As Amazon's Alexa voice assistant becomes smarter each day, it's also showing up in more form factors, with the latest being a small home robot courtesy of, well, a smartwatch brand. Omate's Yumi is, in many ways, a mini version of the ASUS Zenbo: Its head features a 5-inch 720p touchscreen, it runs on wheels, it's powered by Android and it even shares a similar appearance in white. Much like Omate's Rise 3G smartwatch, the Yumi supports Alexa out of the box, so it's effectively an Echo Dot with wheels plus a cute face -- look closer and you'll see the same smile in Amazon's logo. When you do get bored of that look, just pick another face. With a starting price of $349, the 11.7-inch tall Yumi is bound to miss out on some of the bells and whistles found on the $599 Zenbo. For one, Yumi lacks object avoidance and drop avoidance sensors, so you'll have to be careful when it's placed on the table or countertop.

Artificial intelligence is the next big thing in the world of computing, and it is already there. Google's Pixel smartphones, which come with an inbuilt Google Assistant that can get you anything from the daily dose of news to meditation tips, has already started selling in India. Siri is more intelligent than before; it lets you chat her up to find a good place to eat nearby and even draft an email for you. But the biggest use of artificial intelligence might be in bringing down the bar to access for millions of people who find all the text and foreign languages on smartphones hard to comprehend. That is exactly what companies like Karbonn, who think more mass than niche, are working on.

Move over desktop: Mobile is the future of online purchases and commerce, thanks to its ability to connect with location-based and personalized data. Forrester's new report "Predictions 2017: Mobile is the Face of Digital," scheduled to be publicly released Tuesday, explains that mobile has become the new pathway for consumers find brands -- and it is moving past the traditional social media app. Advertisers will increasingly use mobile to connect next year using chatbots, other artificial intelligence-enabled platforms like Apple's Siri or Amazon's Alexa and messaging apps, the report said. The company previously said that people spend more than two hours a day on mobile, and that by 2019 the majority of our billion websites will be on mobile. "The magic of mobile is the immediacy," said Julie Ask, principal analyst at Forrester and co-author of the report.

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.

Today, the virtual assistant landscape is exploding with innovation: New applications and new forms of interaction are constantly emerging. Although the idea of a virtual assistant is decades old, it went mainstream with Apple's introduction of Siri. Siri was created at SRI International based on years of AI research, spun off as an independent venture-backed company in 2007, and acquired by Apple in 2010. The Siri that the world knows enables users to quickly find information and execute important device functions in a fast and friendly way. But Siri was first developed as a "do engine," similar to the emerging crop of AI assistants.

Artificial Intelligence (AI) has always been the subject of dreams and visions about the distant future of humankind. Even though we are nowhere near a conscious robotic system, nowadays, AI systems are ubiquitous and showing tremendous successes in various fields of our everyday life. We are using these on a daily basis, often without even noticing. Whether it is the Virtual Personal Assistants on our mobile phones (such as Siri, Google Now, and Cortana), self-driving cars, the ranking of the web pages given your search query, or the classical textbook examples such as spam filtering and recommendation systems of online media providers and marketplaces like Amazon. Various fields of AI have made a major leap forward in the recent years. As most AI systems are too complex to be defined manually, we have to resort to automatically learning rules and patterns from data using sophisticated Machine Learning (ML) techniques.

Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.

Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferences and item perceptions drift over time. In this paper, we propose a conjugate and numerically stable dynamic matrix factorization (DCPF) based on compound Poisson matrix factorization that models the smoothly drifting latent factors using Gamma-Markov chains. We propose a numerically stable Gamma chain construction, and then present a stochastic variational inference approach to estimate the parameters of our model. We apply our model to time-stamped ratings data sets: Netflix, Yelp, and Last.fm,

Columnist Ed Baig reviews Pixel, which features the high-IQ Google Assistant and a competitive, high-end smartphone camera. A. When Google introduced its Pixel and Pixel XL phones in early October, it picked a hybrid distribution strategy. Instead of selling these $649-and-up smartphones only on its own site, as it had with its earlier Nexus phones, it also signed up Verizon Wireless as a distribution partner. To judge from the ads during the World Series, only the second purchase option exists. They keep touting the Pixel -- "a winner for anyone looking for an excellent phone," USA TODAY's Ed Baig wrote -- as "only on Verizon," something Verizon's own page about the phones repeats.

Like the LIFX White 800, the TP-Link LB120 connects to your network not through a ZigBee bridge but directly, through Wi-Fi. And as with the LIFX, this adds significant size and heft to the bulb, though it is much lighter (less than half the weight) than the LIFX 800 and it retains a largely traditional bulb design. The TP-Link LB120 is designed to work with the TP-Link infrastructure of smart switches, smart plugs, and Wi-Fi gear, but it's compatible with any Wi-Fi product. It is also certified to work with Amazon's Alexa digital assistant. You set up and manage the LB120 through TP-Link's Kasa management app, which has separate sections for managing all of its smart components.