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Lab Notes

A Tech News and News podcast

#55 in Tech News · 🇿🇦
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About the show

A regular look at the newest AI research from academic and frontier labs.

Newest episodes

Ep. #6, Why AI Needs to Argue with Itself with James Evans

1 hr 2 min

On episode 6 of Lab Notes, Amir Zohrenejad speaks with James Evans about the surprising finding that reasoning models simulate a society rather than a single brain. They dig into sycophancy and why users trust AI less but perform better when it has a point of view, the Computational Life experiments that evolved self-replicating programs from a primordial soup of code, and James’s argument that diverse novel ideas are about to become the scarcest resource on the planet.

Ep. #5, The Voice AI Turing Test with Aoden Teo

41 min

In episode 5 of Lab Notes, Amir Zohrenejad speaks with Aoden Teo, co-founder and CEO of Miso Labs, about what it will take for voice AI to become truly humanlike. They explore the limitations of today's speech-to-text and text-to-speech pipelines, the promise of full-duplex speech models, and the technical challenges around audio tokenization, turn-taking, emotion, reasoning, and real-time inference.

Ep. #4, The New Big Data of Inference with Junchen Jiang

53 min

On episode 4 of Lab Notes, Amir Zohrenejad speaks with Junchen Jiang about why KVCache may be better understood as reusable, AI-native data rather than a temporary inference optimization. They explore how LMCache and CacheBlend can reduce redundant computation, move context across distributed inference systems, and help support increasingly complex AI agents. The conversation also covers multimodal workloads, open-source infrastructure, and the future of AI systems research.

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All episodes

Ep. #6, Why AI Needs to Argue with Itself with James Evans

1 hr 2 min

On episode 6 of Lab Notes, Amir Zohrenejad speaks with James Evans about the surprising finding that reasoning models simulate a society rather than a single brain. They dig into sycophancy and why users trust AI less but perform better when it has a point of view, the Computational Life experiments that evolved self-replicating programs from a primordial soup of code, and James’s argument that diverse novel ideas are about to become the scarcest resource on the planet.

Ep. #5, The Voice AI Turing Test with Aoden Teo

41 min

In episode 5 of Lab Notes, Amir Zohrenejad speaks with Aoden Teo, co-founder and CEO of Miso Labs, about what it will take for voice AI to become truly humanlike. They explore the limitations of today's speech-to-text and text-to-speech pipelines, the promise of full-duplex speech models, and the technical challenges around audio tokenization, turn-taking, emotion, reasoning, and real-time inference.

Ep. #4, The New Big Data of Inference with Junchen Jiang

53 min

On episode 4 of Lab Notes, Amir Zohrenejad speaks with Junchen Jiang about why KVCache may be better understood as reusable, AI-native data rather than a temporary inference optimization. They explore how LMCache and CacheBlend can reduce redundant computation, move context across distributed inference systems, and help support increasingly complex AI agents. The conversation also covers multimodal workloads, open-source infrastructure, and the future of AI systems research.

Ep. #3, Exploring Recursive Intelligence with Qizheng Zhang

42 min

On episode 3 of Lab Notes, Amir Zohrenejad sits down with Qizheng Zhang to explore one of the fastest-moving areas of AI research: recursive self-improvement. Together, they discuss Meta-Harness, context engineering, and why the future of AI may depend as much on the software surrounding models as the models themselves. The conversation also examines evaluation, agentic systems, and the limits of today's autonomous AI research.

Ep. #2, The Infrastructure of Intelligence with Hanchen Li

49 min

On episode 2 of Lab Notes, Amir Zohrenejad sits down with Hanchen Li to explore the systems that make modern AI agents faster, more efficient, and better at learning from experience. They discuss KV Cache optimization, long-context inference, prompt learning, continual learning, and why better benchmarks may be just as important as larger models in advancing AI research.

Ep. #1, Why Robots Are Hard with Ren Wang

40 min

On this debut episode of Lab Notes, Amir Zohrenejad is joined by Ren Wang, a researcher and PhD student at UC Berkeley, to explore the state of physical AI and why robotics has progressed differently from large language models. They discuss data scarcity, world models, simulation, dexterity, and the challenges of building robots that can reliably operate in the real world.

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