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Imagining a world without cash
Life, James Hein, Published on 08/05/2024
» How safe are you in a purely digital economy? If you are carrying cash, someone can of course rob you, or you can lose it or give it to someone. You can also have a stash of it at home for emergencies or for buying something from a garage sale. For the most part, you retain control over any cash you manage. Electronic cash can still be stolen and your ability to spend it can be taken by someone else if your details get into the wrong hands. However, it's convenient, just tap and go, or in some cases, just wave your smartphone over a pad. While you have no idea where your money actually is, a small piece of plastic, your watch or a phone can retrieve it for you for a payment. You can even use it to get cash from a wall.
In 2020, China heads into 1984
Life, James Hein, Published on 23/10/2019
» China will have 626 million CCTVs installed by 2020. That's close to one for every two people in the country. By the end of 2019, any application for Internet access will require first having your face scanned. In 2020, if you want to surf the web you will first have to pass a facial recognition process. If you are recognised and your social score is high enough you will be able to connect. This directive comes from the Chinese Ministry of Industry and Technology.
AI-aided hope on the horizon
Life, James Hein, Published on 13/02/2019
» Despite some of my criticisms in the past there are some excellent examples of emerging artificial intelligence technologies. I've mentioned some of these from the medical world in earlier articles but a new one caught my eye this week, figuring out in which hotel a picture was taken. No, not to help people remember where holiday snaps were taken but to track down human trafficking where pics of women are taken to sell them for sex. The three groups behind this identification technology are from George Washington University, Temple University and Adobe, all in the US. Like many AI systems a large amount of source data is used and to help with this more than a million images have been collected from 50,000 hotels worldwide. Using all the room elements in backgrounds a neural network is being trained to identify a hotel chain and then a location.