Megvii, the Chinese startup unicorn known for facial recognition tech, files to go public in Hong Kong – gpgmail


Megvii Technology, the Beijing-based artificial intelligence startup known in particular for its facial recognition brand Face++, has filed for a public listing on the Hong Kong stock exchange.

Its prospectus did not disclose share pricing or when the IPO will take place, but Reuters reports that the company plans to raise between $500 million and $1 billion and list in the fourth quarter of this year. Megvii’s investors include Alibaba, Ant Financial and the Bank of China. Its last funding round was a Series D of $750 million announced in May that reportedly brought its valuation to more than $4 billion.

Founded by three Tsinghua University graduates in 2011, Megvii is among China’s leading AI startups, with its peers (and rivals) including SenseTime and Yitu. Its clients include Alibaba, Ant Financial, Lenovo, China Mobile and Chinese government entities.

The company’s decision to list in Hong Kong comes against the backdrop of an economic recession and political unrest, including pro-democracy demonstrations, factors that have contributed to a slump in the value of the benchmark Hang Seng index. Last month, Alibaba reportedly decided to postpone its Hong Kong listing until the political and economic environment becomes more favorable.

Megvii’s prospectus discloses both rapid growth in revenue and widening losses, which the company attributes to changes in the fair value of its preferred shares and investment in research and development. Its revenue grew from 67.8 million RMB in 2016 to 1.42 billion RMB in 2018, representing a compound annual growth rate of about 359%. In the first six months of 2019, it made 948.9 million RMB. Between 2016 and 2018, however, its losses increased from 342.8 million RMB to 3.35 billion RMB, and in the first half of this year, Megvii has already lost 5.2 billion RMB.

Investment risks listed by Megvii include high R&D costs, the U.S.-China trade war and negative publicity over facial recognition technology. Earlier this year, Human Rights Watch published a report that linked Face++ to a mobile app used by Chinese police and officials for mass surveillance of Uighurs in Xinjiang, but it later added a correction that said Megvii’s technology had not been used in the app. Megvii’s prospectus alluded to the report, saying that in spite of the correction, the report “still caused significant damages to our reputation which are difficult to completely mitigate.”

The company also said that despite internal measures to prevent misuse of Megvii’s tech, it cannot assure investors that those measures “will always be effective,” and that AI technology’s risks and challenges include “misuse by third parties for inappropriate purposes, for purposes breaching public confidence or even violate applicable laws and regulations in China and other jurisdictions, bias applications or mass surveillance, that could affect user perception, public opinions and their adoption.”

From a macroeconomic perspective, Megvii’s investment risks include the restrictions and tariffs placed on Chinese exports to the U.S. as part of the ongoing trade war. It also cited reports that Megvii is among the Chinese tech companies the U.S. government may add to trade blacklists. “Although we are not aware of, nor have we received any notification, that we have been added as a target of any such restrictions as of the date this Document, the existence of such media reports itself has already damaged our reputation and diverted our management’s attention,” the prospectus said. “Whether or not we will be included as a target for economic and trade restrictions is beyond our control.”


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The renaissance of silicon will create industry giants – gpgmail


Every time we binge on Netflix or install a new internet-connected doorbell to our home, we’re adding to a tidal wave of data. In just 10 years, bandwidth consumption has increased 100 fold, and it will only grow as we layer on the demands of artificial intelligence, virtual reality, robotics and self-driving cars. According to Intel, a single robo car will generate 4 terabytes of data in 90 minutes of driving. That’s more than 3 billion times the amount of data people use chatting, watching videos and engaging in other internet pastimes over a similar period.

Tech companies have responded by building massive data centers full of servers. But growth in data consumption is outpacing even the most ambitious infrastructure build outs. The bottom line: We’re not going to meet the increasing demand for data processing by relying on the same technology that got us here.

The key to data processing is, of course, semiconductors, the transistor-filled chips that power today’s computing industry. For the last several decades, engineers have been able to squeeze more and more transistors onto smaller and smaller silicon wafers — an Intel chip today now squeezes more than 1 billion transistors on a millimeter-sized piece of silicon.

This trend is commonly known as Moore’s Law, for the Intel co-founder Gordon Moore and his famous 1965 observation that the number of transistors on a chip doubles every year (later revised to every two years), thereby doubling the speed and capability of computers.

This exponential growth of power on ever-smaller chips has reliably driven our technology for the past 50 years or so. But Moore’s Law is coming to an end, due to an even more immutable law: material physics. It simply isn’t possible to squeeze more transistors onto the tiny silicon wafers that make up today’s processors.

Compounding matters, the general-purpose chip architecture in wide use today, known as x86, which has brought us to this point, isn’t optimized for computing applications that are now becoming popular.

That means we need a new computing architecture. Or, more likely, multiple new computer architectures. In fact, I predict that over the next few years we will see a flowering of new silicon architectures and designs that are built and optimized for specialized functions, including data intensity, the performance needs of artificial intelligence and machine learning and the low-power needs of so-called edge computing devices.

The new architects

We’re already seeing the roots of these newly specialized architectures on several fronts. These include Graphic Processing Units from Nvidia, Field Programmable Gate Arrays from Xilinx and Altera (acquired by Intel), smart network interface cards from Mellanox (acquired by Nvidia) and a new category of programmable processor called a Data Processing Unit (DPU) from Fungible, a startup Mayfield invested in.  DPUs are purpose-built to run all data-intensive workloads (networking, security, storage) and Fungible combines it with a full-stack platform for cloud data centers that works alongside the old workhorse CPU.

These and other purpose-designed silicon will become the engines for one or more workload-specific applications — everything from security to smart doorbells to driverless cars to data centers. And there will be new players in the market to drive these innovations and adoptions. In fact, over the next five years, I believe we’ll see entirely new semiconductor leaders emerge as these services grow and their performance becomes more critical.

Let’s start with the computing powerhouses of our increasingly connected age: data centers.

More and more, storage and computing are being done at the edge; that means, closer to where our devices need them. These include things like the facial recognition software in our doorbells or in-cloud gaming that’s rendered on our VR goggles. Edge computing allows these and other processes to happen within 10 milliseconds or less, which makes them more work for end users.

I commend the entrepreneurs who are putting the silicon back into Silicon Valley.

With the current arithmetic computations of x86 CPU architecture, deploying data services at scale, or at larger volumes, can be a challenge. Driverless cars need massive, data-center-level agility and speed. You don’t want a car buffering when a pedestrian is in the crosswalk. As our workload infrastructure — and the needs of things like driverless cars — becomes ever more data-centric (storing, retrieving and moving large data sets across machines), it requires a new kind of microprocessor.

Another area that requires new processing architectures is artificial intelligence, both in training AI and running inference (the process AI uses to infer things about data, like a smart doorbell recognizing the difference between an in-law and an intruder). Graphic Processing Units (GPUs), which were originally developed to handle gaming, have proven faster and more efficient at AI training and inference than traditional CPUs.

But in order to process AI workloads (both training and inference), for image classification, object detection, facial recognition and driverless cars, we will need specialized AI processors. The math needed to run these algorithms requires vector processing and floating-point computations at dramatically higher performance than general purpose CPUs provide.

Several startups are working on AI-specific chips, including SambaNova, Graphcore and Habana Labs. These companies have built new AI-specific chips for machine intelligence. They lower the cost of accelerating AI applications and dramatically increase performance. Conveniently, they also provide a software platform for use with their hardware. Of course, the big AI players like Google (with its custom Tensor Processing Unit chips) and Amazon (which has created an AI chip for its Echo smart speaker) are also creating their own architectures.

Finally, we have our proliferation of connected gadgets, also known as the Internet of Things (IoT). Many of our personal and home tools (such as thermostats, smoke detectors, toothbrushes and toasters) operate on ultra-low power.

The ARM processor, which is a family of CPUs, will be tasked for these roles. That’s because gadgets do not require computing complexity or a lot of power. The ARM architecture is perfectly designed for them. It’s made to handle smaller number of computing instructions, can operate at higher speeds (churning through many millions of instructions per second) and do it at a fraction of the power required for performing complex instructions. I even predict that ARM-based server microprocessors will finally become a reality in cloud data centers.

So with all the new work being done in silicon, we seem to be finally getting back to our original roots. I commend the entrepreneurs who are putting the silicon back into Silicon Valley. And I predict they will create new semiconductor giants.


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Traces AI is building a less invasive alternative to facial recognition tracking – gpgmail


With all of the progress we’ve seen in deep learning tech in the past few years, it seems pretty inevitable that security cameras become smarter and more capable in regards to tracking, but there are more options than we think in how we choose to pull this off.

Traces AI is a new computer vision startup, in Y Combinator’s latest batch of bets, that’s focused on helping cameras track people without relying on facial recognition data, something the founders believe is too invasive of the public’s privacy. The startup’s technology actually blurs out all human faces in frame, only relying on the other physical attributes of a person.

“It’s a combination of different parameters from the visuals. We can use your hair style, whether you have a backpack, your type of shoes and the combination of your clothing,” co-founder Veronica Yurchuk tells gpgmail.

Tech like this obviously doesn’t scale too great for a multi-day city-wide manhunt and leaves room for some Jason Bourne-esque criminals to turn their jackets inside out and toss on a baseball cap to evade detection. As a potential customer, why forego a sophisticated technology just to stave off dystopia? Well, Traces AI isn’t so convinced that facial recognition tech is always the best solution, they believe that facial tracking isn’t something every customer wants or needs and there should be more variety in terms of solutions.

“The biggest concern [detractors] have is, ‘Okay, you want to ban the technology that is actually protecting people today, and will be protecting this country tomorrow?’ And, that’s hard to argue with, but what we are actually trying to do is propose an alternative that will be very effective but less invasive of privacy,” co-founder Kostya Shysh tells me.

Earlier this year, San Francisco banned government agencies from the use of facial recognition software, and it’s unlikely that they will be the only city to make that choice. In our conversation, Shysh also highlighted some of the backlash to Detroit’s Project Green Light which brought facial recognition surveillance tech city-wide.

Traces AI’s solution can also be a better option for closed venues that have limited data on the people on their premises in the first place. One use case Shysh highlighted was being able to find a lost child in an amusement park with just a little data.

“You can actually give them a verbal description, So if you say, it’s a missing 10-year-old boy, and he had blue shorts and a white t shirt, that will be enough information for us to start a search,” Shysh says.

In addition to being a better way to promote privacy, Shysh also sees the technology as a more effective way to reduce the racial bias of these computer vision systems which have proven less adept at distinguishing non-white faces, and are thus often more prone to false positives.

“The way our technology works, we actually blur faces of the people before sending it to the cloud. We’re doing it intentionally as one of the safety mechanisms to protect from racial and gender biases as well,” Shysh says.

The co-founders say that the U.S. and Great Britain are likely going to be their biggest markets due to the high quantity of CCTV cameras, but they’re also pursuing customers in Asian countries like Japan and Singapore where face-obscuring facial masks are often worn and can leave facial tracking software much less effective.


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Artificial intelligence can contribute to a safer world – gpgmail


We all see the headlines nearly every day. A drone disrupting the airspace in one of the world’s busiest airports, putting aircraft at risk (and inconveniencing hundreds of thousands of passengers) or attacks on critical infrastructure. Or a shooting in a place of worship, a school, a courthouse. Whether primitive (gunpowder) or cutting-edge (unmanned aerial vehicles) in the wrong hands, technology can empower bad actors and put our society at risk, creating a sense of helplessness and frustration.

Current approaches to protecting our public venues are not up to the task, and, frankly appear to meet Einstein’s definition of insanity: “doing the same thing over and over and expecting a different result.” It is time to look past traditional defense technologies and see if newer approaches can tilt the pendulum back in the defender’s favor. Artificial Intelligence (AI) can play a critical role here, helping to identify, classify and promulgate counteractions on potential threats faster than any security personnel.

Using technology to prevent violence, specifically by searching for concealed weapons has a long history. Alexander Graham Bell invented the first metal detector in 1881 in an unsuccessful attempt to locate the fatal slug as President James Garfield lay dying of an assassin’s bullet. The first commercial metal detectors were developed in the 1960s. Most of us are familiar with their use in airports, courthouses and other public venues to screen for guns, knives and bombs.

However, metal detectors are slow and full of false positives – they cannot distinguish between a Smith & Wesson and an iPhone. It is not enough to simply identify a piece of metal; it is critical to determine whether it is a threat. Thus, the physical security industry has developed newer approaches, including full-body scanners – which are now deployed on a limited basis. While effective to a point, the systems in use today all have significant drawbacks. One is speed. Full body scanners, for example, can process only about 250 people per hour, not much faster than a metal detector. While that might be okay for low volume courthouses, it’s a significant problem for larger venues like a sporting arena.

Image via Getty Images

Fortunately, new AI technologies are enabling major advances in physical security capabilities. These new systems not only deploy advanced sensors to screen for guns, knives and bombs, they get smarter with each screen, creating an increasingly large database of known and emerging threats while segmenting off alarms for common, non-threatening objects (keys, change, iPads, etc.)

As part of a new industrial revolution in physical security, engineers have developed a welcomed approach to expediting security screenings for threats through machine learning algorithms, facial recognition, and advanced millimeter wave and other RF sensors to non-intrusively screen people as they walk through scanning devices. It’s like walking through sensors at the door at Nordstrom, the opposite of the prison-like experience of metal detectors with which we are all too familiar. These systems produce an analysis of what someone may be carrying in about a hundredth of a second, far faster than full body scanners. What’s more, people do not need to empty their pockets during the process, further adding speed. Even so, these solutions can screen for firearms, explosives, suicide vests or belts at a rate of about 900 people per hour through one lane.

Using AI, advanced screening systems enable people to walk through quickly and provide an automated decision but without creating a bottleneck. This volume greatly improves traffic flow while also improving the accuracy of detection and makes this technology suitable for additional facilities such as stadiums and other public venues such as Lincoln Center in New York City and the Oakland airport.

Apollo Shield’s anti-drone system.

So much for the land, what about the air?   Increasingly drones are being used as weapons. Famously, this was seen in a drone attack last year against Venezuelan president Nicolas Maduro. An airport drone incident drew widespread attention when a drone shut down Gatwick Airport in late 2018 inconveniency stranded tens of thousands of people.

People are rightly concerned about how easy it is to get a gun. Drones are also easy to acquire and operate, and quite difficult to monitor and to defend against. AI is now being deployed to prevent drone attacks, whether at airports, stadiums, or critical infrastructure. For example, new AI-powered radar technology is being used to detect, classify, monitor and safely capture drones identified as dangerous.

Additionally, these systems use can rapidly develop a map of the airspace and effectively create a security “dome” around specific venues or areas. These systems have an integration component to coordinate with on-the-ground security teams and first responders. Some even have a capture drone to incarcerate a suspicious drone. When a threatening drone is detected and classified by the system as dangerous, the capture drone is dispatched and nets the invading drone. The hunter then tows the targeted drone to a safe zone for the threat to be evaluated and if needed, destroyed.

While there is much dialogue about the potential risk of AI affecting our society, there is also a positive side to these technologies. Coupled with our best physical security approaches, AI can help prevent violent incidents.


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Netflix’s big problem and Apple’s thinnest product yet – gpgmail


Hey. This is Week-in-Review, where I give a heavy amount of analysis and/or rambling thoughts on one story while scouring the rest of the hundreds of stories that emerged on gpgmail this week to surface my favorites for your reading pleasure.

Last week, I talked about the Capital One breach and how Equifax taught us that irresponsible actions only affect companies in the PR department.


Thomas Trutschel/Photothek via Getty Images

The big story

Disney is going to eat Netflix’s lunch.

The content giant announced this week that when Disney+ launches, it will be shipping a $12.99 bundle that brings its Disney+ streaming service, ESPN+ and ad-supported Hulu together into a single-pay package. That price brings those three services together for the same cost as Netflix and is $5 cheaper that what you would spend on each of the services individually.

This announcement from Disney comes after Netflix stuttered in its most recent earnings, missing big on its subscriber add while actually losing subscribers in the U.S.

Netflix isn’t the aggregator it once was; its library is consistently shifting, with original series taking the dominant position. As much as Netflix is spending on content, there’s simply no way that it can operate on the same plane as Disney, which has been making massive content buys and is circling around to snap up the market by acquiring its way into consumers’ homes.

Disney has slowly amassed control of Hulu through buying out various stakeholders, but now that it shifts the platform’s weight, it’s pretty clear that it will use it as a selling point for its time-honed in-house content, which it is still expanding.

The streaming wars have been raging for years, but as the services seem to become more like what they’ve replaced, Disney seems poised to take control.

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On to the rest of the week’s news.

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Trends of the week

Here are a few big news items from big companies, with green links to all the sweet, sweet added context:

  • Apple Card rolls out
    Months after its public debut, Apple has begun rolling out its Apple Card credit card. We got our hands on the new Apple Card app, so check out more about what it’s like here.
  • Amid a struggling smartphone market, Samsung introduces new flagships
    The smartphone market is in a low-key free fall, but there’s not much for hardware makers to do than keep innovating. Samsung announced the release of two new phones for its Note series, with new features including a time-of-flight 3D scanning camera, a larger size and… no headphone jack. Read more here.
  • FedEx ties up ground contract with Amazon
    As Amazon rapidly attempts to build out its own air fleet to compete with FedEx’s planes, FedEx confirmed this week that it’s ending its ground-delivery contract with Amazon. Read more here.

GAFA Gaffes

How did the top tech companies screw up this week? This clearly needs its own section, in order of badness:

  1. Facebook could get fined billions more:
    [Facebook could face billions in potential damages as court rules facial recognition lawsuit can proceed]
  2. Instagram gets its own Cambridge Analytica:
    [Instagram ad partner secretly sucked up and tracked millions of users’ locations and stories]

Extra Crunch

Our premium subscription service had another week of interesting deep dives. My colleague Sarah Buhr had a few great conversations with VCs in the healthtech space and distilled some of their investment theses into a report.

Why is tech still aiming for the healthcare industry? It seems full of endless regulatory hurdles or stories of misguided founders with no knowledge of the space, running headlong into it, only to fall on their faces…

It’s easy to shake our fists at fool-hardy founders hoping to cash in on an industry that cannot rely on the old motto “move fast and break things.” But it doesn’t have to be the code tech lives or dies by.

So which startups have the mojo to keep at it and rise to the top? Venture capitalists often get to see a lot before deciding to invest. So we asked a few of our favorite health VC’s to share their insights.

Here are some of our other top reads this week for premium subscribers. This week, we talked about how to raise funding in August, a month not typically known for ease of access to VCs, and my colleague Ron dove into the MapR fire sale that took place this week:

We’re excited to ramp up The Station, a new gpgmail newsletter all about mobility. Each week, in addition to curating the biggest transportation news, Kirsten Korosec will provide analysis, original reporting and insider tips. Sign up here to get The Station in your inbox beginning this month.




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Why AI needs more social workers, with Columbia University’s Desmond Patton – gpgmail


Sometimes it does seem the entire tech industry could use someone to talk to, like a good therapist or social worker. That might sound like an insult, but I mean it mostly earnestly: I am a chaplain who has spent 15 years talking with students, faculty, and other leaders at Harvard (and more recently MIT as well), mostly nonreligious and skeptical people like me, about their struggles to figure out what it means to build a meaningful career and a satisfying life, in a world full of insecurity, instability, and divisiveness of every kind.

In related news, I recently took a year-long paid sabbatical from my work at Harvard and MIT, to spend 2019-20 investigating the ethics of technology and business (including by writing this column at gpgmail). I doubt it will shock you to hear I’ve encountered a lot of amoral behavior in tech, thus far.

A less expected and perhaps more profound finding, however, has been what the introspective founder Prayag Narula of LeadGenius tweeted at me recently: that behind the hubris and Machiavellianism one can find in tech companies is a constant struggle with anxiety and an abiding feeling of inadequacy among tech leaders.

In tech, just like at places like Harvard and MIT, people are stressed. They’re hurting, whether or not they even realize it.

So when Harvard’s Berkman Klein Center for Internet and Society recently posted an article whose headline began, “Why AI Needs Social Workers…”… it caught my eye.

The article, it turns out, was written by Columbia University Professor Desmond Patton. Patton is a Public Interest Technologist and pioneer in the use of social media and artificial intelligence in the study of gun violence. The founding Director of Columbia’s SAFElab and Associate Professor of Social Work, Sociology and Data Science at Columbia University.

desmond cropped 800x800

Desmond Patton. Image via Desmond Patton / Stern Strategy Group

A trained social worker and decorated social work scholar, Patton has also become a big name in AI circles in recent years. If Big Tech ever decided to hire a Chief Social Work Officer, he’d be a sought-after candidate.

It further turns out that Patton’s expertise — in online violence & its relationship to violent acts in the real world — has been all too “hot” a topic this past week, with mass murderers in both El Paso, Texas and Dayton, Ohio having been deeply immersed in online worlds of hatred which seemingly helped lead to their violent acts.

Fortunately, we have Patton to help us understand all of these issues. Here is my conversation with him: on violence and trauma in tech on and offline, and how social workers could help; on deadly hip-hop beefs and “Internet Banging” (a term Patton coined); hiring formerly gang-involved youth as “domain experts” to improve AI; how to think about the likely growing phenomenon of white supremacists live-streaming barbaric acts; and on the economics of inclusion across tech.

Greg Epstein: How did you end up working in both social work and tech?

Desmond Patton: At the heart of my work is an interest in root causes of community-based violence, so I’ve always identified as a social worker that does violence-based research. [At the University of Chicago] my dissertation focused on how young African American men navigated violence in their community on the west side of the city while remaining active in their school environment.

[From that work] I learned more about the role of social media in their lives. This was around 2011, 2012, and one of the things that kept coming through in interviews with these young men was how social media was an important tool for navigating both safe and unsafe locations, but also an environment that allowed them to project a multitude of selves. To be a school self, to be a community self, to be who they really wanted to be, to try out new identities.


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Facebook could face billions in potential damages as court rules facial recognition lawsuit can proceed – gpgmail


Facebook is facing exposure to billions of dollars in potential damages as a federal appeals court on Thursday rejected Facebook’s arguments to halt a class action lawsuit claiming it illegally collected and stored the biometric data of millions of users.

The class action lawsuit has been working its way through the courts since 2015, when Illinois Facebook users sued the company for alleged violations of the state’s Biometric Information Privacy Act by automatically collecting and identifying people in photographs posted to the service.

Now, thanks to an unanimous decision from the 9th U.S. Circuit Court of Appeals in San Francisco, the lawsuit can proceed.

The most significant language from the decision from the circuit court seems to be this:

 We conclude that the development of face template using facial-recognition technology without consent (as alleged here) invades an individual’s private affairs and concrete interests. Similar conduct is actionable at common law.

The American Civil Liberties Union came out in favor of the court’s ruling.

“This decision is a strong recognition of the dangers of unfettered use of face surveillance technology,” said Nathan Freed Wessler, staff attorney with the ACLU Speech, Privacy, and Technology Project, in a statement. “The capability to instantaneously identify and track people based on their faces raises chilling potential for privacy violations at an unprecedented scale. Both corporations and the government are now on notice that this technology poses unique risks to people’s privacy and safety.”

As April Glaser noted in “Slate”, Facebook already may have the world’s largest database of faces, and that’s something that should concern regulators and privacy advocates.

“Facebook wants to be able to certify identity in a variety of areas of life just as it has been trying to corner the market on identify verification on the web,” Siva Vaidhyanathan told Slate in an interview. “The payoff for Facebook is to have a bigger and broader sense of everybody’s preferences, both individually and collectively. That helps it not only target ads but target and develop services, too.”

That could apply to facial recognition technologies as well. Facebook, thankfully, doesn’t sell its facial recognition data to other people, but it does allow companies to use its data to target certain populations. It also allows people to use its information for research and to develop new services that could target Facebooks billion-strong population of users.

As our own Josh Constine noted in an article about the company’s planned cryptocurrency wallet, the developer community poses as much of a risk to how Facebook’s products and services are used and abused as Facebook itself.

Facebook has said that it plans to appeal the decision. “We have always disclosed our use of face recognition technology and that people can turn it on or off at any time,” a spokesman said in an email to “Reuters”.

Now, the lawsuit will go back to the court of U.S. District Judge James Donato in San Francisco who approved the class action lawsuit last April for a possible trial.

Under the privacy law in Illinois, negligent violations could be subject to damages of up to $1,000 and intentional violations of privacy are subject to up to $5,000 in penalties. For the potential 7 million Facebook users that could be included in the lawsuit those figures could amount to real money.

“BIPA’s innovative protections for biometric information are now enforceable in federal court,” added Rebecca Glenberg, senior staff attorney at the ACLU of Illinois. “If a corporation violates a statute by taking your personal information without your consent, you do not have to wait until your data is stolen or misused to go to court. As our General Assembly understood when it enacted BIPA, a strong enforcement mechanism is crucial to hold companies accountable when they violate our privacy laws. Corporations that misuse Illinoisans sensitive biometric data now do so at their own peril.”

These civil damages could come on top of fines that Facebook has already paid to the U.S. government for violating its agreement with the Federal Trade Commission over its handling of private user data. That resulted in one of the single largest penalties levied against a U.S. technology company. Facebook is potentially on the hook for a $5 billion payout to the U.S. government. That penalty is still subject to approval by the Justice Department.


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Trueface raises $3.7M to recognise that gun, as it’s being pulled, in real time – gpgmail


Globally, millions of cameras are in deployed by companies and organizations every year. All you have to do is look up. Yes, there they are! But the petabytes of data collected by these cameras really only become useful after something untoward has occurred. They can very rarely influence an action in “real-time”.

Trueface is a US-based computer vision company that turns camera data into so-called ‘actionable data’ using machine learning and AI by employing partners who can perform facial recognition, threat detection, age and ethnicity detection, license plate recognition, emotion analysis as well as object detection. That means, for instance, recognising a gun, as it’s pulled in a dime store. Yes folks, welcome to your brave new world.

The company has now raised $3.7M from Lavrock Ventures, Scout Ventures, and Advantage Ventures to scale the team growing partnerships and market share.

Trueface claims it can identify enterprises’ employees for access to a building, detect a weapon as it’s being wielded, or stop fraudulent spoofing attempts. Quite some claims.

However, it’s good enough for the US Air Force as it recently partnered with them to enhance base security.

Originally embedded in a hardware access control device, Trueface’s computer vision software inside one of the first ‘intelligent doorbell’, Chui which was covered by gpgmail’s Anthony Ha in 2014.

Trueface has multiple solutions to run on an array of clients’ infrastructures including a dockerized container, SDKs that partners can use to build their own solutions with, and a plug and play solution that requires no code to get up and running.

The solution can be deployed in various scenarios such as fintech, healthcare, retail to humanitarian aid, age verification, digital identity verification and threat detection. Shaun Moore and Nezare Chafni are the cofounders and CEO and CTO, respectively.

The computer vision market was valued at USD 9.28 billion in 2017 and is now set to reach a valuation of USD 48.32 billion by the end of 2023.

Facial recognition was banned by agency use in the city of San Francisco recently. There are daily news stories about privacy concerns of facial recognition, especially in regards to how China is using computer vision technology.

However, Truface is only deployed ‘on-premise’ and includes features like ‘fleeting data’ and blurring for people who have not opted-in. It’s good to see a company building in such controls, from the word go.

However, it’s then it’s up to the company you work for not to require you to sign a statement saying you are happy to have your face recognized. Interesting times, huh?

And if you want that job, well, that’s a whole other story, as I’m sure you can imagine.


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Google’s Pixel 4 smartphone will have motion control and face unlock – gpgmail


Google’s Pixel 4 is coming out later this year, and it’s getting the long reveal treatment thanks to a decision this year from Google to go ahead and spill some of the beans early, rather than saving everything for one big final unveiling closer to availability. A new video posted by Google today about the forthcoming Pixel 4 (which likely won’t actually be available until fall) shows off some features new to this generation: Motion control and face unlock.

The new “Motion Sense” feature in the Pixel 4 will detect waves of your hand and translate them into software control, including skipping songs, snoozing alarms and quieting incoming phone call alerts, with more planned features to come, according to Google. It’s based on Soli, a radar-based fine motion detection technology that Google first revealed at its I/O annual developer conference in 2016. Soli can detect very fine movements, including fingers pinched together to mimic a watch-winding motion, and it got approval from the FCC in January, hinting it would finally be arriving in production devices this year.

Pixel 4 is the first shipping device to include Soli, and Google says it’ll be available in “select Pixel countries” at launch (probably due to similar approvals requirements wherever it rolls out to consumers).

Google also teased “Face unlock,” something it has supported in Android previously – but Google is doing it very differently than it has been handled on Android in the past with the Pixel 4. Once again, Soli is part of its implementation, turning on the face unlock sensors in the device as it detects your hand reaching to pick up the device. Google says this should mean that the phone will be unlocked by the time you’re ready to use it, since it does this all on the fly, and works from pretty much any authentication.

Face unlock will be supported for authorizing payments and logging into Android apps, as well, and all of the facial recognition processing done for face unlock will occur on the device – a privacy-oriented feature that’s similar to how Apple handles its own Face ID. In fact, Google will also be storing all the facial recognition data securely in its own dedicated on-device Titan M security chip, another move similar to Apple’s own approach.

Google made the Pixel 4 official and tweeted photos (or maybe photorealistic renders) of the new smartphone back in June, bucking the trend of keeping things unconfirmed until an official reveal closer to release. Based on this update, it seems likely we can expect to learn more about the new smartphone ahead of its availability, which is probably going to happen sometime around October based on past behavior.


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