Retailers have a new message for consumers looking to return an item: Keep it. Inc., Walmart Inc. and other companies are using artificial intelligence to decide whether it makes economic sense to process a return. For inexpensive items or large ones that would incur hefty shipping fees, it is often cheaper to refund the purchase price and let customers keep the products. The relatively new approach, popularized by Amazon and a few other chains, is being adopted more broadly during the Covid-19 pandemic, as a surge in online shopping forces companies to rethink how they handle returns. "We are getting so many inquiries about this that you will see it take off in coming months," said Amit Sharma, chief executive of Narvar Inc., which processes returns for retailers.

Elon Musk has overtaken Amazon . Messrs. Musk and Bezos, both founders of rocket companies, have clashed over issues such as Amazon's power over book publishing and Mr. Musk's interest in colonizing the planet Mars. Amazon last year bought a self-driving-car startup that would compete with Tesla. Mr. Musk's net worth totaled around $195 billion Thursday, up from roughly $30 billion a year ago and topping Mr. Bezos's wealth by about $10 billion, according to the Bloomberg Billionaires Index. The value of Tesla's shares rose almost 8% Thursday, enough for Mr. Musk to overtake Mr. Bezos in the wealth ranking.

In some stores, sophisticated systems are tracking customers in almost every imaginable way, from recognizing their faces to gauging their age, their mood, and virtually gussying them up with makeup. The systems rarely ask for people's permission, and for the most part they don't have to. In our season 1 finale, we look at the explosion of AI and face recognition technologies in retail spaces, and what it means for the future of shopping. This episode was reported and produced by Jennifer Strong, Anthony Green, Tate Ryan-Mosley, Emma Cillekens and Karen Hao. Strong: Retailers have been using face recognition and AI tracking technologies for years. And what if you could know about the presence of violent criminals before they act? With Face First you can stop crime before it starts.] It detects faces, voices, objects and claims it can analyze behavior. But face recognition systems have a well-documented history of misidentifying women and people of color. And they're trying to sell it and impose it on the entirety of the country?] Strong: This is Representative Alexandria Ocasio-Cortez at a 2019 congressional hearing on facial recognition.

You probably already have your holiday shopping taken care of and are already looking at a stack of confirmed delivery tracking numbers. But if, say, a friend of yours is still in need of last-minute shopping information, or you just want to double check what's out there, the Engadget Holiday Gift Guides for 2020 are here to help. Zoom has posted update notes for a version of its video chat client that should be available later today. If you have one of Apple's new M1-powered Macs, you'll want to keep an eye out because this update brings the first version with native support for Apple Silicon. There's no version number listed yet, but once it's available, you'll be able to get it with a new installer from the Zoom download page.

We propose a Distributional Approach to address Controlled Text Generation from pre-trained Language Models (LMs). This view permits to define, in a single formal framework, "pointwise" and "distributional" constraints over the target LM -- to our knowledge, this is the first approach with such generality -- while minimizing KL divergence with the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-Based Model) representation. From that optimal representation we then train the target controlled autoregressive LM through an adaptive distributional variant of Policy Gradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the initial LM (GPT-2). We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study we show the effectiveness of our adaptive technique for obtaining faster convergence.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Two women were caught on video fighting over a PS5 video game console at Walmart. The video, which has since gone viral on social media, was taken at a Walmart location in Charlotte, North Carolina, according to TMZ. "What the f–k are you gonna do?" one woman can be heard saying in the video as she takes off her purse and jacket. One man can be heard yelling, "Call the cops!" as a crowd gathered around the two brawling women.

In 2019, Walmart started working with a company called Gatik to test autonomous delivery trucks on a two-mile route between a fulfillment center and a store in Bentonville, Arkansas. After those vehicles logged more than 70,000 miles with a human driver there to make sure nothing went wrong, Walmart and Gatik say they're ready for a new challenge. Next year, there won't be any human drivers in the trucks. That milestone will make Gatik one of the first companies in the space to operate a fully autonomous route in this way. As the startup itself is quick to point, it has its simplified approach to thank for the achievement.

When we look down the memory lane with technological viewpoints, things have evolved significantly. Customer data will keep playing an important role in predictive analytics. Businesses have now started relying on machine learning services for analyzing the shopping habits for optimizing the supply chain and personalizing the offers for their customers. While predictive analytics requires humans to find statistical trends in data, machine learning is a subset of artificial intelligence (AI) that uses computer algorithms to find data trends. Computers can then autonomously make predictions based on those trends (or patterns) -- effectively "learning" without being programmed for a straightforward task.

Retailers are struggling to keep up with a growing demand for online purchases. They have found that the pandemic has completely upended their business models, as customers shift towards online commerce. This has driven many companies to find more innovative ecommerce marketing models that rely on big data. As such, retailers have an even tougher job of keeping on top of supply and demand. However, thankfully, technological tools can make a big difference in this arena.

Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.). These characteristics can then be used by a search engine to return results that better match the query's product intent. Traditional methods for slot-filling require the availability of training data with ground truth slot-annotation information. However, generating such labeled data, especially in e-commerce is expensive and time-consuming because the number of slots increases as new products are added. In this paper, we present distant-supervised probabilistic generative models, that require no manual annotation. The proposed approaches leverage the readily available historical query logs and the purchases that these queries led to, and also exploit co-occurrence information among the slots in order to identify intended product characteristics. We evaluate our approaches by considering how they affect retrieval performance, as well as how well they classify the slots. In terms of retrieval, our approaches achieve better ranking performance (up to 156%) over Okapi BM25. Moreover, our approach that leverages co-occurrence information leads to better performance than the one that does not on both the retrieval and slot classification tasks.