What The Tech

©The Turing Lab

This is a podcast for those who get itchy around black boxes. The ones who don’t just want to know what a new tech does, but why it works — or why it sometimes doesn’t. So we get down to first principles: the math, the physics, the algorithms, to see at the most fundamental level, how stuff works.

Episodes

  1. 3D AGO

    Distillation: Making AI Models Better With Less

    In early 2025, a small Chinese startup called DeepSeek sent a shockwave through the technology sector. Their R1 chatbot didn't just rival the performance of the world’s most advanced reasoning models—it did so at a fraction of the cost, wiping $600 billion off Nvidia's market cap in a single day. This episode deconstructs the controversial "computer science trick" at the heart of this disruption: Knowledge Distillation. We explore how the industry is moving away from "Foundational Giants" that cost hundreds of millions to train, toward a more democratized era where startups and independent researchers can finally compete with the tech titans. We dive into the "Teacher-Student" process that makes this possible. Imagine a master chef teaching a student not just to follow a recipe, but to understand the "intuition" behind every ingredient. We explain the difference between White-Box Distillation, which accesses a model's internal "soft targets" or "dark knowledge," and the more elusive Black-Box Distillation used by DeepSeek to mimic a teacher's behavior through millions of targeted questions. As we look toward 2026, we tackle the growing legal and ethical gray areas of this "AI shrinking ray": what happens to intellectual property when a company’s public-facing product effectively becomes the training data for its competitors?

    19 min

  2. MAY 7

    Physics That Gave Birth to AI

    While we often credit Silicon Valley for the rise of artificial intelligence, the true seeds of modern AI were planted decades ago in the obscure world of theoretical physics. This episode reveals how the "physics of disorder"—specifically the study of random metal alloys called spin glasses—provided the radical blueprint for machines that can learn and remember. You’ll discover how physicists like John Hopfield realized that a network of firing neurons behaves exactly like a grid of atomic magnets, allowing him to model human memory not as a digital warehouse, but as a physical "energy landscape" where memories are deep, stable valleys. The journey continues with the introduction of "temperature" into these digital minds. By repurposing the laws of thermodynamics, researchers like Geoffrey Hinton created the Boltzmann machine, using controlled randomness to help AI escape "local minimums"—shallow, incorrect valleys of thought. This thermal jiggling allowed networks to move beyond simple mimicry to grasp the underlying statistical patterns of data, sparking the first "generative" stirrings of AI. Join us as we trace this incredible evolution from frustrated atoms to the deep learning explosion, proving that the universal drive toward minimum energy is the hidden engine behind the world's most advanced thinking machines

    21 min

  3. APR 23

    Mathematics of VR and AR

    When Apple released the Vision Pro, it wasn't just a new gadget; it marked the official entry into the era of spatial computing. This episode peels back the curtain on the "science of the impossible," exploring how these devices act as translators between messy human biology and rigid silicon logic. We break down the five layers of technology required to build a virtual world—from the "Senses In" army of cameras and LiDAR scanners to the "Life Support" systems that process over one billion pixels per second. We dive deep into the central mathematical challenge of SLAM (Simultaneous Localization and Mapping), revealing how your glasses "know" where you are by constantly blending internal motion data with external visual landmarks to lay 3D "graph paper" over your real-world environment. We also explore the "Illusion Out" stack, comparing the "pancake lenses" that fold light paths to keep headsets thin with the "waveguides" that use microscopic nano-gratings to plumb virtual light directly into your pupils. Finally, we uncover the ultimate computational "cheat" that makes high-resolution VR feasible: foveated rendering. By using infrared cameras to track exactly where your eye is focusing, headsets only render a tiny circle of the screen at full detail, leaving your peripheral vision blurry to save the chip from overheating and the battery from draining. This mathematical triage is the keystone that allows a 23-million-pixel display to function in a wearable device.

    31 min

  4. APR 16

    World Models: Race to Develop A New Kind of LLM

    There’s a revolution unfolding in modern medicine—one that promises to rewrite the destiny of rare and inherited disorders. While CRISPR "molecular scissors" fix genes by cutting DNA, a parallel world of next-generation gene therapies is emerging: "add-a-gene" strategies. Instead of editing existing code, these therapies use custom-engineered viruses to deliver a functional, working copy of a gene so your cells can finally perform their natural functions. As of 2024, approximately 32 of these therapies have been approved globally, treating everything from inherited blindness to spinal muscular atrophy (SMA). We dive into the mechanics of these "cellular delivery trucks," specifically AAV (adeno-associated virus) and lentiviral vectors. AAVs are ideal for long-term expression in non-dividing cells like those in the liver or retina, powering treatments like Zolgensma for SMA and Luxturna for vision loss. Lentiviruses, conversely, can integrate into the genomes of dividing cells, such as bone marrow stem cells. We also tackle the "one-shot gamble": the immense challenge of billion-dollar price tags and the immune responses that often prevent patients from receiving a second dose. Looking toward 2050, we glimpse a future of routine pediatric screenings and synthetic vectors that could make genetic "additions" as common as antibiotics

    28 min

  5. APR 9

    An AI Scientist is on the Horizon

    Is a digital Albert Einstein waiting just over the horizon? In this episode, we explore the provocative question: can we build an AI scientist? We follow the journey of Mario Krenn, a physicist who turned to "computational creativity" after human reason failed to design a complex quantum experiment. Discover how his program, Melvin, began dreaming up experimental recipes that no human had considered, proving that AI can be more than just a calculator—it can be a master of association, piecing together the colossal jigsaw puzzle of scientific data to find the "missing pieces" of our understanding. However, the path to a "scientific creativity machine" is paved with profound ethical and philosophical hurdles. We examine the essential "human in the loop" guardrails required to ensure that AI remains a tool for discovery rather than a replacement for human responsibility. From the strict prohibition of listing AI as a study author to the push for radical transparency in methodology, we discuss how our scientific culture must evolve to ensure these systems boost our curiosity rather than diminish it. Join us as we explore the next great team-up in history: the blending of human gut feeling with machine intelligence to spark the next leap in human knowledge.

    14 min

  6. MAR 25

    When Would AI learn to Smell

    Imagine stepping outside after a rainstorm and being greeted by that rich, earthy scent of petrichor. While we have taught AI to recognize faces, transcribe speech, and even differentiate textures, the sense of smell remains almost completely beyond the grasp of machines. In this premiere episode, we explore the "Silent Sense," uncovering why decoding one trillion different olfactory stimuli is a biological puzzle of staggering complexity. We journey from the 400 distinct receptors in the human nose to the high-stakes world of the 2015 DREAM Olfaction Prediction Challenge, where researchers first began using algorithms to predict how molecules actually smell to a human. We dive into the "Principal Odor Map" and the deep learning revolution that allowed neural networks to analyze raw signals from electronic noses. Discover how the COVID-19 pandemic's focus on smell loss accelerated data sharing through projects like Pyrfume, and how 2024 marked a "multimodal leap" by integrating chemical structures with textual descriptions using Transformer architectures—the same tech behind large language models. As we stand at the frontier of digital olfaction, we ask: what happens when machines can finally "smell" disease, spoiled food, or the subtle shift of a rainstorm?

    20 min
  • 6 Episodes

About

This is a podcast for those who get itchy around black boxes. The ones who don’t just want to know what a new tech does, but why it works — or why it sometimes doesn’t. So we get down to first principles: the math, the physics, the algorithms, to see at the most fundamental level, how stuff works.

Information

  • Creator
    ©The Turing Lab
  • Years Active
    2K
  • Episodes
    6
  • Rating
    Clean
  • Copyright
    ©The Turing Lab
  • Show Website
    What The Tech