Friday, September 06, 2019


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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