Researchers at the Research Center for Molecular Medicine of the Austrian Academy of Sciences have created knowledge-primed neural networks (KPNNs) which utilize signaling pathways and gene-regulatory networks. Each node in a KPNN corresponds to a protein or gene, while each edge possesses a mechanistic biological interpretation. By requiring this closer correspondence, KPNNs integrate deep learning with the interpretability of biological network models, yielding tangible insights into biological systems with high prediction performance. KPNNs are especially applicable to single-cell RNA-seq data, which is produced at massive scale with single-cell sequencing assays. The findings illustrate the future impact that artificial intelligence (AI) and deep learning will have on mechanistic biology as the scientific community learns to add biologically interpretability to AI outcomes, the researchers say.

One of the world's smallest microrobots is able to carry 2.6 times its own body weight thanks to a muscular system powered by alcohol. Conventionally, the "muscles" of small robots have been tethered to an external power source. Alternatively, they have been powered by batteries, the weight and size of which have limited efficiency and how small the robots can be. Top-of-the-range batteries have an energy density of around 1.8 megajoules per kilogram, a fraction of what you get from animal fat, which is about 38 MJ/kg. The methanol-powered muscles used by RoBeetle, an 88-milligram-long microrobot, can use catalytic combustion to reach energy levels up to 20 MJ/kg.

Washington, DC (CNN)Michigan announced Thursday that it's teaming with tech and auto companies to attempt to retrofit a roughly 40-mile stretch of two roads outside Detroit exclusively for self-driving vehicles. Michigan's partners include Ford and Sidewalk Infrastructure Partners, a company that Alphabet has invested in. Alphabet owns Google (GOOG) and Waymo, one of the companies at the forefront of developing self-driving vehicles. Both Interstate 94 and Michigan Avenue between Detroit and Ann Arbor, Michigan, would be retrofitted to include a dedicated lane for self-driving vehicles. Sensors and cameras added to the roads would help the vehicles better understand their surroundings.

The home of hacking in wartime Britain, Bletchley Park, was one of the victims of a major ransomware attack that hit software provider Blackbaud. The firm held data about people who had donated to the trust that manages the Bletchley Park museum. Harvard University has also joined the growing list of victims, which have mostly been charities and universities. Bletchley Park Trust said it was confident any exposed data was now secure. The trust added that data exposed to the hackers might have included names, dates of birth, email addresses, donation history and details of event attendance – but not credit and debit card details or bank account information.

Smart-assistant devices have had their share of privacy missteps, but they're generally considered safe enough for most people. New research into vulnerabilities in Amazon's Alexa platform, though, highlights the importance of thinking about the personal data your smart assistant stores about you--and minimizing it as much as you can. Findings published on Thursday by the security firm Check Point reveal that Alexa's web services had bugs that a hacker could have exploited to grab a target's entire voice history, meaning their recorded audio interactions with Alexa. Amazon has patched the flaws, but the vulnerability could have also yielded profile information, including home address, as well as all of the "skills," or apps, the user had added for Alexa. An attacker could have even deleted an existing skill and installed a malicious one to grab more data after the initial attack.

The report, which is a compilation of 97 countries' position on fully automated weapons, says most of them want to "retain human control over the use of force". Additionally, a growing number of policymakers, artificial intelligence experts, private companies, international and domestic organisations, and ordinary individuals have also endorsed the call to ban fully autonomous weapons. The authors explain that autonomous weapons "would decide who lives and dies, without … inherently human characteristics such as compassion that are necessary to make complex ethical choices."

In the new paper Does BERT Solve Commonsense Task via Commonsense Knowledge?, a team of researchers from Westlake University, Fudan University and Microsoft Research Asia dive deep into the large language model to discover how it encodes the structured commonsense knowledge it leverages on downstream commonsense tasks. The proven successes of pretrained language models such as BERT on various downstream tasks has stimulated research investigating the linguistic knowledge inside the model. Previous studies have revealed shallow syntactic, semantic and word sense knowledge in BERT, however, the question of how BERT deals with commonsense tasks has been relatively unexamined. CommonsenseQA is a multiple-choice question answering dataset built upon the CONCEPTNET knowledge graph. The researchers extracted multiple target concepts with the same semantic relation to a single source concept from CONCEPTNET, where each question has one of three target concepts as the correct answer. For example, "bird" is the source concept in the question "Where does a wild bird usually live?" and "countryside" is the correct answer from the possible target concepts "cage," "windowsill," and "countryside."

Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease of prematurity. Characterized by sudden and progressive intestinal inflammation and tissue death, it affects up to 11,000 premature infants in the United States annually, and 15-30% of affected babies die from NEC. Survivors often face long-term intestinal and neurodevelopmental complications. Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting NEC in premature infants before the disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. The pilot study was presented virtually on July 23 at ACM CHIL 2020.

At the U.S. Army Combat Capabilities Development Command's Army Research Laboratory and the University of Texas at Austin, researchers designed an algorithm that allows an autonomous ground vehicle to improve its existing navigation systems by watching a human drive. The team tested its approach -- called adaptive planner parameter learning from demonstration, or APPLD -- on one of the Army's experimental autonomous ground vehicles. "Using approaches like APPLD, current Soldiers in existing training facilities will be able to contribute to improvements in autonomous systems simply by operating their vehicles as normal," said Army researcher Dr. Garrett Warnell. "Techniques like these will be an important contribution to the Army's plans to design and field next-generation combat vehicles that are equipped to navigate autonomously in off-road deployment environments." Rather than replacing a classical system altogether, APPLD learns how to tune the existing system to behave more like the human demonstration.

As it matures, machine learning has been applied to more and bigger challenges. One of the latest is women's health tech, a space where research has traditionally lagged, and that few companies have addressed, until now. In 2015, women's health tech startups raised only $82 million in funding from investment firms. Since then, that number has risen to $1.1 billion. AI health care company Presagen is one of the companies stepping up in this essential health space, with scalable machine learning that can be used by clinics and patients anywhere in the world.