Python Speed published an interesting and useful blog post about the techniques available for processing large amounts of data, the easiest solution being throw money at the problem by buying more RAM.
Google's Coral embedded AI devices are out of beta and are released to the public. The devices run tensorflow AI onboard, allowing AI solutions to be deployed in the field, such as in hospitals and water treatment plants.
McDonalds have started using AI to control digital drive through displays to suggest menu options based on time of day, weather and product popularity. Some McDonalds restaurants are trialling number plate recognition to add a customer's previous orders into the recommendation system.
Researchers at Portland State University and Adobe have demonstrated being able to generate a 3D Ken Burns effect (parallax) using a single image. The system uses neural networks to generate depth predictions and object boundaries, and context aware in-painting to generate the missing pieces of the video to simluate a moving point of focus. We can tick another Star Trek TNG sci-fi concept off the list.
AI generated voice mimicking software was used to persuade a director of a German energy subsidiary in the UK that their boss was on the phone and allowed theives to order the director to transfer funds to their bank account! This is believed to the first voice-AI assisted theft, so convincing that the director in question who made the transfer said that the software even imitated the tonality of the boss' voice.
Smashing Magazine has a handy tutorial on how front-end developers can start learning machine learning. The post describes how to use a pre-trained model, transfer learning and a custom model using Tensorflow.js.
Trivago deployed machine learning to present images of hotel spas when a user searched hotels with spas to improve user experience. This blog post describes how they tweaked pre-trained convolutional neural networks (CNNs) to label 100+ million hotel images in order to display spa realted images during contextual searches.
Researchers used machine learning to analyse 3.3 million material science abstracts from 1922 to 2018. They found that the ML system captured fundamental knowledge within the field and also historically identified new materials and research to study before the new materials were discovered in real life. The research shows how machine learning can be used to identify latent knoweldge more quickly.