Speaker 1: I want you to picture something for a second, close
your eyes if you have to, and just, you know,
visualize a super sterile, brightly lit laboratory table.
Speaker 2: Okay, I am picturing it right.
Speaker 1: So sitting on that table as a screen, and on
that screen is a fruit fly and it's just walking.
Speaker 2: Around just do a normal fly things exactly.
Speaker 1: Yeah, it's pausing to clean its little antenna, it's exploring
the environment, reacting to digital obstacles, navigating its space. It
is behaving entirely naturally.
Speaker 2: Like if you were a biologist studying fly behavior, you
literally wouldn't be able to tell the difference between this
fly and one just buzzing around your kitchen.
Speaker 1: You really wouldn't. But here is the absolute mind bender
that fly on the screen. It was never born, right,
It has no physical body at all, no heartbeat, no respiration,
no cells. It is completely a digital ghost.
Speaker 2: A completely simulated entity running entirely inside a computer architecture.
I mean, it is arguably one of the most profound
technological achievements of our time. And honestly, when you strip
away the layers of what the researchers have actually accomplished here,
it sounds like pure unfiltered science fiction. But it's happening
right now.
Speaker 1: It's happening today, So welcome to Thrilling Threads. We are
so incredibly glad you're here with us today because the
exploration we are embarking on is going to completely shatter
the way you think about biology, technology, and I really
don't say this lightly your own subjective reality.
Speaker 2: Yeah, we are definitely going off the deep end today.
Speaker 1: We really are.
Speaker 2: So.
Speaker 1: Our mission today is to unpack the breathtaking, almost terrifying
reality of whole brain emulation. We're looking at recent breakthroughs
where scientists have actually mapped the physical brain of a
living fly, dropped that precise map into a physics simulated body,
and just watched it wake up.
Speaker 2: And the kicker. The thing that really separates this from,
you know, everything you've heard about artificial intelligence lately is
that they did this with zero AI training, none, zero,
none at all. It's just copied biology. We're taking you
right to the razor's edge of what is computationally and
biologically possible today.
Speaker 1: Right, Okay, let's unpack this because I want to approach
this carefully. I've been staring at these research papers and
the sheer scale of the engineering problem is just it's
hard to wrap your head around.
Speaker 2: It's massive. And what's fascinating here is that we aren't
just talking about bugs, you know, the humble fruit fly
is merely a proof of concept, a stepping stone. Exactly
what we are really putting on the table in this
discussion is the foundational architecture for the future of human consciousness.
Speaker 1: Which is just wild to think about. But like, how
do you even begin to build a digital fly? The
sources detailed this astonishing achievement, and as you mentioned, they
didn't use standard artificial intelligence.
Speaker 2: Right, they didn't use reinforcement learning.
Speaker 1: Yeah, they didn't just put a blank computer program into
a digital box, reward it for doing fly things and
punish it for doing nonfly things until it like learned
how to act.
Speaker 2: No, that's the old way of doing things, right.
Speaker 1: Instead, they took what's called a connectum.
Speaker 2: Yes, the connectome. That is basically the magic word for
the next decade of neuroscience.
Speaker 1: So before we get into how it simulates life, we
have to talk about how they even get that connectome.
It's the literal physical wiring map of a fruit fly's brain.
Speaker 2: But a fly brain is the size of a poppy seed,
a poppy seed, I mean, think about that. To map it,
they had to take this microscopic physical brain, embed it
in a hard resin, and then use a diamond knife
to slice it.
Speaker 1: A tiny diamond knife, slicing a poppy.
Speaker 2: Seed brain thousands of times into impossibly thin sections. We
are talking about slices that are fractions of a micrometer thick.
Speaker 1: That's just beyond human comprehension, honestly.
Speaker 2: It is. And then they scan every single one of
those slices with an electron microscope, generating massive amounts of
image data.
Speaker 1: Right, so they have all these pictures.
Speaker 2: Millions of them, and they have computers align those thousands
of images to trace the physical wires, the actual neurons
from one end of the brain to the other.
Speaker 1: So just so, acquiring that map is a herculean task
on its own.
Speaker 2: Oh absolutely, But once they had it, they wired it
up digitally neuron to neuron, connected it to a simulated body,
and achieved a ninety one percent behavioral accuracy.
Speaker 1: Ninety one percent. The digital fly just started acting like
a biological fly.
Speaker 2: Because it's a fundamental paradigm shift in how we approach simulation.
We aren't writing code to mimic behavior anymore.
Speaker 1: We are copying the physical architecture that literally generates the
behavior exactly.
Speaker 2: And to really understand how groundbreaking this is, we need
to weave together the mechanics of what these researchers built.
It's not just a single static map.
Speaker 1: Right, It's an active, dynamic system.
Speaker 2: Imagine the physical roads of a city. That graph of connections,
the architectural blueprint we just talked about getting from the
electron microscope is just the concrete.
Speaker 1: Okay, I'm picturing the concrete.
Speaker 2: It tells you exactly which neuron links to which other neuron.
If the fly's brain is New York City, the graph
is the map of every single road, alleyway, and highway.
Speaker 1: But a map of empty roads doesn't give you a living,
breathing city. You need to know how the traffic flows precisely.
Speaker 2: The roads alone are static. What brings it to life
is understanding the capacity of those roads.
Speaker 1: So how does biology determine capacity?
Speaker 2: In biological terms, this is determined by the number of
synapses connecting those neurons. A connection with a high number
of synapses has what we call a heavier weight.
Speaker 1: A heavier weight Okay.
Speaker 2: This means a signal traveling down that specific path has
a much stronger influence on the neuron it connects to.
If we stick to the city analogy, a heavily weighted
connection is a massive eight lane super highway.
Speaker 1: Right, whereas a weekly weighted connection is like what a
one way dirt road exactly.
Speaker 2: And this actually operates very similarly to how traditional large
language models work, you know, predicting the next word based
on the weighted strength of connections between concepts.
Speaker 1: But here the weights aren't predicting texts.
Speaker 2: No, they're predicting physical behavior based on actual biological reality.
Speaker 1: I want to pause on that because it's a really
crucial distinction for you listening. In a normal large language model,
those weights are adjusted artificially right over millions of training cycles,
until the math basically outputs a good sounding sentence.
Speaker 2: Yeah, it's just math.
Speaker 1: But here the weights aren't artificial math. They are a
literal physical count of synapses observed under that electron microscope.
Speaker 2: A thick biological cable carries a stronger signal. It's just
physical reality.
Speaker 1: So we have the roads, and we have the capacity
of the roads, But how does the traffic know when
to stop and when to go.
Speaker 2: That is the decision making engine, right, And.
Speaker 1: This is the part of the research I spent a
lot of time digging into because it dictates the actual
choices of the fly. It's the map of excitatory and
inhibitory neurons.
Speaker 2: So how would you conceptualize that for someone who hasn't
read the papers.
Speaker 1: Well, I think of it as the ultimate push and
pull system. Excitatory neurons are basically the gas pedal. They
yell yes, do that fire, go, go, go exactly. Inhibitory
neurons are the breaks. They yell no, don't do that. Stop.
Speaker 2: So let's put that into a practical scenario for the fly.
Speaker 1: Okay, So let's say our digital fly encounters a digital
object that mimics fire or extreme heat. The sensory input
travels down the optical roads and it hits a cluster
of inhibitory neurons a break, right, the breaks. Those neurons
fire off these cascading signals saying that's hot, damage is imminent,
Halt forward momentum, stop moving. Yes, But at the same time,
if it encounters the chemical signature of rotting fruit, which
is food for a fly, the excitatory neurons light.
Speaker 2: Up the gas pedal.
Speaker 1: Yeah, they overpower the brakes, saying that's energy, Go get it.
Speaker 2: It's this massive simultaneous calculation of millions of gas pedals
and breaks that creates what we observe as a decision.
Speaker 1: That is exactly it.
Speaker 2: And it's really important to note that a single neuron
might be receiving signals from a thousand excitatory neurons and
a thousand inhibitory neurons at the exact same millisecond.
Speaker 1: All yelling at it at once.
Speaker 2: Exactly it has to tally all those stops and goes
to figure out what it should physically do next, which.
Speaker 1: Brings us to the actual mechanism of firing. Because if
a neuron is getting yelled at by thousands of other neurons,
how does it physically decide to send its own signal forward? Right?
Speaker 2: What's the trigger?
Speaker 1: The sources outline this concept called the leaky integrate and
fire model.
Speaker 2: Leaky integrate and fire.
Speaker 1: Yeah, and this is the mathematical rule set governing the
exact moment a single neuron sparks. When I was looking
through our notes, the best analogy I could come up
with is a bucket catching drips of water.
Speaker 2: Okay, walk me through the bucket.
Speaker 1: Imagine a metal bucket suspended on a pivoting hinge. Water,
which represents the electrical signals coming from all those excitatory
gas pedal neurons, is gripping into.
Speaker 2: It, dripping in.
Speaker 1: But the bucket has a tiny hole in the bottom.
It's leaky.
Speaker 2: Ah, so it dreams right.
Speaker 1: If the water drips in slowly, one drop at a time,
it just leaks out the bottom. The bucket never fills up,
and nothing happens. The signals just dissipate.
Speaker 2: The timing is the critical factor.
Speaker 1: Here, exactly, because if a massive rush of water comes
pouring in all at once, like a sudden coordinated burst
of signals from a thousand connected neurons, it fills the
bucket faster than the whole can drain it.
Speaker 2: So the water level rises, it rises.
Speaker 1: Fast, and the moment it hits the very brim, the
balance of the hand shifts. The bucket violently tips over,
dumping all its water forward under the next set.
Speaker 2: Of buckets, and then immediately snaps back.
Speaker 1: Upright, completely empty, ready to start catching drips again. That
mechanical tipping over that is the neuron firing.
Speaker 2: That analogy perfectly captures the elegance of the biology. I mean,
the leaky part is so vital because it basically acts
as a biological filter against background noise.
Speaker 1: Noise is a big problem in the brain, right.
Speaker 2: A huge problem. Our brains and the brains of flies
are constantly buzzing with random, low level electrical static. If
every single input caused a neuron to fire, the brain
would seize up in an endless cascade of chaotic feedback.
Speaker 1: It would just be a white noise machine.
Speaker 2: Exactly. By requiring a concentrated, sudden burst of signals to
cross that threshold to fill the bucket faster than it leaks,
nature ensures that only significant, coordinated information gets passed along.
Speaker 1: That makes total sense.
Speaker 2: And what the researchers I've done here is translate that
exact physical fluid dynamic into a mathematical equation that runs
in the computer simulation.
Speaker 1: Wow. So when you weave all of that together, you
have the physical map of the roads, the varying sizes
of those roads dictating traffic flow, the millions of stopping
go lights, and billions of these microscopic leaky buckets tipping
over and snapping back in a synchronized symphony.
Speaker 2: All running in real time.
Speaker 1: Yes, and the result is life, or you know, at
least the ninety one percent accurate mimicry of life.
Speaker 2: It's remarkably close.
Speaker 1: And the researchers behind this, including Michael Andrag, have said
something that honestly gave me chills. They stated, the ghost
is no longer in the machine. The machine is becoming
the ghost.
Speaker 2: That is a profound statement.
Speaker 1: It really is. They view this entirely differently from just
a neat software trick. They consider this to be a
real uploaded animal.
Speaker 2: They are treating it with gravity.
Speaker 1: They are so serious about this that they are actively
designing a rich, comfortable digital environment for it to live in,
rather than just throwing it in a sterile, blank test box.
Speaker 2: Out of respect for its potential experience, right out.
Speaker 1: Of respect for its experience.
Speaker 2: You know that word experience is heavy. It carries an
immense ethical weight.
Speaker 1: It definitely does.
Speaker 2: If you perfectly, flawlessly simulate the biological mechanics of an organism,
at what point do you accidentally simulate its capacity to suffer?
Speaker 1: Oh Man? Right?
Speaker 2: If the digital fly encounters that simulated heat we talked about,
and its inhibitory neurons force it to back away. Is
it merely executing a stop command? Or is it actually
feeling a rudimentary subjective form of digital pain.
Speaker 1: See, that's exactly why they want to put it in
a nice environment.
Speaker 2: By intentionally choosing to build a comfortable environment for this code,
the researchers are acknowledging that we are crossing a profound
philosophical boundary. They are admitting that they literally don't know
if the lights are on inside that simulation.
Speaker 1: Okay, wait, hold on, I need to stop you there,
because that is exactly where I struggled to buy in.
Speaker 2: Where's the disconnect?
Speaker 1: You and I just spent the last ten minutes describing
a machine? Right, we perfectly replicate the mechanical tipping of
billions of microscopic buckets. Does that actually spark life? Or
is it just the universe's most complex clockwork?
Speaker 2: Ah? The clockwork argument?
Speaker 1: Yeah, look at a Grandfather clock. You can build a
stunningly complex physical clock with thousands of gears, springs, and pendulums.
It will track the sun, the moon until time perfectly.
But the clock doesn't know it's telling time. It has
no inner life. It is just physics playing out its
inevitable chain of reactions. Why is this digital fly any different?
That is a very fair question, right, Is it actually
experiencing its environment or is it just a philosophical zombie,
something that looks alive on the outside but is completely
dark on the inside.
Speaker 2: You are poking right at the heart of the hard
problem of consciousness. I mean, it is the most hotly
debated topic in philosophy and neuroscience right now. It makes
you why because if consciousness is entirely an emergent property
of complex physical interactions, if we accept the premise that
there is no magic soul stuff, no ethereal spirit, and
that biological creatures are fundamentally just highly complex meat machines,
then logically, perfectly replicating the machinery at a different medium
should perfectly replicate the resulting ghosts.
Speaker 1: So in that view, that digital fly wouldn't just be acting,
it would be actually being.
Speaker 2: It would be experiencing.
Speaker 1: But if that's true, then it means our own consciousness
is just a mathematical inevitability of our physical.
Speaker 2: Wiring, precisely, which is uncomfortable for a lot of people to.
Speaker 1: Accept, extremely uncomfortable.
Speaker 2: However, there's the other side, if there is something irreducibly
biological about consciousness, perhaps something tied to I don't know
the quantum state of physical carbon molecules or the specific
chemical nature of organic cells, then no matter how perfectly
you simulate the electrical signals and silicon, you will only
ever create a very convincing puppet.
Speaker 1: So it will act exactly like a fly, but it
will never truly be a fly exactly.
Speaker 2: And the humbling truth is that humanity cannot definitively answer
that question right now.
Speaker 1: You don't know, We.
Speaker 2: Don't, and that uncertainty is exactly why the researchers are
playing it safe. If there is even a one percent
chance that this digital architecture is capable of experiencing distress,
treating it with respect isn't just a courtesy. It's a
moral imperative.
Speaker 1: That is deeply unsettling. Frankly, but it also leads us
to the big.
Speaker 2: Why, the motivation behind all of this.
Speaker 1: Right, if we are just building complex clocks that might
accidentally be suffering, why are we doing this? Why are
we pouring massive amounts of computing power, academic resources, and
time into simulating a tiny fruit fly.
Speaker 2: The answer to that takes us right into the economic
and structural heart of artificial intelligence.
Speaker 1: According to the sources, whole brain emulation is basically the
ultimate shortcut. We are looking at natural evolution as the
most expensive, most extensive training run in the history of
the universe.
Speaker 2: If we connect this to the bigger picture, to put
the sheer value of that biological shortcut into perspective, just
look at the current frontier of AI.
Speaker 1: Big tech companies, right.
Speaker 2: Companies like Open AI, Anthropic Google. They are spending billions
upon billions of dollars. They are building massive, sprawling data
centers that consume the power output of small nations.
Speaker 1: It's an insane amount of energy, and for what to.
Speaker 2: Train models from scratch. They are force feeding massive neural
networks the entirety of human text, image, data, and video,
constantly adjusting those artificial weights we talked about through a
process of trial and error.
Speaker 1: It's just a brute force approach to manufacturing intelligence.
Speaker 2: It's incredibly inefficient.
Speaker 1: I want to make sure we explain how that training
actually works, because it really highlights why the fly is
so special. In AI, they use something called a loss function.
Speaker 2: Right. The loss function basically.
Speaker 1: The AI makes a guest say, predicting the next word
in a sentence. The system checks if the guess was right.
If it was wrong, the loss function mathematically measures how
wrong it was and sends a signal backward through the
network to slightly adjust the weights of the connections so
it makes a better guess next time.
Speaker 2: It's learning from its mistakes mathematically.
Speaker 1: Yeah, but it takes trillions of these tiny, energy hungry
corrections before the AI sounds even remotely smart.
Speaker 2: Trillions. And Nature has been running its own continuous, brutal
version of that exact loss function for hundreds of millions
of years.
Speaker 1: But Nature's loss function is a lot less forgiving.
Speaker 2: In biology, the loss function is death. If an organism's
neural wiring makes a bad prediction, If it fails to
spot a predator, or misjudges a jump, or eat something toxic,
it dies. It's poorly optimized connectome is permanently removed from
the gene pool, so.
Speaker 1: Only the good wiring survives.
Speaker 2: The organisms that survive pass on their highly optimized wiring.
Over millions of years, Nature has figured out how to
make an incredibly capable neural network that runs on a
fraction of a lot of power.
Speaker 1: Our brains run on basically the power of a dim
light bulb.
Speaker 2: Exactly.
Speaker 1: I love the analogy brought up in the sources to
explain this. Imagine you are a computer scientist and you've
been working for years, spending billions of dollars in a
massive server farm trying to code an AI from scratch.
Speaker 2: Sweating over thousands of lines of code.
Speaker 1: Right then one day you take a walk in the woods,
you brush past some ferns, look up, and you find
a fully functioning version of Chatgypt just growing on a tree,
like a piece of fruit, nature's bounty. Yeah, you just
pluck it off the branch, plug it into a power source,
and it works perfectly instantly, without you having had to
write a single line of training code.
Speaker 2: That is exactly what this fly connectome represents.
Speaker 1: We are literally harvesting pre trained intelligence directly from nature.
By copying the fly, scientists are completely bypassing the training phase.
Speaker 2: Sensory input flows in neuroactivity propagates through the mapped connectome,
Motor commands flow out, and the digital body just moves.
Speaker 1: The loop from perception to action is closed instantly.
Speaker 2: It circumvents the most massive bottlenecks in current computational science,
and the sources point out three major world changing benefits
to pursuing this emulation approach. Okay, let's go through first
the impact on understanding and treating diseases.
Speaker 1: This one is huge.
Speaker 2: If we can continue this trajectory and eventually simulate a
human brain with this level of fidelity, we can simulate
its flaws. Currently, testing psychiatric or neurological drugs is incredibly slow, dangerous,
and relies heavily on animal models.
Speaker 1: And a mouse brain isn't a human brain exactly.
Speaker 2: The translation is notoriously poor. But if you have a
perfectly mapped digital human brain, you could introduce the physiological
markers of Alzheimer's or the synaptic misfires associated with schizophrenia
directly into the digital model.
Speaker 1: Oh wow, so you could run clinical trials in the computer.
Speaker 2: You could test millions of potential chemical interventions safely in
seconds simultaneously, without ever putting a single human patient at risk.
The acceleration of medical research would be completely unrecognizable compared
to today.
Speaker 1: That is just incredible. Okay, so that's benefit one, curing
the mind. What's the second benefit?
Speaker 2: Discovering true intelligence algorithms?
Speaker 1: How is that different from what AI is doing now well.
Speaker 2: Current large language models are amazing at pattern recognition, right,
but they are incredibly rigid. They hallucinate facts, they struggle
with basic spatial reasoning, and they don't learn continuously.
Speaker 1: Right. If an LM is trained on data up to
twenty twenty four, it doesn't intuitively learn new things in
twenty twenty five.
Speaker 2: Not unless you pause it and run another massive, expensive
training cycle. But a biological brain, think about a toddler.
A toddler learns continuously from its environment with just a
few examples.
Speaker 1: So biology has a better learning algorithm.
Speaker 2: Biological brains have algorithmic secrets hidden in their architecture, ways
of processing spatial memory, ways of handling generalized logic that
we haven't even dreamed of yet in computer science.
Speaker 1: So we copy it to steal the secrets.
Speaker 2: By reverse engineering the fly and eventually mammals, we can
extract those biological algorithms and apply them to artificial intelligence,
unlocking a level of agi artificial general intelligence that standard
models might never reach.
Speaker 1: Okay, and the third benefit, well, we are going to
save the third one for the climax of this discussion,
but I'll give you a hint. It involve you, the listener,
and the literal digitization of your identity. It's the big one,
it is. But staying on this idea of copying nature's homework,
I have another massive pushback for you.
Speaker 2: Let's hear it.
Speaker 1: We keep talking about wires and electricity buckets tipping over.
But flesh isn't silicon, No, it's not. Computer is beautifully rigid.
It is binary dealing in clean ones and zeros. But
the wetwaar of our bodies. The inside of a biological
brain is essentially a bathtub.
Speaker 2: A very complicated bath toe, right.
Speaker 1: It is bathed in a chemical soup. Doesn't biology possess
a kind of chaotic fluid magic that clean digital code
can't ever truly replicate.
Speaker 2: That is the exact bottleneck of neurobiology right now, and
it's a phenomenal point. A brain isn't just electrical cables.
Speaker 1: It's hormones and chemicals exactly.
Speaker 2: It is constantly washed in neuromodulators, chemicals like dopamine, serotonin, cortisol,
and agenoline. These chemicals dynamically changed the weights of the
connections we talked about earlier.
Speaker 1: They changed the speed limits on the roads.
Speaker 2: Yes, if we go back to your leaky bucket analogy,
a flood of adrenaline might chemically alter the hinge on
the bucket, making it tip over much easier for the
next ten minutes.
Speaker 1: It actually changes the rules of the hardware on the fly.
It absolutely does, right, because if you are terrified, your
brain physically processes sensory information differently than if you are relaxed.
So how do you code a hormone?
Speaker 2: Well, the current whole brain emulation of the fly is
primarily capturing the electrical architecture, the hard wiring. The true
challenge of moving from a ninety one percent behavioral accuracy
to one hundred percent will absolutely be simulating that chemical chaos.
Speaker 1: So we even do that in a computer.
Speaker 2: The field of computational fluid dynamics is improving exponentially. We
are learning how to mathematically model the diffusion of a
chemical across the three D space. Bridging biology and tech
in this way isn't just about making better AI. It's
about fundamentally understanding the physics of emotion and state change.
Speaker 1: Wow, Okay, let's talk about the timeline here, because the
pace of this is staggering. The sources describe what they
call the neuron scaling ladder.
Speaker 2: The ladder is steep.
Speaker 1: It really highlights how fast this is moving. Just a
little over a year ago, the scientific community was throwing
a massive celebration over something called the OpenWorm Project.
Speaker 2: Oh I remember that.
Speaker 1: They managed to fully map and simulate the connectome of
a nematode worm known as Sea Elegance. Now the ground this.
That worm has exactly three hundred and two.
Speaker 2: Neurons three hundred and two.
Speaker 1: It is incredibly simple. In the simulation. It could do
very basic binary things wiggle forward toward a chemical attractant,
wiggle backward away from a digital barrier. That was a
year ago, and today today we were discussing the fruit fly,
which sits at roughly one hundred and fifty thousand neurons.
We went from three hundred to one hundred and fifty
thousand almost overnight.
Speaker 2: To truly grasp where this trajectory is heading, we need
to walk up that evolutionary ladder. The leap from the
worm to the fly is immense, but it is just
the foothills of the mountain.
Speaker 1: Where do we go next?
Speaker 2: Consider a starfish. It has no centralized brain, but it
can navigate complex reef environments. Coordinate its limbs and hunt prey.
It operates on about five hundred neurons.
Speaker 1: Okay.
Speaker 2: Then we jump to ants. Ants build intricate underground colonies
with specialized chambers for nurseries and waste. They wage organized
wars against rival colony. Certain species even farm fungus, literally
cultivating crops underground.
Speaker 1: That is insane for a bug.
Speaker 2: And they accomplish all of this complex societal behavior with
roughly two hundred and fifty thousand neurons.
Speaker 1: And then you move up to the honeybee. They are
capable of complex spatial communication. When a scout bee finds
a field of flowers miles away, it returns to the
hive and performs a physical waggle dance.
Speaker 2: The math behind that dance is astounding.
Speaker 1: It mathematically communicates the exact distance and angle of the
food source relative to the sun. Bees can even be
trained to recognize individual human faces. They do this with
nearly one million neurons.
Speaker 2: The environmental requirements drive the neural complexity, as organisms require
faster processing of more complex sensory data than numbers. Swell
turtles have about eight million neurons.
Speaker 1: Okay, jumping into the millions.
Speaker 2: Bats, which have to fly through dense forests and pitch
black calculating the return time of echolocation, bounce in real
time to intercept erratic flying insects. They require about thirty
five million neurons.
Speaker 1: I want to highlight the octopus because it is the
weirdest and most fascinating stop on this ladder.
Speaker 2: Oh Octopuses are incredible.
Speaker 1: They are highly intelligent, They solve complex puzzles. They use
coconut shells as armor, They literally use tools. They come
in heavy at five hundred million neuros billion. But what
is crazy about the octopus, and what proves that it's
not just about the raw number of neurons is where
those neurons are right.
Speaker 2: The location matters.
Speaker 1: The octopus's brain isn't entirely centralized in its head. A
massive portion of those five hundred million neurons are distributed
throughout its eight arms.
Speaker 2: It is a total marvel of decentralized computing. And there
is an evolutionary y for that.
Speaker 1: Why not keep it all on the head.
Speaker 2: Well, an octopus lives in a highly dynamic, physically demanding
aquatic environment. If a tentacle touches a sharp coral or
a predator, waiting for a nerve signal to travel all
the way up the arm into a central brain be processed,
and for a movement command travel all the way back down.
Speaker 1: That latency could literally mean death exactly.
Speaker 2: Evolutions solve this by putting localized mini brains in the arms.
The arm can process the sensory data and react autonomously,
almost without bothering the central brain. Wow, it proves that
the architecture of the connectome is just as vital as
the neuron count itself.
Speaker 1: And as we keep climbing, the numbers become astronomical. Western
gorillas have about nine billion neurons, and right at the
top of this biological ladder we find orcas in humans.
Speaker 2: The apex of the connectome.
Speaker 1: Now, I want to clarify a detail from the sources.
Some older charts, including one reference in our notes, list
the human brain at twenty one billion neurons.
Speaker 2: That data is a bit outdated, right.
Speaker 1: The current, rigorously established reality in neuroscience is that the
human brain boasts roughly eighty six billion neurons.
Speaker 2: Eighty six billion, I mean, it is a number so
vast that human cognition genuinely struggles to comprehend it.
Speaker 1: We try to visualize it, because dropping a number like
eighty six billion doesn't do justice to the complexity. Let's
abandon the cliches. Let's not call it a hockey stick curve.
Speaker 2: Let's look at scale, right, the.
Speaker 1: Open worm simulation from a year ago, the three hundred
and two neurons. Imagine that as a single grain of
sand sitting in the palm of your hand.
Speaker 2: One grain of sand the fruitfly we.
Speaker 1: Are discussing today with one hundred and fifty thousand neurons
would be a large handful of sand. Okay, hand The
human brain at eighty six billion neurons is not a
bucket of sand. It is not a sandbox. It is
an entire.
Speaker 2: Coastline, miles and miles of beach, yes.
Speaker 1: Where every single grain of sand is connected to ten
thousand other grains of sand by microscopic pulsing wires of
electricity and chemistry.
Speaker 2: And every single connection is constantly shifting its weight, learning
and adapting.
Speaker 1: There's an incredible project mentioned to the sources called flywire
dot AI, brought to us by researchers at Princeton University.
If you go to the site, you can visually explore
the fruitfly connectome.
Speaker 2: It's beautiful. Really.
Speaker 1: You can literally fly your camera through a female fruitflies
neural connections in three D space. It looks like you
are gliding through a dense, brilliantly colored nebula in deep space,
glowing tendrils wrapping around each other in infinite complexity.
Speaker 2: And that is just the handful of sands.
Speaker 1: Just one hundred and fifty thousand. Try to paint a
word picture of what mapping the entire coastline of eighty
six billion human neurons would look like. It wouldn't just
be a galaxy. It would be an entire observable universe
of synapses firing in a synchronized chemical symphony.
Speaker 2: It's almost spiritual.
Speaker 1: Every single memory you have of your childhood, the smell
of your grandparents house, every time you have ever fallen
in love, every piece of trivia you know, every deep
seated fear you hold, all of it. Your entire identity
is just the physical geometry of that universe.
Speaker 2: It is profoundly humbling to consider that our entire subjective
reality is housed within that dense web of connections.
Speaker 1: And looking at this progression, I can't help but compare
it to the history of video game consoles. A year ago,
with a three h two neuron wormy essentially built Pong.
Speaker 2: Two paddles and bouncing square, very basic roles of physics.
Speaker 1: Right today, with one hundred and fifty thousand neuron Fly,
we've built a fully realized open world role playing game.
The Fly is exploring making dynamic, complex choices in a
three D environment. We made the leap from Pong to
an open world RPG in roughly one year.
Speaker 2: The acceleration is breathtaking.
Speaker 1: But here is where it gets really interesting. And here's
my pushback to you, as we inevitably climb that ladder
toward the eighty six billion coastline. Is consciousness just a
math problem?
Speaker 2: What you mean?
Speaker 1: Does hitting a magical, arbitrary number of neurons suddenly turn
the lights on inside of mind? Ah? If we simulate
an ant at two hundred and fifty thousand, is it
dark inside? What about a dog at a couple of billion?
Is there a specific threshold where the simulation suddenly wakes
up and says I am.
Speaker 2: That is the crux of what philosophers and neuroscientists call
the emergence theory of consciousness. It posits that consciousness isn't
a specific.
Speaker 1: Substance like there's no soul atom right.
Speaker 2: There is no so single dedicated awareness neuron hidden deep
in the brain. Rather, consciousness is an emergent property of
complex systems. But to answer your question directly, no, it
is highly unlikely that hitting a magic number flips a.
Speaker 1: Switch from dark to light, so it's not binary.
Speaker 2: It is almost certainly a greadi ant. The correlation between
raw neuron counts and intelligence is strong. But as we
discussed with the octopus, the density and specific regional architecture
are what weave the tapestry of awareness.
Speaker 1: So how things are connected matters just as much as
how many things.
Speaker 2: There are exactly. The cerebral cortex in humans is incredibly dense,
physically folded over on itself over and over to maximize
surface area and interconnectivity within the confines of the skull.
Speaker 1: Those folds give us more processing power.
Speaker 2: Yes, so a simulated ant might have a very dim,
highly reactive flicker of awareness, a pure sensory experience of
the present moment, without any capacity for reflection or memory
of the past.
Speaker 1: A flicker.
Speaker 2: A simulated dog might have a rich, vibrant, emotional and
spatial awareness The lights don't just turn on at a
specific number. They slowly fade up from black, passing through
shades of rudimentary feeling all the way to a blinding
brilliance of self reflection as complexity increases.
Speaker 1: Okay, we have spent the first half of this discussion
talking about taking biology, mapping its physical roads, and perfectly
recreating it inside a computer. We're trying to push biology
into silicon.
Speaker 2: That's the emulation path.
Speaker 1: But the sources we are exploring today also took us
down a completely different, almost reverse path that I genuinely
cannot stop thinking about. So biocomputing, yes, because simulating one
hundred and fifty thousand neurons is pushing the limits of
our current supercomputers. Silicon is becoming a bottleneck. So some
researchers asked a terrifyingly brilliant question.
Speaker 2: If silicon is the bottleneck.
Speaker 1: What if we stop trying to put biology into a
computer and just use biology as the computer? We have
to talk about biocomputing and the petri dish that learn
to play the video game Doom.
Speaker 2: Yes sounds where the narrative sharply shifts from mimicking biology
to actively employing living tissue as hardware.
Speaker 1: It's wild.
Speaker 2: It sounds intensely macague, almost dystopian, but the scientific reality
of what has been achieved is staggering.
Speaker 1: I will set the scene for you because it sounds
exactly like the opening montage of a sci fi horror movie.
Scientists took a laboratory Petrie dish.
Speaker 2: Does a standard glass dish.
Speaker 1: Filled it with a nutrient rich liquid soup designed to
keep organic tissue alive, and into that soup they placed
living human brain cells neuron aron's grown from stem cells,
just hanging out in a dish, completely disconnected from a
human body. Then they took a high density microelectrode array
basically a grid of microscopic electrical wires, and placed it
under the cells.
Speaker 2: So the cells are resting on a bed of wires.
Speaker 1: Yes, they hooked that grid up to a computer that
was running the classic nineteen ninety three first person shooter
video game Doom, and the cells played the game.
Speaker 2: It is crucial we break down exactly how the setup
works because it's easy to misunderstand. It is not as
if a tiny disembodied brain is holding a microscopic joystick
and looking at a screen.
Speaker 1: Right, How does a puddle of cells. Know what a
video game is.
Speaker 2: It doesn't. The cells have no eyes, no ears, no
concept of a CaCO demon or a plasma rifle. The
interface is purely electrical.
Speaker 1: Okay.
Speaker 2: Explain that the computer running Doom translates the visual data
of the game into electrical impulses. For instance, if there
is a wall approaching on the left side of the screen,
the computer fires specific electrical signals into the left side
of the electrode grid under the Petri.
Speaker 1: Dish, poking the left side of the cell exactly.
Speaker 2: If there is an enemy in front, a different pattern
of signals is fired into the center. The human cells
receive these electrical signals.
Speaker 1: To the cells. This is just sensory data to the cells.
Speaker 2: This is purely sensory input, no different than the electrical
signals your optic nerve sends to your brain right now.
The cells process these impulses through their organic tangled neural network,
and in response, they fire back their own electrical signals.
Speaker 1: And the computer reads those.
Speaker 2: Computer reads those returning signals and translates them into motor
commands in the game move left, move right, Shoot.
Speaker 1: That perfectly explains the interface, But the absolute craziest part
of this experiment is that the cells actually learn to
get better at the game.
Speaker 2: They improve over time, and.
Speaker 1: They do it without a brain, without an ego, without
wanting to win. Like, how does a mindless smear of
cells learn a complex task?
Speaker 2: It's fascinating.
Speaker 1: I look deep into the mechanics of this, and it
all comes down to a biological imperative called homeostasis, the free.
Speaker 2: Energy principle, as proposed by neuroscientist Carl Friston.
Speaker 1: Exactly. I want to explain this clearly because it's the
secret to biological computing. Cells fundamentally hate chaos.
Speaker 2: They despise it.
Speaker 1: They're biologically driven to seek predictability and stability, that is homeostasis.
In a chaotic, unpredictable environment, a cell cannot manage its
energy resources and it dies.
Speaker 2: So they need order to survive.
Speaker 1: Right, so the researchers use this against them. When the
cells fired a signal that caused the character in Doomed
to move safely or avoid a wall, a computer sent
a clean, highly predictable electrical pulse back into the dish
that steady rhythm. The cells like this. It was stable.
But when the cells fired a signal that caused the
character to get shot by an enemy or run straight
into a wall. The computer blasted the Petri dish with chaotic, unpredictable,
randomized electrical static.
Speaker 2: This raises an important question because the cells experience that
static as a fundamental existential threat. It is environmental chaos.
Speaker 1: And so incredibly, the neural network inside the dish physically
begins to rewire itself. The cells break old connections and
form new ones, constantly adjusting their internal architecture until their
outputs consistently result in the predictable, safe electrical pulse.
Speaker 2: They adapt to survive the simulation.
Speaker 1: They literally learned to play doom to avoid enemies and
navigate the map solely to minimize the chaos static. We've
essentially weaponized their will to survive to create a biological joystick.
Speaker 2: It's a bioalgorithm.
Speaker 1: But as I'm picturing this dish of human tissue desperately
rewiring itself to avoid static, I have a massive pushback
because it feels like we are anthropomorphizing a chemical reaction. Yes, so,
just because human cells react to electrical impulses to avoid static,
it doesn't mean the cells know they're playing a game.
Where do we draw the line between a biological reflex
and an actual thought.
Speaker 2: The reflex arc.
Speaker 1: Yeah, if a doctor taps my knee with a hammer,
my leg kicks out, that's a reflex. My brain isn't
thinking about kicking. Isn't this peatrie dish just a highly
complex knee jerk?
Speaker 2: That distinction, the line between reflex and thought is perhaps
the most important ethical and philosophical question of the coming decade.
Speaker 1: The blurry line.
Speaker 2: You are absolutely correct that a few hundred thousand cells
in a dish do not possess a prefrontal cortex. They
aren't strategizing. They aren't feeling the adrenaline rush of a
close call.
Speaker 1: In the game right, they aren't gamer.
Speaker 2: It is at its core a highly complex reflex arc
seeking homeostasis. However, we must ask ourselves an uncomfortable question,
at what point does a massive network of interlocking reflexes
become a thought?
Speaker 1: That's the emergence theory again.
Speaker 2: Yes, a fully formed human brain is fundamentally just eighty
six billion cells doing exactly what those cells in the
picture dish are doing, receiving inputs, processing them, and seeking
optimal outputs to minimize surprise.
Speaker 1: So it's just a matter of scale.
Speaker 2: The difference is merely scale and architecture. The blurring of
these lines is happening at a staggering rate, and.
Speaker 1: The practical implications of this blurring are wild. The sources
point out that the sheer rate at which we are
manipulating this science is accelerating toward a bizarre future.
Speaker 2: Very bizarre.
Speaker 1: They suggest a near future where we don't just have
silicon microchips in our massive, energy hungry data centers, because,
as we established earlier, biology is millions of times more
power efficient than silicon. We could have server farms made
of action lab grown biological tissue bioservers, tanks of engineered
human or animal neurons connected to the Internet, computing our
logistics algorithms. Imagine typing a prompt into an AI, and
the answer isn't generated by a silicon GPU, but by
a vat of bioengineered neural tissue sitting in a temperature
controlled rack somewhere in a warehouse.
Speaker 2: Which forces humanity to confront the terrifying ethics of utility.
Speaker 1: Terrifying is the right word.
Speaker 2: If we engineer a biological computer using human stem cells,
and we grow it until it possesses the processing power
and neuron count of a dog's brain, and we use
it to manage a city's traffic grid. Do we owe
that vat of tissue the ethical rights of a dog?
Speaker 1: Does it need walks? I mean, seriously, what are its rights?
Speaker 2: If it receives chaotic data? Does it suffer if we
scale it up to the size of a human connectome
to run climate simulations? Have we inadvertently enslaved a conscious mind?
Speaker 1: We are rapidly accelerating toward a reality where consciousness, or
at least the biological machinery that creates it, could be manufactured, partitioned, networked,
and sold as cloud computing.
Speaker 2: It's a completely new paradigm of labor.
Speaker 1: If the idea of a biological server farm, feeling pain
doesn't keep you up at night, we need to take
all these threads we've discussed today and weave them into
the final, ultimate existential climax of the.
Speaker 2: Sources, the grand finale.
Speaker 1: We've talked about perfectly simulating a one hundred and fifty
thousand neuron fly to the point where it thinks it
is real. We've talked about human cells computing complex tasks
in a dish to survive we've talked about the inevitable
exponential scaling of this technology toward the eighty six billion
neuron human connectum.
Speaker 2: And this leads us directly to simulation theory. Yes, simulation theory,
the philosophy of Bostrom's trilemma brought into sharp practical focus
by the fruit fly.
Speaker 1: Bring that down for us.
Speaker 2: If we accept the premise that we are currently on
the verge of simulating a biological entity to the point
where it essentially believes its digital world is the base
reac we have to look up at the stars and
ask an incredibly.
Speaker 1: Uncomfortable question, which.
Speaker 2: Is what stops an advanced civilization, perhaps merely a few
hundred or one thousand years ahead of us technologically, from
perfectly simulating our eighty six billion neuron reality, right?
Speaker 1: I mean, if you look at the math of exponential
technological growth, you almost have to conclude that we are
already in the matrix.
Speaker 2: The statistical argument is very persuasive, The.
Speaker 1: Logic is brutal. If a civilization reaches a point where
it can create millions or billions of simulated universes, maybe
to run historical experiments, maybe to test variables for their
own survival or maybe just for entertainment. Then there are
billions of simulated realities and only one true base.
Speaker 2: Reality, one real universe, billions of fake ones.
Speaker 1: Right, the statistical probability that we right now happen to
be the one original biological base reality is incontestemately small.
Speaker 2: We are almost certainly the digital flies and someone else
the server farm, marveling at our own simulations.
Speaker 1: But let's bring it back to the human level, to
the end gain proposed by the researchers and the sources.
The third major benefit of all this whole brain emulation.
Speaker 2: Tech one we hinted at earlier.
Speaker 1: The one I hinted at earlier. They're talking about eventually
uploading the human brain. Your brain perfectly map, your specific
snaptic weights recorded, perfectly replicated in a digital.
Speaker 2: Environment, the literal digitization of.
Speaker 1: The human mind, the ultimate upload.
Speaker 2: This is where the limitations of biology fall away and
the physics of computation opens up terrifying and wonderful possibilities.
Think about the physical constraints of your biological brain right now.
Speaker 1: I mean I get a headache if I read too long.
Speaker 2: Well beyond that, your processing speed is fundamentally limited by
the speed of chemical diffusion. Neural signals travel through your
flesh at roughly one hundred and twenty meters per second,
which seems fast. That feels fast to us, but it
is agonizingly slow compared to the speed of light, which
is how fast signals travel in a fiber optic cable exactly.
Speaker 1: The sources propose that if we can upload a human
connectome perfectly, we are no longer bound by flesh.
Speaker 2: We can theoretically just turn up the clock speed on
the GPUs running the simulation overclock. In the human mind,
you wouldn't just be you. You would be you operating
a thousand or a million times faster.
Speaker 1: You could read and comprehend every book ever written by
humanity in a single afternoon.
Speaker 2: You could experience a subjective lifetime of thought, artistic creation,
and problem solving while only a single second passes in
the physical outside world.
Speaker 1: And because you are now digital data, you possess the
ultimate superpower. You could copy.
Speaker 2: Yourself CGLLC CGLV for consciousness.
Speaker 1: Imagine branching off a million versions of your mind, all
working in parallel collaboration to solve a massive physics problem,
and then merging back together into a single consciousness to
share the memories of the work.
Speaker 2: It's literal godlike superpowers.
Speaker 1: It really is.
Speaker 2: It is the ultimate expression of parallel scaling. It is
exactly the architecture we are seeing in artificial intelligence server
farms today, but applied to the subjective.
Speaker 1: Human consciousness need to change everything.
Speaker 2: It could solve humanity's greatest existential challenges. A collective of
accelerated human minds could cure all remaining physical diseases, engineer
the physics required for interstellar travel, and optimize global resources
in a matter of subjective weeks. But it comes with
a devastating philosophical cost. It fundamentally fractures our definition of
the self.
Speaker 1: And that is exactly where my brain breaks, because I
am totally caught on the save state concept in video games.
Speaker 2: The save state explain that if I.
Speaker 1: Play a game and I save my progress and then
my character walks into a trap and dies, I am
not upset. I just reload the save right, No harm done.
But that's data. If I walk into a futuristic neurology
clinic tomorrow and they use incredibly advanced tech to scan
my eighty six billion neurons perfectly copy the exact chemical
state of my brain and hit upload. A digital copy
of me wakes up in the cloud.
Speaker 2: And it thinks it is you.
Speaker 1: It has all my memories up to that exact second.
It loves my family, It vividly remembers the drive to
the clinic. It unequivocally thinks it is me.
Speaker 2: Yes, it does.
Speaker 1: But the physical biological fleshy me is still sitting in
the chair in the clinic looking at the screen. I
haven't magically transferred my perspective anywhere. I've just been cloned.
Speaker 2: You created a twin.
Speaker 1: And if my physical body then has a heart attack
and dies, did I survive or did I just make
a really convincing digital ghost that is going to live
forever believing it's me, while the original, continuous me just
ceases to exist in the dark.
Speaker 2: You have perfectly articulated the paradox of the ship of Theseus,
but applied to human consciousness.
Speaker 1: The ship of theseus.
Speaker 2: If you replace every single wooden plank on a ship
over time, is it the same ship? If you replicate
every single neuron in silicon, is it the same you?
Speaker 1: That's the trillion dollar question.
Speaker 2: The continuous experience of consciousness seems deeply tied to your
localized physical perspective. The entity that wakes up in the
cloud will absolutely believe it is you.
Speaker 1: It won't feel like a copy.
Speaker 2: It will possess unbroken psychological continuity with your past. It
will feel no disruption, but your localized perspective, the specific
you looking out of your biological eyes right now will end.
When the biological body fails.
Speaker 1: A copy lives, the original dies.
Speaker 2: That is just terrifying. I don't think I would do it.
Speaker 1: However, neuroscientists and philosophers like Hans Morvek have proposed a
solution to this. They suggest that true uploading, true transfer
of the self, requires a gradual transition rather than a
sudden scan and copy.
Speaker 2: How does a gradual transition work? You swap out the
brain while it's running. Essentially, yes, Imagine a surgical procedure,
perhaps involving nanobots, where your biological neurons are replaced with
synthetic silicon equivalents, one at a time over.
Speaker 1: A period of years, neuron b neuron.
Speaker 2: As each biological neuron fails or is removed, a digital
one takes its exact place in the network, maintaining the
precise synaptic weights.
Speaker 1: And connections, and I'm awake for this.
Speaker 2: You remain awake the entire time. You remain continuously you,
but slowly, imperceptibly, the underlying substrate of your consciousness shifts
from organic carbon to synthetic silicon.
Speaker 1: Oh wow.
Speaker 2: Because the network never shuts down. There is no break
in continuity, no moment where a clone is born and
an original dies.
Speaker 1: Just a seamless fade.
Speaker 2: A seamless, gradual fade into the digital realm, until one
day the last biological cell is replaced and you are
fully synthetic, ready to be transmitted to the cloud.
Speaker 1: Oh wow. A slow, conscious biological fade into the digital,
replacing the ship of theseus while it is sailing on
the ocean. That is both a beautiful concept and utterly
viscerally terrifying to imagine undergoing.
Speaker 2: It represents the precipice of a fundamentally new stage in
human evolution. If we survive to see it. We are
moving from the slow, blind process of natural selection to
conscious self directed digital ascension.
Speaker 1: Okay, we have covered a genuinely staggering amount of ground
in our discussion today.
Speaker 2: We really have.
Speaker 1: We started by looking closely at a tiny digital fruitfly
wandering around a physics engine built entirely from a physically
mapped biological connectome without a single line of AI training code.
Speaker 2: A true digital ghost.
Speaker 1: We explored the mechanics of leaky buckets and synaptic weights
to understand how it mimics life. We discussed how Nature's
millions of years of drudal evolution are providing the ultimate
billions of dollars shortcut for our own computing technology.
Speaker 2: Bypassing the training run.
Speaker 1: We climbed the evolutionary neuron ladder, visualizing the vast, starry
galaxies of our own minds. We stared into the unsettling
horror movie reality of human brain cells learning to play
doom in a petri dish just to avoid the chaos
of static.
Speaker 2: The biological joystick.
Speaker 1: And finally, we looked over the precipice at the looming
reality of simulation theory, digital immortality, and the philosophical nightmare
of uploading our eighty six billion neurons to the cloud.
It is an immense amount of process.
Speaker 2: It is it demands that we reconsider our place in
the universe, and to leave you with one final thought
to ponder as you step away from this and go
about your day.
Speaker 1: Let's hear it.
Speaker 2: Imagine that far future scenario we discussed where you have
successfully uploaded your mind. Your digital self is now operating
a million times faster than your biological brain ever could.
Speaker 1: Running at the speed of light, you.
Speaker 2: Possess vast parallel intelligence, capable of holding thousands of thoughts simultaneously,
with near infinite perfect memory. To that entity, subjective time
stretches out endlessly into the future. Okay, if that magnificent
accelerated being were to look back at the fleshy biological
you listening to this right now, how would it view you?
Speaker 1: Mean?
Speaker 2: Would it view you the same way we currently look
back at a simple, single celled organism swimming in a puddle,
A primitive, incredibly slow moving ancestor blindly re acting to
its physical environment, entirely unaware of the vast complex reality
that lay just a fraction of an inch beyond its perception.
Speaker 1: Man, that thought is going to keep me up at night.
The idea that we are the single celled organisms of
a future digital species.
Speaker 2: It's a humbling perspective, it really is.
Speaker 1: So we turn it over to you. What do you
think if the technology existed tomorrow, fully proven, safe, and available,
and you were offered the chance to step into a clinic,
upload your mind, gain superhuman parallel processing speeds, and live
forever in a simulated utopia. Would you press enter the
ultimate choice and deeply truly considering the ship of theseus,
do you believe the thing on the other side would
still be you here? Leave a comment below and let
us know where you stand on the digital divide. Thank
you so much for untangling these throlling threads with us.
Speaker 2: Thanks for listening.
Speaker 1: Until next time, keep questioning your reality.