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DataCamp

DataFramed

Valerie Tiberius is the Paul W. Frenzel Chair in Liberal Arts and Professor of Philosophy at the University of Minnesota. She is an expert in ethics, moral psychology, and well-being, and the author of five books including What Do You Want Out of Life? and the forthcoming Artificially Yours: Real Friendship in a World of Chatbots (Princeton University Press, May 2026). She previously served as President of the Central Division of the American Philosophical Association.

In the episode, Richie and Valerie explore the purpose of friendship and whether AI can replicate it, the benefits and risks of chatbot companions for loneliness, how sycophantic AI responses distort advice and self-perception, the dangers of companion chatbots for children's social development, designing ethical AI companions that promote human flourishing, the zone of proximal development as a framework for better AI tools, and much more.

Links Mentioned in the Show:

Artificial Intimacy by Sherry Turkle • 
Being You: A New Science of Consciousness • by Anil Seth
Liberation Day: Stories • by George Saunders
Hard Fork podcast (NYT • )
Connect with ValerieAI-Native Course: Intro to AI for Work • Related Episode: 
#342 — "The Secrets to High AI Adoption" with Stefano Puntoni, Professor at Wharton

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#359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of Minnesota

Almost every AI agent demo lands in roughly the same place: it works most of the time, looks remarkable, and then fails in a way no one anticipated. Self-driving cars hit this wall a decade ago, and agents are running into it now. For data and AI teams, the question is no longer whether agents can complete a task — it's whether they can complete it reliably enough to remove the human reviewer. Which categories of work tolerate a 90% success rate? Which absolutely don't? And where should the next layer of guardrails sit?

Ruslan Salakhutdinov is a UPMC Professor of Computer Science at Carnegie Mellon University and one of Geoffrey Hinton's former PhD students. He has previously served as Director of AI Research at Apple and VP of Research in Generative AI at Meta. His research focuses on deep learning, reasoning, and AI agents.

In the episode, Richie and Russ explore the most exciting use cases of AI agents today, long horizon tasks, the credit assignment problem, multi-agent systems, designing reliable human-in-the-loop workflows, agent safety and guardrails, embodied and physical AI, lessons from self-driving cars, the difference between academia and industry, and much more.

Links Mentioned in the Show:

• Claude Code (Anthropic)

• Yutori

• Waymo

• Apple Project Titan

• DeepSeek-V3 Technical Report

• Kimi K2 Technical Report

• Connect with Ruslan: LinkedIn

• AI-Native Course: Intro to AI for Work

• Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop

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#358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon

The data field has changed shape faster than almost any other. The role that used to be a statistician became a data scientist, became an ML engineer, and is now morphing into AI engineer. Consulting firms are hiring fewer entry-level analysts and more vibe-coders who can ship AI systems to production. For data and AI professionals, this raises immediate questions. Which parts of the work are most exposed to automation, and which are not? Where should you invest your time? And which backgrounds are now producing the strongest hires, whether you are building a team or trying to join one?

Ben Zweig is the CEO and Co-Founder of Revelio Labs, where he leads the development of a universal HR database built on over a billion public employment profiles and more than 5 billion job postings. He holds a PhD in Economics from the CUNY Graduate Center and teaches Data Science and The Future of Work at NYU Stern. Before founding Revelio Labs, he managed Workforce Analytics projects in the IBM Chief Analytics Office and worked as a data scientist at an emerging-markets hedge fund. He is the author of Job Architecture: Building a Workforce Intelligence Taxonomy.

In the episode, Richie and Ben explore why hiring is a broken two-sided market, why jobs are bundles of tasks not skills, building universal taxonomies from billions of job postings, which data careers resist AI, advice for hiring data talent, when traditional NLP beats LLMs, and much more.

Links Mentioned in the Show:

Ben's book — Job Architecture: Building a Workforce Intelligence TaxonomyRevelio LabsO*NET — the US government occupational taxonomy Ben critiquesBaruch Lev — The End of AccountingHaskel & Westlake — Capitalism Without CapitalJustified Posteriors podcast (Andrey Fradkin & Seth Benzell) • Connect with Ben:
 LinkedInAI-Native Course: Intro to AI for Work • Related Episode:
 Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen

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#357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs

Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on?

Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of statistical modeling, machine learning, and production forecasting. He is the author of Hands-On Time Series Analysis with R, an open-source contributor, Docker Captain, and instructor. He holds an MA in Applied Economics and an MS in Actuarial Mathematics from the University of Michigan, where he began his journey learning time series on DataCamp — before going on to build his own course there.

In the episode, Richie and Rami explore time series foundation models and the case for scaling, traditional versus modern forecasting approaches, feature engineering in the business world, backtesting and model selection, risk management in automated forecasting, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more.

Links Mentioned in the Show:

Forecasting: Principles and Practice • (Rob Hyndman)
NixtlaskforecastProphet • Connect with
 RamiAI-Native Course: Intro to AI for Work • Related Episode:
 Developing Better Predictive Models with Graph Transformers

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#356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at Apple

Cloud data platforms now offer hundreds of services, plus a growing menu of SQL, NoSQL, and open source options. Unified environments promise a simpler path, but the hard trade-offs—consistency versus scale, single-writer versus sharded, RPO/RTO targets—still matter. In daily work, you may be deciding between SQL Server, Postgres, and a globally distributed JSON store, while also asking AI tools to draft queries and spot issues. Should you still learn SQL if an agent can write it? How do you validate the intent, performance, and security of generated queries? And can monitoring agents actually reduce on-call pain without taking away needed control?

Shireesh is the CVP of Databases at Microsoft. He leads product management, engineering, and cloud operations for Azure Databases as well as App Development for Microsoft Fabric. The products in his team’s portfolio include Azure SQL Database (on-prem, Hybrid and Cloud), Azure Cosmos DB, Azure PostgreSQL, and Azure MySQL.\\n\\n

Previously, as the Senior Vice President at SingleStore, Shireesh was responsible for end-to-end engineering and product vision of the company. Before moving to SingleStore, Shireesh was a founding member of Cosmos DB, where he architected, designed, and directly contributed to multiple key pieces of the services.\\n\\n

Shireesh has 20+ years of experience on large scale, big data, scale-out, relational and schema agnostic distributed systems across SQL, Azure Cosmos DB and PostgreSQL/Citus.

In the episode, Richie and Shireesh explore how AI agents are reshaping data stacks, why unified platforms like Fabric matter, how semantic models and ontologies reduce confusion in metrics, SQL and NoSQL choices on Azure, Postgres to Cosmos DB with guidance for builders, and much more.

Links Mentioned in the Show:

Microsoft FabricAzure Cosmos DBWhat is Azure SQL Database?Connect with ShireeshAI-Native Course: Intro to AI for WorkRelated Episode: Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at MicrosoftExplore AI-Native Learning on DataCamp

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#355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at Microsoft

Decision intelligence is showing up across data and AI teams as companies move beyond dashboards to decisions made with context. Graphs, entity resolution, and better data products are becoming core tools as messy, siloed data meets stricter risk and compliance needs. In day-to-day work, this means linking “James,” “Jim,” and “Jamie” across systems, enriching records with third‑party sources, and pushing models where the data already lives in your lakehouse. How do you trust your customer counts? Which links in a graph matter, and which are noise? Can graph-based context reduce LLM hallucinations enough for regulated decisions with humans still in-loop.

Jamie Hutton is the Co-founder and Chief Technology Officer of Quantexa, where he leads the company’s global research and development organization in advancing its market-leading Decision Intelligence Platform. With over two decades of experience pioneering data-driven technologies, Jamie has been at the forefront of innovations that connect and unify data at scale to solve complex real-world challenges. He is the creator of dynamic Entity Resolution, a pioneering capability that has redefined how the world’s leading organizations transform raw data into trusted, decision-ready intelligence. This innovation enables enterprises to prepare their data for AI, uncover new revenue streams, and expose hidden connections in even the most sophisticated criminal networks. By providing the foundation for accurate, explainable, and actionable insights, Jamie’s work has empowered governments, financial institutions, and global enterprises to make faster, smarter, and more confident decisions.

Prior to co-founding Quantexa, Jamie held senior technology and analytics leadership roles at SAS and Detica, where he delivered mission-critical solutions for organizations operating in some of the most complex and high-stakes environments in the world. Jamie holds a First-Class master’s degree in computer engineering and is recognized as a leading authority in contextual analytics, data integration, and applied AI for mission-critical decision-making.

In the episode, Richie and Jamie explore decision intelligence beyond BI, entity resolution across siloed data, building context graphs for fraud, AML, credit risk, and growth, how graph analytics separates meaningful links from noise, graph-RAG for LLMs to cut hallucinations, human-in-the-loop workflows, and ways to start today, and much more.

Links Mentioned in the Show:

QuantexaDun & Bradstreet Data EnrichmentConnect with JamieAI-Native Course: Intro to AI for WorkRelated Episode: How Optimization Powers Decision Intelligence with Duke Perrucci & Ed Klotz, CEO and Senior Mathematical Optimization Specialist at Gurobi OptimizationExplore AI-Native Learning on DataCamp

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#354 Beyond BI: Decision Intelligence with Graphs with Jamie Hutton, CTO at Quantexa

Data and AI platforms are racing toward agentic and even autonomous analytics. But the bottleneck is rarely the model—it’s data readiness: governed metrics, clear metadata, and a semantic layer machines can read. For data engineers and analysts, this shifts work from hand-built SQL and dashboard tweaks to designing meaning and trust. If an agent can draft column descriptions, propose a model for a new business question, and build the first dashboard layout, where do you add the most value? What do you measure to prove ROI in 30 days? How do you prevent “shiny demos” from driving strategy too early.

Ketan Karkhanis is the CEO of ThoughtSpot. Prior to joining the company in September 2024, Ketan was the Executive Vice President and General Manager of Sales Cloud at Salesforce. He returned to Salesforce in March 2022 after his time as the COO of Turvo, an emerging supply-chain collaboration platform. Before that, Ketan spent nearly a decade at Salesforce, where he led product areas in Sales, Service Cloud, Lightning Platform, and finally Analytics, wherein as the Senior Vice President & GM of Einstein Analytics, he pioneered incredible innovation, customer success, and business acceleration from launch to over $300M and a 30,000 strong user community. Prior to Salesforce, Ketan was at Cisco Systems where he led various technology initiatives and initiatives spanning Customer Advocacy, Cisco Certifications & eLearning.

In the episode, Richie and Ketan explore AI agents for analytics, why “self‑service BI” often fails, using agents to answer questions, build dashboards, and automate data modeling, how analyst and engineer roles shift toward governance and agent design, how transparency, culture, and ROI drive safe adoption, and much more.

Links Mentioned in the Show:

ThoughtspotThoughspot’s Spotter AgentsConnect with KetanAI-Native Course: Intro to AI for WorkRelated Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNSExplore AI-Native Learning on DataCamp

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

#353 The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot

AI agents are spreading across the data and AI industry, promising to automate everything from research to outreach. At the same time, teams are learning that these tools can hallucinate, leak data, or act in surprising ways. In day-to-day work, the challenge is deciding which tasks to hand off, what data to share, and how to keep the output trustworthy. Do your agents actually add value, or just add noise? Are they running in a secured, ring-fenced environment? How do you balance playful experimentation with critical checking when an agent confidently gets a key fact wrong?

Danielle leads go-to-market strategy at WNS, Capgemini's AI transformation services arm. Previously, Danielle was Chief Data Officer at American Express and Albertsons. She also write The Remix substack on technology trends, and is an Editorial Board Member for CDO Magazine.

In the episode, Richie and Danielle explore AI agents at work, experimentation with guardrails, data privacy, access, tone controls, OpenClaw automation wins and failures, token costs, tying AI plans to P&L strategy, shifts in careers and hiring, how data teams handle unstructured data governance, and much more.

Links Mentioned in the Show:

WNSConnect with DanielleAI-Native Course: Intro to AI for WorkCatch Danielle speaking at RADAR—April 1Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn’t Ready) with Stijn Christiaens, CEO at CollibraExplore AI-Native Learning on DataCamp

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#352 AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNS

World models are emerging as the next step after large language models, pushing AI from book knowledge toward systems that can simulate the physical and social world. Instead of just generating text or short videos, the goal is steerable simulation with long-horizon consistency and planning. For practitioners, this raises practical choices: what data and representations do you need, and when do you mix symbolic reasoning with generative models? How do you test whether a model can follow actions over minutes, not seconds? And where do you start—robotics, driving safety, or synthetic data generation?

Professor Eric Xing is President of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and a world-leading computer scientist whose work spans statistical machine learning, distributed systems, computational biology, and healthcare AI. A fellow of AAAI, IEEE, and the American Statistical Association, he has authored over 400 research papers cited more than 44,000 times.Before MBZUAI, Eric was a Professor of Computer Science at Carnegie Mellon University, where he also founded the Center for Machine Learning and Health. He is the founder and chief scientist of Petuum Inc., recognized as a World Economic Forum Technology Pioneer, and has held visiting roles at Stanford and Facebook. He holds PhDs in both Molecular Biology and Computer Science.

In the episode, Richie and Eric explore world models as simulators for action, the jump from book intelligence to physical and social skills, why long-horizon planning is still hard, architectures, robots, data generation, open K2 Think LLMs, virtual-cell biology, and much more.

Links Mentioned in the Show:

MBZUAIPan World ModelConnect with EricAI-Native Course: Intro to AI for WorkRelated Episode: Developing Better Predictive Models with Graph Transformers with Jure Leskovec, Pioneer of Graph Transformers, Professor at StanfordExplore AI-Native Learning on DataCamp

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#351 Will World Models Bring us AGI? with Eric Xing, President & Professor at MBZUAI

Across the AI industry, high-stakes tools are being deployed in places where errors can harm people: sepsis alerts in hospitals, identity checks, welfare fraud detection, immigration enforcement, and recommendation systems that shape life outcomes. The pattern is familiar: scale and speed go up, while human review becomes rushed, shallow, or punished for disagreeing. In daily work, that can look like a nurse forced to act on false alarms, or a team using an LLM summary in ways the designers never planned. When should you slow down deployment? How do you detect new “wild” use cases early? And what does responsible tracking and oversight look like under real pressure?

Atay Kozlovski is a Postdoctoral Researcher at the University of Zurich’s Center for Ethics. He holds a PhD in Philosophy from the University of Zurich, an MA in PPE from the University of Bern, and a BA from Tel Aviv University. His current research focuses on normative ethics, hard choices, and the ethics of AI.

In the episode, Richie and Atay explore why AI failures keep happening, from automation bias to opaque targeting and hiring models. They unpack “meaningful human control,” accountability, and design in healthcare, government, and warfare. You’ll also hear about deepfakes, consent, digital twins, and AI-driven civic engagement, and much more.

Links Mentioned in the Show:

“Lavender” IDF recommendation systemAmnesty International reports on AI/automation in welfare systems“Meaningful Human Control” (MHC) frameworkConnect with AtayAI-Native Course: Intro to AI for WorkRelated Episode: Harnessing AI to Help Humanity with Sandy Pentland, HAI Fellow at StanfordExplore AI-Native Learning on DataCamp

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#358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon

Lyt når som helst, hvor som helst

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