Marie-Catherine de Marneffe obtained her PhD from Stanford University in 2012, and has been an associate professor in Linguistics at The Ohio State University. She got appointed as a FRNS Research Associate at UCL in 2021. Her research focuses on developing computational linguistic methods that capture what is conveyed by language beyond the literal meaning of the words. She recently worked on "veridicality": how do people interpret events they read about in the news, do they think such events really happen, did not happen, or are just a possibility? Automatic language processing tools such as the ones you can find on translation platforms or YouTube are all around us, using techniques from artificial intelligence and neural networks. What’s the place of linguistics there? If one reads that “Trump's administration does not believe that there is a risk of a pandemic due to the coronavirus", can we deduce that "there is a risk of a pandemic due to the coronavirus"? Can a computer deduce it? In this talk, we will briefly trace the evolution of the techniques used in natural language processing and show that, despite the progress in automatic language processing and high performance ratios (in the 80-90%), today's automatic systems are not yet able to understand language the way we do as humans. As soon as inferences require pragmatics, the systems' answers are wrong. There is therefore still progress to be made both in theoretical linguistics to better understand the factors that play a role in inference and in automatic language processing. For more science visit: • Website: https://www.scienceandcocktails.org • Facebook: https://www.facebook.com/scienceandcocktailscph/ • Youtube: http://www.youtube.com/c/ScienceCocktails • Instagram: https://www.instagram.com/scienceandcocktailsglobal/