When Italian philosopher and essayist Andrea Colamedici released Ipnocrazia: Trump, Musk e La Nuova Architettura Della Realtà (Hypnocracy: Trump, Musk, and the New Architecture of Reality), he wanted to make a statement about the existence of truth in the digital age. The book, published in December, was described as "a crucial book for understanding how control is currently exercised not by repressing truth but by multiplying narratives, making it impossible to locate any fixed point," according to a description by Tlon, a publishing house Colamedici cofounded. While the book attracted buzz in philosophy circles, Italian magazine L'Espresso revealed in April that the book's purported author, Jianwei Xun, did not exist, after one of its editors tried and failed to interview him. Initially described as a Hong Kong–born philosopher based in Berlin, it turned out that Xun was actually a hybrid human-algorithmic creation. Colamedici, listed on the book as translator, used AI to generate concepts and then critique those concepts.

Due to their convenience and high accuracy, face recognition systems are widely employed in governmental and personal security applications to automatically recognise individuals. Despite recent advances, face recognition systems have shown to be particularly vulnerable to identity attacks (i.e., digital manipulations and attack presentations). Identity attacks pose a big security threat as they can be used to gain unauthorised access and spread misinformation. In this context, most algorithms for detecting identity attacks generalise poorly to attack types that are unknown at training time. To tackle this problem, we introduce a differential anomaly detection framework in which deep face embeddings are first extracted from pairs of images (i.e., reference and probe) and then combined for identity attack detection. The experimental evaluation conducted over several databases shows a high generalisation capability of the proposed method for detecting unknown attacks in both the digital and physical domains.

Artificial intelligence is already inextricably woven into everyday life, and its impact will only grow in the coming years. But while this development inspires much discussion among members of the scientific community, public opinion on artificial intelligence has remained relatively unknown. Artificial Intelligence: American Attitudes and Trends, a report published earlier in January by the Center for the Governance of AI, explores this question. Its authors relied on an in-depth survey to analyze American attitudes towards artificial intelligence, from privacy concerns to beliefs about U.S. technological superiority. Some of their findings--most Americans, for example, don't trust Facebook--were unsurprising. But much of their data reflects trends within the American public that have previously gone unnoticed. This month Ariel was joined by Baobao Zhang, lead author of the report, to talk about these findings. Zhang is a PhD candidate in Yale University's political science department and research affiliate with the Center for the Governance of AI at the University of Oxford. Her work focuses on American politics, international relations, and experimental methods. In this episode, Zhang spoke about her take on some of the report's most interesting findings, the new questions it raised, and future research directions for her team.