Could AI ever truly "understand"?

ChatGPT knows how to use the word “tickle” in a sentence but it cannot feel the sensation. Can it then be said to understand the meaning of the word tickle the same way we humans do? In a paper for PNAS, SFI researchers Melanie Mitchell and David C. Krakauer survey the ongoing debate in which AI researchers are teasing apart whether Large Language Models like ChatGPT and Google’s PaLM understand language in any humanlike sense.

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Research News Brief: Economics in nouns and verbs

In the last 50 years, economic theory has come to be based almost solely on mathematics. This brings logical precision, but according to a new paper by SFI economist Brian Arthur, it restricts what economics can easily talk about.

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Study: Why are sustainable practices often elusive?

For at least 200,000 years, humans have been trying to understand their environments and adapt to them. At times, we have succeeded; often, we have not. In a new study, SFI's Stefani Crabtree, Jennifer Dunne, and others analyze how information flows from ecosystems to the societies inhabiting them.

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Recap: Complexity-GAINs International Summer School

This summer, 38 Ph.D. students from the U.S. and Europe gathered in Vienna, Austria, for SFI’s first Complexity-GAINs international summer school to better understand the dynamics of societies, with an eye toward preventing disintegration.

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Research News Brief: The frugal case for energy transition

If you think clean energy is expensive, try fossil fuels. A new report in the journal Joule shows that a rapid transition to renewable energy sources by 2050 could save the global economy trillions of dollars compared to both a gradual transition and to no transition at all.

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Advancing science with machine learning

At the crossroads of computer science and computational science, the emerging field of scientific machine learning focuses on harnessing new ideas in machine learning together with predictive physics-based models to solve complex, real-world problems. On October 10–12, a group met to collaborate on new ideas about using scientific machine learning in complex fields.

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Study: new model for predicting belief change

A new kind of predictive network model could help determine which people will change their minds about contentious scientific issues when presented with evidence-based information. A new study in Science Advances presents a framework to accurately predict whether a person will change their opinion about a certain topic. The approach estimates the amount of dissonance, or mental discomfort, a person has from holding conflicting beliefs about a topic. 

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