Google DeepMind's Vision for AI, Search and Gemini with Oriol Vinyals from Google DeepMind
No Priors: Artificial Intelligence | Technology | Startups
Embrace the Paradox of Sampling and Learning
Nyquist theorem illustrates the importance of sampling at the right frequency to reconstruct signals accurately; insufficient sampling leads to loss of information. However, super-resolution generative models challenge this principle by reconstructing high-resolution images from low-resolution inputs, effectively 'hallucinating' details. This reveals that such models learn underlying structures in the data, enabling them to bypass traditional sampling limits. Moreover, as these models evolve, they may exhibit emergent properties, such as self-correction capabilities, which can simplify the bootstrapping process in certain contexts, though this phenomenon will vary across different domains.
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