Today we’re joined by Andreas Madsen, an independent researcher based in Denmark. While we caught up with Andreas to discuss his ICLR spotlight paper, “Neural Arithmetic Units,” we also spend time exploring his experience as an independent researcher, discussing the difficulties of working with limited resources, the importance of finding peers to collaborate with, and tempering expectations of getting papers accepted to conferences -- something that might take a few tries to get right.

Today we’re joined by Rumman Chowdhury, Managing Director and Global Lead of Responsible AI at Accenture. In our conversation with Rumman, we explored questions like:  • Why is now such a critical inflection point in the application of responsible AI? • How should engineers and practitioners think about AI ethics and responsible AI? • Why is AI ethics inherently personal and how can you define your own personal approach? • Is the implementation of AI governance necessarily authoritarian?

Today we’re joined by Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis, our guests for the recent Advancing Your Data Science Career During the Pandemic panel. In this conversation, we explore ways that Data Scientists and ML/AI practitioners can continue to advance their careers despite current challenges. Our panelists provide concrete tips, advice, and direction for those just starting out, those affected by layoffs, and those just wanting to move forward in their careers.

Visit twimlai.com/blacklivesmatter for resources to support organizations pushing for social equity like Black Lives Matter, and groups offering relief for those jailed for exercising their rights to peaceful protest. 

Andreas Tolias, a Professor of Neuroscience at Baylor College of Medicine, discusses the shortcomings of learning algorithms compared to the brain. Topics include leveraging brain functions for AI advancement, data recording advancements in studying neuron activity, modeling the brain at various levels, data augmentation, bias, and brain-inspired learning in AI, challenges in bridging neuroscience and deep learning, and exciting directions in the intersection of biological systems and deep learning.

Today we’re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley. In our conversation, we explore Joseph’s paper “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,” which looks at compute-efficient training strategies for models. We discuss the two main problems being solved; 1) How can we rapidly iterate on variations in architecture? And 2) If we make models bigger, is it really improving any efficiency?

Today we’re joined by Alpha Lee, Winton Advanced Fellow in the Department of Physics at the University of Cambridge. Our conversation centers around Alpha’s research which can be broken down into three main categories: data-driven drug discovery, material discovery, and physical analysis of machine learning. We discuss the similarities and differences between drug discovery and material science, his startup, PostEra which offers medicinal chemistry as a service powered by machine learning, and much more

Today we’re joined by Emily M. Bender, Professor of Linguistics at the University of Washington. Our discussion covers a lot of ground, but centers on the question, "Is Linguistics Missing from NLP Research?" We explore if we would be making more progress, on more solid foundations, if more linguists were involved in NLP research, or is the progress we're making (e.g. with deep learning models like Transformers) just fine?

Today we’re joined by Nataniel Ruiz, a PhD Student at Boston University. We caught up with Nataniel to discuss his paper “Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems.” In our conversation, we discuss the concept of this work, as well as some of the challenging parts of implementing this work, potential scenarios in which this could be deployed, and the broader contributions that went into this work.

Today we’re joined by Sherri Rose, Associate Professor at Harvard Medical School. We cover a lot of ground in our conversation, including the intersection of her research with the current COVID-19 pandemic, the importance of quality in datasets and rigor when publishing papers, and the pitfalls of using causal inference. We also touch on Sherri’s work in algorithmic fairness, the shift she’s seen in fairness conferences covering these issues in relation to healthcare research, and a few recent pape