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。

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐋𝐚𝐭𝐞𝐬𝐭 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐨𝐟 𝐀𝐈𝐱𝐁𝐥𝐨𝐜𝐤 AIxBlock is an on-premise AI platform that enables you to productize AI models using unused computing resources while ensuring complete privacy. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 1️⃣ 𝐒𝐞𝐥𝐟-𝐇𝐨𝐬𝐭𝐢𝐧𝐠 ☀️ Deploy in minutes. ☀️ 100% privacy control. ☀️ No long-term commitments or upfront costs. 2️⃣ 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬... 𝐓𝐫𝐚𝐢𝐧 & 𝐃𝐞𝐩𝐥𝐨𝐲: Use your data. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐞 & 𝐃𝐞𝐩𝐥𝐨𝐲: Adjust pre-trained models. 𝐃𝐞𝐩𝐥𝐨𝐲 𝐎𝐧𝐥𝐲: Easy model deployment. 𝐋𝐚𝐛𝐞𝐥 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞: Optimize data labeling and validation 𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭? 𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐮𝐬 𝐟𝐨𝐫 𝐚 𝐰𝐚𝐥𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡! #AIxBlock #dataprivacy #SelfHost #SecureYourDatashow more

AIxBlock

22,017 subscribers

114,776 次观看 • 1 年前 •via X (Twitter)

科学技术教育健康养生

Anya Rossi• Live Now

Private livecam show

9 条评论

RA S EL POKEMON 的头像

RA S EL POKEMON1 年前

Nice project

Asraf 的头像

Asraf1 年前

It's really respectable that there's a team that's talking about a good future waiting for it. I'd like to be one of the best project #AlxBlock #solana @Nayonboss1 @Nipahasan12

jashohag33.base.eth 的头像

jashohag33.base.eth1 年前

Gooo

Jahangir Alom 的头像

Jahangir Alom1 年前

❤️

raphael araujo 的头像

raphael araujo1 年前

@AIxBlock

Sojib 的头像

Sojib1 年前

great

مهرشاد حسنپور 的头像

مهرشاد حسنپور1 年前

🤔🤔

sky.no1 ꧁IP꧂ Reddio 的头像

sky.no1 ꧁IP꧂ Reddio1 年前

The best project I've ever seen. It will take the future generation a lot further.Excellent project in a word

AIxBlock 的头像

AIxBlock1 年前

thank you so much for your support 🔥

相关视频

AIxBlock – A Decentralized Platform for End-to-End AI Development & Workflow Automation What can you actually build with AIxBlock? Here’s the full flow — fully customizable and self-hostable: [Curate Data] → [Label] → [Train] → [Deploy] → [Automate] → [Monetize] Pull models from Hugging Face, bring in your own data, train on decentralized GPUs, and automate everything with low-code builders. 🧪 Explore the platform: 🎥 Want to see a real use-case demo? Check our full gallery here:

AIxBlock – A Decentralized Platform for End-to-End AI Development & Workflow Automation What can you actually build with AIxBlock? Here’s the full flow — fully customizable and self-hostable: [Curate Data] → [Label] → [Train] → [Deploy] → [Automate] → [Monetize] Pull models from Hugging Face, bring in your own data, train on decentralized GPUs, and automate everything with low-code builders. 🧪 Explore the platform: 🎥 Want to see a real use-case demo? Check our full gallery here:

AIxBlock

14,330 次观看 • 1 年前

Data ownership & privacy are key to building an internet that benefits & protects its end-users AI is no different — Users’ data must be protected while interacting with AI Oasis' privacy-preserving smart contracts enable users to contribute data to AI models on their own terms

Data ownership & privacy are key to building an internet that benefits & protects its end-users AI is no different — Users’ data must be protected while interacting with AI Oasis' privacy-preserving smart contracts enable users to contribute data to AI models on their own terms

Oasis Labs

32,846 次观看 • 3 年前

What you type into ChatGPT is analyzed, flagged, used to train models, and shared with governments. But you can have both privacy AND AI. Here's how to use AI privately, by self-hosting, or using platforms that don't log your data.

What you type into ChatGPT is analyzed, flagged, used to train models, and shared with governments. But you can have both privacy AND AI. Here's how to use AI privately, by self-hosting, or using platforms that don't log your data.

Naomi Brockwell priv/acc

14,065 次观看 • 5 个月前

Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower and taught by Daniel J. Beutel and Nicholas Lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here!

Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower and taught by Daniel J. Beutel and Nicholas Lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here!

Andrew Ng

64,609 次观看 • 2 年前

Satya Nadella predicts, - if you have AI + Quantum Computing, you may use quantum to generate synthetic data - AI can then use that data to train better models for complex fields like chemistry and physics.

Satya Nadella predicts, - if you have AI + Quantum Computing, you may use quantum to generate synthetic data - AI can then use that data to train better models for complex fields like chemistry and physics.

Haider.

78,677 次观看 • 1 年前

Wow AI is your one-stop shop for everything AI data training: 1️⃣ OTS Dataset 2️⃣ Custom Data Collection 3️⃣ Labeling 4️⃣ Crowdsourcing With Wow AI, you can be confident that your AI training data is of the highest quality, ethical, and compliant. Visit us 👉

Wow AI is your one-stop shop for everything AI data training: 1️⃣ OTS Dataset 2️⃣ Custom Data Collection 3️⃣ Labeling 4️⃣ Crowdsourcing With Wow AI, you can be confident that your AI training data is of the highest quality, ethical, and compliant. Visit us 👉

Wow AI

51,973 次观看 • 2 年前

Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your next rap song. What if your AI was built specifically for your world? Trained on your domain, your data, you become the master of it. Introducing OpenLedger AI Studio. A powerful platform to build and use specialized models with built-in explainability and attribution. Fine-tune using your data through our Model Factory, and deploy them seamlessly. Your models won’t just reason - With OpenLedger, you can explain every decision, trace its data, and reward contributors. Truly Open, Verified, and Explainable AI begins here.

Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your next rap song. What if your AI was built specifically for your world? Trained on your domain, your data, you become the master of it. Introducing OpenLedger AI Studio. A powerful platform to build and use specialized models with built-in explainability and attribution. Fine-tune using your data through our Model Factory, and deploy them seamlessly. Your models won’t just reason - With OpenLedger, you can explain every decision, trace its data, and reward contributors. Truly Open, Verified, and Explainable AI begins here.

OpenLedger

172,568 次观看 • 1 年前

Using Theta Network's cutting-edge AI computing technology, Lavita.AI is building a new era of privacy preserving, AI-powered healthcare data platform powered by Theta EdgeCloud. Learn more at

Using Theta Network's cutting-edge AI computing technology, Lavita.AI is building a new era of privacy preserving, AI-powered healthcare data platform powered by Theta EdgeCloud. Learn more at

Theta Network

98,208 次观看 • 2 年前

AI machine learning models are trained on enormous sets of data, many of which can include sensitive user information In expanding AI adoption, protecting user data and privacy must be prioritized Founder of Oasis Labs, Dawn Song, explains why:

AI machine learning models are trained on enormous sets of data, many of which can include sensitive user information In expanding AI adoption, protecting user data and privacy must be prioritized Founder of Oasis Labs, Dawn Song, explains why:

Oasis Labs

36,083 次观看 • 3 年前

AI glasses show how easy it is to spot anyone and pull their public info in seconds using data people shared long ago and privacy fades fast 😬

AI glasses show how easy it is to spot anyone and pull their public info in seconds using data people shared long ago and privacy fades fast 😬

Tansu Yegen

29,340 次观看 • 7 个月前

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Solana ID 🪷

30,458 次观看 • 1 年前

Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

soulman 🎮

11,702 次观看 • 10 个月前

AI companies are buying up books in bulk, scanning them to train their AI models, and then destroying the books. This is why billionaire-owned AI companies are a scam. 𝐓𝐡𝐞𝐲 𝐚𝐫𝐞 𝐬𝐭𝐞𝐚𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐨𝐟 𝐡𝐮𝐦𝐚𝐧𝐢𝐭𝐲 𝐚𝐧𝐝 𝐭𝐡𝐞𝐧 𝐬𝐞𝐥𝐥𝐢𝐧𝐠 𝐢𝐭 𝐛𝐚𝐜𝐤 𝐭𝐨 𝐮𝐬. Nationalize AI now!

AI companies are buying up books in bulk, scanning them to train their AI models, and then destroying the books. This is why billionaire-owned AI companies are a scam. 𝐓𝐡𝐞𝐲 𝐚𝐫𝐞 𝐬𝐭𝐞𝐚𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐨𝐟 𝐡𝐮𝐦𝐚𝐧𝐢𝐭𝐲 𝐚𝐧𝐝 𝐭𝐡𝐞𝐧 𝐬𝐞𝐥𝐥𝐢𝐧𝐠 𝐢𝐭 𝐛𝐚𝐜𝐤 𝐭𝐨 𝐮𝐬. Nationalize AI now!

Power to the People ☭🕊

18,450 次观看 • 16 天前

Edge Computing 🤝 Federated Learning The next AI breakthrough lies in unlocking private data. By expanding training from public to private data, FLock enables AI models to specialise, delivering deeper insights and continuous innovation. Introducing the FLock FL Alliance.

Edge Computing 🤝 Federated Learning The next AI breakthrough lies in unlocking private data. By expanding training from public to private data, FLock enables AI models to specialise, delivering deeper insights and continuous innovation. Introducing the FLock FL Alliance.

FLock.io

102,558 次观看 • 1 年前

GM Cardano ☀️🩵 Big Tech profits from your digital data! Profila gives you the tools to take control, protect your privacy, and even get paid for sharing your data on your terms. Watch my video to find out how they do it :)

GM Cardano ☀️🩵 Big Tech profits from your digital data! Profila gives you the tools to take control, protect your privacy, and even get paid for sharing your data on your terms. Watch my video to find out how they do it :)

Linda

30,705 次观看 • 1 年前

.Weaviate AI Database is an AI-native vector database that allows you to store data objects and vector embeddings from your favorite machine learning models while scaling seamlessly into billions of data objects. AWS and NVIDIA work together in an ecosystem to deliver cost performance and efficiency for Weaviate. Weaviate is available in AWS Marketplace, making deployment within an AWS environment straightforward and accessible.

.Weaviate AI Database is an AI-native vector database that allows you to store data objects and vector embeddings from your favorite machine learning models while scaling seamlessly into billions of data objects. AWS and NVIDIA work together in an ecosystem to deliver cost performance and efficiency for Weaviate. Weaviate is available in AWS Marketplace, making deployment within an AWS environment straightforward and accessible.

AWS Startups

1,475,959 次观看 • 1 个月前

The #NATIX Drive& app is powered by computer vision AI and edge computing. 🖥️ This enables users to process vast amounts of data safely and efficiently without compromising privacy. 📲

The #NATIX Drive& app is powered by computer vision AI and edge computing. 🖥️ This enables users to process vast amounts of data safely and efficiently without compromising privacy. 📲

NATIX Network

16,461 次观看 • 2 年前