This talk originated as the opening keynote at this year's Association for Computational Linguistics (ACL) meeting, the top conference in NLP. It was designed as a call to action for the NLP community, but much of what I had to say is applicable to AI and even the CS research community more broadly.
Beginning in the mid-1980s, a "statistical revolution" in NLP redefined what it meant to work on language from a computational perspective. Steve Abney, in his essay opening The Balancing Act: Combining Symbolic and Statistical Approaches to Language (Klavans and Resnik, eds., MIT Press, 1996), wrote: "In the space of the last ten years, statistical methods have gone from being virtually unknown in computational linguistics to being a fundamental given". We are again caught up in revolutionary times. Except this time around, the revolution is hurtling forward even faster, and it is redefining not only the research we do, but what it means to do research in the first place, the role of research in society, and society itself. What does this mean for people who do computational work with language -- what defines us as a community, what is our role, and where do we go from here?
Philip Resnik is MPower Professor at University of Maryland with joint appointments in the Department of Linguistics and the Institute for Advanced Computer Studies. He earned his bachelor's in Computer Science at Harvard and his PhD in Computer and Information Science at the University of Pennsylvania, and does research in computational linguistics. Prior to joining UMD, he was an associate scientist at BBN, a graduate summer intern at IBM T.J. Watson Research Center (subsequently awarded an IBM Graduate Fellowship) while at UPenn, and a research scientist at Sun Microsystems Laboratories. In 2020 he was designated a Fellow of the Association for Computational Linguistics. Philip's most recent research has focused in two areas. One is the computational cognitive neuroscience of language, where he has been using computational modeling in connection with brain imaging to look at the role of context and predictive processing during online language comprehension. The other is computational social science, with an emphasis on connecting the signal available in people's language use with underlying mental state -- this has applications in computational political science, particularly in connection with ideology, framing, and beliefs, and in mental health, focusing on the ways that linguistic behavior may help to identify and monitor depression, schizophrenia, and suicidality. Philip is a scientific advisor for NORC at the University of Chicago (a non-partisan, independent social research organization). In entrepreneurial life he was a technical co-founder of CodeRyte (NLP for electronic health records, acquired by 3M in 2012), and is an advisor to FiscalNote (machine learning and analytics for government relations, went public in 2022), and Trustible (a leading technology provider of responsible AI governance).