Over the last few years, artificial intelligence (AI) has advanced rapidly, with developers regularly reporting new breakthroughs. AI algorithms work ever faster. Certain machine behaviors never cease to amaze me. I'm astounded by their ability to learn from their accomplishments and from their interactions with we humans. Will algorithms commit war crimes?

London design studio Sample & Hold has been asked to scan all kinds of things: a shoe, a carrot, the heads of every member of the Barcelona FC team. The firm has even worked with a company in Knightsbridge, London, that makes casts of babies' feet and heads. "Occasionally they have a client who wants a head scan of their kid," explains Sample & Hold director Sam Jackson. Those scans have been used for bronze casts of the child's head, and the 3D scan speeds up that process. Sample & Hold doesn't need lasers to do this 3D scanning.

Newly accessible advanced computing techniques like machine learning and artificial intelligence can help.

Artificial intelligence, machine learning, and data scientists are together changing the world.

You now have a chance to try Apple's machine learning-based Deep Fusion photography if you're willing to live on the bleeding edge. It's releasing an iOS 13.2 developer beta (public likely to follow soon) that makes Deep Fusion available to iPhone 11 and iPhone 11 Pro owners. The technique uses machine learning to create highly detailed, sharper and more natural-looking photos on the primary and telephoto lenses by combining the results of multiple shots. Deep Fusion takes an underexposed photo for sharpness, and blends that with three neutral pictures and a long high-exposure image on a per-pixel level to achieve a highly customized result. The machine learning system examines the context of the picture to understand where a pixel sits on the frequency spectrum.

Recently, self-supervised learning has proved to be effective to learn representations of events in image sequences, where events are understood as sets of temporally adjacent images that are semantically perceived as a whole. However, although this approach does not require expensive manual annotations, it is data hungry and suffers from domain adaptation problems. As an alternative, in this work, we propose a novel approach for learning event representations named Dynamic Graph Embedding (DGE). The assumption underlying our model is that a sequence of images can be represented by a graph that encodes both semantic and temporal similarity. The key novelty of DGE is to learn jointly the graph and its graph embedding. At its core, DGE works by iterating over two steps: 1) updating the graph representing the semantic and temporal structure of the data based on the current data representation, and 2) updating the data representation to take into account the current data graph structure. The main advantage of DGE over state-of-the-art self-supervised approaches is that it does not require any training set, but instead learns iteratively from the data itself a low-dimensional embedding that reflects their temporal and semantic structure. Experimental results on two benchmark datasets of real image sequences captured at regular intervals demonstrate that the proposed DGE leads to effective event representations. In particular, it achieves robust temporal segmentation on the EDUBSeg and EDUBSeg-Desc benchmark datasets, outperforming the state of the art.

Fake news is a well-documented problem. It's obvious that an AI-based technique is needed to counter that threat. AI-generated videos (also known as deepfakes) and images are easier to come up with than ever before. They can range from funny and quirky to much more sinister and dangerous, such as those of political statements that were never given, or shots of events that never took place. It's easy to see how such content published on social networks or by reputable media outlets could be used to manipulate public opinion.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. The media is rife with stories that warn of AI algorithms bringing people back from the dead, AI algorithms developing secret languages, mass technological unemployment, and a looming robot apocalypse. Movies and TV series like Her, The Circle and Westworld, which present a mystic portrayal of conscious machines and human-level AI being just around the corner. Rebooting AI is a refreshing read and a much-needed reality check on the current confusing state of artificial intelligence. Consider the following text, mentioned in Rebooting AI: "Elsie tried to reach her aunt on the phone, but she didn't answer." You don't need to be a genius to quickly make the following assumptions after reading this sentence: But even the most sophisticated AI algorithm would struggle to draw the same conclusions.