Data and AI eXchange (DAX) by Unolabs

Will Satellites Think for Themselves? Future of AI in Space-Tech #ai #space #technology #podcast

#ArtificialIntelligence #FutureOfAI #DeepTech #SpaceTech #satellites What if AI didn’t just power chatbots — but ran satellites, triaged disaster data in orbit, and one day hosted whole data-centres in space? Vishesh Vatsal (CTO, SkyServe) breaks it down.In this episode of Insider Opinion, Vishesh Vatsal — CTO at SkyServe, co-founder of Dfy Graviti, and an IIT Kanpur aerospace engineer — walks us through the near-term and moonshot futures of AI in space: onboard compute, satellite constellations, real-time disaster response, and the wild but plausible idea of data-centres in orbit. We also dig into hiring for deep tech, trust and governance, and practical use cases India should prioritize.Key takeaways- Onboard AI is already feasible — edge GPUs and compressed models make meaningful inference in orbit possible today.- Bandwidth + cost = need for local intelligence — send insights (kilobytes), not raw imagery (gigabytes).- Constellations change the game — frequency, spectrum and resolution improve, but orchestration & collision risk require smart autonomy.- High-value use case: disaster response — faster detection and targeted intervention can save lives.- Moonshot: moving some compute to space (solar-powered data centres) is logically possible and likely within our lifetimes.- Hiring for the AI future: mix of fundamentals + curiosity + at least one AI-skeptic/devil’s advocate on the team.- Governance warning: accelerate safeguards and alignment investment alongside capability development.00:00 — Will AI run satellites in 10 years?02:57 — Coders vs dreamers: hiring for deep tech06:12 — AI in space: from rovers to autonomy09:37 — Disaster response from orbit13:01 — Why space still fears autonomy15:56 — Constellations: frequency, spectrum & resolution20:55 — Moonshot: data centres in space 🚀24:59 — Avoiding collisions: AI & Kessler Syndrome30:06 — India’s role in the global AI race33:39 — Beyond space: AI for Bangalore traffic36:27 — Do you trust AI more than humans?38:40 — Future headline 2040: AI cures all diseases#EdgeComputing #DataCentres #DisasterResponse #UrbanTech #Constellations #RemoteSensing

Data Strategy & Cloud Adoption in the age of AI | Proven Framework #artificialintelligence #ai

#cloud #strategy #data In today’s episode, we dive deep into Data Strategy in the Age of AI with Dr. Shweta Darbha — a seasoned tech leader who has driven digital transformation, cloud adoption, and API-first solutions at some of the world’s top financial institutions.From cloud migration challenges to breaking data silos with APIs, Shweta explains how enterprises can move beyond buzzwords and build smarter, secure, and AI-ready organizations. We unpack common mistakes in cloud adoption, how compliance can coexist with innovation, and why APIs are now the backbone of modern fintech.If you’ve ever wondered how to get real business value out of AI, data, and cloud strategies, this episode will give you clarity and practical insights.What you’ll learn in this podcast:- Why a real data strategy is the backbone of AI- The most common mistakes companies make in cloud adoption- How to innovate while staying compliant in financial services- API-first explained in simple, non-technical terms- Breaking down data silos with cloud + API-first design- What separates winners from laggards in data strategy over the next 5 years00:30 — Episode intro & topic setup 01:23 — Q1: What does a real data strategy look like in the AI era? 03:01 — Data is the backbone of AI, not just a buzzword 04:49 — Defining data strategy: what is AI trying to achieve? 06:05 — Q2: Common mistakes when moving data to the cloud 07:23 — Challenge 1: Lack of clear cloud policy 08:32 — Challenge 2: Teams not cloud-ready, learning on the job 10:41 — Deep-dive: Deciding what transactional data to push to cloud (UPI example) 12:31 — Q3: How can organizations innovate while staying compliant in the cloud? 14:36 — Topic: Data abstraction & built-in security 16:11 — Q4: Why APIs are the backbone of modern digital services 19:03 — Q5: Breaking data silos with cloud + API-first 23:20 — Final Q: Looking ahead 5 years — winners vs laggards 28:36 — Closing & thanks#DataStrategy #AI #CloudAdoption #APIFirst #Fintech #BankingTechnology #DigitalTransformation #APIs #ArtificialIntelligence #CloudComputing #FintechInnovation #DataDriven #TechPodcast #FutureOfBanking #AIinFintechdata strategy in the age of AI, cloud adoption in banking, api first solutions explained, fintech data strategy, ai in financial services, digital transformation in banking, how to break data silos, ai and cloud computing, api integration in fintech, data governance and AI, future of fintech 2025, banking technology trends, enterprise AI strategy, cloud migration mistakes, ai powered digital banking

How to assess AI-readiness of an Organization? Are you 'AI' Ready?

#agenticai #manager #podcast #salesforceEvery boardroom is asking the same question today: “Are we really ready for AI?”In this episode of Data & AI Exchange, we sit down with Hitesh Seth — Chief Architect at Salesforce and a veteran of large-scale Data & AI platforms — to explore what true AI readiness means beyond hype and pilot projects.From financial services to healthcare, Hitesh has helped global enterprises design AI roadmaps, integrate data platforms, and build production-grade AI systems. Here, he shares how leaders can assess data maturity, governance, culture, and talent before scaling AI.What You’ll Learn: - Early signals of an AI-ready organization - How to separate AI theater from real business value - Why strong data foundations & governance are critical - Balancing quick wins vs. long-term AI bets - The non-negotiable roles & teams needed for success - How to measure ROI, fail fast, and pivot effectively - Why a growth mindset is key for leaders in the AI era00:00 – Opening & Introductions01:30 – Early Signals of AI Readiness03:00 – Digital Transformation as a Readiness Indicator04:30 – Data Journeys & Maturity Levels05:40 – AI Theater vs. Real Business Value07:30 – Predictive vs. Generative AI in Enterprises08:30 – What a Solid Data Foundation Looks Like10:30 – Governance & Data Ownership11:40 – Red Flags in Data Quality & Governance13:20 – Quick Wins vs. Strategic Bets15:00 – Talent, Leadership & Change Management16:00 – Before Approving AI Pilots: Tech, APIs & Security18:00 – Efficiency vs. Growth: The Business Value Test19:40 – Building Teams: Upskilling vs. Hiring Externally21:00 – Why Architects Are Critical in AI Readiness22:30 – Non-Negotiable Roles: Data Engineers, MLOps & Governance24:00 – Measuring ROI & Knowing When to Pivot25:30 – Fail Fast, Learn Faster: Lessons from Pilots26:30 – Growth Mindset & Continuous Reinvention27:10 – Closing Thoughts & Takeaways

Can We Trust AI? How far are we from full automation & control to AI #ai #skills #tools #startup

In this episode of Data & AI Exchange (DAX) by Unolabs, we ask the big question: Can AI be trusted in healthcare, pharma, and beyond?Our guest, Dr. Thibault Géoui — a leading AI voice in biotech, an AI consultant, and host of the Tech & Drugs podcast — breaks down the challenges of building trust in AI: from drug discovery to diagnostics and even robotic surgery.What You’ll Learn: - Why “trust in AI” goes beyond accuracy and fairness - The hidden risks of bias in pharma and life sciences data - How AI is already cutting drug R&D timelines from 6 years to 8 months - Real examples where AI outperforms humans in diagnostics - Why benchmarks don’t always capture AI performance - Will we ever trust AI enough for autonomous surgeries or initial diagnostics?Whether you’re an AI enthusiast, healthcare professional, or just curious about the future of technology, this conversation will give you real-world insights into where AI delivers, where it fails, and why trust is the ultimate barrier.Don’t forget to subscribe for more discussions on AI, trust, and the future of technology.00:00 – Can AI replace doctors in the next 10 years?00:29 – Guest intro: Dr. Thibault Géoui, biotech & AI leader01:12 – Why pharma is slow to adopt new technologies02:26 – The gap: 24,000 diseases vs. only 3,000–4,000 drugs03:33 – Drug development costs ($2–6B, 10–12 years) & 95% failure rate04:47 – Why pharma is excited about AI as a new tool05:50 – Trust in AI: accuracy, fairness, reliability, or full lifecycle?07:40 – Pharma’s AI adoption “purgatory” explained09:01 – Why current AI benchmarks don’t tell the full story11:44 – Can bias in AI models ever be fully eliminated?12:53 – Pharma’s own bias: drugs developed for a “typical” patient14:40 – Biological diversity in drug effectiveness (genetics, enzymes, etc.)16:26 – Data sourcing challenges: pharma sitting on tons of unusable data18:07 – Historical vs. new data: accessibility and usability19:17 – Vinod Khosla’s prediction: AI watch for early diagnostics20:15 – AI in cancer imaging: from manual to automated precision22:11 – Specific AI use cases outperforming humans (chess, Go, diagnostics)23:01 – Cutting drug R&D timelines: from 6 years to 8 months24:40 – Preparing oversight mechanisms for AGI-level AI25:16 – Lessons from self-driving cars & autopilot in planes26:20 – Robotic arms in surgery: augmentation vs. full automation27:55 – Sci-fi to reality: Clarke’s novel vs. today’s surgical robots29:07 – AI-assisted vs. autonomous surgery — what’s realistic?29:48 – Closing thoughts: AI’s future in healthcare & pharma#CanAIBeTrusted #ArtificialIntelligence #AIinHealthcare #AItrust #PharmaInnovation #DrugDiscovery #AIMedicine

Middle Managers Upgrading Themselves with AI Skills | AI Tools for High Performance #ai #skills

#agenticai #manager #podcastIs AI Replacing Middle Managers – or Making Them 10x More Powerful?In this power-packed episode of Data & AI Exchange, we dive deep into the evolving role of middle managers in an AI-first corporate world. Are they being replaced? Or are they becoming the most valuable link between humans and machines?Featuring Sandeep Sudarshan CTO at Capgemini UK, with 30+ years of global experience in digital transformation across six continents, this conversation explores: * How AI is shifting the role of managers from supervision to orchestration * Two game-changing AI modes: Co-Pilot vs Co-Thinker * The tools (like Copilot, Gemini, etc.) that are revolutionizing daily productivity * Why data literacy, AI ethics, and prompt engineering are must-have skills * How middle managers can lead AI transformation while supporting their teams * Real-world use cases in sales forecasting and telecom profitability * The risk of falling behind without upskilling in AI * And why AI competency is now tied to promotions and performance reviews Recommended Book: "Generative AI for Managers – by Capgemini & Harvard Business Review"Whether you’re a manager, tech lead, or aspiring leader, this episode gives you a clear roadmap to thrive with AI, not fear it.00:00 – Is AI replacing middle managers or making them 10x better?00:38 – Guest intro: Sepran, CTO at Capgemini UK01:03 – Role of middle managers in an AI-first world01:56 – Co-Pilot vs. Co-Thinker AI usage03:00 – What leadership expects from managers now04:44 – Tools like Copilot & Gemini transforming workflows06:58 – Does AI reduce brainstorming time?08:05 – Shift from FTE to outcome-based models09:55 – Leading AI change with empathy & communication11:08 – Is AI competency affecting promotions?12:24 – Overuse of ChatGPT in evaluations13:50 – Importance of filtering information15:10 – Prompt engineering & agent-building for managers17:34 – AI in sales forecasting & telecom profitability20:04 – Changing performance metrics with AI21:55 – Risks of not upskilling in AI23:05 – Managers as a bridge between AI tools & strategy24:20 – Key skills for future-ready managers26:05 – Layoffs, effort estimation & AI’s role27:38 – Book rec: Generative AI for Managers28:00 – Final wrap-up & thanks#AI #MiddleManagement #DigitalTransformation #AILeadership #Capgemini #GenerativeAI #Productivity #DataDriven #PromptEngineering #Copilot #WorkplaceAI #AIForManagers #LeadershipDevelopment #FutureOfWork #AIUpskillingAI, Middle Management, Digital Transformation, AI Leadership, Capgemini, Generative AI, Productivity, Data Driven, Prompt Engineering, Copilot, Workplace AI, AI for Managers, Leadership Development, Future of Work, AI Upskilling, AI Tools for Managers, AI Strategy, AI in Business, ChatGPT for Work, Enterprise AI