LLamasoft published the results of a global retail supply chain study, which revealed that 73% of retailers believe artificial intelligence (AI) and machine learning can add significant value to their demand forecasting processes. Meanwhile, over half say it will improve 8 other critical supply chain capabilities. The research also found that while 56% of overperforming retailers, also known as'retail winners', use technology to model contingency plans for severe supply chain interruptions, a mere 31% of retailers who are not overperforming do the same. Overall, 56% of retailers surveyed are struggling with the ability to respond to rapid shifts, and the lack of flexibility has cost them during the disruptions such as COVID-19, with many seeing a huge drop in revenue as a result. In addition, 73% of'retail winners' have the foresight and ability to monitor capacity, which allows them to prepare for sudden shifts in demand and supply, compared to 35% of'other' or'under-performing' retailers.

Tesla CEO Elon Musk is criticizing rivaling billionaire and Amazon CEO, Jeff Bezos, on Twitter after the e-tailing giant's splashy acquisition of a self-driving startup. In a tweet after the news, Musk called Bezos a'copy cat' for Amazon's decision to acquire Zoox, a self-driving technology company, for $1billion. 'Jeff Bezos is a copy [cat emoji] haha,' said Musk in the tweet which also linked a report from the Financial Times about Amazon's acquisition of Zoox. Musk's critical tweet no doubt references the CEO's own ventures with the electric vehicle company, Tesla, which has been making its own self-driving vehicles and software for some time. In addition to developing self-driving technology, Zoox, much like Tesla, also develops its own vehicles and aims to use those autonomous cars to let people order driverless rides from their phones.

This week brought good sales on Apple and Amazon devices, as well as some intriguing gaming deals. The Apple Watch Series 5 dropped to $299 again after WWDC kicked off earlier this week and Amazon still has some of its Echo speakers on sale (including the handy Echo Dot with clock). You can grab some extra storage for your Nintendo Switch for less at Newegg and Steam's Summer Sale has just begun. Here are the best deals we found this week that you can still get today. The latest Apple Watch has dropped to its lowest price ever again at Amazon and Walmart.

Amazon.com Inc. has reached an agreement to acquire autonomous-car developer Zoox, the two companies said Friday. The Wall Street Journal reported in May that the Seattle-based e-commerce giant was in advanced talks to buy Zoox, at a price lower than the $3.2 billion valuation Zoox had achieved in a previous fundraising round. Zoox was founded in 2014 and grew quickly amid expanding interest in autonomous vehicles and ride hailing but has more recently struggled to raise funding.

When it comes to smart assistants, we like Google Assistant over Amazon's Alexa here at WIRED. It's easier to set up and is just better at answering voice questions, hands-free. A growing number of smart speakers and smart displays support it, too. It was $574 and dropped down to $300 around March. Now it's the lowest we've seen, and the Amazon price is about $150 cheaper than other major retailers like B&H.

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.

We observe that a crucial inventory management problem ('phantom inventory'), that by some measures costs retailers approximately 4% in annual sales can be viewed as a problem of identifying anomalies in a (low-rank) Poisson matrix. State of the art approaches to anomaly detection in low-rank matrices apparently fall short. Specifically, from a theoretical perspective, recovery guarantees for these approaches require that non-anomalous entries be observed with vanishingly small noise (which is not the case in our problem, and indeed in many applications). So motivated, we propose a conceptually simple entry-wise approach to anomaly detection in low-rank Poisson matrices. Our approach accommodates a general class of probabilistic anomaly models. We extend recent work on entry-wise error guarantees for matrix completion, establishing such guarantees for sub-exponential matrices, where in addition to missing entries, a fraction of entries are corrupted by (an also unknown) anomaly model. We show that for any given budget on the false positive rate (FPR), our approach achieves a true positive rate (TPR) that approaches the TPR of an (unachievable) optimal algorithm at a min-max optimal rate. Using data from a massive consumer goods retailer, we show that our approach provides significant improvements over incumbent approaches to anomaly detection.

These predictions can be troubling for many workers, but less for sales professionals. Artificial Intelligence (AI) technology can help companies analyze customer data and provide meaningful insights from data. These insights are then used to make sales predictions, personalized product recommendations, and important sales moves. Taking over does not mean that AI robots will take sales jobs from people. Taking over means that AI is becoming a necessity for retailers.

Amazon SageMaker Ground Truth now supports labeling 3D point cloud data. For more information about the launched feature set, see this AWS News Blog post. In this blog post, we specifically cover how to perform the required data transformations of your 3D point cloud data to create a labeling job in SageMaker Ground Truth for 3D object tracking use cases. Autonomous vehicle (AV) companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment.

It was just a few months ago that NRF looked at AI in retail, questioning whether 2020 might be the year that it finally took off. How quickly things can change. Artificial intelligence and its companion, machine learning, have been upended along with every other aspect of retail. And while AI's ability to anticipate the future might have been damaged by toilet-paper hoarding, bread-baking shoppers, it also might provide an unexpected roadmap for the future. Machine-learning based AI has been "thrown for a loop by the coronavirus," says Nikki Baird, vice president of retail innovation at Aptos.