Maybe
Facebook could buy Finland?
Finland
is winning the war on fake news. What it’s learned may be crucial
to Western democracy
… Finland
has faced down Kremlin-backed propaganda campaigns ever since it
declared independence from Russia 101 years ago. But in 2014, after
Moscow annexed Crimea and backed rebels in eastern Ukraine, it became
obvious that the battlefield had shifted: information warfare was
moving online.
Toivanen,
the chief communications specialist for the prime minister’s
office, said it is difficult to pinpoint the exact number of
misinformation operations to have targeted the country in recent
years, but most play on issues like immigration, the European Union,
or whether Finland should become a full member of NATO (Russia is not
a fan).
AI
is too easily fooled.
How
we might protect ourselves from malicious AI
We’ve touched previously on the concept of
adversarial examples—the class of tiny changes that, when fed into
a deep-learning model, cause it to misbehave. In March, we covered
UC
Berkeley professor Dawn Song’s talk at our annual EmTech
Digital conference about how she used stickers to trick a
self-driving car into thinking a stop sign was a 45-mile-per-hour
sign, and how she used tailored messages to make a text-based model
spit out sensitive information like credit card numbers. In April,
we similarly talked
about how white hat hackers used stickers to confuse Tesla Autopilot
into steering a car into oncoming traffic.
… A new paper from MIT now points toward a
possible path to overcoming this challenge. It could allow us to
create far more robust deep-learning models that would be much harder
to manipulate in malicious ways. To understand its significance,
let’s first review the basics of adversarial examples.
… It
showed this by identifying a rather interesting property of
adversarial examples that helps us grasp why they’re so effective.
The seemingly random noise or stickers that trigger
misclassifications are actually exploiting very precise, minuscule
patterns that the image system has learned to strongly associate with
specific objects. In other words, the machine isn’t misbehaving
when it sees a gibbon where we see a panda. It is indeed seeing a
pattern of pixels, imperceptible to humans, that occurred far more
often in the gibbon photos than panda photos during training.
Another
huge list. Browsers and their targeting, specialized tools.
New
on LLRX – Online Research Browsers 2019
Via
LLRX
–
Online
Research Browsers 2019 –
Marcus
Zillman’s guide
highlights multifaceted browser alternatives to mainstream search
tools that researchers may regularly use by default. There are many
reliable yet underutilized applications that facilitate access to and
discovery of subject
matter specific documents and sources.
Free applications included here also offer collaboration tools,
resources to build and manage repositories, to employ data
visualization, to create and apply metadata management, citations,
bibliographies, document discovery and data relationship analysis.
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