正在加载视频...

视频加载失败

返回首页

At Axis, every trajectory submitted by our community undergoes a strict replay validation process. We run each submission through checker to verify whether the target task was successfully completed. To see how strict it is, check this demo (Task: Place The Toy Train On The Board Game Box). Real...

axisrobotics's profile picture

Axis Robotics

22,357 subscribers

30,772 次观看 • 3 个月前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

10,000 valid trajectories collected — and we did it ahead of schedule, in just 5 days. We’ve reached our data milestone for "Little Prince's Rose." This community-driven dataset is now entering the pipeline for cleaning, augmentation, and model training, advancing the intelligence of Franka at this very moment. While the Discord role claiming is closing, the experiment will remain open as a welcoming portal for everyone to experience the Axis and how we tackle the data bottleneck for robotics. Robot intelligence is not built by a few; it's built by all.🌹
0:37

10,000 valid trajectories collected — and we did it ahead of schedule, in just 5 days. We’ve reached our data milestone for "Little Prince's Rose." This community-driven dataset is now entering the pipeline for cleaning, augmentation, and model training, advancing the intelligence of Franka at this very moment. While the Discord role claiming is closing, the experiment will remain open as a welcoming portal for everyone to experience the Axis and how we tackle the data bottleneck for robotics. Robot intelligence is not built by a few; it's built by all.🌹

Axis Robotics

23,424 次观看 • 5 个月前

We prepared 18 tasks that were expected to be finished within a week, and they were all completed within 24 hours of launch. This all comes from people's conviction that simulation is a key way to solve the data scarcity in today's physical AI development. Axis has not announced any VC backing yet, and we haven't done any paid marketing. All user growth has been organic, coming from loyal community members who truly believe in what we do and what we are here to achieve. I appreciate everyone's patience and passion for Axis Robotics. Your contribution is valuable. The issue with "task not completed" came from our mission checker being too strict. We are on track to solve this in two ways: 1, We will try to highlight the region in red in the future to provide clear guidance for the mission completion range. 2, We will see if we can provide a tutorial video for each task. keep buidling~
0:27

We prepared 18 tasks that were expected to be finished within a week, and they were all completed within 24 hours of launch. This all comes from people's conviction that simulation is a key way to solve the data scarcity in today's physical AI development. Axis has not announced any VC backing yet, and we haven't done any paid marketing. All user growth has been organic, coming from loyal community members who truly believe in what we do and what we are here to achieve. I appreciate everyone's patience and passion for Axis Robotics. Your contribution is valuable. The issue with "task not completed" came from our mission checker being too strict. We are on track to solve this in two ways: 1, We will try to highlight the region in red in the future to provide clear guidance for the mission completion range. 2, We will see if we can provide a tutorial video for each task. keep buidling~

郡主Christine (✱,✱)

12,955 次观看 • 3 个月前

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:
0:46

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

Jim Fan

332,199 次观看 • 2 年前

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵
0:58

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

Jim Fan

165,246 次观看 • 1 年前

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.
0:36

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

81,285 次观看 • 3 个月前

🔴 People in Developed nations are fighting against Data Centres and here we are being told a Data Centre that is supposed to be run by Adani is the greatest investment our country has ever received..? When will we stop falling for this BS ..?
2:28

🔴 People in Developed nations are fighting against Data Centres and here we are being told a Data Centre that is supposed to be run by Adani is the greatest investment our country has ever received..? When will we stop falling for this BS ..?

Ravi Teja

124,371 次观看 • 4 个月前

If you say that mass immigration has increased crime in Ireland you will be criticized and told that there is no data available to support this claim, yet the government have all the data in relation to the nationality of employment, nationality of our education system, births rates etc. why not collect data in relation to imported crime? Because they are terrified we will know how bad it actually is! That’s my belief. Full episode of The Irish Question out now on usual platforms.
1:17

If you say that mass immigration has increased crime in Ireland you will be criticized and told that there is no data available to support this claim, yet the government have all the data in relation to the nationality of employment, nationality of our education system, births rates etc. why not collect data in relation to imported crime? Because they are terrified we will know how bad it actually is! That’s my belief. Full episode of The Irish Question out now on usual platforms.

Elaine Mullally ☘️

19,522 次观看 • 7 天前

Introducing the #WELL3Ring: Elevating Health Data to On-Chain Assets🔘 At #WELL3, we are leading a transformative shift in how health data is valued and monetized. We cordially invite our #WELLID holders to be the first to experience the benefits of this DePIN wearable. By donning the WELL3 Ring and sharing your health data, you will gain access to unlimited and exclusive rewards🧵
0:40

Introducing the #WELL3Ring: Elevating Health Data to On-Chain Assets🔘 At #WELL3, we are leading a transformative shift in how health data is valued and monetized. We cordially invite our #WELLID holders to be the first to experience the benefits of this DePIN wearable. By donning the WELL3 Ring and sharing your health data, you will gain access to unlimited and exclusive rewards🧵

WELL3

223,003 次观看 • 2 年前

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.
1:00

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Neuracore

40,620 次观看 • 7 个月前

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data. 2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro -> RoboCasa produces N (varying visuals) -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are building tools to enable everyone in the ecosystem to scale up with us. Links in thread:
1:21

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data. 2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro -> RoboCasa produces N (varying visuals) -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are building tools to enable everyone in the ecosystem to scale up with us. Links in thread:

Jim Fan

364,380 次观看 • 1 年前

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."
3:20

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

ANI

163,581 次观看 • 1 年前

In my experience, robot 'generalists' are often jacks of all trades but masters of none. In training across multiple tasks and environments, robot policies fail to generalize robustly and effectively to each particular test setting. What if at test time, we non-parametrically *retrieved* “relevant” data from the training set and used it to significantly improve the performance of few-shot imitation learning to be robust to various test time scenes. Notably, we are *not* collecting lots of new data, just training more on sub-components of the same training data! Now, we’re certainly not the first to suggest retrieval, but in our new work - STRAP, we show how retrieving relevant *sub-trajectories* from offline datasets can significantly increase data reuse across tasks, when paired with an appropriate metric space. A 🧵 (1/7)
0:43

In my experience, robot 'generalists' are often jacks of all trades but masters of none. In training across multiple tasks and environments, robot policies fail to generalize robustly and effectively to each particular test setting. What if at test time, we non-parametrically *retrieved* “relevant” data from the training set and used it to significantly improve the performance of few-shot imitation learning to be robust to various test time scenes. Notably, we are *not* collecting lots of new data, just training more on sub-components of the same training data! Now, we’re certainly not the first to suggest retrieval, but in our new work - STRAP, we show how retrieving relevant *sub-trajectories* from offline datasets can significantly increase data reuse across tasks, when paired with an appropriate metric space. A 🧵 (1/7)

Abhishek Gupta

12,045 次观看 • 1 年前

Maajid Nawaz: The Great War of Our Age Isn’t With Weapons—It’s With Data "We are in an ideological war—not over land or nukes, but over the future of humanity itself. The real battleground? Data. Yuval Noah Harari, on the other side of this conflict, calls it ‘Dataism’—a belief system where AI dictates our choices, our thoughts, even our desires. Autocorrect, algorithmic nudges, predictive search—step by step, we are conditioned to surrender autonomy in exchange for convenience. But this is a Faustian bargain. If every decision is made for us, what remains of free will? If data is the new nuclear weapon, then consciousness is our last line of defense. Our duty—yours and mine—is to protect what it means to be human: to elevate reason, guard the sacred, and resist the commodification of the soul. Because when nothing is sacred, everything is for sale. And when everything is for sale, that’s just another word for slavery. The war is here. Which side will you stand on?" — Maajid Nawaz
1:48

Maajid Nawaz: The Great War of Our Age Isn’t With Weapons—It’s With Data "We are in an ideological war—not over land or nukes, but over the future of humanity itself. The real battleground? Data. Yuval Noah Harari, on the other side of this conflict, calls it ‘Dataism’—a belief system where AI dictates our choices, our thoughts, even our desires. Autocorrect, algorithmic nudges, predictive search—step by step, we are conditioned to surrender autonomy in exchange for convenience. But this is a Faustian bargain. If every decision is made for us, what remains of free will? If data is the new nuclear weapon, then consciousness is our last line of defense. Our duty—yours and mine—is to protect what it means to be human: to elevate reason, guard the sacred, and resist the commodification of the soul. Because when nothing is sacred, everything is for sale. And when everything is for sale, that’s just another word for slavery. The war is here. Which side will you stand on?" — Maajid Nawaz

Camus

18,608 次观看 • 11 个月前

Do you have what it takes to forecast International trade? Today, we are launching AI4trade, a global challenge where teams compete for cash prizes by submitting their best trade forecasts. But it comes with a couple of twists: 1. This is a future out-of-sample challenge, so teams will need to forecast data that will not exist at the time of submission (impossible to train to the test 👹). 2. Forecasts must be better than simply using the latest trade data available at the time of submission (e.g. using July data as a prediction for October numbers ⏰). This should be a fun global competition. People from all parts of the world are welcome to participate and all methods are allowed. We hope the challenge stimulates more work at the intersection of AI and economics. Link in the first comment: 👇
0:56

Do you have what it takes to forecast International trade? Today, we are launching AI4trade, a global challenge where teams compete for cash prizes by submitting their best trade forecasts. But it comes with a couple of twists: 1. This is a future out-of-sample challenge, so teams will need to forecast data that will not exist at the time of submission (impossible to train to the test 👹). 2. Forecasts must be better than simply using the latest trade data available at the time of submission (e.g. using July data as a prediction for October numbers ⏰). This should be a fun global competition. People from all parts of the world are welcome to participate and all methods are allowed. We hope the challenge stimulates more work at the intersection of AI and economics. Link in the first comment: 👇

César A. Hidalgo

22,909 次观看 • 11 个月前

Which one is our priority for securing a digital future for Zimbabwe? The Honourable Speaker's position while providing a clear, logical, and data-informed counter-argument we appreciate his efforts and submission. Let us have a conversation on our priorities in the current Zimbabwean context which we presented yesterday: This is a critical national conversation on our digital future. The proposition of a Google data centre is undoubtedly appealing. Building a centre of Technological Excellence that houses multiple Tech companies and a Start-up Ecosystem is also prudent, though we are calling it a Technology Park. However, I was of the following view which I am willing to be criticised and guided on. The strategic prioritisation of a national ICT Park ecosystem currently is the foundational path to sustainable, sovereign growth and capacity for Zimbabwe. We are reaching out to every province through the Digital Centres, Innovation Hubs at Universities and Colleges. Infrastructure which we need to capacitate better. This is a decentralised system utilising and expanding the infrastructure we have. The central question is not if we want major players like Google; that is not questionable, but when, how and on what terms. Why are we building our own foundation, house, techno park and mini ICT Parks in the provinces and districts? We absolutely need at least a one hyperscale data centre, but centralisation in the current and future environment can be restrictive. This was our plan: 1.The Foundation Before the Skyscraper: Capacity and Traffic We need to increase our national data volumes and internet traffic . Our urgent priority should be upgrading our national backbone, specifically the Optical Fibre on Power Transmission Lines (e.g., Powertel's network from Insukamini to Johannesburg), to peer efficiently with global giants. This builds the foundational "digital highway" we manage. 2.Generating Traffic Through Digitalization A data centre is a response to demand, not a creator of it. We must first drive digitalization aggressively through digital payments, process automation, and e-governance to generate the significant local traffic that would make a data centre viable. Furthermore, we must incorporate Edge Computing strategies to process data closer to the source, a more efficient model for our current needs and in compliance to our Cyber and Data Protection Act which we can amend if need be. Being a regional internet and cybersecurity hub is crucial as the Speaker alluded to me mentioning it to SADC Parliamentary Forum, this is the ultimate goal. 3.PPPs are key to lessen the burden to the fiscus and we are ready to accommodate organisations willing to assist government.(Cont)
3:05

Which one is our priority for securing a digital future for Zimbabwe? The Honourable Speaker's position while providing a clear, logical, and data-informed counter-argument we appreciate his efforts and submission. Let us have a conversation on our priorities in the current Zimbabwean context which we presented yesterday: This is a critical national conversation on our digital future. The proposition of a Google data centre is undoubtedly appealing. Building a centre of Technological Excellence that houses multiple Tech companies and a Start-up Ecosystem is also prudent, though we are calling it a Technology Park. However, I was of the following view which I am willing to be criticised and guided on. The strategic prioritisation of a national ICT Park ecosystem currently is the foundational path to sustainable, sovereign growth and capacity for Zimbabwe. We are reaching out to every province through the Digital Centres, Innovation Hubs at Universities and Colleges. Infrastructure which we need to capacitate better. This is a decentralised system utilising and expanding the infrastructure we have. The central question is not if we want major players like Google; that is not questionable, but when, how and on what terms. Why are we building our own foundation, house, techno park and mini ICT Parks in the provinces and districts? We absolutely need at least a one hyperscale data centre, but centralisation in the current and future environment can be restrictive. This was our plan: 1.The Foundation Before the Skyscraper: Capacity and Traffic We need to increase our national data volumes and internet traffic . Our urgent priority should be upgrading our national backbone, specifically the Optical Fibre on Power Transmission Lines (e.g., Powertel's network from Insukamini to Johannesburg), to peer efficiently with global giants. This builds the foundational "digital highway" we manage. 2.Generating Traffic Through Digitalization A data centre is a response to demand, not a creator of it. We must first drive digitalization aggressively through digital payments, process automation, and e-governance to generate the significant local traffic that would make a data centre viable. Furthermore, we must incorporate Edge Computing strategies to process data closer to the source, a more efficient model for our current needs and in compliance to our Cyber and Data Protection Act which we can amend if need be. Being a regional internet and cybersecurity hub is crucial as the Speaker alluded to me mentioning it to SADC Parliamentary Forum, this is the ultimate goal. 3.PPPs are key to lessen the burden to the fiscus and we are ready to accommodate organisations willing to assist government.(Cont)

Hon Tatenda.A. Mavetera

490,311 次观看 • 8 个月前

This is Anagha Rajesh. And she is crazy. She wants to store data in bacterial DNA. Her startup is called BioCompute. And last year, they actually did it. They stored data in DNA and retrieved it in their tiny lab in Bengaluru. This is a huge moment. But why is she even doing it? Because DNA is the ultimate storage tool available to us. Just 1 gm of DNA can store about 215 petabytes of data - that's like storing over 2 million movies in 4K. On top of that - this data can last for literally 1000s of years. Right now, they still need to figure out a way to make the reading and writing process faster and cheaper. But if BioCompute solves this problem - we could theoretically store all the data created in the world every year in the palm of our hands. And that would be insane. P.S. Check out this video from vyom going to Anagha's lab and actually doing it.
0:21

This is Anagha Rajesh. And she is crazy. She wants to store data in bacterial DNA. Her startup is called BioCompute. And last year, they actually did it. They stored data in DNA and retrieved it in their tiny lab in Bengaluru. This is a huge moment. But why is she even doing it? Because DNA is the ultimate storage tool available to us. Just 1 gm of DNA can store about 215 petabytes of data - that's like storing over 2 million movies in 4K. On top of that - this data can last for literally 1000s of years. Right now, they still need to figure out a way to make the reading and writing process faster and cheaper. But if BioCompute solves this problem - we could theoretically store all the data created in the world every year in the palm of our hands. And that would be insane. P.S. Check out this video from vyom going to Anagha's lab and actually doing it.

Varun Guru

252,873 次观看 • 26 天前

You asked for it, we delivered. The full keynote is live. In this keynote, we unveil our enhanced vision to become the World’s Ears for AI & Robotics. We are creating the world’s first decentralized infrastructure for real-world audio intelligence. Check-out our new website: Start training Voice AI now & get rewarded: From noise-level mapping to multilingual voice data and the future of live audio streams, Silencio is building the critical audio data layer that will power foundation models, embodied AI, autonomous vehicles, and robotics.
23:24

You asked for it, we delivered. The full keynote is live. In this keynote, we unveil our enhanced vision to become the World’s Ears for AI & Robotics. We are creating the world’s first decentralized infrastructure for real-world audio intelligence. Check-out our new website: Start training Voice AI now & get rewarded: From noise-level mapping to multilingual voice data and the future of live audio streams, Silencio is building the critical audio data layer that will power foundation models, embodied AI, autonomous vehicles, and robotics.

Silencio | Voice Data for AI

176,163 次观看 • 10 个月前

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.
0:39

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 次观看 • 8 天前

Another American has just been informed half her property will be seized with eminent domain by Georgia Power to support Data Centers “They are going to demo all of these trees, playhouse, fence, fire pits, the pool, the spa, all of this, including our two neighbors' houses. They're completely demoing theirs. This easement is only going to be 12 feet our house, 12 feet from our bedroom where we sleep” “I am just one of many homeowners who are impacted by Georgia Power's high voltage power lines that that are going in to support data centers that are being built all over” It affects over 330 private properties. Georgia Power says it will negotiate purchases and easements and use eminent domain Georgia Power claims its to strengthen the grid for the growing energy demand in Georgia due to many new data centers We should not be letting this happen. We need protections from data center projects
4:12

Another American has just been informed half her property will be seized with eminent domain by Georgia Power to support Data Centers “They are going to demo all of these trees, playhouse, fence, fire pits, the pool, the spa, all of this, including our two neighbors' houses. They're completely demoing theirs. This easement is only going to be 12 feet our house, 12 feet from our bedroom where we sleep” “I am just one of many homeowners who are impacted by Georgia Power's high voltage power lines that that are going in to support data centers that are being built all over” It affects over 330 private properties. Georgia Power says it will negotiate purchases and easements and use eminent domain Georgia Power claims its to strengthen the grid for the growing energy demand in Georgia due to many new data centers We should not be letting this happen. We need protections from data center projects

Wall Street Apes

338,517 次观看 • 1 个月前

Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.
0:29

Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.

Avi Patel

124,728 次观看 • 5 个月前