Via the
Washington Times:
Researchers at the Georgia Institute of Technology (Georgia Tech) have
found that state-of-the-art object-detection systems, such as the
sensors and cameras used in self-driving cars, are better at detecting
people with lighter skin tones, meaning they are less likely to identify
black people and to stop before crashing into them. The researchers
examined eight image recognition systems and found the bias in each one,
with accuracy 5% lower on average for people with darker skin. The team
proved the hypothesis by dividing a large pool of pedestrian images
into groups of lighter and darker skin using the Fitzpatrick scale—a
scientific way of classifying skin color. “This behavior suggests that
future errors made by autonomous vehicles may not be evenly distributed
across different demographic groups,” the researchers wrote.
Via
the Los Angeles Times:
The New York Police Department (NYPD) is using pattern-recognition
software so analysts can compare robberies, larcenies, and thefts to
hundreds of thousands of crimes logged in the department's database,
finding matches faster than they would manually. The Patternizr
algorithm was launched in December 2016, and NYPD assistant commissioner
of data analytics Evan Levine said, "The more easily that we can
identify patterns in...crimes, the more quickly we can identify and
apprehend perpetrators." Levine and co-developer Alex Chohlas-Wood
trained Patternizr on 10 years of patterns that the department had
manually identified. Patternizr accurately reproduced old crime patterns
a third of the time, and matched parts of patterns 80% of the time. The
software compares factors like method of entry, type of goods stolen,
and distance between crimes, and reduces possible racial bias by not
counting the race of suspects when looking for patterns.
Via
the Wall Street Journal:
Employers across a spectrum of industries are welcoming applicants with
experience in making or playing videogames, believing such backgrounds
can help workers with online collaboration, problem-solving, and other
key workplace skills. For example, General Electric (GE) is hiring
people with game development expertise to train robots to inspect
hazardous areas via virtual reality technology, a role that GE's
Ratnadeep Paul said "came out of the gaming industry." Although some
people still regard gamers as socially maladroit, in recent years that
assumption has been dispelled, partly due to increasingly popular online
multiplayer games that encourage players to form teams and strategize
via online text or voice communication. Said the Rochester Institute of
Technology's Andrew Phelps, "What we used to stereotypically think of as
a weird thing some folks did in their basement is now part of everyday
life. Gaming has become a common touch point for people."
Via
New York Times.com:
The Aravind Eye Hospital in Madurai, India, is working with Google
artificial intelligence (AI) scientists to automate the identification
of diabetic retinopathy. The hospital is using the new AI system to
screen patients, with plans to deploy the technology in surrounding
villages where eye doctors are scarce. The system is based on a neural
network analyzing millions of retinal scans indicating diabetic
blindness so it can learn to identify the disease on its own. The
Aravind installation employs wall-mounted computer screens in waiting
rooms to translate information into the various languages spoken by
patients; the system's performance reportedly equals that of trained
ophthalmologists. However, Luke Oakden-Rayner, director of medical
imaging research at the Royal Adelaide Hospital in Australia, warned,
“On paper, the Google system performs very well, but when you roll it
out to a huge population, there can be problems that do not show up for
years.”
Via
UW Medicine:
International researchers have computer-designed a nanoparticle vaccine
candidate for respiratory syncytial virus (RSV), an infection caught by
nearly all children under three, which is the leading cause of pneumonia
in babies under a year old in the U.S. Computationally-designed protein
nanoparticles enable significantly greater control over key vaccine
properties, including overall size, stability, and the number of
antigens presented to the immune system. University of Washington (UW)
researchers said the vaccine based on the DS-Cav1 protein yielded 10
times more potency than DS-Cav1 alone. UW's Neil King said, "We believe
that computationally-designed nanoparticle vaccines will ultimately be
simpler to manufacture and more effective than traditional vaccines. We
will continue to develop this technology so that we and others can make
new vaccines better, cheaper, and faster."
Via
Reuters:
U.S. regulators have approved Google's deployment of a radar-based
motion sensor, granting it a waiver to use the device at higher power
levels than currently permitted. The U.S. Federal Communications
Commission (FCC) said the Project Soli device "will serve the public
interest by providing for innovative device control features using
touchless hand gesture technology." According to the FCC, the sensor
captures motion in a three-dimensional space using a radar beam to
facilitate touchless control of functions or features that can benefit
users with mobility or speech impediments. Google said the sensor
enables users to press an invisible button between the thumb and index
fingers, or a virtual dial that turns by rubbing the thumb against the
index finger. Said Google, "Even though these controls are virtual, the
interactions feel physical and responsive" as feedback is produced by
the haptic sensation of fingers touching.
And now, news from
Germany.
A new parallel-computing approach can solve combinatorial problems,
according to a study published in Proceedings of the National Academy of
Sciences. Researchers from the Max Planck Institute of Molecular Cell
Biology and Genetics and the Dresden University of Technology
collaborated with an international team on the technology. The
researchers note significant advances have been made in conventional
electronic computers in the past decades, but their sequential nature
prevents them from solving problems of a combinatorial nature. The
number of calculations required to solve such problems grows with the
size of the problem, making them intractable for sequential computing.
The new approach addresses these issues by combining well-established
nanofabrication technology with molecular motors that are very
energy-efficient and inherently work in parallel. The researchers
demonstrated the parallel-computing approach on a benchmark
combinatorial problem that is very difficult to solve with sequential
computers. The team says the approach is scalable, error-tolerant, and
dramatically improves the time to solve combinatorial problems of size
N. The problem to be solved is "encoded" within a network of nanoscale
channels by both mathematically designing a geometrical network that is
capable of representing the problem, and by fabricating a physical
network based on this design using lithography. The network is then
explored in parallel by many protein filaments self-propelled by a
molecular layer of motor proteins covering the bottom of the channels.