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  • #309 What Science Fiction Can Tell Us About the Future of AI with Ken Liu, Sci-Fi Author
    Technology and human consciousness are converging in ways that challenge our fundamental understanding of creativity and connection. As AI systems become increasingly sophisticated at mimicking human thought patterns, we're entering uncharted territory where machines don't just assist creative work—they actively participate in it. But what does this mean for the future of human creativity and our relationship with technology? How do we maintain meaningful human connections in a world where emotional labor is increasingly commoditized? As we navigate this rapidly evolving landscape, the question isn't just whether machines can think, but how their thinking will transform our own.Ken Liu is an American author of speculative fiction. A winner of the Nebula, Hugo, and World Fantasy awards, he wrote the Dandelion Dynasty, a silkpunk epic fantasy series, as well as short story collections The Paper Menagerie and Other Stories and The Hidden Girl and Other Stories. His latest book is All that We See or Seem, a techno-thriller starring an AI-whispering hacker who saves the world. He also translated Cixin Liu’s seminal book series, the Three-Body Problem. He’s often involved in media adaptations of his work. Recent projects include “The Regular,” under development as a TV series; “Good Hunting,” adapted as an episode in season one of Netflix’s breakout adult animated series Love, Death + Robots; and AMC’s Pantheon, with Craig Silverstein as executive producer, adapted from an interconnected series of Liu’s short stories. Prior to becoming a full-time writer, Liu worked as a software engineer, corporate lawyer, and litigation consultant. Liu frequently speaks on a variety of topics, including futurism, machine-augmented creativity, history of technology, bookmaking, and the mathematics of origami.In the episode, Adel and Ken explore the intersection of technology and storytelling, how sci-fi can inform AI's trajectory, the role of AI in reshaping human relationships and creativity, how AI is changing art, and much more.Links Mentioned in the Show:Ken’s BooksKen on Substack, Ken on XSkill Track: AI FundamentalsRelated Episode: What History Tells Us About the Future of AI with Verity Harding, Author of AI Needs YouRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business
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  • Industry Roundup #5: AI Agents Hype vs. Reality, Meta’s $15B Stake in Scale AI, and the First Fully AI-Generated NBA Ad
    Welcome to DataFramed Industry Roundups! In this series of episodes, we sit down to discuss the latest and greatest in data & AI. In this episode, with special guest, DataCamp COO Martijn, we touch upon the hype and reality of AI agents in business, the McKinsey vs. Ethan Mollick debate on simple vs. complex agents, Meta's $15B stake in Scale AI and what it means for data and talent, Apple’s rumored $20B bid for Perplexity amid AI struggles, EU’s push to treat AI skills like reading and math, the first fully AI-generated NBA ad and what it means for creative industries, a new benchmark for deep research tools, and much more.Links Mentioned in the Show:Meta bought Scale AIApple rumoured to buy trying to acquire Perplexity for $20BnMcKinsey's Seizing the Agentic AI Advantage reportThe first fully AI-generated NBA AdEU Generative AI Outlook reportMary Meeker's Trend in AI reportDeep research benchmarkRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business
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  • #308 A Framework for GenAI App and Agent Development with Jerry Liu, CEO at LlamaIndex
    The enterprise adoption of AI agents is accelerating, but significant challenges remain in making them truly reliable and effective. While coding assistants and customer service agents are already delivering value, more complex document-based workflows require sophisticated architectures and data processing capabilities. How do you design agent systems that can handle the complexity of enterprise documents with their tables, charts, and unstructured information? What's the right balance between general reasoning capabilities and constrained architectures for specific business tasks? Should you centralize your agent infrastructure or purchase vertical solutions for each department? The answers lie in understanding the fundamental trade-offs between flexibility, reliability, and the specific needs of your organization.Jerry Liu is the CEO and Co-founder at LlamaIndex, the AI agents platform for automating document workflows. Previously, he led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG, and worked on recommendation systems at Quora.In the episode, Richie and Jerry explore the readiness of AI agents for enterprise use, the challenges developers face in building these agents, the importance of document processing and data structuring, the evolving landscape of AI agent frameworks like LlamaIndex, and much more.Links Mentioned in the Show:LlamaIndexLlamaIndex Production Ready Framework For LLM AgentsTutorial: Model Context Protocol (MCP)Connect with JerryCourse: Retrieval Augmented Generation (RAG) with LangChainRelated Episode: RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI & Adjunct Professor at Stanford UniversityRewatch RADAR AI New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business
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  • #307 Human Guardrails in Generative AI with Wendy Gonzalez & Duncan Curtis, CEO & SVP of Gen AI at Sama
    The line between generic AI capabilities and truly transformative business applications often comes down to one thing: your data. While foundation models provide impressive general intelligence, they lack the specialized knowledge needed for domain-specific tasks that drive real business value. But how do you effectively bridge this gap? What's the difference between simply fine-tuning models versus using techniques like retrieval-augmented generation? And with constantly evolving models and technologies, how do you build systems that remain adaptable while still delivering consistent results? Whether you're in retail, healthcare, or transportation, understanding how to properly enrich, annotate, and leverage your proprietary data could be the difference between an AI project that fails and one that fundamentally transforms your business.Wendy Gonzalez is the CEO — and former COO — of Sama, a company leading the way in ethical AI by delivering accurate, human-annotated data while advancing economic opportunity in underserved communities. She joined Sama in 2015 and has been central to scaling both its global operations and its mission-driven business model, which has helped over 65,000 people lift themselves out of poverty through dignified digital work. With over 20 years of experience in the tech and data space, Wendy’s held leadership roles at EY, Capgemini, and Cycle30, where she built and managed high-performing teams across complex, global environments. Her leadership style blends operational excellence with deep purpose — ensuring that innovation doesn’t come at the expense of integrity. Wendy is also a vocal advocate for inclusive AI and sustainable impact, regularly speaking on how companies can balance cutting-edge technology with real-world responsibility.Duncan Curtis is the Senior Vice President of Generative AI at Sama, where he leads the development of AI-powered tools that are shaping the future of data annotation. With a background in product leadership and machine learning, Duncan has spent his career building scalable systems that bridge cutting-edge technology with real-world impact. Before joining Sama, he led teams at companies like Google, where he worked on large-scale personalization systems, and contributed to AI product strategy across multiple sectors. At Sama, he's focused on harnessing the power of generative AI to improve quality, speed, and efficiency — all while keeping human oversight and ethical practices at the core. Duncan brings a unique perspective to the AI space: one that’s grounded in technical expertise, but always oriented toward practical solutions and responsible innovation.In the episode, Richie, Wendy, and Duncan explore the importance of using specialized data with large language models, the role of data enrichment in improving AI accuracy, the balance between automation and human oversight, the significance of responsible AI practices, and much more.Links Mentioned in the Show:SamaConnect with WendyConnect with DuncanCourse: Generative AI ConceptsRelated Episode: Creating High Quality AI Applications with Theresa Parker & Sudhi Balan, Rocket SoftwareRegister for RADAR AINew to DataCamp?Learn on the go...
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  • #306 The Next Generation of Business Intelligence with Colin Zima, CEO at Omni
    The modern data stack has transformed how organizations work with data, but are our BI tools keeping pace with these changes? As data schemas become increasingly fluid and analysis needs range from quick explorations to production-grade reporting, traditional approaches are being challenged. How can we create analytics experiences that accommodate both casual spreadsheet users and technical data modelers? With semantic layers becoming crucial for AI integration and data governance growing in importance, what skills do today's BI professionals need to master? Finding the balance between flexibility and governance is perhaps the greatest challenge facing data teams today.Colin Zima is the Co-Founder and CEO of Omni, a business intelligence platform focused on making data more accessible and useful for teams of all sizes. Prior to Omni, he was Chief Analytics Officer and VP of Product at Looker, where he helped shape the product and data strategy leading up to its acquisition by Google for $2.6 billion. Colin’s background spans roles in data science, analytics, and product leadership, including positions at Google, HotelTonight, and as founder of the restaurant analytics startup PrimaTable. He holds a degree in Operations Research and Financial Engineering from Princeton University and began his career as a Structured Credit Analyst at UBS.In the episode, Richie and Colin explore the evolution of BI tools, the challenges of integrating casual and rigorous data analysis, the role of semantic layers, and the impact of AI on business intelligence. They discuss the importance of understanding business needs, creating user-focused dashboards, and the future of data products, and much more.Links Mentioned in the Show:OmniConnect with ColinSkill Track: Design in Power BIRelated Episode: Self-Service Business Intelligence with Sameer Al-Sakran, CEO at MetabaseRegister for RADAR AI - June 26New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business
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About DataFramed

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone. Join co-hosts Adel Nehme and Richie Cotton as they delve into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.
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