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Apple researchers recently published a paper describing a new architecture for vision models. The paper's unique approach to vision modeling hints at Apple's likely strategic imperative towards heavily integrating vision models in spatial computing environments.
Apple doesn’t publish a lot of research so we tend to take notice when there’s something that suggests a strategic link.
Apple researchers recently published a paper describing a new architecture for vision models. The paper's unique approach to vision modeling hints at Apple's likely strategic imperative towards heavily integrating vision models in spatial computing environments. This suggests a keen interest in enhancing how devices interact with and interpret the physical world around us, where the models need to seamlessly adapt to rapidly changing visuals and where objects leap from the foreground to the background in a heartbeat.
The main point of the research is that as the AI model gets bigger and has more data to learn from, it performs better at understanding images. This is similar to how large language models get better as they get bigger, and potentially even exhibit emergent reasoning. The correlation between size, data, and efficiency, previously dominant in language models, is now emerging in vision AI.
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