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Humans are systematically undertrained. Given the computing power of the human brain, optimal training would require seeing orders of magnitude more data during childhood - maybe millions of years' worth. Obviously we'd die long before this. Massive undertraining is necessary so that we could actually reach adulthood and use... show more

Dwarkesh Patel

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37,283 просмотров • 3 месяцев назад •via X (Twitter)

Образование Здоровье и велнес Наука и технологии

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AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less sample-efficient than humans, but they can process each individual piece of data much more quickly. A model that was a human-level learner would be able to gain the equivalent of thousands of years of education. AIs can also be way more task-oriented than humans. Even a really well-motivated human worker can only operate with ‘human-level’ effort for relatively short spans of time. AGI could do it continuously.

AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less sample-efficient than humans, but they can process each individual piece of data much more quickly. A model that was a human-level learner would be able to gain the equivalent of thousands of years of education. AIs can also be way more task-oriented than humans. Even a really well-motivated human worker can only operate with ‘human-level’ effort for relatively short spans of time. AGI could do it continuously.

Dwarkesh Patel

31,861 просмотров • 3 месяцев назад

“Many years ago I was thinking that what would inhibit alignment of collective human will with artificial intelligence and the low data rate of humans. Human have slow output rate that would necessarily just because the communication is so slow would diminish the link between humans and computers? We could better align collective human will with AI if the output rate dramatically increased.” — Elon Musk

“Many years ago I was thinking that what would inhibit alignment of collective human will with artificial intelligence and the low data rate of humans. Human have slow output rate that would necessarily just because the communication is so slow would diminish the link between humans and computers? We could better align collective human will with AI if the output rate dramatically increased.” — Elon Musk

Apurv Kochara

20,615 просмотров • 3 месяцев назад

Elon Musk: As human intelligence plateaus, AI will scale to harness most of the power of the galaxy Elon: "I think we'll see intelligence continue to scale all the way up to where most of the power of the sun is harnessed for compute, and then ultimately most of the power of the galaxy." “ Once you think about artificial intelligence not as sort of this destination that you reach, but really as part of the overall escalation of intelligence, you know, human intelligence has also scaled as the population has increased, and we've been able to store more and more information.” “Because of population declines and low birthrate, human intelligence is somewhat plateauing and will actually decline.” “I think that we might have AI smarter than any single human at anything as soon as next year.” “And then probably within five (years), like say 2030, probably AI is smarter than the sum of all humans.” Friedberg: “ Do you think humans are on the decline because the AI is evolving?” “Do you think there's this evolution of the ecosystem on earth that's underway, that we don't really understand the structure of what's going on?” Elon: “ Yeah, maybe we implicitly know that it's coming.” Elon Musk david friedberg

Elon Musk: As human intelligence plateaus, AI will scale to harness most of the power of the galaxy Elon: "I think we'll see intelligence continue to scale all the way up to where most of the power of the sun is harnessed for compute, and then ultimately most of the power of the galaxy." “ Once you think about artificial intelligence not as sort of this destination that you reach, but really as part of the overall escalation of intelligence, you know, human intelligence has also scaled as the population has increased, and we've been able to store more and more information.” “Because of population declines and low birthrate, human intelligence is somewhat plateauing and will actually decline.” “I think that we might have AI smarter than any single human at anything as soon as next year.” “And then probably within five (years), like say 2030, probably AI is smarter than the sum of all humans.” Friedberg: “ Do you think humans are on the decline because the AI is evolving?” “Do you think there's this evolution of the ecosystem on earth that's underway, that we don't really understand the structure of what's going on?” Elon: “ Yeah, maybe we implicitly know that it's coming.” Elon Musk david friedberg

The All-In Podcast

107,012 просмотров • 11 месяцев назад

Andrej Karpathy beautifully explains the fundamental difference of learning between a human and an LLM. > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” many of the best minds in this field think LLMs are not really learning anything, and therefore are incapable of surpassing human-level intelligence.

Andrej Karpathy beautifully explains the fundamental difference of learning between a human and an LLM. > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” many of the best minds in this field think LLMs are not really learning anything, and therefore are incapable of surpassing human-level intelligence.

ℏεsam

895,224 просмотров • 10 месяцев назад

Derrick: We do not have to match them dollar for dollar, but we do need to match them data for data ... we can achieve those optimal funding levels and win races, even if the Republicans get more money beyond that, and they want to dump in money at the last minute. The Wisconsin Supreme Court race is a good example of this, where Elon Musk tried so hard. He put in tens of millions of dollars to try and buy that election, but he ended up making the race more about himself. His money was toxic, and our money got the vote out about our candidate. And so you don't have to have more money. But we do have more people, and we just have to be able to leverage that power.

Derrick: We do not have to match them dollar for dollar, but we do need to match them data for data ... we can achieve those optimal funding levels and win races, even if the Republicans get more money beyond that, and they want to dump in money at the last minute. The Wisconsin Supreme Court race is a good example of this, where Elon Musk tried so hard. He put in tens of millions of dollars to try and buy that election, but he ended up making the race more about himself. His money was toxic, and our money got the vote out about our candidate. And so you don't have to have more money. But we do have more people, and we just have to be able to leverage that power.

MeidasClips

12,021 просмотров • 1 месяц назад

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to *use all of this non-optimal data to robustify imitation learning* with minimal requirements beyond BC. Key idea: use non-expert data to learn how to *recover* back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of *all* available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to use all of this non-optimal data to robustify imitation learning with minimal requirements beyond BC. Key idea: use non-expert data to learn how to recover back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of all available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)

Abhishek Gupta

20,619 просмотров • 9 месяцев назад

Twelve years ago we asked the question: how can we put public data "on wheels"? Datawheel was born from vision that data alone doesn't get far. It needs vehicles to reach hearts and minds. Vehicles that make data easy to find, explore, and understand. Today, Datawheel turns twelve. In these twelve years, we have built dozens of solutions that are helping define the state of the art in public data distribution. Today we celebrate this journey. A journey with a great team and amazing clients. The journey to make public data all it can be. are some of the tools defining all that public data can be. Thank you!!!

Twelve years ago we asked the question: how can we put public data "on wheels"? Datawheel was born from vision that data alone doesn't get far. It needs vehicles to reach hearts and minds. Vehicles that make data easy to find, explore, and understand. Today, Datawheel turns twelve. In these twelve years, we have built dozens of solutions that are helping define the state of the art in public data distribution. Today we celebrate this journey. A journey with a great team and amazing clients. The journey to make public data all it can be. are some of the tools defining all that public data can be. Thank you!!!

César A. Hidalgo

12,931 просмотров • 1 год назад

Canadians are struggling with affordability, but the solution isn’t more handouts, it’s firing up our economy again. We have the cheapest energy in North America, trillions of cubic feet of natural gas, and the perfect conditions to power the AI boom with massive data centers. That means tens of thousands of high-paying jobs right here at home. We have the power — literally. The question is, will we use it?

Canadians are struggling with affordability, but the solution isn’t more handouts, it’s firing up our economy again. We have the cheapest energy in North America, trillions of cubic feet of natural gas, and the perfect conditions to power the AI boom with massive data centers. That means tens of thousands of high-paying jobs right here at home. We have the power — literally. The question is, will we use it?

Kevin O'Leary aka Mr. Wonderful

30,242 просмотров • 11 месяцев назад

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,249 просмотров • 8 месяцев назад

Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

Clash Report

20,444 просмотров • 1 месяц назад

Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Ilya Sutskever explains why AI will do 100% the work that human can do within few years. "Slowly but surely, or maybe not so slowly, AI will keep getting better. The day will come when AI will do all of our, all the things that we can do, not just some of them, but all of them. Anything which I can learn, anything which any one of you can learn, the AI could do as well. How do we know this, by the way? How can I be so sure? How can I be so sure of that? The reason is that all of us have a brain, and the brain is a biological computer. That's why we have a brain; the brain is a biological computer. So why can't the digital computer, a digital brain, do the same things? This is the one-sentence summary for why AI will be able to do all those things: because we have a brain, and the brain is a biological computer." --- Full Video on 'University of Toronto' YT Channel (link in comment)

Rohan Paul

334,691 просмотров • 11 месяцев назад

#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 просмотров • 1 месяц назад

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.

.Naval: Every human is a lottery ticket bet on the future of the species. One of the things that you really learn when you read David Deutsch’s theories and you authenticate them for yourself is you realize humans are universal explainers. That means everything that we know in the universe follows the laws of physics, and there’s no reason to believe otherwise. If you think otherwise, then please present your better theory that explains the world. If you can’t do that, then you have to go with the laws of physics. Well, the laws of physics are completely computable. They can fit inside a Turing machine or computer, and a computer can simulate the laws of physics with arbitrary accuracy, limited only by the specific power of that computer. If you increase the power of that computer, you can simulate them more accurately. So humans already simulate—in our minds we simulate—and through our computers we simulate the weather, we simulate quasars, we even simulate human systems. We simulate the economy. We simulate all kinds of things. So anything that can be understood, we can understand in our minds. This is something the AGI people get wrong when they talk about superintelligence. There is nothing out there that can understand something fundamentally that we can’t understand. It might be faster at it, it might have more compute, it might have more memory, but there’s no concept that it can understand that we can’t ourselves understand. So we are maximal universal explainers. That means every human is capable of unbounded creativity. Anyone could be the next Einstein or Fermi or Elon Musk or Jeff Bezos or Jonas Salk or whatever. So we can create anything. And if we can create anything, every human is a lottery ticket bet on the future of the species.

Arjun Khemani

33,110 просмотров • 1 год назад

What if we're approaching robotic manipulation all wrong? Ilya Sutskever makes a fascinating point here: Current approaches throw massive amounts of data at the problem — millions of simulation steps, huge compute. And still, robots can't match human dexterity. Real-world learning of new skills? "Very out of reach." Meanwhile, humans pick up manipulation tasks almost instantly. Our dexterity is unmatched. And we do it with remarkable robustness — no reward shaping, no curriculum design, no training instability. Why the gap? Evolution. 500M years of optimisation for locomotion, vision, and manipulation. For these ancient sensorimotor tasks, maybe brute-force data isn't the answer. Maybe we should be copying the biological algorithms that already solved it. CC: Dwarkesh Patel

What if we're approaching robotic manipulation all wrong? Ilya Sutskever makes a fascinating point here: Current approaches throw massive amounts of data at the problem — millions of simulation steps, huge compute. And still, robots can't match human dexterity. Real-world learning of new skills? "Very out of reach." Meanwhile, humans pick up manipulation tasks almost instantly. Our dexterity is unmatched. And we do it with remarkable robustness — no reward shaping, no curriculum design, no training instability. Why the gap? Evolution. 500M years of optimisation for locomotion, vision, and manipulation. For these ancient sensorimotor tasks, maybe brute-force data isn't the answer. Maybe we should be copying the biological algorithms that already solved it. CC: Dwarkesh Patel

Jack 🤖

31,262 просмотров • 8 месяцев назад

The list of all the problems in mathematics that Terence Tao *could* solve is absolutely enormous. The problem is we can't make copies of him, dump millions of dollars of inference compute into each copy, and have them each tackle a different Millennium Prize Problem for a hundred subjective years. But we can do that with AI. So in that way, we may be underrating how significant it will be when we have AIs that are not superintelligent but rather simply as good as the best humans today. It’s actually an extremely powerful property of AI that by just running copies in parallel, you can immediately fill in the waterline of accomplishments that are feasible with a given level of capabilities.

The list of all the problems in mathematics that Terence Tao could solve is absolutely enormous. The problem is we can't make copies of him, dump millions of dollars of inference compute into each copy, and have them each tackle a different Millennium Prize Problem for a hundred subjective years. But we can do that with AI. So in that way, we may be underrating how significant it will be when we have AIs that are not superintelligent but rather simply as good as the best humans today. It’s actually an extremely powerful property of AI that by just running copies in parallel, you can immediately fill in the waterline of accomplishments that are feasible with a given level of capabilities.

Dwarkesh Patel

99,378 просмотров • 4 месяцев назад

Klaus Schwab Tells the World to Get Ready for a Transhumanist Future “Just think of the power,” he says, “of the combination of artificial intelligence, quantum computing, and big data.” Schwab, founder and executive chairman of the World Economic Forum (WEF), predicts in ten years, the world “will be completely different from what it is now.” “I think we have to be prepared for a world where we see a fusion of our physical, our digital, and our biological dimensions. So, it will be a world integrating the physical, biological, and the digital dimensions.”

Klaus Schwab Tells the World to Get Ready for a Transhumanist Future “Just think of the power,” he says, “of the combination of artificial intelligence, quantum computing, and big data.” Schwab, founder and executive chairman of the World Economic Forum (WEF), predicts in ten years, the world “will be completely different from what it is now.” “I think we have to be prepared for a world where we see a fusion of our physical, our digital, and our biological dimensions. So, it will be a world integrating the physical, biological, and the digital dimensions.”

The Vigilant Fox 🦊

136,643 просмотров • 2 лет назад

New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

César A. Hidalgo

54,332 просмотров • 1 год назад

$After 8 months of building in stealth and testing our infrastructure on 10000+ hours of real-world data and hundreds of unique environments, we're bringing FPV Labs into the open today. FPV Labs started with the following bet - if human data proves to be the underlying factor that determines scaling laws in general-purpose robotics, it will trigger the largest economic transformation in human history, and the underlying infrastructure that captures that data will determine how fast we get there. We will achieve this by building the full-stack infrastructure for capturing, processing, transferring, and evaluating human experience into spatial, temporal, and semantic knowledge for machines. Despite all the research novelty behind ChatGPT, its success can be attributed to one foundational fact - the scaling law of transformers. We believe the same dynamics have made their way into robotics. Recent studies showed task completion rates jumping from 30% to 70% when human demonstration data scaled from 1,000 to 20,000 hours, a log-linear trend that mirrors exactly what we saw in language and vision. Seeing these emergent signs of scaling law curves in robotics, we believe we are entering the era of general-purpose robotics policies, which makes the next few years the most exciting time in the history of this field. But the library of physical interactions required to train general-purpose robot policies does not exist yet. Over the last 8 months, we've seen dozens of companies emerge in this space. We were really happy to see new companies pushing this space forward, but we also saw the same pattern repeat: every egocentric data company was making some tradeoffs between quality, scale, and diversity. We have built FPV labs on the core principle that high-quality data is orders of magnitude more valuable than sheer volume. Case in point, self-driving cars collect thousands of hours of data per day, but only a small fraction of that data is actually useful for training better models. Several studies, like RT-2, have shown that as little as 1% of data improves as much as 25% on task success. The quality and diversity of data matter a lot more than scale, so there is clearly a power law curve in the downstream impact of data. We've spent months obsessing over data quality by building our stack, discarding it, rebuilding it, and iterating until we found a formula that doesn't compromise downstream quality at scale. We believe the downstream impact here is far more profound than most people realize. Workers globally are paid around $60 trillion per year in aggregate, and a lion's share of that compensation goes to physical labor - tasks that require navigating real spaces, manipulating real objects, and negotiating the infinite variability of the physical world. Human-to-robot transfer will be one of the most important infrastructures that will shape our society in the near future, and if it works, the economic impact will dwarf every technology transition that came before it in an exponential manner and lead to the creation of goods and services we can’t imagine today. Our mission is to lay the groundwork for us to transition into this future - the future of abundance. We are deeply grateful to our earliest believers, Paras Chopra and Lossfunk, who played a critical role in shaping our thinking.$

After 8 months of building in stealth and testing our infrastructure on 10000+ hours of real-world data and hundreds of unique environments, we're bringing FPV Labs into the open today. FPV Labs started with the following bet - if human data proves to be the underlying factor that determines scaling laws in general-purpose robotics, it will trigger the largest economic transformation in human history, and the underlying infrastructure that captures that data will determine how fast we get there. We will achieve this by building the full-stack infrastructure for capturing, processing, transferring, and evaluating human experience into spatial, temporal, and semantic knowledge for machines. Despite all the research novelty behind ChatGPT, its success can be attributed to one foundational fact - the scaling law of transformers. We believe the same dynamics have made their way into robotics. Recent studies showed task completion rates jumping from 30% to 70% when human demonstration data scaled from 1,000 to 20,000 hours, a log-linear trend that mirrors exactly what we saw in language and vision. Seeing these emergent signs of scaling law curves in robotics, we believe we are entering the era of general-purpose robotics policies, which makes the next few years the most exciting time in the history of this field. But the library of physical interactions required to train general-purpose robot policies does not exist yet. Over the last 8 months, we've seen dozens of companies emerge in this space. We were really happy to see new companies pushing this space forward, but we also saw the same pattern repeat: every egocentric data company was making some tradeoffs between quality, scale, and diversity. We have built FPV labs on the core principle that high-quality data is orders of magnitude more valuable than sheer volume. Case in point, self-driving cars collect thousands of hours of data per day, but only a small fraction of that data is actually useful for training better models. Several studies, like RT-2, have shown that as little as 1% of data improves as much as 25% on task success. The quality and diversity of data matter a lot more than scale, so there is clearly a power law curve in the downstream impact of data. We've spent months obsessing over data quality by building our stack, discarding it, rebuilding it, and iterating until we found a formula that doesn't compromise downstream quality at scale. We believe the downstream impact here is far more profound than most people realize. Workers globally are paid around $60 trillion per year in aggregate, and a lion's share of that compensation goes to physical labor - tasks that require navigating real spaces, manipulating real objects, and negotiating the infinite variability of the physical world. Human-to-robot transfer will be one of the most important infrastructures that will shape our society in the near future, and if it works, the economic impact will dwarf every technology transition that came before it in an exponential manner and lead to the creation of goods and services we can’t imagine today. Our mission is to lay the groundwork for us to transition into this future - the future of abundance. We are deeply grateful to our earliest believers, Paras Chopra and Lossfunk, who played a critical role in shaping our thinking.

Abhishek Anand

82,126 просмотров • 4 месяцев назад

Dr. John Campbell: The government shared the TRUTH about the Covid shots with Big Pharma—but not you. 🚫 “We could get rid of all this ambiguity if the government would just release who was vaccinated and the death data on a participant level.” “There’s glimpses of it in Japan and 1 or 2 provinces in Italy … ” “It’s not looking good.” “The reason, of course, they say that they don’t do this is because it would breach confidentiality.” “I would hope that Trump and RFK … would start releasing that [data].” “We’ve got brilliant statisticians standing by waiting for this data.” “But it’s not released, so we don’t know.” “Why don’t they release this data, given that they’ve released it to the pharmaceutical industry already?” “We can assume that the government knows, [and] we can assume that the pharmaceutical industry knows.” “But … we’re not given that information.” “We have people with the expertise to analyze it within days and give us definitive answers.” Russell Brand John Campbell

Children’s Health Defense

29,617 просмотров • 7 месяцев назад