AI Explained

On this episode, we’re joined by Chaoyu Yang, Founder and CEO at BentoML.
AI-forward enterprises across industries are building generative AI applications to transform their businesses. While AI teams need to consider several factors ranging from ethical and social considerations to overall AI strategy, technical challenges remain to deploy these applications into production.
Yang, will explore key aspects of generative AI application development and deployment.

On this episode, we’re joined by Jure Leskovec, Stanford professor and co-founder at Kumo.ai.
Graph neural networks (GNNs) are gaining popularity in the AI community, helping ML teams build advanced AI applications that provide deep insights to tackle real-world problems. Stanford professor and co-founder at Kumo.AI, Jure Leskovec, whose work is at the intersection of graph neural networks, knowledge graphs, and generative AI, will explore how organizations can incorporate GNNs in their generative AI initiatives. 

On this episode, we’re joined by Parul Pandey, Principal Data Scientist at H2O.ai and co-author of Machine Learning for High-Risk Applications.
Although AI is being widely adopted, it poses several adversarial risks that can be harmful to organizations and users. Listen to this episode to learn how data scientists and ML practitioners can improve AI outcomes with proper model risk management techniques.

On this episode, we’re joined by Peter Norvig, a Distinguished Education Fellow at the Stanford Institute for Human-Centered AI and co-author of popular books on AI, including Artificial Intelligence: A Modern Approach and more recently, Data Science in Context.
AI has the potential to improve humanity’s quality of life and day-to-day decisions. However, these advancements come with their own challenges that can cause harm. Listen to this episode to learn considerations and best practices organizations can take to preserve human control and ensure transparent and equitable AI.