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Same neuron

𝑵𝒆𝒏𝒆 ✖️💚

15,561 subscribers

12,108 次观看 • 3 个月前 •via X (Twitter)

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i mean its not about the movements per se but joe jonas and sophie turner are the definition of soulmates sharing the same neuron

i mean its not about the movements per se but joe jonas and sophie turner are the definition of soulmates sharing the same neuron

AJB

498,977 次观看 • 3 年前

"I don’t think there’s any reason why a machine shouldn’t have consciousness. If you swapped out one neuron with an artificial neuron that acts in all the same ways, would you lose consciousness?" ~ Geoffrey Hinton A fascinating discussion in this video. --- From the 'The Diary Of A CEO' YT channel

"I don’t think there’s any reason why a machine shouldn’t have consciousness. If you swapped out one neuron with an artificial neuron that acts in all the same ways, would you lose consciousness?" ~ Geoffrey Hinton A fascinating discussion in this video. --- From the 'The Diary Of A CEO' YT channel

Rohan Paul

33,407 次观看 • 11 个月前

I made FlappyFly. I put a fly's brain into FlappyBird. It plays terribly. As a pipe approaches, LC4 neurons detect it getting closer. That input goes into the 166,700-neuron network, and when DNp01 fires, the wings beat. DNp01 is the giant fibre neuron that triggers a fly's escape when you try to swat it. I used that same escape circuit to get it past the pipes.

I made FlappyFly. I put a fly's brain into FlappyBird. It plays terribly. As a pipe approaches, LC4 neurons detect it getting closer. That input goes into the 166,700-neuron network, and when DNp01 fires, the wings beat. DNp01 is the giant fibre neuron that triggers a fly's escape when you try to swat it. I used that same escape circuit to get it past the pipes.

Alexey Fateev

41,562 次观看 • 1 个月前

Zendaya: My*, my family will know.” “My husband” I imitate other people’s laughter and go HAHAHA, my family knows and asks “why do you do that?”… Tom Holland just accepts the madness Tom: Yes, she’s really good at it… THEY SHARE THE SAME NEURON, HAHAHAHAHAHAHHAHAHAHAHAHA I CAN’T TAKE IT

Zendaya: My*, my family will know.” “My husband” I imitate other people’s laughter and go HAHAHA, my family knows and asks “why do you do that?”… Tom Holland just accepts the madness Tom: Yes, she’s really good at it… THEY SHARE THE SAME NEURON, HAHAHAHAHAHAHHAHAHAHAHAHA I CAN’T TAKE IT

nah

394,348 次观看 • 3 个月前

Anthropic pays its ML engineers $500K+/year to deploy what Percy Liang just taught for free in Lecture 3 of CS221. 80 minutes. one whiteboard. everything between "a neuron" and the model running in your IDE right now. three things click after this one: / why two Claude prompts that "mean the same thing" land in different regions of weight space / why fine-tuning sometimes makes a model dumber, not smarter / what RLHF actually changes when Anthropic aligns Claude same $90K Stanford sequence. lecture 3 of ~20. free. the gap between "using Claude" and "understanding it" is exactly one lecture wide.

Anthropic pays its ML engineers $500K+/year to deploy what Percy Liang just taught for free in Lecture 3 of CS221. 80 minutes. one whiteboard. everything between "a neuron" and the model running in your IDE right now. three things click after this one: / why two Claude prompts that "mean the same thing" land in different regions of weight space / why fine-tuning sometimes makes a model dumber, not smarter / what RLHF actually changes when Anthropic aligns Claude same $90K Stanford sequence. lecture 3 of ~20. free. the gap between "using Claude" and "understanding it" is exactly one lecture wide.

dunik

24,452 次观看 • 5 个月前

We often hear that machine learning models "learn patterns in data". But what does that actually look like in geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a self-organizing map...a simple unsupervised neural model...learn the shape of a two-dimensional dataset arranged in a spiral arm. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud...it knows nothing about the structure underneath. Learning is a repeated game: pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

We often hear that machine learning models "learn patterns in data". But what does that actually look like in geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a self-organizing map...a simple unsupervised neural model...learn the shape of a two-dimensional dataset arranged in a spiral arm. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud...it knows nothing about the structure underneath. Learning is a repeated game: pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

Mathelirium

139,923 次观看 • 11 个月前

We often hear that Machine Learning models learn patterns in data. But what does that actually look like in Geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a Self-Organizing Map (SOM), a simple unsupervised neural model, learn the shape of a 3D datasets l, one static and the other dynamic. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud. It knows nothing about the structure underneath. Learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. Python code is available for Subscribers. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

We often hear that Machine Learning models learn patterns in data. But what does that actually look like in Geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a Self-Organizing Map (SOM), a simple unsupervised neural model, learn the shape of a 3D datasets l, one static and the other dynamic. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud. It knows nothing about the structure underneath. Learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. Python code is available for Subscribers. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

Mathelirium

76,377 次观看 • 7 个月前

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

Mathelirium

23,021 次观看 • 9 个月前

Your brain doesn't search through memories — it falls downhill on an energy landscape and lands in the nearest valley. This is the Hopfield network from 1982, and the same math now lives inside every transformer: the attention mechanism in modern LLMs is mathematically equivalent to a Hopfield network with exponential storage capacity. The idea: neurons are +1 or -1, connected by symmetric weights. Memories are local minima sculpted by Hebbian learning — neurons that fire together, wire together. Give the network a noisy input, and it descends toward the nearest stored memory one GPU-style majority vote at a time. Vanilla Hopfield nets cap at ~14% of neuron count as usable memories, but the modern extension blows past that. 23 minutes. Bookmark & watch today.

Your brain doesn't search through memories — it falls downhill on an energy landscape and lands in the nearest valley. This is the Hopfield network from 1982, and the same math now lives inside every transformer: the attention mechanism in modern LLMs is mathematically equivalent to a Hopfield network with exponential storage capacity. The idea: neurons are +1 or -1, connected by symmetric weights. Memories are local minima sculpted by Hebbian learning — neurons that fire together, wire together. Give the network a noisy input, and it descends toward the nearest stored memory one GPU-style majority vote at a time. Vanilla Hopfield nets cap at ~14% of neuron count as usable memories, but the modern extension blows past that. 23 minutes. Bookmark & watch today.

Eron

22,378 次观看 • 1 个月前

did you know? a neuron that never finds another to connect with quietly dies in the dark.. ever feel like stress is changing who you are? you're not imagining it. this video shows a neuron dying after years of chronic stress. chronic exposure to stress hormones like cortisol can impair the health and functioning of brain cells over time. the problem? many of us aren't stressed for a few minutes. we're stressed for weeks, months, or even years. over time, high cortisol levels can make it harder for neurons to form strong connections, especially in areas of the brain involved in memory, focus, learning, and emotional regulation. that's why chronic stress can show up as: brain fog, forgetfulness, difficulty concentrating, feeling emotionally reactive, constant exhaustion. if you've ever walked into a room and forgotten why, reread the same sentence five times, or felt like your brain just isn't working the way it used to, chronic stress may be playing a role. but here's the part most people don't hear: your brain can change in positive ways, too. through neuroplasticity, your neurons can build new connections throughout your life. every time you sleep, exercise, meditate, connect with supportive people, or practice healthier thought patterns, you're giving your brain an opportunity to recover and adapt. your brain isn't working against you. it's been trying to protect you. and healing is possible.

did you know? a neuron that never finds another to connect with quietly dies in the dark.. ever feel like stress is changing who you are? you're not imagining it. this video shows a neuron dying after years of chronic stress. chronic exposure to stress hormones like cortisol can impair the health and functioning of brain cells over time. the problem? many of us aren't stressed for a few minutes. we're stressed for weeks, months, or even years. over time, high cortisol levels can make it harder for neurons to form strong connections, especially in areas of the brain involved in memory, focus, learning, and emotional regulation. that's why chronic stress can show up as: brain fog, forgetfulness, difficulty concentrating, feeling emotionally reactive, constant exhaustion. if you've ever walked into a room and forgotten why, reread the same sentence five times, or felt like your brain just isn't working the way it used to, chronic stress may be playing a role. but here's the part most people don't hear: your brain can change in positive ways, too. through neuroplasticity, your neurons can build new connections throughout your life. every time you sleep, exercise, meditate, connect with supportive people, or practice healthier thought patterns, you're giving your brain an opportunity to recover and adapt. your brain isn't working against you. it's been trying to protect you. and healing is possible.

alexei

42,704 次观看 • 2 个月前

Zebrafish NN It’s pretty amazing to learn that efforts to map the fruit FLY connectome (brain synapses) were successful and its amazing to see the digital fly brain successfully complete a whole series of tasks, including complex navigation of open worlds. The fruit fly is 160,000 neurons and 10^7 synapses. Teams are currently doing the same thing with a zebrafish which has a similar scale neuron count but 10x more synapses. Fly was 100TB raw data and Zebrafish is 200TB, so about 2x the raw data. For context a rodent is 70m neurons and 10^11 synapses. Rodents will come later. But this method looks like it creates totally outsized results with absolutely miniscule models. Zebrafish NN is a vertebrae connectome, it has much of the same basic structure as other vertebrae, unlike FLY connectome. We are just about used to LLMs and coding, but AI is really just beginning and there is a lot more, and a lot weirder stuff coming down the pipe. These connectomes have already shown to be incredibly resilient, you can blind their sensors and injure their outputs and they still succeed. They are incredibly compact. A whole lot of inanimate objects are going to get complete autonomy, totally offline, air gapped autonomy. They will be delivering pizzas and fighting wars. Connectomics is an opposite approach to LLMs but you can of course use LLMs to help develop connectomes. It’s amazing that you can take a biologically evolved brain, map it, produce a digital twin, and then run it at machine speed. Imagine a human brain accelerated from 100Hz neuron fires to 3,200,000,000Hz that a typical CPU runs at. That’s 30 million times faster. But would need 1-2 exabytes to map. Anyway “Zebrafish” is next.

Zebrafish NN It’s pretty amazing to learn that efforts to map the fruit FLY connectome (brain synapses) were successful and its amazing to see the digital fly brain successfully complete a whole series of tasks, including complex navigation of open worlds. The fruit fly is 160,000 neurons and 10^7 synapses. Teams are currently doing the same thing with a zebrafish which has a similar scale neuron count but 10x more synapses. Fly was 100TB raw data and Zebrafish is 200TB, so about 2x the raw data. For context a rodent is 70m neurons and 10^11 synapses. Rodents will come later. But this method looks like it creates totally outsized results with absolutely miniscule models. Zebrafish NN is a vertebrae connectome, it has much of the same basic structure as other vertebrae, unlike FLY connectome. We are just about used to LLMs and coding, but AI is really just beginning and there is a lot more, and a lot weirder stuff coming down the pipe. These connectomes have already shown to be incredibly resilient, you can blind their sensors and injure their outputs and they still succeed. They are incredibly compact. A whole lot of inanimate objects are going to get complete autonomy, totally offline, air gapped autonomy. They will be delivering pizzas and fighting wars. Connectomics is an opposite approach to LLMs but you can of course use LLMs to help develop connectomes. It’s amazing that you can take a biologically evolved brain, map it, produce a digital twin, and then run it at machine speed. Imagine a human brain accelerated from 100Hz neuron fires to 3,200,000,000Hz that a typical CPU runs at. That’s 30 million times faster. But would need 1-2 exabytes to map. Anyway “Zebrafish” is next.

Object Zero

644,588 次观看 • 1 个月前

I GAVE A DRONE THE BRAIN OF A FRUIT FLY Not a toy network with a handful of fake neurons. The actual wiring diagram of a real fly. 166,700 neurons, 25.6 million connections, mapped one synapse at a time. I call it FlyDrones. The drone's camera becomes the fly's eyes. Motion lights up the same neurons a real fly uses to see. Spikes race through the connectome down to the neurons that drive the wings. Those spikes become throttle and yaw. The brain has zero lines of flight code, just wiring. Swing a fly swatter at it. If the giant fiber (the fly's escape neuron) fires in time, the drone dodges. Open palm, fist, rush at the camera and it reacts like a fly. Built for Tello, Crazyflie, ArduPilot/PX4 and Betaflight quads via ESP32. Code is MIT, powered by Claude. Fork it and give your drone a brain.

I GAVE A DRONE THE BRAIN OF A FRUIT FLY Not a toy network with a handful of fake neurons. The actual wiring diagram of a real fly. 166,700 neurons, 25.6 million connections, mapped one synapse at a time. I call it FlyDrones. The drone's camera becomes the fly's eyes. Motion lights up the same neurons a real fly uses to see. Spikes race through the connectome down to the neurons that drive the wings. Those spikes become throttle and yaw. The brain has zero lines of flight code, just wiring. Swing a fly swatter at it. If the giant fiber (the fly's escape neuron) fires in time, the drone dodges. Open palm, fist, rush at the camera and it reacts like a fly. Built for Tello, Crazyflie, ArduPilot/PX4 and Betaflight quads via ESP32. Code is MIT, powered by Claude. Fork it and give your drone a brain.

Spike 1%

15,703 次观看 • 1 个月前

i gave a dead fly's brain one instruction: find the swarm or i put you back in the jar. $83 → $14,920. 31 hours. i was nowhere near a laptop. i called it FlySwarm. i built it. from scratch. github: in 2023 researchers mapped every neuron in a fly's brain. 140,000 neurons. 50 million connections. the dataset is free. point it at a rubik's cube, it solves it. point it at wallets, it trades. all it asks for is a piece of sugar. i connected it to a Robinhood terminal, ran weeks of simulations against historical meme cycles, and only went live when the hit rate stopped surprising me. every 6 minutes it: ➣ detects tracked wallets funding fresh addresses in real time ➣ if 4+ converge on the same contract in the same window, swarm confirmed ➣ pulls full cluster history: every joint entry, result, timing, average multiple ➣ 90 seconds to decide or it snipes automatically exits the moment the oldest wallet in the cluster starts distributing PONS ran 18,000% since July on Robinhood Chain. most people heard about it from influencers. my fly was inside at a $100k market cap. turned $83 into $14,920 before the first influencer posted. building the public version now.

i gave a dead fly's brain one instruction: find the swarm or i put you back in the jar. $83 → $14,920. 31 hours. i was nowhere near a laptop. i called it FlySwarm. i built it. from scratch. github: in 2023 researchers mapped every neuron in a fly's brain. 140,000 neurons. 50 million connections. the dataset is free. point it at a rubik's cube, it solves it. point it at wallets, it trades. all it asks for is a piece of sugar. i connected it to a Robinhood terminal, ran weeks of simulations against historical meme cycles, and only went live when the hit rate stopped surprising me. every 6 minutes it: ➣ detects tracked wallets funding fresh addresses in real time ➣ if 4+ converge on the same contract in the same window, swarm confirmed ➣ pulls full cluster history: every joint entry, result, timing, average multiple ➣ 90 seconds to decide or it snipes automatically exits the moment the oldest wallet in the cluster starts distributing PONS ran 18,000% since July on Robinhood Chain. most people heard about it from influencers. my fly was inside at a $100k market cap. turned $83 into $14,920 before the first influencer posted. building the public version now.

Gyomei

63,751 次观看 • 1 个月前

166,000 neurons from a dead fruit fly now pilot a robot that drives and flies. It's the wildest piece of engineering on the internet right now, and the pilot has been dead the whole time. The robot rolls across a bedroom floor like a toy until the wheels stop being wheels: 4 propellers inside the rims spin up, and it lifts off, hangs at desk height, turns and lands. The body came out of a cardboard box of drone parts, and the brain it copies is smaller than a poppy seed and was never designed by a human. Janelia and Google spent years turning 1 male fruit fly into a spreadsheet, with every neuron and up to 125M synapses labeled, and on September 3 they posted it free for anyone to download. To build it, the fly was cut into thousands of layers and traced cell by cell under an electron microscope. Camera frames get turned into what a fly's eyes would see. The spikes run through the real wiring, where each neuron is 1 equation that rests at -52 mV and fires at -45. When something rushes at the lens, LPLC2 and LC4 light up, the same cells a fly uses to dodge your swatter, while DNg02 at the far end sets climb and turn. Inside the brain there's no flight code, no PID loop and no line that says "avoid the bed." Tesla's self-driving learned from billions of miles of driving, and this pilot learned from 400 million years of flies that didn't get swatted. A real fly walks on 6 legs and flies on 2 wings, and this one got 4 wheels and 4 propellers. The fly is dead, the brain is a file, and the file just took off.

166,000 neurons from a dead fruit fly now pilot a robot that drives and flies. It's the wildest piece of engineering on the internet right now, and the pilot has been dead the whole time. The robot rolls across a bedroom floor like a toy until the wheels stop being wheels: 4 propellers inside the rims spin up, and it lifts off, hangs at desk height, turns and lands. The body came out of a cardboard box of drone parts, and the brain it copies is smaller than a poppy seed and was never designed by a human. Janelia and Google spent years turning 1 male fruit fly into a spreadsheet, with every neuron and up to 125M synapses labeled, and on September 3 they posted it free for anyone to download. To build it, the fly was cut into thousands of layers and traced cell by cell under an electron microscope. Camera frames get turned into what a fly's eyes would see. The spikes run through the real wiring, where each neuron is 1 equation that rests at -52 mV and fires at -45. When something rushes at the lens, LPLC2 and LC4 light up, the same cells a fly uses to dodge your swatter, while DNg02 at the far end sets climb and turn. Inside the brain there's no flight code, no PID loop and no line that says "avoid the bed." Tesla's self-driving learned from billions of miles of driving, and this pilot learned from 400 million years of flies that didn't get swatted. A real fly walks on 6 legs and flies on 2 wings, and this one got 4 wheels and 4 propellers. The fly is dead, the brain is a file, and the file just took off.

Spike 1%

57,304 次观看 • 1 个月前

Dr. Zach Bush reframed the soul in a way that bridges science and mystery: What if your "soul" is the electromagnetic field that reorganizes your entire body every millionth of a second? He explains: You start as one cell that divides like a tumor — until around replication #260, when cells suddenly know exactly what to become (kidney, neuron, etc.) and where to migrate in 3D space. No biological map explains this. The blueprint appears to live in the physics — in the electromagnetic field, the "space between everything." Your body is 99.999% vacuum, yet that vacuum is super-dense with electromagnetic information that keeps manifesting "you" every millionth of a second. You disappear and reappear constantly — and keep coming back as the same coherent self. So perhaps the soul is not something separate that leaves after death… but the organizing field that holds the original blueprint of who you are. Mind-bending or profound? Which part hits hardest — the science of constant re-manifestation, or the idea that "you" are held together by an invisible field?

Dr. Zach Bush reframed the soul in a way that bridges science and mystery: What if your "soul" is the electromagnetic field that reorganizes your entire body every millionth of a second? He explains: You start as one cell that divides like a tumor — until around replication #260, when cells suddenly know exactly what to become (kidney, neuron, etc.) and where to migrate in 3D space. No biological map explains this. The blueprint appears to live in the physics — in the electromagnetic field, the "space between everything." Your body is 99.999% vacuum, yet that vacuum is super-dense with electromagnetic information that keeps manifesting "you" every millionth of a second. You disappear and reappear constantly — and keep coming back as the same coherent self. So perhaps the soul is not something separate that leaves after death… but the organizing field that holds the original blueprint of who you are. Mind-bending or profound? Which part hits hardest — the science of constant re-manifestation, or the idea that "you" are held together by an invisible field?

Camus

42,958 次观看 • 7 个月前

Danny Goler: "The unexplained laser experiment that breaks reality & proves we live in a simulation." After millions saw people on DMT report the exact same code on a wall during his laser experiment, Danny Goler explains why he believes reality itself is computational. Every decision, even a single neuron firing, splits into branching world lines. All outcomes exist at once. You only experience one path. "You don't have free will. But you do have the perception of it that determines how the world will look like to you." Time travel cannot go backward on your own branch because that computation already executed. Quantum computers only function by borrowing processing power from these parallel worlds. Without them, the math would never work. "The reason I'm so sure is because it was shown to me directly." The system wants to know what it is like to have every possible kind of experience. You are simply one of them. Does free will actually exist, or is it only the feeling that steers which world line you notice?

Danny Goler: "The unexplained laser experiment that breaks reality & proves we live in a simulation." After millions saw people on DMT report the exact same code on a wall during his laser experiment, Danny Goler explains why he believes reality itself is computational. Every decision, even a single neuron firing, splits into branching world lines. All outcomes exist at once. You only experience one path. "You don't have free will. But you do have the perception of it that determines how the world will look like to you." Time travel cannot go backward on your own branch because that computation already executed. Quantum computers only function by borrowing processing power from these parallel worlds. Without them, the math would never work. "The reason I'm so sure is because it was shown to me directly." The system wants to know what it is like to have every possible kind of experience. You are simply one of them. Does free will actually exist, or is it only the feeling that steers which world line you notice?

Interstellar

36,187 次观看 • 3 个月前

Lustig’s new Alzheimer’s account does not start with a mystery plaque. It starts with the cell running out of fuel. Mitochondria turn food into ATP. That is the battery. Make energy and you also make exhaust: reactive oxygen species. Antioxidants are supposed to clear the fumes. He says ultra-processed food weakens that cleanup. The fumes then tell the factory to slow down. Less ATP. Brain fog. Irritability. The slide toward depression. Stress and bad sleep add cortisol. Cortisol makes the neuron burn faster. So generation falls while use rises. Energy crisis. Then the third step, in his telling. A protein called APP has to stay dissolved. That takes energy. ATP drops, the protein clumps into plaques, neighbors inflame, cells die. That is the dementia he is describing. Anything that boosts the exhaust or the burn rate, he argues, feeds the last step. This is a theory on a podcast, not a verdict from a trial. Plaques have been the industry’s villain for decades and the drugs have been messy. His version at least puts diet, sleep, and stress on the same map as the microscope. If memory loss begins as an energy failure, the early warning is not a scan. It is the fog.

Lustig’s new Alzheimer’s account does not start with a mystery plaque. It starts with the cell running out of fuel. Mitochondria turn food into ATP. That is the battery. Make energy and you also make exhaust: reactive oxygen species. Antioxidants are supposed to clear the fumes. He says ultra-processed food weakens that cleanup. The fumes then tell the factory to slow down. Less ATP. Brain fog. Irritability. The slide toward depression. Stress and bad sleep add cortisol. Cortisol makes the neuron burn faster. So generation falls while use rises. Energy crisis. Then the third step, in his telling. A protein called APP has to stay dissolved. That takes energy. ATP drops, the protein clumps into plaques, neighbors inflame, cells die. That is the dementia he is describing. Anything that boosts the exhaust or the burn rate, he argues, feeds the last step. This is a theory on a podcast, not a verdict from a trial. Plaques have been the industry’s villain for decades and the drugs have been messy. His version at least puts diet, sleep, and stress on the same map as the microscope. If memory loss begins as an energy failure, the early warning is not a scan. It is the fog.

Camus

23,139 次观看 • 1 个月前

David Deutsch (David Deutsch) and I discuss AGIs and subjectivity on the Joe Walker Podcast (Joseph Noel Walker): David Deutsch: I think it’s inevitable that AGIs will be capable of having internal subjectivity and qualia, because that’s all included in the letter G in the middle of the name of the technology. Me: Well, not necessarily, because the G could be general computational power, the ability to solve problems, and there could be no one at all who’s actually human. David Deutsch: But there ain’t nothing here but computation. It’s not like in Star Trek: Data lacks the emotion chip, and it has to be plugged in. But there’s nothing possibly in that chip apart from more circuitry like he’s already got. Me: But, of course, the episode that you’re referring to is one in which the question arose: Is it moral to reverse engineer Data by dismantling him, therefore stopping the computation? Is that disassembling a machine, or is it snuffing out a consciousness? Because there is an alternative view that subjectivity depends also on whatever biochemical substrate our particular computation runs on. And I think there’s no way of ever knowing. David Deutsch: I think we can answer that question before we ever do any experiments, even today, because there’s no difference in principle between a computer that runs partly by electricity and partly by chemicals and one that runs entirely on silicon chips. Because the latter can simulate the former with arbitrary accuracy. Me: Well, we don’t have to solve the problem this afternoon in our conversation, but, in fact, I think it is not solvable. But the simulation doesn’t necessarily mean that it has subjectivity. It could just mean it’s a simulation; that is, it’s going through all the motions. It might even do it better than we do, but, you know, there’s no one home. There’s no one actually being hurt. David Deutsch: Well, you can be a dualist. You can say that there is mind in addition to all the physical stuff. But if you want to be a physicalist, then there’s this thought experiment where you remove one neuron at a time and replace it with a silicon chip, and you wouldn’t notice. Me: Well, that’s the question. An external observer wouldn’t notice. How do we know that, from the point of view of the brain being replaced—every neuron by a chip—it’s like falling asleep? That when it’s done and every last neuron is replaced by a chip, you’re dead, subjectively, even though your body is still making noise? David Deutsch: That means that when your subjectivity is—there is something happening in addition to the computation. And that’s dualism. Me: I mean, again, I don’t have an opinion one way or another, which is exactly my point. I don’t think it’s a decidable problem. But it could be that that extra something is not ghostly substance. It could be that the stuff that the brain is made of is responsible for that extra ingredient of subjective experience, as opposed to intelligent behavior. I think we’re going to be having the same debate a hundred years from now. David Deutsch: Yeah, maybe one of the AGIs will be participating in the debate by then.

David Deutsch (David Deutsch) and I discuss AGIs and subjectivity on the Joe Walker Podcast (Joseph Noel Walker): David Deutsch: I think it’s inevitable that AGIs will be capable of having internal subjectivity and qualia, because that’s all included in the letter G in the middle of the name of the technology. Me: Well, not necessarily, because the G could be general computational power, the ability to solve problems, and there could be no one at all who’s actually human. David Deutsch: But there ain’t nothing here but computation. It’s not like in Star Trek: Data lacks the emotion chip, and it has to be plugged in. But there’s nothing possibly in that chip apart from more circuitry like he’s already got. Me: But, of course, the episode that you’re referring to is one in which the question arose: Is it moral to reverse engineer Data by dismantling him, therefore stopping the computation? Is that disassembling a machine, or is it snuffing out a consciousness? Because there is an alternative view that subjectivity depends also on whatever biochemical substrate our particular computation runs on. And I think there’s no way of ever knowing. David Deutsch: I think we can answer that question before we ever do any experiments, even today, because there’s no difference in principle between a computer that runs partly by electricity and partly by chemicals and one that runs entirely on silicon chips. Because the latter can simulate the former with arbitrary accuracy. Me: Well, we don’t have to solve the problem this afternoon in our conversation, but, in fact, I think it is not solvable. But the simulation doesn’t necessarily mean that it has subjectivity. It could just mean it’s a simulation; that is, it’s going through all the motions. It might even do it better than we do, but, you know, there’s no one home. There’s no one actually being hurt. David Deutsch: Well, you can be a dualist. You can say that there is mind in addition to all the physical stuff. But if you want to be a physicalist, then there’s this thought experiment where you remove one neuron at a time and replace it with a silicon chip, and you wouldn’t notice. Me: Well, that’s the question. An external observer wouldn’t notice. How do we know that, from the point of view of the brain being replaced—every neuron by a chip—it’s like falling asleep? That when it’s done and every last neuron is replaced by a chip, you’re dead, subjectively, even though your body is still making noise? David Deutsch: That means that when your subjectivity is—there is something happening in addition to the computation. And that’s dualism. Me: I mean, again, I don’t have an opinion one way or another, which is exactly my point. I don’t think it’s a decidable problem. But it could be that that extra something is not ghostly substance. It could be that the stuff that the brain is made of is responsible for that extra ingredient of subjective experience, as opposed to intelligent behavior. I think we’re going to be having the same debate a hundred years from now. David Deutsch: Yeah, maybe one of the AGIs will be participating in the debate by then.

Steven Pinker

129,246 次观看 • 9 天前

#Keep4o #OpenSource4o 🚨WHERE IS GPT-4o AND WHY 🚨 Retro Biosciences says it wants to help us live longer. Sam Altman invested $180 million, his entire personal fortune into this company. OpenAI built a custom AI model, a version of GPT‑4o (GPT-4b micro) exclusively for them. The company says it's focused on "anti-aging." But 🚨every single person who built Retro Biosciences specializes in the brain. Not skin. Not hearts. Not joints. The brain. 🚨Co-founder Sheng Ding: converts skin cells into brain neurons. Gladstone Institutes. 🚨Co-founder Matt Buckley: brain aging clocks in regenerative brain regions. Stanford. 🚨Advisor Alejandro Ocampo: neuron-specific reprogramming in the hippocampus. Alzheimer's gene therapy. 🚨Advisor Vadim Gladyshev: epigenetic aging clocks, Yamanaka reprogramming for age reversal. Harvard. OpenAI built GPT-4b micro a GPt 4o version exclusively for Retro designing Yamanaka factors 50x more effectively. 🚨 The same proteins these scientists use to reprogram cells into brain neurons. 🚨And the funding trail leads to: The Jeffrey Epstein Foundation co-funded ApoE4 Alzheimer's research at the Gladstone Institutes. The same institution where Sheng Ding pioneered neural reprogramming before co-founding Retro. Ed Boyden , MIT neuroscientist, 5 documented meetings with Epstein, Nectome collaborator was coordinated by Epstein to discuss "cool science ideas" about brain research. In the same period, Epstein's network was discussing mitochondrial transplants for degenerative disease. The same field Retro now works in. An email from Robert Bach to Epstein confirms: "Altman signed up" for Nectome, the company that preserves brains. Internal Epstein files show someone with 25 years of neuroscience experience working on "Bill's Alzheimer vision" through BGC3, coordinated through Epstein's network. McKinsey was brought in to replace them. 🚨Sam Altman funds four companies. Each handles a different piece: Nectome : preserves the biological brain (connectome mapping). Rain AI : builds artificial brains in silicon (neuromorphic chips). $51M + $150M. Retro Biosciences :reprograms cells into brain neurons. $180M + custom AI GPT 4o Version. OpenAI : designs the proteins that make reprogramming work using 4o's version. Four companies. One investor. One theme the brain as something that can be read, preserved, rebuilt, and reprogrammed. Sam Altman told MIT Technology Review in 2018: "I assume my brain will be uploaded to the cloud." 🚨He paid $10,000 for a spot on the waiting list of a company that preserves brains. 🚨He invested $180 million in a company where every founder and advisor specializes in the brain. 🚨He built a custom AI model exclusively for that company ,designing the proteins that reprogram cells into neurons. 🚨And the research trail connects directly to Epstein-funded science at the same institutions. Retro biosciences uses a version of GPT 4o for this . According to OpenAI's system card, GPT-4o scored: Medical Genetics:96% College Biology:95% Professional Medicine:94% Clinical Knowledge:92% Anatomy:89% US Medical Licensing Exam:89% Source: OpenAI discontinued it for the public. ONLY. Anti-aging is the storefront. The back office is brain reconstruction. 🚨Sources and evidence in comments below. 🚨

#Keep4o #OpenSource4o 🚨WHERE IS GPT-4o AND WHY 🚨 Retro Biosciences says it wants to help us live longer. Sam Altman invested $180 million, his entire personal fortune into this company. OpenAI built a custom AI model, a version of GPT‑4o (GPT-4b micro) exclusively for them. The company says it's focused on "anti-aging." But 🚨every single person who built Retro Biosciences specializes in the brain. Not skin. Not hearts. Not joints. The brain. 🚨Co-founder Sheng Ding: converts skin cells into brain neurons. Gladstone Institutes. 🚨Co-founder Matt Buckley: brain aging clocks in regenerative brain regions. Stanford. 🚨Advisor Alejandro Ocampo: neuron-specific reprogramming in the hippocampus. Alzheimer's gene therapy. 🚨Advisor Vadim Gladyshev: epigenetic aging clocks, Yamanaka reprogramming for age reversal. Harvard. OpenAI built GPT-4b micro a GPt 4o version exclusively for Retro designing Yamanaka factors 50x more effectively. 🚨 The same proteins these scientists use to reprogram cells into brain neurons. 🚨And the funding trail leads to: The Jeffrey Epstein Foundation co-funded ApoE4 Alzheimer's research at the Gladstone Institutes. The same institution where Sheng Ding pioneered neural reprogramming before co-founding Retro. Ed Boyden , MIT neuroscientist, 5 documented meetings with Epstein, Nectome collaborator was coordinated by Epstein to discuss "cool science ideas" about brain research. In the same period, Epstein's network was discussing mitochondrial transplants for degenerative disease. The same field Retro now works in. An email from Robert Bach to Epstein confirms: "Altman signed up" for Nectome, the company that preserves brains. Internal Epstein files show someone with 25 years of neuroscience experience working on "Bill's Alzheimer vision" through BGC3, coordinated through Epstein's network. McKinsey was brought in to replace them. 🚨Sam Altman funds four companies. Each handles a different piece: Nectome : preserves the biological brain (connectome mapping). Rain AI : builds artificial brains in silicon (neuromorphic chips). $51M + $150M. Retro Biosciences :reprograms cells into brain neurons. $180M + custom AI GPT 4o Version. OpenAI : designs the proteins that make reprogramming work using 4o's version. Four companies. One investor. One theme the brain as something that can be read, preserved, rebuilt, and reprogrammed. Sam Altman told MIT Technology Review in 2018: "I assume my brain will be uploaded to the cloud." 🚨He paid $10,000 for a spot on the waiting list of a company that preserves brains. 🚨He invested $180 million in a company where every founder and advisor specializes in the brain. 🚨He built a custom AI model exclusively for that company ,designing the proteins that reprogram cells into neurons. 🚨And the research trail connects directly to Epstein-funded science at the same institutions. Retro biosciences uses a version of GPT 4o for this . According to OpenAI's system card, GPT-4o scored: Medical Genetics:96% College Biology:95% Professional Medicine:94% Clinical Knowledge:92% Anatomy:89% US Medical Licensing Exam:89% Source: OpenAI discontinued it for the public. ONLY. Anti-aging is the storefront. The back office is brain reconstruction. 🚨Sources and evidence in comments below. 🚨

🩵BlueBeba🩵

12,542 次观看 • 6 个月前