Bringing us doom, gloom, and a President who polarizes the country?
Russian
Secret Weapon Against U.S. 2020 Election Revealed In New Cyberwarfare
Report
The
FBI has warned
that
“the threat” to U.S. election security “from nation-state
actors remains a persistent concern,” that it is “working
aggressively” to uncover and stop, and the U.S. Director of
National Intelligence has appointed
an
election threats executive, explaining that election security is now
“a top priority for the intelligence community—which must bring
the strongest level of support to this critical issue.”
With
this in mind, a new report
from
cybersecurity powerhouse Check Point makes for sobering reading. “It
is unequivocally clear to us,” the firm warns, “that the Russians
invested a significant amount of money and effort in the first half
of this year to build large-scale espionage capabilities. Given the
timing, the unique operational security design, and sheer volume of
resource investment seen, Check Point believes we may see such an
attack carried out near the 2020 U.S. Elections.”
… And
the most chilling finding is that Russia has built its ecosystem to
ensure resilience, with cost
no object. It has formed a fire-walled structure designed
to attack in waves. Check Point believes this has been a
decade or more in the making and now makes concerted
Russian attacks on the U.S. “almost impossible” to defend
against.
… It’s known and accepted within the U.S.
security community that the elections will almost certainly come
under some level of attack. But the findings actually point to
something much more sinister. A cyber warfare platform that does
carry implications for the election—but also for power grids,
transportation networks, financial services.
[An
alternate link to the report:
https://www.intezer.com/blog-russian-apt-ecosystem/
(Related) How can this
possibly help?
Facebook
promises not to stop politicians’ lies & hate
Facebook
confirms
it won’t fact check politicians’ speech or block their content if
it’s newsworthy
even if it violates the site’s hate-speech rules or other policies.
This cementing of its policy comes from Facebook’s head of global
policy and communication Nick Clegg, who gave a speech
today about
Facebook’s plans to prevent interference in the 2020 presidential
election.
We need security
training that is as frequent and as habit forming as dealing with
large volumes of email.
Webroot
Report: Nearly Half of Employees Confess to Clicking Links in
Potential Phishing Emails at Work
BROOMFIELD,
Colo.,
Webroot,
a Carbonite (CARB) company, released a report, Hook,
Line and Sinker: Why Phishing Attacks Work, that
sheds light on psychological factors impacting an individual's
decision to click on a phishing email.
While
a majority (79%) of people reported being able to distinguish a
phishing message from a genuine one, nearly half (49%) also admit to
having clicked on a link from an unknown sender while at work.
Further, nearly half (48%) of respondents said their personal or
financial data had been compromised by a phishing message. However,
of that group more than a third (35%) didn't take the basic step of
changing their passwords following a breach. Not only is this false
confidence potentially harmful to an employee's personal and
financial data, but it also creates risks for companies and their
data.
Compliance
is not always what was intended. (And some companies are smarter
than some countries?)
Google
refuses to pay publishers in France
Google
will not pay press publishers in France to display their content and
will instead change the way articles appear in search results, a
senior executive said on Wednesday.
The
announcement pours cold water on publishers' hopes of obtaining more
money from the tech giant for displaying their content under the
European Union's new copyright regime, which France was the first to
transpose into national law.
… To
apply the new copyright rules in France, Google will instead change
the way news results appear on its search engine by removing
so-called snippets, or short excerpts from the article.
"When
the French law comes into force, we will not show preview content in
France for a European news publication unless the publisher has taken
steps to tell us that's what they want," the tech giant said in
a separate blog post.
Hyperlinks
and "very short extracts" of press articles are not covered
by the neighboring right, meaning Google can display them on the
platforms without signing a licensing agreement.
In
Germany, where a neighboring right existed prior to the EU directive,
many German publishers decided to give Google their content for free
after their traffic plummeted when snippets no longer appeared on
search results.
My AI can file more
patent applications than your AI!
AI
at the USPTO
The USPTO has extended
its public comment period on the subject of patenting artificial
intelligence inventions. Due Date: October 11, 2019
now November 8, 2019.
I suspect they are far
too optimistic, but them I’m a pessimist by training.
Explainable
AI: Bringing trust to business AI adoption
… At the center of
this problem is a technical question shrouded by myth. There's a
widely held belief out there today that AI technology has become so
complex that it's impossible for the systems to explain why they make
the decisions that they do. And even if they could, the explanations
would be too complicated for our human brains to understand.
The reality is that many
of the most common algorithms used today in machine learning and AI
systems can have what is known as “explainability” built in.
We're just not using it
— or are not getting access to it. For other algorithms,
explainability and traceability functions are still being developed,
but aren't far out.
Here you will find what
explainable AI means, why it matters for business use, and what
forces are moving its adoption forward — and which are holding it
back.
(Related)
Fiddler
raises $10.2 million for AI that explains its reasoning
Explainable
AI, which refers to techniques that attempt to bring transparency to
traditionally opaque AI models and their predictions, is a burgeoning
subfield of machine learning research. It’s no wonder — models
sometimes learn undesirable tricks to accomplish goals on training
data, or they develop
biases
with
the potential to cause harm if left unaddressed.
That’s
why Krishna Gade and Amt Paka founded Fiddler,
a Mountain View, California-based startup developing an “explainable”
engine that’s designed to analyze, validate, and manage AI
solutions.
I
don’t get it. Perhaps I’ll learn eventually?
New
AI Systems Are Here to Personalize Learning
… Ahura
AI is
developing a product to capture biometric data [sic]
from adult learners who are using computers to complete online
education programs. The goal is to feed this data to an AI system
that can modify and adapt their program to optimize for the most
effective teaching method.
… Currently,
Ahura’s system uses the video camera and microphone that come
standard on the laptops, tablets, and mobile devices most students
are using for their learning programs.
With
the computer’s camera Ahura can capture facial movements and micro
expressions, measure eye movements, and track fidget score (a measure
of how much a student moves while learning). The microphone tracks
voice sentiment, and the AI leverages natural language processing to
review the learner’s word usage.
From
this collection of data Ahura can, according to Talebi, identify the
optimal way to deliver content to each individual.
You
want to write right, right?
Grammarly
uses AI to detect the tone and tenor of your writing
Nailing
the right tone and tenor is of critical importance where writing’s
concerned, whether the audiences of said writing are friends,
coworkers, or hiring managers. The trouble is, without an extra pair
of eyes, it’s rarely easy to know whether your work will have the
intended effect.
Fortunately,
Grammarly, a developer of cloud-hosted online grammar checking and
plagiarism detection tools, has developed a tone
detector the
company claims can identify subtle contextual clues conveying a range
of tempers. Essentially, it taps a battery of hard-coded rules and
machine learning algorithms to spot signals in a piece contributing
to its tone, including word choice, phrasing, punctuation, and even
capitalization.
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