ODSC's Ai X Podcast

ODSC
  • ٥٫٠ (٥)
  • التكنولوجيا
  • يتم التحديث أسبوعيًا

With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community. Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/ Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

  1. From AI Hype to Enterprise Execution: How to Actually Scale AI in 2026 with Linda Yao

    ٢٧ مارس

    From AI Hype to Enterprise Execution: How to Actually Scale AI in 2026 with Linda Yao

    In this episode of the ODSC Ai X Podcast, host Cal Al-Dhubaib sits down with Linda Yao, Vice President and General Manager of Hybrid Cloud and AI Solutions at Lenovo Services Group, an $8B global business operating across 180+ countries. Linda shares how enterprises can move beyond AI experimentation into real-world execution by focusing on hybrid AI strategies, governance, and organizational readiness. Drawing from Lenovo’s own internal AI journey, she explains why success depends not just on models, but on systems, people, and process—and what it truly takes to scale AI securely and effectively in today’s enterprise landscape. Key Topics Covered: What “hybrid AI” means and why bringing AI to your data is critical for enterprise success The gap between AI excitement and real-world execution in organizations Why most enterprises are stuck in pilot mode and how to move toward production The importance of data quality, governance, and security as foundational AI requirements Differences in perspective between CEOs, CFOs, and CIOs when adopting AI Real-world enterprise AI use cases, including supply chain optimization and contact centers Human + AI collaboration: augmenting employees rather than replacing them Building trust in AI through responsible AI principles, transparency, and governance The role of organizational readiness across people, process, data, and infrastructure How AI adoption is driving improvements in data management and operational discipline Why orchestration—not just models—is the true source of competitive advantage in AI The rise of agentic and physical AI systems and what’s coming next Career lessons from three waves of enterprise AI and how to take advantage of disruption Memorable Outtakes: “One of the biggest lessons we’ve learned is that AI success is never just about the model or the technology—it’s about all the systems and people around it.” “There’s a lot of excitement around AI, but not always a clear path on how to scale it—especially in a secure and sustainable way.” “Even with the best conversational AI, you can’t talk your way to AI success.” References & Resources: - Linda Yao’s LinkedIn: https://www.linkedin.com/in/lindayao/ - Lenovo news + blog: https://news.lenovo.com/ - Lenovo AI services: https://www.lenovo.com/hk/en/services/ai-services Sponsored by: This episode was sponsored by: ODSC AI East 2026 – The Leading AI Training Conference Join us in Boston from April 28th–30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Learn more: https://odsc.ai/east Use the code podcast for an additional 10% off.

    ٤٥ د
  2. Open Source Coding Agents: A New Developer Workflow with Robert Brennan

    ٢٠ مارس

    Open Source Coding Agents: A New Developer Workflow with Robert Brennan

    In this episode, Sheamus McGovern speaks with Robert Brennan, Co-Founder and CEO of OpenHands, about the rapid evolution of AI coding agents and how they are reshaping software development. Robert shares insights on how developer workflows are changing, from single-agent pair programming to large-scale multi-agent systems operating in parallel. The conversation explores the rise of open source AI, model-agnostic development, emerging roles like “agent orchestrators,” and the practical realities of deploying AI safely in enterprise environments. Speaker: Robert Brennan GitHub: https://github.com/OpenHands Website: https://www.openhands.dev Twitter/X: https://twitter.com/brandondbrennan Key Topics Covered: How AI coding agents have improved with newer models and become part of daily development workflows The shift from developers writing all code to supervising and guiding AI-generated code Differences in enterprise adoption of AI—forward-thinking vs. cautious organizations Why multi-agent “teams” haven’t worked as expected—and the rise of subagents and parallelization Using parallel agents to dramatically speed up tasks like vulnerability remediation The emergence of new roles such as “agent orchestrators” and “agent pilots” Local (IDE-based) vs. cloud-based agent workflows and tooling differences The importance of observability and measuring agent performance at scale Why open source and model-agnostic approaches are becoming more important The OpenHands Index and challenges with benchmarking AI coding performance Risks and realities of open source models, including data sovereignty and geopolitical considerations The concept of inner loop vs. outer loop development and where agents provide the most leverage Real productivity gains (20–40%) vs. hype around AI replacing developers Security risks and lessons from experimental open agent systems like OpenClaw Practical advice for engineers: experiment, build custom workflows, and stay hands-on Memorable Outtakes: “These agents are just getting more and more useful,and harder to ignore in terms of the value they create.” “We’re used to writing every line of code ourselves—and that is changing. That’s a little scary.” “If you’re trying to adopt AI at scale, you should be trying to get more done—not just cut your workforce.” References & Resources: OpenHands: https://www.openhands.dev OpenHands GitHub: https://github.com/OpenHands Laminar (AI observability): https://github.com/lmnr-ai/lmnr OpenHands Index (AI model benchmarking): https://www.openhands.dev/index Agent Skills standard: https://agent-skills.ai ARC Prize: https://arcprize.org Sponsored by: This episode was sponsored by: 🔥 ODSC AI East 2026 – The Leading AI Training Conference Join us in Boston from April 28th–30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Learn more: https://odsc.ai/east

    ٤٦ د
  3. Building the AI Ecosystem in Africa: Innovation, Community, and the Future of Intelligence with Benjamin Rosman

    ١٣ مارس

    Building the AI Ecosystem in Africa: Innovation, Community, and the Future of Intelligence with Benjamin Rosman

    In this episode of the ODSC Ai X Podcast, host Alex Landa speaks with Benjamin Rosman, Professor at the University of the Witwatersrand and Director of the Machine Intelligence and Neural Discovery (MIND) Institute. Rosman is also Director of the RAIL Lab, Co-Founder of Lelapa AI, and Co-Founder of the Deep Learning Indaba. The conversation explores the growth of the AI ecosystem across Africa, the importance of community-driven initiatives like Deep Learning Indaba, and how researchers are tackling challenges such as low-resource languages, healthcare access, and agricultural innovation. Rosman also discusses interdisciplinary research at the MIND Institute and shares his vision for the next decade of AI development on the African continent. Key Topics Covered: - Benjamin Rosman’s journey into artificial intelligence and his early interest in computer science and gaming - The evolution of AI research and education opportunities in Africa over the past two decades - The founding and growth of Deep Learning Indaba and its role in building a pan-African AI community - How grassroots initiatives and community networks are strengthening AI talent across the continent - Real-world AI applications emerging from African researchers, including: + Healthcare diagnostics and medical prioritization systems + Agricultural disease detection using mobile devices + Financial inclusion and access to services + Educational tools for underserved communities - The concept of frugal innovation and designing AI solutions that run on limited infrastructure or mobile devices - Research challenges in Africa, including funding limitations, compute access, and infrastructure constraints - The role of interdisciplinary collaboration at the MIND Institute - AI research topics, ranging from cultural value alignment in AI systems to animal cognition and elephant communication - The importance of social impact, policy engagement, and responsible technology deployment - A vision for the future of Africa’s AI ecosystem and how young technologists can participate Memorable Outtakes from Benjamin Rosman: “There’s always do the coolest things you possibly can—ideally with the coolest people you can.” “You go into places people might think of as completely backward or disconnected, and you find people doing technical work I couldn’t even imagine doing.” “If you can just give young people the tools and resources, they’ll figure out the cool things to build.” Resources: 1. LinkedIn: https://www.linkedin.com/in/benjamin-rosman-a734792b/ 2. Machine Intelligence and Neural Discovery (MIND) Institute: https://www.wits.ac.za/mind/ 3. The Deep Learning Indaba: https://deeplearningindaba.com/ 4. Robotics, Autonomous Intelligence and Learning (RAIL) Lab: https://www.raillab.org/ 5. Lelapa AI: https://lelapa.ai/ 6. TIME Magazine Feature: https://time.com/collections/time100-ai-2025/7305865/benjamin-rosman/ 7. University of the Witwatersrand: https://www.wits.ac.za/ Sponsored by: 🔥 ODSC AI East 2026 – The Leading AI Training Conference Join us in Boston from April 28th–30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Learn more: https://odsc.ai/east

    ٤٢ د
  4. Smarter Per Watt with David vonThenen

    ٦ مارس

    Smarter Per Watt with David vonThenen

    In this episode of the ODSC AIx Podcast, host Sheamus McGovern sits down with David vonThenen, Senior AI/ML Engineer in the Office of the CTO at NetApp. David is a seasoned keynote speaker and open-source contributor with deep expertise in Agentic AI, deep learning, model optimization, cloud-native architectures, and retrieval-augmented generation. Drawing on a career that spans enterprise storage, Kubernetes, and conversational AI, David brings a uniquely grounded, hardware-aware perspective to the fast-moving world of AI systems engineering. The conversation covers the real-world gap between AI demos and production deployments, the industry-wide pivot toward efficiency and smaller models, quantization fundamentals, Hybrid RAG architectures combining vector embeddings and knowledge graphs, multi-agent protocols including MCP and Agent-to-Agent, and the critical role of governance and observability in regulated industries. Key Topics Covered The demo-to-production gap: why RAG and agentic systems that shine in demos often break in real-world deployments The efficiency paradigm shift: industry's move from parameter-count maximalism to smarter-per-watt models The DeepSeek moment: how a cost-efficient open-weight model rattled markets and reframed AI infrastructure investment Quantization explained: what happens when you move from FP16 to INT8 or INT4, including accuracy tradeoffs and hardware compatibility caveats Fine-tune first, quantize second: David's preferred workflow for building specialized, cost-efficient agents Small Language Models (SLMs) as preferred engines for multi-agent systems: cost, speed, reduced hallucination, and domain focus Microservices for cognition: why SLM-powered agents mirror the design principles of microservice architecture Agent swarms and ensemble voting: use cases where redundancy and majority-voting models add genuine value Hybrid RAG: combining vector embeddings with knowledge graphs to add data relationships and improve reasoning quality Is SaaS dead? How agentic AI is reshaping software consumption and the enduring role of human judgment Memorable Outtakes 1. David on why SLMs reduce hallucination: "That subject matter expert is geared towards answering one particular domain. It's much easier to get those small language models to say, 'I don't know what you're talking about.' And by doing that, you can improve the answers overall for your agentic systems when you string these things together." 2. David on the microservices analogy for multi-agent AI: "We're talking about microservice architecture for language models. You're creating purpose-built microservices that handle one specific thing. That's the way I like to think about it." Resources: LinkedIn: https://www.linkedin.com/in/davidvonthenen/ GitHub: https://github.com/davidvonthenen NetApp: https://www.netapp.com bitsandbytes (quantization library by Hugging Face): https://github.com/bitsandbytes-foundation/bitsandbytes Unsloth (open-source quantization and fine-tuning library): https://github.com/unslothai/unsloth Past ODSC Workshops & Code Resources Workshop: Adaptive RAG Systems with Knowledge Graphs - Building Reinforcement-Learning-Driven AI Applications (ODSC East 2025): https://github.com/davidvonthenen/2025-odsc-east-workshop Workshop: Rethinking RAG - How MCP and Agent2Agent Will Transform the Future of Intelligent Search (ODSC West 2025): https://github.com/davidvonthenen/2025-odsc-west-workshop From the Host Sheamus is currently writing The AI Skill Flip, a forthcoming book exploring how AI is reshaping the skills workers need to stay relevant — and how individuals and organizations can get ahead of the shift. Stay tuned for updates. Sponsored By ODSC AI East 2026 - The Leading AI Training Conference Join us in Boston from April 28th-30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Learn more: https://odsc.ai/east

    ٤٨ د
  5. The AI Hiring Gap: Skills, Signals, and “Hidden Workers” Joseph Fuller

    ٢٧ فبراير

    The AI Hiring Gap: Skills, Signals, and “Hidden Workers” Joseph Fuller

    In this episode, host Sheamus McGovern sits down with Joseph B. Fuller, Professor of Management Practice at Harvard Business School, founder and co-leader of HBS’s Managing the Future of Work initiative, to unpack a core paradox: why companies insist they can’t find talent while qualified workers struggle to get hired. Fuller explains how “spot-market, real-time hiring,” weak workforce planning, credential inflation, and broken screening systems created today’s hiring gridlock. We also discuss how AI (especially agentic AI and decision intelligence platforms) may accelerate task displacement, reshape talent pipelines, and force organizations to redesign work rather than “bolt on” tools. Guest: Joseph B. Fuller (Harvard Business School Faculty Page): https://www.hbs.edu/faculty/Pages/profile.aspx?facId=123284 Key Topics Covered: The “talent paradox”: why employers say they can’t hire while qualified workers struggle to get hired How “spot-market” recruiting replaced workforce planning—and why it breaks Credential inflation and why degree requirements became a default hiring proxy Demographics, caregiving, and the shrinking labor supply across developed economies Why “soft skills” drive churn, but most hiring processes don’t measure them well How AI changes work at the task level, and why organizations must redesign workflows Agentic AI and decision-intelligence layers: moving from data → decisions → actions faster The risk to entry-level roles and the talent pipeline as AI boosts senior productivity “Context engineering” as the real differentiator Practical pathways to opportunity: skills-based hiring, apprenticeships, and competency-based education models Joseph Fuller Memorable Outtakes: On the real AI skill: "I'm urging my companies to stop calling people prompt engineers and to call them context engineers. On the two types of AI companies: "My shorthand is that the first pool of companies are AI efficiency companies, and the second pool are AI abundance companies. About 12% of jobs in the United States, AI is already more efficient than an entry-level worker. Both the abundance and the efficiency of the company will just take the win. But the very important distinction has to do with workforce demographics and the necessity of human interaction with AI." On AI outcomes: "There's this huge focus on hallucination results, but if you look at the distribution of results, it's a bell curve. There are plenty of times when the AI is generating an insight that is genuinely valuable, References & Resources: Joseph B. Fuller Faculty Page: https://www.hbs.edu/faculty/Pages/profile.aspx?facId=123284 Managing the Future of Work (Harvard Business School): https://www.hbs.edu/managing-the-future-of-work/Pages/default.aspx About the Project (Managing the Future of Work): https://www.hbs.edu/managing-the-future-of-work/about-the-project/Pages/default.aspx Project on Workforce at Harvard (home): https://pw.hks.harvard.edu/ No Country for Young Grads (Burning Glass Institute): https://www.burningglassinstitute.org/research/no-country-for-young-grads The Expertise Upheaval—How Generative AI’s Impact on Learning Curves Will Reshape the Workplace (HBS item page): https://www.hbs.edu/faculty/Pages/item.aspx?num=68582 The Expertise Upheaval (Burning Glass Institute): https://www.burningglassinstitute.org/research/the-expertise-upheaval Sponsored by: This episode was sponsored by ODSC AI East 2026 – The Leading AI Training Conference Join us in Boston from April 28th–30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Learn more: https://odsc.ai/east

    ٥٦ د
  6. What's on the Chief AI and Data Officer's Mind in 2026 with Salema Rice

    ٢٠ فبراير

    What's on the Chief AI and Data Officer's Mind in 2026 with Salema Rice

    In this episode of the ODSC AI Podcast, host Cal Al-Dhubaib sits down with Salema Rice, a pioneering Chief Data & AI Officer and founder of Intelecon Global. With more than 22 years of executive experience leading data and AI at organizations including Allegis Group, Bain Capital, Accenture, and in partnership with Microsoft, Salema shares what’s really happening behind the scenes in enterprise AI transformation. The conversation explores the human side of AI leadership in 2026—burnout at the executive level, the “frozen middle” in organizations, the importance of data foundations, and why compassion and culture are the true differentiators for long-term AI success. Salema also discusses her work building a global community of 500+ senior data, AI, and technology leaders through Intelecon Global and previews her upcoming data leadership initiative with Microsoft. Key Topics Covered: The real pressures facing Chief Data & AI Officers in 2026 Executive burnout and the growing expectations from boards and CEOs The “frozen middle” problem in AI-driven transformation Why AI success starts with responsible data and strong data governance “Garbage in, garbage out” in the era of generative and agentic AI The importance of building trusted data foundations before scaling AI The origin and global expansion of Intelecon Global Peer-to-peer executive problem-solving and experience-sharing forums Leading with compassion in technical and executive roles How culture remains the #1 challenge for Chief Data Officers Hiring beyond tools: focusing on problem solvers over specific technologies The shift from dashboards to real-time, action-oriented AI insights Moving from AI hype and pilots to measurable business value The evolving role of the Chief Data & AI Officer across industries Why data and AI leaders must think like business value drivers, not cost centers Talent strategy, upskilling, and the future of AI-augmented work Memorable Outtakes: On Data Foundations and AI “AI didn’t abruptly start in November of 2022 with ChatGPT. We’ve been doing AI for a long time. The critical factor to responsible AI is responsible data.” On Leadership and Culture “If you want to trust the data, you have to first trust the data leader. And trusting the data starts with trusting me.” On AI and Human Work “Every job in the world will be X plus AI in the future. But AI can’t replace the human moments that matter most.” On Mindset and Possibility “There’s a lot of magic and pixie dust in the art of possible. Even if you can’t do it all today, it doesn’t mean you can’t do it.” References & Resources: Salema’s LinkedIn: https://www.linkedin.com/in/sjrice/ Cal’s LinkedIn: https://www.linkedin.com/in/dhubaib/ Intelecon Global (Official Website): https://intelliconglobal.com/ Sponsored by: 🔥 ODSC AI East 2026 – The Leading AI Training Conference Join us in Boston from April 28th–30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Learn more: https://odsc.ai/east Use the code podcast for an additional 10% off.

    ١ س ٨ د
  7. Orchestrating AI for Business Outcomes with Michelle Bonat

    ١٣ فبراير

    Orchestrating AI for Business Outcomes with Michelle Bonat

    AI has hit a real turning point: success isn’t just about model accuracy anymore, as it’s about how well the full system works together. In this episode, Sheamus McGovern sits down with Michelle Bonat (Head of AI at Golden Pear Funding; former Chief AI Officer & CTO at AI Squared) to explore what it takes to move from AI vision to orchestrated, observable, and governed systems that drive measurable business results. Drawing on experience leading large AI programs at firms like JPMorgan Chase and in venture-backed startups, Michelle shares practical frameworks for building AI that’s reliable, explainable, auditable, and goal-aligned—and highlights where the real innovation is happening today, from agent coordination and real-time monitoring to compliance automation and trust frameworks. Key Topics Covered: Why “models don’t create value.” The shift from model race to the systems/orchestration era What orchestration means in practice for real AI programs The “system layer”: context, governance, observability, trust The paradox of AI observability and what to measure in production Governance as leverage (not overhead) for faster, safer deployment Avoiding “POC purgatory” by tying AI to one owner and one business metric Memorable Outtakes from Michelle Bonat : “Models don’t create value. Systems do.” “Trust in AI is not a feeling. It’s a behavior.” “Autonomy without orchestration is just automated risk.” “Orchestration is what turns AI from a science project into infrastructure.” References & Resources: Michelle Bonat (LinkedIn): https://www.linkedin.com/in/mbonat/ Michelle Bonat (Website): https://michellebonat.com/ Who is Watching Your AI Agents? How To Do AI Observability with AI (The Paradox of AI Observability): https://www.linkedin.com/pulse/who-watching-your-ai-agents-how-do-observability-michelle-bonat-zplyc/ A Step by Step Guide to Creating your Organization’s AI Playbook: Boat or Moat?: https://www.linkedin.com/pulse/step-guide-creating-your-organizations-ai-playbook-boat-bonat-vqjaf Book reference: Agentic Artificial Intelligence: https://www.amazon.com/Agentic-Artificial-Intelligence-Harnessing-Reinvent-ebook/dp/B0F1DS36YC Sponsored by: This episode was sponsored by: 🔥 ODSC AI East 2026 – The Leading AI Training Conference Join us in Boston from April 28th–30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Use the code podcast for 10% off. Learn more: https://odsc.ai/east

    ٤٤ د
  8. Document AI Workflows: Automating Real-World Document Work with Jerry Liu

    ٦ فبراير

    Document AI Workflows: Automating Real-World Document Work with Jerry Liu

    In this episode of the ODSC AIx Podcast, Sheamus McGovern speaks with Jerry Liu , Co-Founder & CEO, LlamaIndex, about the shift from early “RAG frameworks” to document AI workflows and the practical layer enterprises need to turn PDFs, Word docs, PowerPoints, and spreadsheets into reliable, structured inputs for agents and automation. Jerry explains why legacy OCR and “naive RAG” often fail (loss of structure, bundled packets, fragile templates), how VLM-based parsing changes the game, and what it takes to scale document workflows—from small file-system-based prototypes to production retrieval across millions of documents. Key Topics Covered: Jerry’s background: ML engineering and research experience (Robust Intelligence, Why LlamaIndex says “RAG 1.0 is dead” LlamaIndex’s current mission: document AI workflows Where legacy OCR breaks: tables, charts, layout, “coordinate-based fragility,” VLM-powered document understanding Document parsing outputs that matter downstream: Why “naive RAG” struggles with documents The evolution from search/chat to agentic workflows that “do work” (process automation, exception handling, human-in-the-loop) Enterprise readiness and Trends: coding agents and computer-use agents; Practical advice: scope deterministic vs assistant use cases Memorable Outtakes: Jerry: “It didn’t matter how good the LLM was because you just weren’t able to actually do anything with this data.” Jerry: “If there weren’t regulations, bureaucracy… you could start automating a good 30 to 50% chunk of an entire human’s day.” References & Resources: Jerry Liu (Co-Founder & CEO, LlamaIndex) X (Twitter): https://x.com/jerryjliu0 LinkedIn: https://www.linkedin.com/in/jerry-liu-64390071/ LlamaIndex X (Twitter): https://x.com/llama_index Llama Cloud signup: https://cloud.llamaindex.ai LlamaIndex 10,000 free credits https://login.llamaindex.ai/sign-up Sponsored by: This episode was sponsored by: 🔥 ODSC AI East 2026 – The Leading AI Training Conference Join us in Boston from April 28th–30th for expert-led sessions on Agentic AI, AI Engineering, Data Science, Machine Learning, LLMOps, and AI-driven automation. Learn more: https://odsc.ai/east

    ٥٠ د
مشاهدة الكل (١٠٧)

التقييمات والمراجعات

٥
من ٥
‫٥ من التقييمات‬

حول

With Ai X Podcast, Open Data Science Conference (ODSC) brings its vast experience in building community and its knowledge of the data science and AI fields to the podcast platform. The interests and challenges of the data science community are wide ranging. To reflect this Ai X Podcast will offer a similarly wide range of content, from one-on-one interviews with leading experts, to career talks, to educational interviews, to profiles of AI Startup Founders. Join us every two weeks to discover what’s going on in the data science community. Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here - https://aiplus.training/ Don't miss out on this exciting opportunity to expand your knowledge and stay ahead of the curve.

المعلومات

  • صناع العمل
    ODSC
  • سنوات النشاط
    ٢٠٢٣ - ٢٠٢٦
  • الحلقات
    ١٠٧
  • موقع البرنامج على الويب
    ODSC's Ai X Podcast

قد يعجبك أيضًا