All shoppers are familiar with the offer of a discount at checkout in exchange for taking out a store credit card. Their wallets may already be stuffed with plastic bearing the names of retailers including Amazon.com, Gap, JCPenney, and Lowe's, but there's often one company behind all of them: Synchrony Financial in Stamford, Connecticut, which is one of the world's largest issuers of store credit cards. This summer marks five years since it spun off from its parent company -- General Electric -- and ever since it's been building a digital strategy that uses artificial intelligence and machine learning. Synchrony now deploys AI to speed up everything including credit approvals and fraud detection. Chief Information Officer Carol Juel has led the undertaking.

When I started learning RL three years ago, it was really hard to get practical information about the methods and ways that they could be implemented. Sparse blog posts about individual methods and theoretical papers, without code examples, were the only source of knowledge. To get something to experiment with, lots of time and effort was needed, fighting with weird bugs and misunderstanding mystic math in papers. With the rising popularity of RL, the situation has improved slightly, but, still, there is a lack of structured overview of the modern deep RL methods with a unified code base. This book fills the gap between theory and practice, providing a structured overview of recent RL methods, using clear examples written in uniform style.

A maverick is a person of incredible vision, someone who challenges the norm and forces people to think beyond the ordinary. YourStory is going behind the scenes to uncover the inspirations and secrets of the ultimate maverick in the business world: the entrepreneur. For what is innovation but doing things differently? And as a woman in tech who doesn't code, Ashwini Asokan certainly does not all the stereotypes. At Mad Street Den, the AI startup which she launched in 2013 in Chennai along with her husband Anand Chandrasekaran, Ashwini fills in the shoes of the CEO (with her husband as the CTO).

He thinks AI...is set to save time, lives and money."―The Economist "Topol passionately and persuasively sets out the transformational potential of deep medicine."―Lancet

Imagine this: an online bookstore aims to deliver more personalized recommendations. Based on internal customer data–collected from registrations, purchases and other direct interactions--the bookstore knows it has 1,000 customers who are between 25 and 35 years old, female, and readers of science fiction novels. Relying only on this data, the most that can be done for this group is personalization based on age, gender, and reading preferences. But what if the bookstore knew that 100 people in this group had recently spent several weekends on mountain bike trails? What if it knew that 300 had liked social media posts related to a musician that had just published a biography?

Whatever the country is, when the mobile commerce data from online retailers and brands is compiled, the key findings states that purchase and transaction through mobile devices are continuously increasing. There is nothing surprising as it's a logical extension of the way smartphones are in the use. The way mobile devices are creeping into our lives portrays a positive picture of mobile usage in the future. This is the reason that the retailers have increasingly put their sights on and investing heavily their resources into E-commerce app development. Advocates believe that it's a golden ticket to potentially augment the sales and customer experience.

The only aspect of the code that isn't straightforward is the need to enable EC2 instance termination protection while workers are processing videos, as shown in the following code example: After the job processes, a similar API call disables termination protection. This example application uses termination protection because the jobs are long-running, and you don't want an EC2 instance terminated during a scale-in event if it is still processing a video. You can easily modify the inference code and optimize it for your use case, so this post doesn't spend further time examining it. To review the Dockerfile for the inference code, see the amazon-elastic-inference-eks GitHub repo, under the /Dockerfile directory. The code itself is in the test.py

Warehouse robots are increasingly taking on the task of collecting the items you order online and getting them ready to ship. It used to take 6-8 weeks for an order to arrive - now Amazon and Walmart can get you your stuff the next day. How are online companies getting you your goods so fast? In many cases, robots are helping to speed things along! Recently, I visited Westlake Village, California startup inVia Robotics.

The next four years could see companies double their spend on artificial intelligence (AI) as real-world applications of the technology gather pace. Automated customer service agents, threat intelligence and prevention systems, and sales process recommendation and automation will dominate spend. That's based on a report by the International Data Corporation (IDC) that states that spend on AI solutions will reach US$97.7 billion in 2023, more than double that of the US$37.5 billion estimated in 2019. The figure would represent a compound annual growth rate of 28.4 percent over the period. David Schubmehl, the research director at IDC in charge of Cognitive/Artificial Intelligence systems said; "The use of artificial intelligence and machine learning (ML) is occurring in a wide range of solutions and applications from ERP and manufacturing software to content management, collaboration, and user productivity. "Artificial intelligence and machine learning are top of mind for most organizations today, and IDC expects that AI will be the disrupting influence changing entire industries over the next decade." Banking and retail sectors will lead the shift to AI. According to the report, each of these industries will invest more than US$5 billion in 2019. In the retail sector, nearly half of the spending on technology will go towards automated customer service agents, expert shopping advisors, and product recommendation systems. On the other hand, the banking industry is set to ramp up spend on automated threat intelligence and prevention system, as well as fraud analysis and investigation system. Significant investments would also be made in discrete manufacturing, process manufacturing, healthcare, and professional services. The fastest industries to grow in terms of spending would be the media industry and governments with a five-year CAGR of 33.7 percent and 33.6 percent respectively. According to Marianne D'Aquila, Research Manager at IDC Customer Insight & Analysis, said: "Strategic decision-makers across all industries are now grappling with the question of how to effectively proceed with their AI journey.

Analysts predict that spending on artificial intelligence (AI) in the retail sector will reach $7.3 billion by 2022, a majority of which will be poured into customer-facing conversational solutions like voice assistants and chatbots. That's not surprising, given how the power of conversation is poised to fundamentally transform customer experiences across industries. The use of consumer-grade digital assistants has exploded in recent years. Consumers have quickly moved beyond "talking" to digital systems for basic information (e.g., weather, traffic, trivia, etc.) and now use them to engage in commerce and other activities. For example, half of respondents to a PWC survey last year said that they had made a purchase via a voice assistant, with an additional 25 percent saying they would consider doing so in the future.