Today we continue our ICLR 2021 series joined by Allyson Ettinger, an Assistant Professor at the University of Chicago.
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One of our favorite recurring conversations on the podcast is the two-way street that lies between machine learning and neuroscience, which Allyson explores through the modeling of cognitive processes that pertain to language. In our conversation, we discuss how she approaches assessing the competencies of AI, the value of control of confounding variables in AI research, and how the pattern matching traits of Ml/DL models are not necessarily exclusive to these systems.
Allyson also participated in a recent panel discussion at the ICLR workshop How Can Findings About The Brain Improve AI Systems?, centered around the utility of brain inspiration for developing AI models. We discuss ways in which we can try to more closely simulate the functioning of a brain, where her work fits into the analysis and interpretability area of NLP, and much more!
Thanks to our Sponsor!
I’d like to send a huge thank you to our friends at Qualcomm Technologies for their continued support of the podcast, and their sponsorship of this ICLR series! Qualcomm AI Research is dedicated to advancing AI to make its core capabilities — perception, reasoning, and action — ubiquitous across devices. Their work makes it possible for billions of users around the world to have AI-enhanced experiences on devices powered by Qualcomm Technologies. To learn more about what Qualcomm Technologies is up to on the research front, visit twimlai.com/qualcomm.
Connect with Allyson!
- Paper: Probing for semantic evidence of composition by means of simple classification tasks
- Paper: Assessing Phrasal Representation and Composition in Transformers
- ICLR 2021 Workshop – How Can Findings About The Brain Improve AI Systems?
- Is Linguistics Missing from NLP Research? w/ Emily M. Bender – #376
- Can Language Models Be Too Big? w/ Emily Bender and Margaret Mitchell – #467
- Creating Robust Language Representations with Jamie Macbeth – #477