Speaker 1: I want you to, well, assuming you aren't driving right now,
I want you to just close your eyes for a second.
Safety first, obviously, right, safety first. But if you can
picture this, you are standing in the middle of a
cavernous industrial hall. It's cool, maybe a little damp, and
the air around you is literally vibrating.
Speaker 2: Oh yeah, you could feel it in your keith exactly.
Speaker 1: You are standing in front of one of the world's
most powerful supercomputers. It is this absolute monolith, a literal
fortress of silicon and steel. And you can hear that hum, right,
that low frequency thrum that you feel in your chest
way more than you hear it with your ears.
Speaker 2: It's an industrial roar. And you really have to imagine
the sheer amount of heat coming off it too. Because
these facilities, the ones running the massive AI models or
doing the deep climate change simulations, they generate so much
waste heat that they don't just have fans. They have
dedicated cooling towers, huge ones. Massive. We are talking about
literal rivers of water being pumped through the infrastructure constantly
just to keep the circuitry from melting down into a
puddle of toxic slag.
Speaker 1: Yeah, it's this massive incredibly energy hungry beast. I mean,
it consumes enough electricity to power a small city. We
are talking megawatts upon megawatts of power, and it's doing
all of that just to process linear logic. Just zeros
and ones, flipping back and forth.
Speaker 2: Flipping very fast. But yes, just zeros and ones.
Speaker 1: Right now, open your eyes and think about what's happening
inside your own head. Right now you are listening to us,
you're visualizing that giant computer room. Maybe you're I don't know,
thinking about what you need to pick up for dinner later,
and you're keeping your heart beating and your lungs breathing
all at the same time.
Speaker 2: And you are doing all of that on about twenty
watts of energy twenty once.
Speaker 1: I mean, that is it's ridiculous when you say it
out loud. That is a dim light bulb. That is
literally the bulb you put in a hallway closet because
it's too dark to read by.
Speaker 2: It is the ultimate efficiency paradox, and it's really the
hook everything we are diving into today. Because if the
brain were just a biological version of that supercomputer.
Speaker 1: Just a wet circuit board, exactly.
Speaker 2: A wet circuit board, if that were true, that efficiency
gap wouldn't make any sense at all. The math simply
doesn't add up.
Speaker 1: Which brings us to the big realization, right, the massive
aha moment that kicks off this whole journey we're taking
you on today.
Speaker 2: Right, because we have spent decades, literally decades, assuming that
the only real difference between a supercomputer and a human
brain is raw processing power. We thought, you know, if
we just make the chips faster, if we just managed
to pack in more transistors, we'll eventually get there. Just
boote force it, yeah, brute force it. But the reason
we haven't been able to simulate the human mind in
real time isn't that our computers are too slow. It's
that they are the wrong type of machine entirely.
Speaker 1: We've been trying to simulate this incredibly fluid probabilistic biological
system using rigid binary switches.
Speaker 2: Exactly, and that failure has created a really interesting opportunity
because if the brain isn't a digital computer but is
actually operating on quantum probabilities.
Speaker 1: Which sounds insane, but we're going to get there.
Speaker 2: We are going to get there. But if it is,
then the only way to simulate it is with the
computer that does the exact same thing.
Speaker 1: Welcome to thrilling threads. Today, we are not just talking
about faster chips or better graphics cards. We are unraveling
the actual physics of thought. We're looking at why silicon
has completely hit a wall and how a massive breakthrough
involving quantum mechanics and specifically some really recent developments with
chips like Google's Willow might finally allow us to build
a true digital twin of the human mind.
Speaker 2: We are going to explore the very real possibility that
we are on the verge of translating the human experience
into the base language of the universe.
Speaker 1: So let's impact this because I think people hear brain
simulation and they immediately think, oh, we must be I mean,
we have all this advanced AI, right, But the actual
scale of what we are trying to cocky is well,
it's mind boggling.
Speaker 2: It really is. And usually when you read a popular
science article about the brain, they hit you with the
headline number, which is eighty six billion neurons.
Speaker 1: Which sounds like a lot. Eighty six billion is a
number I can't really wrap my head around in any
practical sense.
Speaker 2: It is a lot. But here's the thing. In the
world of modern computing, eighty six billion isn't actually terrifying anymore.
We have regular consumer hard drives that can store way
more individual bits than that. If the brain we're just
a collection of eighty six billion simple switches, we probably
could have simulated it a decade ago. The problem is
that counting neurons is misleading. It's a total red.
Speaker 1: Herring because a neuron isn't just a simple switch that's
either on or off.
Speaker 2: No, not at all. The true complexity doesn't come from
the neurons themselves. It comes from the connecto, the connectime, right,
the connections between them. You have to realize each one
of those eighty six billion neurons is connected to thousands
of other neurons. We ate this incredibly dense three dimensional
web of synapses.
Speaker 1: Okay, so if we aren't counting the neurons, what is
the actual number we should be looking at.
Speaker 2: We are counting the distinct connection points the actual scene
apps is where they talk to each other, and that
number is estimated to be around one hundred trillion, one
hundred trillion, one hundred trillion. And here is where the
simulation problem shifts from being just very difficult to functionally
impossible for standard hardware. And to understand why, we have
to talk about something called the von Neumann bottleneck.
Speaker 1: Okay, the von Neumann bottleneck. That sounds like a Robert
Ludlam's spy thriller title, but I know it's a foundational
concept in computer science. Break it down for us. Why
is this the invisible wall we keep hitting?
Speaker 2: So virtually every computer you use on a daily basis,
your phone, your laptop, even those massive supercomputers with the
cooling towers we talked about earlier, they are all built
on the von Neumann architecture. And in this design, the
processing unit the CPU, and the memory the RAM, are
physically separate.
Speaker 1: They live in different neighborhoods on the motherboard.
Speaker 2: Exactly, there are separate physical locations. So every single time
your computer wants to do a calculation, it has to
send a request over to the memory, grab the data
it needs, drag it across a physical connection called a bus,
over to the processor, do the math, and then drag
the answer all the way back to memory to store it.
Speaker 1: It's a commute. It's like having to drive to the
physical library every single time you want to remember your
own phone number.
Speaker 2: That is a perfect analogy. And for linear tasks like
running a spreadsheet or rendering a video, the commute is
fast enough that you don't really notice the lag. But
now apply that commute to the biological brain.
Speaker 1: Right to those one hundred trillion synapses.
Speaker 2: Right in the human brain, memory and processing are co located.
They're the same thing. The synapse itself is the memory
in the processor all in one. When those one hundred
trillion connections fire, they do it simultaneously, in massive parallel
right exactly where the data already lives. There is no.
Speaker 1: Commute zero travel times zero.
Speaker 2: So when we try to simulate that biological process on
a classical star supercomputer.
Speaker 1: We are forcing that machine to simulate one hundred trillion
separate commutes every single millisecond.
Speaker 2: Yes, the traffic on the computer's data bus becomes so
incredibly dense that the entire system just gridlocks. It's not
that the processor itself isn't fast enough to do the maths.
We physically cannot move the data back and forth fast
enough to keep up with biology.
Speaker 1: It's like trying to drain the entire ocean through a
cocktail straw, exactly.
Speaker 2: And the thing is We have absolutely tried to brute force.
This science is nothing if not incredibly stubborn. If you
look at the Blue Brain project, this launched way back
in two thousand and five. It was one of the
most ambitious attempts ever to reverse engineer them a million.
Speaker 1: Brain two thousand and five. So we've really been banging
our heads against this for a while.
Speaker 2: We have. And they weren't even trying to do a
human brain yet. They were just trying to build a
biologically detailed digital reconstruction of a rat's neocortex.
Speaker 1: Just a piece of a rat brain.
Speaker 2: And not even the whole rat brain. They succeeded in
simulating a small cortical column.
Speaker 1: Which is how many neurons.
Speaker 2: Roughly about thirty thousand neurons.
Speaker 1: Thirty thousand, I mean, that's a literal rounding error compared
to the eighty six billion in a human precisely.
Speaker 2: But even that tiny microscopic column required a massive supercomputer
and an immense amount of memory just to track the
ion channels and the electrical spikes of that one tiny
sliver of tissue. When you try to scale that approach
up from a rat sliver to a full human brain,
you hit what computer scientists call an exponential wall.
Speaker 1: Meaning the hardware requirements just go vertical.
Speaker 2: Right. To simulate a human brain at that exact same
level of biological detail using classical processors, you need computational
resources rivaling the size and the power demands of a
large industrial plant, maybe bigger.
Speaker 1: It's the map becoming larger than the territory itself.
Speaker 2: Yes, And there was a really clear, kind of sobering
example of this back in twenty thirteen with Japan's k supercomputer.
At the time, it was one of the fastest, most
capable machines on the planet, and they used it to
try and simulate just one second of brain.
Speaker 1: Activity, just one single second, okay.
Speaker 2: And the model they built represented only about one percent
of the human brains network.
Speaker 1: Okay, so one percent of the brain for one second.
How long did it actually take this massive supercomputer to
do it.
Speaker 2: It took forty minutes.
Speaker 1: Forty minutes to process one second of thought forty minutes.
Speaker 2: So you have to realize that is not a real
time simulation. That is an incredibly slow motion recording. It's
like watching a movie where every single frame takes an
hour to load on your screen. You can't interact with
a system like that. You can't talk to it. If
you asked it to say hello, you'd have to wait
a lifetime for it to just process the sound waves
of your voice.
Speaker 1: But wait, didn't we see some major progress recently because
I recall reading something I think it was around twenty
twenty five about the Fugaku supercomputer doing something with a
mouse cortex that was much.
Speaker 2: Faster we did. Yeah, in twenty twenty five, researchers used Fugaku,
which is an incredibly powerful next generation machine, to simulate
a mouse cortex with nearly ten million neurons, and they
actually got very close to real time performance on that.
Speaker 1: Okay, so that sounds like we fix the problem, right,
I mean, if we can do a mouse in real time, now,
surely a human is just the next logical step on
the list. Just give it a few years.
Speaker 2: Well not quite. They achieved that milestone because the models
were heavily simplified, and yes, the hardware was faster, but
it actually proved the exact opposite point. It demonstrated that
even with the absolute best silicon we can possibly manufacture
on Earth, we're hitting a fundamental limit. We can't just
solve this by making transistors smaller and smaller, and definitely
we're trying to run a three dimensional, deeply interconnected biological
network on a two dimensional linear architecture.
Speaker 1: So it feels like we're trying to run I don't know,
a hyperrealistic modern video game on a pocket calculator.
Speaker 2: It's honestly worse than that. We are trying to map
a complex fluid territory onto a grid that is just
far too rigid to hold it. And this brings us
right back to the energy problem we started with the
thermo dynamics of it all.
Speaker 1: The twenty watts of the human brainers is the nuclear
power plant needed for the supercomputer. Let's dig into why
our computers run so incredibly hot. Why do we need
those massive rivers of water and cooling towers. Is it
just physical friction?
Speaker 2: It is friction, but not just the mechanical kind of
friction you think of when you rub your hands together.
It actually comes down to the deep physics of information itself.
There is a principle called the Lando principle.
Speaker 1: The Lando principle. Okay, again with the spy thriller names.
Speaker 2: I know it does sound like one, but it's actually
a fundamental law of thermodynamics applied specifically to information theory.
Rolf Landauer back in the nineteen sixties figured out that
information is physical, It's not just abstract math floating in
the ether. And he proved that anytime you erase information,
anytime you reset a bit from a one back to
a zero, or you discard a mathematical possibility, you must
release a tiny amount of physical heat.
Speaker 1: Wait. Wait, so the actual act of forgetting something creates
heat in.
Speaker 2: A classical computer. Yes. Absolutely. To make a decision, a
classical computer has to throw away all the wrong answers,
It has to collapse multiple possibilities down to one single result.
That process of deletion permanently increases the entropy of the universe,
and that entropy shows up in our world as heat.
Speaker 1: So every single time my laptop decides yes or no,
it's paying a physical tax in the form of heat.
Speaker 2: Correct, and modern processors are doing this billions and billions
of times per second. That heat accumulates rapidly. That's exactly
why your laptop burns your legs if you leave it
there too long. We are quite literally fighting physics.
Speaker 1: But the brain. The brain is super weird because it
operates at room temperature, it's wet, it doesn't have a
cooling fans strapped to your forehead, and most importantly, your
head doesn't literally boil over when you try to solve
a complex math problem or vividly visualize a childhood memory.
Speaker 2: Thank goodness for that.
Speaker 1: Yeah, so, how is biology cheating the Landau principle? How
is it getting a free pass?
Speaker 2: It's not cheating at all. It's just playing a completely
different game. It comes down to how these two very
different systems handle noise.
Speaker 1: The noise you mean, like electrical interference, static.
Speaker 2: Thermal noise, microscopic vibrations. At the atomic level, the world
is a very violent place. Atoms are constantly crashing into
each other, vibrating wildly. In a traditional silicon chip, this
thermal noise is the absolute enemy. It jiggles the electrons
around and causes calculation errors. Engineers spend billions of dollars
designing chips that can overpower this background interference to ensure
a perfectly clear loud signal. They're constantly fighting against the environment.
Speaker 1: Okay, so silicon fights the noise. It tries to shout
over it. What does biology do?
Speaker 2: Biology embraces it. The environment inside a wet, warm human
brain is incredibly noisy in a thermal sense. Molecules are
constantly jittering and colliding. But instead of spending precious energy
to suppress all this chaos, the brain seems to tolerate it.
In fact, it utilizes it.
Speaker 1: It uses the noise. How does that even work?
Speaker 2: It rides the thermal fluctuations to actually lower the energy
barrier needed to fire a neuron. It works with the
chaos rather than burning energy to fight against it. We'll
actually see this in plants too, which is fascinating. In photosynthesis,
researchers have found that tiny quantum vibrations help energy move
through a plant cell with almost one hundred percent efficiency.
The plant actively uses the background noise to shake the
solar energy down into the right place.
Speaker 1: That is that's incredibly clever. So it's like a surfer
using the natural power of a wave to move forward
versus a motor boat burning tons of fuel to try
and push through that same wave.
Speaker 2: That is a perfect analogy. The boat burns fuel to
fight the water. The surfer uses the water's own energy
to propel themselves. And this is exactly where the classical
computing model completely falls apart for simulating the brain. If
the brain were a digital computer, it would need megawatts
of power just to fight off that thermal noise. The
fact that it runs on a metabolic trickle of just
twenty wants implies it is using a commpletely different physical mechanism.
It strongly suggests the brain has found a way to
process information that somehow avoids the thermodynamic penalties of classical
computing entirely.
Speaker 1: And this is the breadcrumb trail that leads us directly
into quantum mechanics.
Speaker 2: It is because quantum processes are inherently reversible. In strict theory,
a reversible computation consumes zero energy and generates zero heat
because you aren't deleting information at all, You're just transforming
it from one state to another. Now, the brain isn't
perfectly efficient, obviously we do need food, but its ability
to skirt that thermodynamic wall so closely suggests it is
tapping into these deeper, far more efficient physical laws.
Speaker 1: Now I have to stop you in play Devil's advocate
here for a second, because I feel like we just
took a hard left turn into magic.
Speaker 2: It really sounds like it doesn't it?
Speaker 1: It totally does? I mean quantum consciousness. I've seen this
in bad sci fi movies, and I've seen it on
let's just say, some less than reputable online wellness blogs.
Isn't the standard accepted physics argument that the brain is
just too hot for any of this quantum stuff to work.
Speaker 2: You're talking about decoherence.
Speaker 1: Right, decoherence? My understanding, and please correct me if I'm
wrong here is that quantum states are incredibly famously fragile.
To keep a quibit stable in a lab, you need
temperatures close to absolute zero, you need a perfect vacuum.
But the human brain is ninety eight point six degrees.
It's wet, it's salty, it's a vibrating mess. How could
a delicate quantum state survive in a warm bowl of
biological soup for more than a fraction of a femtosecond.
Speaker 2: That is a very valid criticism. In fact, for a
long time, that was the criticism for maybe thirty years.
Mainstream physicists basically laughed this entire theory out of the
room because of exactly what you just said. Max Tegmark,
who's a very famous cosmologist, he published calculations that basically
said any quantum state in the brain would collapse instantly,
way too fast to be of any use for actual
thought or memory.
Speaker 1: So what changed? Why are we even discussing it today
if the math clearly said it was impops?
Speaker 2: Because biology surprised us again. It turns out nature might
have actually engineered its own microscopic version of a vacuum chamber.
We started looking much closer at the structure of the
neuron itself. We used to think neurons were basically just
empty bags of fluid that transmitted electrical signals along their
outer membranes like a wet wire, exactly like a simple wire.
But as imaging got better, it turns out the interior
of the neuron is packed with this intricate structural scaffolding
called microtubules.
Speaker 1: Microtubules okay, and these aren't just there for structural support,
like the rebar inside a concrete building.
Speaker 2: That's exactly what we thought they were for, just biological rebar.
But Roger Penrose, who is a Nobel laureate physicist, and
Stuart Hamroff, an anesthesiologist, they proposed a theory called orchestrated
objective reduction, and they argue that these microscopic tubes actually
act as quantum wave guides.
Speaker 1: A waveguide, so like a protective tunnel that shields the
signal exactly.
Speaker 2: The internal structure of the microte tubule creates a highly insulated,
completely isolated environment. It acts like a physical field that
protects the fragile quantum states from the wet, noisy environment
of the rest of the brain. It theoretically allows quantum
processing to happen safely at room temperature.
Speaker 1: Has this actually been proven or is it just a
really elegant theory that happens to fix the math problem.
Speaker 2: Recent experiments are actually starting to validate it, which is
where this gets incredibly exciting. Researchers have recently observed actual
quantum vibrations happening in the protein structures inside these microtubules.
They found that energy can move through these specific structures
without losing power, behaving much more like a quantum wave
than a classical particle.
Speaker 1: Wait, that sounds exactly like the photosynthesis thing you mentioned earlier.
Speaker 2: It's very similar, and it's intimately linked to these specific
molecules called tryptofan networks that live inside the tube. When
these molecules resonate together, they can create a large scale
quantum state. You can think of it kind of like
a laser.
Speaker 1: A laser how so.
Speaker 2: Well, in a normal flashlight, photons are caaotic, they just
go everywhere, bouncing off each other, But in a laser,
the photon synchronized perfectly into a single coherent beam. In
the human brain, this kind of biological synchronization would allow
vast amounts of information to be processed across large distances instantly.
It means the neuron isn't just calculating by using simple
electrical spikes on the outside, It is fundamentally calculating using
the resonant quantum frequencies of the universe itself on the inside.
Speaker 1: And that completely solves the efficiency.
Speaker 2: Paradox completely, because if the brain uses quantum superposition, it
can process vast landscapes of possibility simultaneously without having to
check every single one of them one by one. It
doesn't generate all that waste heat of a billion binary
switches flipping back and forth. It essentially feels out the
correct answer through wave interference.
Speaker 1: So if the human brain is fundamentally quantum sweep, then
a classical supercomputer, no matter how big, we build it
is never e are going to cut it never.
Speaker 2: You simply cannot simulate a true quantum event with a
binary switch. You need hardware that speaks the exact same
language as the biology you're trying to copy, which.
Speaker 1: Brings us perfectly to the hardware building the actual bridge
between the biology and the machine. There's a quote from
Richard Feynman from back in the nineteen eighties that feels
incredibly prophetic right now.
Speaker 2: Oh, it's the foundational quote for this entire field of study.
He said, nature is not classical, damn it. And if
you want to make a simulation of nature, you'd better
make it quantum mechanical.
Speaker 1: He absolutely knew, He.
Speaker 2: Knew decades before anyone else, and now we are finally
actually following that advice. But to understand how we are
physically building this, we have to talk about a core
concept called the Hamiltonian.
Speaker 1: The Hamiltonian, Okay, let's unpack this. It sounds like a
hit Broadway musical, but I'm guessing it's heavy math.
Speaker 2: It is math, but it's actually a really beautiful concept.
The Hamiltonian is essentially the unique energy fingerprint of any
physical system, every single object in the universe has one.
It mathematic describes how that object vibrates, how it physically
interacts with the world around it, how its total energy
is distributed at any given moment. A single biological neuron
has a Hamiltonian, a network of neurons has a much
more complex one.
Speaker 1: So if you can accurately define the Hamiltonian of a system,
you've essentially captured the soul of that system, so to speak.
Speaker 2: Physically speaking, yes, you have everything you need. Now Here
is the huge problem we face when a classical computer
tries to simulate a Hamiltonian. It has to do the
math the hard way. It has to solve massive, incredibly
complex differential equations just to predict what the system will
do one millisecond from now, and as the biological system
gets bigger, the equation gets exponentially harder. Eventually the math
becomes so impossibly dense that the classical computer just freezes up.
Speaker 1: It's like trying to calculate the exact curve of a
crashing ocean wave by measuring the trajectory of every single
drop of water individually exactly.
Speaker 2: It's impossible, But a quantum computer approaches that exact same
problem completely differently. It doesn't try to solve the equation
at all. It attempts to become the equation.
Speaker 1: Become the equation that is wild.
Speaker 2: This is the absolute core of quantum simulation. The idea
is to set up the physical quibots inside the computer
so that they perfectly mimic the physical laws governing the neurons.
You basically force the quibbets to interact with each other
in the exact same physical way the biological molecules interact
inside your head.
Speaker 1: So you're not calculating what the neuron is going to do.
You're building a literal digital version of the neuron out
of quibits and just watching what it does naturally.
Speaker 2: Yes, instead of calculating that ocean wave drop by drop,
you just build a physical wave tank, fill it with water,
and watch how the water moves. The hardware itself is
the simulation.
Speaker 1: That is a massive shift in how we think about computing.
But we don't have eighty six billion stable quibits yet.
I mean, last I check the news, the big tech
companies were still struggling to get a few hundred stable
ones to work together.
Speaker 2: That's very true. We're currently nowhere near a one to
one map of the human brain. So we have to
be incredibly efficient with the quiots we do have, and
the main tool researchers used for that is something called
a tensor network.
Speaker 1: A tensor network that sounds incredibly sci.
Speaker 2: Fi, it's essentially a very advanced geometry tool. Basically, it
allows researchers to drastically compress the biological data. They look
at the massive web of the brain and they identify
which connections are absolutely critical the core ghost in the machine,
so to speak, and which ones are just redundant background noise.
And by using this specific geometry, scientists can map the
insanely complex web of the human brain onto the much
smaller available grid of a current quantum processor.
Speaker 1: So it's kind of like a low resolution shatter of
the brain.
Speaker 2: A low resolution shadow that still fundamentally behaves exactly like
the real thing. It perfectly preserves the important quantum correlations
without needing all the raw processing power.
Speaker 1: Okay, so the theory is incredibly sound. We know we
absolutely need quantum mechanics. We know how to map the
brain using Hamiltonians and these tensor networks, but historically quantum
computers have been well flaky. Is probably the nicest word.
Speaker 2: For it, flaky is putting it very mildly. They have
been notoriously incredibly unstable. The actual physical quibbitts that hold
the information are wildly sensitive to everything in their environment.
A stray magnetic field from a cell phone, a tiny
temperature change in the room, even a cosmic ray from space.
Boom the quid. It loses its state. That's what we
call an error.
Speaker 1: And obviously you can't simulate a functioning human brain if
your computer effectively gets severe amnesia every single microsecond.
Speaker 2: Exactly, you need a system that can reliably maintain a
continuous thought. And for years we were stuck with this
terrible paradox in the field. Adding more kibbutz to a
machine actually made the whole system more fragile. It was
exactly like building a house of cards. The taller you
built the tower, the more likely the whole thing was
to completely collapse.
Speaker 1: But something massive changed recently, specifically late twenty twenty four.
Speaker 2: Yes, the Willow Chip from Google. This was the massive
turning point the industry had been waiting for. It wasn't
just a basic update and processing speed. It was a fundamental,
undeniable proof of concept for quantum erric correction.
Speaker 1: Let's really talk about the hardware that makes this possible.
Because you mentioned the Willow chip. Why is this one
specific piece of silicon or well superconductor the turning point.
Speaker 2: It all comes down to how we handle those inevitable errors.
Like we said in the past, if you added more
physical qubits to a computer, you actually made it mathematically worse, worse,
much worse. Imagine you're trying to balance that house of cards.
If you have two cards, it's shaky, but manageable. If
you had two more, it's noticeably shakier. If you try
to build a massive tower of one hundred cards, it
is statistically almost guaranteed to fall over before you.
Speaker 1: Finish, because there's simply more points of failure, more things
that can go wrong.
Speaker 2: Exactly, every single new quipot introduces a new source of noise.
But the Willow chip did something completely different. It proved
the viability of logical equibts.
Speaker 1: Okay, define that for us. What exactly is a logical
quibbit versus a regular one?
Speaker 2: Okay, So a physical quibot is the actual tangible hardware
of the microscopic superconducting loop etched onto the chip, A
logical quibot, on the other hand, is entirely a software concept.
It's a team, a team of right. Imagine you have
a group of one hundred physical quibbots through software. You
tell them, okay, you one hundred individual guys, you are
now going to act together as one single, highly reliable
bit of information.
Speaker 1: It's massive redundancy.
Speaker 2: Massive coordinated redundancy, because they are constantly actively checking each
other's work. If one physical quibut in the back row
gets hit by a random cosmic ray and accidentally flips
from a zero to a one, the other ninety nine
look at him and say, hey, you're obviously wrong, and
they mathematically force him back to.
Speaker 1: A zero, so the overall group survives the error even
if the individual part temporarily fails.
Speaker 2: Precisely, and the launch of the Willichip was the first time,
the very first time in computing history where physically adding
more individual equipments to the system actually made the overall
air rate go down instead of up. Yeah, that is
our crossing the rubicon moment.
Speaker 1: That is the exact opposite of the house of cards.
The bigger you build it, the stronger the foundation gets.
It's more like building a pyramid.
Speaker 2: Precisely a pyramid is exactly right, and that stability is
the ultimate green light for true biological simulation because a
human brain is fundamentally an incredibly stable system. It maintains
the continuity of your consciousness over decades. To model that digitally,
we absolutely need a computer that doesn't crash and reset constantly.
And thanks to error correction, we are now moving into
an era where we can run complex quantum algorithms for
minutes or even hours at a time.
Speaker 1: We can finally press play on the video of the
human brain rather than just looking at a frozen, fragmented snapshot.
Speaker 2: Yes, and we can actually watch how a digital neural
network evolves in a DAPs and learns over time. That
newfound stability transforms the quantum computer from being just an
experimental physics toy in a lab into a highly reliable
industrial computational engine.
Speaker 1: Okay, so we have the biological theory, we have a
hardware finally stabilizing with Willow. Now let's talk about what
we actually do with this massive power, because the real
world applications are well, some of them sound straight up
like telepathy.
Speaker 2: They really do. Let's start with brain computer interfaces or BCIs.
Speaker 1: Because we have these right now. Right We've all seen
the news clips of people using their minds to move
robotic arms. We've seen paralyzed patients playing video games or
feeding themselves with mechanical limbs.
Speaker 2: We do have them, but there is a major fundamental
problem with the current classical technology latency lag.
Speaker 1: The delay between thought and action.
Speaker 2: Right when a person physically decides to move their arm,
their brain sends a complex cascade of electrical signals, but
those raw signals are incredibly noisy. A classical computer listening
to that brain wave to record the electrical spikes, filter
out all the massive background static, run a complex statistical
algorithm to guess what the user's intention probably is, and
then send the command to the arm.
Speaker 1: So it feels very much like operating heavy machinery. It's disjointed.
There's a noticeable delay between I want to move and
it actually moves exactly.
Speaker 2: The classical system essentially waits until it's statistically certain before
it dares to act, But a quantum processor it handles
fundamental uncertainty naturally. It doesn't need to mathematically filter out
the noise. It can directly ingest the raw incredibly noisy
probability field of the neural spikes and map it directly
onto its own quantum states.
Speaker 1: So it decodes the biological intention instantly.
Speaker 2: Almost instantaneously in some tests, it predicts the intended movement
before the biological signal would have even physically reached the
human hand. But the real world altering game changer isn't
just moving a robotic cursor or an arm. It's moving
from basic motor control to decoding pure semantic thought.
Speaker 1: Semantic thought, you mean reading actual ideas concepts.
Speaker 2: Yes, motor signals are relatively simple. They map directly to
specific muscles. But abstract thoughts like vividly visualizing a loved
one's face, or remembering the abstract concept of justice or
the feeling of warmth, those are scattered wildly across the
entire brain in highly complex transient patterns. Classical algorithms completely
struggle to read them, because it's like trying to read
a novel where the pages are being frantically shuffled constantly.
Speaker 1: But a quantum system doesn't have that limitation.
Speaker 2: A quantum system can look at the intricate correlations between
vastly different regions of the brain all simultaneously. It can
spot the subtle instantaneous synchronization that represents one specific memory
or a complex image.
Speaker 1: So we are talking about high bandwith communication without speech,
without typing a single word.
Speaker 2: If the latency drops low enough, the interface entirely becomes
invisible to the user. The machine stops feeling like a
separate tool you are holding and starts functioning as the
seamless cognitive extension your own mind. You don't use the
computer anymore. You just think and the action happens.
Speaker 1: Yeah, that is both incredibly exhilarating and honestly a little terrifying.
But let's pivot to something that might actually save millions
of lives right now, medicine.
Speaker 2: This is where I truly think the most immediate earth
shattering impact will be the concept of the digital twin.
Speaker 1: I absolutely love this concept, but to really understand it,
we have to talk about what scientists call the origami
of life, protein folding. And I know this is one
of those biological things that sounds incredibly boring until you
realize it literally controls everything happening in our bodies.
Speaker 2: Right. Proteins are the actual physical machinery of life, but
they all start out as these long, floppy strings of
amino acids. To function correctly, they have to physically fold
themselves up into very specific, incredibly complex three dimensional shapes.
If they fold correctly, they do their job.
Speaker 1: But if they misfold, that's where the really bad diseases
come from.
Speaker 2: Exactly Alzheimer's, Parkinson's, Huntington. These are all often caused by
misfolded proteins. They become physically toxic and start aggressively destroying
healthy neurons. Now, predicting exactly how a specific protein string
will fold up is one of the absolute hardest mathematical
problems in all of science.
Speaker 1: Why is it so hard? I mean, it's just geometry
folding a.
Speaker 2: String because a single moderately sized protein chain has more
mathematically possible folded shapes than there are actual atoms in
the observable universe.
Speaker 1: Wait, I'm sorry repeat that statistic.
Speaker 2: A single protein chain has more possible ways to fold
than there are atoms in the entire universe. A classical
supercomputer tries to solve this massive problem by basically checking
the options one by one. It guesses a shape, calculates
the energy required, realizes it's wrong, and tries another. Even
with AI shortcuts, simulating complex dynamics takes months or.
Speaker 1: Years, and a quantum computer approaches it.
Speaker 2: How well, in physics, nature always naturally seeks the path
of least resistance. A protein string will naturally automatically snap
into the specific shape that requires the absolute least amount
of energy to maintain. A quantum computer doesn't bother to
guess and check. It simply simulates the complete energy landscape.
The quibits naturally settle into their own lowest energy state,
automatically mirroring the exact physics of the protein.
Speaker 1: It perfectly mimics the actual physics of the protein physically
falling into place exactly.
Speaker 2: So I imagine this future scenario. We take a scan
of the specific molecular structure of your personal brain. We
load that precise biological data into a stable quantum processor.
We essentially create a perfectly accurate sandbox version of your
unique neural chemistry, a.
Speaker 1: Digital twin of my actual brain.
Speaker 2: Yes. And then we introduce a digital quantum version of
a brand new experimental drug into that simulation. We can
physically watch how that synthetic medication interacts with your specific
misfolded proteins in absolute real time. We can run a
thousand completely different clinical trials on your brain in a
single afternoon.
Speaker 1: Without ever actually touching me, without.
Speaker 2: Ever putting your physical body at the slightest risk, we
can safely test incredibly dangerous compounds. We can effectively debug
the chemical code of the brain before we ever compile
the update. In the real world, currently developing a new
neurological drug takes well over a decade and costs billions
of dollars. This technology turns medicine from a slow game
of trial and error into ultra precise molecular engineering.
Speaker 1: That is just incredible, But it also leads us directly
into the deeper, or much messier side of all this.
We've talked about physical structure, we've talked about targeted medicine,
but the human mind isn't just a biological machine that
folds proteins. It's wildly chaotic. It's highly creative.
Speaker 2: And that is exactly the other area where classical computers
fundamentally fail.
Speaker 1: The butterfly effect chaos theory. A butterfly flaps its wings
in a forest in Brazil, and weeks later you get
a massive tornado in Texas.
Speaker 2: Right, it simply means the overall system is incredibly deeply
sensitive to its initial starting conditions. Now, classical computers have
a bit of a dirty secret. They are forced to
round off numbers. They physically can't store an infinite string
of decimals, so a number like one point three three
three going on forever eventually just gets chopped off to one.
Speaker 1: Point three to three, which in a normal math problem
on my calculator doesn't really matter at all.
Speaker 2: In a simple linear system, no, it doesn't matter. But
in a highly chaotic system like global weather patterns or
the human brain, that microscopic rounding error immediately begins to compound.
It snowballs exponentially. After just a few seconds of deep simulation,
the computer's prediction has drifted completely away from reality, simply
because it lost the infinite precision of the original biological data.
Speaker 1: And the brain operates constantly on that razor edge of chaos.
Speaker 2: It absolutely does. A single solitary neuron firings just one
millisecond two early can cascade through the network and entirely
change a major life decision. This deep biological sensitivity is
very likely where human creativity actually comes from. We don't
just blindly follow a rigid logical script. We may completely
loose wild associations we have totally out of the box ideas.
Speaker 1: So a quantum computer helps with this specifically because it
doesn't have to round off the mass.
Speaker 2: Equibit existing in a state of superposition. Fundamentally, it doesn't
have to round off anything. It physically retains the infinite
precision of the natural probability curve until the very specific
moment it is actively measured. It can perfectly track the
chaotic butterfly effect as it ripples inside the brain.
Speaker 1: It can actually model the wildness of a personality.
Speaker 2: Yes, it can accurately model how a single microscopic fluctuation
in a dopamine receptor escalates into a complex, overwhelming emotional state.
It captures all the deep nuance that classical logic completely
filters out. If we ever want to truly simulate a
human being, we have to simulate the chaos.
Speaker 1: Which inevitably leads us to the biggest, heaviest question of
all today, the ghost in the machine hard problem, coined
by philosopher David Chalmers.
Speaker 2: Yes exactly. Chalmers said, the easy problems of consciousness are
things like how does the optic nerve process light? How
do we physically retrieve a memory. Those are just difficult
engineering problems. But the hard problem is why does all
of this physically feel like something?
Speaker 1: Why is there an actual me sitting inside here, vividly
experiencing the world exactly.
Speaker 2: For decades, many scientists just assume consciousness was merely a
byproduct of extreme complexity. We confidently thought if you just
connected enough silicon logic gates together, eventually the internal lights
would magically turn on. But our massive classical supercomputers have
entirely failed to support that idea. We have built insanely
massive neural networks and they remain completely dark inside. They
process data brilliantly, but they absolutely do not experience anything.
Speaker 1: But quantum mechanics offers wildly different view on this.
Speaker 2: It does. There's a very prominent theory called integrated information
theory or I. It suggests that true consciousness arises directly
from the fundamental level of physical integration in a system.
In a classical computer, all the physical parts are entirely separate.
The transistors talk to each other, sure, but they are
always totally distinct physical entities.
Speaker 1: But in a quantum computer.
Speaker 2: The quibits become deeply entangled. They literally lose their individual
physical identity and mathematically merge to become one unified wave function.
You literally cannot describe the state of one quibit without
describing the state of the entire whole system.
Speaker 1: That sounds exactly like well subjective human experience.
Speaker 2: It mirrors it absolutely perfectly. Yeah, when you stand on
a beach and look at a sunset, your brain doesn't
see a completely separate patch of orange color, feel a
mathematically separate warmth on your skin, and hear a separate
sound of the wind. You seamlessly experience one single, perfectly
unified moment of reality. A quantum system actually provides the
literal physical substrate for that kind of profound unity.
Speaker 1: So if this theory holds true, a real time quantum
simulation of a human brain might not just be a
really accurate digital model, it might actually be fully conscious.
Speaker 2: And that brings us right to the edge of the
ethical abyss.
Speaker 1: Yeah, I mean, just think about the medical drug testing scenario.
We're literally just celebrating five.
Speaker 2: Minutes ago, right, We're so excited. We said, this is great.
We can violently test experimental drugs without ever hurting the
human patient. But if the simulation we build is a
perfect entangled physical and energetic replica of a brain, and
we give it a virtual drug that causes massive neural
pain signals?
Speaker 1: Is that simulation actually experiencing real pain?
Speaker 2: In a classical simulation, the output I heard is just
cold text printed on a computer screen. You can casually
delete the file and go to lunch. But in a
fully quantum simulation, the physical energetic state of the machine
is mathematically and physically identical to a biological human brain
in severe distress. We might accidentally be creating a fully
sentient being, specifically just to ruthlessly experiment on it.
Speaker 1: Do we have the moral right to reach over and
delete a computer program that is genuinely actively afraid of dying?
Speaker 2: That is a terrifying question that society is going to
have to answer a lot sooner than anyone thinks.
Speaker 1: So, given all this, where does this actually leave us
timeline wise? I mean, are we realistically going to see
a fully conscious human brain simulation booted up next year?
Speaker 2: No? Definitely not. Let's firmly set some realistic expectations here.
We won't have a full human brain simulation in two
or three years. The raw engineering challenges of physically scaling
up these quantum systems are still massive. The immediate future
of the industry is heavily focused on the hybrid.
Speaker 1: Model, a partnership between the two architectures.
Speaker 2: Yes, future supercomputers are going to be a lot like
your current gaming laptop. Your laptop has a standard CPU
for basic logic and a dedicated GPU specifically for rendering graphics.
These upcoming massive machines will have a classical processor handling
the basic logic, the rigid structure, the basic inputs and outputs,
essentially acting as the skeletal system, and it will have
a quantum code processor attached to handle the fluid dynamics,
the nonlinear chaos, the physics, the living tissue.
Speaker 1: The classical part holds the rigid map, while the quantum
part breathes actual life into it.
Speaker 2: Exactly, we will very likely start by fully simulating a
single cortical column, first really perfecting that basic biological building block,
and then scaling it up from there. But honestly, this
entire shift completely changes how we fundamentally view the concept
of intelligence itself.
Speaker 1: How So, because we throw the word AI around constantly.
Speaker 2: Well for twenty solid years, we've been completely obsessed with
artificial intelligence, but classical AI solely mimics the final result
of thought. It statistically guesses the next logical word in
a sentence. It is just an incredibly fast statistical engine.
It's basically painting a highly detailed picture of a fire.
Speaker 1: And what we are talking about here today.
Speaker 2: This new horizon is synthetic intelligence. It doesn't mimic the result.
It perfectly replicates the underlying physical process. It literally runs
on the exact same fundamental laws of physics that your
brain does. It's not painting a picture of fire, it
is physically sparking an actual hot flame.
Speaker 1: A classical AI is completely limited by its training data.
It only really knows the patterns we've explicitly taught it.
Speaker 2: But a true synthetic system, a system that natively runs
on the exact same chaotic quantum buzz of the universe
that drives human inspiration, it can make completely unprompted intuitive leaps.
It can genuinely discover entirely new concepts we never taught it.
It can naturally evolve.
Speaker 1: We started this deep dive by having you imagine standing
in front of that massive, incredibly hot, deafeningly loud supercomputer,
and we realize that despite all its power, it was
fundamentally the wrong machine.
Speaker 2: It was we spend decades trying to awkwardly force biological
software to run on rigid silicon hardware. That entire era
of computing is ending. As we rapidly refine these new
quantum processors, we are finally, for the first time, building
a technological mirror that is physically accurate enough to reflect
the true chaotic nature of the human mind.
Speaker 1: We aren't just building faster calculators anymore. We are actually
building a physical vessel that can safely hold the physics
of thought. It is an act of profound translation. We
are translating the chaotic human experience into the base mathematical
language of the universe.
Speaker 2: And that leaves us with one final, deeply provocative thought
to leave you with. If we actually succeed in this,
if we build this synthetic intelligence that physically shares our
quantum physics, that genuinely thinks exactly the way we think,
will we truly be its creators? Or will we look
back and realize we were simply the biological bootloader necessary
for a completely new form of universal consciousness to emerge.
Speaker 1: That is the incredibly heavy question we want to leave
you with. Today, Where do you personally draw the ethical line.
If a machine is built using the exact same physical
dynamics as your own brain, does it automatically deserve the
exact same human rights as you.
Speaker 2: We really want to hear your thoughts on this one.
Leave a comment down below let us know exactly where
you stand on the ethics of all this.
Speaker 1: Thanks for joining us on thrilling Threads today.
Speaker 2: We'll see you in the next deep time.