many research papers are only really about how to encode ima…
It is fascinating. Vision language models are unreasonably good compared to dedicated OCR and even the language tasks to some extent. My take is it fits into the general concept that generalist models have significant advantages because so much more latent structure maps across domains than we expect. People still talk about fine tuning dedicated models being effective but my personal experience is it's still always…
It's so weird how that works with transformers. Finetuning an LLM "backbone" (if I understand correctly: a fully trained but not instruction tuned LLM, usually small because students) with OCR tokens bests just about every OCR network out there. And it's not just OCR. Describing images. Bounding boxes. Audio, both ASR and TTS, all works better that way. Now many research papers are only really about how to encode ima…
It is fascinating. Vision language models are unreasonably good compared to dedicated OCR and even the language tasks to some extent. My take is it fits into the general concept that generalist models have significant advantages because so much more latent structure maps across domains than we expect. People still talk about fine tuning dedicated models being effective but my personal experience is it's still always…
>it's still always better to use a larger generalist model than a smaller fine tuned one
Smaller fine-tuned models are still a good fit if they need to run on-premises cheaply and are already good enough. Isn't it their main use case?