A universal deepfake detector has achieved the best accuracy yet in spotting multiple types of videos manipulated or completely generated by artificial intelligence. The technology may help flag non-consensual AI-generated pornography, deepfake scams or election misinformation videos. The widespread availability of cheap AI-powered deepfake creation tools has fuelled the out-of-control online spread of synthetic videos. Many depict women – including celebrities and even schoolgirls – in nonconsensual pornography. And deepfakes have also been used to influence political elections, as well as to enhance financial scams targeting both ordinary consumers and company executives. But most AI models trained to detect synthetic video focus on faces – which means they are most effective at spotting one specific type of deepfake, where a real person's face is swapped into an existing video.

There is a little shuttle bus in the Inland Empire that's fueled with big aspirations. It's electric, tops out at 25 mph, and can only go on a pre-designated route set up by the Riverside Transit Agency. But here's a catch -- it also drives itself. As of Monday, commuters in Riverside are the first in the country to ride a fully self-driving, publicly accessible bus that is deployed by a city transit agency. "I like to say I have no lesser ambition than to be the new Detroit for vehicle manufacturing," Riverside Mayor Lock Dawson said.

Gravitational-wave observatories like LIGO are large-scale, terrestrial instruments housed in infrastructure that spans a multi-kilometer geographic area and which must be actively controlled to maintain operational stability for long observation periods. Despite exquisite seismic isolation, they remain susceptible to seismic noise and other terrestrial disturbances that can couple undesirable vibrations into the instrumental infrastructure, potentially leading to control instabilities or noise artifacts in the detector output. It is, therefore, critical to characterize the seismic state of these observatories to identify a set of temporal patterns that can inform the detector operators in day-to-day monitoring and diagnostics. On a day-to-day basis, the operators monitor several seismically relevant data streams to diagnose operational instabilities and sources of noise using some simple empirically-determined thresholds. It can be untenable for a human operator to monitor multiple data streams in this manual fashion and thus a distillation of these data-streams into a more human-friendly format is sought. In this paper, we present an end-to-end machine learning pipeline for features-based multivariate time series clustering to achieve this goal and to provide actionable insights to the detector operators by correlating found clusters with events of interest in the detector.

This year, Samsung is planning to open a semiconductor chip manufacturing plant in Taylor, TX, that will cost the company an estimated 17 billion. Intel is building a 20-billion facility in Columbus, OH, and industry leaders GlobalFoundries, TSMC, and Texas Instruments are building their own so-called chip fabs in the U.S. as well. This construction boom has been spurred in part by increasing demand for the smartphones, personal electronic devices, and Artificial Intelligence (AI) services that depend on chips, and the 50 billion in funding that the 2022 CHIPS and Science Act allocated to American semiconductor manufacturing has proven to be a strong incentive. Yet the boom is global, with new plants being developed all over the world. As companies plan these new chip fabs, one of the first questions they need to answer is where they are going to get their water.

Since its introduction two decades ago, there has been increasing interest in the problem of early classification of time series. This problem generalizes classic time series classification to ask if we can classify a time series subsequence with sufficient accuracy and confidence after seeing only some prefix of a target pattern. The idea is that the earlier classification would allow us to take immediate action, in a domain in which some practical interventions are possible. For example, that intervention might be sounding an alarm or applying the brakes in an automobile. In this work, we make a surprising claim. In spite of the fact that there are dozens of papers on early classification of time series, it is not clear that any of them could ever work in a real-world setting. The problem is not with the algorithms per se but with the vague and underspecified problem description. Essentially all algorithms make implicit and unwarranted assumptions about the problem that will ensure that they will be plagued by false positives and false negatives even if their results suggested that they could obtain near-perfect results. We will explain our findings with novel insights and experiments and offer recommendations to the community.

Artificial intelligence researchers are always trying to replicate aspects of human senses through algorithms. AI has been used to dramatically enhance computer vision applications in recent years, and AI has also been used to generate fairly impressive audio samples, even creating whole songs in the style of one artist. Recently, a team of scientists from University of California, Riverside managed to create an AI capable of distinguishing smells from one another based on the chemical makeup of the odor in question. According to cell and systems biologist at UC Riverside, Anandasankar Ray, the researchers tried to base their AI model on how humans perceive smells. The human nose contains approximately 400 olfactory receptors (ORs) that are activated when chemicals enter the nose.

IMAGE: Anandasankar Ray is a professor of molecular, cell and systems biology at UC Riverside. "We now can use artificial intelligence to predict how any chemical is going to smell to humans," said Anandasankar Ray, a professor of molecular, cell and systems biology, and the senior author of the study that appears in iScience. "Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals." Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose. Each OR is activated by a unique set of chemicals; together, the large OR family can detect a vast chemical space.

Raspberry Pi now supports TensorFlow, so you can start your own machine learning projects on the tiny computer. Waymo is beginning to wedge its way into the public transport system in Arizona. AI on Raspberry Pi: The latest version of TensorFlow can now be run on the Raspberry Pi. "Thanks to a collaboration with the Raspberry Pi Foundation, we're now happy to say that the latest 1.9 release of TensorFlow can be installed from pre-built binaries using Python's pip package system," according to a blog post written by Pete Warden, an engineer working on the TensorFlow team at Google. It's pretty easy to install if you've got a Raspberry Pi running Raspbian 9.0 and either Python 2.7 or anything newer than Python 3.4. After that it's only a few simple lines of code, and you're done.

The Model 3 is supposed to be Tesla's humdrum car, the everyday, cut-price offering to the masses. Not the sort of thing that impresses Angelenos, so blasé about celebrities and rich kids valeting their supercars at restaurants. In LA, it takes a special vehicle to stand out. Yet, as I'm driving around town in a (bright red) Model 3, I feel unusually conspicuous, attracting the eyes of passers-by, some of them walking into traffic for a closer look. Other Tesla owners come up to me to chat while charging. Surely, some of them are among the 450,000 people who have already put down a $1,000 deposit for the right to buy this car.

The Times has assembled a panel of distinguished and diverse writers who will regularly contribute to the Books section. The 10 authors who make up the Los Angeles Times Cultural Critics At Large have published works of fiction, nonfiction and poetry. They have won dozens of prizes. A majority have deep connections to Southern California, even though they hail from four different nations. They will help expand the literary conversation, challenging ideas and broadening readers' understanding of literature and culture within the contemporary moment.