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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.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
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.
