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Joelle explains how she ended up at Meta and what her role as VP of AI Research entails before telling us what fascinated her most about AI robotics, why elegance is a key factor in her work, how asking the right questions opens the right doors, and the FRESCO philosophy that forms the foundation off of which Joelle makes all her business decisions.

Mara explains how the team at Amii decides when ML is and isn't appropriate to use. We discuss Amii’s process and its ultimate goal, along with common challenges their partners face when implementing AI solutions.

After attending Meta's event celebrating 10 Years of AI progress at FAIR, Rob shares what he learned with Sama's Director of Machine Learning, Jerome Pasquero, for some much needed technical insight.

The application of AI technologies within industry is growing in popularity. It has the capacity to transform traditional industries while paving the way for innovation in a rapidly evolving landscape. In this episode, we are joined by Bryan DeBois, Director of Industrial AI at RoviSys, to discuss the realm of Industrial AI and its range of applications. RoviSys is a company that specializes in providing automation and information solutions to various industries. It focuses on developing solutions where technical expertise and vendor independence intersect. In our conversation, we discuss the concept of industrial AI, its applications, and how it differs from standard AI processes.

Jason & Duncan discuss the results of Sama's ML Pulse Report, focusing on how to measure model effectiveness, how to increase confidence when moving models into production, and whether they believe generative AI is worth the hype.

Ian shares how AMD partnerships are making powerful models available to the general public and large tech companies alike. Also, will LLMs disrupt search? Clippy is back, and he's BAD.

Doug explains how Amazon CodeWhisperer was designed to save engineering time, and reflects on what it means for something to be truly "generative"

Dalia reflects on the evolving nature of data science, discussing the skills and qualities that are now more crucial than ever for excelling in the field. We also explore why creativity is essential for problem-solving, the value of starting simple, and how to stand out as a data scientist before she explains her unique root cause analysis framework.

In this episode, Colin explains how AgriSynth is “getting humans out of the way” with its closed-loop control system and offers some insight into the sheer volume of data required to train its AI models.

Healthy data culture and practices are essential for modern businesses. They promote informed decision-making, efficiency, innovation, and accountability while helping organizations stay competitive and adaptable in a data-driven world. In this episode, we tackle the age-old struggle many companies face: how to unleash the potential of data while overcoming the hurdles that stand in the way of building a truly healthy data culture. To help us navigate this topic is Jennifer Stirrup, a distinguished expert in artificial intelligence, business intelligence, big data, and data visualization solutions.

Mike breaks down some of the key AI solutions being used at BNY Mellon and shares his thoughts on the many possible future implementations of these emerging technologies. He also unpacks how their enterprise AI team chooses what to prioritize before addressing the importance of demystifying AI capabilities — both within and outside of an organization.

Alex and his team are keen on using AI to increase proficiency at Mercedes while being hyperaware of using this new technology in the most responsible way possible. In our conversation, we learn why Mercedes-Benz thought it necessary to have an entire team dedicated to AI, how they assign AI teams to specific departments in the company, and how the output of descriptive analytics has been improved by machine learning.