Amit Mondal is the VP & Head of Digital Analytics & Experimentation at American Express, where he drives a data-driven culture. He discusses the magic of scaling experimentation to improve customer experiences and the importance of organizational buy-in. Amit underscores the need for clear objectives and robust design in experiments, while addressing challenges like privacy regulations and data literacy. He also emphasizes the power of collaboration across teams to enhance innovation and navigate the complexities of modern experimentation.

Thomas Scialom, Senior Staff Research Scientist at Meta AI, discusses the release of Llama 3.1, the challenges in training LLMs, open vs closed-source models, the GenAI landscape, scalability of AI models, current research, and future trends in AI.

Entrepreneur and author Jordan Goldmeier discusses the enduring relevance of Excel in data science, its use cases, limitations, and future enhancements like generative AI. Topics include Power Query for data cleaning, effective data communication, developing a data mindset, and transitioning tools for improved data analysis.

Mico Yuk, Co-Founder at Data Storytelling Academy, discusses AI productivity, GenAI hype, decision intelligence, data fabrics, and the importance of soft skills for data professionals in this engaging podcast episode.

Vincent Granville discusses custom LLMs, benefits over standard LLMs, architecture, corporate use cases, ethics, and legal considerations. Exploring knowledge graphs, Q&A systems, economic models, ML-skilled engineers in web development, traditional and specialized NLP libraries, and generative AI advancements.

Savin Goyal, Co-Founder & CTO at Outerbounds, discusses the evolution of full stack data scientists integrating software engineering tasks in data science projects for production. Topics include challenges in ML deployment, success stories at companies like Netflix, Metaflow for ML management, and strategies for scalability and robustness in AI production.

Verity Harding, a leader in AI, discusses using history to shape the future of AI by drawing lessons from the space race, the role of government in regulation, and the importance of multi-stakeholder models. She explores the intersection of technology and politics, emphasizing collective action for a positive AI future.

Tom Tunguz, Edo Liberty, and Nick Elprin discuss the future of generative AI, touching on emerging trends, challenges, and potential breakthrough applications. They explore the evolving landscape of AI technology, advancements in LLMs, and the impact on enterprise adoption. The conversation also dives into the challenges of deploying generative AI, automating repetitive tasks with AI for improved productivity, and the concept of brute force innovation in agentic systems.

CEO Barr Moses, Cofounder Prukalpa Sankar, and CEO George Fraser discuss scaling data quality for generative AI. Topics include challenges in data quality and trust, cultural issues, importance of data quality in AI use cases, permissions complexity in AI applications, and impact on organizational success.

Three industry experts, including Barr Moses, CEO of Monte Carlo Data, discuss scaling data quality for generative AI. They explore challenges, best practices, and governance in the age of AI. Topics include AI roles, organizational transformation, evolving AI skills, storytelling in AI, prompt engineering, and navigating the AI job market.