It's been just over a week since Kate Middleton, the internet's favorite "missing person," claimed that a photoshopped image of her with her children on U.K. Mother's Day was edited by her, for unspecified reasons. Then, on Monday, we had our first recorded sighting of the princess, out shopping with Prince William at the Royal Farms Windsor Farm Shop, near Windsor Castle. The video was released by TMZ and the Sun, and stills from it were plastered on the front pages of all the British tabloids Tuesday. Supposedly, it was taken by a 40-year-man, Nelson Silva, who lives nearby and was quoted in TMZ as saying: "Kate looked happy and relaxed. They look happy just to be able to go to a shop and mingle.

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I explain Artificial Intelligence terms and news to non-experts. Do you also have old pictures of yourself or close ones that didn't age well or that you, or your parents, took before we could produce high-quality images? I do, and I felt like those memories were damaged forever. This new and completely free AI model can fix most of your old pictures in a split second. It works well even with very low or high-quality inputs, which is typically quite the challenge.

As you can see in these images, there is a big difference between the synthesized old images and the real old ones. You can see that the synthesized image is already in high definition even with the fake scratches and color changes compared to the other one that contains way fewer details. They addressed this issue by creating their own new network specifically for the task. Basically, they used two variational auto-encoders, also called VAEs, to respectively transform old (degraded) and clean (restored) photos into two latent space. This translation into latent spaces is learned through synthetic paired data but is able to generalize well on real photos since this same domain gap is way smaller on such compact latent spaces. The domain gap from the two latent spaces produced by the VAEs is closed by jointly training an adversarial discriminator.

Yahoo Japan Corp. and two other companies opened a website Wednesday to seek information on wanted fugitives, with artificial intelligence-generated images showing how they could look now. The website, called Tehai, was established by Yahoo Japan, digital marketing business Dentsu Digital Inc. and Party, which creates images of wanted fugitives, in cooperation with the National Police Agency. On Tehai, nine types of images are posted showing how suspects put on wanted lists long ago could look now. The images are created with AI programs that studied vast amounts of facial photo data. The AI-based images take into account how the appearances of fugitives might have changed from those in their old pictures used in conventional posters seeking information about them.

In this article, you learn how to use artificial intelligence, or at least machine learning, to raise the alarm when there are changes in a supposedly static environment, such as a hay barn while the hay is drying after the harvest. I use two methods to achieve this: visual recognition and image comparison. Visual recognition requires more processing than can be done easily on a Raspberry Pi. The solution here is to upload pictures of the IBM Cloud, and ask IBM Watson Visual Recognition to identify the objects in them. If a new object appears, or if an expected object disappears (and doesn't show for a whole day, because objects may only be identifiable under certain lightening conditions), this AI system raises an alarm. Because object recognition using IBM Watson Visual Recognition requires significant bandwidth to upload pictures to the IBM Cloud, I designed an AI system that can work on a low-bandwidth network connection, such as a LoRa connection. To detect changes in such an environment, the second part of this article uses image comparison. Images are taken every ten minutes, and each time the image is compared to the image taken 24 hours prior. This way, the changes in lightening conditions will hopefully be minor enough to prevent false alarms. To implement visual recognition in the cloud, we will base our architecture on the short range architecture in the first article. The devices in the hay barn use WiFi to communicate with an access point in the farmhouse which is connected to the Internet.