In the new episode of Pragmatic Institute’s “Data Chats” podcast, guest host Michael Li—the founder and president of The Data Incubator—sits down with Aishwarya Srinivasan, a data scientist on Google Cloud AI Services team. At Google, she builds machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and AI Platform.
Previously, she was as an Artificial Intelligence and Machine Learning Innovation Leader at IBM, where she worked cross-functionally with the product team, data science team and sales to research AI use cases for clients at IBM Data & AI. Aishwarya holds a postgraduate degree in data science from Columbia University and serves as an ambassador for the Women in Data Science community.
Listen as these two data science experts discuss the importance of building responsible AI systems that aren’t harmful to society, citing examples in the media. Aishwarya describes the responsibilities of team roles like AI ethicists, UX researchers, data scientists and cybersecurity officers in upholding responsible AI standards (read her article for more on these roles.) She also highlights the key issues organizations will have to grapple with as AI continues to evolve, and she shares key takeaways for leveraging machine learning and AI to create business value.
For more Pragmatic Institute thought leadership on designing and building AI applications, read this series of white papers by Sciata President Harish Krishnamurthy, “Making the Leap from AI Investments to Business Results.”
To learn how to leverage data to drive business success, explore Pragmatic Institute’s training offerings. Pragmatic Institute’s data practice provides individuals and teams with actionable guidance, hands-on practice and a business-oriented approach, so they can solve problems and propel decision making with data.
Find links to resources shared in this episode:
- Trusted-AI/AIF360 via Github
- “Adversarial attacks in machine learning: What they are and how to stop them” via VentureBeat
- “What is Adversarial Machine Learning?” via Towards Data Science