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That's why LLM will eventually be used only for initial interaction between the user in their language, to prepare the data to a specialized model. Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
That's why LLM will eventually be used only for initial interaction between the user in their language, to prepare the data to a specialized model. Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
That's why LLM will eventually be used only for initial interaction between the user in their language, to prepare the data to a specialized model. Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
That's actually how vision language models already work, pretty much.
Image gets rasterized into smaller pieces (eg 4x4 pixels) and each of those is assigned a token, similarly how text is broken up into tokens. And the whole thing is fed into a single model.
That's why LLM will eventually be used only for initial interaction between the user in their language, to prepare the data to a specialized model. Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
Do you know that MoE is a thing?
1,000 pings, how many correctly ponged?
Earlier quoted context omitted.
Do you know that MoE is a thing?
The experts in MoEs aren't specialized in any meaningful task sense. From level of what we would think as tasks MoEs are selected essentially arbitrarily per token and per block.
Perhaps one day, all network services will be provided by LLMs natively. Truly, that would be a day in the future.
Earlier quoted context omitted.
That's actually how vision language models already work, pretty much.
Huh? The images are tokenized in the same way language is and it’s just fed into one single model. Not multiple smaller expert models. Image gets rasterized into smaller pieces (eg 4x4 pixels) and each of those is assigned a token, similarly how text is broken up into tokens. And the whole thing is fed into a single model.
> Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
that's p much how it works.