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Natural Language Processing (NLP) conventionally focuses on modeling words, phrases, or documents. However, natural language is generated by people and with the growth of social media and automated assistants, NLP is increasingly tackling human problems that are social, psychological, or medical in nature.
Scientific progress is a key driver of economic growth and prosperity. There is great excitement- but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists.
Scientific progress is a key driver of economic growth and prosperity. There is great excitement- but also concerns- about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Language shared on Facebook and Twitter has already been used to measure characteristics from individual depression and personality to community well-being, mortality, and, recently, COVID symptom rates. In this talk I will summarize recent work from the Human Language Analysis Lab to further NLP towards modeling people as the originators of language. This includes controlling for and correcting biases from extralinguistic variables (demographics, socioeconomics), placing language in time (forecasting future outcomes), and leveraging the inherent multi-level structure (people, who belong to communities, generate language). Taken together, I will suggest that considering the people behind the language not only offers opportunities for improved accuracies but it could be fundamental to NLP's role in our increasingly digital world.
