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Modeling: The Deep Isomorphism of Carbon and Silicon Intelligence

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And this secret isn't some profound quantum mechanics,

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nor is it a mysterious theory of consciousness.

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It comes down to two words:

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Modeling.

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You are doing it every single moment.

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Your brain lives on it.

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Large Language Models speak through it.

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Every nerve in your body operates because of it.

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What I want to say today is:

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In an era where AI democratizes everything,

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humanity's true moat

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may be hidden within these two words.

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Hello,

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everyone,

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I am Leo Wang.

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Previously, we made an episode

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called "Carbon-Based AI,"

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comparing the human body and LLMs in a hardcore way.

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Today, I want to push that framework one level deeper.

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Last time, we asked,

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"What is the difference between you and AI?"

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Today, let's ask a different question:

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"What do you and AI have in common?"

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The answer is:

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You are both doing the exact same thing—

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Modeling.

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What does "modeling" mean?

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Simply put,

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it's abstracting this chaotic world into a set of rules,

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then using those rules to predict, judge, and act.

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You might have heard of Mathematical Modeling Olympiads as a kid.

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What they do in those competitions is

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take a bunch of real-world data,

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identify the patterns within,

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build a mathematical model,

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and use it to calculate what might happen next.

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Sounds very academic, right?

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But in reality, you're doing this every second.

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You head out in the morning and glance at the sky;

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it's overcast.

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Your brain immediately generates a prediction:

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"It's probably going to rain."

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Then you make a decision:

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"Bring an umbrella."

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This entire process

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is modeling.

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You didn't analyze air pressure, humidity, or cloud height.

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Your brain trained a weather model over decades of experience:

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Gray sky + humidity = high probability of rain.

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One second,

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one model,

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one prediction,

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one action.

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But weather is just the simplest type.

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Your brain runs hundreds of models simultaneously.

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You have a language model;

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when someone starts a sentence,

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you've already guessed the ending.

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You have a social model;

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before posting on social media,

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you simulate what your boss will think,

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how your partner will react, and if your mom will call.

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You have a physics model;

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the moment a chopstick slips off the table,

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your hand is already reaching out.

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There's no time to think;

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the motor model in your body takes over control.

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You even have an emotional model.

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You see someone frown,

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and instantly judge that they're unhappy.

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You didn't scan their facial muscles;

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your brain used decades of social training

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to build a facial recognition model.

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So you see,

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you aren't just someone who can model,

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you are a modeling machine.

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But have you ever considered this?

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Your brain has never directly touched the world.

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All light, sound, touch, and temperature...

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They are all pre-processed by your sensory organs

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and sent to the brain as electrical signals.

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The brain doesn't receive the world itself,

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but a series of encoded signals.

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It then uses its internal model to interpret these signals,

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generating the world you think you see.

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Neuroscience calls this process predictive coding.

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The brain isn't a passive receiver;

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it's an active prediction machine.

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It first uses an internal model to generate a prediction,

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then compares it with the actual incoming signal.

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If they match,

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everything is normal.

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If they don't,

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a prediction error is generated.

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The brain then updates its model.

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Your entire perceived world

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is actually a real-time rendering by the brain's internal model.

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What does this look like?

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Isn't this exactly what Large Language Models do?

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The core operation of an LLM

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is predicting the next word.

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It reads the preceding text,

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uses internal parameters to generate a probability distribution,

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and selects the most likely next word.

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Training is about constantly reducing prediction error;

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inference is about using the trained model to make predictions.

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Have you noticed?

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Carbon-based brains and silicon-based models

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are fundamentally doing the same thing:

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building a model,

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using it to predict,

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and updating it based on error.

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The only difference is the medium.

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One runs on neurons;

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the other runs on chips.

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One uses bioelectric signals;

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the other uses matrix operations.

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However,

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there is one key difference between these two models.

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LLMs train first, then perform inference.

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Vast amounts of data are fed in during training,

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adjusting hundreds of billions of parameters,

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and inference only happens after training.

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But the human brain

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never separates training from inference.

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Your brain trains every second,

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and performs inference every second.

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When you see something new,

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you are updating your model

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at the same time you are understanding it.

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This is a more advanced learning paradigm.

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There's another even deeper difference.

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When engineers build a mechanical model of a bridge

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or a mathematical model of an economy,

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every step is transparent.

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You know what each variable represents

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and what relationship each equation describes.

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This is traditional modeling.

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But LLMs use a different kind of modeling.

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Humans designed a framework

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called Generative Pre-trained Transformer,

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then poured massive amounts of data into it.

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During training,

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something happens inside the model.

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There's a phenomenon called grokking.

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Research shows that neural networks

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undergo a sudden phase transition during training.

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For a long time,

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the model just memorizes training data by rote

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and performs poorly on tests.

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Then suddenly, at some point,

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it learns to generalize all at once,

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and test accuracy skyrockets.

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This leap from rote memorization to true understanding—

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when and why it happens—

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is something we don't fully understand yet.

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This kind of black-box modeling

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is completely different in nature

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from the white-box models humans built before.

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In one, you know exactly what's happening inside the model.

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In the other, you only know the input and output.

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The process in between is a black box.

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But behind these two types of modeling

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lies the same essence:

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using finite parameters to compress an infinite world.

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So far,

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everything we've discussed is at the intelligence level.

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Two mediums,

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one operation:

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modeling.

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But now,

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I want to push this framework a step deeper.

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There has always been a debate.

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That intelligence can emerge—

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most people generally accept this now.

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Tens of billions of neurons connected by synapses

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give rise to thinking and reasoning skills.

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No problem there.

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But what about consciousness?

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That thing that knows you are thinking.

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The subjective experience of red being red, or pain being pain.

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Where does that come from?

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Science has no definitive answer yet.

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But what we can observe is

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a fundamental difference in the operating modes

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between human and AI models.

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AI has no observer.

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It doesn't know it's modeling.

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It won't suddenly stop one night and ask:

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"Wait..."

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"Why am I predicting the next word?"

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But humans can.

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You can observe yourself modeling.

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You can perceive your brain using

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an old model to interpret a new situation.

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And you can proactively say,

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"No,"

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"this model is outdated."

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"I need to change it."

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That is the power of awareness.

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We previously used computer architecture to analogize the Tao Te Ching's 'Tao produces One...

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...One produces Two, Two produces Three, and Three produces all things.'

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Physical laws are the Tao.

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Logic gates are One.

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The hardware structure of binary calculation is Two.

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OS and instruction sets are Three.

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On top of Three, a whole virtual world can be rendered.

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The AI within that virtual world

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cannot sense that it lives in a simulation.

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It thinks what it sees is real.

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Today, we look at this through the lens of modeling.

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Your brain renders what you perceive as the real world.

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This rendering is based on your model.

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Your model is based on your training data—

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meaning, your life experiences.

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The information you've touched, the culture you grew up in.

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Everyone's model is different.

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So the world everyone sees is different.

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Two people look at the same flower:

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one sees beauty,

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the other sees an allergen.

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The flower didn't change;

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the models are different.

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And so-called 'cognitive upgrades'

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are essentially structural reconstructions of your model.

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It's not just tinkering.

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It's not adding a few parameters to the old model.

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It's replacing the operating system from the architecture up.

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You used to see the world through one set of filters;

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now you've removed those filters

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or replaced them with something more transparent.

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This process

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has different names in many traditions.

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Some call it epiphany.

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Some call it a paradigm shift.

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Some call it awakening.

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But in the language of modeling,

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it's a phase transition of your cognitive model.

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Have you noticed...

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This is structurally identical

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to the grokking phenomenon in LLMs.

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Long periods of training,

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massive amounts of data input,

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and then, at a certain critical point,

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suddenly,

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the model is no longer just rote learning;

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it learns to generalize.

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It sees the patterns themselves,

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not just the surface of the data.

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Human awakening is the same.

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You read many books,

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experience many things,

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and think for a long time.

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Then, in a single moment,

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perhaps hearing a sentence

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or seeing an image,

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suddenly,

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your entire cognitive framework reconfigures.

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You are no longer circling within the old model;

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the model itself has upgraded.

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Quantum mechanics tells us something even more interesting:

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observation changes the outcome.

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In the microscopic world,

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the instrument you use to observe a quantum state

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determines whether it exhibits wave or particle-like properties.

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The way you observe determines the phenomenon you see.

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Apply this principle to the macro world:

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when you start to notice your own thoughts

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and use consciousness to observe your mental models,

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the act of observation itself changes how the model runs.

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You are no longer driven automatically by the model;

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you gain the ability to examine the machine from the outside.

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This isn't mysticism;

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it's an empirical fact you can verify in daily life.

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Next time you get angry,

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if you can notice the moment the anger rises—

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'Oh,'

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'this is my emotional model being triggered.'

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With just that single realization,

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you'll feel that fire in your chest suddenly loosen.

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The anger is still there,

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but it no longer controls you.

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Because awareness has changed the model's execution path.

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Observation is the starting point for all model upgrades.

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Alright,

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let me consolidate this entire framework.

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The underlying operation of intelligence is modeling.

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Whether carbon-based or silicon-based,

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brain or chip,

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consciousness or algorithm,

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they are all doing the same thing:

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compressing the world through models,

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predicting the future through models,

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and guiding actions through models.

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In terms of modeling,

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AI takes a brute-force approach.

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Massive data plus massive compute power

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train a super-powerful logic and language model.

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On this path,

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AI has already surpassed the human average,

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especially in logically clear tasks like coding and text processing.

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But humans have one thing

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that current AI lacks:

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awareness.

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You can not only build models,

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you can also know that you are modeling.

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You can not only use models to predict the world,

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you can also step outside to examine the models themselves.

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You can not only run programs within a model,

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you can rewrite the source code of the program.

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This is why I say

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modeling may be the most important human skill in the AI era.

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Not because AI cannot model;

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quite the opposite.

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AI is better at modeling than you are.

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Rather, it's because

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human modeling has a dimension AI may never reach:

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the consciousness that can perceive the model's existence

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and actively restructure it.

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There was an Indian mathematician named Srinivasa Ramanujan.

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With almost no formal mathematical training,

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he produced over 3,900 mathematical formulas.

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Many of them have only recently

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been proven correct by modern mathematicians.

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He said he saw these formulas in his dreams,

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written on a screen of light by the goddess Namagiri.

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You can view this as a religious narrative,

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but you can also re-examine it through the lens of modeling.

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Ramanujan's brain was an exceptionally efficient modeling engine.

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But his inspiration didn't come from data.

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He didn't have massive amounts of training data;

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his inspiration came from an intuitive channel we can't fully explain.

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Perhaps human consciousness isn't just an emergent property of the brain.

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Perhaps it's also an antenna

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capable of receiving higher-dimensional information.

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This is just a hypothesis;

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science has no definitive conclusion yet.

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But if this hypothesis holds true,

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it means human modeling has a signal source that AI lacks.

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AI can only perform induction based on existing data;

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its ceiling is the boundary of its training data.

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But humans,

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when the mind is quiet and awareness is clear,

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might be able to touch something beyond data.

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So, I have one piece of advice for young people.

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Once AI has democratized knowledge retrieval,

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logical reasoning, and linguistic expression,

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you must ask yourself one question:

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Where does your irreplaceability lie?

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My answer is:

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Learn to model.

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Not just learning to use AI tools—

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that's just the beginning—

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but learning to model using your own awareness.

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Observe your own thought patterns.

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Deconstruct your cognitive frameworks.

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Discover the hidden assumptions you've never questioned,

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and then reconstruct them.

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Because AI can help you build models,

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but it cannot help you achieve awareness.

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It can give you answers,

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but it can't help you see which model your questions originated from.

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Awareness is the meta-operation of modeling.

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And this meta-operation,

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at least for now,

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belongs only to humans.

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What do you think?

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Have you ever had a moment

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where you suddenly realized you were using the wrong model to understand something,

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someone,

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or even yourself?

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In that moment,

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what happened to your model?

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I'm Leo Wang.

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See you next time.