F-35s, F-22s and other fighter jets will soon use improved "artificial intelligence" to control nearby drone "wingmen" able to carry weapons, test enemy air defenses or perform intelligence, reconnaissance and surveillance missions in high risk areas, senior Air Force officials said. Citing ongoing progress with computer algorithms and some degree of AI (artificial intelligence) already engineered into the F-35, Air Force Chief Scientist Gregory Zacharias said that technology was progressing quickly at the Air Force Research Lab - to the point where much higher degrees of autonomy and manned-unmanned teaming is expected to emerge in the near future. "This involves an attempt to have another platform fly alongside a human, perhaps serving as a weapons truck carrying a bunch of missiles," Zacharias said in an interview with Scout Warrior. An F-35 computer system, Autonomic Logistics Information System, involves early applications of artificial intelligence wherein computers make assessments, go through checklists, organize information and make some decisions by themselves – without needing human intervention. "We are working on making platforms more autonomous with multi-int fusion systems and data from across different intel streams," Zacharias explained.

A startup that has developed artificial intelligence to better manage city water systems is among 10 companies from around the world admitted to a San Francisco accelerator focused on turning drought, leaky pipes and pollution into business opportunities. Emagin, founded in 2016 by Thouheed Abdul Gaffoor and Mohamad Vedut, was among more than 200 applications for the 2017 cohort at the Imagine H2O accelerator in California. After graduating from the University of Waterloo with a degree in environmental engineering, Gaffoor hooked up with Vedut, who graduated from the University of Ontario Institute of Technology with a degree in software engineering. They founded Emagin and moved into the Velocity Garage in the Tannery building in downtown Kitchener while Gaffoor pursues his master's in civil engineering at UW. Two Ontario municipalities are using the startup's artificial intelligence to help operate drinking water and wastewater systems. Emagin's software quickly establishes the normal rates of water use on a system, and alerts operators to problems.

Editor's note: This is the second chapter of a book I'm working on called Demystifying Artificial Intelligence. The goal of the book is to demystify what modern AI is and does for a general audience. So something to smooth the transition between AI fiction and highly mathematical descriptions of deep learning. I'm developing the book over time - so if you buy the book on Leanpub know that there are only two chapters in there so far, but I'll be adding more over the next few weeks and you get free updates. The cover of the book was inspired by this amazing tweet by Twitter user @notajf.

Digital health platform provider HealthTap is betting its new Dr. A.I. mobile app will eliminate the risk of a patient incorrectly self-diagnosing their condition through online searches by providing accurate, online triage that directs patients to the right level of care. The app, which uses artificial intelligence to perform online triage based on a patient's symptoms, can help reduce the risk of a patient incorrectly self-diagnosing her symptoms, which happens frequently with Internet searches, HealthTap says. There are 10 billion symptom-related health searches per year on Google, says HealthTap CEO Ron Gutman. "Search engines can't consult a patient's health records or ask follow-up questions to put a person's symptoms into proper context," Gutman says. "Effective triage requires detailed knowledge of a patient's personal health situation, making context critical to providing optimal care."

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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.