Victor Schmidt Receives the Sixth Antidote Scholarship in Natural Language Processing

Druide informatique is pleased to announce the laureate of the sixth Antidote Scholarship in natural language processing (NLP). The lucky recipient of the $20,000 scholarship is Mr. Victor Schmidt, a Ph.D. student under the supervision of Professor Yoshua Bengio at the MILA Artificial Intelligence Institute. The Antidote Scholarship in NLP is awarded as part of the Druide Fund for Research in Text Analysis, created through a donation from Druide informatique to the Université de Montréal.

Mr. Schmidt’s work centers around the applications of AI to the fight against climate change, like cloud modelling using satellite data. He designed an algorithm that simulates the impact of climate change-related phenomena like flooding, forest fires or intense pollution at a given address. The results of this research can be viewed at ceclimatnexistepas.com. His other research interests include the discovery of materials that can improve the efficiency of hydrogen energy storage using water electrolysis.

The Antidote Scholarship in NLP is awarded each year to a student in the Université de Montréal’s Department of Computer Science and Operations Research (DIRO) whose research is affiliated with MILA. The scholarship comes from the one-million-dollar Druide Fund for Research in Text Analysis, which was created to celebrate the 20-year anniversary of the Antidote software, as well as the 50-year anniversary of the DIRO.

Antidote was already a remarkable mainstream application of artificial intelligence when it was launched in 1996. Its corrector, guides and dictionaries have since benefited from more than 25 years of continuous technological and linguistic development. Today, over a million people use Antidote both in English and in French and Druide is methodically pursuing its development. Antidote 11, launched in October 2021, applies deep learning for the first time, both in the form of a neural correction engine and to provide pronunciations of each word in both languages and in different accents.