AI Explained Fiddler AI

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. 

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.