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Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional...

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Yohan

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45,187 次观看 • 5 个月前 •via X (Twitter)

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Google Earth now lets you create AI images using real-world locations. With the new Nano Banana AI feature, you can pick any place in Google Earth, click “Create Image,” and describe what you want to see. The AI can turn that location into a historical scene, a futuristic city or a fantasy world The feature uses Google Earth’s satellite and 3D map data as the base, so the AI-generated images are built from real locations.
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Google Earth now lets you create AI images using real-world locations. With the new Nano Banana AI feature, you can pick any place in Google Earth, click “Create Image,” and describe what you want to see. The AI can turn that location into a historical scene, a futuristic city or a fantasy world The feature uses Google Earth’s satellite and 3D map data as the base, so the AI-generated images are built from real locations.

Pirat_Nation 🔴

24,731 次观看 • 28 天前

Conducting road safety audits, scheduling maintenance, or planning logistics routes used to require combing through imagery or, worse, driving miles of roads yourself. Not anymore. New AI-powered data layers in Google Earth are changing that. Pulling from billions of Google Street View images, these layers now spot infrastructure assets like stop signs, speed limit signs, and more to map infrastructure for you. By combining these assets into a single project and diving into Street View you can locate and validate assets on Google Earth in seconds. These layers are now available for Professional & Professional Advanced customers on web and Android, with coverage expanding over the coming weeks.
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Conducting road safety audits, scheduling maintenance, or planning logistics routes used to require combing through imagery or, worse, driving miles of roads yourself. Not anymore. New AI-powered data layers in Google Earth are changing that. Pulling from billions of Google Street View images, these layers now spot infrastructure assets like stop signs, speed limit signs, and more to map infrastructure for you. By combining these assets into a single project and diving into Street View you can locate and validate assets on Google Earth in seconds. These layers are now available for Professional & Professional Advanced customers on web and Android, with coverage expanding over the coming weeks.

Google Earth

20,952 次观看 • 2 个月前

Create Stunning Time-Series Satellite Images in Seconds! The GEE Data Catalogs Plugin v0.5 for QGIS is now available and it's a powerful upgrade. You can now create time-series satellite imagery with just a few clicks using a simple interface. The new version also supports direct downloads to your computer, making the workflow faster and more efficient. Key Features: - Access over 80 petabytes of satellite and geospatial datasets from Google Earth Engine - Generate animated time-series imagery effortlessly - Export results directly from QGIS to your local machine Useful Links: QGIS Plugin Page: GitHub Repository: Video Tutorial: #QGIS #geospatial #EarthEngine #Python #datascience #satellite
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Create Stunning Time-Series Satellite Images in Seconds! The GEE Data Catalogs Plugin v0.5 for QGIS is now available and it's a powerful upgrade. You can now create time-series satellite imagery with just a few clicks using a simple interface. The new version also supports direct downloads to your computer, making the workflow faster and more efficient. Key Features: - Access over 80 petabytes of satellite and geospatial datasets from Google Earth Engine - Generate animated time-series imagery effortlessly - Export results directly from QGIS to your local machine Useful Links: QGIS Plugin Page: GitHub Repository: Video Tutorial: #QGIS #geospatial #EarthEngine #Python #datascience #satellite

Qiusheng Wu

14,166 次观看 • 7 个月前

Google just took a big step towards building ChatGPT for Earth. AlphaEarth Foundations does something clever -- instead of drowning in petabytes of Earth observation data, it creates compact summaries of every 10x10m square on Earth by fusing optical, radar, LiDAR, and climate data. The kicker is it can see through clouds in Ecuador and reveal hidden agricultural patterns in Canada. MapBiomas and Global Ecosystems Atlas already using it for conservation work.
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Google just took a big step towards building ChatGPT for Earth. AlphaEarth Foundations does something clever -- instead of drowning in petabytes of Earth observation data, it creates compact summaries of every 10x10m square on Earth by fusing optical, radar, LiDAR, and climate data. The kicker is it can see through clouds in Ecuador and reveal hidden agricultural patterns in Canada. MapBiomas and Global Ecosystems Atlas already using it for conservation work.

Bilawal Sidhu

165,867 次观看 • 1 年前

.Sentient has just integrated Messari by Blockworks 's data and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done via Messari Copilot, which means users can now easily get to Messari’s crypto data without having to dig through extensive reports themselves. Messari's data and research now feeds into Sentient’s Agentic Perplexity, here users can access this in the Agent Hub for all their crypto related questions. Integrating Messari’s research now helps provide an open & community-driven platform for AI-powered search, ensuring that users have access to the best crypto data and insights in real time, while also expanding the functionality of Sentient’s Agent Hub, where users can find and use a growing library of AI agents for various tasks. Now I don't know about you but I know where I'll be getting my stats from moving forward.
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.Sentient has just integrated Messari by Blockworks 's data and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done via Messari Copilot, which means users can now easily get to Messari’s crypto data without having to dig through extensive reports themselves. Messari's data and research now feeds into Sentient’s Agentic Perplexity, here users can access this in the Agent Hub for all their crypto related questions. Integrating Messari’s research now helps provide an open & community-driven platform for AI-powered search, ensuring that users have access to the best crypto data and insights in real time, while also expanding the functionality of Sentient’s Agent Hub, where users can find and use a growing library of AI agents for various tasks. Now I don't know about you but I know where I'll be getting my stats from moving forward.

Polygon Stats

32,718 次观看 • 1 年前

An interesting issue with Tesla Robotaxi where it took us to a Starbucks, but the Google data had the incorrect location. At drop off we were 0.2 miles from the Starbucks, so we had a short walk. Is there a way the Tesla AI team could add functionality in the app so riders can update map info to correct errors or inaccuracies on the underlying map data and then this propagates to the fleet? Being able to do this with a pin drop on the map instead of having to use an address might make this very easy and user friendly! Or, as a bigger ask, would it be possible for the car to be able to use visual images on its own to look for a Starbucks sign and on the fly, get us closer and update the map data on its own?
0:13

An interesting issue with Tesla Robotaxi where it took us to a Starbucks, but the Google data had the incorrect location. At drop off we were 0.2 miles from the Starbucks, so we had a short walk. Is there a way the Tesla AI team could add functionality in the app so riders can update map info to correct errors or inaccuracies on the underlying map data and then this propagates to the fleet? Being able to do this with a pin drop on the map instead of having to use an address might make this very easy and user friendly! Or, as a bigger ask, would it be possible for the car to be able to use visual images on its own to look for a Starbucks sign and on the fly, get us closer and update the map data on its own?

Joe Tegtmeyer 🚀 🤠🛸😎

80,355 次观看 • 1 年前

The companies racing Elon Musk to build AI are paying him more than 2 billion dollars a month to do it. Anthropic pays SpaceX 1.25 billion dollars a month. Google pays 920 million. They are not buying rockets. They are renting compute, the scarce Nvidia chips that train frontier models, from a data center in Memphis called Colossus that Musk's rocket company now owns. SpaceX folded xAI into itself in February, and with it the 220,000 chips built to train Grok. Then it rented them to Grok's rivals. Anthropic took the entire first building. Google leased 110,000 more. The contracts signed so far run past 80 billion dollars. SpaceX is no longer a rocket company that dabbles in AI. It is one of the largest AI compute landlords on Earth, and its biggest tenants are the rivals it is trying to beat. Musk is not choosing between building AI on the ground and building it in space. He is using one to fund the other. The 80 billion in ground contracts is the cash engine. Starmind, the million-satellite constellation built to leave the ground behind, is what the cash builds. So the rivals are financing his exit from the planet. Every dollar Anthropic and Google pay for compute in Memphis helps fund the orbital network designed to strand them on the surface. They are paying the toll on the road they are trying to win, and the toll is building Musk a road no one else can reach. The piece works out who is really renting from whom.
0:10

The companies racing Elon Musk to build AI are paying him more than 2 billion dollars a month to do it. Anthropic pays SpaceX 1.25 billion dollars a month. Google pays 920 million. They are not buying rockets. They are renting compute, the scarce Nvidia chips that train frontier models, from a data center in Memphis called Colossus that Musk's rocket company now owns. SpaceX folded xAI into itself in February, and with it the 220,000 chips built to train Grok. Then it rented them to Grok's rivals. Anthropic took the entire first building. Google leased 110,000 more. The contracts signed so far run past 80 billion dollars. SpaceX is no longer a rocket company that dabbles in AI. It is one of the largest AI compute landlords on Earth, and its biggest tenants are the rivals it is trying to beat. Musk is not choosing between building AI on the ground and building it in space. He is using one to fund the other. The 80 billion in ground contracts is the cash engine. Starmind, the million-satellite constellation built to leave the ground behind, is what the cash builds. So the rivals are financing his exit from the planet. Every dollar Anthropic and Google pay for compute in Memphis helps fund the orbital network designed to strand them on the surface. They are paying the toll on the road they are trying to win, and the toll is building Musk a road no one else can reach. The piece works out who is really renting from whom.

Shanaka Anslem Perera ⚡

97,906 次观看 • 2 个月前

You can now create AI images directly from Google Slides. No need to spend hours searching for images for your presentations. And this feature is available for free. Here's how to activate it: 1. Go to labs .google .com 2. Scroll down to "Google Workspace". 3. Click on the "Learn more" button to access the waitlist. When it's activated, you'll see the button that appears in Google Slides as in the video. Click on it and enter your prompt: E.g.: "a cat in front of a raspberry pie". You can even choose different styles: photography, vector art, sketch, ... This will save a lot of time when creating slideshows! Don't hesitate to follow me to learn how to do more with AI.
0:12

You can now create AI images directly from Google Slides. No need to spend hours searching for images for your presentations. And this feature is available for free. Here's how to activate it: 1. Go to labs .google .com 2. Scroll down to "Google Workspace". 3. Click on the "Learn more" button to access the waitlist. When it's activated, you'll see the button that appears in Google Slides as in the video. Click on it and enter your prompt: E.g.: "a cat in front of a raspberry pie". You can even choose different styles: photography, vector art, sketch, ... This will save a lot of time when creating slideshows! Don't hesitate to follow me to learn how to do more with AI.

Paul Couvert

318,258 次观看 • 2 年前

Set the Grok widget on your Android for instant access to a real AI assistant By default, nearly all Android devices come with the Google Search widget But it’s 2025 - more than 90% of the time, you just need direct answers and an assistant that remembers your past conversations Google Search is basically irrelevant for those cases How to upgrade your home screen to Grok: ➝ Long-press the Google Search widget → tap “Remove” ➝ Long-press the home screen → Widgets → Find “Grok” → Drag to home Grok is fast, powerful, and always ready — a true AI assistant for your daily needs
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Set the Grok widget on your Android for instant access to a real AI assistant By default, nearly all Android devices come with the Google Search widget But it’s 2025 - more than 90% of the time, you just need direct answers and an assistant that remembers your past conversations Google Search is basically irrelevant for those cases How to upgrade your home screen to Grok: ➝ Long-press the Google Search widget → tap “Remove” ➝ Long-press the home screen → Widgets → Find “Grok” → Drag to home Grok is fast, powerful, and always ready — a true AI assistant for your daily needs

X Freeze

20,887 次观看 • 9 个月前

Part of the reason IT people are being laid off is that many of their abilities can be replaced by AI. In the past, the joke was that you would just google it, but that still required some IT knowledge. Now, you can ask AI how to fix an issue and it will walk you through how to fix most things. (if you have admin permissions to your computer) To be fair, no one knows all the weird nuances and issues that can arise with software so google has always been used to find IT fixes - but it took more research and knowing how to implement the fix before.
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Part of the reason IT people are being laid off is that many of their abilities can be replaced by AI. In the past, the joke was that you would just google it, but that still required some IT knowledge. Now, you can ask AI how to fix an issue and it will walk you through how to fix most things. (if you have admin permissions to your computer) To be fair, no one knows all the weird nuances and issues that can arise with software so google has always been used to find IT fixes - but it took more research and knowing how to implement the fix before.

AlphaFox

22,913 次观看 • 2 天前

Okay, this is seriously cool. A team from Google DeepMind, including DeepMind Cofounder Shane Legg, published a paper "From AGI to ASI". In the paper, they include instructions for an AI agent to read along with you. You can open the paper in Codex's in-app browser and have GPT-5.5 read it with you and explain all the concepts. This is the future. AI agents will be part of the target audience, and help us to understand anything we want.
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Okay, this is seriously cool. A team from Google DeepMind, including DeepMind Cofounder Shane Legg, published a paper "From AGI to ASI". In the paper, they include instructions for an AI agent to read along with you. You can open the paper in Codex's in-app browser and have GPT-5.5 read it with you and explain all the concepts. This is the future. AI agents will be part of the target audience, and help us to understand anything we want.

Dan McAteer

53,480 次观看 • 2 个月前

The "Neoclouds are too scared of Jensen to offer TPUs!" tweet reminded me of this quote from the Odd Lots episode with Hudson River Trading's Head of AI: "If you're using TPUs, you're also kind of entering a very close relationship with Google, some feeling of vendor lock in.. It's a complicated thing if you go down that path. But if you're compute hungry like the big labs, you'll take what you can get. I think Anthropic takes TPUs, Trainium, and GPUs, they need them all." You can also just look at the hoops and hurdles Google is going through to find external buyers of TPUs: - Invested $10B+ in Anthropic (and committed to invest $40B+) - Back-stopped Anthropic's data center leases so that Anthropic's data center developers can obtain financing to house the TPU's Anthropic is buying from Google - Created a new neocloud via a JV with Blackstone to offer TPUs (e.g. buy them from Google to rent) CoreWeave and Nebius are already sold out on the GPUs for which they have data center capacity to bring online.. Both want to diversify their customer bases away from hyperscalers and large AI labs. Doesn't seem to make any sense for them to offer TPUs, when Anthropic is the only company actually buying TPUs from Google, and only after being heavily incentivized to do so. $GOOG $CRWV $NBIS
0:18

The "Neoclouds are too scared of Jensen to offer TPUs!" tweet reminded me of this quote from the Odd Lots episode with Hudson River Trading's Head of AI: "If you're using TPUs, you're also kind of entering a very close relationship with Google, some feeling of vendor lock in.. It's a complicated thing if you go down that path. But if you're compute hungry like the big labs, you'll take what you can get. I think Anthropic takes TPUs, Trainium, and GPUs, they need them all." You can also just look at the hoops and hurdles Google is going through to find external buyers of TPUs: - Invested $10B+ in Anthropic (and committed to invest $40B+) - Back-stopped Anthropic's data center leases so that Anthropic's data center developers can obtain financing to house the TPU's Anthropic is buying from Google - Created a new neocloud via a JV with Blackstone to offer TPUs (e.g. buy them from Google to rent) CoreWeave and Nebius are already sold out on the GPUs for which they have data center capacity to bring online.. Both want to diversify their customer bases away from hyperscalers and large AI labs. Doesn't seem to make any sense for them to offer TPUs, when Anthropic is the only company actually buying TPUs from Google, and only after being heavily incentivized to do so. $GOOG $CRWV $NBIS

Rittenhouse Research

13,919 次观看 • 2 个月前

Are you an Insurance Advisor/Mutual Fund Distributor or advise clients on financial products? To make life easier for financial advisors, we have now integrated the ProtectMeWell API into a Google Sheet This means that you can work on a Google Sheet and generate reports (👇) like these for your prospective customers in real-time. 🚀Would you want to be an early adopter of "ProtectMeWell on Google Sheets"? Here is the deal 1⃣ I am seeking the help of 20 passionate advisors for closed user group testing. On a first come, first serve basis. DM me, if you are interested 2⃣ I will extend access to the Google Sheets for two weeks for ₹999. 100% refundable when you provide me with at least 5 actionable areas of improvement from a usability perspective of Google Sheets. 3⃣ When it is launched for the wider audience (late August 2023) you get to use it for free until 31 March 2024 (subject to 10,000 API calls) PS: To clarify, it is not the testing exercise of API (or algorithm), which is robust and has been used by over 100K people. The focus is on the usability of Google Sheets so that a wider audience finds it easy to navigate Sounds interesting? DM me Please amplify for good karma 🙏
0:15

Are you an Insurance Advisor/Mutual Fund Distributor or advise clients on financial products? To make life easier for financial advisors, we have now integrated the ProtectMeWell API into a Google Sheet This means that you can work on a Google Sheet and generate reports (👇) like these for your prospective customers in real-time. 🚀Would you want to be an early adopter of "ProtectMeWell on Google Sheets"? Here is the deal 1⃣ I am seeking the help of 20 passionate advisors for closed user group testing. On a first come, first serve basis. DM me, if you are interested 2⃣ I will extend access to the Google Sheets for two weeks for ₹999. 100% refundable when you provide me with at least 5 actionable areas of improvement from a usability perspective of Google Sheets. 3⃣ When it is launched for the wider audience (late August 2023) you get to use it for free until 31 March 2024 (subject to 10,000 API calls) PS: To clarify, it is not the testing exercise of API (or algorithm), which is robust and has been used by over 100K people. The focus is on the usability of Google Sheets so that a wider audience finds it easy to navigate Sounds interesting? DM me Please amplify for good karma 🙏

Sumit Ramani

42,240 次观看 • 3 年前

For 20 years, Google Earth has been a go-to tool for journalists and investigators seeking to verify ground-level realities. Last week, Google added a feature allowing anyone to generate fake satellite imagery of any real-world location. In just one day, users employed it to fabricate military positions, pass off ordinary buildings as schools, or conjure up a fake nuclear site in Iran from scratch. Real coordinates, yet entirely fictional scenes. Google withdrew the feature after 24 hours. As one investigator noted, Google spent two decades building a global standard, only to add a button capable of fabricating imaginary elements out of thin air.
0:15

For 20 years, Google Earth has been a go-to tool for journalists and investigators seeking to verify ground-level realities. Last week, Google added a feature allowing anyone to generate fake satellite imagery of any real-world location. In just one day, users employed it to fabricate military positions, pass off ordinary buildings as schools, or conjure up a fake nuclear site in Iran from scratch. Real coordinates, yet entirely fictional scenes. Google withdrew the feature after 24 hours. As one investigator noted, Google spent two decades building a global standard, only to add a button capable of fabricating imaginary elements out of thin air.

Anonymous

75,464 次观看 • 22 天前

Terrain impacts nearly every project. Luckily, Google Earth’s measure tool just got a major upgrade. Introducing elevation profiles. Simply draw a line, as you always have, to instantly generate a detailed and interactive elevation profile in the inspector panel. Whether you are tackling complex route and transportation planning, conducting viewshed analysis, or mapping out logistics for heavy machinery, you can now instantly contextualize terrain dips and peaks directly on the map.
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Terrain impacts nearly every project. Luckily, Google Earth’s measure tool just got a major upgrade. Introducing elevation profiles. Simply draw a line, as you always have, to instantly generate a detailed and interactive elevation profile in the inspector panel. Whether you are tackling complex route and transportation planning, conducting viewshed analysis, or mapping out logistics for heavy machinery, you can now instantly contextualize terrain dips and peaks directly on the map.

Google Earth

29,601 次观看 • 3 个月前

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.
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1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.

Sundar Pichai

864,602 次观看 • 1 年前

Building RAG is easy. Parsing real, unstructured data is the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓
0:18

Building RAG is easy. Parsing real, unstructured data is the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓

Charly Wargnier

19,220 次观看 • 5 个月前

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.
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Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

Thomas Kurian

198,018 次观看 • 3 个月前

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷
0:13

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 次观看 • 7 个月前