632nm

How can reinforcement learning help quantum computers learn from their own errors?

In this episode, Vlad Sivak, researcher at Google Quantum AI, joins us to discuss how reinforcement learning can improve quantum computing, from error correction and decoding to the calibration of quantum hardware. Sivak explains how reinforcement learning allows an AI agent to interact with a quantum system, learn from its errors, and discover how to improve its performance without being explicitly told which actions to take.

We explore how quantum error correction works, why decoding error syndromes is such a challenging problem, and how machine learning can help quantum computers adapt to changing error patterns. Sivak describes his team's work using reinforcement learning to dynamically adjust the weights of a quantum error-correction decoder, allowing it to make better-informed decisions as the properties of the hardware change over time. We also discuss why conventional optimization methods can struggle with quantum systems that are high-dimensional, stochastic, and constantly drifting.

Sivak explains how reinforcement learning can improve quantum hardware calibration by compensating for unknown effects that traditional, model-based approaches may miss. Rather than stopping a quantum computer to run dedicated calibration experiments, the long-term vision is to use the stream of error signals generated during computation to continuously update the system's control parameters. This could eventually allow quantum computers to adapt to hardware drift while continuing to operate.

Beyond quantum computing, we discuss the broader potential of reinforcement learning as a paradigm for autonomous discovery. From AlphaGo's unexpected strategies to recent advances in mathematical reasoning and programming, Sivak explores how systems can learn by experimenting, evaluating outcomes, and discovering solutions for themselves. We examine the challenges of scaling reinforcement learning, the relationship between classical AI and quantum computing, and the possibility of building quantum machines that can learn to maintain and improve their own performance.

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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/

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Website: https://www.632nm.com

Timestamps:
00:00 - Intro
00:58 - Protecting Quantum Information
09:28 - Reinforcement Learning for QEC
13:47 - Qubit Calibration
19:10 - Where Do Noise and Error Come From?
21:13 - Scalability of RL Methods
25:28 - Google's AlphaQubit
31:54 - RL vs Human Calibration
33:26 - Settling on RL vs Other ML Approaches
39:57 - Discovery in the Age of AI
44:59 - Collaboration Between Google Deepmind and Quantum AI
46:17 - Combining Quantum and AI
48:35 - Will Human Input Become Obsolete?
55:00 - Controlling Bosonic Codes
1:05:17 - Break Even Point
1:121:51 - Can Syndrome Extraction Be Fault Tolerant?
1:15:41 - Building Gates
1:20:22 - Surface Codes
1:27:38 - Scaling Superconducting Qubits
1:30:22 - How Quantum Decoders Work
1:35:47 - Model Sizes For Larger Quantum Computers
1:37:48 - What Problems Could Be Solved With Unlimited Resources?

What is intelligence, and where does it come from?

Watch Part 1 here: https://youtu.be/t6EFV2gSSmg?si=VIztbK2CM5Nwj7QA

In this episode, Michael Levin, professor of biology at Tufts University, returns to discuss his research into unconventional intelligence, the origins of cognition, and the surprising ways that biological systems solve problems. Building on his work with xenobots and anthrobots, Levin explores how new forms of intelligence can emerge in engineered biological systems and why understanding these systems may require us to rethink the relationship between evolution, learning, and agency.

We discuss Levin’s concept of "Platonic space": the idea that mathematical truths and other patterns may exist independently of the physical world. Levin proposes that biological organisms, artificial intelligence systems, and engineered machines may act as interfaces through which these patterns can take form. This raises fundamental questions about whether intelligence is something we build, something we discover, or something that becomes accessible when we create the right conditions.

Levin also describes research showing that gene regulatory networks can exhibit learning behaviors, including habituation, sensitization, and Pavlovian conditioning. These findings suggest that learning and problem-solving may occur at levels of biological organization that are often overlooked. We explore how collective systems can acquire capabilities that no individual component possesses, and how this relates to the concept of causal emergence: the idea that a system can have properties and capacities beyond those of its parts.

From xenobots and bioelectricity to hybrid biological systems and AI, we examine how engineered systems can display unexpected behaviors that challenge conventional explanations based on natural selection alone. Levin discusses the possibility of developing tools to detect, communicate with, and harness unconventional forms of intelligence, as well as the potential implications for regenerative medicine, biomedical engineering, and artificial intelligence.

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Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/

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Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:16 - Platonic Space
03:55 - Biology in the Absence of Evolution
21:11 - Extracting Knowledge from Platonic Space
37:04 - Development and Loss Functions
50:09 - Investigating Layers of Control
55:35 - Is Computer Science Misleading Our Understanding of Minds?
58:14 - Bioelectric Patterns and Drug Discovery
1:04:07 - How to Build a Predictive Developmental Model
1:13:20 - Levin’s Top Books, Thoughts on Collective Existence
1:18:00 - Questions that Keep Michael Up at Night
1:22:32 - How Does Levin Interact with AI?

#biology #evolution #computerscience #machinelearning #intelligence

Can AI help design personalized cancer vaccines?

Visit Future of Cancer Care Today: https://sijbrandijfoundation.org/fcct

In this episode, we speak with Sid Sijbrandij, founder of GitLab who went "Founder Mode" against his own cancer. Sid discusses personalized cancer vaccines, tumor sequencing, immunotherapy, and the challenge of making cutting-edge cancer treatments accessible to patients outside traditional clinical trials.

We explore how personalized mRNA cancer vaccines work, from sequencing a tumor and identifying mutations that distinguish cancer cells from healthy cells to selecting tumor-specific antigens that can be encoded into an mRNA vaccine. Sid explains why this approach is fundamentally different from conventional chemotherapy and why engaging the patient's own immune system could give immunotherapies the potential to produce durable or even curative responses.

Sid also describes his own experience combining multiple experimental treatment modalities, including personalized mRNA vaccines, immunotherapies, and radioligand therapy. He explains how radioligands can use molecular binders to selectively deliver radiation to tumor tissue, and how modifying the tumor microenvironment may make cancers more susceptible to subsequent immune therapies.

We also discuss why personalized cancer vaccines remain difficult and expensive to manufacture. In melanoma, recent clinical trials have demonstrated that individualized mRNA vaccines can target dozens of tumor-specific antigens, with surprisingly little overlap between patients. This creates a fundamentally different manufacturing problem from producing a conventional drug at scale: every patient's vaccine may need to be designed and manufactured separately.

Finally, Sid explains his effort to make these technologies more accessible through a network of biotech companies. He describes the economics and logistics of producing personalized cancer vaccines, including his experience reducing the time and cost required to manufacture an individualized vaccine, and why he believes falling sequencing costs, AI-assisted biological analysis, and repeated manufacturing experience could eventually make personalized treatments available to far more patients.

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Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Devan Shah: https://www.linkedin.com/in/shahdevan/
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/

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Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:10 - mRNA Vaccine Basics
10:20 - Costs of mRNA Vaccine Development
15:59 - Lessons from Building GitLab
19:19 - Standard of Care for Cancer
24:50 - Sid’s Life Story and GitLab
30:49 - Going Founder Mode Against Cancer
42:03 - Selecting Experimental Treatments
50:38 - How Did Sid Know Treatments Were Working?
58:08 - AI for Treatment Assistance
1:02:54 - Making Personalized Treatments Affordable
1:08:17 - Where are the Levers for Improving Drug Development?
1:10:54 - Most Promising Areas of Drug Development
1:17:14 - The Happy Accident of Sid’s Biotech Investments
1:21:07 - Reflecting on Building GitLab
1:22:46 - How Does Sid Start So Many Companies?
1:26:02 - Finding the Right People for Early Companies
1:34:19 - Will the Bahamas be the Next Singapore?
1:38:42 - Advice for Young Scientists

#cancer #biology #immunology #cancerresearch #gitlab

What does it actually take to build a quantum computer that can scale to millions of qubits?

Learn more about Qolab here: https://qolab.ai/

In this episode, we speak with John Martinis, recipient of the 2025 Nobel Prize in Physics. Martinis is a pioneer in superconducting quantum computing and led the team of engineers at Google Quantum AI during the development of their Sycamore chip, which was the first to demonstrate “Quantum Supremacy,” the outperformance of a quantum computer compared to a classical supercomputer.

John is now the co-founder of Qolab, a company developing new approaches to building scalable superconducting quantum circuits. Martinis discusses why scaling quantum computers is fundamentally an engineering and manufacturing problem, and why the next generation of quantum hardware may require rethinking how the chips themselves are designed and fabricated.

We explore the challenges of building superconducting qubits, from fabrication and packaging to control electronics, wiring, power dissipation, and the subtle imperfections that can determine whether a quantum chip works at all. Martinis explains why adding more qubits is not simply a matter of making existing systems larger. At the scale of hundreds of thousands or millions of qubits, every component has to work together, and improvements in one part of the system can create new problems somewhere else.

Martinis describes the philosophy behind Qolab and its effort to develop a fundamentally different architecture for scalable quantum computing. Rather than simply pushing existing approaches forward, Qolab is trying to remake the individual elements of the system and integrate them in new ways. We discuss wafer-scale fabrication, the challenges of connecting and controlling large numbers of superconducting qubits, and why the manufacturing techniques used to build modern semiconductor chips could be important for the future of quantum computing.

We also discuss the practical engineering lessons Martinis learned while developing superconducting quantum processors, including the difficulty of getting an entire system to work reliably. He recounts the development of the hardware behind Google's early quantum computing efforts, the unexpected failure caused by a circuit board rather than the qubit chip itself, and the many subtle fabrication and engineering issues that can become increasingly important as quantum systems grow larger.

Finally, Martinis explains why he sees quantum computing as a system engineering problem involving dozens of interconnected constraints. From the physics of superconducting qubits to semiconductor fabrication, cryogenic electronics, packaging, and control, building a useful quantum computer requires solving many problems simultaneously. The goal is not simply to build a better qubit, but to develop an architecture that can ultimately support quantum computers at truly large scale.

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Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

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Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:06 - Secrecy of Fabrication
03:24 - 2 Qubit Gate with Transmons
05:18 - New Knowledge of Superconducting Quantum Computers
08:53 - What are Transmons?
35:56 - Lessons from Failure
38:05 - The Role of Theory in Martinis’ Work
44:51 - Two Level States
48:15 - Engineering Tricks in Superconducting Quantum Computers
53:32 - Metrics for Quantum Success
1:00:11 - Scaling Quantum Computers
1:08:08 - Identifying Sources of Error
1:16:15 - Quantum Supremacy Experiment
1:25:45 - What If Quantum Mechanics Failed?
1:33:10 - Is Quantum Supremacy Holding Up?
1:34:42 - Lift-off Fabrication for Superconducting Quantum Computers
1:41:59 - Quantum Flexibility vs Foundries
1:43:40 - Connecting Distant Qubits
1:47:41 - Codesign for Fault-Tolerance
1:49:16 - Martinis’ Nobel Prize
2:01:55 - Advice for Young Scientists

#quantumcomputing #quantumphysics #superconductor #nobelprize #fabrication

What does a cell actually look like when you can see its molecules in action?

In this episode, we speak with Nobel Prize-winning scientist Eric Betzig, whose pioneering work in super-resolution microscopy transformed our ability to see inside living cells. Betzig recounts his decades-long effort to overcome the diffraction limit of light microscopy, from his early work in near-field microscopy to the development of PALM and his eventual focus on watching biological processes unfold in living cells.

We explore why the familiar picture of the cell in biology textbooks may be fundamentally misleading. Much of cell biology has been built by combining observations from biochemistry, molecular biology, and structural biology to construct models of how molecules interact. But, as Betzig explains, we have historically had very little direct information about the spatial organization and dynamics of these molecules inside a living cell. When he and his colleagues used single-molecule microscopy to watch transcription factors in real time, they found that proteins believed to form stable complexes were instead binding to DNA for only a few seconds, forcing them to reconsider how transcription actually works.

We discuss the diffraction limit, why conventional light microscopes cannot resolve structures at the scale of individual proteins, and how super-resolution microscopy made it possible to study molecular processes with unprecedented spatial and temporal resolution. Betzig also explains why imaging living cells can reveal dynamics that are invisible in fixed samples.

Betzig describes his ambitious Cell Observatory project, which combines automated microscopy, large-scale biological experiments, and artificial intelligence to study the enormous complexity of living cells. Rather than trying to build a “virtual cell” from incomplete measurements, he argues that biology first needs to observe these systems at a much larger scale and turn the resulting data into genuine understanding.

Finally, Betzig reflects on what microscopy has taught him about scientific discovery, why the cell may be the most complex form of matter we know, and why better ways of observing life could fundamentally change our understanding of biology.

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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

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Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:30 - The Diffraction Limit
12:30 - Imaging Cells
19:04 - Betzig's Transition from Physics to Biology
32:37 - Getting Fed Up with Science
34:57 - Leaving Science for the Automotive Industry
55:55 - 2008 and the Fall of the Automotive Industry
1:09:50 - Building a Microscope in a Living Room
1:34:01 - Insights from Super-Resolution Microscopy
1:47:53 - AI for Analyzing Petabytes of Data
2:10:35 - Improving Microscopes
2:15:27 - Nuclear Energy and Politics
2:23:18 - The Magic of Bell Labs
2:36:17 - Is SpaceX the New Bell Labs?

#microscopy #cellbiology #superresolution #fluorescence #nobelprize

Why is controlling a quantum hardware becoming one of the biggest challenges in scaling quantum computers?

In this episode, we speak with Yonatan Cohen, co-founder and CTO of Quantum Machines, a company developing advanced control systems for quantum computers. Cohen explains how quantum control sits at the interface between quantum hardware and classical computing, and why this hybrid architecture will become increasingly important as quantum processors scale.

We explore how quantum computers are controlled using precise microwave signals and pulse sequences, the limitations of conventional arbitrary waveform generators, and how Quantum Machines uses FPGA-based pulse processing units to generate waveforms in real time. We also discuss why low-latency classical processing and real-time feedback are essential for calibrating quantum processors, correcting errors, and implementing increasingly complex quantum algorithms.

Cohen explains what changes when moving from small quantum processors to thousands or millions of qubits, including the challenges of data movement, power consumption, control-channel density, and latency. We also discuss quantum error correction, feed-forward operations, hybrid quantum-classical architectures, and the role of CPUs, GPUs, and FPGAs in stabilizing large-scale quantum systems.

We also discuss the origins of Quantum Machines, the company's approach to quantum control, and why building scalable quantum computers requires much more than simply increasing the number of qubits.

Whether you're interested in quantum computing, quantum control, quantum error correction, computer architecture, FPGA technology, or the future of fault-tolerant quantum computers, this episode provides a deep technical look at the control infrastructure required to make large-scale quantum computing possible.

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Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

Subscribe:
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Website: https://www.632nm.com

Timestamps:
00:00 - Intro and Reads
02:37 - Quantum Machines and Hybrid Architecture
06:04 - Why Do Classical Computers Need Quantum Processors?
10:02 - State of the Art Controllers
20:02 - Meeting Itamar
24:01 - FPGAs for Quantum
29:29 - Repurposing FPGAs
35:22 - Remote Direct Memory Access (RDMA)
41:29 - What If We Had Perfect Controllers?
43:26 - Adaptive and Embedded Calibrations
47:15 - Picks and Shovels of Quantum Computing
49:14 - Progress in Different Qubits
53:25 - Keeping Up with Quantum News and Research
56:51 - Core Advantages of Quantum Machines
1:00:06 - Managing Larger Teams
1:02:12 - Discovering New Physics with Quantum Machines
1:14:31 - Reinforcement Models in Quantum
1:16:45 - Yonatan’s Intro to Quantum Computing
1:21:24 - Realtime Correction vs Post Processing
1:32:10 - Channel Numbers and Interfering Signals
1:39:38 - Connecting Multiple Modules
1:46:46 - Early Believers in Quantum Machines
1:51:05 - What Would Yonatan Do With Unlimited Resources?

#quantumcomputing #quantumphysics #computerscience #fpga #coding

How did we go from knowing almost nothing about genes to sequencing the entire human genome?

In this episode, we speak with Nobel Prize-winning molecular biologist Walter Gilbert, whose discoveries helped lay the foundation for modern genomics. Gilbert recounts his remarkable journey from theoretical physics into biology, where he helped discover messenger RNA, uncovered the molecular mechanisms of gene regulation, invented one of the first practical methods for sequencing DNA, and later co-founded Biogen, one of the world's first biotechnology companies.

We explore the race to understand how genes work, the search for the elusive lac repressor, how a chance experiment led to the invention of DNA sequencing, and why Gilbert believed decades in advance that sequencing the human genome would transform biology into an information science. He explains the origins of the Human Genome Project, the rise of computational biology, and why today's era of AI-driven genomics was already visible in the earliest DNA sequence databases.

We also discuss the RNA World hypothesis, how life may have begun with self-replicating RNA molecules, the evolution of gene regulation, exon shuffling, the origins of protein domains, recombinant DNA technology, the birth of the biotechnology industry through Biogen, and how scientific revolutions often emerge from unexpected experiments.

Finally, Gilbert reflects on creativity in both science and art, explaining why, after a lifetime of pioneering discoveries, he left the laboratory to pursue digital abstract art.

Whether you're interested in DNA sequencing, the Human Genome Project, molecular biology, biotechnology, computational biology, the origin of life, RNA World, gene regulation, genomics, or the history of modern biology, this episode offers a firsthand account from one of the scientists who helped build the field.

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Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

Subscribe:
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Website: https://www.632nm.com

Timestamps:
00:00 - Intro
02:34 - Jim Watson and Beginning Biology
06:58 - Lac Repressor
15:27 - Picking Good Problems
17:57 - Developing the First Generation of Sequencing
31:09 - The Birth of the Human Genome Project
40:00 - Origins of Life
43:35 - RNA World Hypothesis
54:45 - Experiments vs Theory in Biology
59:07 - Inspiration from Other Discoveries
1:12:08 - Starting Biogen
1:22:24 - Balancing Industry and Academia
1:27:45 - Advice for CEOs
1:32:48 - Perspectives on Art and Science
1:37:49 - Walter’s Journey through Art
1:44:55 - Walter’s Artistic Process and Inspirations
1:50:32 - The Role of Theory in Biology
1:55:51 - Frontiers and Guidance in Science
1:59:37 - Should Everyone Get Sequenced?

#biology #dnasequencing #originsoflife #genetics #humangenomeproject

How do bacteria power one of the most sophisticated molecular machines in nature?

In this episode, we speak with Dr. Michael Manson, one of the pioneers of bacterial motility research, whose nearly 50-year career has helped uncover how the bacterial flagellar motor works. From the first experiments proving that bacterial flagella rotate to the latest breakthroughs in cryo-EM and single-molecule biology, Manson tells the story of how scientists finally solved the mechanism behind a real working biological motor.

We explore how bacteria move through chemotaxis using a biased random walk, why E. coli alternates between running and tumbling, and how individual molecules can control the direction of a spinning flagellum. Manson explains the experiments that showed proton motive force powers the flagellar motor, how the motor’s rotor and stator generate torque, why it can reverse direction almost instantly, and how bacteria adapt to changing environments by dynamically adjusting their molecular machinery.

We also discuss ATP synthase, proton gradients, molecular motors, bacterial genetics, cryo-electron microscopy, ion channels, self-assembling protein complexes, nanomachines, and the history of the discoveries that transformed modern microbiology.

Whether you’re interested in the bacterial flagellar motor, molecular biology, biophysics, microbiology, ATP synthase, chemotaxis, molecular machines, or the fundamental physics of life, this week we go deep into one of biology’s most remarkable inventions.

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Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro and Reads
02:42 - Biased Random Walk
10:27 - Manson's Work with Howard Berg
13:24 - Proton Motive Force and Flagellum
29:07 - Rotors and Stators of Flagella
37:20 - Mot Proteins
57:34 - CheY and Changing Direction
1:11:48 - Biology and Intelligent Design
1:26:52 - Reversing Proton Flow
1:29:59 - Life at Low Reynolds Number
1:39:15 - Mysteries in the 90s and 2000s
1:48:34 - Applications of Understanding the Nanomotor
1:58:33 - Flagellar Motor Crash Course
2:01:46 - Bacterial Learning and Adaptation
2:05:56 - Giving Up on Birds
2:14:09 - Caltech
2:21:38 - Advice for Young Scientists
2:30:02 - Origins of Life
2:31:19 - What's Left for the Flagellar Motor?

PART 2:
2:33:33 - Building the Nanomotor
2:39:29 - Other Types of Flagella
2:47:06 - MotA and MotB
3:07:17 - Reusing Motors Across Biology
3:10:13 - Benefits of Being Small
3:12:34 - CheY and Changing Direction
3:19:39 - How Physics Shapes Evolution

Why are so many companies betting on neutral atoms to build the first useful quantum computers?

In this episode, we speak with Mark Saffman, professor at the University of Wisconsin–Madison and one of the pioneers of neutral atom quantum computing. Over the past two decades, Saffman has helped transform Rydberg atoms from a theoretical idea into one of the leading architectures for scalable, fault-tolerant quantum computing.

We explore the physics of optical tweezers and Rydberg blockade, how neutral atoms perform quantum logic and create entanglement, and why this platform offers unique advantages in connectivity and scalability. Saffman also discusses the engineering challenges of improving gate fidelity, implementing quantum error correction, and scaling from small laboratory experiments to processors containing millions of qubits.

We also discuss the origins of companies like Infleqtion, the rapid growth of the neutral atom ecosystem, and what it will take for quantum computers to solve meaningful scientific and industrial problems.

Whether you're interested in quantum computing, atomic physics, quantum error correction, computer architecture, or the future of information processing, this episode provides a deep technical look at one of the most promising paths toward practical quantum computers.

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Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro and Reads
02:45 - Neutral Atoms vs Superconductors and Ions
07:30 - Rydberg Atoms
12:49 - Practical Considerations for Rydberg Atoms
19:04 - From Atomic Physics to Quantum Gates
29:49 - Increasing Trap Loading
38:27 - Evolution of Rydberg Gates
45:05 - Limits of Rydberg Fidelity
49:49 - Scaling Neutral Atom Arrays
53:47 - Atomic Species and QEC
1:03:38 - History of Infleqtion
1:10:27 - Mark’s Outlook on the Future
1:15:08 - Caltech and Peter Shor
1:20:00 - Advice for Young Scientists

#quantumphysics #quantumcomputing #physics #computerscience

Why are some of the world's largest technology companies betting on silicon photonics?

In this episode, we speak with John Bowers, professor at UC Santa Barbara and one of the pioneers of silicon photonics, about the technologies that are transforming AI infrastructure and modern data centers. Bowers explains why moving data has become one of the central challenges in computing, how optical communication is overcoming the limitations of traditional electrical interconnects, and why light is increasingly being used to connect processors, servers, and entire data centers.

We explore the origins of silicon photonics, from early optical communications research to the development of integrated photonic devices that can be manufactured using semiconductor processes. Bowers discusses the engineering challenges of combining lasers with silicon, the breakthroughs that enabled heterogeneous integration, and how decades of research helped turn silicon photonics into a commercial technology deployed at global scale.

We examine the growing demands of artificial intelligence, where the movement of information between processors has become just as important as computation itself. Bowers explains why bandwidth, power consumption, and interconnect density are emerging as critical bottlenecks for AI systems, and how optical links are enabling the next generation of large-scale computing architectures.

We also discuss data center networking, optical interconnects, co-packaged optics, heterogeneous integration, semiconductor manufacturing, photonic integrated circuits, telecommunications, AI hardware, and the future of warehouse-scale computing. Throughout the episode, Bowers provides an inside look at how advances in photonics are reshaping the infrastructure that powers modern computing.

Whether you're interested in silicon photonics, optical communications, semiconductor engineering, computer architecture, AI hardware, data center design, networking, integrated photonics, electrical engineering, or the future of computing, this episode provides a deep technical exploration of one of the most important technologies behind the AI revolution.

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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin-168b94130/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:19 - Why Data Centers Need Photonics
05:28 - Bowers's Interest in Physics
10:09 - Lessons From Bell Labs
12:58 - Semiconductor Lasers
18:31 - Teaching Entrepreneurship
23:21 - Heterogeneous Integration
29:40 - Why Silicon Photonics Needed Better Light Sources
32:00 - Heterogeneous Integration vs Direct Growth
44:04 - The Packing Problem in Photonics
47:49 - Narrow Linewidth Lasers
51:31 - Data Centers in Space
59:19 - Lessons from the Telecom Bubble
1:02:17 - Recent Breakthroughs in Photonics
1:04:32 - What is a Frequency Comb?
1:07:07 - Solitons and Microcombs
1:14:48 - Optical Computing and AI
1:19:09 - How Bowers Starts Companies
1:21:56 - Was Bowers Late to Any Trends?
1:22:51 - What would Bowers Build with Unlimited Resources?
1:24:38 - Creating Bell Labs for AI
1:26:35 - Competition, Endurance, and Personality
1:30:41 - The Best Problems for Young Scientists to Tackle
1:37:47 - Advice for Researchers Who Want to Keep Real Depth

#photonics #datacenter #siliconphotonics #computerscience #artificialintelligence

How can a flatworm regenerate a complete head after being cut in half?

In this episode, we speak with Michael Levin, developmental biologist and director of the Allen Discovery Center at Tufts University, about the emerging field of developmental bioelectricity. Levin explains how voltage gradients, ion channels, and gap junctions form a layer of biological control that operates alongside genetics and biochemistry to regulate embryonic development, regeneration, and anatomical patterning.

We explore the experimental foundations of bioelectricity research, including the use of voltage-sensitive dyes, ion channel manipulation, and computational models to read and write electrical information in living tissues. Levin discusses how bioelectric signals help establish left-right asymmetry in embryos, coordinate communication across developing tissues, and encode large-scale anatomical information that individual cells cannot possess on their own.

The conversation examines classic and surprising experiments from the field, including the creation of two-headed planarian worms, the induction of ectopic eyes in frog embryos, and the restoration of normal development after severe genetic and environmental disruptions. Levin explains how bioelectric circuits can act as a control architecture for morphogenesis, allowing tissues to make collective decisions about growth, form, and regeneration.

We also discuss voltage gradients, membrane potentials, gap junction networks, developmental pattern formation, regenerative medicine, collective cellular intelligence, and the relationship between electrophysiology and gene regulation. Throughout the episode, Levin argues that understanding development requires looking beyond genes alone to the dynamic electrical communication networks that coordinate living systems across scales.

Whether you're interested in developmental biology, embryology, regeneration, electrophysiology, bioelectricity, morphogenesis, systems biology, ion channels, pattern formation, or the future of regenerative medicine, this episode provides a deep technical exploration of how electrical signals help shape living organisms.

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Substack: https://632nmpodcast.substack.com/

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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin-168b94130/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:40 - Early Interest in Bioelectricity
05:22 - External Electric Stimulation
19:54 - Two-Headed Planarians
31:40 - Designing Bioelectric Experimental Methods
56:37 - Different Model Organisms
1:07:34 - TAME Theory
1:24:16 - Xenobots and Advice for Young Scientists

#planaria #morphology #neuroscience #biology #bioelectricity

Are quantum computers changing the way we discover cancer treatments?

In this episode, Misha and Yudong spoke with Fred Chong, Seymour Goodman Professor at the University of Chicago, about the future of quantum computer architecture and how quantum algorithms could eventually help solve real-world problems in medicine, optimization, and scientific computing.

Chong explains the transition from the NISQ era toward fault-tolerant quantum computing, why hardware-aware software design remains essential, and how compiler architectures, error correction, and quantum system design all interact across the full computing stack. The conversation explores the challenges of building scalable quantum machines, the tradeoffs between superconducting qubits, trapped ions, and neutral atoms, and why many quantum systems may ultimately function as specialized accelerators alongside classical computers.

We also discuss quantum optimization algorithms like QAOA and how Chong’s group is applying them to cancer biomarker discovery and treatment prediction. By analyzing complex multimodal biological data, including DNA, mRNA, and pathology imaging, these methods aim to uncover patterns that are difficult for conventional machine learning systems to identify without overfitting.

Along the way, Fred shares stories from the early days of supercomputing at Thinking Machines, the origins of his quantum research career, the founding of Super.tech, and his perspective on where quantum computing is genuinely making progress versus where hype still dominates the conversation.

Topics include quantum computing, QAOA, fault-tolerant quantum computing, quantum error correction, quantum compilers, NISQ systems, neutral atoms, superconducting qubits, quantum architecture, cancer biomarkers, biomedical optimization, hybrid quantum-classical systems, and the future of quantum software and hardware co-design.

Follow us for more technical interviews with the world’s greatest scientists:
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Substack: https://632nmpodcast.substack.com/

Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:34 - From Jurassic Park to Quantum Computing
10:13 - Modernizing NISQ Research
13:45 - Designing Around Quantum Hardware
20:30 - Variational Quantum Algorithms
23:07 - Quantum Computers for Cancer Research
30:35 - How Q4Bio Began
37:20 - Will We Need QEC in the Future?
40:25 - What Quantum Computers Can Learn from Classical Architecture
43:08 - Would Fred Return to Classical Computing?
46:11 - Quantum Software and Quantum Compilers
55:19 - Starting Super.tech
1:01:43 - Classical Analogs to Quantum Hardware
1:12:21 - Advice for Young Scientists
1:17:43 - Is AI Impacting Quantum Research?
1:22:38 - Importance of Formal Verification
1:30:40 - QLDPC Codes
1:35:48 - Fred’s Beginnings in Computer Science
1:42:48 - Chicago vs Silicon Valley
1:46:27 - Do We Need More Quantum Software Companies?
1:53:17 - Future of Quantum Computing and Cryptography

#quantumcomputing #quantumalgorithms #cancerresearch #computerscience

How do you measure something as small as a single electron or map quantum behavior at the nanoscale?

In this episode, Misha spoke with Amir Yacoby, professor at Harvard University, about the cutting edge of quantum sensing and the experimental tools redefining how we probe the quantum world.

Yacoby explains how physicists build ultra-sensitive detectors, from single-electron transistors to quantum dots and NV centers in diamond, that can measure charge, spin, and magnetic fields with extraordinary precision. These tools make it possible to study both strongly correlated systems, like those exhibiting the fractional quantum Hall effect, and isolated quantum systems used as qubits.

We explore how accidental discoveries in the lab can evolve into entirely new sensing techniques, including momentum-resolved tunneling and nanoscale imaging methods. The conversation also highlights how quantum sensors are enabling researchers to bridge two regimes: complex many-body systems and controllable quantum devices, opening the door to new insights in topological physics and quantum information processing.

Whether you're interested in quantum measurement, nanoscale imaging, or the future of quantum technologies, this episode offers a detailed look at how new instruments are driving discovery at the frontiers of physics.

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Subscribe:

Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269

Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR

Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:23 - The Process of Creating Quantum Tools
11:28 - Graduate School at Weizmann
14:51 - From Aerospace to Condensed Matter
26:53 - Starting at Harvard
39:44 - Working at Bell Labs
47:42 - Diamond NV Centers
1:00:52 - Spin Waves
1:16:10 - SQUIDs
1:29:57 - State of the Art Sensors
1:33:08 - Motivations for Building Better Sensors
1:36:52 - Fabrication Challenges
1:40:14 - New Sensors
1:45:49 - Majoranas
1:53:25 - Finding New Applications for Sensors
1:57:16 - The Use of AI in Physics
1:58:55 - Advice for Young Scientists

How do you turn a flat piece of nanostructured material into a secure biometric sensor?

In this episode, we speak with Rob Devlin, co-founder and CEO of Metalenz, about how metasurfaces are transforming optics and enabling a new generation of biosecure sensing. Devlin explains how engineers can control light at the subwavelength scale to replace bulky lens stacks with a single flat surface, and why the real breakthrough isn’t just miniaturization, but the ability to mass-produce optics in semiconductor fabs.

We explore how Metalenz scaled metasurfaces from academic prototypes into millions of devices, and what it takes to design optics for manufacturing. Devlin breaks down the transition from building one perfect device in a cleanroom to producing millions that all meet tight specifications.

The conversation focuses on polarization imaging as a new information channel in consumer devices. Unlike traditional cameras that capture only intensity and color, polarization reveals material properties. This enables a new approach to facial recognition that is both more secure and more compact than existing systems.

Rob also shares the story behind Metalenz, from its origins in a Harvard lab to partnerships with major semiconductor manufacturers, and how the company navigated the challenges of finding product-market fit, scaling fabrication, and building a new sensing stack from scratch.

Whether you’re interested in optics, nanofabrication, consumer electronics, or the future of biometric security, this episode explores how controlling light at the nanoscale is opening entirely new possibilities for sensing and identity verification.

Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/

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Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:22 - Making Metalenses Mass-Producible
10:58 - Metasurfaces for Polarimetry
17:10 - Face ID Security and Pitfalls
24:47 - Polar ID Principles
29:02 - Polar ID Demo
39:58 - Meeting Federico Capasso
50:43 - Developing Metasurface Fabrication Techniques
55:58 - Founding Metalenz
1:11:44 - Future of Metalenz and Metasurfaces

#photonics #faceid #biometrics #metasurface #biosecurity #optics

Are data centers in space physically possible, or just another overhyped idea?

In this episode, we speak with Philip Johnston, CEO of Starcloud, about the technical and economic case for putting AI infrastructure in orbit. The idea has gone viral in recent months, drawing strong criticism from science communicators like Scott Manley, Kyle Hill, and Hank Green, but rarely with detailed engagement on the underlying assumptions.

We examine whether space-based data centers can compete with terrestrial infrastructure, and what constraints actually matter: energy generation, cooling, launch costs, and manufacturing at scale. Johnston walks through the core economic model behind Starcloud, including assumptions about SpaceX’s Starship, the cost of solar power in orbit, and why removing terrestrial constraints like land use, permitting, and energy storage could fundamentally change how compute is deployed.

We discuss the physics of radiative cooling in space, the challenges of operating GPUs in a radiation environment, and how orbital systems compare to Earth-based data centers in terms of efficiency and cost structure. The conversation also explores broader questions around AI’s growing energy demands, the limits of terrestrial infrastructure, and whether shifting compute off-world is a niche solution or a long-term inevitability.

Whether you’re interested in space technology, AI infrastructure, energy systems, or the economics of large-scale computing, this episode offers a detailed look at one of the most debated ideas in modern engineering, and a rare opportunity to hear its strongest arguments laid out in full.

Follow us for more technical interviews with the world’s greatest scientists:
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Substack: https://632nmpodcast.substack.com/

Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:12 - What is Starcloud?
02:44 - Why do data centers need to go to space?
06:15 - Can’t we just build more solar panels on earth?
11:10 - Economic analysis of Starcloud
19:56 - How does Starcloud’s cooling work?
28:26 - Training an LLM in space
32:07 - Addressing critics on space Twitter
34:23 - Is Starcloud overfunded?
35:59 - Will demand for data centers keep going up?
38:11 - GPU lifespan and disposal in space
39:47 - Bus structures
41:43 - Starcloud’s origin and founders
49:29 - Fundraising, Competition, and Meeting Expectations
53:29 - Satellite size and collisions
56:29 - Manufacturing Bottlenecks
1:00:20 - Starcloud 1 tests
1:01:57 - Acceleration after YC
1:03:43 - Testing on Earth
1:05:06 - Motivations for Starcloud
1:06:45 - Data centers on the Moon
1:08:12 - Interacting with AI companies
1:08:18 - What’s next for Starcloud?
1:14:01 - Other uses for Starcloud satellites
1:17:56 - Lunar hotels and space elevators
1:24:28 - Complementary business ideas to Starcloud
1:29:51 - Philip’s competitive twin
1:32:18 - Philip and Mike’s thoughts on YC
1:34:45 - Advice for young entrepreneurs

#datacenter #aidatacenter #starlink #spacex #falcon9 #starcloud

How do you actually make quantum algorithms work on real hardware?

Build your own quantum circuits in Crumble: https://algassert.com/crumble

In this episode, we speak with Craig Gidney of Google Quantum AI, whose work focuses on the practical realities of building fault-tolerant quantum computers. Gidney explains how seemingly small implementation choices, like how you perform arithmetic, can dominate the cost of entire quantum algorithms.

We explore why factoring small numbers like 15 in Shor's algorithm can be misleadingly easy, and why scaling to larger numbers requires dramatically more resources due to operations like modular multiplication. He breaks down how quantum circuits are often dominated by classical reversible logic, and why optimizing these routines is critical for making quantum computing viable.

The conversation covers quantum error correction, including why T gates are especially expensive, how magic state factories works, and how different hardware architectures change what “cost” even means. Gidney also explains how resource estimates for breaking cryptography have dropped by orders of magnitude and what drove those improvements.

We also dive into the tools he built, including Stim, Quirk, and Crumble, which help researchers simulate noise, visualize circuits, and track how errors propagate through complex systems. Gidney shares his unconventional path into the field, the role of intuition and tooling in discovery, and how software engineering shapes modern quantum research.

Whether you’re interested in quantum computing, error correction, cryptography, or the engineering challenges behind scalable quantum systems, this episode offers a clear and grounded look at what it really takes to turn quantum algorithms into reality.

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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:22 - Shor’s Algorithm
04:02 - Why are Arithmetic Operations Important?
08:35 - Why are T-Gates Important for QEC?
13:47 - Motivations for Creating Crumble and STIM
18:40 - Can AI Code Quantum Simulators?
22:32 - Journey into Learning Quantum
26:50 - How to Enter the Field of Quantum Computing
31:16 - From Starcraft to Software Engineering
36:05 - Crumble Demo
53:18 - Quirk Demo
1:00:48 - Estimating Resources for Quantum Computation
1:08:58 - Optimizing Measurements for Computation
1:16:40 - How Many Qubits Do We Actually Need?
1:30:49 - Other Research Areas for Improving Fault Tolerance
1:41:23 - Elliptic Curve Discrete Logarithm Problem
1:46:55 - New Tools for Quantum Computing
1:50:23 - What Would Craig Do with Unlimited Funding?
1:52:28 - How Learning Has Changed for Craig with Experience
1:57:31 - Riding the Wave of Innovation vs Sticking to One Idea
1:59:53 - Advice for Young Scientists

#quantumcomputing #quantumphysics #computerscience #googleai #googlequantum

How do neurons convert electrical signals into chemical messages in under a millisecond?

In this episode, we speak with Thomas Südhof, Stanford neuroscientist and Nobel laureate whose discoveries revealed the molecular machinery that allows neurons to communicate at synapses. Südhof explains how an electrical impulse traveling down a neuron triggers the rapid release of neurotransmitters, transforming an electrical signal into a chemical one that can be received by the next cell.

We explore the remarkable precision of synaptic transmission, including how calcium ions trigger vesicle fusion, how specialized proteins organize the release machinery, and why this entire process unfolds on the timescale of a single millisecond. Südhof walks us through the molecular components that make this possible, including the proteins that dock neurotransmitter-filled vesicles and control their release.

The conversation also examines how these discoveries reshaped modern neuroscience by revealing the fundamental mechanisms underlying neuronal communication. Südhof discusses how synapses operate as highly specialized molecular machines and how disruptions in synaptic signaling are linked to neurological and psychiatric disorders.

Whether you’re interested in neuroscience, synapses, brain signaling, neurotransmitters, or the molecular basis of thought, this episode offers a clear explanation of how neurons translate electricity into chemistry, and how this microscopic process makes brain communication possible

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Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)

Timestamps:
00:00 - Intro
01:23 - What is a Synapse?
07:01 - History of Synapse Discovery
12:54 - How Electron Microscopy Helped Neuroscience
15:11 - Early Electrophysiological Experiments
18:31 - Why are Neurotransmitters Needed At All?
21:25 - Electrical Connections Between Cells
22:48 - How Signal Diversity is Created in Synapses
29:04 - Why are Synapses Chemical?
31:06 - How Tom Began his Neuroscience Career
39:32 - Emerging Tools that Allowed for Researching Synapses
44:16 - Discerning Protein Function
49:36 - Discovering Mechanism through Data
52:15 - Isolating Membrane Proteins
55:09 - Voltage Gates
57:50 - How Synapses Change Over Time
1:02:14 - How are Synapses Formed?
1:10:22 - The Need for New Tools
1:11:53 - Implications for Drug Discovery
1:17:07 - Exploring the Mouse Hippocampus
1:22:35 - Tom’s Work on LDL Receptors
1:26:33 - Understanding Molecular Logic

#neuroscience #neuroplasticity #nobelprize #hubermanlab #neurobiology

How do engineers solve problems that seem to violate the laws of physics?

In this episode, we speak with Dan Gelbart, a prolific inventor and precision engineer, about what it really means to work at the limits of physical law. From lasers and optical systems to ultra-precision manufacturing and semiconductor tools, Gelbart has spent decades designing systems where nanometers, noise, and nonlinearities matter, and where small misunderstandings of physics can block real progress.

We discuss the story of the first working laser, built by Theodore Maiman, and why it succeeded only after questioning widely accepted assumptions. Gelbart explains how many “impossible” engineering problems aren’t forbidden by physics at all: they’re constrained by measurement errors, incomplete models, or failure to explore edge cases like pulsed operation, material effects, and boundary conditions.

We explore precision metrology, high-resolution imaging for satellite systems, the culture of engineering education, and the difference between a true physical limit and a design constraint. Gelbart reflects on why mastering fundamentals, mechanics, optics, electromagnetism, matters more than chasing trends, and how breakthroughs often come from carefully re-examining what others assume cannot be done.

Whether you’re interested in physics, engineering, semiconductor manufacturing, lasers, or the philosophy of technological innovation, this conversation offers a rigorous look at how engineers operate at the edge of what nature allows, and sometimes push beyond what others think is possible.

Follow us for more technical interviews with the world’s greatest scientists:
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Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)

Timestamps:
00:00 - Intro
01:35 - The World’s First Laser
07:53 - Solving Impossible Problems
23:37 - Underestimated Problems
39:36 - Dan’s Backstory
43:33 - How to Teach Yourself Anything
47:03 - Shortcomings of Modern Education
53:19 - Developing the Optical Tape Recorder
1:01:39 - Machine Obsolescence
1:08:04 - Why are Scientists Often Bad Businessmen?
1:15:17 - Developing Medical Devices
1:24:52 - Untapped Potential of Materials Science
1:30:47 - Accidental Inventions
1:35:37 - Surviving Bureaucracy
1:42:27 - Humanoid Robots
1:44:11 - Managing an Engineering Team
1:50:06 - Developing the First Good Mobile Data Terminal
1:54:15 - Building an Environment for Solving Problems
2:02:18 - Why Aren’t We Inventing New Things?

#machining #cnc #precisionengineering #metrology #machineshop

How does experience rewire the brain—and why is vision the ideal system for understanding neuroplasticity?

In this episode, we speak with Mark Bear, MIT neuroscientist and a pioneer in the study of experience-dependent plasticity. Bear explains how the visual cortex became a model system for uncovering the synaptic mechanisms that allow the brain to change, adapt, and learn, especially during early development.

We explore how visual experience shapes neural circuits, why the brain undergoes critical periods of heightened plasticity, and what classic experiments in visual deprivation revealed about how connections are strengthened or lost. Bear walks us through the discovery of binocular vision in the cortex, the role of inhibition in closing critical periods, and how these ideas reshaped our understanding of learning and memory.

The conversation also covers modern views of cortical plasticity, including perceptual learning, visual recognition memory, and how the brain distinguishes familiar from novel stimuli. Bear discusses how insights from vision extend to broader questions about brain development, neurological disorders such as amblyopia, and whether adult plasticity can be reopened.

Whether you’re interested in neuroscience, brain development, neuroplasticity, learning and memory, or the biology of vision, this episode offers a clear and authoritative look at how experience shapes the brain at the level of neural circuits and synapses.

Follow us for more technical interviews with the world’s greatest scientists:

Twitter: https://x.com/632nmPodcast
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Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
00:54 - Neuroplasticity in the Visual Cortex
05:45 - Critical Periods for Neuroplasticity
16:50 - Brain Development in Blind People
19:25 - Hallucinations and Sensory Deprivation
25:36 - How Mark’s Vision Disorder Led Him to a Career in Neuroscience
31:35 - Intro to the Visual System
35:52 - Visual System Processing
40:52 - Pop Science Neuroplasticity
45:00 - Memory Enhancing Pharmaceuticals
50:18 - Other Ways of Modifying the Visual Cortex
1:14:50 - Declarative vs Procedural Memory
1:22:36 - Neural Networks and Memory Degradation
1:25:16 - Neuron Transplants and Neurogenesis
1:28:58 - Brain-Machine Interfaces
1:33:46 - Most Prominent Issues in the Field
1:40:47 - Fragile X Syndrome
1:51:10 - Advice for Young Scientists

#neuroplasticity #neuroscience #hubermanlab #braindevelopment #brainplasticity

How can flat surfaces shape light as powerfully as bulky lenses?

In this episode, we speak with Federico Capasso, Harvard physicist and pioneer of metasurfaces, metalenses, and nanophotonics. Capasso traces the path from his work at Bell Labs on quantum cascade lasers to the invention of metasurface optics, showing how a practical challenge—collimating light without traditional lenses—sparked a new way to control light.

We explore the physics behind metasurfaces and generalized Snell’s law, explaining how subwavelength structures enable precise control of wavefronts, phase, and polarization beyond what conventional diffractive optics or Fresnel lenses allow. Capasso clarifies common misconceptions, contrasts metasurfaces with diffraction gratings and phased arrays, and emphasizes the importance of physical intuition and simplicity.

The conversation covers metalenses, polarization optics, holography, and how these ideas moved from theory to large-scale manufacturing in semiconductor foundries, ultimately appearing in consumer devices like smartphones. Capasso also reflects on commercialization, the legacy of Bell Labs, and the blurred boundary between basic science and real-world technology.

Whether you’re interested in metasurfaces, metalenses, nanophotonics, optics, or the process behind breakthrough discoveries, this episode offers a clear and insightful look at how modern optical physics becomes transformative technology.

Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/

Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYin

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)

Timestamps:
00:00 - Intro
01:53 - Transition from Bell Labs to Harvard
09:45 - Generalized Snell's Law
21:25 - Facing the Diffractive Optics Community
31:07 - Benefits of Well-Rounded Education
45:16 - Metalenses
55:55 - Can AI do Physics?
1:07:39 - Industry vs Academia
1:11:44 - Nanophotonics
1:14:44 - What Allowed for the First Metalenses?
1:17:38 - 632nm and Other Lasers
1:20:47 - Quantum applications of Metalenses
1:30:14 - Quantum Entanglement Redefines Spacetime
1:43:22 - Stokes Parameters
1:48:28 - Limits of Metasurface Pixel Size
1:55:20 - Advice for Young Scientists
2:01:45 - Critique of the H Index

#metasurface #metalenses #quantumphysics #materialscience #optics #photonics