Shares of Tesla dipped following the news and were nearly 2% lower when US markets closed. Following the verdict, plaintiffs attorneys said Mr Musk had misrepresented the capabilities of the company's Autopilot driver assistance software. "Tesla designed Autopilot only for controlled-access highways yet deliberately chose not to restrict drivers from using it elsewhere, alongside Elon Musk telling the world Autopilot drove better than humans," said attorney Brett Schreiber in a statement to the BBC. Mr Schreiber said Tesla and Mr Musk had long propped up the company's valuation with "self-driving hype at the expense of human lives." "Tesla's lies turned our roads into test tracks for their fundamentally flawed technology," he added.

When I first became a doctor, I cared for an older man whom I'll call Ted. He was so sick with pneumonia that he was struggling to breathe. His primary-care physician had prescribed one antibiotic after another, but his symptoms had only worsened; by the time I saw him in the hospital, he had a high fever and was coughing up blood. His lungs seemed to be infected with methicillin-resistant Staphylococcus aureus (MRSA), a bacterium so hardy that few drugs can kill it. I placed an oxygen tube in his nostrils, and one of my colleagues inserted an I.V. into his arm. We decided to give him vancomycin, a last line of defense against otherwise untreatable infections.

Due to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This so-called phase problem poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this, we present an approach that utilizes prior knowledge to regularize the training process over larger parameter spaces. We demonstrate the effectiveness of our method in various scenarios, including multilayer structures with box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. By leveraging the input of prior knowledge, we can improve the training dynamics and address the underdetermined ("ill-posed") nature of the problem. In contrast to previous methods, our approach scales favorably when increasing the complexity of the inverse problem, working properly even for a 5-layer multilayer model and an N-layer periodic multilayer model with up to 17 open parameters.

Recently, there has been significant interest in applying machine learning (ML) techniques to X-ray scattering experiments, which proves to be a valuable tool for enhancing research that involves large or rapidly generated datasets. ML allows for the automated interpretation of experimental results, particularly those obtained from synchrotron or neutron facilities. The speed at which ML models can process data presents an important opportunity to establish a closed-loop feedback system, enabling real-time decision-making based on online data analysis. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment. Our data demonstrates the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup.

The insurance industry is one of the largest in the world and has been around for hundreds of years, making it ripe for transformation by newcomers in the space. Over the past five to ten years, technology has pushed the frontier of what's possible, making way for a new breed of digital-first insurance companies to come to life. The use of technology, data, artificial intelligence (AI), and modern design has created a powerful combination, changing what was once a very policy-centric industry to one that's customer-centric. I recently had the chance to speak to Daniel Schreiber, co-founder CEO of Lemonade, the digital insurance company powered by social impact with a mission dedicated to building the "most loveable insurance" available. During our conversation, he spoke about the company's use of AI and big data, how it impacts the customer experience, and the opportunities (and, at times, challenges) it brings to the industry.

And so, after 17 years of false starts, numerous failed attempts at feature films (including a Peter Jackson venture), more than 265 drafts, a reported budget of $200m and a production schedule in Hungary decimated by the pandemic, we are finally set to see a TV series of the video game Halo. Will it have been worth such perseverance? Since the release of the first video game in Microsoft's crown jewel franchise – 2001's Halo: Combat Evolved – the series has sold more than 81m games, generating in excess of $6bn. If a network sticks the landing, a Halo TV show could be a significant weapon in its arsenal. For the uninitiated: Halo takes place at a time of intergalactic war between humans and a collective of quasi-religious alien species known as the Covenant.

Quite how Halo hasn't made it to the screen, small or big, before this is an enigma almost as nebulous as the long-running first person shooter video game's crowded mythos. Luminaries such as Steven Spielberg, Peter Jackson and District 9's Neill Blomkamp have all been involved in trying to get a film based on the explosive exploits of Masterchief across the line for the best part of two decades, yet to no avail. Even this big-budget – it reputedly cost more than $200m and looks like gold – TV series starring Pablo Schreiber as the genetically engineered soldier-hero of the United Nations Space Command (UNSC) has been held up for two years by Covid. Never mind, it's here now, and fans of the games who just want to see their nightly battles with giant space monsters played out on the TV screen will no doubt be more than content with Kyle Killen and Steven Kane's adventurous if somewhat insipid reimagining. Unfortunately, those of us who don't recognise every re-enacted power-up bleep and helmet-cam vision of destruction will probably find ourselves wondering, much of the time, quite what is going on.

The U.S. startup company DeepCure, which works to develop medications with the help of artificial intelligence, said this week that it will be opening a lab and offices in Israel for the first time. DeepCure is part of an emerging wave of companies seeking to improve and accelerate the drug-development process with tools like machine learning and AI. It was formed in 2018 by CEO Kfir Schreiber, alongside Joseph Jacobson and Thrasyvoulos (Thras) Karydis, who are today chief science and chief technology officer, respectively. The three met as students at the Massachusetts Institute of Technology. The company is developing small molecules drugs– in other words, medicines generally sold in capsule form, as opposed to antibody-based biological therapies given as an infusion, for example. DeepCure currently has five development programs underway for therapies against cancer, inflammatory diseases and nervous system diseases.

Lemonade, the fast-growing, machine learning-powered insurance app, put out a real lemon of a Twitter thread on Monday with a proud declaration that its AI analyzes videos of customers when determining if their claims are fraudulent. The company has been trying to explain itself and its business model -- and fend off serious accusations of bias, discrimination, and general creepiness -- ever since. The prospect of being judged by AI for something as important as an insurance claim was alarming to many who saw the thread, and it should be. We've seen how AI can discriminate against certain races, genders, economic classes, and disabilities, among other categories, leading to those people being denied housing, jobs, education, or justice. Now we have an insurance company that prides itself on largely replacing human brokers and actuaries with bots and AI, collecting data about customers without them realizing they were giving it away, and using those data points to assess their risk.

The Final Cut's new album Process was recorded in two places: a cavernous music studio in Berlin, and a Brooklyn dining hall during an immersive culinary experience in which sound was among the items on the menu. "With its swarming, chirping creatures and metallic thuds, it sounds like a cross between a distorted, futuristic version of one of the more patient strains of industrial and drone music," writes a critic for the experimental music magazine Ear Wave Event. Somehow, the anonymous writer claims that the triangulation of Berlin, Brooklyn, and drone music pays homage to Italian culture . Process, if we're to trust the critic, is a messy hodgepodge of instruments, recording processes, and cultural influences. But the Final Cut's album doesn't actually exist.