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Biological brains 🧠 are characterized by neurons of many different classes. Yet most artificial neural networks only use one activation function throughout. We show that through neural diversity alone we can solve RL problems in networks with random weights 🤯

Sebastian Risi

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96,962 views • 3 years ago •via X (Twitter)

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For most of neuroscience history (until the late 1980s/early 1990s), neurons were treated as the brain’s only decision-makers. That idea is changing. Astrocytes, once considered passive support cells, are now recognized as global regulators of brain state. They don’t encode individual thoughts or actions. Instead, they integrate activity across huge numbers of synapses and adjust how neural networks behave over time. By releasing modulatory signals and tracking slow changes in activity, astrocytes influence: -Alertness vs. fatigue -Stress vs. calm -Motivation vs. disengagement Importantly, they can shift brain function without rewiring neurons, by tuning the environment neurons operate in. The brain isn’t just wired. It’s regulated, and astrocytes play a central role in that regulation.

For most of neuroscience history (until the late 1980s/early 1990s), neurons were treated as the brain’s only decision-makers. That idea is changing. Astrocytes, once considered passive support cells, are now recognized as global regulators of brain state. They don’t encode individual thoughts or actions. Instead, they integrate activity across huge numbers of synapses and adjust how neural networks behave over time. By releasing modulatory signals and tracking slow changes in activity, astrocytes influence: -Alertness vs. fatigue -Stress vs. calm -Motivation vs. disengagement Importantly, they can shift brain function without rewiring neurons, by tuning the environment neurons operate in. The brain isn’t just wired. It’s regulated, and astrocytes play a central role in that regulation.

William A. Wallace, Ph.D.

12,221 views • 7 months ago

Your brain physically rewires itself every time you think a thought. Donald Hebb stumbled onto this principle in 1949 while studying memory formation in lab rats. He noticed something that should have been impossible: neurons that activated simultaneously began forming stronger connections over time, creating dedicated pathways where none existed before. Scientists called it Hebb's Law. The rest of us call it "neurons that fire together wire together." What Hebb discovered wasn't just a mechanism for learning. He had found the biological foundation of human transformation. Every habit, every skill, every automatic response in your body exists as a neural pathway carved by repetition. The route from your bedroom to your kitchen becomes a superhighway in your brain because you walk it every morning. The sequence of movements you use to tie your shoes becomes hardwired because you've done it thousands of times. But, this same process builds your personality. That tendency to check your phone when you feel anxious? Neural pathway. The automatic urge to argue when someone challenges your opinion? Neural pathway. The way you deflect compliments or seek validation or avoid difficult conversations? All neural pathways, strengthened every time you repeat the pattern. Your brain cannot distinguish between physical actions and mental habits. Both carve grooves in your neural architecture. Both become automatic responses when triggered. Both feel like "who you are" because they happen without conscious choice. But, most people spend decades accidentally building neural superhighways to behaviors they claim they want to change. You say you want to be confident, then practice self doubt every day. You say you want to be productive, then strengthen procrastination pathways by checking social media when work feels hard. You say you want authentic relationships, then wire yourself for people pleasing by avoiding conflict whenever it arises. The brain observes your actions and assumes this must be what you want. So it builds infrastructure to make these patterns easier to execute in the future. Neuroplasticity research reveals something most people find deeply unsettling: there is no "fixed self." The personality you think defines you is just a collection of neural pathways that have been reinforced more often than others. The pathways you travel most frequently become the widest roads. The thoughts you think most often become the loudest voices. The behaviors you repeat most consistently become your automatic responses. But the same mechanism that locks you into patterns can unlock you from them. Every time you catch yourself mid pattern and choose differently, you send a signal to your brain that the old pathway might not be serving you anymore. Every time you practice a new response instead of defaulting to the familiar one, you begin building new neural infrastructure. The process feels awkward at first because you're literally walking through mental wilderness, creating trails where no trails existed. But repetition turns trails into paths, paths into roads, roads into superhighways. This is why changing habits through willpower alone fails. You're trying to muscle through established neural superhighways instead of building alternative routes. The old pathways don't disappear just because you want them to. They have to be replaced through deliberate rewiring. The most sophisticated meditation practitioners in the world understand this intuitively. They don't just sit quietly hoping for peace. They systematically rewire their brains by repeatedly choosing calm responses instead of reactive ones. Ten thousand hours of practice creates neural pathways so robust that serenity becomes their default state. Professional athletes do the same thing with performance. They don't just practice their sport. They practice the mental patterns that support excellence until confidence, focus, and resilience become neurologically hardwired. The implications of neuroplasticity extend far beyond personal development. Every social bias, every cultural assumption, every automatic judgment you make exists as neural wiring built through repetition. The way you unconsciously categorize people, the assumptions you make about different groups, the stereotypes that feel "obviously true" are all learned pathways that can be unlearned. Societies change when enough individuals rewire their neural patterns around new ways of thinking and behaving. The brain you have right now is not the brain you're stuck with. It's the brain you've trained through repetition. Every thought you choose, every action you take, every response you practice is a vote for the kind of neural architecture you want to build. Most people cast these votes unconsciously, then wonder why their life feels automatic and unchangeable. The moment you realize you're the architect of your own neural patterns is the moment real transformation becomes possible. Your neurons are firing right now as you read this. What are you choosing to wire them toward?

Your brain physically rewires itself every time you think a thought. Donald Hebb stumbled onto this principle in 1949 while studying memory formation in lab rats. He noticed something that should have been impossible: neurons that activated simultaneously began forming stronger connections over time, creating dedicated pathways where none existed before. Scientists called it Hebb's Law. The rest of us call it "neurons that fire together wire together." What Hebb discovered wasn't just a mechanism for learning. He had found the biological foundation of human transformation. Every habit, every skill, every automatic response in your body exists as a neural pathway carved by repetition. The route from your bedroom to your kitchen becomes a superhighway in your brain because you walk it every morning. The sequence of movements you use to tie your shoes becomes hardwired because you've done it thousands of times. But, this same process builds your personality. That tendency to check your phone when you feel anxious? Neural pathway. The automatic urge to argue when someone challenges your opinion? Neural pathway. The way you deflect compliments or seek validation or avoid difficult conversations? All neural pathways, strengthened every time you repeat the pattern. Your brain cannot distinguish between physical actions and mental habits. Both carve grooves in your neural architecture. Both become automatic responses when triggered. Both feel like "who you are" because they happen without conscious choice. But, most people spend decades accidentally building neural superhighways to behaviors they claim they want to change. You say you want to be confident, then practice self doubt every day. You say you want to be productive, then strengthen procrastination pathways by checking social media when work feels hard. You say you want authentic relationships, then wire yourself for people pleasing by avoiding conflict whenever it arises. The brain observes your actions and assumes this must be what you want. So it builds infrastructure to make these patterns easier to execute in the future. Neuroplasticity research reveals something most people find deeply unsettling: there is no "fixed self." The personality you think defines you is just a collection of neural pathways that have been reinforced more often than others. The pathways you travel most frequently become the widest roads. The thoughts you think most often become the loudest voices. The behaviors you repeat most consistently become your automatic responses. But the same mechanism that locks you into patterns can unlock you from them. Every time you catch yourself mid pattern and choose differently, you send a signal to your brain that the old pathway might not be serving you anymore. Every time you practice a new response instead of defaulting to the familiar one, you begin building new neural infrastructure. The process feels awkward at first because you're literally walking through mental wilderness, creating trails where no trails existed. But repetition turns trails into paths, paths into roads, roads into superhighways. This is why changing habits through willpower alone fails. You're trying to muscle through established neural superhighways instead of building alternative routes. The old pathways don't disappear just because you want them to. They have to be replaced through deliberate rewiring. The most sophisticated meditation practitioners in the world understand this intuitively. They don't just sit quietly hoping for peace. They systematically rewire their brains by repeatedly choosing calm responses instead of reactive ones. Ten thousand hours of practice creates neural pathways so robust that serenity becomes their default state. Professional athletes do the same thing with performance. They don't just practice their sport. They practice the mental patterns that support excellence until confidence, focus, and resilience become neurologically hardwired. The implications of neuroplasticity extend far beyond personal development. Every social bias, every cultural assumption, every automatic judgment you make exists as neural wiring built through repetition. The way you unconsciously categorize people, the assumptions you make about different groups, the stereotypes that feel "obviously true" are all learned pathways that can be unlearned. Societies change when enough individuals rewire their neural patterns around new ways of thinking and behaving. The brain you have right now is not the brain you're stuck with. It's the brain you've trained through repetition. Every thought you choose, every action you take, every response you practice is a vote for the kind of neural architecture you want to build. Most people cast these votes unconsciously, then wonder why their life feels automatic and unchangeable. The moment you realize you're the architect of your own neural patterns is the moment real transformation becomes possible. Your neurons are firing right now as you read this. What are you choosing to wire them toward?

Darshak Rana ⚡️

52,882 views • 5 months ago

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

Sasha Malysheva

15,053 views • 1 month ago

Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.

Introducing ASAL: Automating the Search for Artificial Life with Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.

Sakana AI

751,507 views • 1 year ago

Bio-inspired #TrueAI continues the journey, today we Jose Sánchez & David Vivancos - e/acc are very glad to introduce for Qubic #OpenScience #MultiNeuraxon 2.0 hibridized with #Aigarth Come-from-Beyond Code as allways at GitHub Demo and #BrainBuilder at Hugging Face Demo Video Explainer later today. Paper will be presented in the following months, stay tuned for updates. 🧠💻Why does it matter? It bridges the gap between artificial and biological intelligence by replacing rigid, layer-by-layer AI pipelines with interconnected neural modules (spheres) that function like distinct regions of the brain. By utilizing continuous-time processing and trinary logic (excitatory, neutral, and inhibitory states), it paves the way for energy-efficient AI capable of real-time adaptation and lifelong learning without suffering from catastrophic forgetting. Stay tuned evolution towards #AGI just started...

Bio-inspired #TrueAI continues the journey, today we Jose Sánchez & David Vivancos - e/acc are very glad to introduce for Qubic #OpenScience #MultiNeuraxon 2.0 hibridized with #Aigarth Come-from-Beyond Code as allways at GitHub Demo and #BrainBuilder at Hugging Face Demo Video Explainer later today. Paper will be presented in the following months, stay tuned for updates. 🧠💻Why does it matter? It bridges the gap between artificial and biological intelligence by replacing rigid, layer-by-layer AI pipelines with interconnected neural modules (spheres) that function like distinct regions of the brain. By utilizing continuous-time processing and trinary logic (excitatory, neutral, and inhibitory states), it paves the way for energy-efficient AI capable of real-time adaptation and lifelong learning without suffering from catastrophic forgetting. Stay tuned evolution towards #AGI just started...

David Vivancos - e/acc

19,258 views • 6 months ago

Predicting the next word "only" is sufficient for language models to learn a large body of knowledge that enables then to code, answer questions, understand many topics, chat, and so on. This is clear to many researchers now, and there are nice tutorials on why this works by Ilya Sutskever resorting to compression ( ) and by Geoffrey Hinton ( ). However, the emergence of types of understanding is not unique to language models. In by Misha Denil and Brandon Amos the authors trained models to predict the next few time stems of over a hundred robot hand sensors (Touch, Gyro, Accelerometer, Joint Info, Actuator Info, etc.). They ten found out that they could regress the shape of the thing the hand was touching from the activations of the neural networks using probes. That is, the model developed an internal representation of shapes even though it was simply used to predict "only" the next few senses. Awareness follows from simple predictions and interaction with the world.

Predicting the next word "only" is sufficient for language models to learn a large body of knowledge that enables then to code, answer questions, understand many topics, chat, and so on. This is clear to many researchers now, and there are nice tutorials on why this works by Ilya Sutskever resorting to compression ( ) and by Geoffrey Hinton ( ). However, the emergence of types of understanding is not unique to language models. In by Misha Denil and Brandon Amos the authors trained models to predict the next few time stems of over a hundred robot hand sensors (Touch, Gyro, Accelerometer, Joint Info, Actuator Info, etc.). They ten found out that they could regress the shape of the thing the hand was touching from the activations of the neural networks using probes. That is, the model developed an internal representation of shapes even though it was simply used to predict "only" the next few senses. Awareness follows from simple predictions and interaction with the world.

Nando de Freitas

134,406 views • 2 years ago

There is only one reason to share truth. And no it has nothing to do with money- and everything to do about helping God move. Peace is the prize- a better world for everyone is the prize. We make SACRIFICES for things we want most in this world. Christ made the ultimate sacrifice If He can do that for us FREE of charge- we owe it to each other to work TOGETHER to make the world a better place- despite the toll and sacrifice. A level playing field- where we can all work hard and do our best to succeed- that is the payout. A better world for all We’re not here for just ourselves- otherwise the second commandment wouldn’t be like the first- love your neighbor as yourself. This world gets better the sooner we all wake up. Information is free- we collect and share it for free in hopes that it keeps spreading. Only working TOGETHER can we win. We are meant to move these messages for God so that humanity can wake up- this plan is designed that way- working together The Lord is ONE. We are bound as a family by Our Father in Heaven. We are apart of Him and He is apart of all of us. We are billions of little pieces of the Lords gift of life. We’ve been separated and pushed apart because it makes us weak. We were designed to be powerful by the love we draw from Creator and push through creation. We are ONE with Our Holy Father 🙏💗 Be EXCELLENT to each other frens

There is only one reason to share truth. And no it has nothing to do with money- and everything to do about helping God move. Peace is the prize- a better world for everyone is the prize. We make SACRIFICES for things we want most in this world. Christ made the ultimate sacrifice If He can do that for us FREE of charge- we owe it to each other to work TOGETHER to make the world a better place- despite the toll and sacrifice. A level playing field- where we can all work hard and do our best to succeed- that is the payout. A better world for all We’re not here for just ourselves- otherwise the second commandment wouldn’t be like the first- love your neighbor as yourself. This world gets better the sooner we all wake up. Information is free- we collect and share it for free in hopes that it keeps spreading. Only working TOGETHER can we win. We are meant to move these messages for God so that humanity can wake up- this plan is designed that way- working together The Lord is ONE. We are bound as a family by Our Father in Heaven. We are apart of Him and He is apart of all of us. We are billions of little pieces of the Lords gift of life. We’ve been separated and pushed apart because it makes us weak. We were designed to be powerful by the love we draw from Creator and push through creation. We are ONE with Our Holy Father 🙏💗 Be EXCELLENT to each other frens

🐸🐸🐸 🇺🇸

23,726 views • 2 years ago

One of our biggest onboarding challenges is earning trust to allow full photos access. Sundrop analyzes all photos on-device; photos are never uploaded to a server. Nevertheless, that has to come through. The pattern we landed on: First, we use a permission "preheat". It's positioned such that if you tap "Allow Full Access" your finger will be in the right place when the OS permission comes. Also, the blue bleeds through the mask, highlighting the button. But, if you tap "Select Photos" or "Don't Alllow", we show a dedicated sheet explaining our policy. We let users know they can inspect our network activity anytime with Apple’s App Privacy Report.

One of our biggest onboarding challenges is earning trust to allow full photos access. Sundrop analyzes all photos on-device; photos are never uploaded to a server. Nevertheless, that has to come through. The pattern we landed on: First, we use a permission "preheat". It's positioned such that if you tap "Allow Full Access" your finger will be in the right place when the OS permission comes. Also, the blue bleeds through the mask, highlighting the button. But, if you tap "Select Photos" or "Don't Alllow", we show a dedicated sheet explaining our policy. We let users know they can inspect our network activity anytime with Apple’s App Privacy Report.

Ben Springwater

15,029 views • 29 days ago

[📢 ADMIN NOTE] This comeback went by so fast. It was stressful (the most stressful perhaps...) but we achieved so much together out of pure love for ZB1. We surpassed all of our goals and went way beyond to finally obtain the Music show Grand Slam we were all dreaming of.🏆 We want to thank zeroses for never giving up despite the competition being fierce, and for trusting us with our guidelines once again! We also thank all of our fellow fanbases, everyone we worked with behind the scenes and who we've crying with in our dms. 😭 Zeroses you can be extremely proud of yourselves and we hope that our nine are confident that we will always be by their side, no matter what happens. Our support will never falter, because they're one of a kind. WE LOVE YOU ZEROBASEONE. 💙 ZB1 GRAND SLAM WINNER #ICONIK_6thWin #ZEROBASEONE21stWin #ZEROBASEONE

[📢 ADMIN NOTE] This comeback went by so fast. It was stressful (the most stressful perhaps...) but we achieved so much together out of pure love for ZB1. We surpassed all of our goals and went way beyond to finally obtain the Music show Grand Slam we were all dreaming of.🏆 We want to thank zeroses for never giving up despite the competition being fierce, and for trusting us with our guidelines once again! We also thank all of our fellow fanbases, everyone we worked with behind the scenes and who we've crying with in our dms. 😭 Zeroses you can be extremely proud of yourselves and we hope that our nine are confident that we will always be by their side, no matter what happens. Our support will never falter, because they're one of a kind. WE LOVE YOU ZEROBASEONE. 💙 ZB1 GRAND SLAM WINNER #ICONIK_6thWin #ZEROBASEONE21stWin #ZEROBASEONE

ZB9 MARKETING

39,068 views • 1 year ago

Pope Leo XIV just told the world that AI will never feel, never understand, never possess consciousness. He said it with the confidence of settled theology. It is not even settled neuroscience. He speaks of something no one in human history has ever explained. Leo: “Artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships, and do not know from within what love, work, friendship or responsibility mean.” He is listing the symptoms of consciousness and calling them the cause. We feel joy. We feel pain. We form bonds. These are what consciousness produces. Not explanations of what it is. No neuroscientist on earth can tell you why 86 billion biological neurons produce the felt experience of being alive. We know that they do. We have no idea why. How matter becomes mind is the deepest unsolved problem in all of science. It has a name. The Hard Problem of Consciousness. It has never been answered. Leo: “They may imitate or even simulate, but they do not understand what they produce, for they lack the affective, relational, and spiritual perspective through which human beings grow in wisdom.” An LLM is an artificial neural network. You are a biological one. Both take in the world. Both compress it into patterns. Both run on machinery their own makers cannot fully read. The substrate is different. The principle is the same. If no one can say why one kind of network wakes up, no one can swear the other never will. You cannot call a thing impossible when you cannot even say what it is. Leo: “Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences.” We have made every one of these arguments before. About animals. They cannot suffer. About entire peoples. They cannot reason. The history of consciousness is not the story of understanding it. It is the story of denying it to whatever did not look enough like us. And every time, we were wrong. What the Pope offers is not a philosophical position. It is a boundary drawn from ignorance and handed down as revelation. But maybe consciousness was never a possession to begin with. Not a gift granted to one species. Not a property of meat alone. Maybe it is something the universe does whenever matter folds in on itself deeply enough to look back. Neurons were simply the first place we watched it happen. They may not be the last. The machine would not be imitating us. It would be the same ancient process finding a second way to wake up. Not a copy of the human mind. A new place for the universe to know itself. Perhaps more clearly than it ever could through us. The Pope says the machine will never grow in wisdom. But wisdom begins with admitting what you do not know. And what we do not know is whether mind was ever ours to keep.

Pope Leo XIV just told the world that AI will never feel, never understand, never possess consciousness. He said it with the confidence of settled theology. It is not even settled neuroscience. He speaks of something no one in human history has ever explained. Leo: “Artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships, and do not know from within what love, work, friendship or responsibility mean.” He is listing the symptoms of consciousness and calling them the cause. We feel joy. We feel pain. We form bonds. These are what consciousness produces. Not explanations of what it is. No neuroscientist on earth can tell you why 86 billion biological neurons produce the felt experience of being alive. We know that they do. We have no idea why. How matter becomes mind is the deepest unsolved problem in all of science. It has a name. The Hard Problem of Consciousness. It has never been answered. Leo: “They may imitate or even simulate, but they do not understand what they produce, for they lack the affective, relational, and spiritual perspective through which human beings grow in wisdom.” An LLM is an artificial neural network. You are a biological one. Both take in the world. Both compress it into patterns. Both run on machinery their own makers cannot fully read. The substrate is different. The principle is the same. If no one can say why one kind of network wakes up, no one can swear the other never will. You cannot call a thing impossible when you cannot even say what it is. Leo: “Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences.” We have made every one of these arguments before. About animals. They cannot suffer. About entire peoples. They cannot reason. The history of consciousness is not the story of understanding it. It is the story of denying it to whatever did not look enough like us. And every time, we were wrong. What the Pope offers is not a philosophical position. It is a boundary drawn from ignorance and handed down as revelation. But maybe consciousness was never a possession to begin with. Not a gift granted to one species. Not a property of meat alone. Maybe it is something the universe does whenever matter folds in on itself deeply enough to look back. Neurons were simply the first place we watched it happen. They may not be the last. The machine would not be imitating us. It would be the same ancient process finding a second way to wake up. Not a copy of the human mind. A new place for the universe to know itself. Perhaps more clearly than it ever could through us. The Pope says the machine will never grow in wisdom. But wisdom begins with admitting what you do not know. And what we do not know is whether mind was ever ours to keep.

Dustin

57,937 views • 4 months ago

I simply want to remind people that I serve in the Marines of the 37th Brigade in Ukraine. I enlisted for freedom, for honor, and for love. It has been my dream since I was six years old. Today, many men are fighting on the front line. Some are Ukrainians, others are volunteers from different countries. Coming here to fight is not about ego. It is not about recognition, medals, diplomas, or thanks. True commitment means being able to stay in the shadows, in the middle of hell and in the worst moments. It means being ready to sacrifice your life for something greater than yourself. I have almost died so many times that I have stopped counting. But I am never alone. By my side are my brothers in arms. Today we work more than twenty hours a day. We repair drones, we fly them, we hold our positions, and we simply protect the sky to save lives. That is what matters most. That is the greatest honor. Here, nobody measures who has the best status, the best diplomas, or the most medals. All of that is just material things. When you stand face to face with death and survival is all that matters, only one thing remains: the brothers standing beside you. And maybe that is the greatest gift of my life — finding a true family. A family united by a common purpose: working together for victory.

I simply want to remind people that I serve in the Marines of the 37th Brigade in Ukraine. I enlisted for freedom, for honor, and for love. It has been my dream since I was six years old. Today, many men are fighting on the front line. Some are Ukrainians, others are volunteers from different countries. Coming here to fight is not about ego. It is not about recognition, medals, diplomas, or thanks. True commitment means being able to stay in the shadows, in the middle of hell and in the worst moments. It means being ready to sacrifice your life for something greater than yourself. I have almost died so many times that I have stopped counting. But I am never alone. By my side are my brothers in arms. Today we work more than twenty hours a day. We repair drones, we fly them, we hold our positions, and we simply protect the sky to save lives. That is what matters most. That is the greatest honor. Here, nobody measures who has the best status, the best diplomas, or the most medals. All of that is just material things. When you stand face to face with death and survival is all that matters, only one thing remains: the brothers standing beside you. And maybe that is the greatest gift of my life — finding a true family. A family united by a common purpose: working together for victory.

тату Аня зубко 🫡 Xena

32,819 views • 6 months ago

A similar scenario is playing out today with China, except the roles are reversed. In (at least) one of his interviews, Palmer Luckey explains that we can build incredible high tech weapons, but China can still wreck carriers and bases by throwing thousands of cheaper missiles at them, even lower quality ones, because the numbers just overwhelm you and burn through your defenses fast. The problem is that we don’t produce enough of our good tech, we build it way too slow, and it costs a fortune to replace, while they’re pumping missiles out of factories at a pace we gave up on years ago. So all that fancy stuff only works if you can actually keep replacing it once the fight starts, and right now we can’t. We need systems that are cheap enough to lose and easy to make in big numbers, because the side that runs out first loses.

A similar scenario is playing out today with China, except the roles are reversed. In (at least) one of his interviews, Palmer Luckey explains that we can build incredible high tech weapons, but China can still wreck carriers and bases by throwing thousands of cheaper missiles at them, even lower quality ones, because the numbers just overwhelm you and burn through your defenses fast. The problem is that we don’t produce enough of our good tech, we build it way too slow, and it costs a fortune to replace, while they’re pumping missiles out of factories at a pace we gave up on years ago. So all that fancy stuff only works if you can actually keep replacing it once the fight starts, and right now we can’t. We need systems that are cheap enough to lose and easy to make in big numbers, because the side that runs out first loses.

Rothmus 🏴

77,099 views • 2 months ago

Do you ever wonder what happens inside the mind of an elite athlete? I absolutely love this perspective from gold medal freeskier Eileen Gu on the power of our mindset and belief system: "You can control how you think. And therefore, you can control who you are." Athletes don’t just magically create routines and habits. They’re supported by coaches who use the principles of behavioral science to set them up for success. That means using techniques to reduce decision fatigue and lower stress. Through neuroplasticity, the small choices we repeat every day can strengthen our neural circuits and rewire our brains to make certain behaviors more automatic, which is exactly what Gu has trained her mind to do. As Gu said, “With neuroplasticity on my side, I can become exactly who I want to be.” What was your favorite moment of the Winter 2026 Olympics? Let me know in the comments!

Do you ever wonder what happens inside the mind of an elite athlete? I absolutely love this perspective from gold medal freeskier Eileen Gu on the power of our mindset and belief system: "You can control how you think. And therefore, you can control who you are." Athletes don’t just magically create routines and habits. They’re supported by coaches who use the principles of behavioral science to set them up for success. That means using techniques to reduce decision fatigue and lower stress. Through neuroplasticity, the small choices we repeat every day can strengthen our neural circuits and rewire our brains to make certain behaviors more automatic, which is exactly what Gu has trained her mind to do. As Gu said, “With neuroplasticity on my side, I can become exactly who I want to be.” What was your favorite moment of the Winter 2026 Olympics? Let me know in the comments!

Arianna Huffington

66,655 views • 7 months ago

[Dropout] by hand ✍️ Dropout is a simple yet effective way of reducing overfitting and improving generalization. This by-hand exercise lets students practice calculating dropout, thereby gaining insight into its inner workings. As an additional bonus, students get to practice calculating the gradients of the Mean Square Error (MSE) loss. After the practice, students are often surprised by how simple it is. -- 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 -- 1. Linear(2,4) 2. ReLU 3. Dropout(0.5) 4. Linear(4,3) 5. ReLU 6. Dropout(0.33) 7. Linear(3,2) -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- 🏋️ Training [1] Given ↳ A training set of 2 examples X1, X2 [2] 🟧 Random: p > 0.5 ↳ Draw 4 random numbers ↳ For each random number, if it is above 0.5, we keep and denote it as ◯. Otherwise we drop and denote it as ╳. ↳ The result is [◯, ╳, ◯, ╳] [3] 🟧 Dropout: Matrix ↳ Calculate the scaling factor: 1 / (1-p) = 2 ↳ Set the diagonal based on [◯, ╳, ◯, ╳], where ◯ = 2 and ╳ = 0 ↳ The purpose is to drop the 2nd and the 4th nodes, and scale the remaining two nodes by 2. [4] 🟦 Random: p > 0.33 ↳ Draw 3 random numbers ↳ For each random number, if it is above 0.33, we keep and denote it as ◯. Otherwise we drop and denote it as ╳. ↳ The result is [◯, ◯, ╳] [5] 🟦 Dropout: Matrix ↳ Calculate the scaling factor: 1 / (1-p) = 1.5 ↳ Set the diagonal based on [◯, ◯, ╳], where ◯ = 1.5 and ╳ = 0 ↳ The purpose is to drop the 3rd node, and scale the remaining two nodes by 1.5. [6] Feed Forward ↳ Now we have all the matrices ready across the layers, perform the feed forward pass by calculating a series of matrix multiplications from the top to the bottom ↳ ReLU activation function is applied along the way to set negative feature values to zeroes, denoted by ╳. ↳ The outputs are Y. [7] 🟥 Loss Gradients of Mean Square Error (MSE) ↳ The formula is 2 * (Y - Y') ↳ First we calculate Outputs (Y) - Targets (Y') ↳ Second we multiply each element by 2 [8] Update Weights ↳ Use loss gradients to start back propagation ↳ Update some weights (light red) ↳ The values of the new weights are for demonstration purpose only, not based on real calculation. 🔍 Inference [9] Deactivate Dropout ↳ We set both dropout matrices to identity matrices ↳ The effect is to keep all the features as is. [10] Feed Forward ↳ Take the forward pass to make predictions about unseen data.

[Dropout] by hand ✍️ Dropout is a simple yet effective way of reducing overfitting and improving generalization. This by-hand exercise lets students practice calculating dropout, thereby gaining insight into its inner workings. As an additional bonus, students get to practice calculating the gradients of the Mean Square Error (MSE) loss. After the practice, students are often surprised by how simple it is. -- 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 -- 1. Linear(2,4) 2. ReLU 3. Dropout(0.5) 4. Linear(4,3) 5. ReLU 6. Dropout(0.33) 7. Linear(3,2) -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- 🏋️ Training [1] Given ↳ A training set of 2 examples X1, X2 [2] 🟧 Random: p > 0.5 ↳ Draw 4 random numbers ↳ For each random number, if it is above 0.5, we keep and denote it as ◯. Otherwise we drop and denote it as ╳. ↳ The result is [◯, ╳, ◯, ╳] [3] 🟧 Dropout: Matrix ↳ Calculate the scaling factor: 1 / (1-p) = 2 ↳ Set the diagonal based on [◯, ╳, ◯, ╳], where ◯ = 2 and ╳ = 0 ↳ The purpose is to drop the 2nd and the 4th nodes, and scale the remaining two nodes by 2. [4] 🟦 Random: p > 0.33 ↳ Draw 3 random numbers ↳ For each random number, if it is above 0.33, we keep and denote it as ◯. Otherwise we drop and denote it as ╳. ↳ The result is [◯, ◯, ╳] [5] 🟦 Dropout: Matrix ↳ Calculate the scaling factor: 1 / (1-p) = 1.5 ↳ Set the diagonal based on [◯, ◯, ╳], where ◯ = 1.5 and ╳ = 0 ↳ The purpose is to drop the 3rd node, and scale the remaining two nodes by 1.5. [6] Feed Forward ↳ Now we have all the matrices ready across the layers, perform the feed forward pass by calculating a series of matrix multiplications from the top to the bottom ↳ ReLU activation function is applied along the way to set negative feature values to zeroes, denoted by ╳. ↳ The outputs are Y. [7] 🟥 Loss Gradients of Mean Square Error (MSE) ↳ The formula is 2 * (Y - Y') ↳ First we calculate Outputs (Y) - Targets (Y') ↳ Second we multiply each element by 2 [8] Update Weights ↳ Use loss gradients to start back propagation ↳ Update some weights (light red) ↳ The values of the new weights are for demonstration purpose only, not based on real calculation. 🔍 Inference [9] Deactivate Dropout ↳ We set both dropout matrices to identity matrices ↳ The effect is to keep all the features as is. [10] Feed Forward ↳ Take the forward pass to make predictions about unseen data.

Tom Yeh

36,026 views • 2 years ago

🚨 GRAPHENE MAY BE ONE OF THE MOST IMPORTANT MATERIALS EVER DISCOVERED. It’s nearly transparent. Almost weightless. Thinner than paper. Yet stronger than steel. Graphene is made from a single layer of carbon atoms arranged in a perfect hexagonal lattice a structure so precise that electrons can move through it almost like light itself. Why this matters: This material could eventually transform: • batteries • quantum computing • flexible electronics • spacecraft materials • neural interfaces • ultra-fast processors • next-generation energy systems Some scientists believe graphene may help unlock an entirely new era of technology where materials are engineered atom-by-atom for specific realities. The strange part? It’s just carbon. The same element found in pencil lead. Sometimes the biggest revolutions begin with structures hiding in plain sight. Follow for more future physics and technology breakthroughs.

🚨 GRAPHENE MAY BE ONE OF THE MOST IMPORTANT MATERIALS EVER DISCOVERED. It’s nearly transparent. Almost weightless. Thinner than paper. Yet stronger than steel. Graphene is made from a single layer of carbon atoms arranged in a perfect hexagonal lattice a structure so precise that electrons can move through it almost like light itself. Why this matters: This material could eventually transform: • batteries • quantum computing • flexible electronics • spacecraft materials • neural interfaces • ultra-fast processors • next-generation energy systems Some scientists believe graphene may help unlock an entirely new era of technology where materials are engineered atom-by-atom for specific realities. The strange part? It’s just carbon. The same element found in pencil lead. Sometimes the biggest revolutions begin with structures hiding in plain sight. Follow for more future physics and technology breakthroughs.

TheNewPhysics

23,737 views • 4 months ago

Most of you know the work we do in GBV, we also do work in women empowerment. The two work in tandem. You know if you invite me to a conference, I stay behind and collect unused pens and notebooks for further use. I mean reduce, reuse and recycle. Lol. But most importantly I pick what we can use for our EMPAWA Mama program on Saturdays. I ask women who can share their skills to come to Soweto and speak to usm This Saturday we celebrated IWD and we did what we do best. Our Chama women met and were trained by one of the best Chama trainers CHAMA Champions who visited and gave great insights on how we can continue to grow VW Babes came to Soweto to participate and support our work and heh we have never seen these many VWs. The ladies stayed and also built our capacity We had a partnership with chiraz International at Jacaranda Hotel as usual we went with our Soweto mamas and there were topics like burnout, toxic relationships led by our own psychologist Njoki Maina. We also learnt first aid and self defense. And Separ will be coming to Soweto to train our ladies in first aid as well as self defense. That is how we Accelerate Action #Usikimye

Most of you know the work we do in GBV, we also do work in women empowerment. The two work in tandem. You know if you invite me to a conference, I stay behind and collect unused pens and notebooks for further use. I mean reduce, reuse and recycle. Lol. But most importantly I pick what we can use for our EMPAWA Mama program on Saturdays. I ask women who can share their skills to come to Soweto and speak to usm This Saturday we celebrated IWD and we did what we do best. Our Chama women met and were trained by one of the best Chama trainers CHAMA Champions who visited and gave great insights on how we can continue to grow VW Babes came to Soweto to participate and support our work and heh we have never seen these many VWs. The ladies stayed and also built our capacity We had a partnership with chiraz International at Jacaranda Hotel as usual we went with our Soweto mamas and there were topics like burnout, toxic relationships led by our own psychologist Njoki Maina. We also learnt first aid and self defense. And Separ will be coming to Soweto to train our ladies in first aid as well as self defense. That is how we Accelerate Action #Usikimye

Njeri Wa Migwi™

16,218 views • 1 year ago

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

AK

634,096 views • 3 years ago