Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

It seems like almost every day a new headline warns us that artificial intelligence (AI) will soon take over the world, or at the very least steal jobs. Even when AI is not in the news, Hollywood offers up a steady stream of entertainment that depicts a very near future in which life as we know it is threatened by super-intelligent machines. These scenarios have something in common: they oversimplify and misrepresent an important and broader set of transformative technologies that hold great promise for business and society. They indulge in fantasy rather than take into account a rational and better-informed dialogue currently underway in the scientific, policy and business communities about what we consider the third age of computing – the cognitive era. What is Cognitive Computing Cognitive computing -- of which AI is but one part – refers to an entirely new class of technologies whose purpose is to deepen human engagement, scale and elevate expertise, enable new products and services, and enhance exploration and discovery.

Now, researchers from Google DeepMind and Stanford University have updated a theory originally developed to explain how humans and other animals learn. "The evidence seems compelling that the brain has these two kinds of learning systems, and the complementary learning systems theory explains how they complement each other to provide a powerful solution to a key learning problem that faces the brain," explained James McClelland, lead author of the 1995 paper from Stanford University. Components of the neural network architecture that succeeded in achieving human-level performance in a variety of computer games like Space Invaders and Breakout were inspired by complementary learning systems theory. According to DeepMind co-founder Demis Hassabis, "the extended version of the complementary learning systems theory is likely to continue to provide a framework for future research not only in neuroscience but also in the quest to develop Artificial General Intelligence -- our goal at Google DeepMind."

Product shipments of artificial intelligence (AI) systems for vehicles will grow from 7 million in 2015 to 122 million by 2025, which, according to IHS Technology, is a reflection of the automotive industry's growing appetite for AI.

We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the constraint set to prove that the corresponding LP relaxation solves the original integer program. We then exploit connections to combinatorial optimization and develop an efficient primal-dual algorithm, scalable to large datasets. In contrast to our bottom-up approach, which benefits from good RF initialization, conventional methods are top-down acquiring features based on their utility value and is generally intractable, requiring heuristics. Empirically, our pruning algorithm outperforms existing state-of-the-art resource-constrained algorithms.

Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimise promotions, compensation and rebates to drive the desired behaviour across selling channels. Predicting propensity to buy across all channels, making personalised recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are also key. Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimise decisions based on the predictive value of large-scale data sets.

The market research firm expects the attach rate of AI-based systems in new vehicles to increase from 8 percent in 2015 (the vast majority of today's AI systems in cars are focused on speech recognition) to 109% in 2025. IHS sees multiple AI systems of various types to be installed in many cars. In the human-machine interface in vehicles, IHS believes AI will play a role in speech and gesture recognition, eye-tracking, driver monitoring and natural language interfaces. In the autonomous car, AI will advance machine vision systems, while it will also migrate in sensor fusion electronic control units (ECU). In a phone interview with EE Times, Luca De Ambroggi, principal analyst, automotive semiconductors at IHS told us, "AI is viewed as a key enabler for real autonomous vehicles. Everyone in the automotive supply chain is getting pretty bullish."

Innovation is among the key factors driving a country's economic and social growth. But what are the factors that make a country innovative? How do they differ across different parts of the world and different stages of development? In this work done in collaboration with the World Economic Forum (WEF), we analyze the scores obtained through executive opinion surveys that constitute the WEF's Global Competitiveness Index in conjunction with other country-level metrics and indicators to identify actionable levers of innovation. The findings can help country leaders and organizations shape the policies to drive developmental activities and increase the capacity of innovation.

A new method of assembling tyres that promises to improve quality and productivity while eliminating the risk of human error has been introduced within a Bridgestone Corporation plant in Japan. The tyre maker has announced the installation of the new Examation system at its flagship Hikone passenger car tyre production site. The tyre maker reports that Examation "combines Bridgestone's proprietary information and communication technologies with artificial intelligence." The proprietary technologies Bridgestone refers to have been developed in stages over the past two decades: Research into tyre production systems employing ICT and other cutting-edge technologies began at Bridgestone in the late 1990s with the aim of creating more functional tyres and improving quality. In 2002, the company developed the BIRD (Bridgestone Innovative and Rational Development) production system, which was the world's first system for realising complete automation in areas of production ranging from components processes to product inspection processes.

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.