The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software--the pattern, colour and texture-without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in ecommerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software--the pattern, colour and texture-- without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in ecommerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

"We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems," Rastogi said.Rajeev Rastogi, who heads the Machine Learning team at Amazon, explains how the global ecommerce giant employs Artificial Intelligence to improve the online shopping experience.Edited excerpts: In which areas does Amazon use AI? We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems, product recommendations, product search, forecasting future product demand, among others. We have used Deep Learning to do better speech recognition. We use neural networks to convert speech (spoken by users) to text with very high accuracy. The speech recognition and understanding technology in Alexa (Amazon's voice-controlled virtual assistant) is powered by Deep Learning.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software - the pattern, colour and texture-without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in e-commerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

WASHINGTON/BOSTON One of two current members of the U.S. Securities and Exchange Commission raised questions on Thursday for companies like Snap Inc that offer shareholders unequal voting rights, saying the agency should "focus on how some innovations may prove detrimental to investors."

While personalisation remains the most popular place for innovation among US retailers, there is clear evidence that AI has started to gain traction. According to a new report from SailThru called the Digital Retail Innovation Report, "Although marketers have been able to leverage artificial intelligence (AI) for some time, adoption is still limited." "Of the two thirds of marketers (66 per cent) who leverage AI for marketing purposes, the most common applications are in search (37 per cent) and product recommendations (33 per cent), which are employed by at least one third of marketers," the report said. Among the other common responses were application through programmatic advertising (26 per cent) and data science (26 per cent). The authors said, "AI use alone wasn't enough to guarantee marketing success: Marketers leveraging AI in one or more of the listed channels/methods weren't significantly more likely to have met or exceeded their 2016 marketing goals, suggesting that not all applications and methods are equally useful, and that simply employing AI-based solutions is not sufficient for marketing success."

I read Artificial Intelligence for Humans, Volume 1 and then ordered volumes 2 and 3. What I like about this series is the same thing I like about Volume 2, that it's very readable. For someone without a math background, and limited programming prowess, I can understand the concepts. The only things about the book that I don't like are: 1) Some of the context is missing. For instance, I can understand Genetic Algorithms, Partical Swarm Optimization, and Ant Colony Optimization as concepts and I think I could basically code them if I needed to. I would say his forte is explaining the ideas and the math in plain language.

Artificial intelligence (AI) is not just transforming ecommerce. Ecommerce came about because the internet made it easy to find what you were looking for. The digital age made the physical world appear closer, so you could buy things from distant countries. Early adopters assumed ecommerce would be fueled by economics of scale and lower costs, but those weren't the main drivers: It was convenience. Websites made information freely available.

We used this book in a class which was my first academic introduction to data mining. The book's strengths are that it does a good job covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection. Additional bonus appendices cover some elements of linear algebra, dimensionality reduction, probability and statistics, regression analysis, and optimization, in case those concepts are fuzzy for the student. They're by no means thorough enough to learn the topic, merely to remind the reader of salient points they should remember.

A global survey of 4000 consumers reveals that shoppers are being left disappointed with the freshness of their grocery purchases. Set against a backdrop of declining retail profitability and significant changes in consumer lifestyles, grocery retailers are under pressure to deliver the best freshness to their customers, while also turning a profit. McKinsey reports that 40 per cent of grocery revenue is driven by fresh, which puts tremendous pressure on category managers to get it right. Yet category managers in fresh know too well the complexities of delivering the best fresh to their customers: Fresh goods are perishable, demand varies from day to day and supply chain lead-times are difficult to predict. Stock too much and you risk providing a less than satisfactory level of fresh if the stock is not sold in time, or you generate food waste.