I am back from vacation with more news for you! In today’s edition you can find about how Chinese companies are drafting standards or how US Navy saves millions by using biometrics. Enjoy!
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| Source: juniperpublishers.com |
Biometrics
Streaming services trying to prevent password sharing
Netflix, HBO and others are thinking about using biometrics to prevent password sharing, which violates the terms of use.
US Navy to register new personnel using biometrics
US Navy will use biometrics instead of signatures to sign paperwork, which should cost $180,000 compared to current $2.5 million.
How Democrats would regulate facial recognition
USA has still huge influence in the world so the stance of potential future US president should be important for everyone, who cares about facial biometrics and privacy.
Rank of countries in terms of invasive deploying of biometrics
China at the top of countries using biometrics in invasive way is not surprise to me, US in the forth place was surprise to me.
State of the Biometrics in 2019
Biometrics Institute has released a report about current state of biometrics and biometric industry summarizing key events in last two years and stances of member companies and organizations.
Chinese companies drafting standards
While Western world relies heavily on standards from organizations like IEEE, China and Africa are relying often on International Telecommunications Union (ITU). The issue is, that “members of international delegations told FT that ITU standards are increasingly written by companies, rather than governments.”
Other
Is most online advertising a bubble?
This sentence is probably the most important “Annually, eBay was burning a good $20m on ads targeting the keyword ‘eBay’.”. If you want to know how is this possible, read this article.
Hacking neural networks
Facial biometrics is largely based on neural networks, here is a short introduction into hacking them.
Failure Modes in Machine Learning
As biometric systems are heavily relying on machine learning models, you could be interested in the overview of failures in machine learning systems.