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AI completely changes the way we can talk to data. Hex is leading the way by building collaborative, shareable analytics workspaces. I sat down with CEO Barry McCardel to discuss how agent workflows, conversational interfaces, and context-aware models are reshaping analysis. We cover how Hex aims to make everyone...

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Sarah Wang

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41,746 görüntüleme • 8 ay önce •via X (Twitter)

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This guy built an entire AI data science team in Python. Then open-sourced (100% free). It automates data science workflows with AI, including data loading, cleaning, exploratory analysis, and feature engineering. And it tracks each step in a 100% reproducible pipeline. 00:00 Project Overview 01:32 Diving into the AI Data Science Workflow and Data Loading 02:10 Data Wrangling and Cleaning 03:33 Data Visualization Insights & Plotting 04:08 Feature Engineering 05:00 Live 1-Hour Workshop 05:44 AI Data Science Team Python Library 🔗 AI Data Science Team On GitHub (Give it a Star) 🔗 Join My Next Live 1-Hour Agentic AI Workshop (Free):
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This guy built an entire AI data science team in Python. Then open-sourced (100% free). It automates data science workflows with AI, including data loading, cleaning, exploratory analysis, and feature engineering. And it tracks each step in a 100% reproducible pipeline. 00:00 Project Overview 01:32 Diving into the AI Data Science Workflow and Data Loading 02:10 Data Wrangling and Cleaning 03:33 Data Visualization Insights & Plotting 04:08 Feature Engineering 05:00 Live 1-Hour Workshop 05:44 AI Data Science Team Python Library 🔗 AI Data Science Team On GitHub (Give it a Star) 🔗 Join My Next Live 1-Hour Agentic AI Workshop (Free):

Matt Dancho (Business Science)

85,386 görüntüleme • 7 ay önce

1/ New episode – Enabling User-Owned Data to Create User-Owned AI with Anna Kazlauskas from vana Host: Connor Lovely We discuss: - Why private data hasn't mattered much, until now - Building an EVM-compatible L1 for DataDAOs - $VANA tokenomics and how to get involved Timestamps: 00:00 - Introduction 01:45 - Impact of DeepSeek on AI and User Privacy 04:22 - User Awareness and Data Storage Concerns 06:28 - The Shift in AI Narrative 07:14 - Anna's Journey into User-Owned Data 10:45 - Vana's Technical Framework and Privacy 12:20 - The Need for Data Sovereignty in AI 13:27 - Building a Layer 1 Blockchain 15:51 - Data DAOs: A New Approach to Data Ownership 17:15 - Understanding Data DAOs and Their Function 22:47 - The Role of Data Ownership in Social Platforms 24:51 - Negotiating Better Terms for Data Ownership 26:40 - Bringing Data to Vana and AI Applications 28:09 - Privacy-Preserving Compute Solutions 29:22 - Tokenomics of Vana 30:55 - Data DAO Specific Tokens and Their Role 31:22 - Final Thoughts on Vana's Vision 32:30 - Where to Find More Information Watch on all platforms below ⬇️
32:54

1/ New episode – Enabling User-Owned Data to Create User-Owned AI with Anna Kazlauskas from vana Host: Connor Lovely We discuss: - Why private data hasn't mattered much, until now - Building an EVM-compatible L1 for DataDAOs - $VANA tokenomics and how to get involved Timestamps: 00:00 - Introduction 01:45 - Impact of DeepSeek on AI and User Privacy 04:22 - User Awareness and Data Storage Concerns 06:28 - The Shift in AI Narrative 07:14 - Anna's Journey into User-Owned Data 10:45 - Vana's Technical Framework and Privacy 12:20 - The Need for Data Sovereignty in AI 13:27 - Building a Layer 1 Blockchain 15:51 - Data DAOs: A New Approach to Data Ownership 17:15 - Understanding Data DAOs and Their Function 22:47 - The Role of Data Ownership in Social Platforms 24:51 - Negotiating Better Terms for Data Ownership 26:40 - Bringing Data to Vana and AI Applications 28:09 - Privacy-Preserving Compute Solutions 29:22 - Tokenomics of Vana 30:55 - Data DAO Specific Tokens and Their Role 31:22 - Final Thoughts on Vana's Vision 32:30 - Where to Find More Information Watch on all platforms below ⬇️

Proof of Coverage Media

12,484 görüntüleme • 1 yıl önce

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!
1:33

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

DeepLearning.AI

20,833 görüntüleme • 1 yıl önce

Today we're taking the powerful data tools we've built for technical practitioners, and opening them up to everyone! We're releasing end-to-end no-code workflows with: ∑ Spreadsheet-style calculations 🧮 Data browsing 🔏 Updated governance features 🤖 AI speed and accuracy improvements These new workflows live right alongside our existing SQL, Python, and AI tools, so everyone— regardless of technical experience— can come together in Hex to ask and answer questions with data. Watch the launch video below, or read on into the thread for all the details!
11:59

Today we're taking the powerful data tools we've built for technical practitioners, and opening them up to everyone! We're releasing end-to-end no-code workflows with: ∑ Spreadsheet-style calculations 🧮 Data browsing 🔏 Updated governance features 🤖 AI speed and accuracy improvements These new workflows live right alongside our existing SQL, Python, and AI tools, so everyone— regardless of technical experience— can come together in Hex to ask and answer questions with data. Watch the launch video below, or read on into the thread for all the details!

Hex

358,832 görüntüleme • 2 yıl önce

In our latest Partner Podcast, Jeanette Gessler, VP of Box Consulting, Mike Carlino from Deloitte, and Shivani Majewski from DeloitteDigital, dive into the challenges of governing unstructured data. Timestamps 01:08 Data readiness and the importance of foundation for AI 05:58 Leveraging unstructured data and its governance 07:26 The role of clean and structured data in improving client satisfaction 08:39 Adoption and the importance of change management in integrations 11:54 Importance of reliable, clean data in AI use cases 12:35 Insights into practical applications for business improvement 13:12 Successful AI use cases in regulated industries like insurance and banking 16:51 The buzz around AI agents and moving toward practical AI use cases 19:00 How clients react when data is unlocked and actionable 22:12 Tailoring solutions to clients based on maturity and needs
23:14

In our latest Partner Podcast, Jeanette Gessler, VP of Box Consulting, Mike Carlino from Deloitte, and Shivani Majewski from DeloitteDigital, dive into the challenges of governing unstructured data. Timestamps 01:08 Data readiness and the importance of foundation for AI 05:58 Leveraging unstructured data and its governance 07:26 The role of clean and structured data in improving client satisfaction 08:39 Adoption and the importance of change management in integrations 11:54 Importance of reliable, clean data in AI use cases 12:35 Insights into practical applications for business improvement 13:12 Successful AI use cases in regulated industries like insurance and banking 16:51 The buzz around AI agents and moving toward practical AI use cases 19:00 How clients react when data is unlocked and actionable 22:12 Tailoring solutions to clients based on maturity and needs

Box

190,470 görüntüleme • 4 ay önce

We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:
1:33

We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:

Andrew Ng

118,937 görüntüleme • 1 yıl önce

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition
56:10

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition

papiofficial ᛤ

29,967 görüntüleme • 1 yıl önce

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇
0:52

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

FX1

23,913 görüntüleme • 1 yıl önce

Ever wondered how AI could understand your company’s private data the way Google understands the web? Elastic is doing just that by being the foundation for context engineering: ▶️ Linking an organisation’s proprietary data to AI, making its data searchable and actionable. ▶️ Enabling multimodal, multilingual AI experiences in India’s Tier II and III cities. ▶️ Driving global business impact for enterprises and governments alike. In this exclusive conversation with Ash Kulkarni, CEO of Elastic, learn how the company is helping enterprises turn data into clarity and real-world outcomes. See the full article and video here: Shradha Sharma
1:45

Ever wondered how AI could understand your company’s private data the way Google understands the web? Elastic is doing just that by being the foundation for context engineering: ▶️ Linking an organisation’s proprietary data to AI, making its data searchable and actionable. ▶️ Enabling multimodal, multilingual AI experiences in India’s Tier II and III cities. ▶️ Driving global business impact for enterprises and governments alike. In this exclusive conversation with Ash Kulkarni, CEO of Elastic, learn how the company is helping enterprises turn data into clarity and real-world outcomes. See the full article and video here: Shradha Sharma

YourStory

16,476 görüntüleme • 8 ay önce

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
2:24

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,012 görüntüleme • 1 yıl önce

1/ The data parsing market for robotics is just emerging, and it’s already valued in the tens of billions. Data parsing to build data models and software for Robotics is a $20B market in 2024 and is expected to reach $150B+ by 2034 Cherry AI Robotics has built a data parsing tool for robotics, which goes live next week, and will effectively make this project one of the pioneers in data parsing for robotics.
0:25

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

CherryAI Robotics

90,786 görüntüleme • 11 ay önce

1/ New episode – Making the Internet AI-Native with Andrej from Grass Host: Connor Lovely 🎬 We discuss: - An inside look at their revenue and customer base 👀 - How they're upgrading the Internet from the bottom up - What LCR is and why it's so important for our AI future Timestamps: 00:00 - Introduction 01:03 - Shifts in AI and Crypto Landscapes 02:15 - Diminishing Returns in AI Scaling 04:48 - The Evolution of AI Models 06:06 - Grass’s Position in the AI Industry 09:07 - Challenges in Multimodal Data 11:06 - Live Context Retrieval Explained 13:11 - The Future of AI Search 15:07 - The Scraping Dilemma 18:08 - Grass’s Unique Opportunity 22:05 - Current Node Count and Future Goals 24:29 - Grasshopper and Network Expansion 28:08 - Grass’s Identity: DePIN or AI Project? 30:23 - ClipTagger12B and Video Search Innovations 39:40 - Customer Engagement and Revenue Insights 45:04 - Future Growth and Market Potential Watch on other platforms below ⬇️
53:22

1/ New episode – Making the Internet AI-Native with Andrej from Grass Host: Connor Lovely 🎬 We discuss: - An inside look at their revenue and customer base 👀 - How they're upgrading the Internet from the bottom up - What LCR is and why it's so important for our AI future Timestamps: 00:00 - Introduction 01:03 - Shifts in AI and Crypto Landscapes 02:15 - Diminishing Returns in AI Scaling 04:48 - The Evolution of AI Models 06:06 - Grass’s Position in the AI Industry 09:07 - Challenges in Multimodal Data 11:06 - Live Context Retrieval Explained 13:11 - The Future of AI Search 15:07 - The Scraping Dilemma 18:08 - Grass’s Unique Opportunity 22:05 - Current Node Count and Future Goals 24:29 - Grasshopper and Network Expansion 28:08 - Grass’s Identity: DePIN or AI Project? 30:23 - ClipTagger12B and Video Search Innovations 39:40 - Customer Engagement and Revenue Insights 45:04 - Future Growth and Market Potential Watch on other platforms below ⬇️

Proof of Coverage | Tech’s Creative Agency

98,908 görüntüleme • 10 ay önce

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.
1:00

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

Shay Boloor

198,127 görüntüleme • 5 gün önce

We sat down with EvolutionIQOfficial's Tomas Vykruta to talk about AI in the insurance industry, importance of compelling UI, convincing the first customer to share training data with a startup, and other learnings from building a successful AI-powered business.
2:17

We sat down with EvolutionIQOfficial's Tomas Vykruta to talk about AI in the insurance industry, importance of compelling UI, convincing the first customer to share training data with a startup, and other learnings from building a successful AI-powered business.

Artem Burachenok, CEO @alloy.cx

102,816 görüntüleme • 3 yıl önce

AI is transforming the way businesses operate 🤖. It helps companies work faster, cut costs, and make smarter decisions. From automating repetitive tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI can’t function without data 📊. Massive amounts of high-quality data are required to train models and make them effective. Right now, most of this data is controlled by a few centralized giants 🔒, making it expensive, siloed, and out of reach for smaller businesses and innovators. This is where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By combining decentralized storage with AI infrastructure powered by blockchain, OORT makes data open, secure, and accessible to everyone. It’s building a foundation where businesses of all sizes can tap into AI’s full potential and compete in the AI-driven economy 🚀
0:44

AI is transforming the way businesses operate 🤖. It helps companies work faster, cut costs, and make smarter decisions. From automating repetitive tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI can’t function without data 📊. Massive amounts of high-quality data are required to train models and make them effective. Right now, most of this data is controlled by a few centralized giants 🔒, making it expensive, siloed, and out of reach for smaller businesses and innovators. This is where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By combining decentralized storage with AI infrastructure powered by blockchain, OORT makes data open, secure, and accessible to everyone. It’s building a foundation where businesses of all sizes can tap into AI’s full potential and compete in the AI-driven economy 🚀

Sky Wee

58,239 görüntüleme • 9 ay önce

The intersection of crypto and AI, presented by OpenLedger Tim Copeland was joined by OpenLedger core contributor Ram in this sponsored episode to discuss the emerging intersection of decentralized AI and blockchain technology. The conversation covers OpenLedger’s infrastructure that lets users contribute proprietary data to train AI models, tokenomics, and the broader promise of creating a transparent, community-driven AI economy. OUTLINE 00:00 – Introduction 01:02 – What is Openledger? 02:27 – Data Contribution Model 08:47 – Access to Agents 15:16 – Ensuring Quality Data 19:09 – Incentive Structures 23:18 – The Role of Tokens 24:56 – The Convergence of AI and Crypto 27:53 – Competing with Centralized Systems 30:23 – Openledger's Future and Success 33:32 – Data Economy Insights 36:17 – Conclusion
36:50

The intersection of crypto and AI, presented by OpenLedger Tim Copeland was joined by OpenLedger core contributor Ram in this sponsored episode to discuss the emerging intersection of decentralized AI and blockchain technology. The conversation covers OpenLedger’s infrastructure that lets users contribute proprietary data to train AI models, tokenomics, and the broader promise of creating a transparent, community-driven AI economy. OUTLINE 00:00 – Introduction 01:02 – What is Openledger? 02:27 – Data Contribution Model 08:47 – Access to Agents 15:16 – Ensuring Quality Data 19:09 – Incentive Structures 23:18 – The Role of Tokens 24:56 – The Convergence of AI and Crypto 27:53 – Competing with Centralized Systems 30:23 – Openledger's Future and Success 33:32 – Data Economy Insights 36:17 – Conclusion

The Block Media

51,907 görüntüleme • 1 yıl önce

AthenaX Roundtable EP.2 is LIVE! ——— Metis🌿 isn’t just an Ethereum L2 anymore. It wants to become the first AI-native blockchain economy. In this episode, we sit down with Tom Ngo, CEO of Metis, advisor to LazAI Network, to talk about how the project is moving from a typical Ethereum Layer 2 toward an AI-native blockchain ecosystem and what it really means beyond buzzwords. Here’s what we unpack together: Why Metis says “we’re not just an L2” and how their AI pivot has actually been in the works for over 18 months What an AI-native blockchain looks like in real life, not just in theory Why verified data is the core of AI on-chain and how concepts like Lazbubu are making data something you can actually own and interact with How AI agents could make blockchain invisible for users What it takes to lead a team through a major shift while staying true to the original vision ——— Timestamps 00:00 Introduction to Metis and Its Vision 03:55 Transitioning to AI: The New Frontier 06:56 Personal Journey into Blockchain and Metis 09:48 The Importance of Data in AI and Blockchain 13:00 Navigating Market Pressures and User Expectations 16:04 The Unified Experience of Andromeda and Hyperion 19:09 The Future of AI in Blockchain 22:12 LazBubu: Making Data Playable 24:47 Innovation and the Path Forward 27:56 Defining Success in the Blockchain Space ——— Resources Tom Ngo Rea Follow AthenaX on: X: Spotify: TikTok: Instagram: Apple Podcast:
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AthenaX Roundtable EP.2 is LIVE! ——— Metis🌿 isn’t just an Ethereum L2 anymore. It wants to become the first AI-native blockchain economy. In this episode, we sit down with Tom Ngo, CEO of Metis, advisor to LazAI Network, to talk about how the project is moving from a typical Ethereum Layer 2 toward an AI-native blockchain ecosystem and what it really means beyond buzzwords. Here’s what we unpack together: Why Metis says “we’re not just an L2” and how their AI pivot has actually been in the works for over 18 months What an AI-native blockchain looks like in real life, not just in theory Why verified data is the core of AI on-chain and how concepts like Lazbubu are making data something you can actually own and interact with How AI agents could make blockchain invisible for users What it takes to lead a team through a major shift while staying true to the original vision ——— Timestamps 00:00 Introduction to Metis and Its Vision 03:55 Transitioning to AI: The New Frontier 06:56 Personal Journey into Blockchain and Metis 09:48 The Importance of Data in AI and Blockchain 13:00 Navigating Market Pressures and User Expectations 16:04 The Unified Experience of Andromeda and Hyperion 19:09 The Future of AI in Blockchain 22:12 LazBubu: Making Data Playable 24:47 Innovation and the Path Forward 27:56 Defining Success in the Blockchain Space ——— Resources Tom Ngo Rea Follow AthenaX on: X: Spotify: TikTok: Instagram: Apple Podcast:

AthenaX

140,644 görüntüleme • 9 ay önce