Mercedes has announced that it will take legal responsibility for any crashes that occur while its self-driving systems are engaged. The company is currently in the process of deploying "Drive Pilot" technology for its new S-Class and EQS saloon models, which is "Level 3" for autonomy on a six-tier system devised by Society of Automotive Engineers, ranging from Level 0 (no automated driver assistance) to Level 5 (the car drives itself everywhere without any input from the vehicle occupants). Level 3 autonomy means that drivers may take their hands off the wheel and undertake other tasks, such as reading a book, while the car assumes full control of all driving functions. However, this is only in specific conditions, such as in low-speed traffic on motorways, and the person in the driver's seat must be able to retake control within a few seconds of an alert from the car. This is a big leap from Level 2 autonomy, which requires hands-on-wheel supervision from the driver at all times, and which is currently commonplace on new cars in the form of adaptive cruise control and automated lane-keeping. Some cars from the likes of Audi, Mercedes, BMW, Genesis and Tesla have such advanced systems that they are considered somewhere between Levels 2 and 3 -- dubbed by experts as Level 2 .

The MIT researchers said their generative model requires less memory to store than datasets, which can cost millions of dollars to create. MIT researchers have found a way to classify images using synthetic data, which they claim can rival models trained from real data. In the study, the team created a special type of machine learning model to generate extremely realistic synthetic data, which can then train another model for vision-related tasks. The researchers said that currently, massive amounts of data is required to train a machine to perform image classification tasks, such as identifying damage in satellite photos following a natural disaster. However, the datasets required to train the model can cost millions of dollars to generate.

Cancer drugs usually take a scattergun approach. Chemotherapies inevitably hit healthy bystander cells while blasting tumours, sparking a slew of side effects. It is also a big ask for an anticancer drug to find and destroy an entire tumour -- some are difficult to reach, or hard to penetrate once located. A long-dreamed-of alternative is to inject a battalion of tiny robots into a person with cancer. These miniature machines could navigate directly to a tumour and smartly deploy a therapeutic payload right where it is needed. "It is very difficult for drugs to penetrate through biological barriers, such as the blood–brain barrier or mucus of the gut, but a microrobot can do that," says Wei Gao, a medical engineer at the California Institute of Technology in Pasadena.

A four-legged robot called Spot has been deployed to wander around the ruins of ancient Pompeii, identifying structural and safety issues while delving underground to inspect tunnels dug by relic thieves. The dog-like robot is the latest in a series of technologies used as part of a broader project to better manage the archaeological park since 2013, when Unesco threatened to add Pompeii to a list of world heritage sites in peril unless Italian authorities improved its preservation. Spot, made by the US-based Boston Dynamics, is capable of inspecting even the smallest of spaces while "gathering and recording data useful for the study and planning of interventions", park authorities said. The aim, they added, is to "improve both the quality of monitoring of the existing areas, and to further our knowledge of the state of progress of the works in areas undergoing recovery or restoration, and thereby to manage the safety of the site, as well as that of workers." Until Spot came along, no technology of its kind had been developed for archaeological sites, according to Gabriel Zuchtriegel, the director of Pompeii archaeological park. Park authorities have also experimented with a flying laser scanner capable of conducting 3D scans across the 66-hectare (163-acre) site.

Hearing loss is a rapidly growing area of scientific research as the number of baby boomers dealing with hearing loss continues to increase as they age. To understand how hearing loss impacts people, researchers study people's ability to recognize speech. It is more difficult for people to recognize human speech if there is reverberation, some hearing impairment, or significant background noise, such as traffic noise or multiple speakers. As a result, hearing aid algorithms are often used to improve human speech recognition. To evaluate such algorithms, researchers perform experiments that aim to determine the signal-to-noise ratio at which a specific number of words (commonly 50%) are recognized.

Original article by Azfar Adib, who is currently pursuing his PhD in Electrical and Computer Engineering in Concordia University in Montreal. He is a Senior Member in the Institute of Electrical and Electronic Engineers (IEEE). A recent announcement by Meta about terminating the face recognition system in Facebook sparked worldwide attention. It comes as a sort of new reality for many Facebook users, who have been habituated for years to the automatic people recognition feature in Facebook photos and videos. Since the arrival of mankind on earth, facial outlook has remained as the most common identifier for humans.

Hospice is a compassionate approach focusing on quality of life for terminally ill patients and their caregivers, with approximately 1.55 million Medicare beneficiaries enrolled in hospice care for at least one day during 2018 – 17% more than in 2014. However, at least 14% of Medicare beneficiaries enrolled in hospice stayed for more than 180 days, and hospice stays beyond six months can result in substantial excess costs to healthcare organizations under value-based care arrangements. David Klebonis, COO of Palm Beach Accountable Care Organization, has developed highly interpretable machine learning models that, because of the sensitivity of the clinical decision involved, cannot only accurately predict hospice overstays to drive appropriate hospice referrals, but also surface decision criteria that satisfy clinician scrutiny and promote adoption. "Artificial intelligence and machine learning have the potential to use data to predict patients with a high probability of expiring within the next six months, so that physicians can enter into conversations with these patients and their families about the possibility of referral to hospice," he said. Klebonis, who will address the topic this month at HIMSS22, said in Florida about 58% of Medicare decedents were in hospice at the time of death.