In Case You Missed It in March 2025

We had absolutely killer guests and killer conversations on my podcast in March. This isn't bluster; I learned a ton from Andriy, Richmond, Natalie and Varun... Today's episode features all the best highlights!

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

1. The mega-bestselling author of "The 100-Page Machine Learning Book" (and now "The 100-Page Language Models Book"!) Dr. Andriy Burkov on the missing piece of AGI: Why LLMs can't plan or self-reflect.

2. Relatedly, the fascinating and exceptionally well-spoken Natalie Monbiot contrasted artificial intelligence with the human variety, detailing what makes us unique.

3. The charismatic software engineer Richmond Alake (of MongoDB) explained his "A.I. Stack" concept and how you can leverage it to build better A.I. applications.

4. Former Google Gemini engineer Varun Godbole provides a helpful overview of guide to neural network design, the (freely available!) "Deep Learning Tuning Playbook".

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

AI Should Make Humans Wiser (But It Isn’t), with Varun Godbole

Today's trippy, brain-stimulating episode features Varun Godbole, a former Google Gemini LLM researcher who’s turned his attention to the future implications of the crazy-fast-moving exponential moment we're in.

Varun:

• Spent the past decade doing Deep Learning research at Google, across pure and applied research projects.

• For example, he was co-first author of a Nature paper where a neural network beat expert radiologists at detecting tumors.

• Also co-authored the Deep Learning Tuning Playbook (that has nearly 30,000 stars on GitHub!) and, more recently, the LLM Prompt Tuning Playbook.

• He's worked on engineering LLMs so that they generate code and most recently spent a few years as a core member of the Gemini team at Google.

• Holds a degree in Computer Science as well as in Electrical and Electronic Engineering from The University of Western Australia.

Varun mostly keeps today’s episode high-level so it should appeal to anyone who, like me, is trying to wrap their head around how vastly different society could be in a few years or decades as a result of abundant intelligence.

In today’s episode, Varun details:

• How human relationship therapy has helped him master A.I. prompt engineering.

• Why focusing on A.I. agents so much today might be the wrong approach — and what we should focus on instead.

• How the commoditization of knowledge could make wisdom the key differentiator in tomorrow's economy.

• Why the future may belong to "full-stack employees" rather than traditional specialized roles.

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

In Case You Missed It in February 2025

February was another insane month on my podcast. In addition to having stunning smiles, all four guests I hosted are fascinating, highly knowledgeable experts. Today's episode features highlights of my convos with them.

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

1. Professional-athlete-turned-data-engineer Colleen Fotsch on how DBT simplifies data modeling and documentation.

2. Engineer-turned-entrepreneur Vaibhav Gupta on the new programming language, BAML, he created for AI applications. He details how BAML will save you time and a considerable amount of money when calling LLM APIs.

3. Professor Frank Hutter on how TabPFN, the first deep learning approach to become the state of the art for modeling tabular data (i.e., the structured rows and columns of data that, until now, deep learning was feeble at modeling).

4. The ebullient Cal Al-Dhubaib on the keys to scaling (and selling!) a thriving data science consultancy.

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

LLMs and Agents Are Overhyped, with Dr. Andriy Burkov

Andriy Burkov's ML books are mega-bestsellers and his newsletter has a wild 900,000 subscribers. He seldom does interviews so don't miss today's episode, in which he takes compelling, contrarian views on LLMs and agents.

More on Dr. Burkov:

• His indispensable "100-Page Machine Learning Book" seems to be on *every* data scientist / ML engineer's bookshelf.

• He also wrote "ML Engineering" and his latest book, "The 100-Page Language Model Book", was released this year to rave reviews.

• His "Artificial Intelligence" newsletter is subscribed to by 900,000 people on LinkedIn.

• He's the Machine Learning Lead at TalentNeuron, a global labor-market analytics provider.

• He runs his own book-publishing company, True Positive Inc.

• Previously held data science / ML roles at Gartner, Fujitsu and more.

• Holds a PhD in Computer Science (A.I.) from Université Laval in Quebec, where his doctoral dissertation focused on multi-agent decision-making — 15 years ago!

Despite Dr. Burkov being such a technical individual, most of today’s episode should appeal to anyone interested in A.I. (although some parts here and there will be particularly appealing to hands-on machine-learning practitioners).

In today’s episode, Andriy details:

• Why he believes AI agents are destined to fail.

• How he managed to create a chatbot that never hallucinates — by deliberately avoiding LLMs.

• Why he thinks DeepSeek AI crushed Bay Area A.I. leaders like OpenAI and Anthropic.

• What makes human intelligence unique from all other animals and why A.I. researchers need to crack this in order to attain human-level intelligence in machines.

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

How to Grow (and Sell) a Data Science Consultancy, with Cal Al-Dhubaib

Today, my ebullient long-time friend Cal Al-Dhubaib makes his debut on my podcast to spill the beans on how you can launch your own thriving (data science / A.I. / ML) consultancy and, eventually, sell it 💰

Cal:

• Is Head of AI & Data Science at Further, a data and A.I. company based in Atlanta that has hundreds of employees.

• Previously, he was founder and CEO of Pandata, an Ohio-based A.I. and machine learning consultancy that he grew for over eight years until it was acquired by Further a year ago.

• Delivers terrific talks — don’t miss him if you have the chance!

• Holds a degree in data science from Case Western Reserve University in Cleveland.

Today’s episode should appeal to any listener, particularly anyone that would like to drive revenue and profitability from data science or AI projects.

In it, Cal covers:

• Why his first startup was unsuccessful, but how the experience allowed him to discover an untapped market and build Pandata, a thriving data science consultancy.

• His unconventional strategy of requiring clients to make a sizable commitment up front that initially scared away clients but ultimately attracted the best ones.

• The way core values inspired by his "tin can to Mars" thought experiment shaped his hiring and company culture.

• How making data science "boring", helping his clients trust AI systems and delivering a clear return on investment became his formula for success.

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

BAML: The Programming Language for AI, with Vaibhav Gupta

Today's guest, Vaibhav Gupta, has developed BAML, the programming language for AI. If you are calling LLMs, you've gotta check BAML out for instant accuracy improvements and big (20-30%) cost savings.

More on charming and terrifically brilliant Vaibhav:

• Founder & CEO of Boundary (YC W23), a Y Combinator-backed startup that has developed a new programming language (BAML) that makes working with LLMs easier and more efficient for developers.

• Across his decade of experience as a software engineer, he built predictive pipelines and real-time computer vision solutions at Google, Microsoft and the renowned hedge fund The D. E. Shaw Group.

• Holds a degree in Computer Science and Electrical Engineering from The University of Texas at Austin.

This is a relatively technical episode. The majority of it will appeal to folks who interact with LLMs or other model APIs hands-on with code.

In today’s information-dense episode, Vaibhav details:

• How his company pivoted 13 times before settling upon developing a programming language for A.I.

• Why creating a programming language was "really dumb" but why it’s turning out to be brilliant, including by BAML already saving companies 20-30% on their AI costs.

• Fascinating parallels between today's A.I. tools and the early days of web development.

• His unconventional hiring process (I’ve never heard of anything remotely close to it) and the psychology behind why it works.

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

In Case You Missed It in December 2024

Today's "In Case You Missed it Episode"... is one not to miss! Several of the most fascinating conversations I've ever had on the SuperDataScience Podcast I host happened in December.

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

1. The legendary Dr. Andrew Ng on why LLM cost doesn't matter for your A.I. proof of concept.

2. Building directly on Andrew's segment, CTO (and my fellow Nebula.io co-founder) Ed Donner on how to choose the right LLM for a given application.

3. Extremely intelligent and clear-spoken Dr. Eiman Ebrahimi (CEO of Protopia AI) on the future of autonomous systems and data security in our Agentic A.I. future.

4. From our 2024 recap episode, Sadie St. Lawrence's three biggest A.I. "wow" moments of the year... as well as the biggest flop of the year. (One company was behind both!)

5. Harvard/MIT humanist chaplain Greg Epstein (and bestselling author on tech in society) on the ethics of accelerating A.I. advancements. Should we, for example, consider slowing A.I. progress down?

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

2025 AI and Data Science Predictions, with Sadie St. Lawrence

Happy New Year! To prepare you for 2025, today's guest is the clairvoyant Sadie St. Lawrence, who predicts what the biggest A.I. trends will be in the year ahead. We also pick the A.I. winners and losers of 2024.

In a bit more detail, in today’s episode (which will appeal to technical and non-technical listeners alike):

• We cover how Sadie’s predictions for 2024 (which she made a year ago on this show) panned out.

• We award our “wow moment” of 2024, our comeback of the year, our disappointment of the year and our overall winner of 2024.

• And then, of course, we speculate on the five biggest trends to prepare for in 2025.

As with our 2022, 2023 and 2024 predictions episode, our special guest again this year is Sadie St. Lawrence, who is:

• A data science and machine learning instructor whose content has been enjoyed by over 600,000 students.

• The Founder and CEO of the Human Machine Collaboration Institute as well as being founder and chair of Women In Data™️, a community of over 60,000 women across 55 countries.

• Serves on multiple start-up boards.

• Hosts the Data Bytes podcast.

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

In Case You Missed It in November 2024

We had a ton of laughs and I had some seriously mind-expanding moments thanks to my guests on the SuperDataScience Podcast last month. ICYMI, today's episode highlights the most riveting moments from November.

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

1. Deepali Vyas, Global Head of Data and A.I. at executive-search giant Korn Ferry, on how A.I. is transforming recruitment and how job-seekers can stay ahead of the curve.

2. Jess Ramos, data analyst and leading content creator on data careers, on where to start if you yourself are seeking a career in data.

3. Bryan McCann, co-founder and CTO of the rapidly-scaling A.I. platform You.com, on why machines will make much better scientists than humans... and how they will surpass human scientists surprisingly soon.

4. Martin Goodson, CEO of the prestigious British A.I. firm Evolution AI, on how the public figures who are speaking most loudly about A.I. are probably not the people we should be listening to.

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

AI Systems as Productivity Engines, with You.com’s Bryan McCann

Today, wildly intelligent Bryan McCann describes the Agentic A.I. behind his skyrocketing startup You.com and how it will lead to scientific discoveries human scientists couldn't dream of making. Don't miss this episode!

Bryan:

• Co-Founder and CTO of You.com, a prominent Bay Area A.I. startup that has raised $99m in venture capital (including a $50m Series B in September that valued the firm at nearly a billion dollars).

• Was previously Lead Research Scientist at Salesforce and an assistant on courses at Stanford such as Andrew Ng’s wildly popular machine learning course.

• Holds a Master’s in Computer Science, a Bachelor’s in Computer Science and a Bachelor’s in Philosophy, all from Stanford University.

Today’s episode should be fascinating to anyone interested in AI. In it, extremely well-spoken Bryan details:

• The philosophical underpinnings of the breakthroughs that led to the leading A.I. models we have today as well as the ones that will emerge in the coming years.

• How a coding mistake he made serendipitously revealed fundamental insights about meaning and language model alignment.

• Why he believes humanity is entering an existential crisis due to A.I., but nevertheless remains optimistic about the future.

• The fascinating connection between language models and biological proteins.

• Why A.I. systems might soon be able to make scientific discoveries humans could never dream of making.

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

The 10 Reasons AI Projects Fail, with Dr. Martin Goodson

Most A.I. projects fail. In today's episode, the brilliant (and hilarious) Dr. Martin Goodson details the top 10 reasons why A.I. projects fail and how to avoid these common pitfalls.

Martin:

• Is CEO and Chief Scientist at Evolution AI, a firm that uses generative A.I. to extract information from millions of documents a day for their clients.

• Is Founder and Organizer of the London ML Meetup, which (with >15,000 members) is the largest community of AI/ML experts in Europe.

• Previously led data science at startups that apply ML to billions of data points daily.

• Was a statistical geneticist at the University of Oxford (where we shared a small office together)!

Today’s episode will be of interest to anyone even vaguely interested in data science, ML or AI. In today’s episode, Martin details:

• The 10 reasons why data science projects fail and how to avoid these common pitfalls.

• His insights on building A.I. startups that serve large enterprises.

• The importance of open-source A.I. development.

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

PyTorch Lightning, Lit-Serve and Lightning Studios, with Dr. Luca Antiga

Lightning AI makes tons of tools that speed A.I. model dev and deployment, including the wildly popular open-source library PyTorch Lightning. Today, hear from hands-on CTO Dr. Luca Antiga how all the magic happens ⚡️

More on Luca:

• CTO of Lightning AI, which (as one of world’s hottest startups developing A.I. tools) have raised over $80m in venture capital.

• Is also CTO of OROBIX, an A.I. services company that Luca co-founded 15 years ago.

• Holds a PhD in biomedical engineering from Politecnico di Milano… and did his postdoc at the Robarts Research Institute in London, Ontario (coincidentally around the same time I was doing brain-imaging research there).

Today’s episode will probably appeal most to hands-on practitioners like data scientists, software developers and ML engineers, but any tech-savvy professional could find it valuable.

In today’s episode, Luca details:

• How Lightning AI's suite of tools (in addition to PyTorch Lightning, this includes Lightning Studios, LitServe and the Thunder Compiler) is making A.I. development faster and easier.

• The rise of small language models and their potential to rival LLMs.

• His journey from biomedical imaging to deep learning pioneer.

• How software developer’s work will be transformed by A.I. in the coming years.

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

Neuroscience Fueled by ML, with Prof. Bradley Voytek

Today's guest is the extraordinarily intelligent and well-spoken UC San Diego theoretical neuroscience professor, Bradley Voytek. He reveals how AI/ML is accelerating our understanding of the brain.

More on Brad:

• Professor in UC San Diego's Department of Cognitive Science, Data Science Institute, and the Neurosciences Graduate Program.

• Joined Uber as their first data scientist, when it was a 10-person startup, helping build their data science strategy and team.

• Outreach work has appeared in Scientific American, NPR... and Comic-Con!

• Co-authored the amusing book "Do Zombies Dream of Undead Sheep?"

Today’s episode has some brief exchanges that will appeal most to hands-on practitioners, but should overall be fascinating to anyone.

In today’s episode, Brad details:

• How large-scale data science and machine learning are accelerating neuroscience research.

• Discoveries his lab has recently made that overturn nearly a century of neuroscience doctrine.

• Insights on structuring data science education to balance technical skills with creative, practical problem-solving.

• Lessons from using data science to optimize Uber's early ride-prediction algorithms.

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

Are “Citizen Data Scientists” A Myth? With Keith McCormick

In a recent episode, Nick Elprin and I laughed that "citizen data scientists" don't exist. Keith McCormick joins me today to eloquently rebut us and demonstrate the clear value of low-code/no-code tools.

Keith is:

• Data Science Principal at the enterprise A.I. consultancy Further.

• Creator of dozens LinkedIn Learning courses on machine learning and A.I. with, in aggregate, over a million students!

• Author of four statistics books.

Today’s short episode should be of interest to just about any listener. In it, Keith details:

• Common circumstances where low-code/no-code data science tools are the best option for you, even if you are a coding whiz.

• Whether citizen data scientists are myth or reality.

• How AutoML fits into the data science workflow - and why it won't replace data science teams.

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

In Case You Missed It in September 2024

Another month, another set of invaluable conversations on the SuperDataScience Podcast I host. ICYMI, today's episode highlights the most fascinating moments from September.

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

1. Posit PBC engineering manager Dr. Julia Silge explains why Positron, the next-generation IDE she's leading development of, is better-suited to data scientists than any existing IDE.

2. PyTorch expert Luka Anicin provides his top tips for training more accurate and compute-efficient ML models.

3. Exceptional open-source developer Marco Gorelli on why Polars is anywhere from 10 to 100x faster than Pandas, the incumbent Python library for working with DataFrames.

4. Microsoft's Marck Vaisman on what companies hiring data scientists should be looking for... as opposed to what the typically (and mistakenly!) look for today.

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

Data Contracts: The Key to Data Quality, with Chad Sanderson

Before talking to Chad Sanderson, I had never heard of Data Contracts. Now, I'm a proponent of how critical they are for data quality within any platform. Listen in and you may become a proponent too!

Chad is our guest in today's episode. He's:

• An extremely smooth communicator of technical information.

• CEO and Co-Founder of Gable, a platform for data teams that has raised $7m in seed funding.

• Chief Operator of the non-profit Data Quality Camp.

• Author of the forthcoming O'Reilly book “Data Contracts”.

• His informative social-media posts on Data Contracts have enabled him to amass over 80,000 followers on LinkedIn alone.

Today’s episode will appeal most to folks who work with data hands-on or who are involved in management roles that oversee data flows. Init, Chad details:

• What data contracts are.

• The critical concept of "shifting left" in data quality and governance.

• How data debt accumulates and leads to "spaghetti" data architectures.

• Why data quality is fundamentally a change-management problem.

Thanks to Emily Pastewka for suggesting Chad as a guest on the show!

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

PyTorch: From Zero to Hero, with Luka Anicin

Today's episode is on Python's most popular auto-differentiation library, PyTorch, and how you can use it to design, train and deploy deep neural nets, including LLMs. Acclaimed PyTorch instructor Luka Anicin is our guide.

Luka:

• Is one of Udemy’s all-time bestselling instructors on A.I.; over 500,000 students have taken his courses.

• His latest course, available exclusively at SuperDataScience.com, is called “PyTorch: From Zero to Hero”.

• CEO of full-lifecycle A.I. consultancy Datablooz.

• Holds a Bachelor’s in Computer Science, a Master’s in Data Science and is nearing completion of his PhD in Applied A.I.

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

In it, Luka details:

• What the popular Python library PyTorch is for.

• Why you would select PyTorch over TensorFlow or Scikit-learn.

• The tensor building blocks PyTorch provides for designing, training and deploying state-of-the-art deep neural networks, including Large Language Models (LLMs).

• His top tips for accurate and efficient deep learning.

• Guidance on PyTorch portfolio projects.

• Real-world PyTorch case-studies from his experience leading an A.I. consultancy.

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

Scaling Data Science Teams Effectively, with Nick Elprin

Today's episode with (extremely intelligent and wildly successful ML entrepreneur) Nick Elprin covers efficiently scaling data science teams and ensuring A.I. projects are commercial wins 🥇

Nick:

• Is Co-Founder and CEO of Domino Data Lab, a colossal Bay Area startup that has raised over $200m in venture capital from some of the world’s most prestigious VC firms.

• Prior to co-founding Domino Data Lab 11 years ago, he worked as a technologist at Bridgewater Associates, the well-known hedge fund.

• He holds both a BA and MS in Computer Science from Harvard University.

Today’s episode may appeal most to technical folks but has tons of content that will be of interest to anyone in or interested in commercializing data science or A.I.

In this episode, Nick details:

• How organizations can leverage enterprise platforms to efficiently scale their data science teams and data science workflows.

• The exact team size at which integrating such a platform becomes worthwhile.

• How to ensure A.I. projects are commercially successful.

• The tech stack they use at Domino to create such a performant platform.

• His top tip for growing your own colossal data science startup.

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

Agentic AI, with Shingai Manjengwa

Today's episode is all about Agentic A.I. — perhaps the hottest topic in A.I. today. Astoundingly intelligent and articulate Shingai Manjengwa couldn't be a better guide for us on this hot topic 🔥

Shingai:

• Head of A.I. Education at ChainML, a prestigious startup focused on developing tools for a future powered by A.I. agents.

• Founder and former CEO of Fireside Analytics Inc. (developed online data-science courses that have been undertaken by 500,000 unique students).

• Previously was Director of Technical Education at the prominent global A.I. research center, the Vector Institute in Toronto.

• Holds an MSc in Business Analytics from New York University.

Today’s episode should be equally appealing to hands-on practitioners like data scientists as to folks who generally yearn to stay abreast of the most cutting-edge A.I. techniques.

In today’s episode, Shingai details:

• What A.I. agents are.

• Why agents are the most exciting, fastest-growing A.I. application today.

• How LLMs relate to agentic A.I.

• Why multi-agent systems are particularly powerful.

• How blockchain technology enables humans to better understand and trust A.I. agents.

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

In Case You Missed It in July 2024

In July, we had a yet another bevy of extraordinary guests on the SuperDataScience Podcast I host. ICYMI, this episode highlights the most fascinating moments from my convos with them.

Specifically, conversation highlights include:

1. Iconic Daliana Liu (ex-AWS Senior Data Scientist; host of The Data Scientist Show) on the hard skills data scientists need most in today's market.

2. Pulitzer prize-winning journalist and many-time NY Times bestselling author Charles Duhigg on the secrets to being a "Supercommunicator", i.e., getting people invested in your ideas and opening up.

3. Arcee.ai's CEO Mark McQuade and Chief of Frontier Research Charles Goddard detail the frontier "model merging" technique whereby the capabilities of multiple LLMs can be combined without increasing model size.

4. Prolific Google DeepMind researcher Dr. Rosanne Liu (no relation to Daliana!) on her landmark “Beyond the Imitation Game" paper, particularly why all LLM benchmarks are flawed.

5. Andrey Kurenkov, PhD (A.I. Engineering Lead at Astrocade and founder/co-host of my favorite podcast, "Last Week in A.I.") on how Artificial Superintelligence (ASI) may be just a few years and what the implications could be for us as individuals as well as as a society.

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