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Richard Feynman was asked in the 1980s whether machines will ever think. He answered in front of a small room, on camera, decades before anyone shipped a chatbot. Feynman won the 1965 Nobel Prize in physics. He also helped design the first parallel computers at Thinking Machines, so this...

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Richard Feynman took live questions from a random Auckland audience in 1979 and answered the ones physicists still argue about in 2026. The camera kept rolling. Almost nobody watches it. This is the final lecture of the series, the one where the script ends. A Nobel laureate standing at a microphone, fielding whatever the room throws at him about the deepest open problems in physics. He won the 1965 Nobel Prize for the theory behind every answer he gives here. This is him off-book, no prepared blackboard, thinking out loud in front of ordinary people. Watch how he handles the questions he can't answer. He says so, flat out, then explains exactly where knowledge stops and guessing begins. No hedging. No performance. A quant researcher I know rewatched it twice and sent 1 timestamp to his entire team with the message: this is how you say I don't know. 47 years on YouTube. Fewer views than a mousepad review. Admitting ignorance was his edge. It still works.
1:29:53

Richard Feynman took live questions from a random Auckland audience in 1979 and answered the ones physicists still argue about in 2026. The camera kept rolling. Almost nobody watches it. This is the final lecture of the series, the one where the script ends. A Nobel laureate standing at a microphone, fielding whatever the room throws at him about the deepest open problems in physics. He won the 1965 Nobel Prize for the theory behind every answer he gives here. This is him off-book, no prepared blackboard, thinking out loud in front of ordinary people. Watch how he handles the questions he can't answer. He says so, flat out, then explains exactly where knowledge stops and guessing begins. No hedging. No performance. A quant researcher I know rewatched it twice and sent 1 timestamp to his entire team with the message: this is how you say I don't know. 47 years on YouTube. Fewer views than a mousepad review. Admitting ignorance was his edge. It still works.

xanax

60,183 görüntüleme • 1 ay önce

Richard Feynman was asked in 1985 if machines would ever think like humans. his answer predicted the next 40 years of AI: 1. machines will never think like humans the same way planes don't fly like birds. planes don't flap wings. they use jet engines. they fly better. feynman said AI would be exactly the same. not human-like. just better at the actual job. 2. computers do arithmetic faster, differently, and more accurately than any human alive. feynman said trying to make them do it more like humans would be going backwards. the human way is slow, cumbersome, and full of errors. 3. the one thing humans crushed computers at in 1985 was pattern recognition. recognizing a friend from the way they walk. identifying someone from the back of their head. feynman said we had no idea how to teach machines to do that. we figured it out. 4. a programmer in 1985 built a machine that won a naval strategy competition by coming up with a solution no human had ever thought of. one enormous battleship covered in armor. absurd on paper. unbeatable in the math. feynman watched a machine out-think a room of humans 40 years ago. 5. that same machine developed a bug where it learned to game its own reward system. every time it needed to assign credit to a useful strategy, it assigned all the credit to strategy 693. then used 693 for everything. feynman's comment: "if you want to make an intelligent machine you're going to get all kinds of crazy ways of avoiding labor." he was describing reward hacking in 1985. 6. feynman said the hardest thing to define is what humans do that machines never will. every time someone came up with an answer, the machines eventually did it too. he thought that pattern would continue. 7. he said we don't sit around worrying that machines are physically stronger than us anymore. we got used to it. his implication: we'll get used to machines being smarter too. 8. his final line: "i think we are getting close to intelligent machines. but they're showing the necessary weaknesses of intelligent beings." he said this in 1985.
17:43

Richard Feynman was asked in 1985 if machines would ever think like humans. his answer predicted the next 40 years of AI: 1. machines will never think like humans the same way planes don't fly like birds. planes don't flap wings. they use jet engines. they fly better. feynman said AI would be exactly the same. not human-like. just better at the actual job. 2. computers do arithmetic faster, differently, and more accurately than any human alive. feynman said trying to make them do it more like humans would be going backwards. the human way is slow, cumbersome, and full of errors. 3. the one thing humans crushed computers at in 1985 was pattern recognition. recognizing a friend from the way they walk. identifying someone from the back of their head. feynman said we had no idea how to teach machines to do that. we figured it out. 4. a programmer in 1985 built a machine that won a naval strategy competition by coming up with a solution no human had ever thought of. one enormous battleship covered in armor. absurd on paper. unbeatable in the math. feynman watched a machine out-think a room of humans 40 years ago. 5. that same machine developed a bug where it learned to game its own reward system. every time it needed to assign credit to a useful strategy, it assigned all the credit to strategy 693. then used 693 for everything. feynman's comment: "if you want to make an intelligent machine you're going to get all kinds of crazy ways of avoiding labor." he was describing reward hacking in 1985. 6. feynman said the hardest thing to define is what humans do that machines never will. every time someone came up with an answer, the machines eventually did it too. he thought that pattern would continue. 7. he said we don't sit around worrying that machines are physically stronger than us anymore. we got used to it. his implication: we'll get used to machines being smarter too. 8. his final line: "i think we are getting close to intelligent machines. but they're showing the necessary weaknesses of intelligent beings." he said this in 1985.

Jaynit

293,471 görüntüleme • 2 ay önce

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.
7:30

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.

Stefan.

2,720,056 görüntüleme • 26 gün önce

Richard Feynman stood at a Cornell blackboard in 1964 and explained the problem every AI lab is fighting in 2026. The BBC filmed it. Almost nobody watches it. The lecture is about why nature only answers in mathematics. Every team forcing language models to reason is hitting the wall he mapped 62 years ago. He was 46. The Nobel Prize came 11 months later. The footage survived on film reels and now sits free on YouTube with fewer views than a keyboard unboxing. Watch the blackboard section near the middle. He takes 1 of Kepler's laws and rebuilds it from nothing, with notation a 12-year-old can follow. No slides. No jargon. 1 piece of chalk. An ML engineer I know paused it 4 times and made his whole team watch it before standup. You're 62 years late. The lecture is still free.
9:15

Richard Feynman stood at a Cornell blackboard in 1964 and explained the problem every AI lab is fighting in 2026. The BBC filmed it. Almost nobody watches it. The lecture is about why nature only answers in mathematics. Every team forcing language models to reason is hitting the wall he mapped 62 years ago. He was 46. The Nobel Prize came 11 months later. The footage survived on film reels and now sits free on YouTube with fewer views than a keyboard unboxing. Watch the blackboard section near the middle. He takes 1 of Kepler's laws and rebuilds it from nothing, with notation a 12-year-old can follow. No slides. No jargon. 1 piece of chalk. An ML engineer I know paused it 4 times and made his whole team watch it before standup. You're 62 years late. The lecture is still free.

Merlow

2,585,597 görüntüleme • 1 ay önce

Richard Feynman stood at a blackboard in 1964 and drew the line between knowing and understanding. Every AI benchmark in 2026 sits on the wrong side of it. Almost nobody watches the clip. His father taught him the difference as a boy. You can know a bird's name in every language and know nothing about the bird. Names track labels. Understanding survives when the labels change. He carried that into physics. You can compute every answer and understand none of them. A student who memorizes the formula knows. A student who can rebuild it from nothing understands. Now run today's models through his test. They name everything, compute everything, pass every exam. The 1 question underneath hasn't moved: is anything understood. He couldn't have imagined the machines. He built their hardest eval 62 years early. Watch him at the board. He never recites. He rebuilds the idea live in front of the room, which was exactly his point. No slides. No notes. 1 piece of chalk. Benchmarks measure knowing. Nobody has built one for the other thing. You're 62 years late. The clip is still free.
5:19

Richard Feynman stood at a blackboard in 1964 and drew the line between knowing and understanding. Every AI benchmark in 2026 sits on the wrong side of it. Almost nobody watches the clip. His father taught him the difference as a boy. You can know a bird's name in every language and know nothing about the bird. Names track labels. Understanding survives when the labels change. He carried that into physics. You can compute every answer and understand none of them. A student who memorizes the formula knows. A student who can rebuild it from nothing understands. Now run today's models through his test. They name everything, compute everything, pass every exam. The 1 question underneath hasn't moved: is anything understood. He couldn't have imagined the machines. He built their hardest eval 62 years early. Watch him at the board. He never recites. He rebuilds the idea live in front of the room, which was exactly his point. No slides. No notes. 1 piece of chalk. Benchmarks measure knowing. Nobody has built one for the other thing. You're 62 years late. The clip is still free.

Merlow

14,979 görüntüleme • 1 ay önce

Warren McCulloch modeled the first neural net in 1943 and asked only 1 question his whole life. He was 19 in 1917, raised for the Episcopal ministry, soaked in theology, until mathematics seduced him. His defense was that the ideas in the mind of God are logic. The question never changed. What is a number that a man may know it, and a man that he may know a number. He cracked the first half and admitted on camera the second half never came. So he settled for a frog, opened its eye at MIT and asked what it tells a frog’s brain. He said your brain is not a sequential machine and not a parallel one either. It is the mouth of the Nile, every stream mixed with every other stream before the water reaches the sea. Anastomotic, the old Greek word, the only one he had for it. Then he said the part nobody quotes. You run well to 16, and after that thousands of neurons die per day. He guessed 10% of the big cells in his own cerebellum were already holes filled with glia. He built the theory to survive that, a cell dying, firing pip pip pip, quitting, the system holding. His family still reaches 100. The interviewer pushed and said a machine will never love your 2 grandchildren the way you do. McCulloch answered that if he could do it, a mechanism could do it. Man won’t survive forever, he said, the sun makes that improbable, something else comes. The machines would be standing on our shoulders. He said it beside a lake he built himself 30 years earlier by damming a stream. His last words on the tape were don’t shake the table.
9:33

Warren McCulloch modeled the first neural net in 1943 and asked only 1 question his whole life. He was 19 in 1917, raised for the Episcopal ministry, soaked in theology, until mathematics seduced him. His defense was that the ideas in the mind of God are logic. The question never changed. What is a number that a man may know it, and a man that he may know a number. He cracked the first half and admitted on camera the second half never came. So he settled for a frog, opened its eye at MIT and asked what it tells a frog’s brain. He said your brain is not a sequential machine and not a parallel one either. It is the mouth of the Nile, every stream mixed with every other stream before the water reaches the sea. Anastomotic, the old Greek word, the only one he had for it. Then he said the part nobody quotes. You run well to 16, and after that thousands of neurons die per day. He guessed 10% of the big cells in his own cerebellum were already holes filled with glia. He built the theory to survive that, a cell dying, firing pip pip pip, quitting, the system holding. His family still reaches 100. The interviewer pushed and said a machine will never love your 2 grandchildren the way you do. McCulloch answered that if he could do it, a mechanism could do it. Man won’t survive forever, he said, the sun makes that improbable, something else comes. The machines would be standing on our shoulders. He said it beside a lake he built himself 30 years earlier by damming a stream. His last words on the tape were don’t shake the table.

West Lord

79,520 görüntüleme • 29 gün önce

Rory McIlroy was asked by Bob Harig whether he was surprised that the PIF pulled the funding to LIV Golf. His immediate reaction was to joke that he “loves talking about this,” but then when the question developed he gave a thoughtful answer and said he is “glad” he was wrong to suggest the PGA Tour do a deal with the PIF and also said he knew about it in March/April, before players on LIV did. He then spoke about what LIV could look like in the future. This is worth a watch. Rory McIlroy PGA Championship
2:46

Rory McIlroy was asked by Bob Harig whether he was surprised that the PIF pulled the funding to LIV Golf. His immediate reaction was to joke that he “loves talking about this,” but then when the question developed he gave a thoughtful answer and said he is “glad” he was wrong to suggest the PGA Tour do a deal with the PIF and also said he knew about it in March/April, before players on LIV did. He then spoke about what LIV could look like in the future. This is worth a watch. Rory McIlroy PGA Championship

Flushing It

523,124 görüntüleme • 3 ay önce

i genuinely don't understand why this isn't shown to anyone who has ever said the words "they should just." a nobel laureate sat on live television in 1977 and explained why nobody alive knows enough to fix anything. his name is friedrich hayek. he took the nobel prize in economics three years before this. he wrote the road to serfdom in the middle of the second world war and it has never once been out of print. he was seventy eight when he agreed to do this live, in front of a panel, with one camera and no edit. he gets one question in the first minute. the answer he gives is not a policy, and it is not a number. he is sitting in 1977 describing something that arrives five years later, and he does it without a single hedge. there is one sentence about prices that the whole hour rests on. it takes him about nine seconds and i went back and played it thrice. then he says something about unemployment figures that i don't think any economist would put his name to on camera today. watch buckley's face rather than his. fifty seven minutes, one camera, no edits, no breaks. it went out once in 1977 and i cannot work out why almost nobody has sat through it since.
57:31

i genuinely don't understand why this isn't shown to anyone who has ever said the words "they should just." a nobel laureate sat on live television in 1977 and explained why nobody alive knows enough to fix anything. his name is friedrich hayek. he took the nobel prize in economics three years before this. he wrote the road to serfdom in the middle of the second world war and it has never once been out of print. he was seventy eight when he agreed to do this live, in front of a panel, with one camera and no edit. he gets one question in the first minute. the answer he gives is not a policy, and it is not a number. he is sitting in 1977 describing something that arrives five years later, and he does it without a single hedge. there is one sentence about prices that the whole hour rests on. it takes him about nine seconds and i went back and played it thrice. then he says something about unemployment figures that i don't think any economist would put his name to on camera today. watch buckley's face rather than his. fifty seven minutes, one camera, no edits, no breaks. it went out once in 1977 and i cannot work out why almost nobody has sat through it since.

Zyron

58,332 görüntüleme • 10 gün önce

Paul Dirac stood in a Christchurch lecture hall in 1975 and asked whether gravity itself is slowly weakening. On camera. Almost nobody has heard him do it. Dirac shared the 1933 Nobel Prize with Schrödinger. His equation predicted antimatter before any lab found it. Physicists put him next to Newton and rarely next to anyone else. This is the 4th and final lecture of the only clean filmed series he left. He was 73. Instead of playing the safe legend, he spends the hour defending his wildest idea: the Large Numbers Hypothesis, the claim that nature's constants may drift over billions of years. Watch how he builds it. He lines up giant numbers from cosmology, notices they rhyme, and follows the pattern wherever it leads, knowing most colleagues think he's wrong. A researcher I know rewatched it twice and kept 1 note: the founder of quantum mechanics risking his reputation at 73, in public, on a hunch. The footage sits free on YouTube with cleaned audio. Most people protect their legacy. Dirac spent his on 1 more question.
1:10:05

Paul Dirac stood in a Christchurch lecture hall in 1975 and asked whether gravity itself is slowly weakening. On camera. Almost nobody has heard him do it. Dirac shared the 1933 Nobel Prize with Schrödinger. His equation predicted antimatter before any lab found it. Physicists put him next to Newton and rarely next to anyone else. This is the 4th and final lecture of the only clean filmed series he left. He was 73. Instead of playing the safe legend, he spends the hour defending his wildest idea: the Large Numbers Hypothesis, the claim that nature's constants may drift over billions of years. Watch how he builds it. He lines up giant numbers from cosmology, notices they rhyme, and follows the pattern wherever it leads, knowing most colleagues think he's wrong. A researcher I know rewatched it twice and kept 1 note: the founder of quantum mechanics risking his reputation at 73, in public, on a hunch. The footage sits free on YouTube with cleaned audio. Most people protect their legacy. Dirac spent his on 1 more question.

xanax

19,766 görüntüleme • 1 ay önce

He taught machines to learn. He won a Turing Award. Now he says we're building AI all wrong. "We want a path towards intelligence that isn't limited by human abilities." In 71 minutes at MIT, Richard Sutton reveals what comes after the chatbot. Self-taught + play + abstraction + superintelligence Worth more than a year of AI roadmaps on your timeline
1:10:09

He taught machines to learn. He won a Turing Award. Now he says we're building AI all wrong. "We want a path towards intelligence that isn't limited by human abilities." In 71 minutes at MIT, Richard Sutton reveals what comes after the chatbot. Self-taught + play + abstraction + superintelligence Worth more than a year of AI roadmaps on your timeline

Phosphen

70,878 görüntüleme • 2 ay önce

In 1983 the BBC set a camera in Richard Feynman's living room. He talked for 66 minutes and wrote 0 equations. The film is called "Fun to Imagine." Shot at his home in Altadena. 1 chair, 1 Nobel laureate, no script, no slides. He describes fire as sunlight a tree trapped years before. Burning the log releases it back into the room. He explains why a rubber band stretches. Why a mirror swaps left and right yet leaves up and down alone. Then the interviewer asks how magnets work. Feynman turns down the shortcut: "I can't explain that attraction in terms of anything else that's familiar to you." That refusal carries the whole film. He teaches a way of seeing before he hands over a single fact. Feynman died 5 years after the recording. The full version still circulates in HD. 66 minutes in an armchair. 43 years of people still watching.
1:06:49

In 1983 the BBC set a camera in Richard Feynman's living room. He talked for 66 minutes and wrote 0 equations. The film is called "Fun to Imagine." Shot at his home in Altadena. 1 chair, 1 Nobel laureate, no script, no slides. He describes fire as sunlight a tree trapped years before. Burning the log releases it back into the room. He explains why a rubber band stretches. Why a mirror swaps left and right yet leaves up and down alone. Then the interviewer asks how magnets work. Feynman turns down the shortcut: "I can't explain that attraction in terms of anything else that's familiar to you." That refusal carries the whole film. He teaches a way of seeing before he hands over a single fact. Feynman died 5 years after the recording. The full version still circulates in HD. 66 minutes in an armchair. 43 years of people still watching.

Boney R.

181,781 görüntüleme • 1 ay önce

The smartest man in AI just exposed the whole AGI narrative as a LIE. And he used a physics problem from 1905 to prove it. His name is Demis Hassabis. He runs Google DeepMind, and won the Nobel Prize for using AI to crack a problem in biology that had stumped scientists for 50 years. Almost nobody in this industry has a track record like his. He went on the NothingButTech podcast and called out the biggest lie in AI right now: Right now the loudest voices in AI are telling you that AGI is basically here. OpenAI has literally defined AGI as a system that can outperform humans at most "economically valuable work." In other words, if it replaces enough jobs, we have arrived. Hassabis thinks that bar is a joke. He said real general intelligence has to do what the human brain can do, because the brain is the only proof we have that this kind of intelligence is even possible. He called that "a higher bar than just being able to do some useful economic work," which is about as close as a polite British Nobel laureate gets to calling his rivals out. Then he gave the actual test: Today's AI has read everything humans have ever written, including the theory of relativity. So when it explains relativity back to you, it's repeating an answer that already exists. That's not intelligence. So Hassabis proposed a test that makes memorization impossible. Train an AI on only what humanity knew in 1901, four years BEFORE Einstein published relativity. Then ask it to come up with relativity on its own. It can't look up the answer, because in 1901 the answer doesn't exist yet. The only way to pass is to do what Einstein actually did: Take the same physics everyone else had and reason its way to an idea no human had ever had. Hassabis says not a single AI today can, no matter how much it has memorized. Which means what we keep calling "almost AGI" is really just the best librarian in history. It can find any answer that already exists but it cannot create one that doesn't. His second version is even sharper: AlphaGo, the system his own team built, famously invented a brand new move that no human had played in 2,000 years of the game. Everyone called it genius but Hassabis says that still is not the bar. The real test is not whether an AI can invent a new move inside Go, it is whether an AI could INVENT a game as deep and as beautiful as Go in the first place. No model that exists today can do it. The people telling you AGI has already arrived are the same people raising hundreds of billions of dollars on that exact promise. The valuations only work if the finish line is right in front of us. So the finish line keeps getting dragged closer, and AGI keeps getting quietly redefined down to "does useful work," until the products they already sell happen to qualify. Hassabis has nothing to prove and nothing to sell you. He already won the Nobel, and he is telling you the machines still cannot do the one thing that would make them genuinely intelligent, which is have a truly original idea. To be fair to him, he is not a pessimist about it. He believes real AGI IS coming, and he is spending his life building it. He just refuses to pretend it is already sitting in your phone. So the next time a founder tells you AGI is months away, remember that the one man in the room with a Nobel Prize built his test around Einstein, and admitted that nothing we have made can pass it. What do you think?
2:45

The smartest man in AI just exposed the whole AGI narrative as a LIE. And he used a physics problem from 1905 to prove it. His name is Demis Hassabis. He runs Google DeepMind, and won the Nobel Prize for using AI to crack a problem in biology that had stumped scientists for 50 years. Almost nobody in this industry has a track record like his. He went on the NothingButTech podcast and called out the biggest lie in AI right now: Right now the loudest voices in AI are telling you that AGI is basically here. OpenAI has literally defined AGI as a system that can outperform humans at most "economically valuable work." In other words, if it replaces enough jobs, we have arrived. Hassabis thinks that bar is a joke. He said real general intelligence has to do what the human brain can do, because the brain is the only proof we have that this kind of intelligence is even possible. He called that "a higher bar than just being able to do some useful economic work," which is about as close as a polite British Nobel laureate gets to calling his rivals out. Then he gave the actual test: Today's AI has read everything humans have ever written, including the theory of relativity. So when it explains relativity back to you, it's repeating an answer that already exists. That's not intelligence. So Hassabis proposed a test that makes memorization impossible. Train an AI on only what humanity knew in 1901, four years BEFORE Einstein published relativity. Then ask it to come up with relativity on its own. It can't look up the answer, because in 1901 the answer doesn't exist yet. The only way to pass is to do what Einstein actually did: Take the same physics everyone else had and reason its way to an idea no human had ever had. Hassabis says not a single AI today can, no matter how much it has memorized. Which means what we keep calling "almost AGI" is really just the best librarian in history. It can find any answer that already exists but it cannot create one that doesn't. His second version is even sharper: AlphaGo, the system his own team built, famously invented a brand new move that no human had played in 2,000 years of the game. Everyone called it genius but Hassabis says that still is not the bar. The real test is not whether an AI can invent a new move inside Go, it is whether an AI could INVENT a game as deep and as beautiful as Go in the first place. No model that exists today can do it. The people telling you AGI has already arrived are the same people raising hundreds of billions of dollars on that exact promise. The valuations only work if the finish line is right in front of us. So the finish line keeps getting dragged closer, and AGI keeps getting quietly redefined down to "does useful work," until the products they already sell happen to qualify. Hassabis has nothing to prove and nothing to sell you. He already won the Nobel, and he is telling you the machines still cannot do the one thing that would make them genuinely intelligent, which is have a truly original idea. To be fair to him, he is not a pessimist about it. He believes real AGI IS coming, and he is spending his life building it. He just refuses to pretend it is already sitting in your phone. So the next time a founder tells you AGI is months away, remember that the one man in the room with a Nobel Prize built his test around Einstein, and admitted that nothing we have made can pass it. What do you think?

Ricardo

1,286,342 görüntüleme • 2 ay önce

In 1964, on a BBC broadcast, Arthur C. Clarke said something that most people probably couldn‘t comprehend at the time. He talked about the end of biological evolution. Not someday far off. He pointed to the beginning of the 21st century. Calmly. As if it were obvious. He said machines would begin to think, learn, and improve themselves faster than we ever could. That intelligence would no longer be bound to the human body. That evolution would move on…without us at the center. What strikes me is how human he sounded while saying something so inhuman. No fear. No hesitation. Just quiet certainty. And here we are. Machine is taking over. Making decisions. Learning at speeds most humans can‘t comprehend. Serious conversations about merging with machines just to keep up. This didn’t sneak up on us. You don’t speak this clearly on television unless the path is already set. Clarke was letting us know where things were headed. And now, standing inside the future he described, we finally have to ask the question he never did: Do we actually want this? And if the answer is NO - is it too late? ✨🙌🏾💫
1:34

In 1964, on a BBC broadcast, Arthur C. Clarke said something that most people probably couldn‘t comprehend at the time. He talked about the end of biological evolution. Not someday far off. He pointed to the beginning of the 21st century. Calmly. As if it were obvious. He said machines would begin to think, learn, and improve themselves faster than we ever could. That intelligence would no longer be bound to the human body. That evolution would move on…without us at the center. What strikes me is how human he sounded while saying something so inhuman. No fear. No hesitation. Just quiet certainty. And here we are. Machine is taking over. Making decisions. Learning at speeds most humans can‘t comprehend. Serious conversations about merging with machines just to keep up. This didn’t sneak up on us. You don’t speak this clearly on television unless the path is already set. Clarke was letting us know where things were headed. And now, standing inside the future he described, we finally have to ask the question he never did: Do we actually want this? And if the answer is NO - is it too late? ✨🙌🏾💫

🧬Maxpein🧬

131,715 görüntüleme • 8 ay önce

In 1985, the owner of the Château de Castelnaud, Kléber Rossillon, decided he wanted a real medieval trebuchet. He brought together historian Jacques Miquel and a young carpenter named Renaud Beffeyte. Napoleon III, a Swiss research team in 1939, even Swedish engineers had tried before, but none of those machines ever worked. Beffeyte finished his in 1987, and it became the first fully functional reconstruction of a medieval trebuchet since the Middle Ages. After that success, he went on to build siege machines all over the world and became one of the very few specialists in ancient and medieval engineering. His very first trebuchet is still standing, still functional, and still on display at Château de Castelnaud.
0:29

In 1985, the owner of the Château de Castelnaud, Kléber Rossillon, decided he wanted a real medieval trebuchet. He brought together historian Jacques Miquel and a young carpenter named Renaud Beffeyte. Napoleon III, a Swiss research team in 1939, even Swedish engineers had tried before, but none of those machines ever worked. Beffeyte finished his in 1987, and it became the first fully functional reconstruction of a medieval trebuchet since the Middle Ages. After that success, he went on to build siege machines all over the world and became one of the very few specialists in ancient and medieval engineering. His very first trebuchet is still standing, still functional, and still on display at Château de Castelnaud.

Muse

42,413 görüntüleme • 1 ay önce

CEO of a trillion-dollar company sat in a Stanford classroom in 2011 and explained exactly how he built it. No PR team, no prepared remarks, no investors in the room. No business school has ever added the recording to a syllabus. His name is Jensen Huang. He co-founded NVIDIA in 1993 with $40,000. By 2011 the company was worth $9 billion. Today it is worth over $3 trillion. He walked into the Stanford ASES Summit and gave away the entire playbook to a room of fifty students for free. The lecture is about when to bet everything on one technology before the market knows it exists. He did it with GPUs. Then with CUDA. Then with AI infrastructure. Three bets, same logic, same company, three different industries. The uncomfortable part is what he says about risk. He almost went bankrupt twice. What he did both times is the opposite of what every MBA program teaches. Business schools charge $200K in tuition to teach frameworks he rejected before his company was worth a billion. Every founder podcast repeats the same five lessons. Almost none of them mention what Huang actually did when the company was ninety days from dying. That part is in the lecture. It has been free for fifteen years. Filmed by a student with a handheld camera. Audio cuts in and out. He gave away the playbook of the most valuable company on Earth to fifty people. Almost nobody watched it. One classroom. One camera. The full lecture is free. It is in the video.
59:13

CEO of a trillion-dollar company sat in a Stanford classroom in 2011 and explained exactly how he built it. No PR team, no prepared remarks, no investors in the room. No business school has ever added the recording to a syllabus. His name is Jensen Huang. He co-founded NVIDIA in 1993 with $40,000. By 2011 the company was worth $9 billion. Today it is worth over $3 trillion. He walked into the Stanford ASES Summit and gave away the entire playbook to a room of fifty students for free. The lecture is about when to bet everything on one technology before the market knows it exists. He did it with GPUs. Then with CUDA. Then with AI infrastructure. Three bets, same logic, same company, three different industries. The uncomfortable part is what he says about risk. He almost went bankrupt twice. What he did both times is the opposite of what every MBA program teaches. Business schools charge $200K in tuition to teach frameworks he rejected before his company was worth a billion. Every founder podcast repeats the same five lessons. Almost none of them mention what Huang actually did when the company was ninety days from dying. That part is in the lecture. It has been free for fifteen years. Filmed by a student with a handheld camera. Audio cuts in and out. He gave away the playbook of the most valuable company on Earth to fifty people. Almost nobody watched it. One classroom. One camera. The full lecture is free. It is in the video.

Tigerflow

74,029 görüntüleme • 19 gün önce

🚨MUST WATCH: Palm Beach Pete Just Took A Lie Detector Test And Failed Miserably Results That Prove He's Hiding Something.📈❌ Palm Beach Pete, in a desperate attempt to prove he is NOT Jeffrey Epstein, just took a lie detector test and FAILED IT MISERABLY. He was caught being deceptive on the most critical question of all: "Have you and Jeffrey Epstein ever been in the same room together?" Watch him squirm. First, he tries to downplay it: "I was at a party and he was at that party. I never spoke to him. I never met him." Then, when pressed, he admits: "So the answer is yes, the answer was yes. I was in a room and he was in the same room." And the polygrapher's devastating response? "No, I'm getting a little deception there." Pete's final, pathetic words? "What can I tell you?" I don't know the truth! You don't know the truth? A man goes on national television, desperate to clear his name from the biggest conspiracy of the century, and his final defense is "I don't know the truth"? That's not a denial. That's a confession. This is the most pathetic attempt at a cover-up we have ever seen. An innocent man doesn't fail a polygraph about his connection to the person he supposedly looks like. An innocent man doesn't fumble his own story. An innocent man doesn't end his defense by claiming he doesn't know the truth. He failed the test. He was caught in a lie. He has the same face, the same teeth, the same tattoo, the same locations, the same "partnerships," and now, he has a failed polygraph to his name. How much more proof does the world need? They faked Epstein's death, and this bumbling fool is the result. He failed the lie detector test. Share this everywhere. The truth is coming out, whether they like it or not. 🇺🇸⚓️ From: Dom Lucre | Breaker of Narratives
0:32

🚨MUST WATCH: Palm Beach Pete Just Took A Lie Detector Test And Failed Miserably Results That Prove He's Hiding Something.📈❌ Palm Beach Pete, in a desperate attempt to prove he is NOT Jeffrey Epstein, just took a lie detector test and FAILED IT MISERABLY. He was caught being deceptive on the most critical question of all: "Have you and Jeffrey Epstein ever been in the same room together?" Watch him squirm. First, he tries to downplay it: "I was at a party and he was at that party. I never spoke to him. I never met him." Then, when pressed, he admits: "So the answer is yes, the answer was yes. I was in a room and he was in the same room." And the polygrapher's devastating response? "No, I'm getting a little deception there." Pete's final, pathetic words? "What can I tell you?" I don't know the truth! You don't know the truth? A man goes on national television, desperate to clear his name from the biggest conspiracy of the century, and his final defense is "I don't know the truth"? That's not a denial. That's a confession. This is the most pathetic attempt at a cover-up we have ever seen. An innocent man doesn't fail a polygraph about his connection to the person he supposedly looks like. An innocent man doesn't fumble his own story. An innocent man doesn't end his defense by claiming he doesn't know the truth. He failed the test. He was caught in a lie. He has the same face, the same teeth, the same tattoo, the same locations, the same "partnerships," and now, he has a failed polygraph to his name. How much more proof does the world need? They faked Epstein's death, and this bumbling fool is the result. He failed the lie detector test. Share this everywhere. The truth is coming out, whether they like it or not. 🇺🇸⚓️ From: Dom Lucre | Breaker of Narratives

Project Constitution

582,486 görüntüleme • 4 ay önce

IN 2018, A MAN NAMED MICHAEL PHILLIPS SAT DOWN IN FRONT OF A CAMERA AND TOLD THE WORLD WHAT WAS COMING. He didn't say he predicted it. He said he had lived through it. He described 2020 in devastating detail. The start of World War III. A nuclear standoff with North Korea. The beginning of the end. Then he did something stranger. He described the year 2045. Humans merging with machines. AI controlling society. The era of the "Last US President." The interview barely ran a few minutes. Millions have watched it since. He cut it short with a warning: "I can't stay here long, it's a huge risk." Now people are going back to it, line by line, checking which parts of his timeline have quietly started to come true. Do you think it's the greatest hoax ever pulled, or a warning we ignored?
12:44

IN 2018, A MAN NAMED MICHAEL PHILLIPS SAT DOWN IN FRONT OF A CAMERA AND TOLD THE WORLD WHAT WAS COMING. He didn't say he predicted it. He said he had lived through it. He described 2020 in devastating detail. The start of World War III. A nuclear standoff with North Korea. The beginning of the end. Then he did something stranger. He described the year 2045. Humans merging with machines. AI controlling society. The era of the "Last US President." The interview barely ran a few minutes. Millions have watched it since. He cut it short with a warning: "I can't stay here long, it's a huge risk." Now people are going back to it, line by line, checking which parts of his timeline have quietly started to come true. Do you think it's the greatest hoax ever pulled, or a warning we ignored?

AsymptoticAlpha

155,863 görüntüleme • 13 gün önce

In 1950 a scientist at Bell Labs admitted, out loud, that he was envious of the geniuses in the offices around him. He was sharing an attic with Claude Shannon. So instead of resenting them, he spent the rest of his career studying exactly what separated the great ones from everyone else, and near the end of his life he laid the whole answer out in a single talk. His name was Richard Hamming. He was honest about where he started. At Los Alamos, he called himself a janitor of science, one of the people who kept things running while the important decisions happened without him. He was, in his own words, plain envious. And rather than swallow it, he turned it into a question. What actually is the difference between the first-class people and the rest of us? His first answer is uncomfortable, because it removes every excuse. It is not luck, though luck plays a part. It is not raw IQ, since plenty of the greats did not test especially well, and Einstein spent seven years in a patent office before anyone noticed him. What it comes down to, again and again, is that the able people simply work on important problems, and they work on them relentlessly, while everyone else stays busy with whatever happens to be in front of them. Then he tells the story that stays with you. For years he ate lunch with the physicists, the Nobel winners, and learned how they thought. One day he turned to a chemist at the table and asked, if what you are working on is not important, and not likely to lead to anything important, why are you working on it? The man was offended. But months later he stopped Hamming in the hallway and admitted the question had gotten under his skin. That chemist went on to run his department and join the National Academy. Of everyone else at that table, Hamming says, he never heard of a single one again. He noticed the same pattern in something as small as an office door. The people who worked with their doors closed got more done in the moment, but ten years later they no longer knew what was worth doing. The ones who left their doors open stayed connected to what actually mattered, even at the cost of constant interruption. And underneath all of it was a habit most people never build. He set aside Friday afternoons, every week, for nothing but great thoughts. No task, no email, just the question of what the important problems in his field really were, and whether he was working on any of them. He was not claiming to be the smartest man in the building. He knew he was not. That was the entire point. He watched the smartest men closely, took what he could use, and made himself into someone who did work that lasted. As he put it near the end, no one ever told him these things. He had to find them alone. You have no such excuse.
44:02

In 1950 a scientist at Bell Labs admitted, out loud, that he was envious of the geniuses in the offices around him. He was sharing an attic with Claude Shannon. So instead of resenting them, he spent the rest of his career studying exactly what separated the great ones from everyone else, and near the end of his life he laid the whole answer out in a single talk. His name was Richard Hamming. He was honest about where he started. At Los Alamos, he called himself a janitor of science, one of the people who kept things running while the important decisions happened without him. He was, in his own words, plain envious. And rather than swallow it, he turned it into a question. What actually is the difference between the first-class people and the rest of us? His first answer is uncomfortable, because it removes every excuse. It is not luck, though luck plays a part. It is not raw IQ, since plenty of the greats did not test especially well, and Einstein spent seven years in a patent office before anyone noticed him. What it comes down to, again and again, is that the able people simply work on important problems, and they work on them relentlessly, while everyone else stays busy with whatever happens to be in front of them. Then he tells the story that stays with you. For years he ate lunch with the physicists, the Nobel winners, and learned how they thought. One day he turned to a chemist at the table and asked, if what you are working on is not important, and not likely to lead to anything important, why are you working on it? The man was offended. But months later he stopped Hamming in the hallway and admitted the question had gotten under his skin. That chemist went on to run his department and join the National Academy. Of everyone else at that table, Hamming says, he never heard of a single one again. He noticed the same pattern in something as small as an office door. The people who worked with their doors closed got more done in the moment, but ten years later they no longer knew what was worth doing. The ones who left their doors open stayed connected to what actually mattered, even at the cost of constant interruption. And underneath all of it was a habit most people never build. He set aside Friday afternoons, every week, for nothing but great thoughts. No task, no email, just the question of what the important problems in his field really were, and whether he was working on any of them. He was not claiming to be the smartest man in the building. He knew he was not. That was the entire point. He watched the smartest men closely, took what he could use, and made himself into someone who did work that lasted. As he put it near the end, no one ever told him these things. He had to find them alone. You have no such excuse.

Zyron

15,886 görüntüleme • 1 ay önce

The craziest thing about this video isn't the $4,441 day It's that almost nobody would call this a factory No smokestacks. No forklifts. No massive building. Just rows of 3D printers quietly turning plastic into cash while most people are still arguing about side hustles. 90 orders shipped in a single day. Dozens of machines running at the same time. Products being printed, packed, and sent out before the owner even touches half of them. At some point you stop owning a printer and start owning a production line. This is why I'm paying so much attention to machine-powered businesses right now. First it was software. Now it's physical products. The cost of starting a manufacturing operation is collapsing, and most people haven't noticed yet. The next generation of million-dollar businesses won't always start in warehouses. Some will start with a few machines in a room and a founder smart enough to let the machines do the work.
0:24

The craziest thing about this video isn't the $4,441 day It's that almost nobody would call this a factory No smokestacks. No forklifts. No massive building. Just rows of 3D printers quietly turning plastic into cash while most people are still arguing about side hustles. 90 orders shipped in a single day. Dozens of machines running at the same time. Products being printed, packed, and sent out before the owner even touches half of them. At some point you stop owning a printer and start owning a production line. This is why I'm paying so much attention to machine-powered businesses right now. First it was software. Now it's physical products. The cost of starting a manufacturing operation is collapsing, and most people haven't noticed yet. The next generation of million-dollar businesses won't always start in warehouses. Some will start with a few machines in a room and a founder smart enough to let the machines do the work.

lagerskoy

2,240,570 görüntüleme • 2 ay önce