Multi-channel is the new norm – consumers are not completely abandoning brick-and-mortar stores. Instead, they expect seamless shopping experiences across online, mobile and offline stores. They might first browse and research online, then purchase or pick-up in-store--or the other way around. Successful retailers who can gain customer loyalty are those who can deliver a superior seamless experience across all channels. Data is the new gold – The additional touchpoints mean retailers have greater opportunity and more data to identify their customers and discern their preferences. However, without a proper data and analytics infrastructure, many retailers struggle to mine and analyze huge volumes of data generated daily to gain valuable insights that can help them innovate.

Customer preferences alter by the second. Getting in the buyer's good graces once does not guarantee lifelong loyalty. That's why retailers would do anything to lure shoppers. Even something as self-destructive as setting the lowest prices which they can't afford to maintain. Such tools as AI-backed pricing analytics software help businesses create pricing strategies which allow retaining customers and keeping margins at the same time. The thing with the lowest prices in the market does not work unless you are Walmart or Amazon enjoying excellent terms with vendors and pouring millions into marketing.

Amazon Web Services (AWS), a subsidiary of Amazon (NasdaqGS:AMZN), has announced the opening of an infrastructure region in Spain featuring three Availability Zones at launch. It will give Spanish startups, enterprises, and the public sector the ability to "leverage advanced technologies including analytics, artificial Intelligence, database, Internet of Things (IoT), machine learning, mobile services, serverless, and more to drive innovation". "Cloud computing is already powering innovation within businesses, educational institutions, public administrations, and government agencies across Spain, and with this AWS infrastructure region, we look forward to helping accelerate this transformation. Opening an AWS Region in Spain will drive more technology jobs and businesses, boosting the local economy, while enabling organizations across all industries to lower costs, increase security, and improve agility. We're excited to have AWS contribute to the future growth of Spain."

This type of cross-lingual transfer learning can make it easier to bootstrap a model in a language for which training data is scarce, by taking advantage of more abundant data in a source language. But sometimes the data in the source language is so abundant that using all of it to train a transfer model would be impractically time consuming. Moreover, linguistic differences between source and target languages mean that pruning the training data in the source language, so that its statistical patterns better match those of the target language, can actually improve the performance of the transferred model. In a paper we're presenting at this year's Conference on Empirical Methods in Natural Language Processing, we describe experiments with a new data selection technique that let us halve the amount of training data required in the source language, while actually improving a transfer model's performance in a target language. For evaluation purposes, we used two techniques to cut the source-language data set in half: one was our data selection technique, and the other was random sampling.

Artificial Intelligence has great promise to help retailers be more nimble and respond to customer/consumer needs. However, a retailer must step back and have the foundational data pieces in place before trying to implement any AI solutions. I mean foundational pieces such as clean POS data, clean loyalty data and adequate IT systems to allow AI to deliver against its promises. A retailer needs deep pockets to set up their own AI shop with the expertise to manage AI on their payroll. Most retailers will look to the outside for help and a retailer needs to find an outside solution provider that is strategically interested in helping the retailer get their foundational data right FIRST before implementing any AI solution.

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.

Technology is creating ever-changing expectations for people, and Gartner's top predictions for 2020 reflect these new challenges. The predictions were revealed at the Gartner IT Symposium/Xpo 2019 in Orlando, which runs through October 24. More than 9,000 IT leaders and CIO's are in attendance at the conference. "Technology is changing the notion of what it means to be human," said Daryl Plummer, distinguished vice president and Gartner Fellow. "As workers and citizens see technology as an enhancement of their abilities, the human condition changes as well. CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means."

While many chatbots didn't live up to the hype, industries like fintech, healthcare, and retail are quietly adopting the technology to free up busy professionals' time and offer guided, personalized experiences to consumers. In 2016, chatbots were all the rage. That year, Facebook made the Messenger bot platform the centerpiece of its F8 developer conference. Microsoft's Satya Nadella referred to chat as the "third run-time" -- an indispensable piece of operating a platform, second only to the operating system and the web browser. Mentions of chatbots in earnings calls and press releases skyrocketed, and for many, it seemed that chatbots might be the next big disruptive technology. Thousands of companies commissioned their own chatbots in anticipation. In the end, though, the expected paradigm shift didn't happen. There are many reasons why chat didn't take off in 2016. For one, consumers found that many of the tasks the first chatbots were built to perform -- like relaying the news or finding a recipe -- took more time when a bot was involved. Another problem was that bots regularly needed human assistance to understand commands. Even Facebook's much-hyped personal assistant, M, closed down shortly after it was revealed that human handlers were responsible for some 70% of the bot's responses. But while many chatbots didn't meet users' high expectations, they haven't entirely fallen short. Today, the bots are still being used across industries like fintech, healthcare, sales and CRM, retail, and even law -- and they're having important, though quiet, effects. The important chatbots of 2019 aren't all-knowing virtual butlers; they're highly targeted applications of conversational technology. While they may seem less flashy, these bots are advancing their technology and making a demonstrable impact on their industries.

Looking to make the most of current AI technologies and solutions in your business? Register now for the O'Reilly AI Conference in San Jose, September 9–12, 2019, and learn how to leverage AI in your work today. What can artificial intelligence (AI) and machine learning (ML) do to improve customer experience? AI and ML already have been intimately involved in online shopping since, well, the beginning of online shopping. You can't use Amazon or any other shopping service without getting recommendations, which are often personalized based on the vendor's understanding of your traits: your purchase history, your browsing history, and possibly much more.

Some amazing--as well as some downright freaky--innovations in artificial intelligence and machine learning were detailed at the recent Decoded Future NYC Summit, presented by Stylus. Take, for example, technology that will watch consumer faces as they shop online, recording smiles, frowns, or other gestures to measure interest in a product. In the near future, consumers could also be interacting "face-to-face" with virtual assistant machines that have lifelike personalities, emotional responses and character. While those advances may be a few years away, AI and machine learning have already become customary among brands and retailers who are looking to track the right trends in style, color, and pricing. The major players have been taking advantage of this technology for years.