Page 79 - The Economist

The Economist December 9th 2017 Science and technology 79
2 shows and finding ways to make that ma- Environmental engineering waters is unlikely to tempt anyone into
terial more engaging. harvesting them. Previous studies have
Besidesfundamental algorithmswhich Clean-up mussel shown that the ribbed mussel is both har-
firms hope to apply to their own opera- dy and adept at collecting a range of trou-
tions, NIPS is also home to applied re- blesome materials from its environment.
search, particularly in health care and biol- DrGalimanyand DrRose thoughtitwould
ogy. Becks Simpson from Maxwell MRI,a be ideal to help clean up the Bronx River
startupfromBrisbaneinAustralia,showed Anasty-tasting mussel could be justthe Estuary in New York. With an industrial
a way to combine magnetic resonance im- job forcleaning rivers waterfront and wastewaterrun-offs from a
agingwithdeeplearningtoimprovethedi- dense urban environment, the estuary has
agnosis of prostate cancer. Elisabeth Ru- HELLFISH thrive in waters rich in nutri- a long history of suffering from harmful
metshofer from Johannes Kepler Sents. These include the nitrogen used in bacteria and high levels ofnitrogen.
University Linz presented a system that fertiliser, which passes from the land into With a group of colleagues they
could automatically recognise and track rivers and then into the sea. The shellfish moored a six-square-metre commercial
proteins in cells, helping to illuminate the grow, as do the profits of those who har- mussel-growing raft in the estuary and
underlying biology. A team from Duke vest them. The problem comes when dis- populated it with ribbed mussels. They
University in North Carolina had used charges into the sea are tainted with more closely monitored the health of the mus-
machine learning to detect cervical cancer noxious material, such as bacteria that sels over six months and, using a flow-
automatically using a pocket colposcope, pose a threat to human health. Once the through device, also analysed the chemis-
to the same level of accuracy as a human bugsare in the shellfish, theycan be passed try of the water both before and after the
expert. Some used AI to mine doctors’ on to anyone who eats them. mussels had done their filtering. The re-
notesto estimatethe chancesthata patient This problem—and another, of excess sults were impressive.
willbereadmittedtohospital,tocategorise nitrogen that can cause poisonous algal The researchers found that not only did
and understand the allergic reactions of blooms—might be mitigated by shellfish the mussels thrive in the polluted waters
children and to model the geographic dis- thatpeople don’teat, reckon Eve Galimany of the Bronx River Estuary, but they also
tribution of naloxone, which can help and Julie Rose at the National Oceanic and collected a lot of pollutants. More specifi-
blockthe effects ofopioids, in order to get a Atmospheric Administration at Milford cally, a fully stocked raft ofmussels cleared
bettergrip on the use ofsuch drugs. Laboratory in Connecticut. As they report an average of 12m litres of water daily, re-
Other applications ranged from re- in Environmental Science & Technology, moving 160 kilograms of particulate mat-
searchers at the Federal University Lokoja their chosen candidate for the job is the ter, ofwhich 12 kilograms was absorbed by
in Nigeria trying to use machine learning ribbed mussel, more formally known as the mussels’ digestive systems and inte-
to identify potential child suicide bombers Geukensia demissa. grated into their bodies. The remainder
to the DondersInstitute in the Netherlands The ribbed mussel is edible, but it tastes was excreted as waste, which drops down
presenting a system that can reconstruct terrible and so has no commercial value. and is ultimately buried in the river sedi-
pictures of faces that a person sees simply This means growing the mussels in tainted ment. The material filtered out by the mus- 1
by scanning their brains. Google research-
ers used machine learning to hide a com-
plete image inside another picture of the Gender in academia University seminars, relative* share of questions
same size. Whattheymightdo with that re- asked by women…
mains to be seen. ONE theory to explain the low share of Ten countries, 2014-16
New hardware for machine learning women in senior academic jobs is that …when the first question
was on display, too. At its party Intel un- they have less self-confidence than men. was asked by a man Number of seminars
veiled itslatestchip dedicated to solving AI This hypothesis is supported by data in a 20
problems. NVIDIA, a chipmaking rival new working paper, by a team of research-
whose share price has increased ninefold ers from five universities in America and 15
in the past three years thanks to sales of its Europe. In this study, observers counted
graphical-processing units for deep learn- the attendees, and the questions they 10
ing, displayed its latest wares. Graphcore, a asked, at 247 departmental talks and
British startup, caused particular waves. It seminars in biology, psychology and 5
presented benchmarksforitschip’sperfor- philosophy that took place at 35 universi-
mance on common machine-learning ties in ten countries. On average, half of
tasks that tripled speeds for image recogni- each seminar’s audience was female. Women asked Women asked 0
tion and delivered a claimed 200 times im- Men, however, were over 2.5 times more fewer questions* more questions*
provement over NVIDIA for the kinds of likely to pose questions to the speak- …when the first question
machine learning required for speech-rec- ers—an action that may be viewed (right- was asked by a woman
ognition and translation applications. ly or wrongly) as a sign of greater 15
Among older hands at NIPS, especially competence.
those who can remember its origins, there This male skew in question-asking was 10
is a sense that the corporate obsession observable, however, only in those semi-
with machine learning will not last. They nars in which a man asked the first ques- 5
should not be so sure. The systems being tion. When a woman did so, the gender
developed are justbeginningto be a broad- split in question-asking was, on average, 0
ly useful technology, and new algorithms proportional to that of the audience. 80 60 40 20 – 0 + 20 40 60
presented at the conference are likely to be Simply handing the microphone to a Percentage of questions from women minus
adopted rapidly. Powerful computers and woman rather than a man when the floor percentage of attendees who are women, % points
large volumesofdata lie waitingfor exploi- is opened for questions may make a Source: Women’s visibility in academic
tation. The world’s most valuable compa- difference, however small, to one of seminars: women ask fewer questions *Compared to their
than men, A. Carter, A. Croft,
nies have grasped the power of machine academia’s most intractable problems. D. Lukas, G. Sandstrom share of attendance
learning, and they are unlikely to let go. 7