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

In this engaging discussion, Emily M. Bender, a Professor of Linguistics at the University of Washington, explores the intersection of linguistics and NLP. She challenges the current boundaries of NLP research and emphasizes the importance of linguistic insights in improving language models. The conversation dives into the limitations of models like BERT in grasping true meaning and highlights the ethical implications of language in technology. Bender also advocates for interdisciplinary collaboration to enhance understanding and inclusivity in NLP.

Nataniel Ruiz, a PhD student at Boston University specializing in image and video computing, dives into the intricate world of deepfakes. He discusses the importance of adversarial attacks in combating manipulative technology while navigating the ethical implications of image translation networks. The conversation addresses the complexities of protecting digital images and explores potential applications of blockchain in image security. Ruiz highlights the delicate balance needed in executing effective attacks and developing defenses, all while reflecting on the challenges of research amid uncertainty.

Sherri Rose, an Associate Professor at Harvard Medical School, delves into pressing data quality issues in healthcare during the COVID-19 pandemic. She emphasizes the critical need for reliable datasets and rigor in research methodologies. The discussion highlights the rise of algorithmic fairness, particularly its importance for marginalized communities, and critiques current standards in causal inference. Sherri also explores the nuances of risk adjustment in healthcare funding, urging a thoughtful engagement with research to better inform healthcare policies.

Lukas Biewald, Founder and CEO of Weights & Biases, dives into the world of machine learning artifact management. He shares insights about their new tool, Artifacts, designed to track datasets, models, and pipelines seamlessly. The conversation also highlights the challenges of data provenance and reproducibility, alongside the evolution from simplistic methods to more advanced solutions. Biewald emphasizes the importance of user-friendly approaches and the need for organizations to understand their data processes for successful integration and project outcomes.

In this engaging discussion, Richard Socher, Chief Scientist and Executive VP at Salesforce, unveils his groundbreaking work in AI, including the CTRL language model and ProGen, an AI protein generator. He shares insights on how language models are reshaping protein engineering and the ethical implications of AI-generated content. The conversation also dives into Salesforce's innovative AI applications in CRM, exploring how they enhance customer relationships and tackle real-world challenges. Socher emphasizes the importance of balancing research with practical needs in a large tech company.

Manuela Veloso, Head of AI Research at J.P. Morgan Chase and a renowned professor at Carnegie Mellon University, shares her insights on leveraging AI to combat financial crime and enhance client experiences. She discusses the ambitious research goals set by her team and reflects on her journey from robotics to AI in finance. The conversation also touches on her role in founding RoboCup, emphasizing the transformative power of AI in various industries and the importance of collaboration in solving complex financial challenges.

Join Rex Douglass, a computational social scientist, Rob Munro, a crowdsourced data expert, Lea Shanley, a champion of open science, and Gigi Yuen-Reed from IBM as they tackle how data science can ethically combat COVID-19. They dive into the perils of misinformation, the evolution of peer review amidst the pandemic, and the critical need for clear communication in public health. Discover their insights on the collaboration between scientists and citizens to enhance data quality and tackle the ongoing challenges in health crises.

Aleksander Madry, a faculty member at MIT specializing in machine learning robustness, dives into the intriguing world of adversarial examples. He argues these examples shouldn't be seen as mere bugs but as inherent features of AI systems. The conversation highlights the mismatch between human expectations and machine perception, stressing the need for new methodologies to improve interpretability. Madry also shares insights on using robust classifiers for image synthesis and navigates the dual nature of AI technologies, urging a deeper understanding of their implications.

Ben Green, a PhD candidate at Harvard and research fellow at NYU's AI Now Institute, dives into the ethics of AI and its societal implications. He argues that the concept of 'good' in technology isn't enough without a clear definition and a theory of change. The discussion reveals how algorithmic biases can impact marginalized communities and highlights the need for ethical considerations in AI education. Ben also shares insights on the challenges of implementing AI in urban settings and the importance of understanding the political context surrounding technological advancements.

Risto Miikkulainen, Associate VP of Evolutionary AI at Cognizant and a professor at the University of Texas at Austin, discusses the transformative power of evolutionary AI. He highlights its creative problem-solving capabilities in diverse industries like healthcare and manufacturing. Risto dives into neural architecture search, which automates network design, and explores the evolutionary advancements in AI that foster innovative solutions. He also addresses the need for AI standardization and advocates for responsible usage that promotes societal benefits.