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Speaker 1: I want you to picture something for a second, close

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your eyes if you have to, and just, you know,

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visualize a super sterile, brightly lit laboratory table.

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Speaker 2: Okay, I am picturing it right.

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Speaker 1: So sitting on that table as a screen, and on

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that screen is a fruit fly and it's just walking.

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Speaker 2: Around just do a normal fly things exactly.

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Speaker 1: Yeah, it's pausing to clean its little antenna, it's exploring

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the environment, reacting to digital obstacles, navigating its space. It

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is behaving entirely naturally.

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Speaker 2: Like if you were a biologist studying fly behavior, you

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literally wouldn't be able to tell the difference between this

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fly and one just buzzing around your kitchen.

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Speaker 1: You really wouldn't. But here is the absolute mind bender

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that fly on the screen. It was never born, right,

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It has no physical body at all, no heartbeat, no respiration,

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no cells. It is completely a digital ghost.

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Speaker 2: A completely simulated entity running entirely inside a computer architecture.

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I mean, it is arguably one of the most profound

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technological achievements of our time. And honestly, when you strip

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away the layers of what the researchers have actually accomplished here,

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it sounds like pure unfiltered science fiction. But it's happening

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right now.

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Speaker 1: It's happening today, So welcome to Thrilling Threads. We are

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so incredibly glad you're here with us today because the

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exploration we are embarking on is going to completely shatter

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the way you think about biology, technology, and I really

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don't say this lightly your own subjective reality.

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Speaker 2: Yeah, we are definitely going off the deep end today.

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Speaker 1: We really are.

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Speaker 2: So.

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Speaker 1: Our mission today is to unpack the breathtaking, almost terrifying

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reality of whole brain emulation. We're looking at recent breakthroughs

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where scientists have actually mapped the physical brain of a

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living fly, dropped that precise map into a physics simulated body,

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and just watched it wake up.

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Speaker 2: And the kicker. The thing that really separates this from,

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you know, everything you've heard about artificial intelligence lately is

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that they did this with zero AI training, none, zero,

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none at all. It's just copied biology. We're taking you

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right to the razor's edge of what is computationally and

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biologically possible today.

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Speaker 1: Right, Okay, let's unpack this because I want to approach

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this carefully. I've been staring at these research papers and

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the sheer scale of the engineering problem is just it's

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hard to wrap your head around.

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Speaker 2: It's massive. And what's fascinating here is that we aren't

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just talking about bugs, you know, the humble fruit fly

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is merely a proof of concept, a stepping stone. Exactly

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what we are really putting on the table in this

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discussion is the foundational architecture for the future of human consciousness.

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Speaker 1: Which is just wild to think about. But like, how

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do you even begin to build a digital fly? The

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sources detailed this astonishing achievement, and as you mentioned, they

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didn't use standard artificial intelligence.

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Speaker 2: Right, they didn't use reinforcement learning.

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Speaker 1: Yeah, they didn't just put a blank computer program into

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a digital box, reward it for doing fly things and

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punish it for doing nonfly things until it like learned

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how to act.

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Speaker 2: No, that's the old way of doing things, right.

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Speaker 1: Instead, they took what's called a connectum.

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Speaker 2: Yes, the connectome. That is basically the magic word for

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the next decade of neuroscience.

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Speaker 1: So before we get into how it simulates life, we

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have to talk about how they even get that connectome.

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It's the literal physical wiring map of a fruit fly's brain.

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Speaker 2: But a fly brain is the size of a poppy seed,

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a poppy seed, I mean, think about that. To map it,

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they had to take this microscopic physical brain, embed it

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in a hard resin, and then use a diamond knife

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to slice it.

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Speaker 1: A tiny diamond knife, slicing a poppy.

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Speaker 2: Seed brain thousands of times into impossibly thin sections. We

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are talking about slices that are fractions of a micrometer thick.

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Speaker 1: That's just beyond human comprehension, honestly.

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Speaker 2: It is. And then they scan every single one of

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those slices with an electron microscope, generating massive amounts of

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image data.

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Speaker 1: Right, so they have all these pictures.

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Speaker 2: Millions of them, and they have computers align those thousands

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of images to trace the physical wires, the actual neurons

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from one end of the brain to the other.

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Speaker 1: So just so, acquiring that map is a herculean task

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on its own.

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Speaker 2: Oh absolutely, But once they had it, they wired it

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up digitally neuron to neuron, connected it to a simulated body,

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and achieved a ninety one percent behavioral accuracy.

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Speaker 1: Ninety one percent. The digital fly just started acting like

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a biological fly.

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Speaker 2: Because it's a fundamental paradigm shift in how we approach simulation.

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We aren't writing code to mimic behavior anymore.

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Speaker 1: We are copying the physical architecture that literally generates the

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behavior exactly.

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Speaker 2: And to really understand how groundbreaking this is, we need

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to weave together the mechanics of what these researchers built.

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It's not just a single static map.

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Speaker 1: Right, It's an active, dynamic system.

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Speaker 2: Imagine the physical roads of a city. That graph of connections,

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the architectural blueprint we just talked about getting from the

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electron microscope is just the concrete.

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Speaker 1: Okay, I'm picturing the concrete.

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Speaker 2: It tells you exactly which neuron links to which other neuron.

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If the fly's brain is New York City, the graph

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is the map of every single road, alleyway, and highway.

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Speaker 1: But a map of empty roads doesn't give you a living,

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breathing city. You need to know how the traffic flows precisely.

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Speaker 2: The roads alone are static. What brings it to life

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is understanding the capacity of those roads.

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Speaker 1: So how does biology determine capacity?

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Speaker 2: In biological terms, this is determined by the number of

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synapses connecting those neurons. A connection with a high number

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of synapses has what we call a heavier weight.

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Speaker 1: A heavier weight Okay.

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Speaker 2: This means a signal traveling down that specific path has

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a much stronger influence on the neuron it connects to.

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If we stick to the city analogy, a heavily weighted

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connection is a massive eight lane super highway.

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Speaker 1: Right, whereas a weekly weighted connection is like what a

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one way dirt road exactly.

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Speaker 2: And this actually operates very similarly to how traditional large

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language models work, you know, predicting the next word based

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on the weighted strength of connections between concepts.

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Speaker 1: But here the weights aren't predicting texts.

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Speaker 2: No, they're predicting physical behavior based on actual biological reality.

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Speaker 1: I want to pause on that because it's a really

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crucial distinction for you listening. In a normal large language model,

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those weights are adjusted artificially right over millions of training cycles,

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until the math basically outputs a good sounding sentence.

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Speaker 2: Yeah, it's just math.

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Speaker 1: But here the weights aren't artificial math. They are a

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literal physical count of synapses observed under that electron microscope.

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Speaker 2: A thick biological cable carries a stronger signal. It's just

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physical reality.

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Speaker 1: So we have the roads, and we have the capacity

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of the roads, But how does the traffic know when

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to stop and when to go.

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Speaker 2: That is the decision making engine, right, And.

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Speaker 1: This is the part of the research I spent a

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lot of time digging into because it dictates the actual

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choices of the fly. It's the map of excitatory and

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inhibitory neurons.

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Speaker 2: So how would you conceptualize that for someone who hasn't

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read the papers.

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Speaker 1: Well, I think of it as the ultimate push and

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pull system. Excitatory neurons are basically the gas pedal. They

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yell yes, do that fire, go, go, go exactly. Inhibitory

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neurons are the breaks. They yell no, don't do that. Stop.

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Speaker 2: So let's put that into a practical scenario for the fly.

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Speaker 1: Okay, So let's say our digital fly encounters a digital

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object that mimics fire or extreme heat. The sensory input

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travels down the optical roads and it hits a cluster

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of inhibitory neurons a break, right, the breaks. Those neurons

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fire off these cascading signals saying that's hot, damage is imminent,

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Halt forward momentum, stop moving. Yes, But at the same time,

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if it encounters the chemical signature of rotting fruit, which

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is food for a fly, the excitatory neurons light.

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Speaker 2: Up the gas pedal.

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Speaker 1: Yeah, they overpower the brakes, saying that's energy, Go get it.

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Speaker 2: It's this massive simultaneous calculation of millions of gas pedals

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and breaks that creates what we observe as a decision.

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Speaker 1: That is exactly it.

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Speaker 2: And it's really important to note that a single neuron

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might be receiving signals from a thousand excitatory neurons and

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a thousand inhibitory neurons at the exact same millisecond.

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Speaker 1: All yelling at it at once.

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Speaker 2: Exactly it has to tally all those stops and goes

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to figure out what it should physically do next, which.

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Speaker 1: Brings us to the actual mechanism of firing. Because if

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a neuron is getting yelled at by thousands of other neurons,

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how does it physically decide to send its own signal forward? Right?

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Speaker 2: What's the trigger?

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Speaker 1: The sources outline this concept called the leaky integrate and

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fire model.

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Speaker 2: Leaky integrate and fire.

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Speaker 1: Yeah, and this is the mathematical rule set governing the

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exact moment a single neuron sparks. When I was looking

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through our notes, the best analogy I could come up

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with is a bucket catching drips of water.

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Speaker 2: Okay, walk me through the bucket.

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Speaker 1: Imagine a metal bucket suspended on a pivoting hinge. Water,

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which represents the electrical signals coming from all those excitatory

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gas pedal neurons, is gripping into.

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Speaker 2: It, dripping in.

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Speaker 1: But the bucket has a tiny hole in the bottom.

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It's leaky.

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Speaker 2: Ah, so it dreams right.

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Speaker 1: If the water drips in slowly, one drop at a time,

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it just leaks out the bottom. The bucket never fills up,

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and nothing happens. The signals just dissipate.

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Speaker 2: The timing is the critical factor.

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Speaker 1: Here, exactly, because if a massive rush of water comes

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pouring in all at once, like a sudden coordinated burst

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of signals from a thousand connected neurons, it fills the

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bucket faster than the whole can drain it.

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Speaker 2: So the water level rises, it rises.

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Speaker 1: Fast, and the moment it hits the very brim, the

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balance of the hand shifts. The bucket violently tips over,

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dumping all its water forward under the next set.

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Speaker 2: Of buckets, and then immediately snaps back.

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Speaker 1: Upright, completely empty, ready to start catching drips again. That

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mechanical tipping over that is the neuron firing.

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Speaker 2: That analogy perfectly captures the elegance of the biology. I mean,

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the leaky part is so vital because it basically acts

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as a biological filter against background noise.

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Speaker 1: Noise is a big problem in the brain, right.

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Speaker 2: A huge problem. Our brains and the brains of flies

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are constantly buzzing with random, low level electrical static. If

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every single input caused a neuron to fire, the brain

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would seize up in an endless cascade of chaotic feedback.

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Speaker 1: It would just be a white noise machine.

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Speaker 2: Exactly. By requiring a concentrated, sudden burst of signals to

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cross that threshold to fill the bucket faster than it leaks,

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nature ensures that only significant, coordinated information gets passed along.

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Speaker 1: That makes total sense.

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Speaker 2: And what the researchers I've done here is translate that

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exact physical fluid dynamic into a mathematical equation that runs

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in the computer simulation.

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Speaker 1: Wow. So when you weave all of that together, you

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have the physical map of the roads, the varying sizes

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of those roads dictating traffic flow, the millions of stopping

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go lights, and billions of these microscopic leaky buckets tipping

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over and snapping back in a synchronized symphony.

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Speaker 2: All running in real time.

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Speaker 1: Yes, and the result is life, or you know, at

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least the ninety one percent accurate mimicry of life.

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Speaker 2: It's remarkably close.

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Speaker 1: And the researchers behind this, including Michael Andrag, have said

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something that honestly gave me chills. They stated, the ghost

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is no longer in the machine. The machine is becoming

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the ghost.

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Speaker 2: That is a profound statement.

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Speaker 1: It really is. They view this entirely differently from just

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a neat software trick. They consider this to be a

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real uploaded animal.

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Speaker 2: They are treating it with gravity.

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Speaker 1: They are so serious about this that they are actively

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designing a rich, comfortable digital environment for it to live in,

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rather than just throwing it in a sterile, blank test box.

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Speaker 2: Out of respect for its potential experience, right out.

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Speaker 1: Of respect for its experience.

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Speaker 2: You know that word experience is heavy. It carries an

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immense ethical weight.

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Speaker 1: It definitely does.

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Speaker 2: If you perfectly, flawlessly simulate the biological mechanics of an organism,

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at what point do you accidentally simulate its capacity to suffer?

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Speaker 1: Oh Man? Right?

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Speaker 2: If the digital fly encounters that simulated heat we talked about,

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and its inhibitory neurons force it to back away. Is

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it merely executing a stop command? Or is it actually

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feeling a rudimentary subjective form of digital pain.

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Speaker 1: See, that's exactly why they want to put it in

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a nice environment.

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Speaker 2: By intentionally choosing to build a comfortable environment for this code,

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the researchers are acknowledging that we are crossing a profound

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philosophical boundary. They are admitting that they literally don't know

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if the lights are on inside that simulation.

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Speaker 1: Okay, wait, hold on, I need to stop you there,

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because that is exactly where I struggled to buy in.

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Speaker 2: Where's the disconnect?

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Speaker 1: You and I just spent the last ten minutes describing

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a machine? Right, we perfectly replicate the mechanical tipping of

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billions of microscopic buckets. Does that actually spark life? Or

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is it just the universe's most complex clockwork?

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Speaker 2: Ah? The clockwork argument?

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Speaker 1: Yeah, look at a Grandfather clock. You can build a

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stunningly complex physical clock with thousands of gears, springs, and pendulums.

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It will track the sun, the moon until time perfectly.

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But the clock doesn't know it's telling time. It has

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no inner life. It is just physics playing out its

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inevitable chain of reactions. Why is this digital fly any different?

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That is a very fair question, right, Is it actually

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experiencing its environment or is it just a philosophical zombie,

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something that looks alive on the outside but is completely

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dark on the inside.

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Speaker 2: You are poking right at the heart of the hard

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problem of consciousness. I mean, it is the most hotly

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debated topic in philosophy and neuroscience right now. It makes

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you why because if consciousness is entirely an emergent property

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of complex physical interactions, if we accept the premise that

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there is no magic soul stuff, no ethereal spirit, and

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that biological creatures are fundamentally just highly complex meat machines,

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then logically, perfectly replicating the machinery at a different medium

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should perfectly replicate the resulting ghosts.

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Speaker 1: So in that view, that digital fly wouldn't just be acting,

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it would be actually being.

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Speaker 2: It would be experiencing.

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Speaker 1: But if that's true, then it means our own consciousness

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is just a mathematical inevitability of our physical.

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Speaker 2: Wiring, precisely, which is uncomfortable for a lot of people to.

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Speaker 1: Accept, extremely uncomfortable.

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Speaker 2: However, there's the other side, if there is something irreducibly

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biological about consciousness, perhaps something tied to I don't know

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the quantum state of physical carbon molecules or the specific

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chemical nature of organic cells, then no matter how perfectly

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you simulate the electrical signals and silicon, you will only

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ever create a very convincing puppet.

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Speaker 1: So it will act exactly like a fly, but it

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will never truly be a fly exactly.

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Speaker 2: And the humbling truth is that humanity cannot definitively answer

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that question right now.

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Speaker 1: You don't know, We.

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Speaker 2: Don't, and that uncertainty is exactly why the researchers are

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playing it safe. If there is even a one percent

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chance that this digital architecture is capable of experiencing distress,

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treating it with respect isn't just a courtesy. It's a

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moral imperative.

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Speaker 1: That is deeply unsettling. Frankly, but it also leads us

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to the big.

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Speaker 2: Why, the motivation behind all of this.

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Speaker 1: Right, if we are just building complex clocks that might

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accidentally be suffering, why are we doing this? Why are

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we pouring massive amounts of computing power, academic resources, and

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time into simulating a tiny fruit fly.

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Speaker 2: The answer to that takes us right into the economic

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and structural heart of artificial intelligence.

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Speaker 1: According to the sources, whole brain emulation is basically the

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ultimate shortcut. We are looking at natural evolution as the

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most expensive, most extensive training run in the history of

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the universe.

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Speaker 2: If we connect this to the bigger picture, to put

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the sheer value of that biological shortcut into perspective, just

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look at the current frontier of AI.

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Speaker 1: Big tech companies, right.

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Speaker 2: Companies like Open AI, Anthropic Google. They are spending billions

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upon billions of dollars. They are building massive, sprawling data

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centers that consume the power output of small nations.

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Speaker 1: It's an insane amount of energy, and for what to.

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Speaker 2: Train models from scratch. They are force feeding massive neural

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networks the entirety of human text, image, data, and video,

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constantly adjusting those artificial weights we talked about through a

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process of trial and error.

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Speaker 1: It's just a brute force approach to manufacturing intelligence.

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Speaker 2: It's incredibly inefficient.

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Speaker 1: I want to make sure we explain how that training

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actually works, because it really highlights why the fly is

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so special. In AI, they use something called a loss function.

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Speaker 2: Right. The loss function basically.

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Speaker 1: The AI makes a guest say, predicting the next word

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in a sentence. The system checks if the guess was right.

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If it was wrong, the loss function mathematically measures how

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wrong it was and sends a signal backward through the

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network to slightly adjust the weights of the connections so

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it makes a better guess next time.

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Speaker 2: It's learning from its mistakes mathematically.

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Speaker 1: Yeah, but it takes trillions of these tiny, energy hungry

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corrections before the AI sounds even remotely smart.

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Speaker 2: Trillions. And Nature has been running its own continuous, brutal

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version of that exact loss function for hundreds of millions

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of years.

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Speaker 1: But Nature's loss function is a lot less forgiving.

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Speaker 2: In biology, the loss function is death. If an organism's

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neural wiring makes a bad prediction, If it fails to

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spot a predator, or misjudges a jump, or eat something toxic,

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it dies. It's poorly optimized connectome is permanently removed from

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the gene pool, so.

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Speaker 1: Only the good wiring survives.

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Speaker 2: The organisms that survive pass on their highly optimized wiring.

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Over millions of years, Nature has figured out how to

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make an incredibly capable neural network that runs on a

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fraction of a lot of power.

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Speaker 1: Our brains run on basically the power of a dim

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light bulb.

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Speaker 2: Exactly.

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Speaker 1: I love the analogy brought up in the sources to

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explain this. Imagine you are a computer scientist and you've

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been working for years, spending billions of dollars in a

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massive server farm trying to code an AI from scratch.

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Speaker 2: Sweating over thousands of lines of code.

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Speaker 1: Right then one day you take a walk in the woods,

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you brush past some ferns, look up, and you find

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a fully functioning version of Chatgypt just growing on a tree,

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like a piece of fruit, nature's bounty. Yeah, you just

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pluck it off the branch, plug it into a power source,

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and it works perfectly instantly, without you having had to

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write a single line of training code.

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Speaker 2: That is exactly what this fly connectome represents.

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Speaker 1: We are literally harvesting pre trained intelligence directly from nature.

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By copying the fly, scientists are completely bypassing the training phase.

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Speaker 2: Sensory input flows in neuroactivity propagates through the mapped connectome,

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Motor commands flow out, and the digital body just moves.

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Speaker 1: The loop from perception to action is closed instantly.

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Speaker 2: It circumvents the most massive bottlenecks in current computational science,

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and the sources point out three major world changing benefits

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to pursuing this emulation approach. Okay, let's go through first

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the impact on understanding and treating diseases.

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Speaker 1: This one is huge.

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Speaker 2: If we can continue this trajectory and eventually simulate a

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human brain with this level of fidelity, we can simulate

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its flaws. Currently, testing psychiatric or neurological drugs is incredibly slow, dangerous,

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and relies heavily on animal models.

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Speaker 1: And a mouse brain isn't a human brain exactly.

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Speaker 2: The translation is notoriously poor. But if you have a

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perfectly mapped digital human brain, you could introduce the physiological

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markers of Alzheimer's or the synaptic misfires associated with schizophrenia

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directly into the digital model.

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Speaker 1: Oh wow, so you could run clinical trials in the computer.

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Speaker 2: You could test millions of potential chemical interventions safely in

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seconds simultaneously, without ever putting a single human patient at risk.

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The acceleration of medical research would be completely unrecognizable compared

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to today.

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Speaker 1: That is just incredible. Okay, so that's benefit one, curing

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the mind. What's the second benefit?

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Speaker 2: Discovering true intelligence algorithms?

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Speaker 1: How is that different from what AI is doing now well.

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Speaker 2: Current large language models are amazing at pattern recognition, right,

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but they are incredibly rigid. They hallucinate facts, they struggle

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with basic spatial reasoning, and they don't learn continuously.

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Speaker 1: Right. If an LM is trained on data up to

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twenty twenty four, it doesn't intuitively learn new things in

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twenty twenty five.

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Speaker 2: Not unless you pause it and run another massive, expensive

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training cycle. But a biological brain, think about a toddler.

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A toddler learns continuously from its environment with just a

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few examples.

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Speaker 1: So biology has a better learning algorithm.

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Speaker 2: Biological brains have algorithmic secrets hidden in their architecture, ways

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of processing spatial memory, ways of handling generalized logic that

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we haven't even dreamed of yet in computer science.

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Speaker 1: So we copy it to steal the secrets.

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Speaker 2: By reverse engineering the fly and eventually mammals, we can

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extract those biological algorithms and apply them to artificial intelligence,

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unlocking a level of agi artificial general intelligence that standard

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models might never reach.

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Speaker 1: Okay, and the third benefit, well, we are going to

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save the third one for the climax of this discussion,

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but I'll give you a hint. It involve you, the listener,

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and the literal digitization of your identity. It's the big one,

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it is. But staying on this idea of copying nature's homework,

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I have another massive pushback for you.

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Speaker 2: Let's hear it.

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Speaker 1: We keep talking about wires and electricity buckets tipping over.

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But flesh isn't silicon, No, it's not. Computer is beautifully rigid.

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It is binary dealing in clean ones and zeros. But

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the wetwaar of our bodies. The inside of a biological

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brain is essentially a bathtub.

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Speaker 2: A very complicated bath toe, right.

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Speaker 1: It is bathed in a chemical soup. Doesn't biology possess

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a kind of chaotic fluid magic that clean digital code

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can't ever truly replicate.

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Speaker 2: That is the exact bottleneck of neurobiology right now, and

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it's a phenomenal point. A brain isn't just electrical cables.

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Speaker 1: It's hormones and chemicals exactly.

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Speaker 2: It is constantly washed in neuromodulators, chemicals like dopamine, serotonin, cortisol,

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and agenoline. These chemicals dynamically changed the weights of the

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connections we talked about earlier.

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Speaker 1: They changed the speed limits on the roads.

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Speaker 2: Yes, if we go back to your leaky bucket analogy,

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a flood of adrenaline might chemically alter the hinge on

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the bucket, making it tip over much easier for the

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next ten minutes.

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Speaker 1: It actually changes the rules of the hardware on the fly.

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It absolutely does, right, because if you are terrified, your

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brain physically processes sensory information differently than if you are relaxed.

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So how do you code a hormone?

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Speaker 2: Well, the current whole brain emulation of the fly is

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primarily capturing the electrical architecture, the hard wiring. The true

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challenge of moving from a ninety one percent behavioral accuracy

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to one hundred percent will absolutely be simulating that chemical chaos.

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Speaker 1: So we even do that in a computer.

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Speaker 2: The field of computational fluid dynamics is improving exponentially. We

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are learning how to mathematically model the diffusion of a

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chemical across the three D space. Bridging biology and tech

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in this way isn't just about making better AI. It's

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about fundamentally understanding the physics of emotion and state change.

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Speaker 1: Wow, Okay, let's talk about the timeline here, because the

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pace of this is staggering. The sources describe what they

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call the neuron scaling ladder.

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Speaker 2: The ladder is steep.

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Speaker 1: It really highlights how fast this is moving. Just a

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little over a year ago, the scientific community was throwing

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a massive celebration over something called the OpenWorm Project.

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Speaker 2: Oh I remember that.

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Speaker 1: They managed to fully map and simulate the connectome of

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a nematode worm known as Sea Elegance. Now the ground this.

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That worm has exactly three hundred and two.

477
00:23:07,880 --> 00:23:09,200
Speaker 2: Neurons three hundred and two.

478
00:23:09,559 --> 00:23:12,640
Speaker 1: It is incredibly simple. In the simulation. It could do

479
00:23:12,839 --> 00:23:16,839
very basic binary things wiggle forward toward a chemical attractant,

480
00:23:17,000 --> 00:23:19,519
wiggle backward away from a digital barrier. That was a

481
00:23:19,559 --> 00:23:22,799
year ago, and today today we were discussing the fruit fly,

482
00:23:23,279 --> 00:23:26,000
which sits at roughly one hundred and fifty thousand neurons.

483
00:23:26,440 --> 00:23:28,559
We went from three hundred to one hundred and fifty

484
00:23:28,559 --> 00:23:30,000
thousand almost overnight.

485
00:23:30,160 --> 00:23:33,279
Speaker 2: To truly grasp where this trajectory is heading, we need

486
00:23:33,319 --> 00:23:36,119
to walk up that evolutionary ladder. The leap from the

487
00:23:36,119 --> 00:23:38,640
worm to the fly is immense, but it is just

488
00:23:38,680 --> 00:23:39,920
the foothills of the mountain.

489
00:23:40,119 --> 00:23:41,000
Speaker 1: Where do we go next?

490
00:23:41,200 --> 00:23:44,039
Speaker 2: Consider a starfish. It has no centralized brain, but it

491
00:23:44,079 --> 00:23:48,640
can navigate complex reef environments. Coordinate its limbs and hunt prey.

492
00:23:48,960 --> 00:23:50,880
It operates on about five hundred neurons.

493
00:23:50,880 --> 00:23:51,119
Speaker 1: Okay.

494
00:23:51,759 --> 00:23:55,079
Speaker 2: Then we jump to ants. Ants build intricate underground colonies

495
00:23:55,119 --> 00:23:58,720
with specialized chambers for nurseries and waste. They wage organized

496
00:23:58,759 --> 00:24:03,200
wars against rival colony. Certain species even farm fungus, literally

497
00:24:03,240 --> 00:24:04,960
cultivating crops underground.

498
00:24:05,079 --> 00:24:06,440
Speaker 1: That is insane for a bug.

499
00:24:06,519 --> 00:24:09,240
Speaker 2: And they accomplish all of this complex societal behavior with

500
00:24:09,319 --> 00:24:11,359
roughly two hundred and fifty thousand neurons.

501
00:24:11,640 --> 00:24:13,960
Speaker 1: And then you move up to the honeybee. They are

502
00:24:14,000 --> 00:24:18,359
capable of complex spatial communication. When a scout bee finds

503
00:24:18,359 --> 00:24:21,240
a field of flowers miles away, it returns to the

504
00:24:21,319 --> 00:24:23,640
hive and performs a physical waggle dance.

505
00:24:24,319 --> 00:24:27,240
Speaker 2: The math behind that dance is astounding.

506
00:24:27,519 --> 00:24:31,279
Speaker 1: It mathematically communicates the exact distance and angle of the

507
00:24:31,279 --> 00:24:35,200
food source relative to the sun. Bees can even be

508
00:24:35,279 --> 00:24:39,480
trained to recognize individual human faces. They do this with

509
00:24:39,599 --> 00:24:41,440
nearly one million neurons.

510
00:24:41,960 --> 00:24:45,960
Speaker 2: The environmental requirements drive the neural complexity, as organisms require

511
00:24:46,039 --> 00:24:49,319
faster processing of more complex sensory data than numbers. Swell

512
00:24:49,920 --> 00:24:52,279
turtles have about eight million neurons.

513
00:24:51,920 --> 00:24:54,079
Speaker 1: Okay, jumping into the millions.

514
00:24:53,799 --> 00:24:56,839
Speaker 2: Bats, which have to fly through dense forests and pitch

515
00:24:56,880 --> 00:25:00,599
black calculating the return time of echolocation, bounce in real

516
00:25:00,640 --> 00:25:04,359
time to intercept erratic flying insects. They require about thirty

517
00:25:04,359 --> 00:25:05,480
five million neurons.

518
00:25:05,599 --> 00:25:08,079
Speaker 1: I want to highlight the octopus because it is the

519
00:25:08,119 --> 00:25:10,599
weirdest and most fascinating stop on this ladder.

520
00:25:10,640 --> 00:25:12,039
Speaker 2: Oh Octopuses are incredible.

521
00:25:12,079 --> 00:25:15,000
Speaker 1: They are highly intelligent, They solve complex puzzles. They use

522
00:25:15,039 --> 00:25:18,319
coconut shells as armor, They literally use tools. They come

523
00:25:18,319 --> 00:25:21,039
in heavy at five hundred million neuros billion. But what

524
00:25:21,160 --> 00:25:23,920
is crazy about the octopus, and what proves that it's

525
00:25:23,960 --> 00:25:26,599
not just about the raw number of neurons is where

526
00:25:26,640 --> 00:25:27,920
those neurons are right.

527
00:25:28,000 --> 00:25:29,119
Speaker 2: The location matters.

528
00:25:29,200 --> 00:25:32,839
Speaker 1: The octopus's brain isn't entirely centralized in its head. A

529
00:25:32,920 --> 00:25:36,440
massive portion of those five hundred million neurons are distributed

530
00:25:36,480 --> 00:25:37,640
throughout its eight arms.

531
00:25:37,720 --> 00:25:40,400
Speaker 2: It is a total marvel of decentralized computing. And there

532
00:25:40,440 --> 00:25:41,839
is an evolutionary y for that.

533
00:25:42,119 --> 00:25:43,599
Speaker 1: Why not keep it all on the head.

534
00:25:43,680 --> 00:25:47,720
Speaker 2: Well, an octopus lives in a highly dynamic, physically demanding

535
00:25:47,799 --> 00:25:51,920
aquatic environment. If a tentacle touches a sharp coral or

536
00:25:51,920 --> 00:25:55,079
a predator, waiting for a nerve signal to travel all

537
00:25:55,119 --> 00:25:58,279
the way up the arm into a central brain be processed,

538
00:25:58,640 --> 00:26:01,400
and for a movement command travel all the way back down.

539
00:26:01,759 --> 00:26:04,200
Speaker 1: That latency could literally mean death exactly.

540
00:26:04,599 --> 00:26:08,119
Speaker 2: Evolutions solve this by putting localized mini brains in the arms.

541
00:26:08,680 --> 00:26:12,279
The arm can process the sensory data and react autonomously,

542
00:26:12,480 --> 00:26:15,640
almost without bothering the central brain. Wow, it proves that

543
00:26:15,680 --> 00:26:18,440
the architecture of the connectome is just as vital as

544
00:26:18,440 --> 00:26:19,640
the neuron count itself.

545
00:26:19,839 --> 00:26:23,720
Speaker 1: And as we keep climbing, the numbers become astronomical. Western

546
00:26:23,759 --> 00:26:27,680
gorillas have about nine billion neurons, and right at the

547
00:26:27,720 --> 00:26:31,799
top of this biological ladder we find orcas in humans.

548
00:26:31,480 --> 00:26:32,920
Speaker 2: The apex of the connectome.

549
00:26:33,079 --> 00:26:35,200
Speaker 1: Now, I want to clarify a detail from the sources.

550
00:26:35,359 --> 00:26:38,359
Some older charts, including one reference in our notes, list

551
00:26:38,440 --> 00:26:41,640
the human brain at twenty one billion neurons.

552
00:26:41,920 --> 00:26:44,160
Speaker 2: That data is a bit outdated, right.

553
00:26:44,240 --> 00:26:47,640
Speaker 1: The current, rigorously established reality in neuroscience is that the

554
00:26:47,720 --> 00:26:53,119
human brain boasts roughly eighty six billion neurons.

555
00:26:53,400 --> 00:26:56,039
Speaker 2: Eighty six billion, I mean, it is a number so

556
00:26:56,319 --> 00:26:59,400
vast that human cognition genuinely struggles to comprehend it.

557
00:27:00,079 --> 00:27:02,559
Speaker 1: We try to visualize it, because dropping a number like

558
00:27:02,599 --> 00:27:05,599
eighty six billion doesn't do justice to the complexity. Let's

559
00:27:05,599 --> 00:27:08,200
abandon the cliches. Let's not call it a hockey stick curve.

560
00:27:08,279 --> 00:27:09,839
Speaker 2: Let's look at scale, right, the.

561
00:27:09,799 --> 00:27:12,319
Speaker 1: Open worm simulation from a year ago, the three hundred

562
00:27:12,319 --> 00:27:14,880
and two neurons. Imagine that as a single grain of

563
00:27:14,920 --> 00:27:16,400
sand sitting in the palm of your hand.

564
00:27:16,519 --> 00:27:18,240
Speaker 2: One grain of sand the fruitfly we.

565
00:27:18,240 --> 00:27:21,039
Speaker 1: Are discussing today with one hundred and fifty thousand neurons

566
00:27:21,160 --> 00:27:23,440
would be a large handful of sand. Okay, hand The

567
00:27:23,519 --> 00:27:27,559
human brain at eighty six billion neurons is not a

568
00:27:27,599 --> 00:27:30,759
bucket of sand. It is not a sandbox. It is

569
00:27:30,799 --> 00:27:31,519
an entire.

570
00:27:31,400 --> 00:27:34,400
Speaker 2: Coastline, miles and miles of beach, yes.

571
00:27:34,759 --> 00:27:37,200
Speaker 1: Where every single grain of sand is connected to ten

572
00:27:37,240 --> 00:27:41,279
thousand other grains of sand by microscopic pulsing wires of

573
00:27:41,319 --> 00:27:42,759
electricity and chemistry.

574
00:27:42,440 --> 00:27:45,720
Speaker 2: And every single connection is constantly shifting its weight, learning

575
00:27:45,799 --> 00:27:46,559
and adapting.

576
00:27:47,079 --> 00:27:50,319
Speaker 1: There's an incredible project mentioned to the sources called flywire

577
00:27:50,359 --> 00:27:54,440
dot AI, brought to us by researchers at Princeton University.

578
00:27:54,519 --> 00:27:56,960
If you go to the site, you can visually explore

579
00:27:57,000 --> 00:27:58,200
the fruitfly connectome.

580
00:27:58,359 --> 00:27:59,480
Speaker 2: It's beautiful. Really.

581
00:27:59,519 --> 00:28:02,839
Speaker 1: You can literally fly your camera through a female fruitflies

582
00:28:03,119 --> 00:28:06,079
neural connections in three D space. It looks like you

583
00:28:06,119 --> 00:28:09,960
are gliding through a dense, brilliantly colored nebula in deep space,

584
00:28:10,279 --> 00:28:13,319
glowing tendrils wrapping around each other in infinite complexity.

585
00:28:13,359 --> 00:28:15,319
Speaker 2: And that is just the handful of sands.

586
00:28:15,240 --> 00:28:17,599
Speaker 1: Just one hundred and fifty thousand. Try to paint a

587
00:28:17,640 --> 00:28:20,559
word picture of what mapping the entire coastline of eighty

588
00:28:20,559 --> 00:28:23,359
six billion human neurons would look like. It wouldn't just

589
00:28:23,440 --> 00:28:26,319
be a galaxy. It would be an entire observable universe

590
00:28:26,359 --> 00:28:29,440
of synapses firing in a synchronized chemical symphony.

591
00:28:29,440 --> 00:28:30,319
Speaker 2: It's almost spiritual.

592
00:28:30,440 --> 00:28:33,680
Speaker 1: Every single memory you have of your childhood, the smell

593
00:28:33,720 --> 00:28:36,480
of your grandparents house, every time you have ever fallen

594
00:28:36,480 --> 00:28:39,559
in love, every piece of trivia you know, every deep

595
00:28:39,599 --> 00:28:42,359
seated fear you hold, all of it. Your entire identity

596
00:28:42,759 --> 00:28:45,079
is just the physical geometry of that universe.

597
00:28:45,279 --> 00:28:48,519
Speaker 2: It is profoundly humbling to consider that our entire subjective

598
00:28:48,559 --> 00:28:51,559
reality is housed within that dense web of connections.

599
00:28:51,839 --> 00:28:54,279
Speaker 1: And looking at this progression, I can't help but compare

600
00:28:54,319 --> 00:28:57,480
it to the history of video game consoles. A year ago,

601
00:28:57,759 --> 00:29:02,240
with a three h two neuron wormy essentially built Pong.

602
00:29:02,240 --> 00:29:05,240
Speaker 2: Two paddles and bouncing square, very basic roles of physics.

603
00:29:05,319 --> 00:29:08,039
Speaker 1: Right today, with one hundred and fifty thousand neuron Fly,

604
00:29:08,359 --> 00:29:11,279
we've built a fully realized open world role playing game.

605
00:29:11,640 --> 00:29:14,759
The Fly is exploring making dynamic, complex choices in a

606
00:29:14,799 --> 00:29:17,480
three D environment. We made the leap from Pong to

607
00:29:17,519 --> 00:29:19,519
an open world RPG in roughly one year.

608
00:29:19,680 --> 00:29:21,559
Speaker 2: The acceleration is breathtaking.

609
00:29:21,920 --> 00:29:24,200
Speaker 1: But here is where it gets really interesting. And here's

610
00:29:24,240 --> 00:29:27,319
my pushback to you, as we inevitably climb that ladder

611
00:29:27,359 --> 00:29:30,720
toward the eighty six billion coastline. Is consciousness just a

612
00:29:30,720 --> 00:29:31,359
math problem?

613
00:29:31,400 --> 00:29:31,799
Speaker 2: What you mean?

614
00:29:32,000 --> 00:29:35,079
Speaker 1: Does hitting a magical, arbitrary number of neurons suddenly turn

615
00:29:35,079 --> 00:29:38,000
the lights on inside of mind? Ah? If we simulate

616
00:29:38,039 --> 00:29:40,240
an ant at two hundred and fifty thousand, is it

617
00:29:40,319 --> 00:29:43,839
dark inside? What about a dog at a couple of billion?

618
00:29:44,000 --> 00:29:46,880
Is there a specific threshold where the simulation suddenly wakes

619
00:29:46,960 --> 00:29:48,480
up and says I am.

620
00:29:48,640 --> 00:29:51,640
Speaker 2: That is the crux of what philosophers and neuroscientists call

621
00:29:51,720 --> 00:29:56,319
the emergence theory of consciousness. It posits that consciousness isn't

622
00:29:56,319 --> 00:29:57,359
a specific.

623
00:29:56,960 --> 00:29:59,680
Speaker 1: Substance like there's no soul atom right.

624
00:29:59,559 --> 00:30:03,160
Speaker 2: There is no so single dedicated awareness neuron hidden deep

625
00:30:03,200 --> 00:30:06,559
in the brain. Rather, consciousness is an emergent property of

626
00:30:06,599 --> 00:30:10,200
complex systems. But to answer your question directly, no, it

627
00:30:10,279 --> 00:30:13,039
is highly unlikely that hitting a magic number flips a.

628
00:30:13,039 --> 00:30:15,440
Speaker 1: Switch from dark to light, so it's not binary.

629
00:30:15,720 --> 00:30:18,960
Speaker 2: It is almost certainly a greadi ant. The correlation between

630
00:30:19,079 --> 00:30:22,200
raw neuron counts and intelligence is strong. But as we

631
00:30:22,279 --> 00:30:25,759
discussed with the octopus, the density and specific regional architecture

632
00:30:25,960 --> 00:30:28,240
are what weave the tapestry of awareness.

633
00:30:28,319 --> 00:30:30,319
Speaker 1: So how things are connected matters just as much as

634
00:30:30,359 --> 00:30:31,160
how many things.

635
00:30:30,960 --> 00:30:35,480
Speaker 2: There are exactly. The cerebral cortex in humans is incredibly dense,

636
00:30:35,720 --> 00:30:38,920
physically folded over on itself over and over to maximize

637
00:30:38,960 --> 00:30:42,319
surface area and interconnectivity within the confines of the skull.

638
00:30:42,680 --> 00:30:45,160
Speaker 1: Those folds give us more processing power.

639
00:30:45,279 --> 00:30:48,680
Speaker 2: Yes, so a simulated ant might have a very dim,

640
00:30:48,920 --> 00:30:52,920
highly reactive flicker of awareness, a pure sensory experience of

641
00:30:52,920 --> 00:30:56,359
the present moment, without any capacity for reflection or memory

642
00:30:56,359 --> 00:30:56,880
of the past.

643
00:30:57,000 --> 00:30:57,519
Speaker 1: A flicker.

644
00:30:57,960 --> 00:31:01,200
Speaker 2: A simulated dog might have a rich, vibrant, emotional and

645
00:31:01,319 --> 00:31:04,759
spatial awareness The lights don't just turn on at a

646
00:31:04,759 --> 00:31:08,480
specific number. They slowly fade up from black, passing through

647
00:31:08,519 --> 00:31:11,839
shades of rudimentary feeling all the way to a blinding

648
00:31:11,880 --> 00:31:14,759
brilliance of self reflection as complexity increases.

649
00:31:14,839 --> 00:31:17,160
Speaker 1: Okay, we have spent the first half of this discussion

650
00:31:17,200 --> 00:31:21,839
talking about taking biology, mapping its physical roads, and perfectly

651
00:31:21,880 --> 00:31:25,720
recreating it inside a computer. We're trying to push biology

652
00:31:25,799 --> 00:31:26,599
into silicon.

653
00:31:26,799 --> 00:31:28,079
Speaker 2: That's the emulation path.

654
00:31:28,160 --> 00:31:30,599
Speaker 1: But the sources we are exploring today also took us

655
00:31:30,640 --> 00:31:33,759
down a completely different, almost reverse path that I genuinely

656
00:31:33,759 --> 00:31:37,799
cannot stop thinking about. So biocomputing, yes, because simulating one

657
00:31:37,880 --> 00:31:40,440
hundred and fifty thousand neurons is pushing the limits of

658
00:31:40,440 --> 00:31:44,640
our current supercomputers. Silicon is becoming a bottleneck. So some

659
00:31:44,720 --> 00:31:46,839
researchers asked a terrifyingly brilliant question.

660
00:31:47,359 --> 00:31:49,480
Speaker 2: If silicon is the bottleneck.

661
00:31:49,039 --> 00:31:51,319
Speaker 1: What if we stop trying to put biology into a

662
00:31:51,359 --> 00:31:54,720
computer and just use biology as the computer? We have

663
00:31:54,759 --> 00:31:57,519
to talk about biocomputing and the petri dish that learn

664
00:31:57,640 --> 00:31:58,920
to play the video game Doom.

665
00:31:59,160 --> 00:32:03,559
Speaker 2: Yes sounds where the narrative sharply shifts from mimicking biology

666
00:32:03,640 --> 00:32:07,079
to actively employing living tissue as hardware.

667
00:32:07,279 --> 00:32:07,880
Speaker 1: It's wild.

668
00:32:08,039 --> 00:32:13,000
Speaker 2: It sounds intensely macague, almost dystopian, but the scientific reality

669
00:32:13,039 --> 00:32:15,079
of what has been achieved is staggering.

670
00:32:15,200 --> 00:32:17,079
Speaker 1: I will set the scene for you because it sounds

671
00:32:17,119 --> 00:32:20,000
exactly like the opening montage of a sci fi horror movie.

672
00:32:20,559 --> 00:32:22,880
Scientists took a laboratory Petrie dish.

673
00:32:22,720 --> 00:32:24,319
Speaker 2: Does a standard glass dish.

674
00:32:24,319 --> 00:32:26,920
Speaker 1: Filled it with a nutrient rich liquid soup designed to

675
00:32:26,960 --> 00:32:30,519
keep organic tissue alive, and into that soup they placed

676
00:32:30,559 --> 00:32:34,240
living human brain cells neuron aron's grown from stem cells,

677
00:32:34,279 --> 00:32:36,720
just hanging out in a dish, completely disconnected from a

678
00:32:36,759 --> 00:32:40,039
human body. Then they took a high density microelectrode array

679
00:32:40,559 --> 00:32:44,440
basically a grid of microscopic electrical wires, and placed it

680
00:32:44,559 --> 00:32:45,640
under the cells.

681
00:32:45,400 --> 00:32:47,279
Speaker 2: So the cells are resting on a bed of wires.

682
00:32:47,400 --> 00:32:49,720
Speaker 1: Yes, they hooked that grid up to a computer that

683
00:32:49,759 --> 00:32:52,839
was running the classic nineteen ninety three first person shooter

684
00:32:53,240 --> 00:32:56,640
video game Doom, and the cells played the game.

685
00:32:56,920 --> 00:32:59,359
Speaker 2: It is crucial we break down exactly how the setup

686
00:32:59,400 --> 00:33:02,599
works because it's easy to misunderstand. It is not as

687
00:33:02,680 --> 00:33:06,759
if a tiny disembodied brain is holding a microscopic joystick

688
00:33:06,960 --> 00:33:08,000
and looking at a screen.

689
00:33:08,240 --> 00:33:10,039
Speaker 1: Right, How does a puddle of cells. Know what a

690
00:33:10,119 --> 00:33:10,880
video game is.

691
00:33:11,039 --> 00:33:13,319
Speaker 2: It doesn't. The cells have no eyes, no ears, no

692
00:33:13,400 --> 00:33:16,440
concept of a CaCO demon or a plasma rifle. The

693
00:33:16,519 --> 00:33:18,279
interface is purely electrical.

694
00:33:18,640 --> 00:33:18,920
Speaker 1: Okay.

695
00:33:18,960 --> 00:33:23,359
Speaker 2: Explain that the computer running Doom translates the visual data

696
00:33:23,400 --> 00:33:27,319
of the game into electrical impulses. For instance, if there

697
00:33:27,359 --> 00:33:29,480
is a wall approaching on the left side of the screen,

698
00:33:30,000 --> 00:33:33,200
the computer fires specific electrical signals into the left side

699
00:33:33,200 --> 00:33:35,279
of the electrode grid under the Petri.

700
00:33:35,119 --> 00:33:37,319
Speaker 1: Dish, poking the left side of the cell exactly.

701
00:33:37,599 --> 00:33:40,160
Speaker 2: If there is an enemy in front, a different pattern

702
00:33:40,200 --> 00:33:43,359
of signals is fired into the center. The human cells

703
00:33:43,400 --> 00:33:45,319
receive these electrical signals.

704
00:33:44,960 --> 00:33:47,519
Speaker 1: To the cells. This is just sensory data to the cells.

705
00:33:47,599 --> 00:33:50,640
Speaker 2: This is purely sensory input, no different than the electrical

706
00:33:50,680 --> 00:33:53,599
signals your optic nerve sends to your brain right now.

707
00:33:53,759 --> 00:33:58,400
The cells process these impulses through their organic tangled neural network,

708
00:33:58,680 --> 00:34:02,240
and in response, they fire back their own electrical signals.

709
00:34:02,000 --> 00:34:03,279
Speaker 1: And the computer reads those.

710
00:34:03,279 --> 00:34:06,240
Speaker 2: Computer reads those returning signals and translates them into motor

711
00:34:06,240 --> 00:34:08,639
commands in the game move left, move right, Shoot.

712
00:34:08,760 --> 00:34:13,760
Speaker 1: That perfectly explains the interface, But the absolute craziest part

713
00:34:13,800 --> 00:34:17,840
of this experiment is that the cells actually learn to

714
00:34:17,840 --> 00:34:18,760
get better at the game.

715
00:34:18,800 --> 00:34:20,360
Speaker 2: They improve over time, and.

716
00:34:20,360 --> 00:34:23,360
Speaker 1: They do it without a brain, without an ego, without

717
00:34:23,360 --> 00:34:26,719
wanting to win. Like, how does a mindless smear of

718
00:34:26,760 --> 00:34:29,000
cells learn a complex task?

719
00:34:29,199 --> 00:34:30,119
Speaker 2: It's fascinating.

720
00:34:30,199 --> 00:34:32,239
Speaker 1: I look deep into the mechanics of this, and it

721
00:34:32,280 --> 00:34:36,280
all comes down to a biological imperative called homeostasis, the free.

722
00:34:36,239 --> 00:34:39,440
Speaker 2: Energy principle, as proposed by neuroscientist Carl Friston.

723
00:34:39,719 --> 00:34:42,159
Speaker 1: Exactly. I want to explain this clearly because it's the

724
00:34:42,199 --> 00:34:46,880
secret to biological computing. Cells fundamentally hate chaos.

725
00:34:47,000 --> 00:34:47,880
Speaker 2: They despise it.

726
00:34:47,960 --> 00:34:52,159
Speaker 1: They're biologically driven to seek predictability and stability, that is homeostasis.

727
00:34:52,559 --> 00:34:56,119
In a chaotic, unpredictable environment, a cell cannot manage its

728
00:34:56,199 --> 00:34:58,159
energy resources and it dies.

729
00:34:58,400 --> 00:35:00,119
Speaker 2: So they need order to survive.

730
00:35:00,199 --> 00:35:04,199
Speaker 1: Right, so the researchers use this against them. When the

731
00:35:04,199 --> 00:35:06,840
cells fired a signal that caused the character in Doomed

732
00:35:06,920 --> 00:35:09,760
to move safely or avoid a wall, a computer sent

733
00:35:09,840 --> 00:35:14,079
a clean, highly predictable electrical pulse back into the dish

734
00:35:14,119 --> 00:35:16,760
that steady rhythm. The cells like this. It was stable.

735
00:35:17,400 --> 00:35:19,199
But when the cells fired a signal that caused the

736
00:35:19,280 --> 00:35:21,599
character to get shot by an enemy or run straight

737
00:35:21,639 --> 00:35:26,719
into a wall. The computer blasted the Petri dish with chaotic, unpredictable,

738
00:35:27,000 --> 00:35:28,760
randomized electrical static.

739
00:35:29,119 --> 00:35:32,280
Speaker 2: This raises an important question because the cells experience that

740
00:35:32,400 --> 00:35:37,280
static as a fundamental existential threat. It is environmental chaos.

741
00:35:36,840 --> 00:35:40,239
Speaker 1: And so incredibly, the neural network inside the dish physically

742
00:35:40,280 --> 00:35:44,079
begins to rewire itself. The cells break old connections and

743
00:35:44,159 --> 00:35:47,840
form new ones, constantly adjusting their internal architecture until their

744
00:35:47,840 --> 00:35:52,039
outputs consistently result in the predictable, safe electrical pulse.

745
00:35:52,000 --> 00:35:54,039
Speaker 2: They adapt to survive the simulation.

746
00:35:54,159 --> 00:35:57,320
Speaker 1: They literally learned to play doom to avoid enemies and

747
00:35:57,400 --> 00:36:01,559
navigate the map solely to minimize the chaos static. We've

748
00:36:01,760 --> 00:36:05,599
essentially weaponized their will to survive to create a biological joystick.

749
00:36:05,679 --> 00:36:06,920
Speaker 2: It's a bioalgorithm.

750
00:36:07,159 --> 00:36:10,519
Speaker 1: But as I'm picturing this dish of human tissue desperately

751
00:36:10,559 --> 00:36:14,800
rewiring itself to avoid static, I have a massive pushback

752
00:36:15,400 --> 00:36:19,199
because it feels like we are anthropomorphizing a chemical reaction. Yes, so,

753
00:36:20,000 --> 00:36:23,400
just because human cells react to electrical impulses to avoid static,

754
00:36:24,000 --> 00:36:26,039
it doesn't mean the cells know they're playing a game.

755
00:36:26,719 --> 00:36:29,360
Where do we draw the line between a biological reflex

756
00:36:29,679 --> 00:36:30,639
and an actual thought.

757
00:36:30,840 --> 00:36:31,679
Speaker 2: The reflex arc.

758
00:36:31,800 --> 00:36:33,639
Speaker 1: Yeah, if a doctor taps my knee with a hammer,

759
00:36:33,800 --> 00:36:36,440
my leg kicks out, that's a reflex. My brain isn't

760
00:36:36,480 --> 00:36:39,239
thinking about kicking. Isn't this peatrie dish just a highly

761
00:36:39,639 --> 00:36:40,920
complex knee jerk?

762
00:36:41,280 --> 00:36:44,280
Speaker 2: That distinction, the line between reflex and thought is perhaps

763
00:36:44,360 --> 00:36:47,840
the most important ethical and philosophical question of the coming decade.

764
00:36:48,039 --> 00:36:49,079
Speaker 1: The blurry line.

765
00:36:49,119 --> 00:36:51,760
Speaker 2: You are absolutely correct that a few hundred thousand cells

766
00:36:51,760 --> 00:36:54,639
in a dish do not possess a prefrontal cortex. They

767
00:36:54,639 --> 00:36:58,119
aren't strategizing. They aren't feeling the adrenaline rush of a

768
00:36:58,159 --> 00:36:58,960
close call.

769
00:36:58,760 --> 00:37:00,079
Speaker 1: In the game right, they aren't gamer.

770
00:37:00,519 --> 00:37:04,400
Speaker 2: It is at its core a highly complex reflex arc

771
00:37:04,519 --> 00:37:09,280
seeking homeostasis. However, we must ask ourselves an uncomfortable question,

772
00:37:09,719 --> 00:37:12,719
at what point does a massive network of interlocking reflexes

773
00:37:12,760 --> 00:37:13,360
become a thought?

774
00:37:13,599 --> 00:37:14,880
Speaker 1: That's the emergence theory again.

775
00:37:14,960 --> 00:37:18,800
Speaker 2: Yes, a fully formed human brain is fundamentally just eighty

776
00:37:18,840 --> 00:37:21,800
six billion cells doing exactly what those cells in the

777
00:37:21,840 --> 00:37:25,360
picture dish are doing, receiving inputs, processing them, and seeking

778
00:37:25,400 --> 00:37:27,559
optimal outputs to minimize surprise.

779
00:37:28,119 --> 00:37:29,519
Speaker 1: So it's just a matter of scale.

780
00:37:29,639 --> 00:37:33,199
Speaker 2: The difference is merely scale and architecture. The blurring of

781
00:37:33,239 --> 00:37:36,199
these lines is happening at a staggering rate, and.

782
00:37:36,159 --> 00:37:39,880
Speaker 1: The practical implications of this blurring are wild. The sources

783
00:37:39,880 --> 00:37:41,880
point out that the sheer rate at which we are

784
00:37:41,880 --> 00:37:45,400
manipulating this science is accelerating toward a bizarre future.

785
00:37:45,679 --> 00:37:46,440
Speaker 2: Very bizarre.

786
00:37:46,519 --> 00:37:48,800
Speaker 1: They suggest a near future where we don't just have

787
00:37:48,880 --> 00:37:52,639
silicon microchips in our massive, energy hungry data centers, because,

788
00:37:52,679 --> 00:37:56,280
as we established earlier, biology is millions of times more

789
00:37:56,320 --> 00:37:59,519
power efficient than silicon. We could have server farms made

790
00:37:59,519 --> 00:38:04,599
of action lab grown biological tissue bioservers, tanks of engineered

791
00:38:04,679 --> 00:38:08,079
human or animal neurons connected to the Internet, computing our

792
00:38:08,119 --> 00:38:12,440
logistics algorithms. Imagine typing a prompt into an AI, and

793
00:38:12,480 --> 00:38:15,519
the answer isn't generated by a silicon GPU, but by

794
00:38:15,559 --> 00:38:19,000
a vat of bioengineered neural tissue sitting in a temperature

795
00:38:19,000 --> 00:38:21,400
controlled rack somewhere in a warehouse.

796
00:38:21,159 --> 00:38:25,519
Speaker 2: Which forces humanity to confront the terrifying ethics of utility.

797
00:38:25,960 --> 00:38:27,079
Speaker 1: Terrifying is the right word.

798
00:38:27,239 --> 00:38:30,599
Speaker 2: If we engineer a biological computer using human stem cells,

799
00:38:31,000 --> 00:38:33,599
and we grow it until it possesses the processing power

800
00:38:33,639 --> 00:38:36,159
and neuron count of a dog's brain, and we use

801
00:38:36,199 --> 00:38:39,599
it to manage a city's traffic grid. Do we owe

802
00:38:39,679 --> 00:38:42,480
that vat of tissue the ethical rights of a dog?

803
00:38:42,639 --> 00:38:45,360
Speaker 1: Does it need walks? I mean, seriously, what are its rights?

804
00:38:45,400 --> 00:38:48,480
Speaker 2: If it receives chaotic data? Does it suffer if we

805
00:38:48,519 --> 00:38:50,760
scale it up to the size of a human connectome

806
00:38:51,000 --> 00:38:55,639
to run climate simulations? Have we inadvertently enslaved a conscious mind?

807
00:38:55,840 --> 00:38:59,920
Speaker 1: We are rapidly accelerating toward a reality where consciousness, or

808
00:39:00,079 --> 00:39:05,000
at least the biological machinery that creates it, could be manufactured, partitioned, networked,

809
00:39:05,000 --> 00:39:06,480
and sold as cloud computing.

810
00:39:06,599 --> 00:39:08,840
Speaker 2: It's a completely new paradigm of labor.

811
00:39:09,039 --> 00:39:11,800
Speaker 1: If the idea of a biological server farm, feeling pain

812
00:39:11,880 --> 00:39:14,000
doesn't keep you up at night, we need to take

813
00:39:14,079 --> 00:39:16,480
all these threads we've discussed today and weave them into

814
00:39:16,480 --> 00:39:19,199
the final, ultimate existential climax of the.

815
00:39:19,159 --> 00:39:20,360
Speaker 2: Sources, the grand finale.

816
00:39:20,599 --> 00:39:23,760
Speaker 1: We've talked about perfectly simulating a one hundred and fifty

817
00:39:23,760 --> 00:39:26,000
thousand neuron fly to the point where it thinks it

818
00:39:26,039 --> 00:39:30,400
is real. We've talked about human cells computing complex tasks

819
00:39:30,400 --> 00:39:33,519
in a dish to survive we've talked about the inevitable

820
00:39:33,639 --> 00:39:37,679
exponential scaling of this technology toward the eighty six billion

821
00:39:37,760 --> 00:39:39,639
neuron human connectum.

822
00:39:39,679 --> 00:39:43,920
Speaker 2: And this leads us directly to simulation theory. Yes, simulation theory,

823
00:39:44,000 --> 00:39:48,159
the philosophy of Bostrom's trilemma brought into sharp practical focus

824
00:39:48,199 --> 00:39:48,920
by the fruit fly.

825
00:39:49,440 --> 00:39:50,400
Speaker 1: Bring that down for us.

826
00:39:51,280 --> 00:39:54,400
Speaker 2: If we accept the premise that we are currently on

827
00:39:54,480 --> 00:39:57,199
the verge of simulating a biological entity to the point

828
00:39:57,199 --> 00:39:59,719
where it essentially believes its digital world is the base

829
00:39:59,760 --> 00:40:02,159
reac we have to look up at the stars and

830
00:40:02,239 --> 00:40:03,840
ask an incredibly.

831
00:40:03,360 --> 00:40:04,960
Speaker 1: Uncomfortable question, which.

832
00:40:04,760 --> 00:40:08,360
Speaker 2: Is what stops an advanced civilization, perhaps merely a few

833
00:40:08,440 --> 00:40:11,559
hundred or one thousand years ahead of us technologically, from

834
00:40:11,599 --> 00:40:15,559
perfectly simulating our eighty six billion neuron reality, right?

835
00:40:15,639 --> 00:40:18,280
Speaker 1: I mean, if you look at the math of exponential

836
00:40:18,320 --> 00:40:21,039
technological growth, you almost have to conclude that we are

837
00:40:21,079 --> 00:40:22,000
already in the matrix.

838
00:40:22,079 --> 00:40:24,400
Speaker 2: The statistical argument is very persuasive, The.

839
00:40:24,440 --> 00:40:27,559
Speaker 1: Logic is brutal. If a civilization reaches a point where

840
00:40:27,559 --> 00:40:31,679
it can create millions or billions of simulated universes, maybe

841
00:40:31,719 --> 00:40:35,119
to run historical experiments, maybe to test variables for their

842
00:40:35,159 --> 00:40:38,800
own survival or maybe just for entertainment. Then there are

843
00:40:39,039 --> 00:40:44,159
billions of simulated realities and only one true base.

844
00:40:43,960 --> 00:40:47,480
Speaker 2: Reality, one real universe, billions of fake ones.

845
00:40:47,719 --> 00:40:51,800
Speaker 1: Right, the statistical probability that we right now happen to

846
00:40:51,800 --> 00:40:57,119
be the one original biological base reality is incontestemately small.

847
00:40:57,400 --> 00:40:59,960
Speaker 2: We are almost certainly the digital flies and someone else

848
00:41:00,159 --> 00:41:02,400
the server farm, marveling at our own simulations.

849
00:41:02,599 --> 00:41:04,760
Speaker 1: But let's bring it back to the human level, to

850
00:41:04,840 --> 00:41:07,360
the end gain proposed by the researchers and the sources.

851
00:41:07,800 --> 00:41:10,960
The third major benefit of all this whole brain emulation.

852
00:41:10,599 --> 00:41:12,480
Speaker 2: Tech one we hinted at earlier.

853
00:41:12,159 --> 00:41:14,920
Speaker 1: The one I hinted at earlier. They're talking about eventually

854
00:41:15,039 --> 00:41:18,719
uploading the human brain. Your brain perfectly map, your specific

855
00:41:18,800 --> 00:41:22,400
snaptic weights recorded, perfectly replicated in a digital.

856
00:41:22,079 --> 00:41:24,480
Speaker 2: Environment, the literal digitization of.

857
00:41:24,480 --> 00:41:26,239
Speaker 1: The human mind, the ultimate upload.

858
00:41:26,679 --> 00:41:30,039
Speaker 2: This is where the limitations of biology fall away and

859
00:41:30,079 --> 00:41:34,400
the physics of computation opens up terrifying and wonderful possibilities.

860
00:41:35,079 --> 00:41:38,519
Think about the physical constraints of your biological brain right now.

861
00:41:39,159 --> 00:41:41,400
Speaker 1: I mean I get a headache if I read too long.

862
00:41:41,679 --> 00:41:45,480
Speaker 2: Well beyond that, your processing speed is fundamentally limited by

863
00:41:45,519 --> 00:41:49,400
the speed of chemical diffusion. Neural signals travel through your

864
00:41:49,480 --> 00:41:52,280
flesh at roughly one hundred and twenty meters per second,

865
00:41:52,320 --> 00:41:55,119
which seems fast. That feels fast to us, but it

866
00:41:55,199 --> 00:41:58,760
is agonizingly slow compared to the speed of light, which

867
00:41:58,800 --> 00:42:01,920
is how fast signals travel in a fiber optic cable exactly.

868
00:42:02,000 --> 00:42:04,920
Speaker 1: The sources propose that if we can upload a human

869
00:42:04,960 --> 00:42:08,320
connectome perfectly, we are no longer bound by flesh.

870
00:42:08,360 --> 00:42:10,880
Speaker 2: We can theoretically just turn up the clock speed on

871
00:42:10,920 --> 00:42:13,880
the GPUs running the simulation overclock. In the human mind,

872
00:42:14,320 --> 00:42:16,880
you wouldn't just be you. You would be you operating

873
00:42:16,880 --> 00:42:18,639
a thousand or a million times faster.

874
00:42:18,880 --> 00:42:21,719
Speaker 1: You could read and comprehend every book ever written by

875
00:42:21,760 --> 00:42:23,440
humanity in a single afternoon.

876
00:42:23,599 --> 00:42:27,320
Speaker 2: You could experience a subjective lifetime of thought, artistic creation,

877
00:42:27,599 --> 00:42:30,519
and problem solving while only a single second passes in

878
00:42:30,559 --> 00:42:32,039
the physical outside world.

879
00:42:32,360 --> 00:42:36,119
Speaker 1: And because you are now digital data, you possess the

880
00:42:36,360 --> 00:42:39,400
ultimate superpower. You could copy.

881
00:42:39,159 --> 00:42:43,360
Speaker 2: Yourself CGLLC CGLV for consciousness.

882
00:42:44,119 --> 00:42:47,679
Speaker 1: Imagine branching off a million versions of your mind, all

883
00:42:47,760 --> 00:42:51,280
working in parallel collaboration to solve a massive physics problem,

884
00:42:51,320 --> 00:42:54,559
and then merging back together into a single consciousness to

885
00:42:54,599 --> 00:42:55,880
share the memories of the work.

886
00:42:56,079 --> 00:42:58,679
Speaker 2: It's literal godlike superpowers.

887
00:42:58,719 --> 00:42:59,480
Speaker 1: It really is.

888
00:42:59,599 --> 00:43:02,719
Speaker 2: It is the ultimate expression of parallel scaling. It is

889
00:43:02,800 --> 00:43:06,079
exactly the architecture we are seeing in artificial intelligence server

890
00:43:06,159 --> 00:43:09,159
farms today, but applied to the subjective.

891
00:43:08,679 --> 00:43:10,639
Speaker 1: Human consciousness need to change everything.

892
00:43:10,719 --> 00:43:14,400
Speaker 2: It could solve humanity's greatest existential challenges. A collective of

893
00:43:14,440 --> 00:43:18,760
accelerated human minds could cure all remaining physical diseases, engineer

894
00:43:18,760 --> 00:43:22,760
the physics required for interstellar travel, and optimize global resources

895
00:43:22,800 --> 00:43:25,360
in a matter of subjective weeks. But it comes with

896
00:43:25,400 --> 00:43:29,320
a devastating philosophical cost. It fundamentally fractures our definition of

897
00:43:29,320 --> 00:43:29,800
the self.

898
00:43:29,960 --> 00:43:32,920
Speaker 1: And that is exactly where my brain breaks, because I

899
00:43:32,920 --> 00:43:35,760
am totally caught on the save state concept in video games.

900
00:43:35,840 --> 00:43:38,039
Speaker 2: The save state explain that if I.

901
00:43:38,000 --> 00:43:40,480
Speaker 1: Play a game and I save my progress and then

902
00:43:40,519 --> 00:43:42,880
my character walks into a trap and dies, I am

903
00:43:42,880 --> 00:43:45,840
not upset. I just reload the save right, No harm done.

904
00:43:45,880 --> 00:43:49,920
But that's data. If I walk into a futuristic neurology

905
00:43:49,960 --> 00:43:53,639
clinic tomorrow and they use incredibly advanced tech to scan

906
00:43:53,800 --> 00:43:57,800
my eighty six billion neurons perfectly copy the exact chemical

907
00:43:57,800 --> 00:44:01,360
state of my brain and hit upload. A digital copy

908
00:44:01,400 --> 00:44:02,920
of me wakes up in the cloud.

909
00:44:02,679 --> 00:44:03,679
Speaker 2: And it thinks it is you.

910
00:44:04,000 --> 00:44:06,679
Speaker 1: It has all my memories up to that exact second.

911
00:44:06,920 --> 00:44:10,159
It loves my family, It vividly remembers the drive to

912
00:44:10,199 --> 00:44:12,559
the clinic. It unequivocally thinks it is me.

913
00:44:12,760 --> 00:44:13,440
Speaker 2: Yes, it does.

914
00:44:13,599 --> 00:44:17,000
Speaker 1: But the physical biological fleshy me is still sitting in

915
00:44:17,079 --> 00:44:18,960
the chair in the clinic looking at the screen. I

916
00:44:18,960 --> 00:44:22,079
haven't magically transferred my perspective anywhere. I've just been cloned.

917
00:44:22,280 --> 00:44:23,280
Speaker 2: You created a twin.

918
00:44:23,480 --> 00:44:25,760
Speaker 1: And if my physical body then has a heart attack

919
00:44:25,840 --> 00:44:28,920
and dies, did I survive or did I just make

920
00:44:28,920 --> 00:44:31,480
a really convincing digital ghost that is going to live

921
00:44:31,519 --> 00:44:35,239
forever believing it's me, while the original, continuous me just

922
00:44:35,280 --> 00:44:36,599
ceases to exist in the dark.

923
00:44:36,760 --> 00:44:39,880
Speaker 2: You have perfectly articulated the paradox of the ship of Theseus,

924
00:44:40,239 --> 00:44:41,800
but applied to human consciousness.

925
00:44:41,800 --> 00:44:42,920
Speaker 1: The ship of theseus.

926
00:44:43,199 --> 00:44:46,599
Speaker 2: If you replace every single wooden plank on a ship

927
00:44:46,679 --> 00:44:49,239
over time, is it the same ship? If you replicate

928
00:44:49,280 --> 00:44:51,920
every single neuron in silicon, is it the same you?

929
00:44:52,440 --> 00:44:53,880
Speaker 1: That's the trillion dollar question.

930
00:44:54,039 --> 00:44:57,639
Speaker 2: The continuous experience of consciousness seems deeply tied to your

931
00:44:57,800 --> 00:45:01,400
localized physical perspective. The entity that wakes up in the

932
00:45:01,400 --> 00:45:03,639
cloud will absolutely believe it is you.

933
00:45:03,960 --> 00:45:05,239
Speaker 1: It won't feel like a copy.

934
00:45:05,280 --> 00:45:09,119
Speaker 2: It will possess unbroken psychological continuity with your past. It

935
00:45:09,119 --> 00:45:13,960
will feel no disruption, but your localized perspective, the specific

936
00:45:14,039 --> 00:45:17,599
you looking out of your biological eyes right now will end.

937
00:45:17,679 --> 00:45:19,360
When the biological body fails.

938
00:45:19,679 --> 00:45:22,239
Speaker 1: A copy lives, the original dies.

939
00:45:22,440 --> 00:45:24,599
Speaker 2: That is just terrifying. I don't think I would do it.

940
00:45:24,800 --> 00:45:28,559
Speaker 1: However, neuroscientists and philosophers like Hans Morvek have proposed a

941
00:45:28,559 --> 00:45:32,400
solution to this. They suggest that true uploading, true transfer

942
00:45:32,440 --> 00:45:35,800
of the self, requires a gradual transition rather than a

943
00:45:35,840 --> 00:45:37,000
sudden scan and copy.

944
00:45:37,280 --> 00:45:39,519
Speaker 2: How does a gradual transition work? You swap out the

945
00:45:39,519 --> 00:45:43,039
brain while it's running. Essentially, yes, Imagine a surgical procedure,

946
00:45:43,159 --> 00:45:47,039
perhaps involving nanobots, where your biological neurons are replaced with

947
00:45:47,119 --> 00:45:50,440
synthetic silicon equivalents, one at a time over.

948
00:45:50,280 --> 00:45:52,480
Speaker 1: A period of years, neuron b neuron.

949
00:45:52,800 --> 00:45:56,960
Speaker 2: As each biological neuron fails or is removed, a digital

950
00:45:57,000 --> 00:45:59,920
one takes its exact place in the network, maintaining the

951
00:46:00,079 --> 00:46:01,679
precise synaptic weights.

952
00:46:01,360 --> 00:46:03,079
Speaker 1: And connections, and I'm awake for this.

953
00:46:03,480 --> 00:46:06,679
Speaker 2: You remain awake the entire time. You remain continuously you,

954
00:46:07,480 --> 00:46:13,760
but slowly, imperceptibly, the underlying substrate of your consciousness shifts

955
00:46:13,760 --> 00:46:16,199
from organic carbon to synthetic silicon.

956
00:46:16,400 --> 00:46:16,840
Speaker 1: Oh wow.

957
00:46:17,280 --> 00:46:19,960
Speaker 2: Because the network never shuts down. There is no break

958
00:46:19,960 --> 00:46:22,840
in continuity, no moment where a clone is born and

959
00:46:22,880 --> 00:46:23,880
an original dies.

960
00:46:24,360 --> 00:46:25,840
Speaker 1: Just a seamless fade.

961
00:46:25,559 --> 00:46:29,039
Speaker 2: A seamless, gradual fade into the digital realm, until one

962
00:46:29,119 --> 00:46:32,159
day the last biological cell is replaced and you are

963
00:46:32,280 --> 00:46:35,000
fully synthetic, ready to be transmitted to the cloud.

964
00:46:35,119 --> 00:46:39,119
Speaker 1: Oh wow. A slow, conscious biological fade into the digital,

965
00:46:39,400 --> 00:46:41,760
replacing the ship of theseus while it is sailing on

966
00:46:41,800 --> 00:46:44,760
the ocean. That is both a beautiful concept and utterly

967
00:46:44,920 --> 00:46:47,039
viscerally terrifying to imagine undergoing.

968
00:46:47,320 --> 00:46:51,199
Speaker 2: It represents the precipice of a fundamentally new stage in

969
00:46:51,320 --> 00:46:55,039
human evolution. If we survive to see it. We are

970
00:46:55,119 --> 00:46:59,320
moving from the slow, blind process of natural selection to

971
00:46:59,440 --> 00:47:02,400
conscious self directed digital ascension.

972
00:47:02,599 --> 00:47:06,159
Speaker 1: Okay, we have covered a genuinely staggering amount of ground

973
00:47:06,159 --> 00:47:07,039
in our discussion today.

974
00:47:07,079 --> 00:47:07,639
Speaker 2: We really have.

975
00:47:08,039 --> 00:47:11,199
Speaker 1: We started by looking closely at a tiny digital fruitfly

976
00:47:12,039 --> 00:47:15,440
wandering around a physics engine built entirely from a physically

977
00:47:15,480 --> 00:47:20,119
mapped biological connectome without a single line of AI training code.

978
00:47:19,920 --> 00:47:21,599
Speaker 2: A true digital ghost.

979
00:47:21,920 --> 00:47:25,079
Speaker 1: We explored the mechanics of leaky buckets and synaptic weights

980
00:47:25,119 --> 00:47:28,559
to understand how it mimics life. We discussed how Nature's

981
00:47:28,599 --> 00:47:31,519
millions of years of drudal evolution are providing the ultimate

982
00:47:31,800 --> 00:47:34,719
billions of dollars shortcut for our own computing technology.

983
00:47:34,880 --> 00:47:36,480
Speaker 2: Bypassing the training run.

984
00:47:36,480 --> 00:47:40,360
Speaker 1: We climbed the evolutionary neuron ladder, visualizing the vast, starry

985
00:47:40,480 --> 00:47:43,639
galaxies of our own minds. We stared into the unsettling

986
00:47:43,719 --> 00:47:46,719
horror movie reality of human brain cells learning to play

987
00:47:46,760 --> 00:47:49,519
doom in a petri dish just to avoid the chaos

988
00:47:49,599 --> 00:47:50,480
of static.

989
00:47:50,199 --> 00:47:51,480
Speaker 2: The biological joystick.

990
00:47:51,719 --> 00:47:54,800
Speaker 1: And finally, we looked over the precipice at the looming

991
00:47:54,840 --> 00:47:59,480
reality of simulation theory, digital immortality, and the philosophical nightmare

992
00:47:59,760 --> 00:48:03,079
of uploading our eighty six billion neurons to the cloud.

993
00:48:03,639 --> 00:48:05,320
It is an immense amount of process.

994
00:48:05,480 --> 00:48:08,559
Speaker 2: It is it demands that we reconsider our place in

995
00:48:08,599 --> 00:48:11,599
the universe, and to leave you with one final thought

996
00:48:11,639 --> 00:48:13,599
to ponder as you step away from this and go

997
00:48:13,679 --> 00:48:14,280
about your day.

998
00:48:14,480 --> 00:48:14,960
Speaker 1: Let's hear it.

999
00:48:15,440 --> 00:48:18,599
Speaker 2: Imagine that far future scenario we discussed where you have

1000
00:48:18,679 --> 00:48:22,400
successfully uploaded your mind. Your digital self is now operating

1001
00:48:22,440 --> 00:48:25,800
a million times faster than your biological brain ever could.

1002
00:48:26,000 --> 00:48:27,519
Speaker 1: Running at the speed of light, you.

1003
00:48:27,440 --> 00:48:32,599
Speaker 2: Possess vast parallel intelligence, capable of holding thousands of thoughts simultaneously,

1004
00:48:32,880 --> 00:48:37,679
with near infinite perfect memory. To that entity, subjective time

1005
00:48:37,719 --> 00:48:41,199
stretches out endlessly into the future. Okay, if that magnificent

1006
00:48:41,280 --> 00:48:44,599
accelerated being were to look back at the fleshy biological

1007
00:48:44,639 --> 00:48:47,760
you listening to this right now, how would it view you?

1008
00:48:47,920 --> 00:48:48,119
Speaker 1: Mean?

1009
00:48:48,239 --> 00:48:50,960
Speaker 2: Would it view you the same way we currently look

1010
00:48:51,039 --> 00:48:55,000
back at a simple, single celled organism swimming in a puddle,

1011
00:48:55,639 --> 00:49:00,559
A primitive, incredibly slow moving ancestor blindly re acting to

1012
00:49:00,679 --> 00:49:05,559
its physical environment, entirely unaware of the vast complex reality

1013
00:49:05,639 --> 00:49:08,760
that lay just a fraction of an inch beyond its perception.

1014
00:49:09,559 --> 00:49:11,400
Speaker 1: Man, that thought is going to keep me up at night.

1015
00:49:11,880 --> 00:49:15,000
The idea that we are the single celled organisms of

1016
00:49:15,039 --> 00:49:16,400
a future digital species.

1017
00:49:16,440 --> 00:49:18,360
Speaker 2: It's a humbling perspective, it really is.

1018
00:49:18,679 --> 00:49:20,239
Speaker 1: So we turn it over to you. What do you

1019
00:49:20,239 --> 00:49:25,119
think if the technology existed tomorrow, fully proven, safe, and available,

1020
00:49:25,199 --> 00:49:27,159
and you were offered the chance to step into a clinic,

1021
00:49:27,519 --> 00:49:31,280
upload your mind, gain superhuman parallel processing speeds, and live

1022
00:49:31,320 --> 00:49:34,639
forever in a simulated utopia. Would you press enter the

1023
00:49:34,800 --> 00:49:38,239
ultimate choice and deeply truly considering the ship of theseus,

1024
00:49:38,760 --> 00:49:40,360
do you believe the thing on the other side would

1025
00:49:40,360 --> 00:49:42,719
still be you here? Leave a comment below and let

1026
00:49:42,800 --> 00:49:44,800
us know where you stand on the digital divide. Thank

1027
00:49:44,840 --> 00:49:47,159
you so much for untangling these throlling threads with us.

1028
00:49:47,320 --> 00:49:48,199
Speaker 2: Thanks for listening.

1029
00:49:48,400 --> 00:49:50,679
Speaker 1: Until next time, keep questioning your reality.