An Italian researcher has claimed to have found the long-lost burial place of the famed Greek philosopher Plato who died around 348 BC. Graziano Ranocchia used AI to decipher the Herculaneum scrolls, charred papyrus found buried by the Mount Vesuvius eruption in 79AD, revealing new text that pointed to an exact location in Athens. The analysis showed Plato was buried in'The Academy,' a famous school founded by the philosopher in 387 BC, near the so-called Museion - a small building sacred to the Muses that no longer stands among the ruins. Ranocchia and his team uncovered 1,000 words, corresponding to 30 percent of the text, using the'bionic eye' - and believe they will have the papyrus completely analyzed by 2026. The analysis showed Plato was buried in'The Academy,' a famous school founded by the philosopher in 387 BC, near the so-called Museion - a small building sacred to the Muses The team uncovered 1,000 words, corresponding to 30 percent of the text, using the'bionic eye' - and believe they will have the papyrus completely analyzed by 2026 'Compared to previous editions, there is now an almost radically changed text, implying a number of new and concrete facts about various academic philosophers,' Ranocchia said in a statement.

Researchers say they have found a hardware exploit with Tesla's infotainment system that could unlock paid upgrades for free, including Full Self-Driving (FSD) and heated rear seats. They used a technique called voltage glitching, which involves tinkering with the supply voltage of the infotainment system's processor. "If we do it at the right moment, we can trick the CPU into doing something else," Christian Werling told TechCrunch. "It has a hiccup, skips an instruction and accepts our manipulated code. That's basically what we do in a nutshell."

With Valentine's Day on the horizon, many singletons might be swiping on their dating apps with a little more urgency than normal. Unfortunately, a new study from the University of Vienna has found that this excessive searching could be doing more harm than good in the quest for love. Psychologists surveyed 464 young people on their dating app use, including how much they swipe and how they decide whether to go left or right on a profile. They were also asked if they compare themselves to others or become overwhelmed when browsing profiles, as well as about their feelings towards being single. A correlation was found between excessive swiping and a fear of being alone forever, feeling bad about one's life and so-called'partner choice overload'.

Here's something reassuring to think about: researchers using machine-learning artificial intelligence (AI) often don't know precisely how their algorithms are solving the problems they are tasked with. Take for instance the AI that can identify race from X-rays where no human can see how, or the Facebook AI that began to develop its own language. Joining these may be everyone's favorite text-to-image generator, DALLE-2. Computer Science PhD student Giannis Daras noticed that the DALLE-2 system, which creates images based on a text input prompt, would return nonsense words as text under certain circumstances. "A known limitation of DALLE-2 is that it struggles with text," he wrote in a paper published on pre-print server Arxiv.

Biometrics can effectively be used to detect deepfakes, according to a paper from a team of Italian and German researchers reported by Unite.AI, and could be a less "unwieldy" method of doing so than detecting synthetic artefacts and other methods. The framework for the method specifies the use of at least ten genuine videos of the subject to train the biometric model, the researchers from the University of Federico II in Naples and the Technical University of Munich write. The research into'Audio-Visual Person-of-Interest DeepFake Detection' has been posted to Arxive, and describes what the authors say is a new state-of-the-art in deepfake detection. In testing against well-known datasets, the researchers improved area under curve (AUC) scores by 3 and 10 for accuracy identifying genuine high and low-quality videos, respectively, and 7 percent for deepfake videos. Interestingly, on high-quality videos, the worst-performing system delivered deepfake detection accuracy of above 69 percent.

A new research collaboration between France and the UK casts doubt on growing industry confidence that synthetic data can resolve the privacy, quality and availability issues (among other issues) that threaten progress in the machine learning sector. Among several key points addressed, the authors assert that synthetic data modeled from real data retains enough of the genuine information as to provide no reliable protection from inference and membership attacks, which seek to deanonymize data and re-associate it with actual people. Furthermore, the individuals most at risk from such attacks, including those with critical medical conditions or high hospital bills (in the case of medical record anonymization) are, through the'outlier' nature of their condition, most likely to be re-identified by these techniques. 'Given access to a synthetic dataset, a strategic adversary can infer, with high confidence, the presence of a target record in the original data.' The paper also notes that differentially private synthetic data, which obscures the signature of individual records, does indeed protect individuals' privacy, but only by significantly crippling the usefulness of the information retrieval systems that use it.

Human interpreters make choices unique to them, consciously or unconsciously, when translating one language into another. They might explicate, normalize, or condense and summarize, creating fingerprints known informally as "translationese." In machine learning, generating accurate translations has been the main objective thus far. But this might be coming at the expense of translation richness and diversity. In a new study, researchers at Tilburg University and the University of Maryland attempt to quantify the lexical and grammatical diversity of "machine translationese" -- i.e., the fingerprints made by AI translation algorithms.

Researchers at Duke Kunshan University, Wuhan University, Lenovo, and Sun Yat-sen University in Guangzhou claim to have developed an AI system that detects whether a person is wearing a mask from the sound of their muffled speech. They say that in experiments, it achieves 78.8% accuracy on one metric, demonstrating that sound could be a useful means of enforcing mask-wearing during the pandemic. The team's work is a submission to the 11th annual Computational Paralinguistics Challenge (ComParE) at the upcoming Interspeech 2020 conference, an open challenge dealing with the states and traits of speakers as manifested in their speech. This year saw the introduction of a "mask sub-challenge" in which the goal is to develop algorithms capable of determining whether a person is wearing a mask from the sound of their voice. For the sub-challenge, every competitor -- the coauthors of this study included -- must use the same corpus of 32 German speakers recorded for 10 hours in an audio studio wearing Lohmann & Rauscher face coverings.

By carrying out advanced experiments on neuronal cultures and large scale simulations, a group of scientists from Bar-Ilan University in Israel claims to have created a new type of ultra-fast artificial intelligence algorithm. This algorithm is based on the dynamics of the human brain, which, despite computing at a much slower rate than modern computers, is extremely fast and efficient. In an article published today in the journal Scientific Reports, researchers claim to be rebuilding the bridge between neuroscience and advanced artificial intelligence algorithms that, they say, has taken a backseat for almost 70 years. "The current scientific and technological viewpoint is that neurobiology and machine learning are two distinct disciplines that advanced independently," the study's lead author, Prof. Ido Kanter, of Bar-Ilan University's Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center said in a press release. "The absence of expectedly reciprocal influence is puzzling."

Ravens and great apes may not be quite as smart as we think. Researchers have found that while they are able to plan ahead, it does not require thinking. Instead, they can make plans instinctively through prior experiences. 'Some researchers have suggested that planning in great apes and ravens develops through thinking, that they simulate future scenarios and make decisions based on such mental simulations,' said Johan Lind, associate professor in Ethology, at Centre for Cultural Evolution, Stockholm University, author of the study. 'My study shows that planning behaviours and self-control in non-human animals instead can emerge through associative learning.'