Globally the popularity of chatbot has reached all time high and the estimated market value of chatbots crossed $190 million in 2016 and it is supposed to grow at faster pace in the coming years. So, Chatbots are not only here to stay but to become an era defining aspect of modern web development and digital experience. A recent study conducted by Aspect Software Research is an eye opener in this respect. According to this study 44% of customers across niches openly expressed their preference for a chatbot over human customer service professionals. According to predictions of Gartner, by the time we reach 2020, more than 80% of customer services and direct customer engagement will happen through chatbots.

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.

Over the past several years, the retail industry has undergone a tremendous transformation. With the rapid growth of eCommerce, spearheaded by tech giants like Amazon, retail has become as much as an online experience as a physical one. It's been estimated that by the end of 2019, 1.92 billion people in the world will be shopping online. With such a large growing audience, shifting consumer preferences, and innovative new technology, it's no wonder that the retail industry is in a constant state of transformation. In order to shed some light on how much the industry has evolved, I had a conversation with Ajoy Krishnamurti, Chief Business Officer for Retail and eCommerce at Crayon Data.

Amazon.com's Alexa has mastered Hindi in just a few years. The voice assistant introduced to India in 2017 gets a major local makeover for one of the largest retail markets. From Wednesday, Amazon launches a version that now speaks Hindi and Hinglish -- a unique blend with English. It can also switch automatically between all three. The new, improved Alexa and Echo speakers hit the market in time for the Diwali shopping season.

Robotics often involves training complex sequences of behaviors. For example, consider a robot designed to follow or track another object. Although the goal is easy to describe (the closer the robot is to the object, the better), creating the logic that accomplishes the task is much more difficult. Reinforcement learning (RL), an emerging Machine Learning technique, can help develop solutions for exactly these kinds of problems. This post is an introduction to RL and it explains how we used AWS RoboMaker to develop an application that trains a TurtleBot Waffle Pi to track and move toward a TurtleBot Burger.

As brick-and-mortar retailers continue to struggle against online competitors, some are seeking out services that leverage big data and personalization to increase e-commerce sales. "During the rise of big data, it was said that data was the new oil," Brian Solis, principal analyst at Altimeter, told TechRepublic. "In an era of AI and machine learning however, personalized data is the new competitive advantage and will only become standard CX on the horizon." Indeed, 72% of retailers reported that AI will be a "competitive necessity" in the next five years, according to a recent Oxford Economics survey. One such tech option for retailers looking to fight off the competition is uSizy, a recommendation technology for fashion apparel and footwear businesses, which unveiled its latest product, uSizy Smart Business, on Wednesday.

The phrase "artificial intelligence" in pop culture often conjures up dystopian images such as the sentient computer Hal 9000 from the 1968 film "2001: A Space Odyssey" that killed people for its self preservation; or the cyborg assassin with a metal endoskeleton in director James Cameron's "The Terminator." In recent years, our fascination with the potential of AI has taken a more starry-eyed turn, as shown in the 2013 sci-fi drama "Her," where the main character falls in love with a virtual assistant. In reality, artificial intelligence (AI) technology is quickly permeating every aspect of our lives. From Amazon's voice-activated Alexa to writing technology that helps managers craft job postings, AI is in our hearts, homes and workplaces. And it's only going to become a bigger part of our lives: Experts call the rise of AI the driving force behind the fourth industrial revolution. On a recent afternoon at the NVIDIA robotics research lab in Seattle's University District, researchers use a simulated kitchen to test robots' ability to perform simple tasks such as grabbing objects.

G4 instances provide the industry's most cost-effective machine learning inference for applications, like adding metadata to an image, object detection, recommender systems, automated speech recognition, and language translation. G4 instances also provide a very cost-effective platform for building and running graphics-intensive applications, such as remote graphics workstations, video transcoding, photo-realistic design, and game streaming in the cloud. Machine learning involves two processes that require compute – training and inference. Training entails using labeled data to create a model that is capable of making predictions, a compute-intensive task that requires powerful processors and high-speed networking. Inference is the process of using a trained machine learning model to make predictions, which typically requires processing a lot of small compute jobs simultaneously, a task that can be most cost-effectively handled by accelerating computing with energy-efficient NVIDIA GPUs.

Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science.

If you're one of the majority of Americans who have shopped online, you're already familiar with retailers' tendencies to make buying suggestions for you based on your past purchases and on-site behavior. You might also like: this air mattress, this camping grill and this sleeping bag. And if the suggestions are spot on, you're probably thankful that the company saved you a couple extra clicks. This is just one example of artificial intelligence and machine learning using data to identify patterns and relationships, then offer intelligent suggestions based on these findings. Unsurprisingly, forward-thinking businesses across every industry are harnessing AI and ML in an attempt to derive maximum value from their stored data.