It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".

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Shopping from home during the coronavirus has necessarily caused a surge in online retail transactions. That, in turn, has pushed a big spike in support calls, which has posed some problems, especially for larger e-tailers. Fortunately, chatbot technology has been there to take on the brunt of this burden. Recent research by Digital360Commerce quantifies the spike at a whopping 426 percent increase in chatbot-driven customer service sessions in April, 2020 as compared to the preceding February. The challenge for human service agents is that despite the ease with which most voice over IP (VoIP) call centers claim they can handle agents that work in distributed environments (at home, for instance), most support services aren't set up that way with only their central call centers having the network infrastructure to handle the new volume of support calls.

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

Amazon Prime Day, previously postponed due to the coronavirus pandemic, is now scheduled for Oct. 13-14, and Amazon just might compensate for the three month-long postponement with even better deals and lower prices. The new date was revealed in internal Amazon emails sent to employees, who were also told not to take any vacation leaves Oct. 13-20. One memo said the company will officially confirm Oct. 13-14 in an announcement on Sunday, according to The Verge. "Stay tuned for more details on Prime Day," said an Amazon spokesperson, who also said customers can ask Alexa devices to keep them updated about Prime Day. Prime Days have normally been held July 15, the anniversary Amazon's founding anniversary.

Now similar experiments are spreading as companies double down on plans to reduce social contact in stores. A survey published by McKinsey & Co. in July found that most consumers in the U.S. and China who changed their shopping habits during the pandemic expect the change to stick after the crisis. Get weekly insights into the ways companies optimize data, technology and design to drive success with their customers and employees. Giant Eagle Inc. recently hired cashier-free technology company Grabango Co. to introduce checkout-free shopping at a GetGo convenience store in Pittsburgh. And Mastercard is introducing its Shop Anywhere system, built on Accel Robotics Corp.'s computer vision technology, in the final quarter of this year.

The Kroger Co. plans to roll out Everseen's Visual AI technology chainwide to detect and reduce customer errors at self-checkout stations. Ireland-based Everseen said its artificial intelligence and machine learning platform began deployment in Kroger stores in March and is slated to be installed at 2,500 stores in the coming months. The Visual AI platform watches video in real time to recognize regular processes and "intelligently" step in whenever something is amiss, Evergreen explained. For Kroger shoppers, the technology flags errors occasionally experienced at self-checkout and enables customers to self-correct or, if they're unable to rectify the problem, an associate is summoned to help. For example, if a customer scanning groceries at the self-checkout kiosk has an item that doesn't scan properly, Evergreen's solution identifies the non-scan incident and alerts a store associate via a mobile device to intervene and rescan the item.

Despite the growth of e-commerce, brick-and-mortar stores are still the preferred destinations for many people. In this paper, we present ISA, a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. ISA assists users by leveraging advanced techniques in computer vision, speech processing, and natural language processing. An in-store user only needs to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product. The assistant can also guide the user through the purchase process or recommend other similar products to the user. We take a data-driven approach in building the engines of ISA's natural language processing component, and the engines achieve good performance.

Walmart is starting to deliver at-home COVID-19 tests by drone. A trial got underway in North Las Vegas today and the deliveries will expand to Cheektowaga, New York early next month. It's delivering the kits to qualifying patients who live within a mile of certain Walmart Supercenters in both locales. Patients will self-administer a nasal swab, which they'll send to Quest Diagnostics for testing. Walmart says there's no kit or delivery cost for those who opt to receive a test by drone, and there's a prepaid shipping label to return it.

This paper proposes a new approach to sales forecasting for new products with long lead time but short product life cycle. These SKUs are usually sold for one season only, without any replenishments. An exponential factorization machine (EFM) sales forecast model is developed to solve this problem which not only considers SKU attributes, but also pairwise interactions. The EFM model is significantly different from the original Factorization Machines (FM) from two-fold: (1) the attribute-level formulation for explanatory variables and (2) exponential formulation for the positive response variable. The attribute-level formation excludes infeasible intra-attribute interactions and results in more efficient feature engineering comparing with the conventional one-hot encoding, while the exponential formulation is demonstrated more effective than the log-transformation for the positive but not skewed distributed responses. In order to estimate the parameters, percentage error squares (PES) and error squares (ES) are minimized by a proposed adaptive batch gradient descent method over the training set. Real-world data provided by a footwear retailer in Singapore is used for testing the proposed approach. The forecasting performance in terms of both mean absolute percentage error (MAPE) and mean absolute error (MAE) compares favourably with not only off-the-shelf models but also results reported by extant sales and demand forecasting studies. The effectiveness of the proposed approach is also demonstrated by two external public datasets. Moreover, we prove the theoretical relationships between PES and ES minimization, and present an important property of the PES minimization for regression models; that it trains models to underestimate data. This property fits the situation of sales forecasting where unit-holding cost is much greater than the unit-shortage cost.