Level 5 vehicles, which are self-driving in all road situations, do not yet exist. Moreover, it's not clear when that will change. Governments, consumers, and engineers alike want to know how close the automotive world is to producing a fully autonomous Level 5 vehicle. While some experts say such vehicles could hit the road in the next few years, they're a shrinking minority. Most forecasts say a truly self-driving car is at least a decade away -- and maybe much longer, because it requires disruptive technology that has yet to be invented.

A safe-technology advocacy group issued claims Tuesday that Tesla's full self-driving software represents a potentially lethal threat to child pedestrians, the latest in a series of claims and investigations into the technology to hit the world's leading electric carmaker. According to a safety test conducted by the Dawn Project, the latest version of Tesla Full Self-Driving (FSD) Beta software repeatedly hit a stationary, child-sized mannequin in its path. The claims that the technology apparently has trouble recognizing children form part of an ad campaign urging the public to pressure Congress to ban Tesla's auto-driving technology. In several tests, a professional test driver found that the software – released in June – failed to detect the child-sized figure at an average speed of 25mph and the car then hit the mannequin. The Dawn Project' founder, Dan O'Dowd, called the results "deeply disturbing."

Customs and Border Protection is making progress testing and deploying their facial recognition technology at air, sea and land ports across the country. CBP is using the technology to scan travelers at 26 seaports and 159 land-ports and airports across the country, the Government Accountability Office told the House Committee on Homeland Security hearing on July 27. CBP installed facial recognition technology to biometrically confirm travelers' identities for all arriving and departing travelers.

Researchers from Michigan State University have devised a way for synthetic faces to take a break from the deepfakes scene and do some good in the world – by helping image recognition systems to become more accurate. The new controllable face synthesis module (CFSM) they've devised is capable of regenerating faces in the style of real-world video surveillance footage, rather than relying on the uniformly higher-quality images used in popular open source datasets of celebrities, which do not reflect all the faults and shortcomings of genuine CCTV systems, such as facial blur, low resolution, and sensor noise – factors that can affect recognition accuracy. CFSM is not intended specifically to authentically simulate head poses, expressions, or all the other usual traits that are the objective of deepfake systems, but rather to generate a range of alternative views in the style of the target recognition system, using style transfer. The system is designed to mimic the style domain of the target system, and to adapt its output according to the resolution and range of'eccentricities' therein. The use-case includes legacy systems that are not likely to be upgraded due to cost, but which can currently contribute little to the new generation of facial recognition technologies, due to poor quality of output that may once have been leading-edge.

The structure of the vitellogenin protein -- a precursor of egg yolk -- as predicted by the AlphaFold tool.Credit: DeepMind From today, determining the 3D shape of almost any protein known to science will be as simple as typing in a Google search. Researchers have used AlphaFold -- the revolutionary artificial-intelligence (AI) network -- to predict the structures of some 200 million proteins from 1 million species, covering nearly every known protein on the planet. The data dump will be freely available on a database set up by DeepMind, Google's London-based AI company that developed AlphaFold, and the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), an intergovernmental organization near Cambridge, UK. "Essentially you can think of it covering the entire protein universe," DeepMind CEO Demis Hassabis, said at a press briefing. The 3D shape, or structure, of a protein is what determines its function in cells.

Known as OceanOneK, the robot allows its operators to feel like they're underwater explorers, too. OceanOneK resembles a human diver from the front, with arms and hands and eyes that have 3D vision, capturing the underwater world in full color. The back of the robot has computers and eight multidirectional thrusters that help it carefully maneuver the sites of fragile sunken ships. OceanOneK, here doing an experiment in a swimming pool at Stanford University, resembles a human diver. When an operator at the ocean's surface uses controls to direct OceanOneK, the robot's haptic (touch-based) feedback system causes the person to feel the water's resistance as well as the contours of artifacts.

Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision-making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.

A CT scan of a tumor in human lungs. Researchers are experimenting with AI algorithms that can spot early signs of the disease.Credit: K. H. Fung/SPL From biomedicine to political sciences, researchers increasingly use machine learning as a tool to make predictions on the basis of patterns in their data. But the claims in many such studies are likely to be overblown, according to a pair of researchers at Princeton University in New Jersey. They want to sound an alarm about what they call a "brewing reproducibility crisis" in machine-learning-based sciences. Machine learning is being sold as a tool that researchers can learn in a few hours and use by themselves -- and many follow that advice, says Sayash Kapoor, a machine-learning researcher at Princeton.

A stick-on patch that can take an ultrasound scan of a person's insides as they go about their daily life has been hailed as a revolution in medical imaging. The wearable patch, which is the size of a postage stamp, can image blood vessels, the digestive system and internal organs for up to 48 hours, giving doctors a more detailed picture of a patient's health than the snapshots provided by routine scans. In laboratory tests, researchers used the patches to watch people's hearts change shape during exercise, their stomachs expand and shrink as they drank and passed drinks, and their muscles pick up microdamage when weightlifting. Prof Xuanhe Zhao at Massachusetts Institute of Technology, who led the research team, said the patches could "revolutionise" medical imaging because existing scans are very brief, sometimes lasting only seconds, and usually have to be performed in hospitals. Ultimately, Zhao envisions people buying boxes of the patches over the counter and using them, with help from smart algorithms on their mobile phones, to monitor their heart, lungs and digestive systems for early signs of disease or infection, or their muscles during rehabilitation or physical training.

In the years to come, artificial intelligence will be a key feature of cross border criminal investigations, a joint report by Eurojust and eu-LISA, the union's official IT agency found. AI technologies can increase cooperation between EU member states in tackling crime, however, authorities must be careful since machine learning algorithms are prone to biases. AI was listed as a priority in the EU's e-Justice Action plan for 2019-2023. In a world where crime is borderless and criminals employ sophisticated communication tools and technologies, including encryption and AI; tackling crime requires cross-border cooperation by EU Member States and the application of technologies on par with those used by the criminal groups, urged Friday's report. "The field of justice is undergoing digital transformation, and artificial intelligence, as a set of different technologies, has great potential to contribute to and further enhance this process, allowing for a significant improvement in both the efficiency and effectiveness of operation of the judicial authorities," the report said.