Alfonzo Macias looks unremarkable at first glance -- bearded, bespectacled, with a short widow's peak. But his strangely distorted glasses and the dissolving background behind him hint at a discomforting truth: Macias never existed. Undetectable to the naked eye, the uncannily human face is in fact the creation of an algorithm -- one used by pro-Trump media outlet TheBL to give an identity to one of the many fake Facebook accounts that it uses to drive traffic to its website. While less attention-grabbing than the viral deepfake videos that have manipulated the speech and actions of politicians and celebrities to popular effect in recent years, static artificial intelligence-generated faces are becoming an increasingly common tool for misinformation, experts say. Instead of making real people appear to say and do things they have not, the technique works by generating entirely "new" people from scratch.

After years of working with the world's leading direct-to-consumer brands, we see that it is easy to scale the number of interactions but still very hard to scale the quality of these interactions to drive efficient growth,

Quality journalism often comes from long-term investigative projects, requiring time consuming work sifting through giant collections of documents, images and audio recordings. As part of Journalist Studio, our new suite of tools to help reporters do their work more efficiently, securely, and creatively through technology, we're launching Pinpoint, a new tool that brings the power of Google Search to journalists. Pinpoint helps reporters quickly sift through hundreds of thousands of documents by automatically identifying and organizing the most frequently mentioned people, organizations and locations. Reporters can sign up to request access to Pinpoint starting this week. For many topics, seeing is key to understanding.

Now that the majority of New York Fashion Week's runway shows have gone digital, designers are seeking to replicate the aura and grandeur of the fashion show outside of the catwalk's limitations. From Dior's live-streamed presentations, to Louis Vuitton's short films, to Loewe's FedEx-shipped "Show in a Box", high-fashion has demonstrated how collections can be shared with consumers in new, socially-distant ways. However, one of the main limitations of runway shows was the necessity of models -- and a lot of them. Real-time, in-person runways saw models walking out one after the other. With digital showings-- such as the pre-photographed Resort 2021 collections -- the necessity for more-than-a-couple-of-models is much lower.

Brain "plasticity" is one of the great discoveries in modern science, but neuroscientist David Eagleman thinks the word is misleading. Unlike plastic, which molds and then retains a particular shape, the brain's physical structure is continually in flux. But Eagleman can't avoid the word. "The whole literature uses that term plasticity, so I use it sparingly," he says. Eagleman also discounts computer analogies to the brain. He's coined the term "livewired" (the title of his new book) to point out that the brain's hardware and software are practically inseparable. Eagleman is a man of prodigious energy. An adjunct professor at Stanford University, he's also been a novelist, TV host of PBS's The Brain, and science advisor for the HBO series Westworld.

Abstract: Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training.

But that doesn't mean the technology is entirely useless. Sony's new Spatial Reality Display (or SR Display), for example, uses eye-tracking technology to render believable 3D objects, without the need to wear 3D glasses or put on a VR headset. It's something CG and VR artists could use to preview their work easily. And no, it's not meant for consumers -- not at its $5,000 price, anyway. Sony first previewed the SR Display at CES this year, where it was called its "Eye-Sensing Light Field Display."

Intelligent cameras could be one step closer thanks to a research collaboration between the Universities of Bristol and Manchester who have developed cameras that can learn and understand what they are seeing. Roboticists and artificial intelligence (AI) researchers know there is a problem in how current systems sense and process the world. Currently they are still combining sensors, like digital cameras that are designed for recording images, with computing devices like graphics processing units (GPUs) designed to accelerate graphics for video games. This means AI systems perceive the world only after recording and transmitting visual information between sensors and processors. But many things that can be seen are often irrelevant for the task at hand, such as the detail of leaves on roadside trees as an autonomous car passes by.

Using customer behavioral analysis, AI, machine learning and bots, Futurism's Dimensions creates that personalized experience.

Discussion includes the latest data-analysis technologies, creating a data-driven culture, leveraging artificial intelligence to uncover granular insights, …