正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

One of the biggest bottlenecks in deploying visual AI and computer vision is annotation, which can be both costly and time-consuming. Today, we’re introducing Verified Auto Labeling, a new approach to AI-assisted annotation that achieves up to 95% of human-level performance while cutting labeling costs by up to 100,000x... show more

Voxel51

1,665 subscribers

12,835 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

2 条评论

Voxel51 的头像

Voxel511 年前

Verified Auto Labeling combines our expertise in data curation with automated labeling and QA workflows. Our research paper, Auto-Labeling Data for Object Detection, establishes new benchmarks for zero-shot auto-labeling, demonstrating that Verified Auto Labeling is 100,000× cheaper and 5,000× faster than traditional annotation.

Voxel51 的头像

Voxel511 年前

Labeling 3.4 million objects on a single NVIDIA L40S GPU cost only $1.18 and took just over an hour. While manually labeling the same dataset would cost roughly $124,092 and take nearly 7,000 hours. Read the full paper: Read our blog to dive deeper:

相关视频

Announcing Roboflow Rapid: the first prompt-based model creation engine. Data labeling is dead. Go from idea to deployed model in minutes without labeling data. Upload a video, type a text prompt, and get an API. No data labeling teams. No manual annotation. No infra / dependency hell. If you are spending 95% of your time labeling data and only 5% building your app, you are doing it wrong.

Announcing Roboflow Rapid: the first prompt-based model creation engine. Data labeling is dead. Go from idea to deployed model in minutes without labeling data. Upload a video, type a text prompt, and get an API. No data labeling teams. No manual annotation. No infra / dependency hell. If you are spending 95% of your time labeling data and only 5% building your app, you are doing it wrong.

Roboflow

22,959 次观看 • 9 个月前

Introducing Razer ID verified by World ID – a safe and secure way for gamers to verify that they are human in the age of AI. Verify your Razer ID to World ID today -

Introducing Razer ID verified by World ID – a safe and secure way for gamers to verify that they are human in the age of AI. Verify your Razer ID to World ID today -

R Λ Z Ξ R

139,557 次观看 • 1 年前

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

David Fields

13,648 次观看 • 2 年前

CPAI Demo CPAI’s advanced AI labeling technology delivers unmatched accuracy and saves you money. What’s the game-changer? Automated data labeling - a process that traditionally requires costly manual labor. CPAI is: • Reducing human overhead • Improving efficiency • Eliminating errors • Lowering costs • Increasing data processing speeds Watch @DerekMoonTax demonstrate a head-to-head comparison, highlighting the key differences that sets CPAI tech against one of the leading products in the industry.

CPAI Demo CPAI’s advanced AI labeling technology delivers unmatched accuracy and saves you money. What’s the game-changer? Automated data labeling - a process that traditionally requires costly manual labor. CPAI is: • Reducing human overhead • Improving efficiency • Eliminating errors • Lowering costs • Increasing data processing speeds Watch @DerekMoonTax demonstrate a head-to-head comparison, highlighting the key differences that sets CPAI tech against one of the leading products in the industry.

CPAI

66,715 次观看 • 1 年前

Introducing GPT-4o, our new model which can reason across text, audio, and video in real time. It's extremely versatile, fun to play with, and is a step towards a much more natural form of human-computer interaction (and even human-computer-computer interaction):

Introducing GPT-4o, our new model which can reason across text, audio, and video in real time. It's extremely versatile, fun to play with, and is a step towards a much more natural form of human-computer interaction (and even human-computer-computer interaction):

Greg Brockman

4,362,928 次观看 • 2 年前

OPENAI JUST CUT MODEL PRICES AS THE AI PRICE WAR HEATS UP 🔥 OpenAI is cutting the price of Luna by 80% and Terra by 20%. It is also rolling out a faster mode that runs at twice the speed, but costs more. The bigger picture is clear -- cheaper Chinese models and tighter corporate AI budgets are forcing the biggest AI companies to compete on price, not just performance.

OPENAI JUST CUT MODEL PRICES AS THE AI PRICE WAR HEATS UP 🔥 OpenAI is cutting the price of Luna by 80% and Terra by 20%. It is also rolling out a faster mode that runs at twice the speed, but costs more. The bigger picture is clear -- cheaper Chinese models and tighter corporate AI budgets are forcing the biggest AI companies to compete on price, not just performance.

CryptosRus

12,382 次观看 • 1 个月前

69% of baseball fans say they'd rather have a computer vision AI system call balls and strikes than a human umpire. This season, the MLB gave them one. For the first time in league history, a human ump's ball-strike call is not final. A Sony computer vision system called Hawk-Eye makes the ruling. It can read the seam pattern on the ball, measure spin axis, and detect spin decay mid-flight. Hawk-Eye's Head of Computer Vision Engineering says the pipeline runs "various AI and machine learning models" from camera capture to data output. On Saturday, one umpire had 6 of 8 challenged calls overturned. Three of those missed by over 2 inches. The team ran out of challenges by the fourth inning trying to correct him, then the manager got thrown out for arguing a call they couldn't challenge. An AI computer vision system accurate to a sixth of an inch, sitting next to a human who misses by two. The crowd cheered for the machine.

69% of baseball fans say they'd rather have a computer vision AI system call balls and strikes than a human umpire. This season, the MLB gave them one. For the first time in league history, a human ump's ball-strike call is not final. A Sony computer vision system called Hawk-Eye makes the ruling. It can read the seam pattern on the ball, measure spin axis, and detect spin decay mid-flight. Hawk-Eye's Head of Computer Vision Engineering says the pipeline runs "various AI and machine learning models" from camera capture to data output. On Saturday, one umpire had 6 of 8 challenged calls overturned. Three of those missed by over 2 inches. The team ran out of challenges by the fourth inning trying to correct him, then the manager got thrown out for arguing a call they couldn't challenge. An AI computer vision system accurate to a sixth of an inch, sitting next to a human who misses by two. The crowd cheered for the machine.

The Rundown AI

198,549 次观看 • 6 个月前

Introducing Kled Labeling, the next major update of the Kled app. For the first time, everyday people can earn real income from AI’s fastest growing sector: data labeling. Users now have the ability to label content in the most intuitive way possible from the hottest Silicon Valley startups, with millions of dollars allocated in rewards. So whether you're on your couch watching TV, at the dinner table, in bed, anywhere you can earn just with the tap of a button. We’re not hiring VAs. We’re not outsourcing overseas. We’re building a collective of users to outpace the billion dollar giants like Mercor, Scale AI, Surge, and more. This is the first public application that lets anyone earn by labeling data. Every American and global citizen will become the network.

Introducing Kled Labeling, the next major update of the Kled app. For the first time, everyday people can earn real income from AI’s fastest growing sector: data labeling. Users now have the ability to label content in the most intuitive way possible from the hottest Silicon Valley startups, with millions of dollars allocated in rewards. So whether you're on your couch watching TV, at the dinner table, in bed, anywhere you can earn just with the tap of a button. We’re not hiring VAs. We’re not outsourcing overseas. We’re building a collective of users to outpace the billion dollar giants like Mercor, Scale AI, Surge, and more. This is the first public application that lets anyone earn by labeling data. Every American and global citizen will become the network.

Kled AI

58,972 次观看 • 11 个月前

🚨BREAKING NEWS! The biggest news out of the Global AI Summit on Africa is the Africa Declaration on Artificial Intelligence, a groundbreaking show of commitment by both the public and private sector, which also includes a $60 billion fund to invest in developing and expanding AI infrastructure, scaling African AI enterprises, building a robust pipeline of AI practitioners, and strengthening domestic AI research capabilities, while upholding principles of equity and inclusion. Read the full Declaration here: #GAISAfrica2025

🚨BREAKING NEWS! The biggest news out of the Global AI Summit on Africa is the Africa Declaration on Artificial Intelligence, a groundbreaking show of commitment by both the public and private sector, which also includes a $60 billion fund to invest in developing and expanding AI infrastructure, scaling African AI enterprises, building a robust pipeline of AI practitioners, and strengthening domestic AI research capabilities, while upholding principles of equity and inclusion. Read the full Declaration here: #GAISAfrica2025

Rwanda Centre for the Fourth Industrial Revolution

16,324 次观看 • 1 年前

Introducing Orgo, Computers for AI Agents. AI apps today are constrained to a chat interface and text responses. With Orgo, you can give a model like GPT access to a full computer. Orgo provides full desktop environments which can be controlled by AI models like GPT and Claude. Available now for developers.

Introducing Orgo, Computers for AI Agents. AI apps today are constrained to a chat interface and text responses. With Orgo, you can give a model like GPT access to a full computer. Orgo provides full desktop environments which can be controlled by AI models like GPT and Claude. Available now for developers.

Spencer Kinney

280,660 次观看 • 1 年前

One of the biggest area I’m working on right now is trying to bridge the gap between linear chat interfaces and the non-linear nature of human thinking. Benji Taylor’s Agentation already shows that markup and annotation resonate strongly with visual feedback, and I’ve found they feel more natural in text-based environments as well. One tiny step, but in the right direction. Coming tonight to

One of the biggest area I’m working on right now is trying to bridge the gap between linear chat interfaces and the non-linear nature of human thinking. Benji Taylor’s Agentation already shows that markup and annotation resonate strongly with visual feedback, and I’ve found they feel more natural in text-based environments as well. One tiny step, but in the right direction. Coming tonight to

Balint Orosz

41,202 次观看 • 6 个月前

AI UGC has reached a point where it outperforms traditional UGC on both cost AND quality this video is 100% AI and cost under $5 with my new system we can mass produce ads and scale offers across dozens of accounts while constantly testing new GEOs what used to take weeks can now be done in a few hours, while cutting costs by 99%

AI UGC has reached a point where it outperforms traditional UGC on both cost AND quality this video is 100% AI and cost under $5 with my new system we can mass produce ads and scale offers across dozens of accounts while constantly testing new GEOs what used to take weeks can now be done in a few hours, while cutting costs by 99%

MAX

30,367 次观看 • 3 个月前

Automate Atlas capture tasks and pass the assessment . 🚨 If you’re doing Atlas Capture tasks manually, you’re probably making them harder than they need to be. I’m putting together a 3-part series showing how I use AI to make the workflow faster and more accurate. But before touching automation, you need to understand the rules. Part 1: → How Atlas Capture labeling actually works → The mistakes that cost you assessment points → How to write better segment labels → How to use AI without breaking the task guidelines → How to approach the assessment properly Then in Parts 2 & 3, we’ll get into the actual AI-assisted workflow. 🎥 Watch Part 1 below. Join The AI Task Hub for the rest of the series + new AI data annotation platforms, guides and opportunities:

Automate Atlas capture tasks and pass the assessment . 🚨 If you’re doing Atlas Capture tasks manually, you’re probably making them harder than they need to be. I’m putting together a 3-part series showing how I use AI to make the workflow faster and more accurate. But before touching automation, you need to understand the rules. Part 1: → How Atlas Capture labeling actually works → The mistakes that cost you assessment points → How to write better segment labels → How to use AI without breaking the task guidelines → How to approach the assessment properly Then in Parts 2 & 3, we’ll get into the actual AI-assisted workflow. 🎥 Watch Part 1 below. Join The AI Task Hub for the rest of the series + new AI data annotation platforms, guides and opportunities:

Sage

72,318 次观看 • 1 个月前

This is my vision for the future of Gemini and AI Assistants. Computer Use + Browser Use + Command Line Agent + On Screen Annotation. I finally got around to building a quick experimental product and demo, using the Gemini models If you also love this space, let's chat!

This is my vision for the future of Gemini and AI Assistants. Computer Use + Browser Use + Command Line Agent + On Screen Annotation. I finally got around to building a quick experimental product and demo, using the Gemini models If you also love this space, let's chat!

Jonathan Ouyang

23,994 次观看 • 7 个月前

One of the why questions we’re always asking is how can we help operations employees access inventory more efficiently? We’re taking a big leap forward by introducing DeepFleet, an exciting new AI model that makes our one million+ robot fleet work smarter. Think of DeepFleet as an intelligent traffic management system for our fulfillment centers. Just like smart traffic systems reduce congestion in cities, DeepFleet coordinates our robots’ movements to find optimal paths and reduce bottlenecks. By leveraging our deep expertise in robotics and combining it with AWS tools like SageMaker, we’ve built an AI model that learns from our extensive data on inventory movement to make real-time decisions that improve robot travel time by 10% (which at our scale is pretty significant). What makes this particularly exciting is how AI and robotics create value across the board. For customers, it means faster delivery times and lower costs. For employees, it opens up new opportunities – our robots handle physically demanding tasks and heavy lifting, making our fulfillment centers safer, while our people take on more technical roles. This combination of robotics and generative AI is just getting started, and am looking forward to what we’ll invent next.

One of the why questions we’re always asking is how can we help operations employees access inventory more efficiently? We’re taking a big leap forward by introducing DeepFleet, an exciting new AI model that makes our one million+ robot fleet work smarter. Think of DeepFleet as an intelligent traffic management system for our fulfillment centers. Just like smart traffic systems reduce congestion in cities, DeepFleet coordinates our robots’ movements to find optimal paths and reduce bottlenecks. By leveraging our deep expertise in robotics and combining it with AWS tools like SageMaker, we’ve built an AI model that learns from our extensive data on inventory movement to make real-time decisions that improve robot travel time by 10% (which at our scale is pretty significant). What makes this particularly exciting is how AI and robotics create value across the board. For customers, it means faster delivery times and lower costs. For employees, it opens up new opportunities – our robots handle physically demanding tasks and heavy lifting, making our fulfillment centers safer, while our people take on more technical roles. This combination of robotics and generative AI is just getting started, and am looking forward to what we’ll invent next.

Andy Jassy

42,327 次观看 • 1 年前

Introducing LDA, a latent world action foundation model that, for the first time, unifies the utilization of heterogeneous embodied data across simulation and reality, humans and robots, and varying levels of action quality and annotation. By breaking long-standing data silos in embodied intelligence, LDA enables the field, much like GPT did for language, to benefit continuously from scaling data, marking the transition into a new era of scalable learning. #Galbot #Robotics #Innovation #AI #Technology #Humanoid #WorldModel

Introducing LDA, a latent world action foundation model that, for the first time, unifies the utilization of heterogeneous embodied data across simulation and reality, humans and robots, and varying levels of action quality and annotation. By breaking long-standing data silos in embodied intelligence, LDA enables the field, much like GPT did for language, to benefit continuously from scaling data, marking the transition into a new era of scalable learning. #Galbot #Robotics #Innovation #AI #Technology #Humanoid #WorldModel

Galbot

40,796 次观看 • 5 个月前