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Eye on Apple
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 researchers published a paper on a new architecture for vision models that scales up better and learns from diverse image data. This suggests Apple's strategic interest in enhanced spatial computing and interpreting the physical world.
- The approach mirrors techniques used in language models—predicting the next part based on previous parts to understand complex patterns. This allows for more effective scaling and adaptation as the model grows.
- The paper shows Apple innovating in model architecture itself, not just compute architecture. It also indicates a direction towards complex vision prediction and interpretation relevant for dynamic 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.