What has Hong Kong triggered?
Growing
backlash in China against A.I. and facial recognition
China’s seemingly unfettered push into facial
recognition is getting some high-level pushback.
Face-swapping
app Zao went viral last
weekend, but it subsequently triggered a backlash from media — both
state-run and private — over the apparent lack of data privacy
protections.
… “The
future has come, artificial intelligence is not only a test for
technological development, but a test for governance,” city
newspaper The Beijing News, wrote Sunday in Chinese, according to a
CNBC translation.
For my Disaster Recovery class. Grab the PDF!
How to
(Inadvertently) Sabotage Your Organization
In
1944, the Office of Strategic Services (OSS), the Central
Intelligence Agency’s predecessor — headed by legendary William
“Wild Bill” Donovan — put together a secret field
manual for
sabotaging enemy organizations. The manual encouraged “simple
acts” of destruction that required no special training, tools, or
equipment, with minimal “danger of injury, detection, and
reprisal,” and that, crucially, could be executed by “ordinary
citizens.”
What standards? What oversight?
More Than
Half of U.S. Adults Trust Law Enforcement to Use Facial Recognition
Responsibly
But
the public is less accepting of facial recognition technology when
used by advertisers or technology companies:
“The ability of governments and law enforcement agencies to
monitor the public using facial recognition was once the province of
dystopian science fiction. But modern technology is increasingly
bringing versions of these scenarios to life. A
recent investigation found
that U.S. law enforcement agencies are using state Department of
Motor Vehicles records to identify individual Americans without their
consent, including those with no criminal record. And countries such
as China have
made facial recognition technology a cornerstone of their strategies
to police the behaviors and activities of their publics. Despite
these high-profile examples from fiction and reality, a new
Pew Research Center survey finds
that a majority of Americans (56%) trust law enforcement agencies to
use these technologies responsibly. A similar share of the public
(59%) says it is acceptable for law enforcement to use facial
recognition tools to assess security threats in public spaces…”
(Related)
An
ICO spokesperson said:
“We will be reviewing the judgment carefully. We welcome the court’s finding that the police use of Live Facial Recognition (LFR) systems involves the processing of sensitive personal data of members of the public, requiring compliance with the Data Protection Act 2018. This new and intrusive technology has the potential, if used without the right privacy safeguards, to undermine rather than enhance confidence in the police.
“Our investigation into the first police pilots of this technology has recently finished. We will now consider the court’s findings in finalising our recommendations and guidance to police forces about how to plan, authorise and deploy any future LFR systems.
“In the meantime, any police forces or private organisations using these systems should be aware that existing data protection law and guidance still apply.”
So
if you are not already aware of the High Court’s finding that the
use of live (real-time) facial recognition systems by police is
lawful, you can find the press summary here
(pdf)
Conclusion: We better do something. Free ebook
available.
Rand Report
– Hostile Social Manipulation
Hostile
Social Manipulation – Present Realities and Emerging Trends:
“The role of information warfare in global strategic competition
has become much
more apparent
in recent years. Today’s practitioners of what this report’s
authors term hostile
social manipulation employ
targeted social media campaigns, sophisticated forgeries,
cyberbullying and harassment of individuals, distribution of rumors
and conspiracy theories, and other tools and approaches to cause
damage to the target state. These emerging tools and techniques
represent a
potentially significant threat
to U.S. and allied national interests. This
report represents an effort to better define and understand the
challenge
by focusing on the activities of the two leading authors of such
techniques — Russia and China.
Okay, why do humans matter?
Will AI
replace university lecturers? Not if we make it clear why humans
matter
… Forget robo-lecturers whirring away in front
of whiteboards: AI teaching will mostly happen online, in 24/7
virtual classrooms. AI machines will learn to teach by ferreting out
complex patterns in student behaviour – what you click, how long
you watch, what mistakes you make, even what time of day you work
best. This will then be linked to students’ “success”, which
might be measured by exam marks, student satisfaction or
employability.
… AI
edtech developers are nothing if not ambitious: this month, UK
company Century Tech will partner the Flemish regional government to
launch AI
assistants in schools across half of Belgium.
Until
now there’s been one big challenge to wholesale takeover by
teaching machines: AI requires vast amounts of data to train on
before it can spot patterns. But a large dataset
now exists for student
behaviour,
thanks to the hundreds of thousands of students who have followed
MOOCs (massive online open courses) over the past decade.
The
big question mark around MOOCs was how they could survive by giving
away course content for free. With uncomfortable echoes of recent
data controversies, it may
turn out that building the training database for AI teaching was the
MOOC business plan all along.
… Replacing
all lecturers with AI is probably still
some years off.
The ethical and educational challenges, which include AI’s inbuilt
biases,
the importance of lecturers’ pastoral role amid increasing mental
health concerns, and the idea that “consuming content” is
equivalent to learning, are so unsettling I’d like to think we
wouldn’t let it happen. But I worry that the combined pressures of
technology and economics frequently prove irresistible. If machines
can replace doctors,
why not academics too?
Perspective.
Streaming
makes up 80 percent of the music industry’s revenue
More
people are streaming music through services like Apple
Music and
Spotify,
and the record industry is seeing a major lift.
Revenue
made from streaming services in the United States grew by 26 percent
in the first six months of the year, according to trade group
Recording Industry Association of America, as
reported by The Wall
Street Journal.
That makes for a revenue of $4.3 billion, according to research
conducted by the group, which represents approximately 80
percent of the music industry’s overall revenue.
Perspective.
(Video)
The
internet's second revolution | The Economist
The
second half of humanity is joining the internet. People in countries
like India will change the internet, and it will change them.
Capabilities.
Having just attended the Huawei keynote here at
the IFA trade show, there were a couple of new features enabled
through AI that were presented on stage that made the hair on the
back of my neck stand on end. Part of it is just an impression on
how quickly AI in hand-held devices is progressing, but the other
part of it makes me think to how it can be misused.
"Real-Time Multi-Instance Segmentation"
Firstly, AI detection in photos is not new.
Identifying objects isn’t new. But Huawei showed a use case where
several people were playing musical instruments, and the smartphone
camera could detect both the people from the background, and the
people from each other. This allowed the software to change the
background, from an indoor scene to an outdoor scene and such. What
this also enabled was that individuals could be deleted, moved, or
resized.
… Detecting Health Rate with Cameras
The second feature was related to Health and AR.
By using a pre-trained algorithm, Huawei showed the ability for your
smartphone to detect your
heart rate simply by the front facing camera (and assuming
the rear facing camera too). It does this by looking at small facial
movements between video frames, and works on the values it predicts
per pixel to get an overall picture.
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