Applied Machine Learning
Tuesday, October 26, 2021
Wednesday, October 27, 2021
FEATURED TALK: (AI): Responsible AI into Practice - Deliver Trust in Artificial Intelligence Solution
AI has been a key driver in innovation in every industry Organizations have ramped up their effort on leveraging AI to gain a competitive advantage. However, AI solution comes with its own challenges and risk, particularly in regulated industries. There have been numerous instances when AI introduced bias. Organizations must use a balanced approach to accelerating the adoption of AI and prioritize AI governance to ensure trust in the AI system. While AI regulation landscape is still evolving, now is the time for organizations to start taking steps to understand and mitigate AI risks. Responsible AI framework provides guidelines around AI governance for building fair, transparent, ethical, and accountable AI solutions. In this session you will learn about how organizations can follow Responsible AI guidelines and operationalize trust in AI solutions by incorporating AI governance throughout the AI/ML life cycle.
Starting with ML tutorials seems easy. But how do you scale your ML models from detecting cats and dogs to a full scale business ML model?
From search engine results to social media feeds, the applications powered by AI are ubiquitous in our day to day lives. However, there are many dangers of using AI, from amplifying historical biases to making decisions that we cannot interpret. With the rise of AI-based solutions, the need for us to understand the motivation behind these black-box models is imperative. In this session, we explore real scenarios that show the perils of using AI in the wild and understand why simply optimizing for accuracy or performance is not enough. Learn how these risks can be addressed through the use of various techniques throughout the model development and deployment process.