When it comes to online dating, Giovanni Wolfram, a 25-year-old living in Santa Fe, New Mexico, isn't all too worried about whether his fellow dating app users will find him attractive. Rather, his biggest fear is that he might come off as "cringey." "You can get away with being ugly," Wolfram says. "But being cringey is just like--that's a character that's imprinted on you." Since he first joined Hinge at 18, he has worked hard to scrub his profile of sincerity.

Though the beauty industry seeks to eradicate them, research has shown that having wrinkles can be a positive thing. Researchers from Western University and the University of Miami found that human brains are pre-wired to view people as more sincere if they have wrinkles around their eyes when they smile and frown. People who have the so-called'Duchenne marker' are viewed as conveying more intense and more sincere emotions. Researchers used a method called visual rivalry and showed study participants photographs of expressions with and without the Duchenne marker to see which expressions are perceived as more important. When different images are shown in each eye, the brain alternates between these two images, but will bring the image that is perceived as more relevant into perceptual awareness more often.

In The Last Invention of Man, an excerpt from his book Life 3.0: Being Human in the Age of Artificial Intelligence, physicist Max Tegmark imagines how AI might take over the world. "The Omega Team was the soul of the company," Tegmark writes. "Whereas the rest of the enterprise brought in the money to keep things going, by various commercial applications of narrow AI, the Omega Team pushed ahead in their quest for what had always been the CEO's dream: building general artificial intelligence. Most other employees viewed'the Omegas,' as they affectionately called them, as a bunch of pie-in-the-sky dreamers, perpetually decades away from their goal. They happily indulged them, however, because they liked the prestige that the cutting-edge work of the Omegas gave their company, and they also appreciated the improved algorithms that the Omegas occasionally gave them. What they didn't realize was that the Omegas had carefully crafted their image to hide a secret: They were extremely close to pulling off the most audacious plan in human history. Their charismatic CEO had handpicked them not only for being brilliant researchers, but also for ambition, idealism, and a strong commitment to helping humanity. He reminded them that their plan was extremely dangerous, and that if powerful governments found out, they would do virtually anything -- including kidnapping -- to shut them down or, preferably, to steal their code. But they were all in, 100 percent, for much the same reason that many of the world's top physicists joined the Manhattan Project to develop nuclear weapons: They were convinced that if they didn't do it first, someone less idealistic would."

This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that both retrieves documents from a full-text document corpus and from within individual documents, and locates passages likely to contain information about important problem-solving features of cases. SPIRE uses two case-bases, one containing past precedents, and one containing excerpts from past case texts. Both are used by SPIRE to automatically generate queries, which are then run by the INQUERY full-text retrieval engine on a large text collection in the case of document retrieval and on individual text documents for passage retrieval.

The concept of common ground — the mutual understanding of context and conventions — is central to philosophical accounts of mendacity; its use is to determine the meaning of linguistic expressions and the significance of physical acts, and to distinguish certain statements as conveying a conventional promise, warranty, or expectation of sincerity. Lying necessarily involves an abuse of common ground, namely the willful violation of conventions regulating sincerity. The ‘lying machine’ is an AI system that purposely abuses common ground as an effective means for practicing mendacity and lesser deceptions. The machine's method is to conceive and articulate sophisms — perversions of normative reason and communication — crafted to subvert its audience's beliefs. Elements of this paper (i) explain the described use of common ground in philosophical accounts of mendacity, (ii) motivate arguments and illusions as stratagem for deception, (iii) encapsulate the lying machine's design and operation, and (iv) summarize human-subject experiments that confirm the lying machine's arguments are, in fact, deceptive.

Voting theory can provide useful insights for multiagent preference aggregation. However, the standard setting assumes voters with preferences that are total orders, as well as a ballot language that coincides with the preference language. In typical AI scenarios, these assumptions do not hold: certain alternatives may be incomparable for some agents, and others may have their preferences encoded in a format that is different from how the preference aggregation mechanism wants them. We study the consequences of dropping these assumptions. In particular, we investigate the consequences for the important notion of strategy-proofness. While strategy-proofness cannot be guaranteed in the classical setting, we are able to show that there are situations in our more general framework where this is possible. We also consider computational aspects of the problem.