Since complex diseases such as cancer, diabetes and so on pose a very big threat to human health, they have been extensively studied in the past decades1. However, the underlying pathogenesis of complex diseases is still not clearly known. With the rapid development of genomics technologies, the big data of variations on DNA level such as SNP and CNV (copy number variation) allow comprehensive characterization of complex diseases and provide potential biomarkers to predict the status of complex diseases. Due to the'missing heritability' and lack of reproducibility, the exploration of relationships between SNPs and complex diseases have been transferred from single variation to biomarkers interactions which are defined as epistasis2. First, as the number of variants increases, the combination space expands exponentially, resulting in the'curse of dimensionality' problem.
Lawmakers, child development experts, and privacy advocates are expressing concerns about two new Amazon products targeting children, questioning whether they prod kids to be too dependent on technology and potentially jeopardize their privacy. In a letter to Amazon CEO Jeff Bezos on Friday, two members of the bipartisan Congressional Privacy Caucus raised concerns about Amazon's smart speaker Echo Dot Kids and a companion service called FreeTime Unlimited that lets kids access a children's version of Alexa, Amazon's voice-controlled digital assistant. "While these types of artificial intelligence and voice recognition technology offer potentially new educational and entertainment opportunities, Americans' privacy, particularly children's privacy, must be paramount," wrote Senator Ed Markey (D-Massachusetts) and Representative Joe Barton (R-Texas), both cofounders of the privacy caucus. The letter includes a dozen questions, including requests for details about how audio of children's interactions is recorded and saved, parental control over deleting recordings, a list of third parties with access to the data, whether data will be used for marketing purposes, and Amazon's intentions on maintaining a profile on kids who use these products. Echo Dot Kids is the latest in a wave of products from dominant tech players targeting children, including Facebook's communications app Messenger Kids and Google's YouTube Kids, both of which have been criticized by child health experts concerned about privacy and developmental issues.
Bottom Line: Zero Trust Security (ZTS) starts with Next-Gen Access (NGA). Capitalizing on machine learning technology to enable NGA is essential in achieving user adoption, scalability, and agility in securing applications, devices, endpoints, and infrastructure. Zero Trust Security provides digital businesses with the security strategy they need to keep growing by scaling across each new perimeter and endpoint created as a result of growth. ZTS in the context of Next-Gen Access is built on four main pillars: (1) verify the user, (2) validate their device, (3) limit access and privilege, and (4) learn and adapt. The fourth pillar heavily relies on machine learning to discover risky user behavior and apply for conditional access without impacting user experience by looking for contextual and behavior patterns in access data.
Observing the world's oceans is increasingly a mission assigned to autonomous underwater vehicles (AUVs) -- marine robots that are designed to drift, drive, or glide through the ocean without any real-time input from human operators. Critical questions that AUVs can help to answer are where, when, and what to sample for the most informative data, and how to optimally reach sampling locations. MIT engineers have now developed systems of mathematical equations that forecast the most informative data to collect for a given observing mission, and the best way to reach the sampling sites. With their method, the researchers can predict the degree to which one variable, such as the speed of ocean currents at a certain location, reveals information about some other variable, such as temperature at some other location -- a quantity called "mutual information." If the degree of mutual information between two variables is high, an AUV can be programmed to go to certain locations to measure one variable, to gain information about the other.
When shooting a photo in low light, a low-ISO long-exposure photo requires a stable camera and blurs movement in the frame while a high-ISO short-exposure photo can be plagued with noise and poor quality. Now AI is bridging the cap, opening the door to low-ISO image quality while shooting at faster shutter speeds. A group of researchers at the University of Illinois Urbana-Champaign and Intel have published a new paper titled Learning to See in the Dark. It explains how they trained an AI to do low-light image processing and produce results that are much cleaner and more usable than traditional high-ISO photos. The team put together a set of photo pairs, with each pair containing a RAW short-exposure photo and a long-exposure version.
Ride-hailing service Uber announced plans for a flying taxi on Wednesday that could provide relief from road congestion for the commuters of the future. This is a rendering of UberÕs VTOL concept., flying car, an electric vertical take-off and landing vehicle. SAN FRANCISCO -- Uber executives continue to grapple with a host of challenges to their ride-hailing business, from taxi industry pushback in cities such as London to political fallout due to a self-driving car death in Arizona. But none of that has put the brakes on the company's futuristic -- and somewhat outlandish -- plans to develop a network of flying taxis, a project that gained a bit more altitude at Tuesday's kickoff of the two-day Uber Elevate conference in Los Angeles. Uber announced new partnerships with government officials and aircraft manufacturers aimed at further developing eVTOL (electric vertical takeoff and landing) craft, which use wing-mounted propellers to provide lift, as with a helicopter, and a tail-mounted propeller to generate forward thrust, as with a plane.
An Uber self-driving test car which killed a woman crossing the street detected her but decided not to react immediately, a report has said. The car was travelling at 40mph (64km/h) in self-driving mode when it collided with 49-year-old Elaine Herzberg at about 10pm on 18 March. Herzberg was pushing a bicycle across the road outside of a crossing. She later died from her injuries. Although the car's sensors detected Herzberg, its software which decides how it should react was tuned too far in favour of ignoring objects in its path which might be "false positives" (such as plastic bags), according to a report from the Information.