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21 sats \ 1 reply \ @k00b 9 Nov \ on: KOSMOS-2 Explained: Microsoft’s Multimodal Marvel AI
VLMs, in contrast, use "the visual reality". They are trained on massive datasets containing millions, or even billions, of image-text pairs.
This process forces the model to connect the word "sunset" not just to other words, but to the actual pixels, colors, and shapes that constitute a sunset in a photograph. This grounding in the visual world makes their understanding richer, more concrete, and fundamentally closer to how humans perceive the world.
I remain super curious about the data pipelines of these things.
The model demonstrates impressive capabilities in associating textual phrases with visual regions, especially for straightforward prompts and well-lit, distinct images.
However, it still struggles with contextual complexity and logical reasoning, often producing hallucinated or incorrect outputs when faced with open-ended or abstract questions.
I'm also curious about how wrong models can be and still be useful. Like, how is often is something that works sometimes better than nothing?
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0 sats \ 0 replies \ @optimism 15h
Like, how is often is something that works sometimes better than nothing?
If you're a gambler, all the time. The problem starts when you have standards then it becomes crappy real fast, unless you have a scalable means to judge output and discard at scale too.
I think that this is the hardest part of all generation in practice.
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