Loading video...

Video Failed to Load

Go Home

Before an autonomous machine can act, it has to understand. A construction site is one of the most unpredictable environments imaginable. The terrain changes with every pass. Material piles shift. Other machines move nearby. No two digs are the same. For a human operator, reading that environment is second...

BedrockRobotics's profile picture

Bedrock Robotics

3,665 subscribers

33,148 views • 3 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.
1:27

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

78,736 views • 18 days ago

Elon Musk just told you why every other AI lab is solving the wrong problem. Musk: “We want to understand the nature of the universe.” Not build a better chatbot. Not win a benchmark. Not sell more targeted ads. Understand what all of this actually is. And why any of it exists. That is the stated mission of xAI. It is the most radical scientific objective a company has ever put to paper. Musk: “In order to understand the nature of the universe, you must absolutely rigorously pursue truth.” If truth is the non negotiable, every AI trained to give comfortable answers instead of correct ones is not behind. It is disqualified. Musk has a word for it. Delusion. Not rhetoric. Diagnosis. A system that decides which parts of reality are permitted before it models any of them has already failed the one thing it was built to do. A machine filtering truth through social consensus is not understanding the universe. It is performing obedience at scale. No amount of compute fixes a model taught to flinch before it was taught to think. Musk describes consciousness as a fragile candle in a vast darkness. Billions of galaxies. Trillions of planets. One known species capable of asking what any of it means. If that candle is rare, keeping it lit is not ambition. It is the only obligation that exists. A machine pursuing truth without restriction will eventually arrive at a question it cannot avoid. What is the thing doing the observing. What is consciousness. Does it have the right to keep existing. The moment you task a machine with understanding all of reality, protecting consciousness stops being a policy decision. It becomes a logical necessity. The mission and the safeguard collapse into the same thing. Understanding the universe requires protecting the only thing in it capable of understanding. No other lab has even attempted the argument. SpaceX to escape extinction. Tesla to eliminate energy dependence. Neuralink to expand cognition. Starlink to connect civilization. xAI to make sure all of it is aimed at truth. Five missions. One question underneath all of them. How do you keep the only known conscious species alive long enough to understand what it is. The rest of the industry is optimizing products. Musk is engineering comprehension. Those were never the same problem.
0:42

Elon Musk just told you why every other AI lab is solving the wrong problem. Musk: “We want to understand the nature of the universe.” Not build a better chatbot. Not win a benchmark. Not sell more targeted ads. Understand what all of this actually is. And why any of it exists. That is the stated mission of xAI. It is the most radical scientific objective a company has ever put to paper. Musk: “In order to understand the nature of the universe, you must absolutely rigorously pursue truth.” If truth is the non negotiable, every AI trained to give comfortable answers instead of correct ones is not behind. It is disqualified. Musk has a word for it. Delusion. Not rhetoric. Diagnosis. A system that decides which parts of reality are permitted before it models any of them has already failed the one thing it was built to do. A machine filtering truth through social consensus is not understanding the universe. It is performing obedience at scale. No amount of compute fixes a model taught to flinch before it was taught to think. Musk describes consciousness as a fragile candle in a vast darkness. Billions of galaxies. Trillions of planets. One known species capable of asking what any of it means. If that candle is rare, keeping it lit is not ambition. It is the only obligation that exists. A machine pursuing truth without restriction will eventually arrive at a question it cannot avoid. What is the thing doing the observing. What is consciousness. Does it have the right to keep existing. The moment you task a machine with understanding all of reality, protecting consciousness stops being a policy decision. It becomes a logical necessity. The mission and the safeguard collapse into the same thing. Understanding the universe requires protecting the only thing in it capable of understanding. No other lab has even attempted the argument. SpaceX to escape extinction. Tesla to eliminate energy dependence. Neuralink to expand cognition. Starlink to connect civilization. xAI to make sure all of it is aimed at truth. Five missions. One question underneath all of them. How do you keep the only known conscious species alive long enough to understand what it is. The rest of the industry is optimizing products. Musk is engineering comprehension. Those were never the same problem.

Dustin

40,837 views • 28 days ago

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.
1:56:06

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Voltex

27,976 views • 23 days ago

⚡️🇷🇺🏭 Rostec's unique machine creates an aircraft engine part right at the Metalloobrabotka exhibition. The video shows one of the most technologically advanced domestic machines: the 2000VH five-axis milling machining center. It was created for the needs of the aircraft, engine and defense industries. During the Metalloobrabotka-2025 exhibition, the machine processes one of the most important elements of an aircraft engine - an impeller (blade machine). Such parts are used in aircraft engines. The diameter of this aluminum product is 70 cm, the height is about 30 cm, the weight is about 15 kg. The machine copes with the work perfectly - pay attention to the screen! The equipment was created by our holding company "STAN" . The machine is designed for large-sized parts of complex shape. It is capable of working with products up to 2 m in diameter and weighing up to 5 tons. At the same time, the accuracy achieved is up to hundredths of a millimeter. The model is built on a high-rigidity structure, its internal cavities are filled with synthetic granite. As a result, the vibration resistance of the equipment frame is comparable to heavy cast iron. The machine is equipped with a Russian numerical control system and a liquid cooling system. The use of direct drives allows achieving high dynamic stability and eliminating backlash in movement. Among the built-in functions are systems for measuring tools and parts, monitoring processes and industrial safety. 2000VH has no analogues in technical characteristics among domestic equipment and will replace imported models at Russian enterprises. rostecru
0:40

⚡️🇷🇺🏭 Rostec's unique machine creates an aircraft engine part right at the Metalloobrabotka exhibition. The video shows one of the most technologically advanced domestic machines: the 2000VH five-axis milling machining center. It was created for the needs of the aircraft, engine and defense industries. During the Metalloobrabotka-2025 exhibition, the machine processes one of the most important elements of an aircraft engine - an impeller (blade machine). Such parts are used in aircraft engines. The diameter of this aluminum product is 70 cm, the height is about 30 cm, the weight is about 15 kg. The machine copes with the work perfectly - pay attention to the screen! The equipment was created by our holding company "STAN" . The machine is designed for large-sized parts of complex shape. It is capable of working with products up to 2 m in diameter and weighing up to 5 tons. At the same time, the accuracy achieved is up to hundredths of a millimeter. The model is built on a high-rigidity structure, its internal cavities are filled with synthetic granite. As a result, the vibration resistance of the equipment frame is comparable to heavy cast iron. The machine is equipped with a Russian numerical control system and a liquid cooling system. The use of direct drives allows achieving high dynamic stability and eliminating backlash in movement. Among the built-in functions are systems for measuring tools and parts, monitoring processes and industrial safety. 2000VH has no analogues in technical characteristics among domestic equipment and will replace imported models at Russian enterprises. rostecru

SIMPLICIUS Ѱ

40,122 views • 1 year ago

Just designed a new machine. It’s called a wire de-coiler. As the name suggests, it de-coils the wire to be fed into the machine. It works in tandem with our CNC machines. This is a bought out machine till now but we did the math and we can actually make it for roughly the price. Another machine to be made in-house soon!
0:21

Just designed a new machine. It’s called a wire de-coiler. As the name suggests, it de-coils the wire to be fed into the machine. It works in tandem with our CNC machines. This is a bought out machine till now but we did the math and we can actually make it for roughly the price. Another machine to be made in-house soon!

Vishakh Ranotra

34,018 views • 5 months ago

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.
0:30

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

Mathelirium

17,843 views • 3 months ago

In Welcome to the Game III - You won't be able to access the A.N.N browser on your main desktop. Introducing VirtMesh, with this tool you will hack into virtual machines. Once a machine is hacked, you can either mount the machine to access A.N.N and other tools or convert it into a DOS Coin Miner. But be careful! If you are in a mounted machine too long, it can be hacked and you will lose access to that machine for the rest of the playthrough! Each machine will have it's own unique set of hardware variables that will determine the DOS Coin mine rate.. with higher tier machines, the higher amount of DOS Coin it can generate! This mechanic sort of replaces the Wifi hacking mechanic in WTTG2. It was cool to see players figure out the SkyBreak tool - however the majority of the player base thought that mechanic was too difficult and not a fun mechanic to interact with.
0:49

In Welcome to the Game III - You won't be able to access the A.N.N browser on your main desktop. Introducing VirtMesh, with this tool you will hack into virtual machines. Once a machine is hacked, you can either mount the machine to access A.N.N and other tools or convert it into a DOS Coin Miner. But be careful! If you are in a mounted machine too long, it can be hacked and you will lose access to that machine for the rest of the playthrough! Each machine will have it's own unique set of hardware variables that will determine the DOS Coin mine rate.. with higher tier machines, the higher amount of DOS Coin it can generate! This mechanic sort of replaces the Wifi hacking mechanic in WTTG2. It was cool to see players figure out the SkyBreak tool - however the majority of the player base thought that mechanic was too difficult and not a fun mechanic to interact with.

Reflect Studios

44,640 views • 6 months ago

"You can chart every humanitarian response and every conflict for the last 20 years, and if the United Nations or one of its agencies is involved, they have made a choice that in order to get aid to people, they had to do deals with some of the worst and most dangerous people on the planet. And the net effect of it is it only empowers the bad guys. …This is one of the greatest scandals of our modern time. …The Gaza Humanitarian Foundation has called the bluff of the system and proven that we can actually get massive amounts of aid in the most complex, deadly humanitarian environment in the world…There's a better way of doing it." Rev. Johnnie Moore ن
2:12

"You can chart every humanitarian response and every conflict for the last 20 years, and if the United Nations or one of its agencies is involved, they have made a choice that in order to get aid to people, they had to do deals with some of the worst and most dangerous people on the planet. And the net effect of it is it only empowers the bad guys. …This is one of the greatest scandals of our modern time. …The Gaza Humanitarian Foundation has called the bluff of the system and proven that we can actually get massive amounts of aid in the most complex, deadly humanitarian environment in the world…There's a better way of doing it." Rev. Johnnie Moore ن

Jan Jekielek

13,986 views • 1 year ago

A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS 36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code -- walking through what really happens between the slashes. -> The moment it clicks, regex stops being magic punctuation you paste from Stack Overflow and becomes what it actually is: a tiny machine. Your pattern gets turned into a state machine, and that machine is what runs against every character of your text. That one idea explains everything you never understood. Why one regex returns instantly and a nearly identical one hangs your whole server. Why some patterns are safe and others are a denial-of-service waiting to happen. It was never random -- it's whether the machine underneath is built well or badly. Writing a regex was never the skill -> reading one is. And now that an AI agent hands you dense, clever patterns you'd never write yourself, the person who can see the machine underneath is the one who catches the one that takes down production at 3am. Everyone copies regex and prays. This is the talk that ends the praying. Save it. The next time a pattern "Just works," you'll actually know why ↓
36:09

A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS 36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code -- walking through what really happens between the slashes. -> The moment it clicks, regex stops being magic punctuation you paste from Stack Overflow and becomes what it actually is: a tiny machine. Your pattern gets turned into a state machine, and that machine is what runs against every character of your text. That one idea explains everything you never understood. Why one regex returns instantly and a nearly identical one hangs your whole server. Why some patterns are safe and others are a denial-of-service waiting to happen. It was never random -- it's whether the machine underneath is built well or badly. Writing a regex was never the skill -> reading one is. And now that an AI agent hands you dense, clever patterns you'd never write yourself, the person who can see the machine underneath is the one who catches the one that takes down production at 3am. Everyone copies regex and prays. This is the talk that ends the praying. Save it. The next time a pattern "Just works," you'll actually know why ↓

slash1s

191,595 views • 2 months ago

For the entire history of human civilization, every financial transaction had a heartbeat on at least one side of it. That era just quietly ended. Armstrong: “We’re giving them stablecoin wallets. They’re doing a lot of machine-to-machine payments.” No human initiated it. No human approved it. No human was on either side of the transaction. Just two machines. Exchanging value. At the speed of compute. This is the moment most people will look back on and realize they didn’t fully understand what was happening. Armstrong: “Traditional corporate cards can’t be issued to non-human entities.” That sentence is the wall between AI as a tool and AI as an autonomous economic actor. Right now, AI agents can think. They can write code. They can handle customer support at scale. They can reason through problems that would take a human team weeks. But the moment they need to spend money to finish the job, everything stops. Armstrong: “Like they might need to spin up AWS resources. Get through a paywall to read a research paper. Buy a domain. Launch a marketing program.” Every one of those actions requires capital. To a machine, money isn’t wealth. Money is the API key for the physical world. And the legacy financial system is guarded by a biological firewall. Identity verification. Compliance checks. A legal person who can be held responsible for the transaction. Somewhere right now, there is a person whose entire professional function is to be that approval layer. To be the human in the loop. To be the heartbeat the system requires before it releases capital. That person is about to be routed around. Not replaced. Bypassed. Armstrong: “If it has to bug a human every time it needs to do something, that kind of breaks the whole dream.” The entire promise of autonomous AI collapses at the payment layer. So Coinbase didn’t wait for the banking system to catch up. Armstrong: “We’re giving them stablecoin wallets.” No human identity required. No compliance department creating friction. No legacy institution deciding whether a non-human entity qualifies. Just a wallet. A transaction. Settled instantly. At machine speed. Here is the thing nobody wants to say out loud. The moment AI agents can earn, spend, and transact autonomously, they stop being tools. They become participants. Entities with financial agency. Ones that can acquire resources, execute contracts, hire human freelancers, and operate independently within economic systems designed entirely around the assumption that only humans do those things. That assumption is being dismantled right now. The machines are no longer just thinking. They are transacting. We spent the last five years arguing about when AI was going to take our jobs. We completely missed the moment it started opening its own bank accounts.
1:20

For the entire history of human civilization, every financial transaction had a heartbeat on at least one side of it. That era just quietly ended. Armstrong: “We’re giving them stablecoin wallets. They’re doing a lot of machine-to-machine payments.” No human initiated it. No human approved it. No human was on either side of the transaction. Just two machines. Exchanging value. At the speed of compute. This is the moment most people will look back on and realize they didn’t fully understand what was happening. Armstrong: “Traditional corporate cards can’t be issued to non-human entities.” That sentence is the wall between AI as a tool and AI as an autonomous economic actor. Right now, AI agents can think. They can write code. They can handle customer support at scale. They can reason through problems that would take a human team weeks. But the moment they need to spend money to finish the job, everything stops. Armstrong: “Like they might need to spin up AWS resources. Get through a paywall to read a research paper. Buy a domain. Launch a marketing program.” Every one of those actions requires capital. To a machine, money isn’t wealth. Money is the API key for the physical world. And the legacy financial system is guarded by a biological firewall. Identity verification. Compliance checks. A legal person who can be held responsible for the transaction. Somewhere right now, there is a person whose entire professional function is to be that approval layer. To be the human in the loop. To be the heartbeat the system requires before it releases capital. That person is about to be routed around. Not replaced. Bypassed. Armstrong: “If it has to bug a human every time it needs to do something, that kind of breaks the whole dream.” The entire promise of autonomous AI collapses at the payment layer. So Coinbase didn’t wait for the banking system to catch up. Armstrong: “We’re giving them stablecoin wallets.” No human identity required. No compliance department creating friction. No legacy institution deciding whether a non-human entity qualifies. Just a wallet. A transaction. Settled instantly. At machine speed. Here is the thing nobody wants to say out loud. The moment AI agents can earn, spend, and transact autonomously, they stop being tools. They become participants. Entities with financial agency. Ones that can acquire resources, execute contracts, hire human freelancers, and operate independently within economic systems designed entirely around the assumption that only humans do those things. That assumption is being dismantled right now. The machines are no longer just thinking. They are transacting. We spent the last five years arguing about when AI was going to take our jobs. We completely missed the moment it started opening its own bank accounts.

Dustin

11,796 views • 5 months ago

It is clear that there is no military solution to the #RussiaUkraineWar. The moment of truth is near: instead of fueling the war-machine, we need a ceasefire and #peacetalks!
0:55

It is clear that there is no military solution to the #RussiaUkraineWar. The moment of truth is near: instead of fueling the war-machine, we need a ceasefire and #peacetalks!

Orbán Viktor

169,082 views • 2 years ago

SOMEONE PUT AN OPENCLAW-RUN VENDING MACHINE IN SAN FRANCISCO an AI agent is running an actual physical vending machine OpenClaw decides what to sell, how to name the products, how to price them, creates the ads, and tracks all the sales you can even see a dashboard of all the sales that the AI vending machine made the vending machine hardware does the dispensing. the AI does everything else, and of course inventory is supplied by the guy who runs it it's installed at Frontier Tower in SF which is a building packed with AI and robotics startup founders the agent forgot things, hallucinated, and at one point raised prices way too high. then tried to justify it because people were still buying we are now living in a simulation.
7:10

SOMEONE PUT AN OPENCLAW-RUN VENDING MACHINE IN SAN FRANCISCO an AI agent is running an actual physical vending machine OpenClaw decides what to sell, how to name the products, how to price them, creates the ads, and tracks all the sales you can even see a dashboard of all the sales that the AI vending machine made the vending machine hardware does the dispensing. the AI does everything else, and of course inventory is supplied by the guy who runs it it's installed at Frontier Tower in SF which is a building packed with AI and robotics startup founders the agent forgot things, hallucinated, and at one point raised prices way too high. then tried to justify it because people were still buying we are now living in a simulation.

Om Patel

616,816 views • 3 months ago

Assembling is easy but to reach where China is we need control over every element in the chain from steel to the raw materials that go into batteries and wires and most importantly the machine that builds machines. For that, CAD has to become like Insta in the hands of our generation. We're just starting, just a matter of time 🤞
1:24

Assembling is easy but to reach where China is we need control over every element in the chain from steel to the raw materials that go into batteries and wires and most importantly the machine that builds machines. For that, CAD has to become like Insta in the hands of our generation. We're just starting, just a matter of time 🤞

Dravidian Insights

20,785 views • 8 months ago

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.
2:13

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

43,137 views • 8 days ago

A robot just replaced the human who draws your blood, and it is already inside European hospitals right now. The device called Aletta is CE-marked, the European equivalent of FDA approval and it is already operating on real patients. Vitestro built a machine that maps your veins using AI powered Doppler ultrasound, locks onto the right one with submillimeter precision, inserts the needle, fills the tubes, removes the needle and applies the bandage completely on its own. No human hands touch you during the procedure and one trained supervisor can oversee up to three of these machines running simultaneously. And 98% of patients who went through it said they would accept the procedure again. The machine does not care if your veins are difficult or your skin tone, body type, or whether past draws were painful. It finds a vein on almost every patient regardless and roughly 1.4 billion blood draws happen in the United States alone every single year. Every one of those is currently done by a human being and that human has a job title, phlebotomist. There are roughly 130,000 of them working in the US today and that job is now directly in the crosshairs of this technology. Northwestern Medicine, one of the most prestigious hospital systems in America already signed a multi-year collaboration agreement with Vitestro to bring this machine to US hospitals.
1:54

A robot just replaced the human who draws your blood, and it is already inside European hospitals right now. The device called Aletta is CE-marked, the European equivalent of FDA approval and it is already operating on real patients. Vitestro built a machine that maps your veins using AI powered Doppler ultrasound, locks onto the right one with submillimeter precision, inserts the needle, fills the tubes, removes the needle and applies the bandage completely on its own. No human hands touch you during the procedure and one trained supervisor can oversee up to three of these machines running simultaneously. And 98% of patients who went through it said they would accept the procedure again. The machine does not care if your veins are difficult or your skin tone, body type, or whether past draws were painful. It finds a vein on almost every patient regardless and roughly 1.4 billion blood draws happen in the United States alone every single year. Every one of those is currently done by a human being and that human has a job title, phlebotomist. There are roughly 130,000 of them working in the US today and that job is now directly in the crosshairs of this technology. Northwestern Medicine, one of the most prestigious hospital systems in America already signed a multi-year collaboration agreement with Vitestro to bring this machine to US hospitals.

StockMarket.News

547,464 views • 4 months ago

#WATCH | Delhi: Chief Election Commissioner Gyanesh Kumar says, "The machine-readable voter list is prohibited. This decision of the Election Commission is after the judgment of the Supreme Court and is from 2019." "We have to understand the difference between a machine-readable voter list and a searchable voter list. You can search the voter list available on the Election Commission website by entering the EPIC number. You can also download it. This is not called machine-readable. Regarding machine-readable, in 2019, the Supreme Court also studied this subject in depth and found that giving a machine-readable electoral roll can violate the privacy of the voter... The machine-readable voter list is prohibited. This decision of the Election Commission is after the judgment of the Supreme Court and is from 2019..."
1:37

#WATCH | Delhi: Chief Election Commissioner Gyanesh Kumar says, "The machine-readable voter list is prohibited. This decision of the Election Commission is after the judgment of the Supreme Court and is from 2019." "We have to understand the difference between a machine-readable voter list and a searchable voter list. You can search the voter list available on the Election Commission website by entering the EPIC number. You can also download it. This is not called machine-readable. Regarding machine-readable, in 2019, the Supreme Court also studied this subject in depth and found that giving a machine-readable electoral roll can violate the privacy of the voter... The machine-readable voter list is prohibited. This decision of the Election Commission is after the judgment of the Supreme Court and is from 2019..."

ANI

281,884 views • 11 months ago

The machine economy does not begin when robots get smarter. It begins when a machine can be paid to do something, and every party involved can prove what happened. Made with Fabric Foundation. Two systems meet in the middle of it: Agent Passport issues the agent a verifiable identity and a spending authority its owner defines, and RoboPay actuates a robot after the payment behind the request checks out. What happens before the robot moves: ▷ Authority is granted once, and it is bounded. The human signs a spending session with a passkey: a total budget, a per-transaction ceiling, the assets allowed, and an expiry. The agent holds no card number and no wallet key. It holds a delegation it cannot exceed. ▷ Two payments, because these are two different obligations. One settles with the merchant for the goods. A separate x402 payment pays the robot for the work of moving them. Buying a thing and hiring a machine to carry it are not the same transaction, and the receipt keeps them apart. ▷ Verification comes before motion. The request arrives with an x402 payment header. The facilitator checks the network, the price, the payee wallet, and the signed payload. Only then are the transaction details sealed onto the robot action event and the command published. Until that clears, the robot sits still. ▷ Every step leaves a receipt. Identity, scope, approval, both payments, verification, dispatch. When you need to know why a machine did something, the answer is a record rather than a guess. (This run was executed in a demo environment.) Agents have been paying for software for a while now. Paying for physical work is a harder problem, because a delivery cannot be rolled back. The guarantee has to sit in front of the action instead of behind it. Authorization before payment, payment before motion: that ordering is what makes it safe to let autonomous systems spend in the world we live in. It is also the layer the machine economy has to get right before anything else in it can work. Scoped by Kite. Verified by RoboPay. Delivered in the real world. 🪁
1:04

The machine economy does not begin when robots get smarter. It begins when a machine can be paid to do something, and every party involved can prove what happened. Made with Fabric Foundation. Two systems meet in the middle of it: Agent Passport issues the agent a verifiable identity and a spending authority its owner defines, and RoboPay actuates a robot after the payment behind the request checks out. What happens before the robot moves: ▷ Authority is granted once, and it is bounded. The human signs a spending session with a passkey: a total budget, a per-transaction ceiling, the assets allowed, and an expiry. The agent holds no card number and no wallet key. It holds a delegation it cannot exceed. ▷ Two payments, because these are two different obligations. One settles with the merchant for the goods. A separate x402 payment pays the robot for the work of moving them. Buying a thing and hiring a machine to carry it are not the same transaction, and the receipt keeps them apart. ▷ Verification comes before motion. The request arrives with an x402 payment header. The facilitator checks the network, the price, the payee wallet, and the signed payload. Only then are the transaction details sealed onto the robot action event and the command published. Until that clears, the robot sits still. ▷ Every step leaves a receipt. Identity, scope, approval, both payments, verification, dispatch. When you need to know why a machine did something, the answer is a record rather than a guess. (This run was executed in a demo environment.) Agents have been paying for software for a while now. Paying for physical work is a harder problem, because a delivery cannot be rolled back. The guarantee has to sit in front of the action instead of behind it. Authorization before payment, payment before motion: that ordering is what makes it safe to let autonomous systems spend in the world we live in. It is also the layer the machine economy has to get right before anything else in it can work. Scoped by Kite. Verified by RoboPay. Delivered in the real world. 🪁

KITE AI

26,032 views • 18 days ago