A Tesla in Autopilot mode crashed Saturday into a Florida Highway Patrol cruiser parked on the side of the road in Orlando. The crash happened while a federal investigation into Tesla's partially automated driving system is underway after nearly a dozen crashes involving emergency responder vehicles. The official police report from the Florida Highway Patrol states an Orlando man stopped his disabled vehicle in the travel lane of the highway. A 28-year-old trooper parked his patrol vehicle, a 2018 Dodge Charger, directly behind the disabled vehicle and activated the Dodge's emergency lights. The trooper then exited the vehicle to assist the driver. The 2019 Tesla apparently failed to stop and struck the left side of the patrol cruiser, then proceeded to strike the disabled vehicle.

SpaceX will attempt to catch a massive rocket using "robot chopsticks", according to Elon Musk. The audacious plan could be carried out later this year during a major test of the Mars-bound Starship craft, which will see it blasted into orbit by a Super Heavy booster rocket. The so-called chopsticks refer to mechanical arms attached to SpaceX's launch tower – named'Mechazilla' by Mr Musk – which will help guide the booster rocket back down onto the pad. This system could eventually allow for rapid reusability and allow for multiple Starship launches in a single day, though chances of early success are far from guaranteed. "SpaceX will try to catch largest ever flying object with robot chopsticks," Mr Musk tweeted on Monday.

Ian Watson, Rosina O Weber, David Leake Case-based reasoning is reasoning from experience, solving new problems and interpreting new situations by retrieving and adapting prior cases. The Twenty-Eight International Conference on Case-Based Reasoning (ICCBR2020) was held from June 8-12, 2020, with program chairs Ian Watson and Rosina Weber. The conference was originally scheduled for Salamanca, Spain, a World Heritage site, under the auspices of local chair Juan Manuel Corchado and the University of Salamanca. Its theme, "CBR Across Bridges", reflected the goal of bringing together researchers and practitioners with relevant work across various AI areas. Before the conference, the pandemic struck, with tragic effects. The conference chairs resolved to continue with a safe alternative: the first virtual ICCBR. With researchers unable to travel, the virtual conference not only bridged AI areas but geographic ones: 141 conference attendees participated from 23 countries.

Toyota has apologised for the "overconfidence" of a self-driving bus after it ran over a Paralympic judoka in the athletes' village and said it would temporarily suspend the service. The Japanese athlete, Aramitsu Kitazono, will be unable to compete in his 81kg category this weekend after being left with cuts and bruises following the impact with the "e-Palette" vehicle. His injuries prompted a personal intervention from the president of Toyota, Akio Toyoda. As part of its sponsorship of Tokyo 2020, Toyota has been showcasing its autonomous vehicles via a shuttle service, which has been running around the clock in the athletes' village. On Thursday, however, one of the buses pulled away from a T-junction and drove through a pedestrian crossing while Kitazono, a visually impaired athlete, was walking across.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Given that natural language processing (NLP) is a subset of artificial intelligence (AI), models need to train on large volumes of data. Unfortunately, many researchers are unable to access or develop the models and datasets necessary for robust systems -- they are mostly the purview of large technology giants. Hugging Face, the winner of VentureBeat's Innovation in Natural Language Process/Understanding Award for 2021, is looking to level the playing field. The team, launched by Clément Delangue and Julien Chaumond in 2016, was recognized for its work in democratizing NLP, the global market value for which is expected to hit $35.1 billion by 2026.

The artificial intelligence systems used by image recognition tools, such as those that certain connected cars use to identify street signs, can be tricked to make an incorrect identification by a low-cost but effective attack using a camera, a projector and a PC, according to Purdue University researchers. A research paper describes an Optical Adversarial Attack, or OPAD, which uses a projector to project calculated patterns that alter the appearance of the 3D objects to AI-based image recognition systems. The paper will be presented in October at an ICCV 2021 Workshop. In an experiment, a pattern was projected onto a stop sign, causing the image recognition to read the sign as a speed limit sign instead. The researchers say this attack method could also work with image recognition tools in applications ranging from military drones to facial recognition systems, potentially undermining their reliability.

The application of statistical machine learning techniques in chemistry has a long history1. Algorithmic innovation, improved data availability, and increases in computer power have led to an unprecedented growth in the field2,3. Extending the previous generation of high-throughput methods, and building on the many extensive and curated databases available, the ability to map between the chemical structure of molecules and materials and their physical properties has been widely demonstrated using supervised learning for both regression (for example, reaction rate) and classification (for example, reaction outcome) problems. Notably, molecular modelling has benefited from interatomic potentials based on Gaussian processes4 and artificial neural networks5 that can reproduce structural transformations at a fraction of the cost required by standard first-principles simulation techniques. The research literature itself has become a valuable resource for mining latent knowledge using natural language processing, as recently applied to extract synthesis recipes for inorganic crystals6.

Artificial intelligence leads the top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform. The steps being taken to integrate artificial intelligence (AI) into healthcare and the use of AI techniques in the detection and management of various diseases were popularly discussed in Q2. Rafael Grossmann, a surgeon and clinical innovator, shared an article on two new companies namely Anumana and Lucem Health being launched by healthcare company Mayo Clinic that will collect and analyse patient data gathered from remote monitoring devices and tools to enable early detection and diagnosis of diseases. Mayo Clinic will launch a remote monitoring platform that will enable clinicians and physicians to make quicker and better decisions with the help of the collected and analysed patient data thereby speeding up the diagnosis before symptoms appear. It will also allow patients to take more control of their health and related decisions.

US federal agencies are planning to expand use of facial recognition systems, according to a report by the Government Accountability Office (GAO) despite continuous backlash over the technology's application for more than a year. The report, published Tuesday, assessed the use of facial recognition systems by federal agencies, and how they plan to expand the use of the technology in the future. Eighteen of the 24 surveyed agencies, including the US Departments of Justice, Defense, Education, Housing and Urban Development, reported using facial recognition technology (FRT) for one or more purposes, the GAO report said. The survey also found that 10 of the agencies plan to broaden their use of the technology by 2023, with two of them investing in its research and development. While most of the facial recognition systems used by the federal agencies are government owned, the report says six such systems come from commercial vendors like Clearview AI, and Acuant FaceID.

Excelling at a test often does not translate into excelling at the skills the test purports to measure. This is true not only of humans but also of AI systems, and the more so the greater the claims of the test's significance. This became evident less than a decade after the introduction of the Winograd Schema Challenge (WSC),3 a test designed to measure an AI system's commonsense reasoning (CSR) ability by answering simple questions. An example would be, given the information: The sculpture rolled off the shelf because it wasn't anchored, answering: What wasn't anchored? There are multiple AI systems2 that achieve human performance on the WSC but are not capable of performing CSR.