From enhancing in-store customer experiences to streamlining back-end supply chain operations, AI is revolutionizing the retail industry. Attendees at this year's GPU Technology Conference in Silicon Valley can learn how AI improves loss prevention and powers shopper tracking and autonomous checkout for more personalized customer experience and cost-effective business. Here are six can't-miss retail-focused sessions at GTC to attend: How Walmart Improves Forecast Accuracy with NVIDIA GPUs -- John Bowman, director of data science at Walmart Labs, shows how GPU computing has enabled the retailing giant to significantly improve forecast accuracy while remaining within execution time windows -- using RAPIDS open source data science and machine learning libraries. Semantic Understanding for E-Commerce Search -- Learn how deep learning-fueled semantic understanding is helping solve the problem of e-commerce search, with Somnath Banerjee, director of machine learning at Walmart Labs. What Every Industry Can Learn About AI from Retail -- A panel of experts from GOAT, Focal Systems, Fellow Robots and NVIDIA outline the success criteria that any industry can apply to its strategy -- from supply chain optimization to better customer experience.

FedEx Corp. has today announced the "SameDay Bot" – an autonomous delivery service, designed to help retailers make same-day and last-mile deliveries to their customers. Using this machine, retailers will be able to accept orders from nearby customers and deliver items directly to homes or businesses the same day. FedEx is collaborating with companies such as AutoZone, Lowe's, Pizza Hut, Target, Walgreens and Walmart to help assess retailers' autonomous delivery needs. "The FedEx SameDay Bot is an innovation designed to change the face of local delivery and help retailers efficiently address their customers' rising expectations," said Brie Carere, executive vice president for FedEx. "The bot represents a milestone in our ongoing mission to solve the complexities and expense of same-day, last-mile delivery for the growing e-commerce market in a manner that is safe and environmentally friendly."

In my previous blog post, I explored how reinforcement learning is taking the guesswork out of marketing to deliver great experiences. Let's take a look at two additional areas where AI is transforming the customer experience: recommendations and natural language processing. We've all had online shopping experiences where an item is recommended that seems so irrelevant, and we realise this company does not understand us as a customer at all. There are typically two approaches to recommendations. The customer-centric approach looks for similarities in the behaviour or characteristics between users or customers and recommends products that other similar users have bought.

Jeff Bezos, the world's richest man and one frequently tagged a cheapskate when it comes to giving away money, has emerged as a leading philanthropist. According to the latest Philanthropy 50 list, a ranking of America's top 50 donors compiled by the Chronicle of Philanthropy, Bezos actually made more charitable donations than anyone else in the US last year, including philanthropists with comparable fortunes such as former New York mayor Michael Bloomberg and Microsoft's Bill Gates. Bezos, who holds Amazon stock valued at about $137bn, contributed a total of $2bn to good causes in 2018 through his Bezos Day One Fund, a philanthropic vehicle founded by his soon-to-be ex-wife MacKenzie Bezos. According to the foundation's website, the Bezos fund was established to support existing not-for-profits addressing homelessness and poverty, in addition to funding preschool education in low-income communities. While the Bezos gifts may still be far behind Gates' lifetime donations of about $45bn to global poverty alleviation and education, or Bloomberg's current $6bn in gifts, his surprise entry into the top 50 comes as overall giving among the group declined by half last year compared to 2017.

Once qualified, the apprentices will work across Amazon's UK corporate and operations sites, including its UK head office in London and three development centers in Edinburgh, Cambridge in eastern England and in the capital where teams work on innovations including Alexa, machine learning, Prime Video, Prime Air and Amazon's advanced distribution centers.

Many of today's most popular AI systems are, at their core, classifiers. They classify inputs into different categories: this image is a picture of a dog, not a cat; this audio signal is an instance of the word "Boston", not the word "Seattle"; this sentence is a request to play a video, not a song. But what happens if you need to add a new class to your classifier -- if, say, someone releases a new type of automated household appliance that your smart-home system needs to be able to control? The traditional approach to updating a classifier is to acquire a lot of training data for the new class, add it to all the data used to train the classifier initially, and train a new classifier on the combined data set. With today's commercial AI systems, many of which were trained on millions of examples, this is a laborious process.

Amazon founder Jeff Bezos has said he wants to use his vast wealth to build the infrastructure needed to make space accessible for startups, rather than simply sending humans to Mars. Speaking at a private event at the Yale Club in New York, Bezos revealed that he considered his space company Blue Origin to be "the most important work" that he does. But such work is only possible by liquidating $1 billion of Amazon stock each year to bankroll it. Other space startups, such as Elon Musk's SpaceX and Richard Branson's Virgin Galactic, require similar funding from their billionaire backers due to the high barriers of entry into the space industry. Bezos said space startups are currently unable to flourish in the same way that Amazon and Facebook did because the infrastructure that facilitated such tech firms does not yet exist for space.

In this special guest feature, Amjad Hussain, founder and CEO of Algo.ai,, observes that whereas 20 years ago, labels were able to flood stores with CDs in the knowledge surplus inventory would inevitably sell over time, doing so now could lead to huge losses. As such, companies are increasingly turning to new smart technology which harnesses the power of machine learning AI to accurately predict future demand using existing data. Algo.ai is a workflow automation platform which connects artificial intelligence, augmented reality, and automation to retailers, distributors, and manufacturers. In the age of Spotify, it may appear CDs are predestined to suffer the same fate as'the radio star'. However, while sales are dropping year over year, the mid-year music revenue statistics from the RIAA (Recording Industry Association of America) report $245 million in CD sales in the first part of 2018 alone.

The demise of Sears provides a perfect cautionary tale. By all accounts, the former retail giant -- which was in a "death spiral for well over a decade" -- is paying a steep price for its failure to innovate in new technology. Yet, the means for Sears to do just that was available. Business technology has been advancing at a rapid pace over the past decade. Deep Learning and Artificial Intelligence (AI), along with an unprecedented availability of data, have made it possible to extract business insights like never before.

Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation results are a mixed bag. While many AI applications have yielded increased ROI--this case study of AI in retail marketing segmentation is one example--others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.