ML security has the same goal as all cybersecurity measures: reducing the risk of sensitive data being exposed. If a bad actor interferes with your ML model or the data it uses, that model may output incorrect results that, at best, undermine the benefits of ML and, at worst, negatively impact your business or customers. "Executives should care about this because there's nothing worse than doing the wrong thing very quickly and confidently," says Zach Hanif, vice president of machine learning platforms at Capital One. And while Hanif works in a regulated industry--financial services--requiring additional levels of governance and security, he says that every business adopting ML should take the opportunity to examine its security practices. Devon Rollins, vice president of cyber engineering and machine learning at Capital One, adds, "Securing business-critical applications requires a level of differentiated protection. It's safe to assume many deployments of ML tools at scale are critical given the role they play for the business and how they directly impact outcomes for users."

Tesla will play a major role in a manslaughter trial this week over a fatal crash caused by a vehicle operating on autopilot, in what could be a defining case for the self-driving car industry. At the trial's heart is the question of who is legally responsible for a vehicle that can drive – or partially drive – itself. Kevin George Aziz Riad is on trial for his role in a 2019 crash. Police say Riad exited a freeway in southern California in a Tesla Model S, ran a red light and crashed into a Honda Civic, killing Gilberto Lopez and Maria Guadalupe Nieves-Lopez. Tesla's autopilot system, which can control speed, braking and steering, was engaged at the time of the crash that killed the couple, who were on their first date.

Artificial Intelligence is widely used in various industries to automate routine tasks and increase the productivity and efficiency of business strategies. One more area where AI is becoming extremely useful is user experience and user interface (UX/UI) design. While AI can save designers time and perform routine tasks, such as image cropping, designers can focus on more complex tasks that require a high level of creativity. However, this is not the only advantage of Artificial Intelligence. ML models get "smarter" and can drive better UI/UX design.

When the concept artist and illustrator RJ Palmer first witnessed the fine-tuned photorealism of compositions produced by the AI image generator Dall-E 2, his feeling was one of unease. The tool, released by the AI research company OpenAI, showed a marked improvement on 2021's Dall-E, and was quickly followed by rivals such as Stable Diffusion and Midjourney. Type in any surreal prompt, from Kermit the frog in the style of Edvard Munch, to Gollum from The Lord of the Rings feasting on a slice of watermelon, and these tools will return a startlingly accurate depiction moments later. Cosmopolitan trumpeted the world's first AI-generated magazine cover, and technology investors fell over themselves to wave in the new era of "generative AI". The image-generation capabilities have already spread to video, with the release of Google's Imagen Video and Meta's Make-A-Video.

Developed at KAUST (King Abdullah University of Science and Technology), machine learning differentiates the gases according to the way they induce slight temperature changes in the sensor as they interact with it. Smart electronic sensors have applications from medical diagnostics to the detection of industrial gas leaks. The challenge is to accurately detect the target gas among the complex mixture of chemicals typically found in the air, said Usman Yaqoob, a postdoc in the labs of Mohammad Younis, who led the research. "Existing sensing technologies still suffer from cross-sensitivity," Yaqoob said in a statement. On the hardware side is a heated strip of silicon called a microbeam resonator.

Can a three-dimensional (3D) piece of origami be flattened without damaging it? Merely by looking at the piece, the answer is difficult to predict because every fold in the origami has to be well-suited for flattening. This is an instance of a combinatorial problem. A new study by the University of Amsterdam's (UvA) Institute of Physics and research institute AMOLF has shown that machine learning algorithms can exactly and efficiently answer these types of questions. This hopefully will give a boost to the artificial intelligence (AI)-supported design of complex and functional (meta)materials.

Trapped in a metal cage in a corner of a 350,000 sq ft Amazon warehouse outside Boston last week a lonely yellow robot arm sorted through packages, preparing items to be shipped out to customers who demand ever-faster delivery. Soon it will be joined by others in a development that could mean the end of thousands of jobs and, Amazon argues, the creation of thousands of others. As the robot worked, a screen displayed its progress. It carefully packed a tub of protein powder, next came a box of napkin rings then … a tube of hemorrhoid cream. As 100 journalists from around the world snapped pictures, someone switched the screen to hide the cream.

While univariate models for representing time series are useful in many applications, jointly modeling multiple time series can increase accuracy of various tasks, such as forecasting, anomaly detection and pattern discovery. There are two main hindrances to applying these models more widely. The first is wide variability of behaviors of different time series, making it harder to capture their different behaviors in a single model. Solutions may be borrowed from other challenges ML researchers face in other data domains: for example, in other machine learning tasks, different scales between features is addressed using normalization techniques. A second challenge is joint modeling of time series that are measured/reported at different time intervals (e.g., reporting every second, minute or hour), and sometimes reported at irregular intervals.

Indoor positioning technologies are one of the driving forces behind the digital transformation of the industrial sector. The ability to track objects, assets and people accurately and cheaply could save resources, time and money for companies in various sectors, from logistics to health care. Xavier Vilajosana, the Professor in the Faculty of Computer Science, Multimedia and Telecommunications and leader of the Wireless Networks group (WINE) at the Internet Interdisciplinary Institute (IN3) at the UOC (Universitat Oberta de Catalunya) is coordinating the university's participation in a new European project which is developing innovative solutions to improve location in indoor environments. DUNE uses deep learning techniques combined with distributed computing systems, which take advantage of both cloud and edge computing. In other words, these are computing architectures that operate both on remote servers and near where the data are generated.

But while the video game seemingly looks and plays much like other online strategy games, under the hood it's a very different story. That's because it doesn't rely on the servers running popular online strategy games like Eve Online and World of Warcraft. Instead, Dark Forest runs completely on a blockchain, in a way that means no one is in control of how it plays out. Its early success doesn't just reflect a fun way of making games that work in an entirely different way. It also helps prove that blockchains can be used for far more interesting and complex applications than just moving digital money around, something some blockchain boosters have been saying since the technology first emerged.