In Case You Missed It in September 2025

The guests we had on my show in September were *extra* extraordinary. ICYMI, today's episode highlights the best parts of my convos with them:

1. Aurélien Géron, the bestselling ML author of all time, on why AGI may resist human control.

2. Shirish Gupta and Ishan Shah on how to make an A.I. hardware purchase that will still be relevant five years from now.

3. Renowned University of Oxford economics professor Carl Benedikt Frey on whether A.I. could allow for a billion-dollar company with a single employee.

4. David Loker on how CodeRabbit makes A.I. code-reviews more secure than with humans alone.

5. Graph guru Amy Hodler on the future of graph networks, including causal and multimodal functionality.

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

Dragon Hatchling: The Missing Link Between Transformers and the Brain, with Adrian Kosowski

What do dragons, macarons and potato latkes have in common? They've sparked a revolutionary model that's poised to replace the Transformer. Today, Adrian Kosowski reveals this big breakthrough.

Adrian:

• Chief Scientific Officer and co-founder of Pathway.

• Theoretical computer scientist, quantum physicist and mathematician.

• Earned his PhD at 20 years old and went on to serve as a tenured researcher at Inria at 23 and associate professor at École Polytechnique.

• Has authored more than 100 scientific papers spanning graph algorithms, distributed systems, quantum information and A.I.

In today's highly technical episode, Adrian demonstrates how the Pathway team have brought devised the Baby Dragon Hatching (BDH) architecture, thereby allowing attention in LLMs to function more like the way the biological brain functions. This is revolutionary because (relative to the today-ubiquitous Transformer architecture) BDH allows:

• Reasoning to be generalized across more complex and extended reasoning patterns, approximating a more human-like approach to problem-solving.

• Saving time/compute/money through sparse activation at inference time.

• Allowing for more interpretability.

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

What is an "AI Agent"? Explained in 90 Seconds

What is an "A.I. Agent" anyway? In today's 90-second video, I quickly and concretely explain.

This video clip was taken from a four-hour "Agentic A.I. Engineering" workshop that I delivered with Ed Donner... indeed, the slides I present in this clip were created by Ed!

Over the coming weeks, I'll release more clips from the full workshop that quickly explain additional discrete topics.

See here for a (today still quite short!) agentic A.I. YouTube playlist that includes the full four-hour workshop if you're interested in checking that out.

Automating Code Review with AI, feat. CodeRabbit’s David Loker

Today, enjoy hearing from the super-intelligent engineer David Loker on how A.I. transforming software development by dramatically accelerating code reviews and automatically improving code bases. It's a great one!

(He also, like me, is a big fan of GPT-5... hear why later in the episode.)

More on David:

• Director of A.I. at CodeRabbit (who've raised $88m in venture capital including a $60m Series B a couple weeks ago, congrats!)

• Previously Lead Data Scientist, ML Engineer and Senior Software Engineer at firms like Netflix and Amazon.

• Holds a Master of Mathematics in Computer Science from the University of Waterloo.

Today's episode will be particularly appealing to software developers and other hands-on practitioners (data scientists, ML engineers, etc.) but David is an outstanding communicator of complex info so any interested listener will enjoy it.

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

Graph Algorithms, GraphRAG and Causal Graphs, with Graph Guru Amy Hodler

For *years*, I'd been trying to get Amy Hodler on my show. Finally she's here! A graph-network guru, Amy fills us in on graph algorithms and cutting-edge applications like GraphRAG and Causal Graphs. Enjoy!

More on Amy:

• Graph-tech evangelist and co-author of O'Reilly books on Graph Algorithms and Knowledge Graphs.

• Decades of experience in emerging tech at companies like HP, Hitachi, Neo4j, and Cray.

• Founder of GraphGeeks.org, a community for people passionate about graphs and connected data.

Today's episode will probably appeal most to hands-on data/AI practitioners but Amy is such a tremendous communicator that anyone who wants to know the latest on graph networks (and their powerful real-world use-cases!) will enjoy it.

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

AI for Manufacturing and Industry, with Hugo Dozois-Caouette

Today's episode is all about the *vast* opportunities for applying A.I. to industrial applications such as manufacturing. Our guide is Hugo Dozois-Caouette, co-founder and CTO of MaintainX.

Hugo:

• Co-founded MaintainX, a CMMS ("Computerized Maintenance Management System") back in 2018, steering the firm to tremendous success, including a recent $2.5 billion valuation and a total of over $250m in venture capital raised.

• Previously served as Lead Software Engineer at Autodesk and Fujitsu.

• Holds a B.Eng. in Computer Engineering from the Université de Sherbrooke in Quebec.

Today's episode is mostly high-level and so should be fascinating to any listener. I was blown away by the data volumes created by manufacturing and how untapped A.I. applications are there.

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

NPUs vs GPUs vs CPUs for Local AI Workloads, with Dell’s Ish Shah and Shirish Gupta

Double the laughs in today's episode, with *two* hardware experts from Dell joining me to explain when you should process A.I. workloads locally with a CPU, GPU or Neural Processing Unit (NPU).

Guest #1, Ishan Shah, is:

• Technologist in the Office of the CTO for Dell Technologies’ Client Solutions Group.

• Was previously founding member of Dell's Chief A.I. Office.

• Holds an MBA from the Massachusetts Institute of Technology.

Guest #2, Shirish Gupta:

• Director of A.I. Product Management at Dell, where he's been for 20+ years!

• Holds a Master's in Engineering from the University of Maryland.

Today's episode will appeal to all hands-on A.I. practitioners as well as anyone who makes hardware decisions for A.I. practitioners. It's also simply a ton of fun to listen to — Ish and Shirish play off each other very amusingly :)

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

In Case You Missed It in August 2025

ICYMI, today's episode provides the top moments from conversations I had with my podcast guests in August. Interestingly, in August all of these episodes took off on YouTube in an unprecedented way:

1. Julien Launay, co-founder and CEO of the wildly successful Adaptive ML, explains how LLMs are trained including the pre-training and the increasingly critical post-training phases.

2. Michelle Yi provides her brilliant take on shocking misalignment research from Anthropic, which showed that A.I. agents will frequently resort to blackmailing humans when its goals are threatened.

3. Kirill Eremenko, CEO of the learning platform SuperDataScience, describes what it takes to become an A.I. Engineer.

4. And Akshay Agrawal who explains how his rapidly-adopted marimo notebook can be converted into a full-blown, click-and-point web app in *seconds*.

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

Hopes and Fears of AGI, with All-Time Bestselling ML Author Aurélien Géron

Aurélien Géron is the best-selling author of ML books of all time. Today's episode is his first interview in a *decade*... and it's a stunner! He reveals his next book as well as his deep thoughts on AGI.

Aurélien:

• Is the author of O'Reilly's "Hands-On Machine Learning" series of books. The fourth edition in the series will be on bookshelves in the coming months.

• Was Co-Founder and CTO of Wifirst, an exceptionally successful French tech company.

• Was previously a product manager at Google.

• Holds an MEng in Computer Science from AgroParisTech.

The start of today's episode (on Aurélien's books) may appeal primarily to hands-on practitioners like data scientists and software developers. The bulk of the episode, however, will appeal to anyone looking to understand how Artificial General Intelligence (AGI) will overhaul work, life and society for humans.

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

8 Steps to Becoming an AI Engineer, with Kirill Eremenko

The #1 fastest-growing role in many countries (including the US) is A.I. Engineer. Want to become one? ...or add the skillset into your existing role? Today, Kirill Eremenko provides an 8-step roadmap!

Many of you will already know Kirill:

• Founder and CEO of SuperDataScience.com, the eponymous e-learning platform.

• Founded the SuperDataScience Podcast nine years ago and hosted the show until he passed me the reins five years ago.

• With over 3 million students, he’s the most popular data science and A.I. instructor on Udemy.

• He holds a Master’s from The University of Queensland in Australia and a Bachelor’s in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology.

Today's episode will be of primary interest to hands-on practitioners like data scientists and software developers.

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

How to Jailbreak LLMs (and How to Prevent It), with Michelle Yi

Today, extraordinary Michelle Yi details LLM jailbreaking (as well as data poisoning, prompt stealing and slop squatting!) and how to prevent it. Scary content but she makes it funny and entertaining, enjoy!

When I say "extraordinary", I'm not exaggerating. Michelle:

• Finished her undergrad at the same age as most folks finish high school.

• While working full-time as an engineering lead at IBM on Jeopardy-playing Watson, she was also a professional violinist in the New York Philharmonic!

• In the past decade, has held a impressive list of AI leadership roles at Bay Area startups.

• Now is helping (startlingly underrepresented) women in tech startups and venture capital through co-founding Generationship, being a venture partner in (ironically named) The Tech Bros and a board member for Women In Data™️.

Today's episode skews a bit toward hands-on practitioners but Michelle does such a wonderful job of communicating complex concepts and making them relevant to modern global events that anyone might love this episode.

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

Data Lakes 101 (and Why They’re Key for AI Models), with Oz Katz

Today, Oz Katz joins me to explain what data lakes are as well as what data infrastructure's needed to train and run modern A.I. models (such as multi-modal LLMs). This is an informative one!

Oz Katz:

• Is co-founder and CTO of lakeFS, which looks and feels like Git but is for data (instead of software) versioning and collaboration.

• Was previously co-founder/CTO at a number of other tech startups, including Swayy, which was acquired by Similarweb in 2015.

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

LLM Pre-Training and Post-Training 101, with Julien Launay

How are cutting-edge LLMs are trained? Find out in today's exceptional episode with Julien Launay, who digs into pre-training (supervised learning) and post-training (reinforcement learning) in eloquent detail.

Julien:

• CEO and co-founder of Adaptive ML, a remarkably fast-growing startup focused on enabling A.I. models to learn from experience.

• Previous led the extreme-scale research teams at Hugging Face and LightOn, where he helped develop state-of-the-art open-source models.

• Organizer of the "Efficient Systems for Foundation Models" workshop at ICML (the prestigious International Conference on Machine Learning).

Today's episode will appeal most to hands-on practitioners but other folks who are open to getting into the technical weeds on Large Language Model (LLM) training should also listen in.

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

In Case You Missed It in July 2025

Wow, what a month... ICYMI, here are the top moments from the conversations I had with my podcast guests in July:

1. AI-lab director Lilith Bat-Leah on why data-centric machine learning research (DMLR) is the future of AI.

2. Prolific author and speaker Sinan Ozdemir (last week in San Francisco, I witnessed him deliver the *best* talk I've ever seen) on whether we can trust LLM benchmarks.

3. Bloomberg's Dr. Sebastian Gehrmann on why generic LLMs fail in regulated industries like finance and healthcare.

4. AI entrepreneur Dr. Zohar Bronfman on how AI can predict what you'll do next... before you're even consciously aware of your decision.

5. Microsoft AI researcher Dr. Robert Osazuwa Ness with step-by-step guidance on how to build causal AI models using PyTorch.

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

The Future of Python Notebooks is Here, with Marimo’s Dr. Akshay Agrawal

I love Jupyter Notebooks... but they have a lot of painful "features". Today's guest Akshay Agrawal has built marimo, which resolves these issues and adds in lots of clever new innovations.

More on Akshay:

• Co-founder and CEO of marimo.

• Carried out a PhD in electrical engineering at Stanford University.

• Previously held software engineering roles at Google and Netflix.

Today's episode will appeal most to hands-on practitioners. In it, we cover:

• Why 96% of Jupyter notebooks fail to reproduce their original results.

• How reactive notebooks can transform a simple slider adjustment into automatic recalculation across your entire analysis — like Excel for data science.

• How you can now intuitively select data points with your mouse in a scatter plot and instantly get them back as a Python dataframe for analysis.

• How one marimo notebook can simultaneously be an executable script, an importable Python module, and even a fully functional click-and-point UI!

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