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Infrastructure Scaling and Compound AI Systems with Jared Quincy Davis - #740

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Kandungan disediakan oleh TWIML and Sam Charrington. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh TWIML and Sam Charrington atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

In this episode, Jared Quincy Davis, founder and CEO at Foundry, introduces the concept of "compound AI systems," which allows users to create powerful, efficient applications by composing multiple, often diverse, AI models and services. We discuss how these "networks of networks" can push the Pareto frontier, delivering results that are simultaneously faster, more accurate, and even cheaper than single-model approaches. Using examples like "laconic decoding," Jared explains the practical techniques for building these systems and the underlying principles of inference-time scaling. The conversation also delves into the critical role of co-design, where the evolution of AI algorithms and the underlying cloud infrastructure are deeply intertwined, shaping the future of agentic AI and the compute landscape.

The complete show notes for this episode can be found at https://twimlai.com/go/740.

  continue reading

777 episod

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iconKongsi
 
Manage episode 495904581 series 2355587
Kandungan disediakan oleh TWIML and Sam Charrington. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh TWIML and Sam Charrington atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

In this episode, Jared Quincy Davis, founder and CEO at Foundry, introduces the concept of "compound AI systems," which allows users to create powerful, efficient applications by composing multiple, often diverse, AI models and services. We discuss how these "networks of networks" can push the Pareto frontier, delivering results that are simultaneously faster, more accurate, and even cheaper than single-model approaches. Using examples like "laconic decoding," Jared explains the practical techniques for building these systems and the underlying principles of inference-time scaling. The conversation also delves into the critical role of co-design, where the evolution of AI algorithms and the underlying cloud infrastructure are deeply intertwined, shaping the future of agentic AI and the compute landscape.

The complete show notes for this episode can be found at https://twimlai.com/go/740.

  continue reading

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