Deep 6 AI was founded in 2015 and is based in Pasadena, CA.Deep 6 AI finds patients for clinical trials in minutes rather than months. Using artificial intelligence on clinical data, Deep 6 AI's software accelerates patient recruitment exponentially, getting life-saving cures to people faster. Deep 6 AI got its start by beating out some of the biggest names in analytics in a U.S. government-sponsored contest Deep 6 AI has focused exclusively on healthcare, participating in the Techstars Healthcare Accelerator in partnership with Cedars-Sinai, the Healthbox modified-accelerator program, and Stanford's StartX Accelerator.

At what point does a factory worker lose capacity because of a co-bot? What if a logistics artificial intelligence (AI) system is fooled by new data and makes a fatal error? What if board executives are duped by hostile chatbots and act on misinformation? None of these instances of bot-gone-bad scenarios are science fiction fantasy. Don't forget the Tesla driverless car that mistook a trailer for the sky, the racist Microsoft bot that learnt from bad examples and the chatbots that influenced the 2016 US presidential election.

Almost a year ago, after heavy criticism from President-elect Donald J. Trump, Ford Motor Company canceled plans to build a $1.6 billion car plant in Mexico and announced that it would instead equip a Michigan factory to make electric and hybrid models. Now the automaker is changing its plans again, saying it intends to assemble new battery-powered cars in Mexico, not Michigan. But the Michigan location will get an even larger investment than previously planned and will focus on making a range of self-driving cars. The switch comes as the Trump administration has been pushing to renegotiate the North American Free Trade Agreement with Canada and Mexico. Few industries are more heavily affected by the accord than the auto sector, which has rushed to build plants in Mexico over the last several years to take advantage of lower labor costs and that country's extensive network of trade agreements. Late last month, Vice President Mike Pence met with top executives from Ford, General Motors and Fiat Chrysler to discuss trade and the renegotiation effort.

Politics has become a technological arms race. In 2016, the Republicans fought back, using big-data analytics and microtargeting of online ads to help propel Donald Trump into the White House. Raffi Krikorian wants to get the Democrats out ahead again. As the chief technology officer of the Democratic National Committee, the MIT graduate is reshaping his party's tech strategy. Krikorian, an expert in software engineering, previously led Uber's Advanced Technologies Center and got its first fleet of driverless cars on the road.

While the field of artificial intelligence (AI) has been around for some 60 years, it's now finally a part of our daily lives -- including how we work, bank, shop, interact, invest, drive and get insured. The term AI means different things to different people, but at PwC we think about it on a continuum, moving from assisted to augmented and, finally, autonomous intelligence. Here, I am primarily focusing on assisted intelligence -- applications that help us better perform tasks we're already doing today. This includes things like email filtering, automated processing of insurance claims and customer service chatbots, just to name a few applications. Of course when you're talking about AI, the question of automation and its potential to replace human jobs isn't far behind.

In my first blog in this short series on AI (Artificial Intelligence) in defense, I examined three areas where AI can enable defense agencies to'do things differently'. In this second blog, I raise my sights to look at how AI can also allow agencies to'do different things' that simply weren't possible before. This is where things start getting really interesting. As ever, it's best to start with some context. Successful use of AI in defense essentially revolves around three elements: first, the sensors (physical or virtual) that collect the data; second, the AI itself that provides the "brain" and thinking based on that data; and third, the actors – objects, agents or personnel – who do things based that thinking.

Digital transformation is rapidly moving the transportation industry from a closed, proprietary and analog ecosystem to open, networked, always-on mobility platform. It is already a prime example of the efficiency and revenue-generating potential of the Internet of Things (IoT) and soon, as we are promised by legacy and upstart automakers, it will become the prototype of the autonomous, AI-driven, robotic future. Becoming digital, however, means a new life in the cybersecurity trenches. Cisco's 2017 Midyear Cybersecurity Report includes interesting findings from a survey of 180 chief information security officers and security operations professionals in the transportation industry. Here are the highlights, buttressed by commentary from Joe Kirk, CIO of the Tennessee Department of Transportation (TDOT), "moving Tennessee forward" for more than 100 years and winner of a 2017 National Roadway Safety Award.

This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer). The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning. We show that even a simple probabilistic model achieves state-of-the-art on a standard k-shot learning dataset by a large margin. Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning.

Soft robotics has made leaps and bounds over the last decade as researchers around the world have experimented with different materials and designs to allow once rigid, jerky machines to bend and flex in ways that mimic and can interact more naturally with living organisms. However, increased flexibility and dexterity has a trade-off of reduced strength, as softer materials are generally not as strong or resilient as inflexible ones, which limits their use. Now, researchers at the Wyss Institute at Harvard University and MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created origami-inspired artificial muscles that add strength to soft robots, allowing them to lift objects that are up to 1,000 times their own weight using only air or water pressure, giving much-needed strength to soft robots. The study is published this week in Proceedings of the National Academy of Sciences (PNAS). "We were very surprised by how strong the actuators [aka, "muscles"] were. We expected they'd have a higher maximum functional weight than ordinary soft robots, but we didn't expect a thousand-fold increase. It's like giving these robots superpowers," says Daniela Rus, Ph.D., the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and one of the senior authors of the paper.

There is a war a-brewin', but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin's declaration that "the nation that leads in AI (Artificial Intelligence) will be the ruler of the world," the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet. Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings / environment. See the blog "Artificial Intelligence is not Fake Intelligence" for a more detailed explanation on artificial intelligence and machine learning. The Fast Company article "How to Stop Worrying and Love the Great AI War of 2018," projected that the AI battle would ultimately boil down between the "AI Big 6": Alphabet/Google, Amazon, Apple, Facebook, IBM, and Microsoft.