Representation of language networks. A talk between the Brain and Artificial Intelligence

Chairs: Bruno Bianchi,Juan Esteban Kamienkowski

[fwduvp preset_icccd="SAN2020" playlist_id="S07"]
Neuroscience and Artificial Intelligence (AI) have a long and connected common history. On the one side, a better understanding of the brain could play a key role in building intelligent machines. On the other side, AI models boost Neuroscience research, providing very powerful techniques to analyze the growing data torrent that is available these days. One example of this virtuous cycle is the study of language. Human language has a unique level of complexity, allowing us to generate abstractions of concepts and to communicate it to other people that can understand these abstractions. The study of this capacity has historically been of great interest for linguists, who studies how language ‚Äčis structured, and for neuroscientists, who try to understand how it is implemented in the brain, based on several experimental techniques, such as invasive and non-invasive electrophysiology and fMRI. Recently, Computer Science has joined these fields by studying how fluid communication is achieved between humans, and between humans and machines, providing methods and models to the other disciplines. In particular, the great development in Natural Language Processing (NLP) algorithms seen in recent years has made it possible to solve highly complex linguistic tasks similar to humans. In this symposium, we will be hearing different voices in the talk between Neuroscience and Artificial Intelligence in terms of understanding brain representations of language.