What initially began as a Facebook event has morphed into a cultural moment, a juxtaposition of the previous day's inauguration of America's 45th president, Donald Trump. Heather Whaling is CEO of Geben Communication, a PR and social media agency with offices in Columbus, Ohio, and Chicago. She serves on the board of The Women's Fund of Central Ohio, mentors women entrepreneurs, and is a vocal advocate for paid parental leave. On the issues, it's increasingly difficult to find commonalities between Trump supporters and the marchers who will flock to DC and other cities around the country. Yet both groups share at least one tool in their toolbox: A mastery of social media as the go-to channel to amplify viewpoints and shape perceptions.

Machine-learning has predicted death risk in people with serious heart disease faster and more accurately than current methods. New software, developed by scientists at Imperial College London, has created virtual 3D hearts of each patient that replicate the way the organ contracts with each beat. Artificial intelligence is able to rapidly learn which features of cardiac function best predict heart failure and death. The system uses magnetic resonance imaging (MRI) of the heart together with information from blood tests and other observations. The technology has been tested on patients with pulmonary hypertension, a condition that leads to heart failure if not treated appropriately.

Here's a sentence you don't get to read everyday: Kristen Stewart has surprised the artificial intelligence community by publishing a paper on machine learning. The Twilight actress recently made her directorial debut with the short film Come Swim, and in it used a machine learning technique known as "style transfer" (where the aesthetics of one image or video is applied to another) to create an impressionistic visual style. Along with special effects engineer Bhautik J Joshi and producer David Shapiro, Stewart has co-authored a paper on this work in the film, publishing it in the popular online repository for non-peer reviewed work, arXiv. The paper itself is titled "Bringing Impressionism to Life with Neural Style Transfer in Come Swim," and offers a detailed case study on how to use this sort of machine learning in a film. The paper describes Come Swim as a "poetic, impressionistic portrait of a heartbroken man underwater," with the film's aesthetic grounded by a painting of Stewart's showing a "man rousing from sleep." The team used existing neural networks to transfer the style of this painting onto a test frame, and then fine-tuned their setup by adding "blocks of color and texture" until they'd created the desired painting-like effect.

Second, it cites all of 13 sources, most of which are github links, other arXiv articles, or conference presentations–not terribly rigorous scholarship. Third, only one author (the lead) has contact information, which calls into question how much the second (Stewart) and third authors contributed to the research and authoring of the article. Fourth, getting second author credit does not mean "co-wrote," it means that she contributed to the paper in some way. You see a lot of papers where a graduate (or even undergraduate) research assistant who entered data into a spreadsheet gets credit as co-author. Her contribution could be significant, or it could be next to nothing.

Quantitative investing, one of the latest paths available to hedge fund managers and incorporating computer analytics in innovative new ways in order to make precision investment decisions, may have a new challenger emerging from within its own ranks. According to a report in January by Eurekahedge, the quickly-changing landscape of alternative investing strategies has seen a sudden rise in the prominence of artificial intelligence-based (AI) funds, and that many of these funds are vastly outperforming so-called "traditional quants", as well as human-led management teams. According to a report by ValueWalk, Eurekahedge's AI/Machine Learning Hedge Fund Index, monitoring performance of 23 hedge funds utilizing this investment strategy, has managed to outperform generalized hedge funds as well as traditional quant funds decisively since 2010. In fact, AI funds have netted annual returns of 8.44% for the past 6 years. This is dramatically higher than the other indices that Eurekahedge uses, including the CTA/Managed Futures index, with 2.62% returns for the same period, and the trend following index, which saw a mere 1.62% return level at the same time.

As we blast into 2017, marketers are wondering: what innovative technological developments will this year bring? How will these changes affect my strategy? What are my competitors doing, and more importantly, what are my customers expecting? These big, important questions require extensive research, if not a sixth sense for weeding out what's merely a fad, and what's as equally as groundbreaking as, say, the internet. To save you time, we've done the work for you.

A team of artificial intelligence researchers from Northwestern University have built a robot on CogSketch model that will mimic the understanding level of common human beings. This computational model of analogy is based on the structure mapping theory of Northwestern psychology professor Dedre Gentner and the same artificial intelligence platform was previously developed in Forbus' Laboratory. According to Ken Forbus this model has the ability to understand the world as adult Americans do with an accuracy of 75 percentages. He further added that the things those are difficult for humans to understand are also difficult to recognise by these robots; best proof that it is mimicking human cognition. However; it can solve complex visual problems citing as one of the hallmarks of human intelligence.

Chip giant Intel last week rolled out a new deep learning framework that runs as a Spark job atop Hadoop. Called BigDL, the open source software is designed to take advantage of hardware acceleration capabilities that Intel has built into its Xeon CPUs. BigDL, which Intel released on Github, is modeled after Torch, an open source deep learning framework used in scientific computing. Intel says the framework supports numeric computing via Tensor, as well as high level neural networks, and can be used to run prebuilt Caffe and Torch models on Spark. While many deep learning frameworks today leverage GPUs, Intel is taking a different route with BigDL, for obvious reasons.

When psychologist Dr. Paul Ekman visited the Fore tribe in the highlands of Papua New Guinea in 1967, he probably didn't imagine that his work would become the foundation for some of the latest developments in artificial intelligence (AI). After studying the tribe, which was still living in the preliterate state it had been in since the Stone Age, Ekman believed he had found the blueprint for a set of universal human emotions and related expressions that crossed cultures and were present in all humans. A decade later he created the Facial Action Coding System, a comprehensive tool for objectively measuring facial movement. Ekman's work has been used by the FBI and police departments to identify the seeds of violent behavior in nonverbal expressions of sentiment. He has also developed the online Atlas of Emotions at the behest of the Dalai Lama.

Photo taken on Jan. 17, 2017 shows Xuewei Qi, a leading researcher at the University of California, Riverside, is using vehicle connectivity and evolutionary algorithms to improve the efficiency of Plug-In Hybrid Electric Vehicles.