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You know, when you think about cutting-edge

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artificial intelligence today, you, you probably picture massive

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warehouses just full of glowing servers, humming away,

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pulling down enough electricity to power a small

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city. Yeah. Exactly. I mean, we basically treat

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intelligence like an industrial mining operation right now.

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We just throw more power, more data, well,

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more brute force at the problem. But today's

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deep dive flips that entirely on its head.

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We're unpacking a really dense, honestly mind-bending

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whitepaper on biomorphic AGI. Right. And the

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core mission here is to understand why this

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brute force approach of massive datacenters is

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hitting a physical wall. And why the ultimate

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solution is a biological blueprint for AI that

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runs on just 20 Watts of power. Okay.

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Let's unpack this. Why can't we just keep

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building bigger digital datacenters? Like, what is

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physically stopping us? Well, it comes down to

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a structural flaw called the Von Neumann bottleneck.

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So in modern digital hardware, your memory storage

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and your computational processors are—they're physically separated.

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And moving the data between those two places

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actually takes, like, a hundred to a thousand

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times more energy than doing the actual math.

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So you hit a thermal and economic ceiling

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just, you know, shuttling data around. Oh, wow.

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So it's I mean, it's like commuting a

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100 miles to a kitchen every single time

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every single time you need to chop a

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carrot for a soup. You're spending all your

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energy on the commute, not the actual cooking.

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That's spot on. And what's fascinating here is

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that biological brains bypass this entirely. They actually

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compute in memory. Wait, in memory? Yeah, so

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your synapses are simultaneously the hard drive and

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the processor. Computing happens physically via chemistry right

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where the data lives. Which makes a biological

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brain like billions of times more energy efficient

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than our best silicon chips. So the old

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digital model is doomed by that commute, basically.

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And that's why this paper suggests we need

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borrow three loops from biology. Right? Phylogeny, is

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evolution, cognition, or active inference, and ontogeny.

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Right. Ontogeny, which is self-repair. Yeah. And

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here's where it gets really interesting, because I

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have to push back here. Are we talking

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about actual physical wires regrowing in a machine?

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Or is this, you know, just happening in

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software? No. It's physical hardware behaving like biology.

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It uses this mathematical framework called neural cellular

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automata. The hardware isn't a rigid static grid

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at all. If the network gets physically damaged,

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the local cells actually feel this sudden drop

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in spatial gradients. They literally sense the void

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where their neighbors used to be. Oh, wow.

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Right. Triggered by that physical disruption, they autonomously

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regrow their connections along new chemical gradients to

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restore function. And this is entirely without human

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intervention. Let me stop you there. How is

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a machine physically regrowing its own pathways? Like,

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is this even made of? So the system

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uses analog memristive crossbars. Compute instantly via Ohm's

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law. And the paper also mentioned something wild

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called Holomorphic Equilibrium Propagation. I mean, we get

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a plain English breakdown of what a memorised

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of crossbar actually is? Yeah. Absolutely. Think of

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it as a grid of wires that can

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remember how much electricity has flowed through them,

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and then they change their own electrical resistance

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accordingly. So instead of shuttling ones and zeros

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around, it just lets current flow? Exactly. The

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computation happens instantly based on basic physics and

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that Holomorphic Equilibrium Propagation. That's just a fancy

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way of saying the system learns by finding

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the path of least resistance. Oh, so it

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actively uses its own physical hardware flaws to

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its advantage? Yeah. Like the natural wire resistance,

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it uses that to calculate the right answer.

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That is brilliant. It uses imperfections to learn.

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But wait, if this hardware is constantly finding

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new paths and rewriting its own resistance to

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save energy, wouldn't it just overwrite everything it

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learned yesterday? Yes, which is the famous catastrophic

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forgetting problem. Right. So what does this all

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mean for how it actually remembers things? Well,

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to solve it, the system uses dual speed

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memory. So it has a fast memory for

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new experiences and a slow memory for core

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knowledge. But to manage the transfer between the

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two, this 20-Watt device literally needs to

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sleep. Wait, what? How does a physical piece

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of hardware sleep? It enters a state of

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homeostatic idling. It drops its power usage, shuts

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out new external data, and rapidly replays the

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electrical patterns of its daily episodic memories. Doing

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this offline. Exactly. It gently merges those new

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experiences into its core structure at a fraction

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of the learning rate. So it adapts its

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physical shape without breaking its foundational knowledge. That

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is just wild. And the relevance here to

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you, the listener, is huge. We are looking

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at a future where AI isn't dependent on

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giant megawatt server farms at all. No. We're

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talking about untethered 20-Watt devices learning dynamically

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in real time right on the edge. Right

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in your pocket, basically. Yeah. And we connect

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this to the bigger picture. It challenges our

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entire assumption that intelligence is just abstract software.

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It shows intelligence is fundamentally a physical process.

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Meaning it's about a physical body adapting its

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shape to minimize energy, which, you know, brings

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us back to that massive server farm humming

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with electricity. If genuine general intelligence strictly requires

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biological constraints like physical embodiment and actually needing

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a sleep state to consolidate its memories, does

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that mean the first true AGI will inherently

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know what it feels like to be exhausted?