Over the past few years the CES trade show has become a familiar post-holidays pilgrimage for many of the country's biggest marketers. They see the event as a way to get a sneak peek at the latest tech gadgets and technologies that can help them engage with their customers. This year marketing executives from companies such as Coca-Cola, Unilever, Johnson & Johnson, Campbell Soup and PepsiCo Inc. made their way to Las Vegas for the gathering. The convention was jam-packed with everything from self-driving cars to robots that play chess to Procter & Gamble's air-freshener spray that can connect with Alphabet Inc.'s Nest home to automatically release pleasant scents in the home. But there was one category that seemed to especially win over marketers: virtual assistants.
Editor's Note: Tech Tracker looks at different technologies that are disrupting the industry and changing the way restaurants operate and interact with customers. Through a partnership with online reservation platform Resy, several critically acclaimed and buzzworthy restaurants across the country are hosting "Off Menu Week" throughout the year starting in late February. Off Menu Week was designed as an alternative to traditional restaurant weeks, which occur in various cities throughout the year. Off Menu Week, by contrast, celebrates experimentation and risk. "As diners, we crave connection to the creative people behind our favorite restaurants. We thought, let's throw out the dated premise of restaurant week and bring to life a program that's fundamentally about that connection and creativity," Resy co-founder and CEO Ben Leventhal said in a statement.
Assuming the decision maker behaves according to the EU model, we investigate the elicitation of generalized additively decomposable utility functions on a product set (GAI-decomposable utilities). We propose a general elicitation procedure based on a new graphical model called a GAI-network. The latter is used to represent and manage independences between attributes, as junction graphs model independences between random variables in Bayesian networks. It is used to design an elicitation questionnaire based on simple lotteries involving completely specified outcomes. Our elicitation procedure is convenient for any GAI-decomposable utility function, thus enhancing the possibilities offered by UCP-networks.
More than 80 Amazon scientists and engineers will attend this year's International Conference on Machine Learning (ICML) in Stockholm, Sweden, with 11 papers co-authored by Amazonians being presented. "ICML is one of the leading outlets for machine learning research," says Neil Lawrence, director of machine learning for Amazon's Supply Chain Optimization Technologies program. "It's a great opportunity to find out what other researchers have been up to and share some of our own learnings." At ICML, members of Lawrence's team will present a paper titled "Structured Variationally Auto-encoded Optimization," which describes a machine-learning approach to optimization, or choosing the values for variables in some process that maximize a particular outcome. The first author on the paper is Xiaoyu Lu, a graduate student at the University of Oxford who worked on the project as an intern at Amazon last summer, then returned in January to do some follow-up work.
The next time you call room service for extra towels, your order may be delivered by a robot. It might not be able to change your sheets, but Savioke's Relay hospitality robot can bring everything from toothpaste to Starbucks, and it uses Wi-Fi and 3D cameras to navigate. The robot is already being used by some hotels in the US, and with recent funding of $15 million, autonomous butlers could soon become a lot more popular. The next time you call room service for a new tube of toothpaste, your order may be delivered by a robot. It might not be able to change your sheets, but Savioke's Relay hospitality robot can bring everything from clean towels to Starbucks, and it uses Wi-Fi and 3D cameras to navigate Each of the Relay robots stands roughly three feet tall.