Discover how top universities are transforming teaching methods to prepare students for a future where AI is an essential co-pilot, not a crutch. In this eye-opening episode, seasoned professors from UVA’s business and commerce schools reveal the revolutionary shift from traditional teaching to a dynamic, AI-augmented learning experience.

You’ll hear from Ryan Wright, a UVA professor working to integrate AI into learning and research, Tim Laseter, who reflects on how the case method is evolving, and Keith McCormick, who brings a practical machine-learning perspective on AI’s real impact in organizations. Together, they unpack how students should be using AI as a co-creator, why judgment and critical thinking matter more than ever, and how educators can preserve genuine learning in an AI-driven world.

You'll learn how case method classrooms are becoming interactive labs where students collaborate with AI tools, analyze real-world data, and develop judgment skills that no AI can replace.

Ryan Wright wonders how we can teach the students not just to be ‘AI janitor,’ cleaning up and refining outputs, but rather co-creator partners of AI. Tim Laseter explores how to harness AI in experiential learning and live cases, making students thrive in an uncertain future. Keith McCormick highlights the importance of understanding organization memory and how AI can augment, rather than replace, the uniquely human skills of empathy and judgment.

In this episode, we also break down concrete tactics for educators and students alike: from using AI-driven peer reviews and meta-analysis to cultivating critical thinking through iterative questioning. The conversation then tackles the delicate balance of assessment in a world where AI is ubiquitous: should exams be oral, project-based, or involve real-time interrogation of AI outputs?

Beyond individual skills, the episode underscores a profound insight: how should we shape creativity in students? Possibly, a liberal arts foundation is the best strategy for future-proofing careers in the AI era. As AI accelerates change, the ability to see beyond AI-generated answers, ask the right questions, and think critically becomes invaluable.

This episode is essential listening for educators, students, and anyone curious about shaping the next generation’s skills in an AI-powered world. Whether you're at the start of your teaching journey or a seasoned professional grappling with assessment strategies, this discussion offers a fresh perspective on how to stay ahead. Prepare to rethink what it means to learn, teach, and succeed with AI as your co-creator, not your substitute.

It’s always a pleasure talking to Luis and Joshua. This episode, we covered ground that ranged from practical career advice to the cutting edge of AI agents and time series modeling.

What We Discussed

Career Paths in AI: Luis and Joshua shared insights on navigating roles in machine learning and data science, from industry trajectories to the skills that actually matter when building real systems.

AI Agents: We explored the state of autonomous agents. What’s hype, what’s working, and why the problems are harder than they initially appear. The conversation cut through the noise to focus on what’s actually deployable.

Foundational Models for Time Series: The deep dive here was substantial. Time series modeling with large language models is a frontier area. Neither Luis nor Joshua pulled punches about the complexity. We discussed architectural choices, training considerations, and the gap between benchmark results and production performance.

Why This Matters

The intersection of general-purpose foundation models and domain-specific challenges like time series is where a lot of real innovation happens. If you’re building forecasting systems or working with temporal data at scale, the patterns we discussed apply directly.

Thanks to Luis and Joshua for diving deep. This is the work that doesn’t make it into papers. It’s what practitioners actually need.

For the latest episode of My Data Guest, I sat down with Antonio Malia, a dear friend since our Pisa days, now founder of Seltz, a startup rethinking web search from the ground up. Not for humans this time. For machines.

The core problem

Google, Bing, and every search engine we grew up with were built around a simple human loop: type a query, scan snippets, click a link, read the page. Antonio’s insight is that this entire interface breaks down for an LLM. A snippet is, in his words, a movie trailer. It hints at the answer but doesn’t give it. So the model either guesses or burns tokens and seconds fetching the full page. Multiply that by every agentic loop, and you get slow, expensive, unreliable agents.

Seltz’s bet: build the crawler, index, retrieval, and ranking stack from scratch, optimized for a completely different reward function: one where the “user” is a language model, not a person.

Why it matters: latency and trust

Two things stood out from the conversation:

* Latency compounds. If an agent can get a search result back in 100ms instead of a second, it can run ten iterations in the time a traditional engine takes for one. That’s the difference between a shallow answer and genuinely complex task completion.

* Trust is the real target. Antonio framed it sharply: AGI, to him, is the point where we trust a machine’s completed process more than we’d trust another person’s. Web search is just the first, most obvious place models need reliable, constantly-updated access to the world. No more “airplane mode” after training cutoff.

Full stack, small team, Rust

Seltz owns everything: crawler, data connectors, knowledge pipeline, ranking. Building in Rust was a pragmatic call: efficiency, memory safety, and (notably) a much larger, more excited talent pool than C++ offers today. The team, partly ex-Amazon, partly IR-research veterans, is explicitly modeled on the “flat, high-trust, senior-heavy” structure Antonio admired at places like Netflix.

On coding agents

Antonio’s take on AI coding tools was refreshingly unfussy: they’re a boost only if you already know exactly what needs to be done. Used to offload thinking, they slow you down. Used to execute a decision you’ve already made, and to be challenged when they propose something better, they’re a genuine multiplier. Design decisions stay human.

What’s next

The next six months are about making Seltz’s product the main driver of value for customers who integrate it, validating the underlying research day by day rather than chasing a big splashy release. If you want to try it yourself: sign up in the console and connect via API/MCP, or just book a call with the team.

Hiring in tech has always moved fast, but few corners of it are shifting as quickly right now as recruitment itself. In this episode, I sat down with Bruno Lomardo — “Loma” to most people — who leads talent acquisition at Rasa, the conversational AI platform. We talked about what generative AI is doing to hiring, the strange new problem of deep fake candidates, and why getting diversity right isn’t a box-ticking exercise but a competitive advantage.

From recruiter to AI talent lead

Loma’s path into tech recruitment wasn’t a straight line, and that turns out to be part of why he sees the field clearly. He’s lived through hiring booms and the cooldowns that follow, watching the market swing from “we’ll take anyone who can write a for-loop” to far pickier, signal-hungry hiring. That cyclical experience shapes how he reads the current moment — because the latest swing isn’t really about headcount. It’s about the tools.

“We’ve been working with conversational AI for years,” he points out, and that long runway matters. At Rasa, AI isn’t a buzzword bolted onto the hiring process; it’s the product. So when generative AI started reshaping how candidates apply and how teams screen, the team had a head start in understanding both the upside and the failure modes.

What generative AI actually changed

The most visible effect of generative AI on recruitment is volume. Applications are easier to produce than ever, which sounds great until you realize a polished cover letter no longer tells you much. When everyone can generate a flawless, perfectly tailored application in thirty seconds, the signal that used to come from effort and craft starts to disappear.

That creates a real discrepancy in the market. On paper, candidates look stronger and more uniform than ever. In conversation, the gaps show. Loma’s takeaway is that the screening burden has shifted — away from filtering for basic competence on paper and toward verifying that the person behind the application is real, capable, and who they say they are.

Which leads to the most unsettling part of the conversation.

The deep fake problem

“Deep fakes are getting better and harder to detect,” Loma says, and he means it literally. Recruiters are now encountering candidates whose video presence, voice, or even live interview behavior may be synthetically generated or assisted. What started as an edge-case curiosity has become a credibility problem teams have to actively manage.

The detection methods are evolving alongside the threat — verification steps, live and unscripted interactions, and tooling built specifically to flag manipulated media. But Loma is candid that this is an arms race, and the honest position is to assume the fakes will keep improving. The practical defense is process: build interviews that are hard to fake your way through, and don’t outsource your judgment entirely to a tool that can be gamed.

Doing diversity right

Where the conversation turned genuinely energizing was on diversity, equity, and inclusion. Loma’s framing cuts through the usual debate: this isn’t charity and it isn’t optics. “Doing diversity right creates better teams,” he says — and the emphasis lands on right.

Done badly, diversity efforts become quotas chasing optics, which helps no one and breeds resentment. Done well, they widen the pipeline, surface candidates conventional processes overlook, and produce teams that make better decisions because they bring more perspectives to the table. The work is in the how: structured interviews, debiased job descriptions, broader sourcing, and consistent evaluation criteria that give every candidate a fair read.

The road ahead

Looking forward, Loma sees AI playing an even larger role in hiring — handling the repetitive front end, surfacing strong candidates faster, and freeing recruiters to do the human work that actually predicts success on a team. But the same technology that makes hiring more efficient is also what makes it harder to trust. The recruiters who thrive will be the ones who treat AI as a tool to sharpen judgment, not replace it.

If there’s one thread running through the whole conversation, it’s that: the fundamentals of good hiring — verification, fairness, and genuine human evaluation — matter more now, not less.

Educators today face one of the hardest challenges in history: preparing the next generation for a world we cannot yet imagine.

What will the job market of tomorrow actually require? Will we have to show AI fluency in a curriculum? Will critical thinking become the most valuable skill on a CV? Will knowing how to think alongside AI matter more than knowing how to use it?

Santosh Nair, Head of Data and AI at Siemens Healthineers, shares provocative insights on how AI is reshaping both learning and work and why human skills, like critical thinking and social intelligence, are becoming even more essential.

You’ll discover why protecting critical thinking, curiosity, and social skills is more vital than ever, especially for children and young adults navigating a rapidly changing world. Santosh challenges the traditional approach, emphasizing a human-centric education that evolves alongside technology.

We break down how AI is transforming roles like software engineering, from coding to problem-solving, and why mastering new skills like “AI fluency” will become the entry ticket to future jobs. Learn why old paradigms of assessment and learning need an overhaul and how teachers can turn AI into their greatest ally, fostering a richer, more connected classroom experience.

Stay tuned for actionable frameworks like the “six C’s” (Curiosity, Creativity, Connection, Collaboration, Critical thinking, and Courage) that can help us thrive in an AI-powered world.

Is AI a friend or a fiend to students? In this episode we talk about the AI friend; that AI that helps students reach academic success; that AI that allows students to enrich their curricula. In this compelling episode, we explore how universities are leveraging AI not to replace, but to empower educators, streamline operations, and personalize student support at scale.

Discover how Ai and classic predictive analytics and early warning systems are identifying at-risk students long before failure occurs, allowing institutions to intervene proactively instead of reactively. We break down real-world examples, from monitoring assignment engagement to financial support alerts, illustrating how AI helps advisors focus on students who need help most without replacing human oversight.

You’ll hear why scaling personalization in education is crucial when managing thousands of students and how AI supports tailored interventions that boost retention and success. We delve into the different facets of AI adoption in higher education, covering operational automation, predictive modelling, and chatbots, and discuss the importance of human oversight. The usage of AI always comes with some warnings. In this episode, you will gain insights into the challenges of data ethics, transparency, bias, and of course privacy

Moving onto the classroom, you’ll hear how educators are adapting assignments and assessments to prevent reliance on AI, which might lead to a loss of critical thinking. AI has changed the way we learn, making tests and note writing obsolete. You will hear about specific strategies for fostering genuine learning in a tech-enabled classroom.

In summary, this episode stresses that AI is a powerful tool when used responsibly. AI-personalized education can reduce administrative burdens, enhance early detection of academic issues, and enable educators to focus on mentorship and meaningful human connection.

This is an episode for educators, administrators, and policy makers eager to harness AI responsibly for tangible impact. Don’t miss this insightful discussion on the future of AI-powered education.

AI is changing the way developers build software.

Not only because models are getting better, but because the tools around them are becoming more flexible, more powerful, and more integrated into real business workflows.

In this episode of My Data Guest, I spoke with Alan Nichols, CTO and co-founder of Rasa, about how AI is reshaping developer tools, what enterprises need from AI agents, and why building scalable and maintainable systems is still one of the biggest challenges.

Rasa started almost ten years ago with a clear goal: bridge the gap between academic research in dialogue systems and practical tools that developers could actually use.

At the time, many teams were building chatbots for platforms like Slack and Facebook Messenger. But most systems were limited. They could handle simple flows, but they struggled with more complex conversations.

Rasa focused on giving developers the building blocks to create more robust dialogue systems. Instead of hiding everything behind a black box, the goal was to give teams control, flexibility, and the ability to build systems that could be maintained over time.

That developer-first mindset is still relevant today.

With the rise of large language models, the AI landscape has changed dramatically. Before ChatGPT, many enterprise teams were mostly worried about preventing errors. They wanted control, predictability, and strict guardrails.

After LLMs became mainstream, the conversation changed.

Companies started to see the potential of AI systems that could understand language more naturally, assist users more effectively, and create better experiences. The tradeoff is that these systems are less predictable than traditional software.

That means the challenge is no longer only about building something impressive. It is about building AI systems that enterprises can trust.

A key part of this is designing systems that can improve over time. AI agents should not only answer questions. They should learn from interactions, adapt to user needs, and provide developers with the feedback needed to make the system better.

This is where developer tools become critical.

Good AI tooling should help teams understand what the system is doing, where it fails, and how to improve it. For enterprises, maintainability matters as much as raw model capability.

The main takeaway from the conversation is simple: AI is transforming developer tools, but the fundamentals still matter.

Developers need control.Enterprises need reliability.Users need better experiences.And AI systems need to be designed so they can evolve.

The future of AI agents will not only depend on better models. It will also depend on better platforms, better workflows, and better tools for the people building them.

Listen to the episode

In this conversation, we cover:

* Rasa’s journey from dialogue systems to AI agents

* how AI is changing developer platforms

* the impact of LLMs on enterprise adoption

* why companies moved from skepticism to experimentation

* how to build AI systems with confidence

* why maintainability matters in enterprise AI

* the future of developer tools and AI agents

You connect an LLM to a few tools, give it a task, and suddenly it looks like the system can reason, plan, and act on its own.

But production is a different story.

In this episode of My Data Guest, I spoke with Dipanjan Sarkar, AI engineer and community leader at Analytics Vidhya, about what it really takes to build agentic AI systems that work reliably outside a notebook.

The main point was clear: agentic AI is not just a prompt, a framework, or a clever demo. It is an engineering problem.

A production system has to deal with messy user requests, tool failures, long context, security risks, hallucinations, monitoring, and evaluation. These are not details you add at the end. They need to shape the system from the beginning.

One topic we discussed was context engineering. In agentic systems, the model does not only receive a user prompt. It may also receive tool outputs, retrieved documents, previous steps, instructions, and business rules. If the context becomes too long or poorly structured, the system can fail in unpredictable ways.

As Dipanjan put it:

“Context window limitations cause system crashes.”

We also talked about hallucinations. Giving an LLM access to tools does not magically solve the problem. The model can still choose the wrong tool, misunderstand the output, or produce an answer that sounds correct but is not grounded in reality.

Another key point was evaluation. Asking the model how confident it is is not enough.

“Confidence scores are unreliable in LLMs.”

For real systems, teams need custom evaluation metrics, tracing, and monitoring. It is not enough to check the final answer. You also need to understand the steps the agent took to get there.

Security is another major concern. Once an agent can access APIs, documents, databases, or internal tools, the risks become much larger. Prompt injection, data leakage, unsafe actions, and poor governance all become real production problems.

The conclusion of the episode is simple: if you want to build agentic AI for production, start thinking like an engineer from day one.

Design for failure.Add observability.Evaluate the actual workflow.Set clear permissions.Keep humans in the loop where needed.

Agentic AI has huge potential, but the companies that succeed will not be the ones with the flashiest demos. They will be the ones that build systems that are reliable, observable, and safe enough to be trusted.

Listen to the episode

In this conversation, we cover:

* common mistakes when deploying agentic AI

* why demos often fail in production

* context engineering

* hallucinations and model limitations

* debugging and monitoring strategies

* evaluation challenges

* security and governance

* what may happen in the next few years