Navrina Singh, Founder and CEO of Credo AI, dives into the pivotal role of AI governance in driving innovation. With a rich background at Microsoft and Qualcomm, she discusses why responsible AI practices are critical for all industries, not just the regulated ones. Gain insights on the challenges of implementing transparent and fair AI, the impact of the EU AI Act, and the complexities of state-level regulations in the U.S. Singh also underscores the need for trust, accountability, and collaboration to navigate the evolving landscape of generative AI.

Jonathan Cohen, VP of Applied Research at NVIDIA and leader of the NeMo platform, dives into the vital role of AI in enterprise applications. He discusses how NeMo Guardrails enhance AI security and observability, crucial for responsible deployments. Jonathan shares insights on the evolving landscape of AI agents, balancing automation with human oversight. Real-world examples illustrate the power of AI, like successful implementations in telecommunications, showcasing how organizations can leverage advanced AI while navigating security challenges.

On this episode, we’re joined by Kevin Schawinski, CEO and Co-Founder at Modulos AG The EU AI Act was passed to redefine the landscape for AI development and deployment in Europe. But what does it really mean for enterprises, AI innovators, and industry leaders?  Schawinski will share actionable insights to help organizations stay ahead of the EU AI Act, and discuss risk implications to meeting transparency requirements, while advancing responsible AI practices.

In this insightful discussion, Robert Nishihara, Co-founder and CEO of Anyscale, dives into the complexities of scaling generative AI in enterprises. He highlights the challenges of building robust AI infrastructure and the journey from theoretical concepts to practical applications. Key topics include the integration of Ray and PyTorch for efficient distributed training and the critical role of observability in AI workflows. Nishihara also addresses the nuances of evaluating AI performance metrics and the evolution of retrieval-augmented generation.

In this episode, we’re joined by Pradeep Javangula, Chief AI Officer at RagaAI Deploying LLM applications for real-world use cases requires a comprehensive workflow to ensure LLM applications generate high-quality and accurate content. Testing, fixing issues, and measuring impact are critical steps of the workflow to help LLM applications deliver value.  Pradeep Javangula, Chief AI Officer at RagaAI will discuss strategies and practical approaches organizations can follow to maintain high performing, correct, and safe LLM applications. 

In this episode, we’re joined by Amal Iyer, Sr. Staff AI Scientist at Fiddler AI.  Large-scale AI models trained on internet-scale datasets have ushered in a new era of technological capabilities, some of which now match or even exceed human ability. However, this progress emphasizes the importance of aligning AI with human values to ensure its safe and beneficial societal integration. In this talk, we will provide an overview of the alignment problem and highlight promising areas of research spanning scalable oversight, robustness and interpretability.

On this episode, we’re joined by Kathy Baxter, Principal Architect of Responsible AI & Tech at Salesforce. Generative AI has become widely popular with organizations finding ways to drive innovation and business growth. The adoption of generative AI, however, remains low due to ethical implications and unintended consequences that negatively impact the organization and its consumers.  Baxter will discuss ethical AI practices organizations can follow to minimize potential harms and maximize the social benefits of AI. 

On this episode, we’re joined by Patrick Hall, Co-Founder of BNH.AI. We will delve into critical aspects of AI, such as model risk management, generating adverse action notices, addressing algorithmic discrimination, ensuring data privacy, fortifying ML security, and implementing advanced model governance and explainability.

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.