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.
Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of monopolistic firms and assumed product relationships, e.g., complementarity in usage. We develop a new machine-learning-driven methodology for designing bundles in a large-scale, cross-category retail setting. We leverage historical purchases and consideration sets created from clickstream data to generate dense continuous representations of products called embeddings. We then put minimal structure on these embeddings and develop heuristics for complementarity and substitutability among products. Subsequently, we use the heuristics to create multiple bundles for each product and test their performance using a field experiment with a large retailer. We combine the results from the experiment with product embeddings using a hierarchical model that maps bundle features to their purchase likelihood, as measured by the add-to-cart rate. We find that our embeddings-based heuristics are strong predictors of bundle success, robust across product categories, and generalize well to the retailer's entire assortment.
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.
We propose a novel neural topic model in the Wasserstein autoencoders (W AE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.
Artificial intelligence and machine learning solutions have taken over the enterprise sector. Smart automation, intelligent business decisions, and recommendation engines have changed the way companies do business today. Companies that have adopted artificial intelligence (AI) and machine learning (ML) in their operations have seen great success. Apart from freeing up human labor from mundane tasks and automating repetitive ones, AI has fundamentally changed the way some companies operate. Let's look at some companies that were highly successful in adopting AI. Why Is AI Adoption on the Rise?