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.

With their rainbow of hues and myriad party tricks, color-tunable LEDs get all the press in the world of smart lighting. It's fun stuff, but the reality is that most of us will rarely find much of a need to turn all the lights in the house blue or red--unless it's time to celebrate our team winning the World Series. Even then, you'll probably want to turn them all back to white after the celebration. White light is also important in its own right, as today there is plenty of science to show how various shades of white--with variations in color temperature--impact our psychological state. Cool light that's closer to blue has an energizing effect, and is best in the morning.

US tech giants like Facebook, Google, Amazon, and Microsoft are investing hundreds of millions of dollars into artificial intelligence as they look to make their platforms and personal assistants that it smarter. Part of this effort involves finding and hiring the brightest minds in the world. But with so many large companies involved in the so-called "AI race" it's not always easy to recruit the best talent. Yann LeCun, the director of Facebook AI Research and one of the world's most prominent AI academics, told Business Insider last week that he employs certain tactics to get people to come and work for him. "There's various things ... but a lot of it is nurturing relationships with academic laboratories that have a track record of producing interesting students," said LeCun, who is also a professor at New York University.

Who here remembers taking computer programming in school? Whether you learned programming by punching holes in a never ending series of cards, or by writing simple DOS or other computer language commands, the fact remained that computers needed an incredibly precise set of instructions to accomplish a task. The more complicated the task, the more complicated your instructions had to be. Machine learning is inherently different. Rather than telling a computer exactly how to solve a problem, the programmer instead tells it how to go about learning to solve the problem for itself.

AI -- looms on the horizon as the next game-changer for business operations, including sales. Salespeople tend to adopt technologies as consumers first and then expand use into their professional lives, so sales leaders thinking about AI will play may be focusing on assistive intelligence like Amazon Echo, Cortana and Siri. There is strong consumer demand: Strategy Analytics predicts that the use of voice-activated AI assistants will rise to almost 350 million by 2020. It's also easy to imagine the ways a voice-enabled assistant like Siri could become a great sales sidekick. Picture AI providing a spoken-worded guided selling system in CPQ, interacting with sales management systems while the salesperson is behind the wheel on a road trip, producing custom reports based on the salesperson's spoken requests and then displaying the results on a screen.