Our CEO @harjivsingh moderates "Unleashing Human Potential in the Age of Intelligence" at the @USISPForum Leadership Dialogue, #IndiaAIImpactSummit2026. Joining the US CEO delegation alongside @Microsoft @Adobe @FedEx @generalcatalyst @rubrikInc, in a conversation on building AI fluency at scale. Panelists: → Vijay Guntur, CTO @hcltech → Girish Raghavan, CTO @GEHealthCare → @rohitksingh, Former Secretary to Government of India → Kumaresh Pattabiraman, @LinkedInIndia Feb 18 · The Leela Palace, New Delhi The future belongs to those who know how to work with AI. #PeopleOverPlatforms

🛡️ @PerceptronNTWK : 🔸The world is generating massive and rapidly growing amounts of data: Approximately 181 zettabytes of global data by 2025 (growing at 23% per year), with roughly 2.5 quintillion bytes (2.5 million trillion bytes) created every single day. The vast majority (~70%) consists of user-generated data coming from real-world interactions, conversations, and communities on platforms like Telegram, Discord, WeChat, and similar services. 🔸 AI depends heavily on high-quality data, yet current methods of data collection and processing remain outdated — primarily relying on centralized data centers and controlled by a small number of large providers. Core problems of the centralized model : ▫️ Data is expensive and access is restricted → High-quality datasets (especially specialized/domain-specific ones) can cost up to 40 times more than generic data → Only about 12% of businesses have production-ready data systems → This severely limits startups, independent researchers, and creates deep inequality in AI development. ▫️ Centralization leads to inefficiency → Infrastructure is extremely energy-intensive and costly → This drives up data prices, slows innovation, and makes it especially difficult to access real-time or highly specialized data. ▫️ AI “craves” real-world signals → The most valuable data is found in real-time, dynamic interactions, but centralized systems struggle to capture it efficiently, ethically, and at sufficient scale. ▫️ Users are rendered “invisible” → People who create the data (everyday users) contribute enormously to training AI, yet they are structurally disconnected from the value chain, lacking proper incentives, recognition, or fair rewards. 🔥 Overall consequence : This imbalance is becoming increasingly obvious and unsustainable as AI is applied more broadly across society. The global data & analytics market is already enormous (estimated at hundreds of billions of USD in 2025, potentially reaching trillions in the 2030s), but the current approach is actively constraining its full potential. 📌 We need to rethink how AI acquires data — likely moving toward decentralized models, democratizing access to data, empowering the users who actually create it, or developing entirely new mechanisms to more effectively and fairly capture value from real-world data. #Perceptron #PERC

AI won't fully replace jobs, but it could automate 80% of tasks. A new industrial revolution is here. 🌐🔧 tweaktown.com/news/103221/nv… #AI #Jobs #Automation

Join us for a revolutionary talk on 'Indian Education & Decentralized Learning' with Stanley Bishop at @DeSciKolkata!🎓 A tech architect at the forefront of AI and machine learning, @ScienceStanley is ready to share his expertise on reshaping education.🧠 #DeSci #AI #Education