The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Ayanna Howard, the Dean of the College of Engineering at The Ohio State University, is a prominent robotics researcher focusing on human-robot interaction. In this conversation, she dives into her book, exploring the social dynamics between people and AI, including biases that arise from both data and model design. She highlights the ethical responsibilities of AI builders and discusses her transition to OSU, emphasizing future projects that address AI's societal impacts, trust issues during emergencies, and the need for a mindful approach to technology.

Join Ayanna Howard, Dean of the College of Engineering at The Ohio State University and founder of Zyrobotics, as she dives into her book on the intricate dynamics between humans and robots. She discusses how AI carries societal biases, particularly regarding gender, emphasizing the responsibility of developers in mitigating these issues. The conversation explores the complexities of emotion recognition in AI and highlights the ethical implications of technology in society. Howard also reflects on her new role and the future of applied AI, advocating for equitable advancements.

Penousal Machado, an Associate Professor at the University of Coimbra, combines his expertise in evolutionary computation with a passion for images and graphics. He discusses the link between creativity and humanity, challenging the anthropocentric view of AI. The conversation also delves into the evolution of mating habits across species and how these insights can inform machine learning. By exploring the connections between art, nature, and technology, Machado highlights the transformative potential of AI in both creative fields and scientific understanding.

Arul Menezes, a Distinguished Engineer at Microsoft with 30 years of experience, discusses the evolution of machine translation technology. He highlights key breakthroughs from seq2seq to transformer models and the use of multilingual transfer learning. Arul also delves into domain-specific improvements, optimization techniques like document-level translation, and the challenges in voice translation technology. His insights into integrating speech recognition and enhancing translation accuracy for diverse languages showcase the future of this field.

In this engaging discussion, Luna Dong, Sr. Principal Scientist at Amazon, shares her expertise in product knowledge graphs and their role in enhancing search and recommendation systems. She explores the integration of machine learning and discusses the challenges of data curation. Luna delves into the unique dynamics of media versus retail knowledge graphs and the necessity of human intervention for data quality. The conversation also highlights practical applications of knowledge graphs and the importance of standardization across the industry.

In this discussion, Sarah M. Brown, an Assistant Professor at the University of Rhode Island, dives into the crucial need for a systems-level approach to fairness in AI. She introduces Wiggum, a groundbreaking tool for detecting bias that fosters collaboration between technical experts and social scientists. The conversation also touches on how aggregated data can mislead perceptions of fairness, emphasizing the significance of granular analysis. Brown explores the complexities of defining fairness and the importance of innovative educational frameworks in addressing ethical algorithmic challenges.

Join Andrew Trister, Deputy Director for Digital Health Innovation at the Bill & Melinda Gates Foundation, as he shares insights from his impressive background, including his work on Apple's ResearchKit. He discusses innovative AI applications aimed at enhancing community-based healthcare, particularly in underserved areas. The conversation highlights tackling COVID-19 and improving malaria testing accuracy using Bayesian models. Andrew also addresses challenges in scaling digital health technologies and the importance of community engagement in driving successful health solutions.

Drago Anguelov, Distinguished Scientist and Head of Research at Waymo, dives into the exciting world of autonomous vehicles. He shares insights on the significant advancements in AV technology and the focus on achieving level 4 driving capabilities. Drago breaks down critical ML use cases like Perception and Simulation, showcasing how Waymo’s fully autonomous systems are performing in Phoenix. He also discusses the socioeconomic impacts of self-driving cars and the potential influence of AV systems on future enterprise machine learning.

Ya Xu, the Head of Data Science at LinkedIn, shares her journey from a statistics background to leading a global ML team. She describes the crucial three phases—building, adoption, and maturation—of any successful platform. Ya discusses the challenges of collaboration between application and platform teams, as well as the importance of overcoming 'hero syndrome'. She also dives into LinkedIn's approach to differential privacy, the evolution of experimentation in ML, and how these strategies enhance user experience while ensuring security.

Jesse Engel, a Staff Research Scientist at Google focused on the Magenta Project, dives into the intersection of creativity and AI. He shares insights on how Differentiable Digital Signal Processing (DDSP) marries classical sound design with deep learning. Jesse discusses training music generation models that efficiently capture polyphonic relationships while navigating the challenges of traditional audio techniques. He also highlights collaborative projects enhancing music, language, and visual art, promoting a future of democratized musical creativity.