You can choose among multiple artificial intelligence (AI) frameworks to develop AI algorithms. You also have a choice of a wide range of hardware to train and deploy AI models. The diversity of frameworks and hardware is crucial to maintaining the health of the AI ecosystem. This diversity, however, also introduces several challenges to AI developers. This post briefly addresses these challenges and introduces a compiler solution that can help solve them.

An Ambient Intelligence revolution is coming. In the past ten years, we've witnessed an astonishing mobile computing revolution. Smartphone installation base is expected to reach 6 billion by 2020, according to research firm IHS Markit. Over half of the world's population are carrying personal devices that have powerful processors, sensors, cameras, high speed communications and intelligent applications. In the next ten years, an Ambient Intelligence (AmI) revolution is coming where all the above technologies will be embedded in our homes, grocery stores, offices, hospitals and transportation services. AmI will be one of the key elements of the Fourth Industrial Revolution.

Today, the business is entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Though, it's not a simple process for companies to incorporate machine learning into their existing business intelligence systems. But as AI has gained momentum, prominent application providers have gone beyond creating traditional software to developing more holistic platforms and solutions that better automate business intelligence and analytics processes. In case you run an online store, you've certainly noticed several changes in customer behavior.

Predictive analytics can be defined as a form of data mining that uses statistical modeling to analyze historical patterns, and then uses these models to project future outcomes. The deployment of artificial intelligence allows analytics technologies to spot relationships between variables that humans are simply incapable of seeing. In this article, we want to bring that theory to life with five predictive analytics use cases. There have been some newsworthy stories in this field, notably the "Target Knows When You're Pregnant" headlines that garnered so much attention a few years ago. Things have developed quite a bit since then. The evolution of widely available and accessible analytics platforms has provided access to sophisticated statistical models for companies of all sizes.

Amazon has big plans for the future of grocery stores. The company's recent acquisition of Whole Foods serves as proof that the online retailer is preparing to change the way we shop for groceries. We can expect AI and IoT technology -- similar to that in the recently launched Amazon Go store -- to be integrated into future iterations of Whole Foods and the broader Amazon family of services. Fully automating supermarkets, which is likely Amazon's goal, takes advantage of one of the company's core competencies: digitizing commerce that used to be purely physical. The possibilities for the future of grocery stores are seemingly endless with Amazon driving innovation in the space. Amazon Go stores produce a ton of valuable consumer data from IoT sensors and back-end tech.

Trunk Club, an apparel subscription service owned by Nordstrom, has increased Pinterest engagement by more than 100 percent in recent months by embedding artificial intelligence into its digital-images marketing. AI-image search is central to its ambitions of building a data-science model that drives highly relevant product offerings to consumers across the social web. More broadly, the effort speaks to how AI search is quickly becoming the retail sector's next big digital shopping experience. "We are trying to understand how one pair of jeans plays out against another pair that was released in another season," explained Justin Hughes, vp of product development and design at Trunk Club. "We want to get really granular and understand what really works."

Machine learning, a type of artificial intelligence powered by large-scale data that provides computers with the ability to learn without being explicitly programmed, will change the face of retail over the next few years. Managing director of Dunnhumby SA, Graeme Tulloch comments, "Through new data science techniques and increases in available data, the company is now using machine learning to assist retailers in predicting the future, by simulating scenarios that forecast outcomes and pinpoint critical action areas within an enormous amount of possibilities. "Most retailers are already forecasting to some extent. When a product goes on promotion, they have an idea of the amount of extra product they might expect to sell, however, a large percentage of the promotions are not profitable, so predicting with accuracy the performance of the promotions using advanced machine learning (to replace simple linear regression) is critical. Improving predictability helps ensure promotions will deliver great ROI and optimises the ordering inventory to ensure stock is available in store to avoid customer dissatisfaction."

This is an excellent book. I have hundreds of papers and books on Neural Nets from the time of Rosenblatt's Perceptron on through autoencoders, recurrent NNs, convolutional NNs, RBM's, DNN's, greedy pretraining, Kolmogrov's universal approximation theorem, optimization methods for weight training, and more. I found this book to provide a conceptual overview of the DNNs and the architectures (feed forward, deep belief, unsupervised pre-trained, convolutional, recurrent, long and short term memory, and recursive, networks). The book provides the conceptual connective tissue that are the muscles that the practitioner must bond to the architectural bones to move forward in Deep Learning. The book is a remarkable debrief by two lead developers of the DL4J framework; Josh Patterson and Adam Gibson.

Mike Toscano, the former president of the Association for Unmanned Vehicle Systems International, emphatically declared at the September RobotLab forum that "anyone who claims to know the future of the [robotics] industry is lying, I mean no one could've predicted the computing mobile revolution." These words acted as a guiding principle when walking around RoboBusiness in Silicon Valley last week. The many keynotes, pitches and exhibits in the Santa Clara Convention Center had the buzz of an industry racing towards mass adoption, similar to the early days of personal computing. The inflection point in the invention that changed the world, the PC, was 1995. During that year, Sun Microsystems released Java to developers with promise of "write once, publish anywhere," followed weeks later by Microsoft's consumer software package, Windows '95.

Amazon is happy to announce that Alexa, the brain that powers Amazon Echo, is coming to India, along with three Alexa-enabled devices: Amazon Echo, Echo Plus, and Echo Dot. Customers who want to help further the development of Alexa and the Echo family of devices may request an invitation to purchase devices beginning today. This opens up new opportunities for developers in India and worldwide. Starting today, you can build for voice with Alexa and reach customers in India with the Alexa Skills Kit (ASK), our collection of self-service APIs, tools, documentation, and code samples. Hardware manufacturers can start developing Alexa-enabled products for Indian customers with the Alexa Voice Service (AVS) by participating in a developer preview.