In Case You Missed It in April 2025

We had a record number of guests on my podcast in April — and they were spectacular! In today's "In Case You Missed It" episode, hear the best parts of my conversations with each of them.

The specific conversation highlights included in today's episode are:

1. Sama Bali from NVIDIA and Logan Lawler from Dell Technologies fill us in on the AI software stack on NVIDIA GPUs, including libraries like CUDA.

2. Continuing on the A.I. hardware topic, Emily Webber details Amazon Web Services (AWS)'s own A.I. accelerator chips.

3. Zerve AI's co-founder Dr. Greg Michaelson describes how data scientists can deploy A.I. models to production without needing to call on an engineering team.

4. Kai Beckmann, CEO of Merck KGaA's semiconductor business, describes intricate details of the semiconductors that make A.I. systems hum.

5. Finally, Shirish Gupta explains his "A-I-P-C" framework for finding out if you should be using edge compute for local A.I. inference instead of relying on cloud compute.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Serverless, Parallel, and AI-Assisted: The Future of Data Science is Here, with Zerve’s Dr. Greg Michaelson

What are "code nodes" and "RAG DAGs"? Listen to today's episode with the highly technical (but also highly hilarious) Dr. Greg Michaelson to get a glimpse into the future of data science and A.I. model development.

Greg:

• Is a Co-Founder of Zerve AI, a super-cool platform for developing and delivering A.I. products that launched to the public on this very podcast a little over a year ago.

• Previously spent 7 years as DataRobot’s Chief Customer Officer and 4 years as Senior Director of Analytics & Research for Travelers.

• Was a baptist pastor while he obtained his PhD in Applied Statistics!

Today’s episode is on the technical side and so will appeal most to hands-on practitioners like data scientists, AI/ML engineers and software developers… but Greg is such an engaging communicator that anyone interested in how the practice of data science is rapidly being revolutionized may enjoy today’s episode.

In it, Greg details:

• How Zerve's collaborative, graph-based coding environment has matured over the past year, including their revolutionary 'Fleet' feature (in beta) that allows massive parallelization of code execution without additional cost.

• How AI assistants are changing the coding experience by helping build, edit, and connect your data science projects.

• Why the rise of LLMs might spell trouble for many SaaS businesses as building in-house solutions becomes increasingly viable.

• The innovative ways companies are using retrieval-augmented generation (RAG) to create more powerful A.I. applications.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Become Your Best Self Through AI Augmentation — feat. Natalie Monbiot

The deep-thinking and highly articulate Natalie Monbiot returns to my podcast today for a can't-miss episode (one of my favorite convos ever) on how A.I. will overhaul our lives, our work, our society in the coming years.

More on Natalie:

• Through her consultancy, Virtual Human Economy, she advises on virtual humans and A.I. clones, including to startups like Wizly and investment firms like Blue Tulip Ventures.

• Was previously Head of Strategy at Hour One, a leading virtual-human video-generation startup.

• Regularly speaks at the world's largest conferences, including Web Summit and SXSW.

• Holds a Master's in Modern Languages and Literature from the University of Oxford.

Today’s fascinating episode will be of great interest to all listeners. In it, Natalie details:

• How A.I. is making us dumber — and what we can do about it.

• Why the "virtual human economy" could be the next evolution of human civilization.

• The two states of being humans are seeking (and how A.I. could help us achieve them).

• Why focusing on merely 10x’ing our capabilities misses the much bigger opportunity of A.I.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

In Case You Missed It in January 2025

Happy Valentine's Day 💘 ! My high-calorie gift to you is today's episode, which features the best highlights from conversations I had with the (absolutely epic!) guests I hosted on my podcast in January.

The specific conversation highlights included in today's episode are:

1. Famed futurist Azeem Azhar on how to break your linear mindset to prepare for the exponential technological change that we are experiencing (and will experience even more rapidly in years to come).

2. Global quantum-computing expert Dr. Florian Neukart on practical, real-world applications of quantum computing today.

3. Kirill Eremenko and Hadelin de Ponteves — who have together taught over 5 million people data science — with their 12-step checklist for selecting an appropriate foundation model (e.g., large language model) for a given application.

4. Brooke Hopkins (former engineer at Waymo, now founder and CEO of Y Combinator-backed startup Coval) on why you should evaluate A.I. agents with reference-free metrics.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Positron IDE, Tidy NLP and MLOps with Dr. Julia Silge

Prepare to have your brain tickled by Dr. Julia Silge. In today's episode, Julia details the IDE she's been developing for data scientists, "Tidy" NLP, and open-source libraries that make MLOps a breeze.

More on Julia:

• Engineering Manager at Posit PBC (makers of RStudio... and the company formerly known as RStudio).

• Authored the bestselling O'Reilly books “Text Mining with R” and “Tidy Modeling with R".

• Previously worked as a Data Scientist at Stack Overflow and Datassist.

• Prior to joining industry, was an academic researcher and professor at Yale University.

• Holds a PhD in Astronomy from The University of Texas at Austin.

Today’s episode will probably appeal most to hands-on practitioners like data scientists, software developers and ML engineers. In it, Julia details:

• The brand-new IDE Positron (free to use and source-available) that she’s been developing.

• Her favorite LLMs for code generation.

• The open-source software libraries that make MLOps easy.

• Her top tips for effective Natural Language Processing, including when more traditional NLP techniques should be used instead of an LLM.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Superintelligence and the Six Singularities, with Dr. Daniel Hulme

Artificial Superintelligence (ASI) could be realized in our lifetime... some even think within a few years. Today's brilliant guest, Dr. Daniel Hulme, details the six major ways society could be overhauled by ASI.

More on Daniel:

• Chief A.I. Officer at marketing giant WPP.

• CEO of A.I. consulting-services company Satalia.

• Entrepreneur-in-Residence at one of the world's top A.I.-research universities, UCL.

• Co-founder of Faculty and speaker at Singularity University.

• Holds an Eng.D. in computational complexity from UCL.

Today’s episode should be of interest to everyone. In it, Daniel details:

• How and when Artificial Super Intelligence (ASI) may arise.

• The six types of Singularity ASI is expected to unleash.

• Neuromorphic computing.

• How to align A.I. interests with human interests.

• Ways human work could be dramatically automated not just in the future… but this very day.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Deep Learning Classics and Trends, with Dr. Rosanne Liu

Today's guest is the amazing Google DeepMind research scientist, Dr. Rosanne Liu!

Rosanne:

• Is a Research Scientist at Google DeepMind in California.

• Is Co-Founder and Executive Director of ML Collective, a non-profit that provides global ML research training and mentorship.

• Was a founding member of Uber AI Labs, where she served as a Senior Research Scientist.

• She has published deep learning research in top academic venues such as NeurIPS, ICLR, ICML and Science, and her work has been covered in publications like WIRED and the MIT Tech Review.

• Holds a PhD in Computer Science from Northwestern University.

Today’s episode, particularly in the second half when we dig into Rosanne’s fascinating research, is relatively technical so will probably appeal most to hands-on practitioners like data scientists and ML engineers.

In today’s episode, Rosanne details:

• The problem she founded the ML Collective to solve.

• How her work on the “intrinsic dimension” of deep learning models inspired the now-standard LoRA approach to fine-tuning LLMs.

• The thorny problems with LLM evaluation benchmarks and how they might be solved.

• The pros and cons of curiosity- vs goal-driven ML research.

• The positive impacts of diversity, equity and inclusion in the ML community.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Reinforcement Learning from Human Feedback (RLHF), with Dr. Nathan Lambert

In today's episode, the renowned RLHF thought-leader Dr. Nathan Lambert digs into the origins of RLHF, its role today in fine-tuning LLMs, emerging alternatives to RLHF... and how GenAI may democratize (human) education!

Nathan:

• Is a Research Scientist at the Allen Institute for AI (AI2) in Seattle, where he’s focused on fine-tuning Large Language Models (LLMs) based on human preferences as well as advocating for open-source AI.

• He’s renowned for his technical newsletter on AI called "Interconnects".

• Previously helped build an RLHF (reinforcement learning from human feedback) research team at Hugging Face.

• Holds a PhD from University of California, Berkeley in which he focused on reinforcement learning and robotics, and during which he worked at both Meta AI and Google DeepMind.

Today’s episode will probably appeal most to hands-on practitioners like data scientists and machine learning engineers, but anyone who’d like to hear from a talented communicator who works at the cutting edge of AI research may learn a lot by tuning in.

In today’s episode, Nathan details:

• What RLHF is and how its roots can be traced back to ancient philosophy and modern economics.

• Why RLHF is the most popular technique for fine-tuning LLMs.

• Powerful alternatives to RLHF such as RLAIF (reinforcement learning from A.I. feedback) and direct distilled preference optimization (dDPO).

• Limitations of RLHF.

• Why he considers AI to often be more alchemy than science.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

MLOps: The Job and The Key Tools, with Demetrios Brinkmann

Today, global MLOps community leader Demetrios Brinkmann details why MLOps is essential, how it differs from related roles like LLMOps, DevOps and A.I. Engineering, and the best tools for deploying and scaling LLMs.

Demetrios:

• Is Founder and CEO of MLOps Community, an organization dedicated to supporting MLOps professionals that has quickly grown to over 20,000 members.

• Was previously founder of the Data on Kubernetes community.

• Before that, worked in public-facing roles at a number of European tech startups.

Today’s episode will be of interest to anyone who’s keen to better understand the critical function of MLOps in bringing machine learning models to the real world.

In today’s episode, Demetrios details:

• What exactly MLOps is and how it relates to other jobs like LLMOps, DevOps and A.I. Engineer.

• The key MLOps tools and approaches.

• What it takes to build a thriving community of tens of thousands of professionals in just a few years.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Aligning Large Language Models, with Sinan Ozdemir

For today’s quick Five-Minute Friday episode, the exceptional author, speaker and entrepreneur Sinan Ozdemir provides an overview of what it actually means for an LLM to be “aligned”.

More on Sinan:
• Is Founder and CTO of LoopGenius, a generative AI startup.
• Has authored several excellent books, including, most recently, the bestselling "Quick Start Guide to Large Language Models".
• Is a serial AI entrepreneur, including founding a Y Combinator-backed generative AI startup way back in 2015 that was later acquired.

This episode was filmed live at the Open Data Science Conference (ODSC) East in Boston last month. Thanks to ODSC for providing recording space.

The Super Data Science Podcast is available on all major podcasting platforms and a video version is on YouTube. This is episode #784!

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Generative A.I. for Solar Power Installation, with Navdeep Martin

A startling 70% of solar-power projects fail. In today's episode, hear how Navdeep Martin's startup Flypower is using Generative A.I. to ensure we install renewable energy sources more effectively and efficiently.

Navdeep:

• Co-founder and CEO of Flypower, a generative A.I. startup dedicated to ensuring clean-energy projects, particularly solar-power projects, succeed.

• Previously held senior product leadership roles at VC-backed Bay Area AI startups as well as for AI products at Comcast and The Washington Post.

• Before that, was a software engineer for the CIA.

• Holds a degree in computer science from William & Mary and an MBA from the University of Virginia.

Today’s episode will appeal to anyone who’d like to hear about the evolution of generative A.I. technologies in products and applications, including how you can best make use of the various categories of Gen-A.I. technologies today and how, in particular, A.I. is being used to overcome the social and regulatory hurdles associated with combating climate change.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Ensuring Successful Enterprise AI Deployments, with Sol Rashidi

Prodigious Sol Rashidi has deployed nearly 40 large-scale data and A.I. projects at Fortune 100 companies. Her rich insights on doing this successfully fill her new book and are distilled into today's fun episode.

Sol ☀:

• Has been a C-suite data/analytics/A.I. leader at Estée Lauder, Merck pharmaceuticals, Sony Music and Royal Caribbean Cruise Lines.

• Was Senior Partner leading the Digital and Innovation Practice at EY and was the Partner who led the Watson go-to-market at IBM.

• Has been involved in over three dozen large-scale data/A.I. deployments.

• Is recognized with a string of international awards for her leadership.

• Holds eight patents with many more pending.

Today’s episode will be invaluable to anyone who’d like to succeed at deploying A.I. models commercially. In it, Sol details:

• Her straightforward system for selecting the enterprise A.I. projects that will be successfully deployed.

• What kinds of A.I. projects should always be avoided.

• Why larger enterprises drag their feet on impactful A.I. projects and how to overcome such corporate logjams.

• When you should patent an innovation.

• Why Chief Data Officers and related C-suite roles have such high turnover.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Tidyverse of Essential R Libraries and their Python Analogues, with Dr. Hadley Wickham

Many-time bestselling author and prolific open-source R developer Hadley Wickham is our guest today. In it, we discuss Posit's rebrand and why the Tidyverse needs to be in every data scientist's toolkit.

More on Hadley:
• Chief Scientist at Posit PBC
• Adjunct Professor of Statistics at Stanford University, Rice University and The University of Auckland.
• Is best-known as the creator of the Tidyverse suite of open-source R libraries for data science, including the essential libraries dplyr and ggplot2.
• Has written seminal books on R programming for O'Reilly, Springer and CRC Press, including the mega-bestselling "R for Data Science".

Today’s episode will primarily be of interest to hands-on practitioners like data scientists and machine learning engineers. In it, Hadley details:
• Why the iconic open-source company RStudio rebranded to Posit.
• The philosophy of the tidyverse, amusing backstories on its most iconic packages and why the tidyverse is invaluable for all data scientists to be familiar with.
• The open-source projects he’s most excited about today.
• How you can easily get involved with career-bolstering open-source projects yourself.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Generative AI in Practice, with Bernard Marr

In today's episode, Bernard Marr — world-leading futurist (>4m social-media followers) and prolific author (20+ books!) — details how GenAI will revolutionize industries, enhance our lives and solve pressing global issues.

In case he isn’t already on your radar, Bernard:

• World-leading futurist who’s consulted with NVIDIA, Google, Microsoft, Amazon and many more on digital transformation and A.I. in business.

• His 20+ books have been translated into 20+ languages and earned several business and management "book of the year" awards; many have also been bestsellers.

• His writing has been featured in The Guardian, Financial Times, The Wall Street Journal, the Harvard Business Review and many other leading media outlets.

• Has over 4 million combined social media followers.

Today’s episode will be of interest to anyone who’d like to better understand Generative A.I. and how to adopt GenAI effectively at work or at home.

In this episode, Bernard details:

• The history of GenAI.

• How GenAI will pair with other industries like energy, healthcare and education to accelerate hyper-innovation across every aspect of society.

• The regulatory and ethical challenges associated with GenAI and how we can overcome them.

• How AI paradoxically makes us more human.

• How to successfully implement GenAI both professionally and personally.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Deep Reinforcement Learning for Maximizing Profits, with Prof. Barrett Thomas

Today, Prof. Barrett Thomas blends his rich technical understanding of Deep Reinforcement Learning with his commercial savviness to eloquently detail how Deep RL can be leveraged to minimize costs and maximize profits.

Barrett:

• Is Research Professor in Business Analytics and Senior Associate Dean at the University of Iowa’s College of Business.

• As will soon be unsurprising to you when you hear how well he communicates complex concepts, he’s won multiple teaching awards (amongst other academic prizes).

• He holds a PhD in Industrial and Operations Engineering from the University of Michigan.

Today’s episode is a technical one that will appeal primarily to hands-on practitioners like data scientists, software developers and machine learning engineers.

In this episode, Barrett details:

• What Markov Decision Processes are and how they relate to Deep Reinforcement Learning.

• How operations research leverages neural networks to minimize business costs and maximize business profits.

• How same-day delivery has been made possible by machine learning.

• How aerial drones and autonomous vehicles will revolutionize supply chains and transportation.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Best A.I. Startup Opportunities, with venture capitalist Rudina Seseri

How should an A.I. startup find product-market fit? How do some A.I. startups become spectacularly successful? The renowned (and highly technical!) A.I. venture-capital investor Rudina Seseri answers these questions and more in today's episode.

Rudina:

• Founder and Managing Partner of Glasswing Ventures in Boston.

• Led investments and/or served on the Board of Directors of more than a dozen SaaS startups, many of which were acquired.

• Was named Startup Boston's 2022 "Investor of the Year" amongst many other formal recognitions.

• Is a sought-after keynote speaker on investing in A.I. startups.

• Executive Fellow at Harvard Business School.

• Holds an MBA from Harvard University.

Today’s episode will be interesting to anyone who’s keen on scaling their impact with A.I., particularly through A.I. startups or investment.

In this episode, Rudina details:

• How data are used to assess venture capital investments.

• What makes particular AI startups so spectacularly successful.

• Her "A.I. Palette" for examining categories of machine learning models and mapping them to categories of training data.

• How Generative AI isn’t a fad, but it is still only a component of the impact that AI more broadly can make.

• The automated systems she has built for staying up to date on all of the most impactful AI developments.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Gemini Ultra: How to Release an A.I. Product for Billions of Users, with Google’s Lisa Cohen

Google recently released Gemini Ultra, their largest language model. I love Ultra and now use it instead of GPT-4 on many tasks. Today's guest, Lisa Cohen, leads Gemini's rollout; hear from her how a company with billions of users rolls out new A.I. products.

More on Gemini Ultra:

• The only LLM with comparable capabilities to GPT-4 (in my experience as well as on benchmark evaluations, although I know benchmarking has plenty of issues!)

• Ultra maintains attention across large context windows (Gemini 1.5 Pro has a million-token context, btw!), competently generating natural language and code.

• Like GPT-4V, Ultra is multi-modal and so accepts both an image and text as input at the same time.

• Piggybacking on Google's excellence at search, I’ve found Gemini Ultra to be particularly effective at tasks that involve real-time search (the Google "Bard" project that focused on real-time information retrieval was renamed "Gemini" when Gemini Ultra was released).

Lisa Cohen is perhaps the best person on the planet to be speaking to about the momentous Gemini releases because Lisa is Director of Data Science & Engineering for Google's Gemini, Assistant and Search Platforms. In addition, she:

• Was previously Senior Director of Data Science at Twitter and Principal Director of Data Science at Microsoft.

• Holds a Master's in Applied Math from Harvard University.

In this episode, Lisa details:

• The three LLMs in Google’s Gemini family and how the largest one, Gemini Ultra, fits in.

• The many ways you can access Gemini models today.

• How absolutely enormous LLM projects are carried out and how they’re rolled out safely and confidently to literally billions of users.

• How LLMs like Gemini Ultra are transforming life and work for everyone from data scientists to educators to children, and how this transformation will continue in the coming years.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Humans Love A.I.-Crafted Beer

I recently recorded tipplers' reactions as they had their first taste of the A.I.-crafted "Krohn&Borg" lager I co-developed. Today's episode illustrates the result: Humans love A.I. beer! There's also cool content on using CRISPR-Cas9 to modify yeast genes.

Thanks again to Beau Warren, Head Brewer at Species X Beer Project, for the opportunity to collaborate on this delicious project. You can check out Episode #755 for tons of detail on the ML packages used and the models developed to craft beer with A.I.

And thanks to all of the guests/judges in today's episode:

• Rehgan Avon of AlignAI

• Alexandra Hagmeyer (Dauterman) of Path Robotics

• Kelsey Dingelstedt of Women in Analytics (WIA)

• William McFarland of Omega Yeast

• Jim Lachey of the Super Bowl XXVI-winning Washington Commanders

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.