Neuroscience, AI and the Limitations of LLMs, with Dr. Zohar Bronfman

I was blown away by today's guest, the brilliant dual-PhD Zohar Bronfman as we discussed neuroscience, A.I., and why predictive models offer a better ROI than generative ones. Enjoy!

Dr. Bronfman:

• Is the co-founder and CEO of Pecan AI, a predictive analytics platform that has raised over $100m in venture capital.

• Holds two PhDs — one in computational neuroscience and another in philosophy — bringing a deep, multidisciplinary lens to the design and impact of A.I. systems.

• Focuses on the evolution of machine learning from statistical models to agentic systems that influence real-world outcomes.

Today’s episode will be fascinating for every listener.

In it, Zohar details:

• The trippy implications of the reality that your brain makes decisions hundreds of milliseconds before you're consciously aware of them.

• The intelligence feat that bumblebees can do that current A.I. cannot, with implications for the realization of human-like intelligence in machines.

• Why predictive models are more important than generative models for businesses but how generative LLMs can nevertheless make building and deploying predictive models much easier and accessible.

• The rollercoaster journey that led him to create a sensationally successful A.I. startup immediately upon finishing his academic degrees.

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

Why RAG Makes LLMs Less Safe (And How to Fix It), with Bloomberg’s Dr. Sebastian Gehrmann

In today's episode, A.I. researcher Dr. Sebastian Gehrmann details what RAG is and why it makes LLMs *less* safe... despite popular perception of the opposite.

Sebastian:

• Is Head of Responsible A.I. at Bloomberg, the New York-based financial, software, data, and media company that (with 20,000 employees) is huge.

• Previously, as Head of NLP at Bloomberg, he directed the development and adoption of language technology to bring the best A.I.-enhanced products to the Bloomberg Terminal.

• Prior to Bloomberg, was a senior researcher at Google, where he worked on the development of large language models, including the groundbreaking BLOOM and PaLM models.

• He holds a Ph.D. in computer science from Harvard University.

Today’s episode skews slightly toward our more technical listeners like data scientists, A.I. engineers and software developers, but anyone who’d like to be up to date on the latest A.I. research may want to give it a listen.

In today’s episode, Sebastian details:

• The shocking discovery that retrieval augmented generation (RAG) actually makes LLMs LESS safe, despite the popular perception of the opposite.

• Why the difference between 'helpful' and 'harmless' A.I. matters more than you may think.

• The hidden “attack surfaces” that emerge when you combine RAG with enterprise data.

• The problems that can happen when you push LLMs beyond their intended context window limits.

• What you can do to ensure your LLMs are Helpful, Honest and Harmless for your particular use cases.

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

Agentic AI Hands-On in Python: MCP, CrewAI and OpenAI Agents SDK (by Jon Krohn and Ed Donner)

Now live! Four hours long and 100% free, this hands-on workshop covers all the Agentic A.I. theory and tools you need to develop and deploy multi-agent teams with Python.

Beautifully shot by a professional film crew (led by the exceptional Lucie McCormick) at the Open Data Science Conference (ODSC) East in Boston a few weeks ago and then meticulously edited by SuperDataScience's inimitable Mario Pombo, this training (within the GenAI-forward Cursor IDE) features all of today's essential agent frameworks:

• OpenAI Agents SDK

• CrewAI

• Anthropic's Model Context Protocol (MCP)

From design considerations through to practical implementation tips, by completing all four modules in this video, you will have all the knowledge and skills needed to create effective multi-agent systems. The four modules are:

1. Defining Agents

2. Designing Agents

3. Developing Agents

4. The Future of Agents

The coding elements are led by the wonderful Ed Donner, whom many of you will already know as one of the very best in the world at creating and teaching hands-on A.I. content.

We received rave reviews for the session at ODSC East and the lecture hall was standing-room only for the entire duration, so I anticipate that you'll love it too!

Watch the full training here: youtu.be/LSk5KaEGVk4

Conversational AI is Overhauling Data Analytics, with Martin Brunthaler

Fascinating new episode for you from serial entrepreneur/CTO Martin Brunthaler on how GenAI and Agentic A.I. are transforming data analytics today... and how analytics will continue to evolve in the coming years.

Martin Brunthaler:

• CTO of Adverity, an Austrian data analytics platform he co-founded a decade ago and that has since raised over $160m in venture capital.

• Before Adverity, Martin was co-founder and CTO at two other European tech start-ups, giving him over 20 years of combined experience in starting, scaling and exiting companies across multiple industries including eCommerce, media and mobile.

• Holds an engineering diploma (equivalent to a Bachelor's degree) from the Salzburg University of Applied Sciences in Austria.

Today’s episode should be of interest to just about anyone who’d be interested in this podcast because it touches on data analytics, transforming user experiences with modern AI capabilities and growing tech businesses.

In today’s episode, Martin details:

How a childhood fascination with computer programming evolved into founding a globally leading platform for marketing data analytics.

• What "data democratization" really means and how the traditional dashboard-based approach to data reporting is failing businesses.

• Why data analysts are spending too much time on "busy work" instead of delivering business value.

• How conversational AI is overhauling how data insights are gleaned for hands-on data practitioners and business users alike.

• His no-nonsense tips for tech startup success.

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

Generative AI for Business, with Kirill Eremenko and Hadelin de Ponteves

Craving an intro to building and deploying commercially successful Generative A.I. applications? In today's episode, superstar data-science instructors Kirill and Hadelin (>5 million students between them) will fill you in!

Kirill Eremenko is one of our two guests today. He's:

• Founder and CEO of SuperDataScience, an e-learning platform.

• Founded the SuperDataScience Podcast in 2016 and hosted the show until he passed me the reins four years ago.

Our second guest is Hadelin de Ponteves:

• Was a data engineer at Google before becoming a content creator.

• In 2020, took a break from Data Science content to produce and star in a Bollywood film featuring "Miss Universe" Harnaaz Sandhu.

Together, Kirill and Hadelin:

• Have created dozens of data science courses; they are the most popular data science instructors on the Udemy platform, with over five million students between them!

• They also co-founded CloudWolf, an education platform for quickly mastering Amazon Web Services (AWS) certification.

• And, in today’s episode, they announce (for the first time anywhere!) another (brand-new) venture they co-founded together.

Today’s episode is intended for anyone who’s interested in real-world, commercial applications of Generative A.I. — a technical background is not required.

In today’s episode, Kirill and Hadelin detail:

• What generative A.I. models like Large Language Models are and how they fit within the broader category of “Foundation Models”.

• The 12 crucial factors to consider when selecting a foundation model for a given application in your organization.

• The 8 steps to ensuring foundation models are deployed commercially successfully.

• Many real-world examples of how companies are customizing A.I. models quickly and at remarkably low cost.

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

Flexible AI Deployments Are Critical, with Chris Bennett and Joseph Balsamo

Today's episode features heavyhitters from Dell (Chris Bennett) and Iternal (Joseph Balsamo) detailing why we must have flexibility in our A.I. model deployment (and why generative A.I. is overhyped)!

In a bit more detail, today's guests are:

• Chris Bennett: Global CTO for Data & A.I. Solutions at Dell Technologies

• Joseph Balsamo: Sr VP of Product Development at Iternal Technologies

This episode was filmed live at Insight Partners' ScaleUp:AI conference in New York a few weeks ago. Thanks to George Mathew, Jennifer Jordan, Kristen Zeck and Deanna Uzarski for inviting me and making the magic of this session happen.

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

Virtual Humans and AI Clones, with Natalie Monbiot

Today, the clever and astoundingly well-spoken Natalie Monbiot provides a fascinating, mind-expanding episode on virtual humans, A.I. clones and the emerging virtual-human economy.

Natalie:

• Is Head of Strategy and a Founding Team member of Hour One, a leader in virtual-human video generation that raised $20m in a Series A led by Insight Partners.

• Through her own consultancy, EKLEKTIK, she advises virtual-human and A.I.-clone companies.

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

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

Today's episode will of interest to everyone. In it, Natalie details:

• What virtual humans are.

• How virtual humans will buy us time and unleash a virtual-human economy.

• The ethical quandaries and challenges associated with creating virtual twins.

• What distinguishes virtual humans from deep fakes.

(P.S.: This is the first time we've ever shot an episode with three video cameras... if you watch the video version, let me know if you think it's worth the extra effort and investment!)

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

How to Be a Supercommunicator, with Charles Duhigg

Today, Pulitzer Prize winner and NY Times bestselling author Charles Duhigg reveals how you can become a "Supercommunicator", allowing you to connect with anyone, form deep bonds and get more done with others.

More on Charles:

• Pulitzer prize-winning journalist who currently writes for The New Yorker.
• His first book, "The Power of Habit", was published in 2012, spent over three years on New York Times bestseller lists and was translated into 40 languages.
• His second book, "Smarter Faster Better", was published in 2016 and was also a New York Times bestseller.
• Is a graduate of Yale University and Harvard Business School.

Today’s episode should be of great interest to everyone. In it, Charles provides the key takeaways from "Supercommunicators" including:

• Step-by-step instructions on how to connect meaningfully with anyone.
• The three types of conversation and how to ascertain which one you’re in at any given moment.
• How to have productive conflicts without the conversation spiraling out of control.
• How generative A.I. is transforming our conversations today and how the technology may transform them even more dramatically in the future.

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.

How to Catch and Fix Harmful Generative A.I. Output

Today, the A.I. entrepreneur Krishna Gade joins me to detail open-source solutions for overcoming the safety and security issues associated with generative A.I. systems, such as those powered by Large Language Models (LLMs).

The remarkably well-spoken Krishna:
• Is Co-Founder and CEO of Fiddler AI, an observability platform that has raised over $45m in venture capital to build trust in A.I. systems.
• Previously worked as an engineering manager on Facebook’s Newsfeed, as Head of Data Engineering at Pinterest, and as a software engineer at both Twitter and Microsoft.
• Holds a Masters in Computer Science from the University of Minnesota.

In this episode, Krishna details:
• How the LLMs that enable Generative A.I. are prone to inaccurate statements, can be biased against protected groups and are susceptible to exposing private data.
• How these undesirable and even harmful LLM outputs can be identified and remedied with open-source solutions like the Fiddler Auditor that his team has built.

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

Generative Deep Learning, with David Foster

Today, bestselling author David Foster provides a fascinating technical introduction to cutting-edge Generative A.I. concepts including variational autoencoders, diffusion models, contrastive learning, GANs and (my favorite!) "world models".

David:
• Wrote the O'Reilly book “Generative Deep Learning”; the first edition from 2019 was a bestseller while the second edition was released just last week.
• Is a Founding Partner of Applied Data Science Partners, a London-based consultancy specialized in end-to-end data science solutions.
• Holds a Master’s in Mathematics from the University of Cambridge and a Master’s in Management Science and Operational Research from the University of Warwick.

Today’s episode is deep in the weeds on generative deep learning pretty much from beginning to end and so will appeal most to technical practitioners like data scientists and ML engineers.

In the episode, David details: 
• How generative modeling is different from the discriminatory modeling that dominated machine learning until just the past few months.
• The range of application areas of generative A.I.
• How autoencoders work and why variational autoencoders are particularly effective for generating content.
• What diffusion models are and how latent diffusion in particular results in photorealistic images and video.
• What contrastive learning is.
• Why “world models” might be the most transformative concept in A.I. today.
• What transformers are, how variants of them power different classes of generative models such as BERT architectures and GPT architectures, and how blending generative adversarial networks with transformers supercharges multi-modal models.

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

Open-Source “Responsible A.I.” Tools, with Ruth Yakubu

In today's episode, Ruth Yakubu details what Responsible A.I. is and open-source options for ensuring we deploy A.I. models — particularly the Generative variety that are rapidly transforming industries — responsibly.

Ruth:
• Has been a cloud expert at Microsoft for nearly seven years; for the past two, she’s been a Principal Cloud Advocate that specializes in A.I.
• Previously worked as a software engineer and manager at Accenture.
• Has been a featured speaker at major global conferences like Websummit.
• Studied computer science at the University of Minnesota.

In this episode, Ruth details:
• The six principles that underlie whether a given A.I. model is responsible or not.
• The open-source Responsible A.I. Toolbox that allows you to quickly assess how your model fares across a broad range of Responsible A.I. metrics.

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

The A.I. and Machine Learning Landscape, with investor George Mathew

Today, razor-sharp investor George Mathew (of Insight Partners, which has a whopping $100-billion AUM 😮) brings us up to speed on the Machine Learning landscape, with a particular focus on Generative A.I. trends.

George:
• Is a Managing Director at Insight Partners, an enormous New York-based venture capital and growth equity firm ($100B in assets under management) that has invested in the likes of Twitter, Shopify, and Monday.com.
• Specializes in investing in A.I., ML and data "scale-ups" such as the enterprise database company Databricks, the fast-growing generative A.I. company Jasper, and the popular MLOps platform Weights & Biases.
• Prior to becoming an investor, was a deep operator at fast-growing companies such as Salesforce, SAP, the analytics automation platform Alteryx (where he was President & COO) and the drone-based aerial intelligence platform Kespry (where he was CEO & Chairman).

Today’s episode will appeal to technical and non-technical listeners alike — anyone who’d like to be brought up to speed on the current state of the data and machine learning landscape by a razor-sharp expert on the topic.

In this episode, George details:
• How sensational generative A.I. models like GPT-4 are bringing about a deluge of opportunity for domain-specific tools and platforms.
• The four layers of the "Generative A.I. Stack" that supports this enormous deluge of new applications.
• How RLHF — reinforcement learning from human feedback — provides an opportunity for you to build your own powerful and defensible models with your proprietary data.
• The new LLMOps field that has emerged to support the suddenly ubiquitous LLMs (Large Language Models), including generative models.
• How investment criteria differ depending on whether the prospective investment is seed stage, venture-capital stage, or growth stage.
• The flywheel that enables the best software companies to scale extremely rapidly.

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