Generative AI proposes several large language models (LLMs) to automatically generate a message in response to users' requests. Such scientific breakthroughs promote new writing assistants but with some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT by comparing its generated messages with those of the recent French presidents. To achieve this, we compare end-of-the-year addresses written by Chirac, Sarkozy, Hollande, and Macron with those automatically produced by ChatGPT. We found that ChatGPT tends to overuse nouns, possessive determiners, and numbers. On the other hand, the generated speeches employ less verbs, pronouns, and adverbs and include, in mean, too standardized sentences. Considering some words, one can observe that ChatGPT tends to overuse "to must" (devoir), "to continue" or the lemma "we" (nous). Moreover, GPT underuses the auxiliary verb "to be" (^etre), or the modal verbs "to will" (vouloir) or "to have to" (falloir). In addition, when a short text is provided as example to ChatGPT, the machine can generate a short message with a style closed to the original wording. Finally, we reveal that ChatGPT style exposes distinct features compared to real presidential speeches.

Does Thought Require Sensory Grounding? Presidential Address delivered under the title "Can a Large Language Model Think?" at the one hundred nineteenth Eastern Division meeting of the American Philosophical Association on January 6, 2023. Does the capacity to think require the capacity to sense? A lively debate on this topic runs throughout the history of philosophy and now animates discussions of artificial intelligence. In favor of a positive answer, Aristotle says, "The soul never thinks without an image." Aquinas says, "There's nothing in the intellect that wasn't previously in the senses." Hume says, "All our simple ideas in their first appearance are derived from simple impressions." With some minimal assumptions, all three of these statements suggest that thinking requires the capacity to sense, or at least requires having had the capacity to sense at some point. Contrasting with these empiricist theses, rationalist philosophers have often denied that thinking requires sensing. Plato holds that we can think about the forms before we have senses and a body. Descartes holds that the pure intellect thinks independently of the senses. Navigating between empiricism and rationalism, Kant discusses the issue extensively ("Thoughts without content are empty"); unsurprisingly, his final views on the matter are complicated. In recent decades, this philosophical debate has become central to debates in artificial intelligence and cognitive science. He and others held that for symbols to have meaning, they must be causally grounded in sensory connections to the environment. To be meaningful, the symbol "RED" must be grounded in seeing red. The symbol "WATER" must be grounded in a sensory connection to water. If we assume that thinking and meaning go together in AI systems, then this amounts to another version of the thesis that thinking requires sensing. In the last few years, discussion of symbol grounding has become especially widespread in the debate over large language models (LLMs) such as the GPT systems. Can large language models think, mean, or understand?

Every two years, the current AAAI president gives the opening address at the AAAI Conference on Artificial Intelligence. This year it was the turn of Bart Selman. In his talk he reviewed the current state of AI and presented examples of three different applications of AI to aid scientific discovery. Bart began his talk by considering the deep-learning "revolution", highlighting some of the areas that it has transformed, namely computer vision, natural language processing, machine translation, game play, and reinforcement learning. He noted that the field is undergoing a rapid acceleration at the moment, with an incredible rate of progress.

To celebrate International Women's Day, we take a look back over the past year of AIhub content and highlight some of our favourite articles, interviews, podcasts and videos, by, or featuring, women in the field. Falaah Arif Khan is an engineer/scientist by training and an artist by nature. She is currently Artist-in-Residence at the Center for Responsible AI at New York University. When we interviewed Falaah in 2020 she had just completed her first comic book, Meet AI. She has since teamed up with other AI researchers on other exciting projects.

This is the first presidential address of AAAI, the American Association for Artificial Intelligence. In the grand scheme of history of artificial intelligence (AI), this is surely a minor event. The field this scientific society represents has been thriving for quite some time. No doubt the society itself will make solid contributions to the health of our field. But it is too much to expect a presidential address to have a major impact.

This article, derived from the 1996 Association for the Advancement of Artificial Intelligence Presidential Address, explores the notion of intelligence from a variety of perspectives and finds that it "are" many things. It has, for example, been interpreted in a variety of ways even within our own field, ranging from the logical view (intelligence as part of mathematical logic) to the psychological view (intelligence as an empirical phenomenon of the natural world) to a variety of others. One goal of this article is to go back to basics, reviewing the things that we, individually and collectively, have taken as given, in part because we have taken multiple different and sometimes inconsistent things for granted. I believe it will prove useful to expose the tacit assumptions, models, and metaphors that we carry around as a way of understanding both what we're about and why we sometimes seem to be at odds with one another. Intelligence are also many things in the sense that is a product of evolution.

The 2018 AAAI Conference on Artificial Intelligence, the 2018 Innovative Applications of Artificial Intelligence, and the 2018 Symposium on Educational Advances in Artificial Intelligence were held February 2–7, 2018 at the Hilton New Orleans Riverside, New Orleans, Louisiana, USA.  This report, based on the prefaces contained in the AAAI-18 proceedings and program, summarizes the events of the conference.

Schultz will speak on moving toward Intelligence Conference (IAAI-Vancouver. Oren Etzioni (University of Washington of New South Wales), distinguished for and Farecast) is known for his his work in automated reasoning, will technical work in intelligent agents, speak on representing and reasoning data mining and web search. Coolest Web Sites" and PC World's "20 progress report on logic for automated Applications of AI Conference Intelligent Arts) was the technical director and agents is another research area that experienced designer for Electronic Art's FIFA franchise He is obsessed Careful thought was put into inviting College Park) is a prominent with creating the illusion of life world class speakers to this year's conference. Getoor will speak on graph application of AI. In his talk, entitled Alan Mackworth (University of British identification, specifically methods "Big'A,' Small'I': Smart Ends from Columbia). For the technical AAAI-07 that transform an observed input Simple Means," Brown will cover various conference, speakers include Alan graph into an inferred output graph.

Creativity is sometimes taken to be an inexplicable aspect of human activity. By summarizing a considerable body of literature on creativity, I hope to show how to turn some of the best ideas about creativity into programs that are demonstrably more creative than any we have seen to date. I believe the key to building more creative programs is to give them the ability to reflect on and modify their own frameworks and criteria. That is, I believe that the key to creativity is at the metalevel.

Human intelligence is shaped by what is most important to us -- the things that cause ecstasy, despair, pleasure, pain, and other intense emotions. The ability to separate the important from the unimportant underlies such faculties as attention, focusing, situation and outcome assessment, priority setting, judgment, taste, goal selection, credit assignment, the selection of relevant memories and precedents, and learning from experience. AI has for the most part focused on logic and reasoning in artificial situations where only relevant variables and operators are specified and has paid insufficient attention to processes of reducing the richness and disorganization of the real world to a form where logical reasoning can be applied. This article discusses the role of importance judgment in intelligence; provides some examples of research that make use of importance judgments; and offers suggestions for new mechanisms, architectures, applications, and research directions for AI.