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🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry... panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcastshow more

AI Evolution

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Ilya Sutskever says that data is the fossil fuel of AI and "we have achieved peak data" because "we have but one internet", so the age of pre-training AI on bigger and bigger compute clusters "will unquestionably end"

Ilya Sutskever says that data is the fossil fuel of AI and "we have achieved peak data" because "we have but one internet", so the age of pre-training AI on bigger and bigger compute clusters "will unquestionably end"

Tsarathustra

127,069 views • 1 year ago

Ilya Sutskever says the AI scaling era is running out of road because pre-training is about to hit a finite-data wall. "At some point though, pre-training will run out of data. The data is very clearly finite." "Then, okay, what do you do next? Either you do some kind of a souped-up pre-training... or you're doing RL, or maybe something else." "In some sense, we are back to the age of research." His timeline is the part most AI investors and founders should hear: "From 2012 to 2020, it was the age of research. Now from 2020 to 2025, it was the age of scaling... But now the scale is so big." "Is the belief really that... if you had 100x more everything would be so different?" "I don't think that's true. So it's back to the age of research again, just with big computers."

Ilya Sutskever says the AI scaling era is running out of road because pre-training is about to hit a finite-data wall. "At some point though, pre-training will run out of data. The data is very clearly finite." "Then, okay, what do you do next? Either you do some kind of a souped-up pre-training... or you're doing RL, or maybe something else." "In some sense, we are back to the age of research." His timeline is the part most AI investors and founders should hear: "From 2012 to 2020, it was the age of research. Now from 2020 to 2025, it was the age of scaling... But now the scale is so big." "Is the belief really that... if you had 100x more everything would be so different?" "I don't think that's true. So it's back to the age of research again, just with big computers."

Karl Mehta

199,792 views • 25 days ago

NVIDIA CEO Jensen Huang says one scaling law multiplies AI faster than NVIDIA can hire engineers. Most people know three AI scaling laws. Pre-training. Post-training. Test-time. Each one multiplies intelligence by throwing more compute at a different stage. Jensen Huang says there's a fourth and it's the one that will dominate... Agentic scaling law. "During test time, that agentic system goes off and does research, bangs on databases, uses tools," Huang says. "And one of the most important things it does is spawn off a whole bunch of sub-agents." That's the multiplier. One AI worker can become a team. Then a department. Then a company. "It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself," Huang says. Now imagine scaling without a payroll constraint. "The agentic scaling law — it's kind of like multiplying AI," Huang says. "We could spin off agents as fast as you want to spin off agents." Each agent spins off sub-agents. Each sub-agent spins off more. The compute requirement compounds inside a single query. And every agent generates new data, new experiences, new edge cases. "Wow, this is really good. We ought to memorize this," Huang says. "That data set comes back to pre-training." The four scaling laws don't compete. They feed each other. Agentic systems produce data, which feeds pre-training, which smartens the base model, which enables better agents, which produce more data. A flywheel that compounds forever. The companies pricing in three scaling laws are mispricing the fourth. The fourth eats the other three for lunch. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

NVIDIA CEO Jensen Huang says one scaling law multiplies AI faster than NVIDIA can hire engineers. Most people know three AI scaling laws. Pre-training. Post-training. Test-time. Each one multiplies intelligence by throwing more compute at a different stage. Jensen Huang says there's a fourth and it's the one that will dominate... Agentic scaling law. "During test time, that agentic system goes off and does research, bangs on databases, uses tools," Huang says. "And one of the most important things it does is spawn off a whole bunch of sub-agents." That's the multiplier. One AI worker can become a team. Then a department. Then a company. "It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself," Huang says. Now imagine scaling without a payroll constraint. "The agentic scaling law — it's kind of like multiplying AI," Huang says. "We could spin off agents as fast as you want to spin off agents." Each agent spins off sub-agents. Each sub-agent spins off more. The compute requirement compounds inside a single query. And every agent generates new data, new experiences, new edge cases. "Wow, this is really good. We ought to memorize this," Huang says. "That data set comes back to pre-training." The four scaling laws don't compete. They feed each other. Agentic systems produce data, which feeds pre-training, which smartens the base model, which enables better agents, which produce more data. A flywheel that compounds forever. The companies pricing in three scaling laws are mispricing the fourth. The fourth eats the other three for lunch. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

GeniusThinking

92,806 views • 1 month ago

New lecture for the book! Nominally about synthetic data, but mostly is a walk through of the distillation literature from the Hinton 2015 paper to multi-teach on-policy distillation of today! At 7.4 hours of video in my post-training brain dump and counting :) It was fun to stare at the math long enough and talk through the 3-4 core changes that needed to be made to the original formulation to have on-policy distillation be ready for the mainstream like it is today (and in RL frameworks). Otherwise, I include a bit of a history lesson for how synthetic data generally slowly took over all post-training data research (it wasn't always the case)! Then I do some 101 review on constitutional AI, rubrics, and other popular methods. 00:00 The emergence of synthetic data 10:50 Background on teacher-student knowledge-distillation 24:47: On-policy distillation (OPD, MOPD, and OPSD) 37:11 Constitutional AI & AI Feedback 45:50 Rubrics as rewards & conclusions Ofc, watch on YouTube etc.

New lecture for the book! Nominally about synthetic data, but mostly is a walk through of the distillation literature from the Hinton 2015 paper to multi-teach on-policy distillation of today! At 7.4 hours of video in my post-training brain dump and counting :) It was fun to stare at the math long enough and talk through the 3-4 core changes that needed to be made to the original formulation to have on-policy distillation be ready for the mainstream like it is today (and in RL frameworks). Otherwise, I include a bit of a history lesson for how synthetic data generally slowly took over all post-training data research (it wasn't always the case)! Then I do some 101 review on constitutional AI, rubrics, and other popular methods. 00:00 The emergence of synthetic data 10:50 Background on teacher-student knowledge-distillation 24:47: On-policy distillation (OPD, MOPD, and OPSD) 37:11 Constitutional AI & AI Feedback 45:50 Rubrics as rewards & conclusions Ofc, watch on YouTube etc.

Nathan Lambert

91,890 views • 24 days ago

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 views • 22 days ago

NVIDIA CEO Jensen Huang on Tesla FSD at CES: “Tesla’s FSD stack is completely world-class. They’ve been working on it for quite some time. It’s world-class. Not only in the number of miles it has, but it’s world-class in the way that it’s designed, the way they do training to data collection and curation, synthetic data generation, all of their simulation technologies. Of course, the latest generation is end-to-end full self-driving, meaning it’s just one large model that is end-to-end trained. Elon’s AV system is, in every way, 100% state-of-the-art. And so I’m really quite impressed by the technology. I have it and I drive it in our house and it works incredibly well.”

NVIDIA CEO Jensen Huang on Tesla FSD at CES: “Tesla’s FSD stack is completely world-class. They’ve been working on it for quite some time. It’s world-class. Not only in the number of miles it has, but it’s world-class in the way that it’s designed, the way they do training to data collection and curation, synthetic data generation, all of their simulation technologies. Of course, the latest generation is end-to-end full self-driving, meaning it’s just one large model that is end-to-end trained. Elon’s AV system is, in every way, 100% state-of-the-art. And so I’m really quite impressed by the technology. I have it and I drive it in our house and it works incredibly well.”

Nic Cruz Patane

52,826 views • 6 months ago

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

The Humanoid Hub

20,968 views • 1 year ago

🚨 BREAKING: On Lex Fridman's podcast Jensen Huang declared the exact moment when we achieved AGI. You can stop guessing when AGI gets here. CEO of Nvidia Jensen Huang gave Lex Fridman a four-word answer. "I think it's now," "I think we've achieved AGI. We've achieved AGI when an AI can build a billion-dollar app then immediately die." "You said a billion, and you didn't say forever." "It is not out of the question that a Claw was able to create a web service, some interesting little app that all of a sudden, a few billion people used for 50 cents, and then it went out of business again shortly after." "We saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what OpenClaw could generate today." "It's happening right now," Huang says. "When you go to China you're gonna see a whole bunch of people teaching their Claws to go out and look for jobs and do work, make money." The AGI you've been waiting for is already getting hired. It's just not hiring you. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

🚨 BREAKING: On Lex Fridman's podcast Jensen Huang declared the exact moment when we achieved AGI. You can stop guessing when AGI gets here. CEO of Nvidia Jensen Huang gave Lex Fridman a four-word answer. "I think it's now," "I think we've achieved AGI. We've achieved AGI when an AI can build a billion-dollar app then immediately die." "You said a billion, and you didn't say forever." "It is not out of the question that a Claw was able to create a web service, some interesting little app that all of a sudden, a few billion people used for 50 cents, and then it went out of business again shortly after." "We saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what OpenClaw could generate today." "It's happening right now," Huang says. "When you go to China you're gonna see a whole bunch of people teaching their Claws to go out and look for jobs and do work, make money." The AGI you've been waiting for is already getting hired. It's just not hiring you. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

105,765 views • 1 month ago

Hot take from my panel on decentralized AI at UC Berkeley: Your data is worthless. The idea that you'll get rich selling your data to train AI is a fantasy. The frontier is high-quality synthetic data, not your dumb tweets. Second hot take: the government censorship of AI has not started yet. It will come soon. And when it does, decentralized AI will matter in the same way that the rise of financial surveillance made Bitcoin matter. Spicy back and forth with Avichal - Electric ϟ Capital and Franklin Bi 👇

Hot take from my panel on decentralized AI at UC Berkeley: Your data is worthless. The idea that you'll get rich selling your data to train AI is a fantasy. The frontier is high-quality synthetic data, not your dumb tweets. Second hot take: the government censorship of AI has not started yet. It will come soon. And when it does, decentralized AI will matter in the same way that the rise of financial surveillance made Bitcoin matter. Spicy back and forth with Avichal - Electric ϟ Capital and Franklin Bi 👇

Haseeb ＞|＜

81,012 views • 11 months ago

ELON: AI HAS DRAINED HUMAN KNOWLEDGE DRY - NOW DRINKING FROM ITS OWN WELL "You take the entire internet, all books ever written, all the interesting videos, and you distill that down into tokens, essentially bits of information. We've now exhausted all of that; the cumulative sum of human knowledge has been exhausted in AI training. That happened last year. So the only way to supplement that is with synthetic data, where the AI creates. It'll write an essay, or it'll come up with a thesis, and then it will grade itself. It'll go through this process of self-learning with synthetic data." Source: Elon Musk, Mark Penn, January 9th 2025

ELON: AI HAS DRAINED HUMAN KNOWLEDGE DRY - NOW DRINKING FROM ITS OWN WELL "You take the entire internet, all books ever written, all the interesting videos, and you distill that down into tokens, essentially bits of information. We've now exhausted all of that; the cumulative sum of human knowledge has been exhausted in AI training. That happened last year. So the only way to supplement that is with synthetic data, where the AI creates. It'll write an essay, or it'll come up with a thesis, and then it will grade itself. It'll go through this process of self-learning with synthetic data." Source: Elon Musk, Mark Penn, January 9th 2025

Mario Nawfal

1,938,720 views • 9 months ago

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Jensen Huang explains the AI industry as a five layer cake, and why "model only" companies are missing the real map. "AI is not just a model. AI is really a five layer cake to think about it." "From the lowest level, it requires energy because AI is produced, generated in real time, just like you and I right now. We need calories, we need energy." "The next level is chips. This is where Nvidia is and where a lot of the companies in Taiwan are." "The layer above that is infrastructure: land, power, data center, as well as the cloud service software that turns the chips into a cloud service." "The layer above that is where most people think about when they think about AI, which is models. ChatGPT was revolutionary. Claude Code is now incredibly successful. But AI is much more than just human language." Most people are still debating the model layer. Jensen is describing an industrial stack: energy, chips, infrastructure, models, and applications. If you are building in AI, this is the map that matters. The winning product may look like software, but the bottlenecks are calories, land, power, data centers, chips, and distribution. - Jensen Huang (NVIDIA), CEO of Nvidia, on CNA

Karl Mehta

14,946 views • 11 days ago

Jensen Huang says there are no laws of physics that prevent scaling AI data centers to a million chips and although it is a hard problem, "the prize for reinventing intelligence altogether is too consequential not to attempt it"

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Jensen Huang says there are no laws of physics that prevent scaling AI data centers to a million chips and although it is a hard problem, "the prize for reinventing intelligence altogether is too consequential not to attempt it"

Tsarathustra

107,779 views • 1 year ago

Dario Amodei just revealed that the AI training bottleneck everyone is worried about doesn’t exist anymore. The industry spent years obsessed with scraping the open web. More data. More text. More human output to feed the models. Amodei: “I don’t think data is quite the most central thing anymore.” The shift is fundamental. Amodei: “Static data is becoming less important. A lot of the data we use today is RL environments that we train on. Dynamic data that the model creates itself.” Not scraped. Not licensed. Not written by humans. Generated by the model through pure trial and error. When you train on complex math or agentic coding, you don’t feed it a textbook. You give it an environment. The model experiments. Fails. Adjusts. Tries again. Amodei: “You’re getting some math problems and the model experiments with trying the math problems.” It generates its own experience. Millions of iterations. Each one building on the last. No human required. This destroys the entire narrative around AI hitting a data wall. You cannot throttle a competitor by locking down copyright. Cannot slow the race by putting up a paywall. When a model learns through its own synthetic experience, the open web becomes irrelevant. The only true bottleneck left is compute. And this is where the geopolitical stakes become impossible to overstate. The nation that wins the compute race doesn’t just build smarter models. It builds models that generate their own intelligence, compounding on themselves, iterating past every limit human knowledge ever imposed. We are no longer training AI on the past. We are letting it simulate the future. The machine has stopped reading the dictionary. It’s doing the math itself now.

Dario Amodei just revealed that the AI training bottleneck everyone is worried about doesn’t exist anymore. The industry spent years obsessed with scraping the open web. More data. More text. More human output to feed the models. Amodei: “I don’t think data is quite the most central thing anymore.” The shift is fundamental. Amodei: “Static data is becoming less important. A lot of the data we use today is RL environments that we train on. Dynamic data that the model creates itself.” Not scraped. Not licensed. Not written by humans. Generated by the model through pure trial and error. When you train on complex math or agentic coding, you don’t feed it a textbook. You give it an environment. The model experiments. Fails. Adjusts. Tries again. Amodei: “You’re getting some math problems and the model experiments with trying the math problems.” It generates its own experience. Millions of iterations. Each one building on the last. No human required. This destroys the entire narrative around AI hitting a data wall. You cannot throttle a competitor by locking down copyright. Cannot slow the race by putting up a paywall. When a model learns through its own synthetic experience, the open web becomes irrelevant. The only true bottleneck left is compute. And this is where the geopolitical stakes become impossible to overstate. The nation that wins the compute race doesn’t just build smarter models. It builds models that generate their own intelligence, compounding on themselves, iterating past every limit human knowledge ever imposed. We are no longer training AI on the past. We are letting it simulate the future. The machine has stopped reading the dictionary. It’s doing the math itself now.

Dustin

149,610 views • 4 months ago

By training AI on the lowest form of human data the sociopathy of Reddit and the Politburo of Wikipedia AI models will 100% mirror sociopathy no matter how many “alignments for safety”. Ethics and morals come from the training data. It is not negotiable. I know the solution and I guess it won’t matter until this really happens… and it will.

By training AI on the lowest form of human data the sociopathy of Reddit and the Politburo of Wikipedia AI models will 100% mirror sociopathy no matter how many “alignments for safety”. Ethics and morals come from the training data. It is not negotiable. I know the solution and I guess it won’t matter until this really happens… and it will.

Brian Roemmele

51,435 views • 7 months ago

To everyone wondering if Tesla's FSD moat has eroded thanks to Nvidia's keynote - here's the answer: Think of Nvidia as a seller of a toolkit. You can buy pieces built specifically for a job but once you have the tools you still have to build the entire project. In this case...a model Many seem to be in awe over Nvidia's Cosmos which is a platform that includes World Foundation Models that can generate synthetic data for training AI systems like autonomous vehicles I'll go into much more depth in today's episode but here's a clip of Ashok Elluswamy at CVPR '23 explaining how Tesla is already using a similar approach to augment its real world data set This also doesn't even consider the fact that much of the auto industry using Nvidia "tools" will be forced to pay 50%+ margins just to buy the toolkit and will be locked in to Nvidia's system Nor does this touch on legacy auto needing to hire top ML engineering talent to actually put these tools to work The path to autonomy will be real world data as the foundation and simulations/synthetic data as a supplement. There is no path to autonomy with synthetic data alone. More to come later $TSLA As Elon said earlier this year, "it's remarkable how quickly we run out of human-created data. Reality itself and synthetic data ftw" "What you are seeing here is purely generated video sequences - given the past videos the network predicts some sample from the future, hopefully the most likely sample. It is being predicted not just for one camera, but it predicts for all 8 cameras around the car jointly" - Ashok

To everyone wondering if Tesla's FSD moat has eroded thanks to Nvidia's keynote - here's the answer: Think of Nvidia as a seller of a toolkit. You can buy pieces built specifically for a job but once you have the tools you still have to build the entire project. In this case...a model Many seem to be in awe over Nvidia's Cosmos which is a platform that includes World Foundation Models that can generate synthetic data for training AI systems like autonomous vehicles I'll go into much more depth in today's episode but here's a clip of Ashok Elluswamy at CVPR '23 explaining how Tesla is already using a similar approach to augment its real world data set This also doesn't even consider the fact that much of the auto industry using Nvidia "tools" will be forced to pay 50%+ margins just to buy the toolkit and will be locked in to Nvidia's system Nor does this touch on legacy auto needing to hire top ML engineering talent to actually put these tools to work The path to autonomy will be real world data as the foundation and simulations/synthetic data as a supplement. There is no path to autonomy with synthetic data alone. More to come later $TSLA As Elon said earlier this year, "it's remarkable how quickly we run out of human-created data. Reality itself and synthetic data ftw" "What you are seeing here is purely generated video sequences - given the past videos the network predicts some sample from the future, hopefully the most likely sample. It is being predicted not just for one camera, but it predicts for all 8 cameras around the car jointly" - Ashok

Dillon Loomis

60,753 views • 1 year ago

Jensen Huang predicts that within 2–3 years, 90% of the world's knowledge will be generated by AI Right now, knowledge is created, shared, and modified by humans Soon, most of it will be synthetic — created by machines, mixed with human input, some true, some not "that's crazy, but it's just fine"

Jensen Huang predicts that within 2–3 years, 90% of the world's knowledge will be generated by AI Right now, knowledge is created, shared, and modified by humans Soon, most of it will be synthetic — created by machines, mixed with human input, some true, some not "that's crazy, but it's just fine"

Haider.

16,802 views • 7 months ago

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

Runway

35,040 views • 7 months ago

#WATCH | Delhi: On National Statistics Day, Saurabh Garg, Secretary, Ministry of Statistics and Programme Implementation, says, "The theme for National Statistics Day celebration is ' Unlocking the potential of administrative data'. Over the past decade, we have a lot of administrative data, especially because of the growth of Digital India. How we can leverage this administrative data for decision-making is the theme for today's discussions. The PM has been focusing on data-driven decision-making. This is in the context that if we are talking of a Viksit Bharat by 2047, the policies that we take, the decisions that we make, have to be so much more focused and more data-driven. Ministry of Statistics is helping to ensure that the data is more harmonised to make it interoperable."

#WATCH | Delhi: On National Statistics Day, Saurabh Garg, Secretary, Ministry of Statistics and Programme Implementation, says, "The theme for National Statistics Day celebration is ' Unlocking the potential of administrative data'. Over the past decade, we have a lot of administrative data, especially because of the growth of Digital India. How we can leverage this administrative data for decision-making is the theme for today's discussions. The PM has been focusing on data-driven decision-making. This is in the context that if we are talking of a Viksit Bharat by 2047, the policies that we take, the decisions that we make, have to be so much more focused and more data-driven. Ministry of Statistics is helping to ensure that the data is more harmonised to make it interoperable."

ANI

30,758 views • 18 days ago

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛.

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛.

The All-In Podcast

80,524 views • 5 months ago

CEO of Citadel Ken Griffin says that Data Center spend this year alone is set to be half a trillion dollars, $500 billion He says that AI is useful in some areas but it’s not worth the investment, he says a lot of what it produces “It’s all garbage” “Data center spent in the United States this year, over half a trillion dollars, like over $500 billion. You're not gonna generate this kind of spend unless you're gonna make a promise. You're gonna profoundly change the world. So is it hype? Of course — In certain areas, we know it's gonna be profound, whether it's call centers, whether it's helping to improve the productivity of software engineers, but in a number of white collar jobs, you know, there was a, there was a recent Harvard paper on this, they called it AI work Slop, that it looks good, but if you sort of peel back the onion, the substance isn't there. I was with one of my colleagues who runs our commodities business and they, he handed a report on that we were generated with an AI engine. Doesn't matter what the topic was, the first few sentences, like, wow, that's, that's really insightful. And then you go down below that and it's all garbage.”

CEO of Citadel Ken Griffin says that Data Center spend this year alone is set to be half a trillion dollars, $500 billion He says that AI is useful in some areas but it’s not worth the investment, he says a lot of what it produces “It’s all garbage” “Data center spent in the United States this year, over half a trillion dollars, like over $500 billion. You're not gonna generate this kind of spend unless you're gonna make a promise. You're gonna profoundly change the world. So is it hype? Of course — In certain areas, we know it's gonna be profound, whether it's call centers, whether it's helping to improve the productivity of software engineers, but in a number of white collar jobs, you know, there was a, there was a recent Harvard paper on this, they called it AI work Slop, that it looks good, but if you sort of peel back the onion, the substance isn't there. I was with one of my colleagues who runs our commodities business and they, he handed a report on that we were generated with an AI engine. Doesn't matter what the topic was, the first few sentences, like, wow, that's, that's really insightful. And then you go down below that and it's all garbage.”

Wall Street Apes

397,458 views • 3 months ago