I was at a meetup when Simon Knowles, CTO of Graphcore, was giving his talk on the latest development at Graphcore, and that is also where I met with Peter Bloomfield from Digital Catapult (@digicatapult). Peter was spreading the word about Machine Intelligence Garage which is an amazing opportunity created by a collaboration between the government and industry leaders like Google, Nvidia, AWS, etc. to help startups and small businesses access compute resources, which they would have otherwise never been able to get hold of. Sometime later, we decided to have a chat discussing the usual questions like what is Machine Intelligence Garage, what is the history, why, how, who, when, and at what stage is the program at and how do people get involved? As you would imagine, I found our conversation interesting and informative and hence decided to write about it and share it with the rest of us. Mani: Hey, Peter, great meeting you and learning about the initiative from Digital Catapult, can you please share with me the history about this initiative?

AI and machine learning are going to start making a lot more decisions. They probably still won't be used in the near future to make "big" decisions like whether to put a 25 percent tariff on a commodity and start a trade war with a partner. However, nearly anything you've stuck in Excel and massaged, coded, or sorted is a good clustering, classification, or learning-to-rank problem. Anything that is a set of values that can be predicted is a good machine learning problem. Anything that is a pattern or shape or object that you just go through and "look for" is a good deep learning problem.

The military is authorized to shoot down drones flying over bases, but at least two naval bases are still struggling to get operators to stop getting too close. Now, the FAA has issued a stricter warning against flying drones too near Naval Base Kitsap (Washington) and Naval Submarine Base Kings Bay (Georgia) in order "to address concerns about potentially malicious drone operations over certain, high-priority maritime operations." More specifically, the FAA is restricting drone flights near the US Navy and US Coast Guard vessels operating in those bases. Kitsap is one of Navy's strategic nuclear weapons facilities, while Kings Bay houses the country's nuclear missile submarines. At the request of the @DeptofDefense and @USCG, the #FAA is restricting #drone operations near two naval bases in #Washington and #Georgia.

In this article, we will see the implementation of Convolutional Neural Network (CNN) using Keras on MNIST data set and then we will compare the results with the regular neural network. It is highly recommended to first read the post "Convolutional Neural Network -- In a Nutshell" before moving on to CNN implementation to develop intuition about CNN. The MNIST dataset is most commonly used for the study of image classification. The MNIST database contains images of handwritten digits from 0 to 9 by American Census Bureau employees and American high school students. It is divided into 60,000 training images and 10,000 testing images.

Every two seconds, sensors measuring the United States' electrical grid collect 3 petabytes of data – the equivalent of 3 million gigabytes. Data analysis on that scale is a challenge when crucial information is stored in an inaccessible database. But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. They are using data from the U.S. Department of Energy National Labs' sensors, called phasor measurement units, that collect information on the electrical power grid about voltages, currents and power generation. Because these values can vary, keeping the power grid stable involves continuously monitoring the sensors.

Apple stays quiet about it, but the company is clearly developing a self-driving car. And this week the world was given a glimpse inside this project, showing that it's much larger than many had previously thought. The FBI arrested a former employee on Apple's autonomous car design team for allegedly downloading proprietary information and attempting take it to a rival car company in China. The resulting documentation reveals the number of employees on the project. There's no room for doubt about what Xiaolang Zhang, the former Apple employee, was working on. "ZHANG was hired at Apple starting December 7, 2015, to work on a project to develop software and hardware for use in autonomous vehicles (the Project)," wrote Eric Proudfoot, the FBI Special Agent investigating this crime, in the official criminal complaint (PDF).

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination with other secondary users. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network (RNN), called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.

The AI humanoid, 'Sophia' (see above), has been making a worldwide tour on behalf of her creator, Hanson Robotics of Hong Kong, and made an unexpected stop to the Caucasus this week. The Caucasus stop meant granting the world's first ever visa granted to a robot, a process that took just two minutes thanks to some smart technology. Sophia's visit was organized by UN public service award winner ASAN (Azerbaijan Service and Assessment Network) xidmet, a government agency in Azerbaijan; which has been reducing the bureaucracy by creating one-stop centres for delivering services to the public. The agency recently took over all the country's e-government initiatives - no mean feat although in Azerbaijani, the word "asan" means easy. To underscore its electronic kinship with Sophia, and its prowess at delivering e-government services, ASAN issued her an electronic visa upon her arrival at Baku International Airport in the nation's capital.

"Everything is true…everything anybody has ever thought," Philip K. Dick – Do Androids Dream of Electric Sheep. It is impossible to escape from the fact that technology, and increasingly artificial intelligence (AI), has transformed everyday life. It all started with how we play our music, but Apple's Siri and Amazon's Alexa (along with other similar "virtual assistants") now have a daily interface with many of us. We are also, increasingly, now daily users of the Internet of Things (IoT) – connecting up smart fridges, boilers and alarm systems, each controllable from a smartphone. The "everyday" form of AI is almost unavoidable in the modern home, but, while not necessarily as obvious to you and me, there is also an ongoing, yet unseen growth in AI in the manufacturing sector. What is still lacking, however, is concrete regulation in place for the use and development of AI in the industry.

We are delighted to announce that the results of the highly anticipated Advanced Endpoint Protection (AEP) 2.0 Group Test are now available.Twenty of the industry's leading AEP vendors participated in this test, and our place in the SVM reflects 100% block rate in 5/8 of the test's attack categories and high TCO ratings. Get the full report, and join our joint webinar with NSS Labs. Cybersecurity is a crowded space in which thousands of companies operate. CISOs are occasionally bombarded with many solutions claiming to stop the next attacks. We continue to lead the pack and innovate with our static and behavioral AI technologies, powered by deep visibility.