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AI is hitting an inflection point—and it's not just about computing power or model layers. Mobile World Live reported from this year's #HWIDI Forum in Paris to fully unpack the theme "Data Awakening, Infra Evolving." As they discovered, the real race is focusing on #Data itself—indeed "the next chapter... show more

Huawei IT Products & Solutions

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34,870 просмотров • 1 месяц назад •via X (Twitter)

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🚀 Arweave: The Epic Journey Towards Timeless Data Storage Embark on a journey with me as we unravel the marvel that is Arweave, a project meticulously crafted to safeguard data for an eternity without compromise. Let's dive into the intricacies that make this revolutionary endeavor a beacon of #permanence . 🌐 How Does #Arweave Achieve Timeless Data Storage? ✨ Cryptographic Proofs: Imagine a world where data integrity is not just a promise but a #cryptographic reality. Arweave's #foundation lies in cryptographic proofs, ensuring that every new block added to the network is not just stored but validated with unyielding certainty. 🏰 Storage Endowment: Arweave doesn't just store data; it endows it with longevity. Through a groundbreaking approach, data stored on Arweave is prepaid for 20 replicas spanning the next 200 years. This is not a mere estimate; it's a conservative projection, fortifying Arweave's commitment to #securing your data for the long term. 💡 Incentivized Evolution: The #world evolves, and so does Arweave. The #protocol is open, ready for change, and embraces an incentivized evolution model. Arweave positions itself not just as a #platform but as a versatile framework, encouraging innovative forks rewarded through a fair fork methodology. The beauty lies in the compatibility of these forks at the protocol layer, fostering cooperation over competition. 🌍 Unified Dataset for Permanence: Arweave isn't just about storing data; it's about unity. The encouragement of forks aligns seamlessly with the mission of permanent data storage. Keeping the dataset unified is in everyone's best interest, ensuring a collective stride towards a future where data stands the test of time. 🔍 Breaking It Down: Cryptographic Proofs: Ensuring the replication and secure storage of substantial parts of the Arweave dataset. Storage Endowment: A commitment to safeguarding data for a minimum of 200 years. Innovative Forks: Encouraging a continuous stream of innovation for permanent data storage. 📖 For those seeking a deeper understanding, explore DRAFT 17. Delve into the intricacies of Arweave's design, where proofs drive replication, the endowment fortifies storage, and the approach to forks sparks innovation. Join us on this odyssey towards a future where your data remains eternally secure and unaltered. Türkçe için : Arweave Ecosystem Only Arweave ar.io WeaveVM is now Load Network (@useload)

🚀 Arweave: The Epic Journey Towards Timeless Data Storage Embark on a journey with me as we unravel the marvel that is Arweave, a project meticulously crafted to safeguard data for an eternity without compromise. Let's dive into the intricacies that make this revolutionary endeavor a beacon of #permanence . 🌐 How Does #Arweave Achieve Timeless Data Storage? ✨ Cryptographic Proofs: Imagine a world where data integrity is not just a promise but a #cryptographic reality. Arweave's #foundation lies in cryptographic proofs, ensuring that every new block added to the network is not just stored but validated with unyielding certainty. 🏰 Storage Endowment: Arweave doesn't just store data; it endows it with longevity. Through a groundbreaking approach, data stored on Arweave is prepaid for 20 replicas spanning the next 200 years. This is not a mere estimate; it's a conservative projection, fortifying Arweave's commitment to #securing your data for the long term. 💡 Incentivized Evolution: The #world evolves, and so does Arweave. The #protocol is open, ready for change, and embraces an incentivized evolution model. Arweave positions itself not just as a #platform but as a versatile framework, encouraging innovative forks rewarded through a fair fork methodology. The beauty lies in the compatibility of these forks at the protocol layer, fostering cooperation over competition. 🌍 Unified Dataset for Permanence: Arweave isn't just about storing data; it's about unity. The encouragement of forks aligns seamlessly with the mission of permanent data storage. Keeping the dataset unified is in everyone's best interest, ensuring a collective stride towards a future where data stands the test of time. 🔍 Breaking It Down: Cryptographic Proofs: Ensuring the replication and secure storage of substantial parts of the Arweave dataset. Storage Endowment: A commitment to safeguarding data for a minimum of 200 years. Innovative Forks: Encouraging a continuous stream of innovation for permanent data storage. 📖 For those seeking a deeper understanding, explore DRAFT 17. Delve into the intricacies of Arweave's design, where proofs drive replication, the endowment fortifies storage, and the approach to forks sparks innovation. Join us on this odyssey towards a future where your data remains eternally secure and unaltered. Türkçe için : Arweave Ecosystem Only Arweave ar.io WeaveVM is now Load Network (@useload)

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What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

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Matrix AI Network

60,855 просмотров • 1 месяц назад

Neil Oliver dissects Larry Fink's admission that "intermittent power like wind and solar" cannot reliably power all the new data centres required by "the world of data harvesting". "Our data is the new oil, the new gold, and its storage must not be entrusted to the renewables, so-called, that the rest of us are expected to settle for." "They de-industrialised Europe in pursuit of the wind and solar future... Britons pay more for energy than just about anyone else on Earth." "And now, Larry Fink... says the world of data—the world we are all expecting or expected to inhabit—needs the kind of power you only get from oil and gas, and the like." Credit: Neil Oliver

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Wide Awake Media

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“Without data, there is no AI.” As Micron Chairman, President and CEO Sanjay Mehrotra shares, AI’s future depends on the ability to move, store, and access data at incredible scale — and that’s exactly where Micron delivers. From cloud to edge, our memory and storage innovations help transform raw data into real intelligence. Learn more about how we’re powering the #AI era: #IntelligenceAccelerated Sanjay Mehrotra

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FX1

23,913 просмотров • 1 год назад

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.

Matrix AI Network

15,296 просмотров • 2 месяцев назад

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.

CherryAI Robotics

90,786 просмотров • 10 месяцев назад

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

Sky Wee

67,555 просмотров • 10 месяцев назад

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

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304,297 просмотров • 8 месяцев назад

🚨LINDA YACCARINO: GROK GROWING FASTER THAN ANY AI MODEL BECAUSE OF REAL-TIME X DATA “What’s really important is that Grok is the only AI trained on real-time data from X. That’s why Grok is growing faster than any other competitor. Grok 3.5 is about to drop and will be the smartest model in the world.” Source: Axios Linda Yaccarino Elon Musk

🚨LINDA YACCARINO: GROK GROWING FASTER THAN ANY AI MODEL BECAUSE OF REAL-TIME X DATA “What’s really important is that Grok is the only AI trained on real-time data from X. That’s why Grok is growing faster than any other competitor. Grok 3.5 is about to drop and will be the smartest model in the world.” Source: Axios Linda Yaccarino Elon Musk

Mario Nawfal

377,487 просмотров • 1 год назад

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Nichole Wischoff

36,782 просмотров • 3 месяцев назад

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 просмотров • 1 месяц назад

A sneak peek at Huawei IDI Forum 2026 in Paris: fresh perspectives on data storage, real-world technology innovation in action, and an immersive exhibition experience showcasing next-gen infrastructure. - Insight meets experience - Technology you can explore, not just read about - Built for what's next Follow the journey to Paris and see what's coming next.

A sneak peek at Huawei IDI Forum 2026 in Paris: fresh perspectives on data storage, real-world technology innovation in action, and an immersive exhibition experience showcasing next-gen infrastructure. - Insight meets experience - Technology you can explore, not just read about - Built for what's next Follow the journey to Paris and see what's coming next.

Huawei IT Products & Solutions

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Is the future of AI $NEAR … ? OpenAI is racing toward a $1T IPO, quickly becoming a centralized beast of epic proportions. But what happens to your data in that world?? As chronear explains, “We can't let centralized actors determine how they store or how they use user data. It should be the users who actually control the custody or use of their data." NEAR AI is building a truly open AI, open source, where users control their data and store their data locally. What AI do you use daily? And would you switch if a better decentralized option existed?

Is the future of AI $NEAR … ? OpenAI is racing toward a $1T IPO, quickly becoming a centralized beast of epic proportions. But what happens to your data in that world?? As chronear explains, “We can't let centralized actors determine how they store or how they use user data. It should be the users who actually control the custody or use of their data." NEAR AI is building a truly open AI, open source, where users control their data and store their data locally. What AI do you use daily? And would you switch if a better decentralized option existed?

Bri Teresi

25,101 просмотров • 3 месяцев назад

AI shouldn’t just belong to a few big companies 🤖 OORT | The Data Cloud for Decentralized AI is creating a decentralized AI cloud where your data, storage, and compute help power the next generation of AI , and you get rewarded for it. Global, transparent, and community-driven. Are you ready to join? 🌍⚡ #OORT #DecentralizedAI #Blockchain #Web3 #AICommunity #CryptoTech #DataRevolution #Onchain #Innovation #FutureOfAI

AI shouldn’t just belong to a few big companies 🤖 OORT | The Data Cloud for Decentralized AI is creating a decentralized AI cloud where your data, storage, and compute help power the next generation of AI , and you get rewarded for it. Global, transparent, and community-driven. Are you ready to join? 🌍⚡ #OORT #DecentralizedAI #Blockchain #Web3 #AICommunity #CryptoTech #DataRevolution #Onchain #Innovation #FutureOfAI

Sky Wee

194,643 просмотров • 6 месяцев назад

President Trump has extracted Indian data from the Prime Minister. The most valuable asset that India has is the data we produce. Any talk about AI has to be based on data, and our 1.4 billion people produce the largest amount on the planet. This includes medical data, data on movement of people, and communication data. All of it has been signed off by PM Modi. And the final thing is that he has promised President Trump that we will buy billions of dollars of imports from the United States every year. : LoP Shri Rahul Gandhi 📍 Keralam

President Trump has extracted Indian data from the Prime Minister. The most valuable asset that India has is the data we produce. Any talk about AI has to be based on data, and our 1.4 billion people produce the largest amount on the planet. This includes medical data, data on movement of people, and communication data. All of it has been signed off by PM Modi. And the final thing is that he has promised President Trump that we will buy billions of dollars of imports from the United States every year. : LoP Shri Rahul Gandhi 📍 Keralam

Congress

13,901 просмотров • 4 месяцев назад

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

DeepLearning.AI

20,833 просмотров • 1 год назад

one of my favorite aspects of this new work is that it is generative, not based on an arbitrary random number, but based on the chaos of real life. the physical sculpture is constantly pulling in news and real-time data from all over the world and the NFT is updated daily as well with this hectic collage of information overload... i believe in the future more work will be dynamic like this allowing us to reimagine real-time data in new ways based on different events happening in the world...

one of my favorite aspects of this new work is that it is generative, not based on an arbitrary random number, but based on the chaos of real life. the physical sculpture is constantly pulling in news and real-time data from all over the world and the NFT is updated daily as well with this hectic collage of information overload... i believe in the future more work will be dynamic like this allowing us to reimagine real-time data in new ways based on different events happening in the world...

beeple

155,750 просмотров • 1 год назад

After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

Databricks

10,457 просмотров • 21 дней назад