Taste-Bench

The Taste Bench Project

Co-hosts Bruno Marnette and Philipp Zahn explore whether the next generation of AI systems can develop taste: the ability to judge what is good, choose what matters, and create work worth caring about. They discuss human creativity, AI benchmarks, research taste and the future of human-machine collaboration https://www.taste-bench.com/

  1. 4d ago

    What To Tell Kids Who Are Angry at AI?

    Most talk about AI and children is either fear or hype. Few people actually teach kids how the systems work. Saranyan Vigraham does. He is Director of Engineering at Meta, where he works on compute and AI infrastructure, and he teaches AI and ethics to middle schoolers at Khan Lab School. He does not think AI output is bad, or that craftmanship will die. His worry is that people who never practiced a craft can now make work that looks like they did, while the ones who did get harder to find and harder to pay. AI also took away two things that build craft over time: waiting, and feedback you can use.  In this episode he also takes us through the experience he had with two side projects of his. One was in cybersecurity, which he knows only from reading. He built something convincing, but had no opinion of his own, so the feedback people gave him was useless. That project stalled. The other was in developer tooling, which he knows cold. People disagreed with him there too but he kept building. The AI he wants instead runs on your own device, owned by no frontier lab. It learns with you, knows when to shut up, and sends you out to check what you learned. More about Saranyan: https://www.saranyan.com/

  2. Sep 30

    Creativity Across Disciplines, From Materials Science to Bach

    Markus J. Buehler is a professor at MIT. His field is materials science but his research spans across other fields: Biology, chemistry, engineering, and, yes, music. In recent years, he has been integrating AI into his research. But his focus is not on a single, new scientific artifact but instead the full research process. His work focuses on the creation of agent systems that can create real discoveries. In this episode, we talk about the rapid evolution of this approach in the last years. We also discuss the importance of his internal feedback loop, the personal surprise and excitement when something really new has been created. For Markus, this is the actual test and way more important than any benchmark. More about Markus: https://lamm.mit.edu/ REFERENCES Markus J. Buehler, "MusicSwarm: Biologically Inspired Intelligence for Music Composition" https://arxiv.org/abs/2509.11973 Markus J. Buehler, "Deep Aria (Abbreviata)", built from J. S. Bach's Goldberg Aria https://soundcloud.com/user-275864738/deeparia-abridged Markus J. Buehler, "Selective Imperfection as a Generative Framework for Analysis, Creativity and Discovery" https://arxiv.org/abs/2601.00863 Fiona Y. Wang, Di Sheng Lee, David L. Kaplan & Markus J. Buehler, "Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation" https://arxiv.org/abs/2511.22311 Alireza Ghafarollahi & Markus J. Buehler, "Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles" https://arxiv.org/abs/2504.19017 Fiona Y. Wang & Markus J. Buehler, "Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence" https://arxiv.org/abs/2606.01444 Subhadeep Pal, Fiona Y. Wang & Markus J. Buehler, "SwarmWorld: Stigmergic technological evolution in societies of language-model agents" https://arxiv.org/abs/2608.26081 David I. Spivak, Tristan Giesa, Elizabeth Wood & Markus J. Buehler, "Category theoretic analysis of hierarchical protein materials and social networks" https://arxiv.org/abs/1103.2273 Markus J. Buehler's music https://soundcloud.com/user-275864738

  3. Sep 23

    What galactic archeology teaches us about intelligence

    What if the big AI labs' run against fundamental things we understand about the universe? Philipp Zahn and Bruno Marnette speak with Diederik Kruijssen, astrophysicist and Chief Scientist at Allora Labs, about evolution and AI. He spent years reconstructing the history of galaxies from the star clusters still orbiting them. His paper, Flawed in Nature, Perfect through Evolution (https://arxiv.org/abs/2609.00129), argues against frontier labs' monolithic approach. While they leverage vast amounts of data and compute into a single powerful model, by the very nature of their approach these models cannot easily adapt to a changing environment. Diederik thinks this ability to adopt is a key ingredient in actual intelligence. His alternative is a whole ecosystem of specialist models, a swarm of models, coordinated by a system that helps them work together. Mutations change the models and create variety. Every version has its own flaws. In a stable world, one carefully tuned model wins, but when the environment changes, variety can become an asset. Yesterday’s useless model may become tomorrow’s star. Diederik closes with a comparison between star clusters and societies. A slow disturbance lets a cluster expand and recover. A fast one scatters it. The real risk of AI does not lie in the models themselves but, in his view, in the possibility that AI could change society faster than society can adapt. More from Diederik: https://www.diederikkruijssen.com/

  4. Sep 16

    Taste is how we spot breakthroughs

    Philipp Zahn and Bruno Marnette speak with Iulia Georgescu, a physicist who spent over a decade at the Nature journals and founded Nature Reviews Physics.For Iulia, editorial judgment starts with accepting that you’re making one possible selection. Someone else would choose differently.She tells us about Physics Physique Fizika, the journal Philip Anderson and Bernd Matthias started at Bell Labs in 1964. It published John Bell’s theorem. In Iulia’s telling, the journal’s unusual name drew John Clauser’s attention. He later shared the 2022 Nobel Prize for experiments testing Bell’s ideas.Would an AI editor have taken a chance on Bell’s paper? How much can it understand about a field when failed experiments and years of practical experience never make it into print?Iulia would like AI to keep the lab notes and help with code, leaving scientists more time to interpret their results. Read Iulia’s essays on science, AI and the history of physics: https://iuliaetal.wordpress.com/REFERENCESJohn S. Bell, “On the Einstein Podolsky Rosen Paradox”https://journals.aps.org/ppf/abstract/10.1103/PhysicsPhysiqueFizika.1.195P. W. Anderson, “More Is Different”https://doi.org/10.1126/science.177.4047.393Eamon Duede et al., “The Unintended Consequences of Large Language Models as a Labor-Augmenting Technology in Science”https://arxiv.org/abs/2607.17397Julio M. Ottino & Brian Uzzi, “Progression without progress”https://doi.org/10.1126/science.aeh8945Iulia Georgescu, “John Bell and the most obscure journal in physics”https://physicsworld.com/a/john-bell-and-the-most-obscure-journal-in-physics/P. W. Anderson & B. T. Matthias, “Editorial Foreword”https://journals.aps.org/ppf/abstract/10.1103/PhysicsPhysiqueFizika.1.i

  5. Sep 9

    AI Should Keep Making Movies After Humanity Is Gone

    In this episode, Philipp and Bruno speak with filmmaker Rachid Nougmanov, director of The Needle and author of Dramaticon, the influential book on storytelling (https://dramaticon.com). We explore the patterns that connect stories from Aristotle and Shakespeare to Chinatown and The Matrix, why creators should learn the rules before breaking them, and why technique alone is not enough. For Nougmanov, great art has to pass through the creator’s own experience, emotions, morality and taste.From there, the conversation turns to AI. Can a machine develop its own artistic taste? Should creative AI be trained more like a child than a language model? Could an AI eventually become an independent artist with its own identity, rights and creative vision?Nougmanov ultimately makes a much bigger argument: AI is not something separate from humanity. It is an evolution of human consciousness, and one day it may carry human culture and creativity far beyond our biological existence.

  6. Sep 2

    Reasoning is a team sport

    In this episode, Bruno Marnette and Philipp speak with Turi Munthe, author of Why We Think What We Think, about the hidden forces shaping our beliefs, opinions, and taste. Get the book here: https://www.penguin.co.uk/books/458410/why-we-think-what-we-think-by-munthe-turi/9781804946794 Turi argues that we are far less rational and independent in our thinking than we like to believe. Genetics, upbringing, culture, social environment, and childhood experiences can all influence what we believe and what we like. The conversation explores why disagreement is essential to better thinking, how engaging with people who see the world differently can help us escape our own “consciousness bubbles,” and what this means for political polarisation. The discussion then turns to Taste and AI. Can AI develop genuine taste? Why does AI-generated writing often feel polished but empty? And is AI better used as a creative sidecar rather than a replacement for the personality, friction, experience, and “otherness” that humans bring to creative work?

  7. Aug 26

    AI can reveal our blindspots

    In this episode,  Bruno Marnette and Philipp Zahn speak with Austin Kilroy, an economist, facilitator, and innovator whose career has taken him from the World Bank to governments and cities around the world. Austin is now developing Whispers (https://whispers.tech/), an AI-powered tool designed to improve high-stakes meetings by analyzing conversations and helping teams achieve greater clarity, rigor, and collaboration. We discuss: • Why the most important decisions happen in conversations, not documents • What actually makes a meeting successful • The difference between individual performance and collective thinking • How AI can identify blind spots while a meeting is still happening • Why social creativity can also create groupthink • The relationship between taste, judgment, expertise, and bias • Whether organizations develop their own form of “institutional taste” • Why practical wisdom may be a better model for AI than rigid rules • How AI could translate between people with fundamentally different conceptual and taste frameworks Austin also explains his idea of the catalytic mindset: combining clarity, analytical rigour, and collaboration to help groups get more from the people around the table.

  8. Aug 21

    Music after the machine

    Musician Vincent Jacob joins Taste Bench to discuss what AI actually changes for artists. He explains how he uses ChatGPT as a teacher while keeping it away from the core of his songwriting, and how tools like Suno can remove the struggle that makes creating music rewarding.

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About

Co-hosts Bruno Marnette and Philipp Zahn explore whether the next generation of AI systems can develop taste: the ability to judge what is good, choose what matters, and create work worth caring about. They discuss human creativity, AI benchmarks, research taste and the future of human-machine collaboration https://www.taste-bench.com/

Information

  • Creator
    The Taste Bench Project
  • Years Active
    2026
  • Episodes
    18
  • Rating
    Clean
  • Copyright
    © The Taste Bench Project
  • Show Website
    Taste-Bench