MIT Open Aggregated Podcast Feed

MIT Open Aggregated Podcast Feed

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MIT Open Aggregated Podcast Feed episodes

  • Grow Enterprise AI Maturity for Bottom-Line Impact
    Stephanie Woerner reads MIT CISR's August 2025 research briefing, which she co-authored with Ina Sebastian, Peter Weill, and Evgeny Káganer. See the text version and related content at https://cisr.mit.edu/publication/2025_0801_EnterpriseAIMaturityUpdate_WoernerSebastianWeillKaganer.
    Abstract: Last year, MIT CISR introduced a four-stage enterprise AI maturity framework to help leaders identify how to create value from AI (artificial intelligence) technology. A new MIT CISR survey has found that enterprises today are making significant progress in their AI maturity, with the greatest financial impact seen in the progression from stage 2, where enterprises build pilots and capabilities, to stage 3, where enterprises develop scaled AI ways of working. In this briefing we describe how enterprises mature from piloting AI to scaling it, illustrated by case studies of the Guardian Life Insurance Company of America and Italgas Group.
    16 min
  • The Homework Machine Ep 4 "Busted!"

    Students tell us that they know learning in schools is important. But sometimes, turning to ChatGPT to get their work done feels like the best option. AI might help with what they perceive as busy work, or they might be confused about what counts as a legitimate use, and what counts as cheating.

    And sometimes, students tell us, they know they’re crossing a line. When that happens, it’s usually because they’ve hit a wall in the learning process, and generative AI presents a quick and easy way through the blockage. For teachers, there is no single, off the shelf solution, that will ensure students make wise decisions, but understanding why students turn to AI can be a helpful starting point.

    This episode was produced by Andrew Meriwether Jesse Dukes. We had editing from Ruxandra Guidi and Alexandra Salomon. Reporting and research from Natasha Esteves, Holly McDede, Andrew Parsons, Marnette Federis, and Chris Bagg. Sound design and music supervision by Steven Jackson. Production help from Yebu Ji. Data analysis from Manee Ngozi Nnamani and Manasa Kudumu. Special thanks to Josh Sheldon and Eric Klopfer. Administrative support from Jessica Rondon.

    Featured guests include Miriam Reichenberg, Kaitleen Evangelista, as well as anonymous students. Thanks to InTandem for facilitating interviews.

    Thanks to Greer Murphy and Jessa Kirk, at UC Santa Cruz's Academic Integrity Office. Check out Greer Murphy's co-authored survey of academic integrity policies.

    Original music for this series was created by Steven Jackson, Andrew Meriwether and Jesse Dukes, as part of the music project Cue Shop. Thanks to Will Grueb, Andy Wilds, and the MIT Music Department for letting us use the MIT Harpsichord.
    The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT.

    We had support from Google’s Academic Research Awards program.

    0 min
  • Jamie Wang, "Reimagining the More-Than-Human City: Stories from Singapore" (MIT Press, 2024)
    As climate change accelerates and urbanization intensifies, our need for more sustainable and livable cities has never been more urgent. Yet, the imaginary of a flourishing urban ecofuture is often driven by a specific version of sustainability that is tied to both high-tech futurism and persistent economic growth. What kinds of sustainable futures are we calling forth, and at what and whose expense? In Reimagining the More-Than-Human City: Stories from Singapore (MIT Press, 2024), Jamie Wang attempts to answer these questions by critically examining the sociocultural, political, ethical, and affective facets of human-environment dynamics in the urban nexus, with a geographic focus on Singapore.Widely considered a model for the future of urbanism and an emblematic new world city, Singapore, Wang contends, is a fascinating site to explore how modernist sustainable urbanism is imagined and put into practice. Drawing on field research, this book explores distinct and intrarelated urban imaginaries situated in various sites, from the futuristic, authoritarian Supertree Grove, positioned as a technologically sustainable solution to a velocity-charged and singular urban transportation system, to highly protected nature reserves and to the cemeteries, where graves and memories continue to be exhumed and erased to make way for development. Wang also attends to more contingent yet hopeful alternatives that aim to reconfigure current urban approaches. In the face of growing enthusiasm for building high-tech, sustainable, and “natural” cities, Wang ultimately argues that urban imaginings must create space for a more relational understanding of urban environments.
    51 min
  • Hannah Star Rogers, "Art, Science, and the Politics of Knowledge (MIT Press, 2022)
    'Art, Science, and the Politics of Knowledge (MIT Press, 2022)' by Hannah Star Rogers
    When I sat down with Hannah Star Rogers to discuss her new book Art, Science, and the Politics of Knowledge, I found myself nodding along to a refreshingly obvious yet somehow radical proposition: why do we insist on keeping art and science in separate corners? Rogers makes a compelling case that this artificial boundary isn't just limiting our understanding of both fields, it's actively distorting how we think about knowledge itself.
    What struck me most during our conversation was Rogers' articulation of Art-STS (ASTS) as an emerging field that refuses to play by the old rules os separation and siloed study. The field, and Rogers, recognizes that both artists and scientists are engaged in the same fundamental project - making sense of the world through experimentation, observation, and yes, imagination. When we acknowledge this shared enterprise, the implications ripple outward. Who gets to produce legitimate knowledge? Whose methods count as valid? These questions matter because they shape everything from funding decisions to educational curricula to which voices we trust in public discourse.
    Rogers doesn't just theorize about these connections; she shows us what happens when we take them seriously. The experimental collaborations she documents reveal knowledge production as a deeply social, often messy, always political process. This isn't a bug in the system, it's the system itself. And maybe, just maybe, admitting that is the first step toward building more honest and inclusive ways of understanding our world.
    Notes:
    Routledge Handbook of Art, Science, and Technology Studies
    Picturing the Invisible
    Science Communication as a Boundary Space: An Interactive Installation about the Social Responsibility of Science
    Gaïa Global Circus: A Climate Tragicomedy
    Shot on LiDAR, a Short Film Examines the Contradictions of Urban Surveillance
    59 min
  • The Duplicitous Nature of Humanity

    Teachers have all sorts of opinions about AI. Some are optimistic, some are pessimistic. But the most common topic that came up in our interviews was cheating.

    While students have always taken shortcuts to complete their work, ChatGPT and other generative AI have a historically unique power to quickly, convincingly and comprehensively do a students’ assignment. This is proving a powerful temptation to students.

    So how do teachers help their students make good decisions? Teachers know that schools have historically struggled to manage discipline fairly but they also recognize that letting students get away with cheating isn’t doing them a favor. Teachers share how they’re navigating the Scylla and Charybdis of school discipline in the AI age.

    Listen to a bleeped version of this episode (Coming soon!).

    Transcript coming soon!

    This episode was produced by Jesse Dukes with Yebu Ji.
    Editing: Alexandra Salomon and Ruxandra Guidi
    Reporting and research from Natasha Esteves, Andrew Meriwether, Holly McDede, Andrew Parsons, Marnette Federis, and Chris Bagg.
    Sound design and music supervision by Steven Jackson.
    Production assistance from Nathan Ray.
    Data analysis from Manee Ngozi Nnamani and Manasa Kudumu.
    Special thanks to Josh Sheldon, Camila Lee, Liz Hutner, and Eric Klopfer.
    Administrative support from Jessica Rondon.

    Thanks to the teachers who spoke to us including Joe O'Hara, Alec Jensen, Schuyler Hunt, Anna Rose Pandey, Ray Salazar, and Jessica Petit-Frere. And thanks to all the teachers and students who partipated in our research.

    Thanks to Greer Murphy and Jessa Kirk, at UC Santa Cruz's Office of Academic Integrity. Check out Greer Murphy's co-authored survey of academic integrity policies.

    The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT.

    We had support from Google’s Academic Research Awards program.

    The Homework Machine is a program of the MIT Teaching Systems Lab, Justin Reich, director.

    33 min
  • Frances Egan, "Deflating Mental Representation" (MIT Press, 2025)
    The human mind has the curious, even mysterious, ability to generate thoughts about things with which we are not in causal contact, such as when we think about yesterday’s tennis final, or Aristotle, or unicorns. Naturalizing mental content has usually meant explaining how this is possible in terms that eliminate the mystery while retaining commitment to a substantive relationship between mind and world that undergirds this ability. In Deflating Mental Representation (MIT Press), Frances Egan argues that we should give up this commitment in favor of a naturalistic account that treats attributions of content as abstract glosses of neural mechanisms. According to Egan, who is emeritus professor of philosophy at Rutgers University—New Brunswick, representational glosses play ineliminable roles in commonsense psychology and our explanations of human behavior, but they should not be taken literally. Egan forcefully challenges many leading theories of mental representation, making her book a must-read for those interested in the concept of mental representation in the cognitive sciences.
    Deflating Mental Representation is available open-access and free here. 
    1 hr 2 min
  • Paul Thagard, "Bots and Beasts: What Makes Machines, Animals, and People Smart?" (MIT Press, 2021)
    Octopuses can open jars to get food, and chimpanzees can plan for the future. An IBM computer named Watson won on Jeopardy! and Alexa knows our favorite songs. But do animals and smart machines really have intelligence comparable to that of humans? In Bots and Beasts: What Makes Machines, Animals, and People Smart? (MIT Press, 2021), Paul Thagard looks at how computers (“bots”) and animals measure up to the minds of people, offering the first systematic comparison of intelligence across machines, animals, and humans.
    Thagard explains that human intelligence is more than IQ and encompasses such features as problem solving, decision making, and creativity. He uses a checklist of twenty characteristics of human intelligence to evaluate the smartest machines—including Watson, AlphaZero, virtual assistants, and self-driving cars—and the most intelligent animals—including octopuses, dogs, dolphins, bees, and chimpanzees. Neither a romantic enthusiast for nonhuman intelligence nor a skeptical killjoy, Thagard offers a clear assessment. He discusses hotly debated issues about animal intelligence concerning bacterial consciousness, fish pain, and dog jealousy. He evaluates the plausibility of achieving human-level artificial intelligence and considers ethical and policy issues. A full appreciation of human minds reveals that current bots and beasts fall far short of human capabilities.
    Galina Limorenko is a doctoral candidate in Neuroscience with a focus on biochemistry and molecular biology of neurodegenerative diseases at EPFL in Switzerland. To discuss and propose the book for an interview you can reach her at [email protected].
    1 hr 1 min
  • The Jagged Frontier

    ChatGPT is the most well known of the Large Language Models (LLMs) but what is an LLM? We go deep into how this remarkable new technology is built, and why their performance is inconsistent — or jagged — across similar tasks. We dive into the techniques AI engineers use to align these tools’ behavior with our values, and explain why they don’t always work, and sometimes we get hallucinations or biased output. 

     

    This episode was produced by Steven Jackson and Jesse Dukes

    Editing:  Alexandra Salomon and Ruxandra Guidi  

    Reporting and research from Holly McDede, Natasha Esteves, Andrew Parsons, Andrew Meriwether, Marnette Federis, and Chris Bagg.

    Sound design and music supervision by Steven Jackson. 

    Production assistance from Yebu Ji and Nathan Ray. 

    Data analysis from Manee Ngozi Nnamani and Manasa Kudumu. 

    Special thanks to Josh Sheldon, Camila Lee, Liz Hutner, and Eric Klopfer. 

    Administrative support from Jessica Rondon. 

    The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT. 

    We had support from Google’s  Academic Research Awards program. 

    The Homework Machine is a program of the MIT Teaching Systems Lab, Justin Reich, director. 

    33 min
  • How AI is Reshaping Medical Imagery with MIT CSAIL Professor Polina Golland
    AI is transforming radiology, but not at the expense of skilled technicians. In the same way that personal computers and spreadsheets didn’t eliminate accountants, AI is not going to replace radiologists but will instead transform the way they work.
    MIT CSAIL Professor Polina Golland’s research sits at the intersection of machine learning and healthcare, specifically medical imaging. In this episode, she discusses her team’s groundbreaking work on algorithms that analyze subtle patterns in x-rays, helping detect diseases earlier and understand them more deeply.
    This conversation covers:
    2:00 - How do doctors diagnose heart failure?
    5:27 - Converting medical imagery to numbers
    8:20 - Code generation for radiologists
    9:25 - Weaknesses in the medical system that computing can strengthen
    16:48 - The choreography of treating a patient
    20:31 - Turning an algorithm into a product
    24:26 - Will radiologists be replaced by AI?
    30:21 - How will AI change medical imagery?
    Connect with CSAIL Alliances:
    On our site: cap.csail.mit.edu/about-us/meet-our-team
    On LinkedIn: linkedin.com/company/mit-csail #MITCSAIL #AI #GenerativeAI #Leadership #Technology #CSAILPodcast
    37 min
  • Researcher Spotlight: Gautam Rao on co-producing evidence with policymakers
    In this episode, host Sambhav Choudhury speaks with Gautam Rao, Associate Professor of Economics at UC Berkeley. From his unconventional journey from electronics engineering to economics, Gautam shares insights on his behavioral economics and mental health research in India. He discusses his collaborative study on psychotherapy's long-term effects in Goa, explores effective strategies for building partnerships with policymakers, and emphasizes the importance of fieldwork in challenging academic assumptions.
    15 min

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