While Jamal Khashoggi was being carefully slaughtered in the Saudi consulate in Istanbul, a (clumsy and not much alike) man was trying out his shoes and clothes. The plan was for the imposter to appear on CCTV cameras while exiting the consulate and walk back to Khashoggi's residence. The plan eventually blew up, because the Turkish intelligence had already bugged the consulate and recorded exactly what had happened. This was one of the first attempts by state actors to manipulate other states (or publics) through CCTV footage. However, recent actions of the Iranian state television have taken this type of information warfare to a different level.

In a sparsely decorated corner office of the Danish Public Benefits Administration sits one of Denmark's most quietly influential people. Annika Jacobsen is the head of the agency's data mining unit, which, over the past eight years, has conducted a vast experiment in automated bureaucracy. Blunt, and with a habit of completing others' sentences, Jacobsen is clear about her mission: "I'm here to catch cheaters." Denmark's Public Benefits Administration employs hundreds of people who oversee one of the world's most well-funded welfare states. The country spends 26 percent of its GDP on benefits--more than Sweden, the United States, and the United Kingdom.

The documents also include forms that local police officers can use to submit a photo to the FBI's Facial Analysis, Comparison and Evaluation (FACE) Services Unit, which then runs it through a facial recognition search and returns possible matches. Officers can use the form to request the photos also be run through a biometric database of foreign citizens and combatants run by the Defense Department and the passport and visa photos managed by the State Department, the documents show.

AI models designed to closely simulate a person's voice are making it easier for bad actors to mimic loved ones and scam vulnerable people out of thousands of dollars, The Washington Post reported. Quickly evolving in sophistication, some AI voice-generating software requires just a few sentences of audio to convincingly produce speech that conveys the sound and emotional tone of a speaker's voice, while other options need as little as three seconds. For those targeted--which is often the elderly, the Post reported--it can be increasingly difficult to detect when a voice is inauthentic, even when the emergency circumstances described by scammers seem implausible. Tech advancements seemingly make it easier to prey on people's worst fears and spook victims who told the Post they felt "visceral horror" hearing what sounded like direct pleas from friends or family members in dire need of help. One couple sent $15,000 through a bitcoin terminal to a scammer after believing they had spoken to their son.

This article is part of TPM Cafe, TPM's home for opinion and news analysis. It first appeared on our publisher Joe Ragazzo's newsletter, Rhapsody. These online applications--which allow a person to converse with a "bot" powered by artificial intelligence via text--interface are a potential window into the future of technology. A few weeks ago, ChatGPT burst on the scene and spawned a million takes. It was promptly banned from some school districts.

Amid lingering concerns about surveillance and safety, the Los Angeles City Council is expected to vote Tuesday on whether to accept the donation of a dog-like robot for the LAPD. The vote will determine whether the department gets the controversial device, which would be paid for with a nearly $280,000 donation from the Los Angeles Police Foundation. The Police Commission and the council's public safety committee have approved the move. The department said it intends to deploy the device in limited scenarios and primarily for reconnaissance. Nicknamed Spot, it can climb stairs, open doors and navigate rugged terrain, giving police a set of eyes in potentially dangerous situations while keeping officers out of harm's way, officials say.

WASHINGTON – Ukraine has broadened a request for controversial cluster bombs from the United States to include a weapon that it wants to cannibalize to drop the anti-armor bomblets it contains on Russian forces from drones, according to two U.S. lawmakers. Kyiv has urged members of Congress to press the White House to approve sending the weapons but it is by no means certain that the Biden administration will sign off on that. Cluster munitions, banned by more than 120 countries, normally release large numbers of smaller bomblets that can kill indiscriminately over a wide area, threatening civilians. Ukraine is seeking the MK-20, an air-delivered cluster bomb, to release its individual explosives from drones, said U.S. Representatives Jason Crow and Adam Smith, who both serve on the House of Representatives Armed Services Committee. That is in addition to 155 mm artillery cluster shells that Ukraine already has requested, they said.

Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive MCMC methods, or use stochastic losses that have high variance. To avoid these problems, we augment flows with annealed importance sampling (AIS) and minimize the mass-covering $\alpha$-divergence with $\alpha=2$, which minimizes importance weight variance. Our method, Flow AIS Bootstrap (FAB), uses AIS to generate samples in regions where the flow is a poor approximation of the target, facilitating the discovery of new modes. We apply FAB to multimodal targets and show that we can approximate them very accurately where previous methods fail. To the best of our knowledge, we are the first to learn the Boltzmann distribution of the alanine dipeptide molecule using only the unnormalized target density, without access to samples generated via Molecular Dynamics (MD) simulations: FAB produces better results than training via maximum likelihood on MD samples while using 100 times fewer target evaluations. After reweighting the samples, we obtain unbiased histograms of dihedral angles that are almost identical to the ground truth.

Neologisms are certain uses, expressions, and words that did not traditionally exist in a language, but are incorporated into it due to the need of speakers to adapt to a new reality [1]. That is, neologisms are those new words and expressions that speakers incorporate into a language, as new things and new ways of doing to name arise. They are the exact opposite of archaisms. The appearance of neologisms is a common and ordinary process in all languages, forced as they are to adapt and update or die. However, a word can be considered a neologism only for a certain time, since once it has been incorporated and normalized as part of the language, it simply ceases to be a novelty. The simplest way to classify neologisms would be from the method used to create them, thus we have: 1. morphological neologisms: they are built using words that already exist in the language, through the processes of composition or derivation. For example, the word "aircraft" was once a neologism, made up of the prefix "air" and the suffix "craft". This also happens with "teleoperators" or with "biosecurity".

Libraries are increasingly relying on computational methods, including methods from Artificial Intelligence (AI). This increasing usage raises concerns about the risks of AI that are currently broadly discussed in scientific literature, the media and law-making. In this article we investigate the risks surrounding bias and unfairness in AI usage in classification and automated text analysis within the context of library applications. We describe examples that show how the library community has been aware of such risks for a long time, and how it has developed and deployed countermeasures. We take a closer look at the notion of '(un)fairness' in relation to the notion of 'diversity', and we investigate a formalisation of diversity that models both inclusion and distribution. We argue that many of the unfairness problems of automated content analysis can also be regarded through the lens of diversity and the countermeasures taken to enhance diversity.