Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: It is February twenty twenty six, and I actually want
Speaker 2: to start by asking you to just take a second.
Speaker 2: And you know, exist sure, that sounds easy enough, is it? Though?
Speaker 2: I mean, look around you, look at the device you
Speaker 2: are listening to this on, or just look out the window,
Speaker 2: at the clouds moving, the traffic flowing by. Right. We
Speaker 2: are living through a moment right now, mid February twenty
Speaker 2: twenty six where the tectonic plates of computing are kind
Speaker 2: of shifting right beneath our feet. And I don't mean
Speaker 2: a new smartphone release or a slightly faster chatbot.
Speaker 3: No, definitely not. We are talking about the invisible engine
Speaker 3: that really drives modern civilization exactly.
Speaker 2: And the crazy part to me is that most of
Speaker 2: us are looking in the completely wrong direction. Like everyone
Speaker 2: is watching generative AI, which is huge obviously, don't get
Speaker 2: me wrong, but there is something happening in the labs
Speaker 2: at Sandia National Laboratories. Right now, that challenges the fundamental
Speaker 2: physics of how we compute reality.
Speaker 3: It is a subtle shift, but an absolutely seismic one.
Speaker 3: It's one of those moments in scientific history where you
Speaker 3: might look back ten years later and realize, oh, that
Speaker 3: was the day the rules changed.
Speaker 2: Yeah. And to understand why it matters so much, we
Speaker 2: have to talk about a contradiction, a contradiction that I
Speaker 2: think most of us, myself included, honestly, have just accepted
Speaker 2: as a fundamental law of the universe. It basically goes
Speaker 2: like this. There are creative tasks and there are rigid tasks.
Speaker 3: Right. The classic dichotomy it is the split between the
Speaker 3: artist and the accountant.
Speaker 2: Essentially exactly. On one side, you have art, language, poetry, intuition.
Speaker 2: We tend to think of those as things the human
Speaker 2: brain does exceptionally well. Biological intelligence is messy, right, It's fuzzy,
Speaker 2: it's highly creative.
Speaker 3: And then on the exact opposite side you have the
Speaker 3: rigid stuff complex mathematics, physics, simulations calculating the trajectory of
Speaker 3: a Falcon nine rocket, or modeling how a massive suspension
Speaker 3: bridge holds up weight in a category five hurricane, and.
Speaker 2: We think of those as things conventional computers.
Speaker 3: Do well exactly, logic processors, silicon chips, cold hard binary calculation.
Speaker 3: That has been the prevailing wisdom for what seventy years now.
Speaker 3: Brains are for thinking, computers are for calculating.
Speaker 2: Well, we are here to tell you that as of
Speaker 2: this week, that assumption has been flipped completely upside down.
Speaker 2: We are doing an analysis today on breaking news coming
Speaker 2: out of Sandy and Natural laboratories, specifically a new study
Speaker 2: published in Nature Machine Intelligence that basically says we were
Speaker 2: entirely wrong.
Speaker 3: The headline itself is startling. Researchers have found that brain
Speaker 3: inspired computers, what we call neuromorphic hardware, can now solve
Speaker 3: super computer life math problems.
Speaker 2: And let's be super specific here. We aren't talking about
Speaker 2: basic arithmetic. We aren't talking about a chip calculating a
Speaker 2: fifteen percent tip at a restaurant. Yeah, we are talking
Speaker 2: about partial differential equation.
Speaker 3: Pdase, the really really hard stuff, the kind of math
Speaker 3: that describe how the universe flows and changes over time.
Speaker 2: And the kicker here, the thing that makes this such
Speaker 2: a huge deal is that this was previously thought to
Speaker 2: be completely impossible for this specific type of hardware.
Speaker 3: Impossible is literally the word they used with a widely
Speaker 3: accepted fact in the computer science community that you simply
Speaker 3: could not do this kind of rigorous math on a
Speaker 3: neuromorphic chip.
Speaker 2: But they did it. And that breakthrough is what we
Speaker 2: are going to explore today, because it is not just
Speaker 2: about building a faster calculator, is it not at all?
Speaker 3: It is a breakthrough in efficiency, in overall capability, and
Speaker 3: perhaps most importantly, and this is the part that genuinely
Speaker 3: keeps me of at night, it's a breakthrough in understanding
Speaker 3: biological intelligence itself.
Speaker 2: So here is our mission for this exploration. We are
Speaker 2: going to unpack how a computer modeled physically after biological
Speaker 2: gray matter, after the squishy stuff in our skulls, can
Speaker 2: outperform traditional logic processors at their own game.
Speaker 3: We will be pulling directly from that key study in
Speaker 3: Nature Machine intelligence, along with reports from the Department of
Speaker 3: Energy and the technical notes provided by Sandy and National Laboratories.
Speaker 2: And just so you understand the stakes here as you listen,
Speaker 2: this isn't just academic, This isn't just about math geeks
Speaker 2: getting hyped about a new algorithm in a lab.
Speaker 3: No, the real world applications are massive.
Speaker 2: Right, we are talking about national security, We're talking about
Speaker 2: global climate modeling and potentially unlocking the underlying secrets of
Speaker 2: diseases like Alzheimer's.
Speaker 3: It brilliantly connects the very large nuclear stockpile simulations to
Speaker 3: the very small down to the individual neurons firing in
Speaker 3: your brain right now as you process my voice.
Speaker 2: So buckle up. We are going on a deep exploration
Speaker 2: of the impossible calculation.
Speaker 3: I'm ready.
Speaker 2: Let's start with what I like to call the intuition trap,
Speaker 2: because I thin I think this is where most of
Speaker 2: us get entirely tripped up when we compare computers to brains.
Speaker 3: It is a very common trap. It is actually closely
Speaker 3: related to something called morvex paradox in early AI research.
Speaker 2: Oh right, give us the rundown on the paradox.
Speaker 3: Well. In the early days of artificial intelligence back in
Speaker 3: the eighties, researchers assume the hard stuff to teach a
Speaker 3: computer would be high level reasoning, playing chess, proving logic theorems,
Speaker 3: that sort of thing. They thought the easy stuff would
Speaker 3: be basic sensor motor tasks, walking across the room, folding
Speaker 3: a towel, or just recognizing a face, and.
Speaker 2: It turned out to be the exact opposite, exactly.
Speaker 3: A nineteen eighties computer could beat a chess grand master,
Speaker 3: but you couldn't get a robot to walk over a
Speaker 3: pile of laundry without it immediately falling on its face.
Speaker 3: The stuff that feels hard to us, like logic and
Speaker 3: high level math, is actually computationally simple for a machine.
Speaker 3: But the stuff that feels completely easy to us, perception
Speaker 3: fluid movement, is a computational nightmare.
Speaker 2: So let's apply that directly to this Sandia study. I
Speaker 2: want you to picture something. As you listen, Imagine you
Speaker 2: are standing on a tennis court. It is a beautiful
Speaker 2: sunny day. Someone serves a ball to you. It is
Speaker 2: flying across the net at I don't know, one hundred
Speaker 2: miles an.
Speaker 3: Hour, fast, very fast.
Speaker 2: You see it, You step forward, you plant your foot,
Speaker 2: you swing your racket, and whack you return the serve
Speaker 2: right down the line.
Speaker 3: A tech book example of censorimotor integration.
Speaker 2: Now, be honest, how does that feel to you, to
Speaker 2: the human being actually doing it?
Speaker 3: It feels instinctive, It feels purely reactive. You don't stand
Speaker 3: there with a notepad and calculate the wind speed. You
Speaker 3: don't pull out a protractor to measure the angle of
Speaker 3: the sun. You just do it. It feels entirely easy.
Speaker 2: Right, It feels effortless. Now, imagine I sit you down
Speaker 2: at a sterile desk with a blank sheet of paper
Speaker 2: and a pencil, and I say, okay, I need you
Speaker 2: to solve this partial differential equation describing the fluid dynamics
Speaker 2: of air resistance on a fuzzy sphere traveling at forty
Speaker 2: five meters per second with a localized grosswind.
Speaker 3: Yeah, most humans, myself certainly included, would break out in
Speaker 3: a cold, sweaty meatia. That feels incredibly hard.
Speaker 2: Exactly, hitting the ball feels easy. The math on paper
Speaker 2: feels hard. But here is where the experts at Sandia,
Speaker 2: specifically brad Amone, one of the computational neuroscientists on this project,
Speaker 2: say we are totally completely wrong in how we view that.
Speaker 3: This is the insight that really flips the script. Em
Speaker 3: Moone points out that our intuition about effort is an illusion.
Speaker 3: In reality, the motor control required to hit that moving
Speaker 3: tennis ball involves what he calls EXAs scale level physics
Speaker 3: computations EXAs scale.
Speaker 2: That is a word usually reserved for the absolute most
Speaker 2: powerful massive supercomputers on Earth. We are talking quintillions of
Speaker 2: calculations per single.
Speaker 3: Second, precisely. Think about what is actively happening under the
Speaker 3: hood when you swing that racket. It is not magic,
Speaker 3: It is hard physics. Your brain is actively processing the
Speaker 3: trajectory of the ball in three D space using stereoscopic vision.
Speaker 3: It is accounting for wind resistance, It is calculating gravity.
Speaker 2: And the ball has spin on it too, right, the
Speaker 2: Magnus effect.
Speaker 3: It curs in the air, right, it has spin, And simultaneously,
Speaker 3: while tracking all that, your brain is adjusting this specific
Speaker 3: tension in hundreds of individual muscles, your bicep, your triceps,
Speaker 3: your lads, your calves. It is constantly balancing your center
Speaker 3: of mass so you don't tip over. It is predicting
Speaker 3: where the ball will be in exactly zero point five seconds,
Speaker 3: all in real time, all within milliseconds.
Speaker 2: And it is doing all of that while the ball
Speaker 2: is still actively in the air.
Speaker 3: If you tried to program a traditional robot to do
Speaker 3: that using explicit math, literally writing out the equations for
Speaker 3: every single variable, every gust of wind, every tiny muscle twitch,
Speaker 3: you would need a staggering amount of processing power. You
Speaker 3: would be solving complex physics equations explicitly.
Speaker 2: But our brains do it how because I definitely don't
Speaker 2: feel like a supercomputer when I'm playing tennis. I just
Speaker 2: feel like I'm swinging my arm.
Speaker 3: We do it cheaply, we do it incredibly efficiently. The
Speaker 3: quote from brad Amon in the material is fantastic. He says,
Speaker 3: these are very sophisticated computations. They are exast scale level
Speaker 3: problems that our brains are cap of doing very cheaply.
Speaker 2: Cheaply meaning low energy, barely.
Speaker 3: Any energy at all. Your brain runs on about twenty wants.
Speaker 2: Of power, twenty watts like a dim light ball, the very.
Speaker 3: Dim light bulb, maybe refrigerator light. Meanwhile, a supercomputer capable
Speaker 3: of doing those same exact physics calculations explicitly simulating the air,
Speaker 3: the ball the human body would require megawatts of power.
Speaker 3: It would literally need its own dedicated power plant.
Speaker 2: So the massive revelation here is that our brains are
Speaker 2: already solving these complex physics equations. We are solving partial
Speaker 2: differential equations constantly, just by existing in the physical world.
Speaker 2: We just aren't consciously aware of the math.
Speaker 3: We are walking talking physics engines. We just happen to
Speaker 3: have a very user friendly interface that hides all the
Speaker 3: complex code from our conscious mind.
Speaker 2: That is such a cool way to think about it.
Speaker 2: We are running the physics code in.
Speaker 3: The background constantly. If you casually catch a set of
Speaker 3: keys someone throws at you from across the room, you
Speaker 3: just organically solved a parabolic arc differential equation. If you
Speaker 3: merge your car onto a busy highway, you're rapidly calculating
Speaker 3: relative velocities and friction coefficients.
Speaker 2: Okay, so we have dropped this term a few times now.
Speaker 2: Partial differential equations or PDEs. I want to pause here
Speaker 2: for a second. We need to define this because this
Speaker 2: is the hard math that the new computer chips are
Speaker 2: finally solving. What exactly are these equations?
Speaker 3: So simply put, PDEs are the mathematical language of the
Speaker 3: physical world.
Speaker 2: The language of reality itself.
Speaker 3: Essentially, yes, in regular high school algebra, you might solve
Speaker 3: for x and x is just a single static number
Speaker 3: like ex equals five. In a partial differential equation, the
Speaker 3: solution isn't a single number. The solution is a function.
Speaker 3: It describes how things continuously change over space and time,
Speaker 3: because in the actual universe nothing is perfectly static. Everything
Speaker 3: is moving, flowing, heating up, cooling down, stressing, bending. PDEs
Speaker 3: are the equations we use to mathematically map those constant changes.
Speaker 2: Can you give us some conc create examples from the
Speaker 2: source material? What are we normally using these for in
Speaker 2: the real world.
Speaker 3: The classic example is forecasting weather patterns. That is a
Speaker 3: massive PDE problem. You have air, pressure, temperature, moisture, wind speed,
Speaker 3: all dynamically interacting over the entire surface of the globe.
Speaker 2: And they all directly affect each other. Right If the
Speaker 2: temperature drops, the pressure changes, which immediately changes the wind
Speaker 2: direction exactly.
Speaker 3: It is a tightly coupled system. Another huge example is
Speaker 3: fluid dynamics, how water flows through a complex municipal pipe system,
Speaker 3: or how air flows over the curved weghing of a
Speaker 3: commercial airplane. Or think about structural mechanics calculating exactly how
Speaker 3: a steel building material will stress and eventually snap under
Speaker 3: a heavy physical.
Speaker 2: Load okay that paints a clear picture.
Speaker 3: Or modeling invisible electromagnetic fields. These are all strictly governed
Speaker 3: by partial differential equations.
Speaker 2: And the key thing for everyone to understand here is
Speaker 2: that these are not simple arithmetic problems. You can't just
Speaker 2: plug them into a standard desktop calculator and hit enter.
Speaker 3: No, they are incredibly, incredibly resource intensive. The way we
Speaker 3: solve them traditionally in computer science is by breaking the
Speaker 3: physical world down into tiny little geometric cubes. We call
Speaker 3: it a mesh, like pixelating reality exactly like that, you
Speaker 3: pixelate reality into a highly detailed three D grid. Then
Speaker 3: you have the computer calculate the physics for every single
Speaker 3: little isolated cube and simultaneously calculate how every single cube
Speaker 3: interacts with its immediate neighbors. To do that at a
Speaker 3: high enough resolution to be useful, like safely simulating a
Speaker 3: nuclear explosion or mapping a global climate model, you need
Speaker 3: massive energy hungry supercomputers. You need to painfully crunch billions
Speaker 3: and billions of numbers just to get a single answer.
Speaker 2: Because you're simulating millions of little microscopic interactions happening all
Speaker 2: at the exact same time exactly.
Speaker 3: And that has always been the fundamental bottleneck in science.
Speaker 3: We have the math, We've had the maths for hundreds
Speaker 3: of years. Newton and Liivenis started this whole thing, but
Speaker 3: actually running the math that costs an absolute fortune in
Speaker 3: energy and hardware infrastructure.
Speaker 2: Which perfectly brings us to the hardware side of this
Speaker 2: breakthrough because the solution Sandy have found wasn't just hey,
Speaker 2: let's build a slightly bigger computer. It was let's build
Speaker 2: a completely different kind of.
Speaker 3: Computer, right, and this is where we finally get into
Speaker 3: neuromorphic computing.
Speaker 2: Neuromorphic it sounds like something straight out of a futuristic
Speaker 2: sci fi novel.
Speaker 3: It literally just means taking the form of the brain,
Speaker 3: neuro meaning brain morphic meaning form.
Speaker 2: So how is this physically different from the laptop I
Speaker 2: have sitting right here in front of me, or the
Speaker 2: massive server farms running the internet.
Speaker 3: Your laptop, your phone, and the massive supercomputers we have
Speaker 3: relied on for decades are all based on what we
Speaker 3: call the von Neumann architecture.
Speaker 2: Okay, let's unpack that.
Speaker 3: Von Neuman John von Neuman brilliant mathematician. In the nineteen forties,
Speaker 3: he proposed a logical structure for computers that we literally
Speaker 3: still use today. In a traditional computer, you have very
Speaker 3: clear physical separation. You have the processor, the CPU, the
Speaker 3: logic center where the math happens, and you have the
Speaker 3: memory the ram where the data is actually stored.
Speaker 2: So they live in different houses.
Speaker 3: They live in entirely different neighborhoods. On a motherboard, when
Speaker 3: the computer wants to do a single calculation, it has
Speaker 3: to physically go to the memory, grab a specific piece
Speaker 3: of data, drive it all the way over to the processor,
Speaker 3: do the math, and then drive the answer all the
Speaker 3: way back to memory to store it.
Speaker 2: And that driving, that physical moving of electronic data back
Speaker 2: and forth is the main problem.
Speaker 3: It is the infamous Vunnowmen bottleneck. It takes time, and crucially,
Speaker 3: it takes a massive amount of energy. A surprisingly huge
Speaker 3: percentage of the energy your computer uses isn't actually doing
Speaker 3: any math. It's just moving numbers back and forth across
Speaker 3: tiny wires.
Speaker 2: It's like having a chef who has to walk three
Speaker 2: blocks to the grocery store every single time they need
Speaker 2: a pinch AsSalt or a single egg.
Speaker 3: That is a perfect analogy. It is incredibly inefficient. If
Speaker 3: you are trying to rapidly cook a massive ten course banquet,
Speaker 3: you end up spending way more time and energy walking
Speaker 3: than you do actually cooking.
Speaker 2: So how is a neuromorphic chip.
Speaker 3: Different neuromorphic computers are modeled physically and architecturally after the
Speaker 3: human brain. Instead of rigid silicon logic gits, they have
Speaker 3: artificial neurons and synapses. They process information in parallel, all
Speaker 3: at once, rather than one tiny step at a time.
Speaker 3: And crucially, and this is the absolute key to the
Speaker 3: energy savings, we see the memory and the processing happen
Speaker 3: in the exact same physical location.
Speaker 2: Just like the biological brain. My memories aren't stored in
Speaker 2: the separate biological hard drive in my foot, They are
Speaker 2: right there in the network of neurons where the thinking
Speaker 2: happens exactly.
Speaker 3: This is referred to in the industry as in memory computing.
Speaker 3: You don't move the data to the processor. You process
Speaker 3: the data directly where it already lives.
Speaker 2: That makes total sense intuitively, So why haven't we been
Speaker 2: using these brain chips for physics all along? If they
Speaker 2: are so incredibly efficient, why are we still building massive,
Speaker 2: power hungry GPU clusters, Because.
Speaker 3: For a very long time there was a heavy stigma
Speaker 3: attached to this kind of hardware in the scientific community.
Speaker 2: A stigma really.
Speaker 3: Well, maybe limitation is a slightly better word. Because these
Speaker 3: chips work organically like brains, they are inherently.
Speaker 2: Noisy, noisy what does that mean for a computer?
Speaker 3: They use spikes of electricity to communicate, very much like
Speaker 3: biological neurons firing. They aren't always wanted to invent rigidly
Speaker 3: precise in the way a traditional digital calculator is. A
Speaker 3: standard calculator gives you exactly five point zero zero zero
Speaker 3: zero zero zero zero zero every single time. Neuromorphic chip
Speaker 3: might give you an output that is statistically five, but
Speaker 3: it fluctuates slightly. It has inherent mathematical randomness.
Speaker 2: Oh I see. So the broader scientific community thought they
Speaker 2: were only really good for fuzzy.
Speaker 3: Tasks, right, task like pattern recognition showing an artificial intelligence
Speaker 3: a thousand photos and asking is this a cat or
Speaker 3: a dog? Neuromorphic chips are absolutely fantastic at that. They
Speaker 3: could look at the gestalt, the whole messy picture and
Speaker 3: make a fast, efficient judgment call.
Speaker 2: But you definitely wouldn't trust a fuzzy judgment call to
Speaker 2: calculate the structural integrity of a suspension bridge you're driving over.
Speaker 3: Precisely. That was the core assumption. The skepticism was incredibly deep.
Speaker 3: It was widely assumed that because they lacked that rigid
Speaker 3: linear decimal point precision, because they were so called noisy.
Speaker 3: They simply couldn't handle rigorous math like PDEs. You don't
Speaker 3: want a fuzzy ballpark answer when you are dealing with
Speaker 3: critical nuclear physics. You want the exact precise answer.
Speaker 2: So everyone just naturally assumed neuromorphic chips are for AI
Speaker 2: looking at cat photos and giant traditional supercomputers are for
Speaker 2: serious match.
Speaker 3: Until right now, until this publication in February twenty twenty six.
Speaker 2: Enter Brad Siloman and Brad Amone, the two.
Speaker 3: Brads, the dynamic duo at Sandy National Labs.
Speaker 2: So what did they actually do to break this assumption?
Speaker 2: Did they have to invent and manufacture a completely new
Speaker 2: kind of physical chip.
Speaker 3: Interestingly, no, the hardware itself wasn't the primary novelty hear,
Speaker 3: They actually used existing state of the art neuromorphic architecture.
Speaker 3: The massive breakthrough was the algorithm. They created a fundamentally
Speaker 3: new way of operating the hardware.
Speaker 2: Okay, walk us through this. How do you force a noisy,
Speaker 2: fuzzy brain chip to do rigorous hard math.
Speaker 3: They essentially realized that the physical structure of these neuromorphic chips,
Speaker 3: the specific way the artificial neurons connect and fire, could
Speaker 3: be mathematically mapped directly to the structure of partial differential equations.
Speaker 2: Wait, so the math itself physically looks like the brain
Speaker 2: structure in.
Speaker 3: A highly abstract way. Yes, this is the fascinating part
Speaker 3: of the paper. They took a circuit model that was
Speaker 3: already very well known in neuroscience. It is a model
Speaker 3: of how biological cortical networks work.
Speaker 2: Cortical networks being the outer layer of the brain right.
Speaker 3: Right, the cerebral cortex, the advanced thinking part of the brain.
Speaker 3: This specific circuit model had been kicking around in academia
Speaker 3: for about twelve years. Neuroscientists used it strictly to understand
Speaker 3: how our biological neurons inhibit and excite each other to
Speaker 3: process visual or sense reinformation. Okay, but nobody had ever
Speaker 3: deeply looked at that biological model and said, hey, wait
Speaker 3: a minute, this exact pattern of inhibition and excitation looks
Speaker 3: exactly like the mathematical operations we used to solve diffusion
Speaker 3: equations on a computer.
Speaker 2: Ah. So that is the twelve year gap mentioned in
Speaker 2: the Sander report.
Speaker 3: Exactly, the information was already there. The biological circuit was
Speaker 3: heavily documented, but the direct link to applied mathematics wasn't
Speaker 3: made until Thielman and Amone finally connected the dots.
Speaker 2: It is a classic case of academic silos, isn't it.
Speaker 2: The neuroscientists are in one building studying biology, the applied
Speaker 2: mathematicians are in another building studying equations, and rarely.
Speaker 3: Do they ever sit down over coffee and realize they
Speaker 3: were looking at the exact same map from two different angles.
Speaker 3: The neuroscientists were looking at it strictly as a model
Speaker 3: of wet biology. The mathematicians were blindly looking for a
Speaker 3: faster way to solve dry equations. Fielman and Emohen realized
Speaker 3: it was fundamentally the same mechanism.
Speaker 2: So they applied this new outme algorithm. They essentially program
Speaker 2: the neuromorphic chip to behave exactly like this biological cortical network.
Speaker 2: And what actually happened.
Speaker 3: They successfully proved that these brain like systems can rapidly
Speaker 3: solve sparse finite element problems that is the highly technical
Speaker 3: term for the underlying math behind these complex simulations, and
Speaker 3: they could do it incredibly efficiently.
Speaker 2: They broke the cardinal rule. They got a fuzzy computer
Speaker 2: to do hard, precise math.
Speaker 3: And they got the right answers. They empirically showed that
Speaker 3: you can actually control the inherent noise of the chip.
Speaker 3: You can mathematically tune the system so that the precision
Speaker 3: is high enough for rigorous scientific work.
Speaker 2: That is just incredible. But I have to play Devil's
Speaker 2: advocate for a second here just to ground this. Please
Speaker 2: do why does it actually matter how we solve the math?
Speaker 2: I mean, if I have a massive, traditional supercomputer that
Speaker 2: can reliably simulate a nuclear explosion and it works perfectly fine,
Speaker 2: why do I urgently need a neuromorphic brain chip to
Speaker 2: do it instead? Is it just because it is scientifically elegant?
Speaker 3: It is deeply elegant. Yes, but no, that is absolutely
Speaker 3: not the primary driver. The driver is the global energy crisis.
Speaker 2: Okay, let's get into that.
Speaker 3: We hinted at this earlier with the megawatts comparison. Traditional
Speaker 3: supercomputers are massive energy hogs. We are talking about colossal
Speaker 3: data centers that consume as much electricity as entire small cities.
Speaker 2: I have read a lot about this with the recent
Speaker 2: AI boom. The sheer amount of power needed just to
Speaker 2: train these new language models is staggering.
Speaker 3: It is, and traditional scientific simulation is just as bad.
Speaker 3: If not worse. When you run a massive simulation, let's say,
Speaker 3: modeling the entire global climate for the next one hundred
Speaker 3: years at a very high resolution, you are burning a
Speaker 3: tremendous amount of physical power. It is wildly expensive, and paradoxically,
Speaker 3: it has a massive carbon footprint.
Speaker 2: Right you are literally burning coal and gas to power
Speaker 2: the computer that is modeling the disastrous effects of burning
Speaker 2: coal and gas.
Speaker 3: It is a deeply vicious cycle. The neuromorphic advantage, as
Speaker 3: they call it, is raw efficiency because these brain like
Speaker 3: systems mimic biological computation, and because biological brains are incredibly efficient.
Speaker 3: Remember twenty watts.
Speaker 2: The dim refrigerator light.
Speaker 3: Bulb, the light bulb. If we can successfully move these
Speaker 3: massive math problems over to neuromorphic hardware, we can logically
Speaker 3: solve the exact same problems using a tiny fraction of
Speaker 3: the energy. We're talking about orders of magnitude less power consumption.
Speaker 2: That completely changes the game for who can actually forward
Speaker 2: to run these simulations.
Speaker 3: Right absolutely, it totally opens the door to a true
Speaker 3: neuromorphic supercomputer. Imagine a machine with the raw calculating power
Speaker 3: of today's giants, something like the Frontier supercomputer, but it
Speaker 3: physically fits in a much smaller room and doesn't require
Speaker 3: a dedicated hydro electric dam just to turn it on.
Speaker 2: And this naturally leads us to the highly specific applications
Speaker 2: mentioned in the Department of Energy reports. Sandia National Laboratories
Speaker 2: isn't just doing this for fun academic research. They have
Speaker 2: a very specific directive.
Speaker 3: Their core mission is national security, specifically working with the
Speaker 3: National Nuclear Security Administration the NSA.
Speaker 2: Which sounds very serious and very high stakes.
Speaker 3: It is the highest stakes imaginable. They are directly responsible
Speaker 3: for the safety, security, and reliability of the entire United
Speaker 3: States nuclear stockpile. Now you have to remember how this
Speaker 3: works today. We don't verify nuclear weapons by physically blowing
Speaker 3: them up anymore. We haven't done a live test since
Speaker 3: the early.
Speaker 2: Nineties, right, No more underground detonations in the Nevada desert exactly.
Speaker 3: So how do we actually know the aging weapons still work?
Speaker 3: How do we know the complex internal mechanisms are still
Speaker 3: safe to store? We test them entirely inside.
Speaker 2: Computers, pure simulation, massive.
Speaker 3: Incredibly complex, hyper precise physics simulations to ensure the nuclear
Speaker 3: deterrent is both safe and reliable. This requires the absolute
Speaker 3: peak of human computational power. It is arguably the most
Speaker 3: difficult and resource intensive computing task on the.
Speaker 2: Planet, and currently doing that burns a massive amount of
Speaker 2: energy on traditional silicon.
Speaker 3: Moving these critical simulations to neuromorphic hardware is a massive
Speaker 3: strategic move. It saves power, yes, but it also potentially
Speaker 3: drastically speeds up the analysis. If you can process these
Speaker 3: incredibly dense physics interactions in parallel exactly like a biological
Speaker 3: brain does, you might be able to run high stake
Speaker 3: security scenarios vastly faster than a conventional linear logic processor
Speaker 3: ever could.
Speaker 2: So it's not just about being green and saving the
Speaker 2: planet's power grid. It is about being tactically better.
Speaker 3: Faster, leaner, and smarter. In a national security context, especially
Speaker 3: with global tensions, computational speed is absolutely.
Speaker 2: Everything, and beyond the classified nuclear stuff, the source material
Speaker 2: explicitly mentions broader impacts for the rest of us. We
Speaker 2: briefly touched on weather forecasting.
Speaker 3: Climate modeling is a truly huge one. We desperately need
Speaker 3: better higher resolution models to fully understand climate change. We
Speaker 3: need to know exactly what happens regionally if the ocean
Speaker 3: temperature rises by one point five degrees versus two degrees.
Speaker 3: We need to accurately model fluid cloud form, which is
Speaker 3: notoriously difficult for traditional computers.
Speaker 2: And doing that complex fluid math on a brain based
Speaker 2: chip is vastly cheaper.
Speaker 3: Much much cheaper. It means researchers can run significantly more models,
Speaker 3: we can test far more variable simultaneously, we can iterate
Speaker 3: our predictions faster. Neuromorphic computing could realistically break the cycle
Speaker 3: of high carbon computing blocking vital climate research.
Speaker 2: That is a remarkably hopeful vision for the near future.
Speaker 3: It is it's a rare case of advanced technology directly
Speaker 3: solving the foundational problems created by older technology.
Speaker 2: I want to pivot the conversation now. We have talked
Speaker 2: deeply about the hardware, the complex math, the global energy implications.
Speaker 2: But there is a part four to this Sandia story
Speaker 2: that I find arguably the most fascinating of all. It
Speaker 2: is what I call the mirror effect. Ah.
Speaker 3: Yes, this is where we cross over from physics and
Speaker 3: get deeply philosophical and highly medical.
Speaker 2: So we are currently using advanced technology to mimic the
Speaker 2: biological brain to do hard math. But does this process
Speaker 2: actually teach us anything about the biological brain? Itself.
Speaker 3: The neuroscience experts involved say absolutely yes, it works both ways.
Speaker 3: It is a brilliant two way street of discovery.
Speaker 2: Explain how that work.
Speaker 3: By actively forcing a physically brain like structure, this neuromorphic chip,
Speaker 3: to do high level math, we are essentially running a
Speaker 3: perfectly controlled scientific experiment on how our own biological hardware
Speaker 3: naturally processes information.
Speaker 2: We are successfully reverse engineering ourselves in.
Speaker 3: A very real sense. Yes, remember earlier we noted that
Speaker 3: thom And and Emone specifically used a biological cortical network algorithm.
Speaker 3: The mathematically proved that this natural biological structure inherently solves
Speaker 3: partial differential equations.
Speaker 2: Right, the diffusion equations mapped perfectly to the neurons.
Speaker 3: This logically implies, and this is the really big conceptual leap,
Speaker 3: that our own human brains, which obviously have this exact
Speaker 3: cortical structure, might be naturally solving PDEs as their primary
Speaker 3: foundational way of functioning in the world.
Speaker 2: So when I am just sitting here, thinking, or moving
Speaker 2: my hand or just existing, my physical neurons are essentially
Speaker 2: organically crunching complex differential equations.
Speaker 3: That is the leading hypothesis emerging from this. It beautifully
Speaker 3: takes us right back to the tennis ball analogy. You
Speaker 3: aren't just reacting to a fuzzy visual stimulus, you are
Speaker 3: actively organically computing high level physics.
Speaker 2: And if that is fundamentally true, it leads to a
Speaker 2: truly radical new theory about human brain diseases that the
Speaker 2: researcher is brought up.
Speaker 3: The source material provocatively called this the concept of diseases
Speaker 3: of computation.
Speaker 2: Diseases of computation that sounds almost robotic, like a sci
Speaker 2: fi dystopia.
Speaker 3: It does sound a bit clinical, but it is an
Speaker 3: incredibly powerful new framework. Brade Moon explicitly raises this specific point.
Speaker 3: He suggests that devastating neurological conditions, things like Alzheimer's and
Speaker 3: Parkinson's disease, might not just be simple biological failures. They
Speaker 3: might actually be algorithmic failures.
Speaker 2: What does that mean in practical terms?
Speaker 3: Think of it this way. If the healthy brain's primary
Speaker 3: job is to constantly run these massive physics equations to
Speaker 3: smoothly manage your physical movement and your coherent thoughts, then
Speaker 3: a tragic disease like Parkinson's which deeply affects fluid movement
Speaker 3: and causes tremors, might essentially be a glitch in the
Speaker 3: math itself. Wow, It might literally be that this specific
Speaker 3: neural networks are no longer able to efficiently solve the
Speaker 3: precise PDE required to move your hands smoothly to pick
Speaker 3: up a coffee cup. The mathematical equation is visibly breaking
Speaker 3: down in real time because the hardware, the biological neurons,
Speaker 3: is slowly degrading.
Speaker 2: That is a completely radically different way of looking at neurology. Usually,
Speaker 2: when we talk about Alzheimer's or dementia in the news,
Speaker 2: we only hear about the biology, the physical plaques, the
Speaker 2: neurofibrillary tangles, the specific toxic proteins building up exactly.
Speaker 3: We only ever look at the biological gup the wetwear.
Speaker 3: But this breakthrough suggests we should also be looking intensely
Speaker 3: at the data processing layer. We should be looking at
Speaker 3: the software run on that wetwear.
Speaker 2: If that is true, If cognitive decline in Alzheimer's is
Speaker 2: functionally a math error caused by degrading hardware, what does
Speaker 2: that actually mean for future treatment?
Speaker 3: It offers a completely new avenue of profound hope. It
Speaker 3: suggests that if we can fully master the math of
Speaker 3: these artificial neuromorphic chips. If we can see exactly how
Speaker 3: the algorithmic code breaks down when we deliberately damage the
Speaker 3: silicon chip or introduce electrical noise, then we might perfectly
Speaker 3: understand mathematically what is happening in the failing human brain.
Speaker 2: We could literally model the progression of the human disease
Speaker 2: on the silicon chip exactly.
Speaker 3: We're rapidly closing the historic gap between applied theoretical mathematics
Speaker 3: and clinical neuroscience. We could potentially develop entirely new diagnostic
Speaker 3: tools that scan for these specific computational glitches years before
Speaker 3: the physical biological symptoms ever appear in the patient, or.
Speaker 2: Even developed treatments that try to essentially patch the biological.
Speaker 3: Code potentially yes, or the very least, therapies that specifically
Speaker 3: help the rest of the brain adapt and compensate for
Speaker 3: those localized math errors. It gives the medical field a
Speaker 3: completely new, rigorous language to describe the problem, and historically,
Speaker 3: usually when you find a precise new language for an
Speaker 3: old problem, you eventually find completely new solutions.
Speaker 2: That is genuinely mind blowing. Truly, it takes us from
Speaker 2: being a somewhat dry story about computer chips and power
Speaker 2: grids and turns it into a profoundly human story about
Speaker 2: the very future of human health and longevity.
Speaker 3: It is the ultimate convergence of deeply separated scientific fields,
Speaker 3: and as we've seen throughout history, that convergence is usually
Speaker 3: exactly where the absolute biggest leaps forward happen when the
Speaker 3: applied mathematician finally talks to the clinical neuroscientist and they
Speaker 3: both sit down to talk to the computer hardware engineer.
Speaker 2: So stepping back, we have covered a truly massive amount
Speaker 2: of ground in this analysis. Today. We started with the
Speaker 2: simple image of swinging at a tennis ball.
Speaker 3: The intuitive biological phys ex engine we all carry around
Speaker 3: in our skulls.
Speaker 2: We move from that to defining the hard math of PDEs,
Speaker 2: the literal equations that describe the flowing universe. We looked
Speaker 2: closely at the physical hardware, at the brain chips that
Speaker 2: everyone loudly claimed were way too fuzzy for the serious stuff,
Speaker 2: but surprisingly turned out to be biologically perfect for it all.
Speaker 3: Thanks to a brilliant new algorithmic approach that beautifully bridged
Speaker 3: a frustrating twelve year gap in academic knowledge.
Speaker 2: We talked about the critical need to save the planet's
Speaker 2: power grid, or at least massively cut down electricity use
Speaker 2: while simultaneously securing the national nuclear.
Speaker 3: Stockpile, a massively critical dual benefit, unprecedented computational efficiency paired
Speaker 3: with enhanced national security.
Speaker 2: And we ended up looking deeply in the mirror wondering
Speaker 2: if our own grandmother's tragic memory loss is actually at
Speaker 2: its core, a breakdown and complex biological calculus.
Speaker 3: A literal disease of computation.
Speaker 2: It is genuinely astonishing how one single study coming out
Speaker 2: of Sandia National Laboratories in early ties twenty twenty six
Speaker 2: can fundamentally touch on so many wildly different aspects of
Speaker 2: our shared reality.
Speaker 3: And remember, the researchers themselves heavily emphasize that this specific
Speaker 3: PDE breakthrough is really just the beginning, the foot in
Speaker 3: the door as they called it, right, Brad's Eyelman explicitly said,
Speaker 3: we have a foot in the door. Now. They successfully
Speaker 3: prove that basic applied math works beautifully on these neural chips.
Speaker 3: The immediate next question the entire industry is asking is
Speaker 3: can far more advanced math follow right.
Speaker 2: If they can crush PDEs, what else can they casually
Speaker 2: solve Exactly.
Speaker 3: Can these neuromorphic systems seamlessly handle chaotic, unpredictable systems, can
Speaker 3: they efficiently handle massive quantum mechanics simulations that currently baffle
Speaker 3: our best sloopercomputers. We are rapidly entering a completely new
Speaker 3: era where our machines don't just blindly compute linear logic anymore.
Speaker 3: They actively think through physics. They dynamically process the physical
Speaker 3: world exactly like a highly evolved biological entity would.
Speaker 2: It is a total paradigm shift. We used to rigidly think, Okay,
Speaker 2: we need to make the computer more strictly logical to
Speaker 2: make it better. Now we are empirically proving no, make
Speaker 2: the computer more biological, and it magically becomes vastly better
Speaker 2: At the hardest math.
Speaker 3: Human intuition about what conventional computers can actually do is
Speaker 3: often totally wrong, and as this study shows, our deep
Speaker 3: intuition about what our own biological brains are constantly doing
Speaker 3: is also often completely wrong.
Speaker 2: Which perfectly brings me to a final, slightly provocative thought
Speaker 2: I really want to leave you with as you finished
Speaker 2: listening today, Well, herett, we have solidly established today that
Speaker 2: your subconscious brain is essentially functioning as an organic, highly
Speaker 2: efficient supercomputer. It is constantly quietly solving dense physics equations
Speaker 2: just to help you seamlessly walk across a room, talk
Speaker 2: to a friend, and physically navigate the world. You are
Speaker 2: actively doing extreme calculus right now, just by automatically balancing
Speaker 2: your spine to sit upright in your chair.
Speaker 3: Without even dedicating a single conscious thought to it. Your
Speaker 3: brain is flawlessly balancing the thing of gravity, constant muscle tension,
Speaker 3: and massive streams of sensory input.
Speaker 2: So here is the real question to ponder. If our
Speaker 2: biological brains are already effortlessly doing this supposedly impossible math
Speaker 2: entirely in the background, what other massive mathematical secrets are
Speaker 2: currently hidden deep in our subconscious.
Speaker 3: That is the ultimate question? Right there?
Speaker 2: Are we organically solving other impossible equations? Are we constantly
Speaker 2: processing environmental data in ways we don't even have scientific
Speaker 2: names for yet, And as we continue to actively build
Speaker 2: advanced machines that mimic our biology more and more perfectly,
Speaker 2: will we finally build a machine capable of unlocking those
Speaker 2: deep secrets within ourselves?
Speaker 3: We might just find out that the wet, messy human
Speaker 3: brain has actually been miles ahead of our brightest mathmeditions
Speaker 3: all along.
Speaker 2: I really want you to think deeply about that exact
Speaker 2: concept the very next time you casually catch a set
Speaker 2: of keys someone unexpectedly throws at you. Don't just think,
Speaker 2: oh good catch, Think to yourself, nice calculation of.
Speaker 3: Flawless real time SOL allusion to a complex partial differential equation.
Speaker 2: Exactly. Thank you for joining us on this deep exploration
Speaker 2: of the Sandia breakthrough today. It has been a genuinely
Speaker 2: fascinating ride.
Speaker 3: Always a profound pleasure.
Speaker 2: Stay entirely curious about the world around you. We will
Speaker 2: see you next time.