This year, the company also plans to hire more programmers for its AI projects. Other prominent users, the Journal noted, ranged from industrial giant General Electric and health provider Massachusetts General Hospital to financial institutions such as Fannie Mae or Mastercard. AI, company officials said, quickly completes routine jobs and enables human employees to conduct other business. In coming years, however, its capabilities will allow it to spot trends and aid in making decisions with less and less reliance on programmers. An October report from International Data Corp. predicted that the global AI market would increase from $8 billion in 2016 to more than $47 billion in 2020.

The idea that robots could replace humans in the workplace dates back to science fiction writers a century ago, and it has been a recurring theme in political life for almost as long. Back in 1964, US President Lyndon B. Johnson created a national commission to examine the impact of automation on the economy and employment. Automation should be viewed as an ally, not an enemy, he said at the time. "If we understand it, if we plan for it, if we apply it well, automation will not be a job destroyer or a family displaced. Instead, it can remove dullness from the work of man and provide him with more than man has ever had before."

Computers can already hold a massive amount of instantly-retrievable data in a manner that puts most humans to shame, but getting them to actually display intelligence is an entirely different challenge. A team of researchers from Northwestern University just made a huge stride towards that goal with a computational model that actually outperforms the average American adult in a standard intelligence test. As PhysOrg reports, the witty computer system utilizes an AI platform called CogSketch that gives it the power to solve visual problems just by looking at them, which is something that has traditionally held back many examples of artificial intelligence. Being able to visually understand, interpret, and then use that data to come to a solution brings the computer system closer to the functioning of the human brain than many before it, and so the team pitted its creation against a popular standardized test called Raven's Progressive Matrices. The Raven's test (or RPM for short) is comprised of 60 multiple choice questions that measure the taker's ability to reason, using visual puzzles.

Deep learning is helping solve everyday inconveniences, both serious and superficial. Artificial intelligence has been used to manage the global financial market, predict heart failure, and help cars navigate city streets autonomously. But not every AI application is so serious. A Brown University student recently developed a system that invents futuristic and ridiculous baby names. And last year the first AI-judged beauty contest was held.

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, microscopic image analysis, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. This book describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database. Dr. Yang is the founder of the Biomedical Image Computing and Imaging Informatics (BICI2) lab (http://www.bme.ufl.edu/labs/yang/). His major research interests are focus on biomedical image analysis and imaging informatics, computer vision, biomedical informatics and machine learning.

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.