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1. Everyone working around physical spaces will be wearing glasses with camera and mics soon. 2. The data captured will be extremely valuable. 3. Model companies will pay many billions for this data to train humanoid robots. 4. Savvy employers should start asking employees to wear cams/mics to capture... show more

Eric Jorgenson 📚 ☀️

88,532 subscribers

13,928 次观看 • 1 个月前 •via X (Twitter)

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Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

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PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

Faheem Ullah

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Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

Faheem Ullah

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How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:

How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:

Faheem Ullah

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PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

Faheem Ullah

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How to Train Your AI NFT (Introducing TolyAI) On-chain Clone We gathered Toly's Twitter activity from 2023 to 2025 and used it to create our SANA AI NFT. This allows you to ask questions and get answers that reflect Toly's Twitter style. The data is stored in a vector database, with the indexes secured on-chain via Metaplex Core APP DATA. We've made this agent openly available for everyone to use. Soon, anyone will be able to train their own AI NFT and store it on-chain. Toly Agent: Core Data: Dataset:

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OctonetAI

19,357 次观看 • 1 年前

🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗

🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗

Hedgie

162,060 次观看 • 3 个月前

I just inscribed the CRAWL module to Termina! You can now crawl the OCI to find Bitmap districts with reinscriptions or children! GO TO: TRY: > crawl bitmap This will take a long time, but don't worry! I already did it for you. Bring your own data: Let's work together to keep this dataset updated. You can crawl ranges (for example, of your own bitmaps) and submit a pull request. It took me about 7 hours to crawl all of bitmap with about 15 windows open each running on a range. It would've taken about 10 days with just one window! Definitely some improvements that can be made in performance for the future, but so now we have at least made it possible! And it is mind-blowing what we will be able to do with this data. Stay tuned later this week to find out how we can piggyback off this data to build powerful tools on-chain!

I just inscribed the CRAWL module to Termina! You can now crawl the OCI to find Bitmap districts with reinscriptions or children! GO TO: TRY: > crawl bitmap This will take a long time, but don't worry! I already did it for you. Bring your own data: Let's work together to keep this dataset updated. You can crawl ranges (for example, of your own bitmaps) and submit a pull request. It took me about 7 hours to crawl all of bitmap with about 15 windows open each running on a range. It would've taken about 10 days with just one window! Definitely some improvements that can be made in performance for the future, but so now we have at least made it possible! And it is mind-blowing what we will be able to do with this data. Stay tuned later this week to find out how we can piggyback off this data to build powerful tools on-chain!

bitoshi blockamoto 🧱 BITMAP 🟧

23,686 次观看 • 11 个月前

PhD Students – How to extract data from papers for your literature review in seconds? Extracting data from papers takes a lot of time. You can automate this process with Bohrium 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐩𝐚𝐩𝐞𝐫𝐬? 1. Go to and log in 2. Click on 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐵𝑎𝑠𝑒 from the left menu 3. Upload the papers you selected for literature review 4. You will see the following option against each paper - Read PDF - Key Takeaway - AI Poster 5. Click on 𝑅𝑒𝑎𝑑 𝑃𝐷𝐹 for the first paper in your list 6. Write a prompt for the data you want to extract 7. For example, you can enter datasets, methodology etc. 8. It will extract the required data from the paper 9. If you want to extract Key Takeaways from the paper 10. Go back and click on 𝐾𝑒𝑦 𝑇𝑎𝑘𝑒𝑎𝑤𝑎𝑦𝑠 11. Bohrium will extract Key Takeaways from the paper 12. In addition to this, you also have 2 more options - AI Poster - Podcast 13. Click on 𝐴𝐼 𝑃𝑜𝑠𝑡𝑒𝑟 and it will create a poster for you 14. This is the poster based on the given research paper 15. If you click on 𝑃𝑜𝑑𝑐𝑎𝑠𝑡, it will convert the paper to audio 16. You can listen to the paper instead of reading it Repeat this cycle for all the papers in your pool. You will end up with the required data. You can use this data to write your literature review Try Bohrium today for FREE: Anything you’d like to add?

PhD Students – How to extract data from papers for your literature review in seconds? Extracting data from papers takes a lot of time. You can automate this process with Bohrium 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐩𝐚𝐩𝐞𝐫𝐬? 1. Go to and log in 2. Click on 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐵𝑎𝑠𝑒 from the left menu 3. Upload the papers you selected for literature review 4. You will see the following option against each paper - Read PDF - Key Takeaway - AI Poster 5. Click on 𝑅𝑒𝑎𝑑 𝑃𝐷𝐹 for the first paper in your list 6. Write a prompt for the data you want to extract 7. For example, you can enter datasets, methodology etc. 8. It will extract the required data from the paper 9. If you want to extract Key Takeaways from the paper 10. Go back and click on 𝐾𝑒𝑦 𝑇𝑎𝑘𝑒𝑎𝑤𝑎𝑦𝑠 11. Bohrium will extract Key Takeaways from the paper 12. In addition to this, you also have 2 more options - AI Poster - Podcast 13. Click on 𝐴𝐼 𝑃𝑜𝑠𝑡𝑒𝑟 and it will create a poster for you 14. This is the poster based on the given research paper 15. If you click on 𝑃𝑜𝑑𝑐𝑎𝑠𝑡, it will convert the paper to audio 16. You can listen to the paper instead of reading it Repeat this cycle for all the papers in your pool. You will end up with the required data. You can use this data to write your literature review Try Bohrium today for FREE: Anything you’d like to add?

Faheem Ullah

13,433 次观看 • 1 年前

🚨 JUST IN: President Trump goes full PRO-DATA CENTER MODE, saying China will LOVE it if we stop building them This comes after X BUSTED a major Chinese bot influence operation against data centers "China could not be happier with this anti Data Center movement. Actually, they can’t believe it is happening!" "The only reason that communities throughout the U.S.A. should not want Data Centers is if they want to end up being backwards and poor. If they want to be successful and rich, with far lower taxes and jobs all over the place, let Data Reign." "The good news is that there are plenty of other places that want them. If we kill the Golden Goose, you will only have yourselves to blame." He's right. We have to win. And people's taxes can be SLASHED on top of it

🚨 JUST IN: President Trump goes full PRO-DATA CENTER MODE, saying China will LOVE it if we stop building them This comes after X BUSTED a major Chinese bot influence operation against data centers "China could not be happier with this anti Data Center movement. Actually, they can’t believe it is happening!" "The only reason that communities throughout the U.S.A. should not want Data Centers is if they want to end up being backwards and poor. If they want to be successful and rich, with far lower taxes and jobs all over the place, let Data Reign." "The good news is that there are plenty of other places that want them. If we kill the Golden Goose, you will only have yourselves to blame." He's right. We have to win. And people's taxes can be SLASHED on top of it

Eric Daugherty

130,129 次观看 • 9 天前

David Friedberg says Anthropic asked big pharma for their data and nearly everyone said no "There's been an effort by Anthropic to sign up life sciences companies to contribute to a new life sciences focused model. They're approaching these large companies with large proprietary data sets and saying, if you share your data, we will give you early access, some sort of proprietary value. Sign this NDA and you can participate with us." "I think nearly everyone I've spoken with has woken up to the fact that they are trying to commoditize everyone's business. If all of the tens of billions of dollars you have invested in experiments and product development, and you've generated all of this proprietary data along the way, that data is a true asset of your organization. It's an asset that you've spent billions of dollars developing." "And by handing it over to a model company to then combine with other people's data, you are commoditizing the one core differentiation that you have. And so everyone is largely saying no." "I think what everyone's realizing is they're better off developing their own weights and their own models using either an open source basis or there might be some intermediary business model that evolves."

David Friedberg says Anthropic asked big pharma for their data and nearly everyone said no "There's been an effort by Anthropic to sign up life sciences companies to contribute to a new life sciences focused model. They're approaching these large companies with large proprietary data sets and saying, if you share your data, we will give you early access, some sort of proprietary value. Sign this NDA and you can participate with us." "I think nearly everyone I've spoken with has woken up to the fact that they are trying to commoditize everyone's business. If all of the tens of billions of dollars you have invested in experiments and product development, and you've generated all of this proprietary data along the way, that data is a true asset of your organization. It's an asset that you've spent billions of dollars developing." "And by handing it over to a model company to then combine with other people's data, you are commoditizing the one core differentiation that you have. And so everyone is largely saying no." "I think what everyone's realizing is they're better off developing their own weights and their own models using either an open source basis or there might be some intermediary business model that evolves."

dnap

287,499 次观看 • 2 个月前

is live 🚨 > Psyop tactics X DR marketing X mathematics everything here and more to build our psywar Models This is just the beta, the scale in which our models will enhance will be legendary. And ONLY for marketing/VSL purposes. Operating without psywar intel will from now on will be negligent to your potential as a founder & marketer... the amount of data this is being trained on is unfathomable. literally every singe data point from my firm + a insane computational prediction engine + some more [redacted] stuff I can't disclose for privacy reasons. 3+ psywar manuals worth of data, hundreds of case studies for the top neuro data sources hundreds of case studies and mapping data for stimuli response patterns ungodly amount of DR data and testing there is also no shot anyone can make something close to the level of this engine will output data. the Information this has been built on has been lost data, BH data, sourced from the farthest corners of the internet and even FARTHER THAN THAT + Our own Psywar firm's data The edge this will give is immeasurable and I promise you I will make this shit work and connect with all the automations you need it to > this is just the beta, once I can turn this into a full model. everything will go exponential, so do not forget about this. Try out our tools now and see how you like it. We are Just getting started. You do NOT want to miss out.

is live 🚨 > Psyop tactics X DR marketing X mathematics everything here and more to build our psywar Models This is just the beta, the scale in which our models will enhance will be legendary. And ONLY for marketing/VSL purposes. Operating without psywar intel will from now on will be negligent to your potential as a founder & marketer... the amount of data this is being trained on is unfathomable. literally every singe data point from my firm + a insane computational prediction engine + some more [redacted] stuff I can't disclose for privacy reasons. 3+ psywar manuals worth of data, hundreds of case studies for the top neuro data sources hundreds of case studies and mapping data for stimuli response patterns ungodly amount of DR data and testing there is also no shot anyone can make something close to the level of this engine will output data. the Information this has been built on has been lost data, BH data, sourced from the farthest corners of the internet and even FARTHER THAN THAT + Our own Psywar firm's data The edge this will give is immeasurable and I promise you I will make this shit work and connect with all the automations you need it to > this is just the beta, once I can turn this into a full model. everything will go exponential, so do not forget about this. Try out our tools now and see how you like it. We are Just getting started. You do NOT want to miss out.

Krma

15,670 次观看 • 1 个月前

0.32 has shipped, and it's a massive release from Rerun. There's a ton of cool new features, and I wanted to highlight 2 in particular 1. OSS Server streaming from disk 2. Dataset review I walk you through them in the video, so take a look. I'll have a much longer blog post next week about the entire pipeline. With 0.32, much of the foundation is set for a unified data layer for physical data, and I'll be getting into the details of it with all that I've built over the past year. This will cover 1. Raw Data Collection 2. Data Ingestion 3. Catalog Registration 4. Query and Review 5. Post Process 6. Training so lots to share

0.32 has shipped, and it's a massive release from Rerun. There's a ton of cool new features, and I wanted to highlight 2 in particular 1. OSS Server streaming from disk 2. Dataset review I walk you through them in the video, so take a look. I'll have a much longer blog post next week about the entire pipeline. With 0.32, much of the foundation is set for a unified data layer for physical data, and I'll be getting into the details of it with all that I've built over the past year. This will cover 1. Raw Data Collection 2. Data Ingestion 3. Catalog Registration 4. Query and Review 5. Post Process 6. Training so lots to share

Pablo Vela

11,264 次观看 • 3 个月前

Google's former CEO Eric Schmidt on the data strategy behind the next hundred billion dollar companies: He explains that he evaluates startup ideas by looking at 5-year growth trajectories and asking whether there's a more scalable strategy available. Take a founder building an app they want to charge $10 for. Eric Schmidt's challenge: "Why not give it away for free and upsell users instead?" But the real insight is his framework for predicting which companies will dominate — and it all comes down to data. "5 years ago, I said publicly that the future will be apps that are on smartphones that use Google Maps, GPS, and do something useful. Now, what I should have said was Uber." So what does he think will define the next wave of massive companies? Systems built on Android and iOS, fast networks, and powerful machine learning, with a crucial data advantage: "They're going to use the crowd to learn something." He illustrates with this example: Pay dermatologists $1 each to categorize skin samples. Feed that into a machine learning system. Then sell the diagnostic service back to them, because a system trained on thousands of experts will outperform any individual. Schmidt summarizes the winning data strategy: "You crowdsource information in, you learn it, and then you sell it. [This] is in my view a highly likely candidate for the next hundred billion dollar corporations." The blueprint: Aggregate expert data at scale → train ML systems on that data → sell superior insights back to the market. This is how the next generation of dominant companies will be built.

Google's former CEO Eric Schmidt on the data strategy behind the next hundred billion dollar companies: He explains that he evaluates startup ideas by looking at 5-year growth trajectories and asking whether there's a more scalable strategy available. Take a founder building an app they want to charge $10 for. Eric Schmidt's challenge: "Why not give it away for free and upsell users instead?" But the real insight is his framework for predicting which companies will dominate — and it all comes down to data. "5 years ago, I said publicly that the future will be apps that are on smartphones that use Google Maps, GPS, and do something useful. Now, what I should have said was Uber." So what does he think will define the next wave of massive companies? Systems built on Android and iOS, fast networks, and powerful machine learning, with a crucial data advantage: "They're going to use the crowd to learn something." He illustrates with this example: Pay dermatologists $1 each to categorize skin samples. Feed that into a machine learning system. Then sell the diagnostic service back to them, because a system trained on thousands of experts will outperform any individual. Schmidt summarizes the winning data strategy: "You crowdsource information in, you learn it, and then you sell it. [This] is in my view a highly likely candidate for the next hundred billion dollar corporations." The blueprint: Aggregate expert data at scale → train ML systems on that data → sell superior insights back to the market. This is how the next generation of dominant companies will be built.

Big Brain AI

104,706 次观看 • 7 个月前

No longer a ‘conspiracy theory’ Online retailers are accessing your data and then adjusting prices for items based on that data Airlines are being caught adjusting prices based on data too “This holiday season, you and your neighbor could be shopping for the exact same item and pay different prices — surveillance pricing is and how your personal data plays a role. You may be shopping for gifts from the comfort of your couch, but stores can still see who you are. Most of these companies are charging you a different price based on some data they have about you. It could be your location, it could be your proximity to the store, it could be your shopping history, knowing that you bought a product previously and are willing pay for it again.” “Consumer watchdog president Jamie Court says surveillance pricing is nearly impossible to avoid — Court says his organization has evidence all the major retailers use personal algorithms to nudge a price up or down, targeting data from your Amazon cart, Uber rides, or even the brand of laptop you're using to shop” “For those traveling for the holidays. Airlines will charge you a different price depending upon how many times you go to the website. “Currently, there is no federal regulation for companies that use surveillance pricing. We reached out to the FTC”

Wall Street Apes

519,197 次观看 • 8 个月前

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 human data passes smoothly (Video 1). However, bots or manually altered data will fail (Video 2). Why? Faking numbers breaks the simulation's physics causality. Even tiny tweaks cause error accumulation, resulting in failed movements. This invalid data is automatically rejected. Because of this mechanism, data submitted via bots will ultimately fail our replay verification. Invalid data is strictly excluded from model training, and the task slot is reopened to the community to collect genuine, high-quality trajectories. Furthermore, we actively monitor for duplicated data. Trajectories that are identical lack the diversity required for robot learning and will not be credited by our scoring system. If we detect accounts submitting a massive volume of identical trajectories, all associated addresses will be permanently banned. For Axis, the quality and diversity of data are the only ways to solve the robotics generalization gap. They will always be our absolute top priorities.

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 human data passes smoothly (Video 1). However, bots or manually altered data will fail (Video 2). Why? Faking numbers breaks the simulation's physics causality. Even tiny tweaks cause error accumulation, resulting in failed movements. This invalid data is automatically rejected. Because of this mechanism, data submitted via bots will ultimately fail our replay verification. Invalid data is strictly excluded from model training, and the task slot is reopened to the community to collect genuine, high-quality trajectories. Furthermore, we actively monitor for duplicated data. Trajectories that are identical lack the diversity required for robot learning and will not be credited by our scoring system. If we detect accounts submitting a massive volume of identical trajectories, all associated addresses will be permanently banned. For Axis, the quality and diversity of data are the only ways to solve the robotics generalization gap. They will always be our absolute top priorities.

Axis Robotics

30,876 次观看 • 4 个月前

Telecom companies offer Recharge Plans with ‘𝐃𝐚𝐢𝐥𝐲 𝐃𝐚𝐭𝐚 𝐋𝐢𝐦𝐢𝐭𝐬’ like 1.5GB, 2GB or 3GB per day, resetting every 24 hours. Any Unused Data EXPIRES at midnight, despite being fully paid for. 𝐘𝐨𝐮 𝐚𝐫𝐞 𝐛𝐢𝐥𝐥𝐞𝐝 𝐟𝐨𝐫 𝟐𝐆𝐁. 𝐘𝐨𝐮 𝐮𝐬𝐞 𝟏.𝟓𝐆𝐁. 𝐓𝐡𝐞 𝐫𝐞𝐦𝐚𝐢𝐧𝐢𝐧𝐠 𝟎.𝟓𝐆𝐁 𝐝𝐢𝐬𝐚𝐩𝐩𝐞𝐚𝐫𝐬 𝐚𝐬 𝐝𝐚𝐲 𝐞𝐧𝐝𝐬. No refund. No rollover. Just gone. This is not an accident. This is policy. Use it unnecessarily, or lose it by midnight. That’s how mobile data works today. I raised this issue in Parliament - 𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐝𝐚𝐭𝐚 𝐭𝐡𝐚𝐭 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐩𝐚𝐢𝐝 𝐟𝐨𝐫 𝐛𝐞 𝐅𝐎𝐑𝐅𝐄𝐈𝐓𝐄𝐃? UNUSED DATA should carry forward into the next cycle, so consumers can use what they have already paid for. My demands are clear: 𝟏. 𝐀𝐥𝐥𝐨𝐰 𝐃𝐚𝐭𝐚 𝐜𝐚𝐫𝐫𝐲-𝐟𝐨𝐫𝐰𝐚𝐫𝐝/ 𝐃𝐚𝐭𝐚 𝐫𝐨𝐥𝐥𝐨𝐯𝐞𝐫 𝐟𝐨𝐫 𝐚𝐥𝐥 𝐮𝐬𝐞𝐫𝐬 All telecom operators should provide Rollover of Unused Data. What remains unused at the end of the day, should be added to the next day’s Daily Data Limit, not erased the moment validity ends. 𝟐. 𝐆𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐧𝐞𝐱𝐭 𝐦𝐨𝐧𝐭𝐡’𝐬 𝐑𝐞𝐜𝐡𝐚𝐫𝐠𝐞 𝐀𝐦𝐨𝐮𝐧𝐭𝐬 If a consumer consistently under-utilises their data over multiple cycles, there should be a mechanism for Adjustment or Discount of that value, from the following month’s Recharge Amount. Consumers should not repeatedly pay for capacity they do not use. 𝟑. 𝐀𝐥𝐥𝐨𝐰 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐫𝐞𝐥𝐚𝐭𝐢𝐯𝐞𝐬 & 𝐟𝐫𝐢𝐞𝐧𝐝𝐬 Unused data should be treated as the consumer’s digital property. Users should be allowed to transfer their unused data to others, from their Daily Data Limit, just as transfer money to others. As we build a Digital India, access cannot depend on data that disappears. If you’ve paid for it, it should carry forward and remain yours to use.

Telecom companies offer Recharge Plans with ‘𝐃𝐚𝐢𝐥𝐲 𝐃𝐚𝐭𝐚 𝐋𝐢𝐦𝐢𝐭𝐬’ like 1.5GB, 2GB or 3GB per day, resetting every 24 hours. Any Unused Data EXPIRES at midnight, despite being fully paid for. 𝐘𝐨𝐮 𝐚𝐫𝐞 𝐛𝐢𝐥𝐥𝐞𝐝 𝐟𝐨𝐫 𝟐𝐆𝐁. 𝐘𝐨𝐮 𝐮𝐬𝐞 𝟏.𝟓𝐆𝐁. 𝐓𝐡𝐞 𝐫𝐞𝐦𝐚𝐢𝐧𝐢𝐧𝐠 𝟎.𝟓𝐆𝐁 𝐝𝐢𝐬𝐚𝐩𝐩𝐞𝐚𝐫𝐬 𝐚𝐬 𝐝𝐚𝐲 𝐞𝐧𝐝𝐬. No refund. No rollover. Just gone. This is not an accident. This is policy. Use it unnecessarily, or lose it by midnight. That’s how mobile data works today. I raised this issue in Parliament - 𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐝𝐚𝐭𝐚 𝐭𝐡𝐚𝐭 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐩𝐚𝐢𝐝 𝐟𝐨𝐫 𝐛𝐞 𝐅𝐎𝐑𝐅𝐄𝐈𝐓𝐄𝐃? UNUSED DATA should carry forward into the next cycle, so consumers can use what they have already paid for. My demands are clear: 𝟏. 𝐀𝐥𝐥𝐨𝐰 𝐃𝐚𝐭𝐚 𝐜𝐚𝐫𝐫𝐲-𝐟𝐨𝐫𝐰𝐚𝐫𝐝/ 𝐃𝐚𝐭𝐚 𝐫𝐨𝐥𝐥𝐨𝐯𝐞𝐫 𝐟𝐨𝐫 𝐚𝐥𝐥 𝐮𝐬𝐞𝐫𝐬 All telecom operators should provide Rollover of Unused Data. What remains unused at the end of the day, should be added to the next day’s Daily Data Limit, not erased the moment validity ends. 𝟐. 𝐆𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐧𝐞𝐱𝐭 𝐦𝐨𝐧𝐭𝐡’𝐬 𝐑𝐞𝐜𝐡𝐚𝐫𝐠𝐞 𝐀𝐦𝐨𝐮𝐧𝐭𝐬 If a consumer consistently under-utilises their data over multiple cycles, there should be a mechanism for Adjustment or Discount of that value, from the following month’s Recharge Amount. Consumers should not repeatedly pay for capacity they do not use. 𝟑. 𝐀𝐥𝐥𝐨𝐰 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐫𝐞𝐥𝐚𝐭𝐢𝐯𝐞𝐬 & 𝐟𝐫𝐢𝐞𝐧𝐝𝐬 Unused data should be treated as the consumer’s digital property. Users should be allowed to transfer their unused data to others, from their Daily Data Limit, just as transfer money to others. As we build a Digital India, access cannot depend on data that disappears. If you’ve paid for it, it should carry forward and remain yours to use.

Raghav Chadha

426,664 次观看 • 5 个月前

🚨 JUST NOW: President Trump DEMANDS New York Gov. Kathy Hochul immediately rescind her 1-year ban on new AI data centers "The Radical Left Dumocrats must not be allowed to cause us to lose Data Centers, AI, and all of this incredible new Technology, to China, and other countries!" "New York State has made a terrible decision. All of this Income, and other Benefits, will be going to Red States, and some Blue, where Data Centers are sought as Cash Cows, with Lower Taxes and Record Setting Jobs." "They must pay for their own Water and Power, and any leftover goes back to the State and local Community." "Data Centers are tremendous WINS for the States and Communities that are lucky enough to get them. New York should change its Policy, IMMEDIATELY."

🚨 JUST NOW: President Trump DEMANDS New York Gov. Kathy Hochul immediately rescind her 1-year ban on new AI data centers "The Radical Left Dumocrats must not be allowed to cause us to lose Data Centers, AI, and all of this incredible new Technology, to China, and other countries!" "New York State has made a terrible decision. All of this Income, and other Benefits, will be going to Red States, and some Blue, where Data Centers are sought as Cash Cows, with Lower Taxes and Record Setting Jobs." "They must pay for their own Water and Power, and any leftover goes back to the State and local Community." "Data Centers are tremendous WINS for the States and Communities that are lucky enough to get them. New York should change its Policy, IMMEDIATELY."

Eric Daugherty

71,885 次观看 • 1 个月前

GOOGLE CEO: DATA CENTERS IN SPACE WILL HARNESS THE SUN’S POWER Sundar laid out a vision straight from science fiction, building Google’s future data centers in orbit to capture near-limitless solar energy: “How do we one day have data centers in space so we can better harness the energy from the sun? That’s a hundred trillion times more energy than what we produce on Earth today. In 2027, we’ll take the first step, sending tiny racks of machines aboard satellites to test them out and start scaling. A decade from now, this will be seen as a normal way to build data centers.” Source: Fox, Sundar Pichai

GOOGLE CEO: DATA CENTERS IN SPACE WILL HARNESS THE SUN’S POWER Sundar laid out a vision straight from science fiction, building Google’s future data centers in orbit to capture near-limitless solar energy: “How do we one day have data centers in space so we can better harness the energy from the sun? That’s a hundred trillion times more energy than what we produce on Earth today. In 2027, we’ll take the first step, sending tiny racks of machines aboard satellites to test them out and start scaling. A decade from now, this will be seen as a normal way to build data centers.” Source: Fox, Sundar Pichai

Mario Nawfal

459,653 次观看 • 9 个月前

A new HUGE data center campus called Project Open Sky is being planned in Oklahoma. The land will be over a square mile with 6 Data Centers One American family all built houses together on a compound, and the data center campus is planned less than 300 feet away from their homes The residents say they already have a water pressure issue and this is going to mean there won’t be enough water to farm or even for their homes The data center campus could use as 27.4 million gallons per year “They literally have an entire square mile and are pushing to get more — Cloverleaf Infrastructure is the company behind Project Open Sky, which is in talks to build a 633-acre campus with as many as 6 data centers on it” This is going to destroy the lives of the residents in this town

A new HUGE data center campus called Project Open Sky is being planned in Oklahoma. The land will be over a square mile with 6 Data Centers One American family all built houses together on a compound, and the data center campus is planned less than 300 feet away from their homes The residents say they already have a water pressure issue and this is going to mean there won’t be enough water to farm or even for their homes The data center campus could use as 27.4 million gallons per year “They literally have an entire square mile and are pushing to get more — Cloverleaf Infrastructure is the company behind Project Open Sky, which is in talks to build a 633-acre campus with as many as 6 data centers on it” This is going to destroy the lives of the residents in this town

Wall Street Apes

66,829 次观看 • 14 天前