AI + a16z Remaking the UI for AI
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Apr 19, 2024 Anjney Midha, A16z General Partner, discusses the future of AI hardware, focusing on the potential at the inference layer for everyday wearable devices. He highlights the demand for private interactions with language models and the need for on-device processing. Midha emphasizes the emergence of new chips to handle inference workloads, driven by open source models and a renaissance of efficient hardware.
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Nvidia's Training Edge, Inference Opportunity
- Nvidia dominates AI training because of its deep investment in software and scale over a decade.
- Inference workloads offer a more open field for innovation and new chip architectures.
Two Lineages of Computing
- Computing advances follow two lineages: reasoning (intelligence) and interfaces.
- Current shift is both in AI reasoning (LLMs) and new AI companion interfaces combining text, voice, and vision.
AI Needs Better Interfaces for Context
- Existing interfaces can't fully capture human context for AI models to act proactively.
- Seamless multimodal sensing (voice, vision, eye tracking) is needed for computers to understand user intent naturally.