Overview


A Guide to AWS

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Even those new to IT have probably heard that everyone is "moving to the cloud." This transition from standard infrastructure is thanks in large part to Amazon Web Services. Currently, AWS offers "over 90 fully featured services for computing, storage, networking, analytics, application services, d...


How AI & Chatbots Are Changing Education Worldwide

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Is the FHE teaching capability and capacity improving as fast as it should? Some of our knowledge about teaching and learning go back to Greek times and still hold true. But that is not to say that more recent research and technology should be ignored. It is generally accepted that Moore's Law is ...


Quantum Machine Learning: An Overview

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At a recent conference in 2017, Microsoft CEO Satya Nadella used the analogy of a corn maze to explain the difference in approach between a classical computer and a quantum computer. In trying to find a path through the maze, a classical computer would start down a path, hit an obstruction, backtrac...


What is Artificial Intelligence? Financial Services Technology Advisory Blog

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The pace of change in digital technology is accelerating exponentially, particularly in the field of artificial intelligence (AI). All major technology companies are reorganizing around this red-hot sector, and it dominated discussions and presentations at the recent World Economic Forum 2017 in Davos. In this series, I will try to provide an overview of what constitutes AI, how it is already being applied to banking, insurance and capital markets today, and how organizations can craft a value-driven AI strategy. Why are we experiencing the current level of growth in the AI field? There are several factors: At Accenture, we believe the time to move on AI is now. Low barriers to entry for AI will accelerate the rate of competition. This trend coupled with the exponential growth of AI accelerates high-performing organizations and will leave the slow movers behind. We view AI as a collection of multiple technologies that enable machines to sense, comprehend and act--and learn--either on their own or to augment human activities. As a new factor of production, AI has the potential to introduce new sources of growth, changing how work is done and reinforcing the role of people to drive growth in business. The key is to comprehend AI as a capital–labor hybrid. AI can replicate labor activities at much greater scale and speed, and even perform some tasks beyond the capabilities of humans. AI can also take the form of physical capital, such as robots and intelligent machines. And unlike conventional capital, thanks to its self-learning capabilities, AI can actually improve over time. Today's definition of AI refers to multiple technologies that can be combined in different ways to: All three capabilities are underpinned by the ability to learn from experience and adapt over time. To learn more, take a look at Accenture's Top 5 Technology Trends 2017, and at how we envision AI to change the Future of Business.


A guide to receptive field arithmetic for Convolutional Neural Networks

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The receptive field is perhaps one of the most important concepts in Convolutional Neural Networks (CNNs) that deserves more attention from the literature. All of the state-of-the-art object recognition methods design their model architectures around this idea. However, to my best knowledge, current...



LiveWireLabs

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What is a Voice Interface? Ranging from Apple's Siri, Amazon's Alexa and Microsoft's Cortana the use of voice-based communication through devices like a Mobile App is in demand. You might be doubtful with written instructions on the computer but the use of voice interface communication has exceeded...


10 Principles for Winning the Game of Digital Disruption

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A version of this article appeared in the Spring 2018 issue of strategy business. If you haven't noticed, a high-stakes global game of digital disruption is currently under way. It is fueled by the latest wave of technology: advances in artificial intelligence, data analytics, robotics, the Interne...


Key facts about Chatbots

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As we sweep into the 4th Industrial Revolution driven by artificial intelligence, organisations are scrambling to implement Chatbots to be the face of their new machine-driven operations. Here are a few things you should know about them: Chatbots are often just seen as an automated text chat channe...


Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering

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The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of patterns and the categorization of data into specific sets. Predictive modeling and optimization methods allow unknown events to be categorized based on statistics and classifiers input by researchers. The Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering is a critical reference source that provides comprehensive information on the use of optimization techniques and predictive models to solve real-life engineering and science problems. Through discussions on techniques such as robust design optimization, water level prediction, and the prediction of human actions, this publication identifies solutions to developing problems and new solutions for existing problems, making this publication a valuable resource for engineers, researchers, graduate students, and other professionals.