This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

The impact of technology like artificial intelligence (AI) in the country can be measured from the influence of digital technologies on the economic elements and GDP which is 8 percent. The percentage is expected to increase to 60 percent in the next two years. Providing the relevant data, Anant Maheshwari, President, Microsoft India said that India has the highest involvement of AI in the workplace. Tech, Media and Entertainment companies hoped for a zoomed focus on disruptive technologies in their pre-budget expectations. During the Budget 2019 presentation speech which was delivered on February 1, 2019, Acting Finance Minister Piyush Goyal said "In order to take the benefits of artificial intelligence and related technologies to the people, a national programme on AI has been envisaged".

This podcast interview focuses on product strategy principles that, if applied, have the power to deliver transformative innovation with triple-digit ROI. My guest is Richard Boyd, CEO at Tanjo.AI. Over the last twenty-six years, Richard has led or helped create some of the most innovative game technology companies in the industry. He has served as a game technology consultant for a wide variety of industries including energy, healthcare, education and motion pictures. At Aerospace giant Lockheed Martin he created and led a group of innovative engineers and designers across all mission areas called Virtual World Labs.

Etihad Airways, the national airline of the UAE, has announced a strategic partnership with Microsoft to launch the first ever in-house AI Academy in the region, which will revolutionise the way the airline serves its customers by upskilling its workforce, optimising operations and creating alternate revenue streams. As part of the AI Academy, all Etihad employees will be given access to an online training programme, and instructor led classes, to drive companywide AI literacy, empowering every employee to deliver more value to the company and its customers. Microsoft specialists will also conduct a series of AI business workshops and hands-on technical lab sessions to help identify business challenges that can be optimised with AI. Etihad is currently embarking on a digital transformation journey in order to enhance the capacity and quality of its services to the almost 20 million passengers it carries each year. "There is a simple reason that we are long-term partners with Microsoft – we think alike," said, Tony Douglas Chief Executive Officer, Etihad Aviation Group.

Our existence as a species is, in all likelihood, limited. Whether the downfall of the human race begins as a result of a devastating asteroid impact, a natural pandemic, or an all-out nuclear war, we are facing a number of risks to our future, ranging from the vastly remote to the almost inevitable. Global catastrophic events like these would, of course, be devastating for our species. Even if nuclear war obliterates 99% of the human race however, the surviving 1% could feasibly recover, and even thrive years down the line, with no lasting damage to our species' potential. There are some events that there's no coming back from though.

This course is to quickly learn all the technologies required to implement IoT solutions. The technologies to gather data, build messaging layer, quickly process real-time events, Analysis of data at rest, visualizations, machine learning and more. You will see good example scenarios where IoT is used at its best. A scenario explained end-to-end with all participating technology stack. You will get clear understanding of each technology component and you will be able to decide which technology to be used for which requirement.

The term neural networks refers to networks of neurons in the mammalian brain. Neurons are its fundamental units of computation. In the brain they are connected together in networks to process data. This can be a very complex task, and the dynamics of neural networks in the mammalian brain in response to external stimuli can therefore be quite intricate. Inputs and outputs of each neuron vary as functions of time, in the form of so-called spike trains, but also the network itself changes. We learn and improve our data-processing capacities by establishing reconnections between neurons. Neural-networkalgorithms are inspired by the architecture and the dynamics of networks of neurons in the brain. Yet the algorithms use neuron models that are highly simplified, compared with real neurons. Nevertheless, the fundamental principle is the same: artificial neural networks learn by reconnection.

In my previous blog, we looked at using TensorFlow models in dataflow pipelines to generate predictions and classifications in real-time. In this blog post, I will walk you through using Databricks ML models in StreamSets Data Collector for low-latency inference. In Machine Learning there are two major phases. This can be broken down in two categories. Like TensorFlow Serving, MLeap, and PMML, Databricks ML Model Export is also targeted at low-latency, lightweight ML-powered applications.

In 1933, a chicken keeper and amateur photographer decided to find the culprit who was stealing his eggs. Since its inception, security cameras are everywhere nowadays, most of the claimed "smart ones" work by streaming videos back to a monitor or a server so as someone or some software can analyze video frames and hopefully find some useful information from them. They consume a large amount of network bandwidth and power to stream videos even though ten image frames are all we need to know who was stealing the eggs. They are also facing a dilemma of out of service when the network is unstable, images cannot be analyzed and the "smart" becomes "dumb". Edge computing is a network model which enables data processing occurs at the edge of the network where the camera is located, eliminating the need to send videos to a central server for processing.

Last year, I started writing about my experiences taking courses on machine learning and artificial intelligence. One of the big, unexpected problems I ran into was calculus and linear algebra. I've found that many online courses say you don't need much mathematics fundamentals to be a programmer, but inevitably, even in beginner courses, the underlying math was important to understand what was going on. The need for remedial math seems widespread enough that even a simple Google search for'calculus and artificial intelligence' turns up a bunch of blogs and additional courses on how to understand the math underlying these assignments. After spending a lot of time online trying to sort through this haystack of do-it-yourself calculus blogs, college class PDFs, and other resources, I came away with two websites that were outstanding for teaching basic calculus and linear algebra: Khan Academy and an on-demand tutoring service called Yup.