Privacy

Sensitive information about the location and staffing of military bases
and spy outposts around the world has been revealed by a fitness tracking
company.

The details were released by Strava in a data visualisation map that shows
all the activity tracked by users of its app, which allows people to record
their exercise and share it with others.

The map,
released in November 2017, shows every single activity ever uploaded to
Strava – more than 3 trillion individual GPS data points, according
to the company. The app can be used on various devices including
smartphones and fitness trackers like Fitbit to see popular running routes
in major cities, or spot individuals in more remote areas who have unusual
exercise patterns.

Last November, for instance, the company unveiled a program that uses
artificial intelligence to monitor Facebook users for signs of self-harm.
But it did not open the program to users in Europe, where the company
would have had to ask people for permission to access sensitive health
data, including about their mental state.

Tech

Since its discovery over a hundred years ago, the 240-page Voynich
manuscript, filled with seemingly coded language and inscrutable
illustrations, has confounded linguists and cryptographers. Using artificial
intelligence, Canadian researchers have taken a huge step forward in
unraveling the document’s hidden meaning.

[...] For Greg Kondrak, an expert in natural language processing at the
University of Alberta, this seemed a perfect task for artificial
intelligence. With the help of his grad student Bradley Hauer, the computer
scientists have taken a big step in cracking the code, discovering that the
text is written in what appears to be the Hebrew language, and with letters
arranged in a fixed pattern. To be fair, the researchers still don’t know
the meaning of the Voynich manuscript, but the stage is now set for other
experts to join the investigation.

Austerity is an
algorithm. I've just
discovered this magazine,
Logic, and it looks
promising. This article is another reminder that social issues cannot and
perhaps should not be solved using only technological means.

The methodology of the algorithm itself was riddled with flaws. It
calculates the average of an individual’s annual income reported to the
Australian Tax Office by their employer over twenty-six fortnightly periods
and compares it with the fortnightly earnings reported to Centrelink by the
welfare recipient. All welfare recipients are required to declare their
gross earnings (income accrued before tax and other deductions) within this
fourteen-day period. Any discrepancy between the two figures is interpreted
by the algorithm as proof of undeclared or underreported income, from which
a notice of debt is automatically generated.

Previously, these inconsistencies would be handled by Centrelink staff,
who
would call up your employer, confirm the amount you received in fortnightly
payments, and cross-index that figure with the one calculated in the system.
But the automation of the debt recovery process has outsourced authority
from humans to the algorithm itself.

It’s certainly efficient: it takes the algorithm one week to generate
20,000
debt notices, a process that would take up to a year if done manually. But
it’s not a reliable method of fraud detection. It’s blunt, unwieldy, and
error-prone. It assumes that variations in the data sets are deliberate, and
that recipients have received more than what they are entitled to. What’s
more, the onus is on the welfare recipient to prove their income has been
reported correctly and that the entitlements they have received are
commensurate within twenty-one days.

Machines are starting to take the place of the people who flip burgers,
drive across town and, lately, manage stock portfolios.

Artificial intelligence is taking on a bigger role in making investment
decisions.

A.I., including an ability to analyze data and actually learn from it, is
considered useful in executing certain investing models, such as
high-frequency trading, and in helping fund managers with tasks that rely on
gathering and interpreting reams of information. Going a step further, an
exchange-traded fund introduced in October uses A.I. algorithms to choose
long-term stock holdings.