In this Five-Minute Friday, Cofounder and CTO of lakeFS Oz Katz talks to Jon Krohn about data warehouses, data lakes, and how companies can handle increasingly complex data infrastructures and formats. Hear about lakeFS’s collaboration with Legofest, lakeFS’s approach to helping users collaborate on data lakes, and how to overcome the challenges of working with multimodal data.

Additional materials: ⁠www.superdatascience.com/914⁠

This episode is brought to you by the ⁠Dell AI Factory with NVIDIA⁠.

Julien Launay launched Adaptive to give data science teams in business enterprises their “RLOps tooling” to make reinforcement learning easier. Talking to Jon Krohn, Julien says, “Most of our users are data scientists who write Python codes to interface with the system”. Adaptive is also able to work with companies without data science teams, collaborating with partners like Deloitte to add the necessary personnel. Julien is currently working on making his platform more widely available.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/913⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this episode of In Case You Missed It, we look back on five great interview episodes from July. Hear from Lilith Bat-Leah (Episode 901), Sinan Ozdemir (Episode 903), Sebastian Gehrmann (Episode 905), Zohar Bronfman (Episode 907) and Robert Ness (Episode 909). They’ll tell you why data-centric machine learning is so important across disciplines, starting with law, and how we can use AI benchmarks and “red teaming” to refine our search for the best AI models. 

Additional materials: ⁠⁠⁠⁠www.superdatascience.com/912

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Reproducibility, Python notebooks, and data science communities: Software developer Akshay Agrawal speaks to Jon Krohn about Marimo, the next-generation computational notebook for Python, how he built and fostered a thriving community around the product, and what makes this notebook so versatile and accessible for users. 

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/911⁠⁠⁠⁠⁠

This episode is brought to you by ⁠Trainium2, the latest AI chip from AWS ⁠and by the ⁠Dell AI Factory with NVIDIA⁠.

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this Five-Minute Friday, Jon Krohn looks into AI’s disruption of the journalism industry and how it has fundamentally reshaped news production. Multiple news outlets’ suing of ChatGPT over its use of copyrighted materials may have taken the most headlines to date, but this isn’t to say news media is rebuffing AI entirely. On the contrary, several outlets have launched summarization and analysis tools for both internal and external use, such as The New York Times’s Echo and The Washington Post’s Haystacker. This episode looks into the ways major news outlets are utilising AI, and what this means for journalists.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/910⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Researcher at Microsoft Robert Usazuwa Ness talks to Jon Krohn about how to achieve causality in AI with correlation-based learning, the right libraries, and handling statistical inference. When dealing with causal AI, Robert notes how important it is to keep aware of variables in the data that may mislead us and force inaccurate assumptions. Not all variables will be useful. It is essential, then, that any assumptions are grounded in a deeper understanding of how the data were gathered, and not what appears in the dataset. Listen to the episode to hear how you can apply causal AI to your projects.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/907⁠⁠⁠⁠

This episode is brought to you by Trainium2, the latest AI chip from AWS and by the Dell AI Factory with NVIDIA.

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

The moral and ethical implications of letting AI take the wheel in business, as revealed by Anthropic: Jon Krohn looks into Anthropic’s latest research on how to use and deploy LLMs safely, specifically in business environments. The team designed scenarios to test the behavior of AI agents when given a goal and a set of obstacles to reach it. Those obstacles included 1) threats to the AI’s continued operation, and 2) conflict between the AI’s goals and the goals of the company. Hear Jon break down the results of this research in this Five-Minute Friday.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/908⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

“Intelligence has many forms,” says Zohar Bronfman, who speaks with Jon Krohn about the fascinating intersection between computational neuroscience and philosophy, and how it has brought him closer to understanding what is necessary to develop human-like intelligence in machines, as well as his motivations for launching Pecan AI and why predictive models outstrip generative models in business. 

Additional materials: ⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/907⁠⁠⁠

This episode is brought to you⁠⁠⁠ by, ⁠⁠⁠⁠Adverity, the conversational analytics platform⁠⁠⁠⁠ and by the ⁠⁠⁠⁠Dell AI Factory with NVIDIA⁠⁠⁠⁠.

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this episode you will learn:

Jason Corso speaks to Jon Krohn in this Five-Minute Friday all about Voxel51’s latest tool, Verified Auto-Labelling, and the company’s incredible success in developing popular tools for computer vision.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/906⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

RAG LLMs are not safer: Sebastian Gehrmann speaks to Jon Krohn about his latest research into how retrieval-augmented generation (RAG) actually makes LLMs less safe, the three ‘H’s for gauging the effectivity and value of a RAG, and the custom guardrails and procedures we need to use to ensure our RAG is fit-for-purpose and secure. This is a great episode for anyone who wants to know how to work with RAG in the context of LLMs, as you’ll hear how to select the best model for purpose, useful approaches and taxonomies to keep your projects secure, and which models he finds safest when RAG is applied.

Additional materials: ⁠⁠⁠⁠⁠⁠www.superdatascience.com/905⁠⁠

This episode is brought to you⁠ by, ⁠⁠⁠Adverity, the conversational analytics platform⁠⁠⁠ and by the ⁠⁠⁠Dell AI Factory with NVIDIA⁠⁠⁠.

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this episode you will learn: