It's tough to do much of anything involving technology these days without running into a virtual assistant. Pick up your Android phone or Chromebook, and there's Google Assistant waiting for a chat. Apple's got Siri, poor Samsung's got Bixby, and even random companies like Bank of America are getting in on the action with their own woefully unnecessary A.I. personalities (sorry, "Erica"). We've talked plenty about the reasons why everyone and their mother wants you to get friendly with their flavor of robot aid -- and why that, in turn, has led to what I call the post-OS era, in which a device's operating system is less important than the virtual assistant threaded throughout it. It's no coincidence that Google is slowly expanding Assistant into a platform of its own, and what we're seeing now is almost certainly just the tip of the iceberg.

In the race among tech companies to bring their voice recognition technology into the realm of personal medicine, Google is the furthest along, according to a study published on Thursday in the journal Nature Digital Medicine. Researchers Yan Fossat and Adam Palanica from lab company Klick Health in Toronto tested technology from Google, Amazon and Apple to gauge how well their services comprehended the 50 most commonly prescribed medicines and whether they could provide accurate information to users. Fossat and Palanica said they activated Google Assistant, Amazon Alexa and Apple's Siri and played individual audio clips from 46 English speaking people with the prompt, "Tell me about," followed by the medication name. "We reviewed all the literature, and identified this one area of medication comprehension that is under studied," Fossat told CNBC. "It's especially important to research these voice assistant tools, given the growing demand for them in health care."

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. The conventional approach, for both context-aware and standard models, is to retrieve items and apply the constraints as independent operations. The order in which these two steps are executed can induce significant problems. For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i.e., less popular items). As a result, the additional information that the constraint brings about user intent may not be accurately captured. In this paper we propose integrating the information provided by the contextual constraint into the similarity computation, by merging constraint application and retrieval into one operation in the embedding space. This technique allows us to generate high-quality recommendations for the specified constraint. Our approach learns constraints representations jointly with the user and item embeddings. We incorporate our methods into a matrix factorization model, and perform an experimental evaluation on one internal and two real-world datasets. Our results show significant improvements in predictive performance compared to context-aware and standard models.

Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multi-taxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes time-varying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In trace-driven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.

Twitter announced on Monday it has acquired Fabula AI, a London-based machine learning research company. Fabula AI's team will join Twitter and work alongside Sandeep Pandey as part of Twitter's research group focused on natural language processing, reinforcement learning, machine learning ethics, recommendation systems and graph deep learning. Fabula's graph deep learning research is used to detect network manipulation. With this acquisition, Twitter aims to use Fabula's capabilities to better identify bad actors and malicious behavior on the platform, in addition to enhancing its recommendations processes. While the Fabula research team will initially focus on improving the health of conversations happening on Twitter, the company said the team's efforts will expand in the future, aiming to help stop spam and abuse, as well as improve recommendations, the explore tab and the onboarding experience.

Turning the lights on (and off) has never been easier thanks to Amazon's Alexa. If you're a diehard Alexa user, then you probably already know about a few of the different smart bulb options, but there are more options at your disposal than you might think. Whether you're looking to upgrade your current smart home lighting system or are ready to dip your toes in the water for the first time, we are here to help. The smart home industry is moving quickly, and it's hard to keep track of what's available, what works best, and what should be avoided. Smart bulbs were one of the first smart devices available to consumers, so there are many, many on the market.

If you haven't hopped aboard the smart speaker train, now is as good a time as any, as there are two relatively inexpensive ones on the market today--the Google Home Mini and the Amazon Echo Dot. If you're looking for a device that can play music, give you the weather, and operate your smart gadgets, you may have been eyeballing these small-but-mighty smart speakers. The question is, though, which one is better? How do the features compare? There are distinct differences in how each speaker--the Echo Dot, left, and Google Home Mini--lights up when given a command.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. Think of all the things a microphone can pick up: voices, noises, whispers, conversations, arguments, confessions – even people alone, in a room, mumbling to themselves. Think of all the things you say in a private space, all the weird things you do. Once those sounds have been saved, that data can be stored, edited, and manipulated and shared.

Machine Learning (ML), the subset of artificial intelligence, is gaining fresh momentum. Powerful and affordable computational processing, growing volumes of huge data sets, affordable data storage options and the ability to automatically apply complex mathematical calculations to big data faster than before are the factors responsible for the resurging interest. ML is basically the idea of training machines to recognize patterns in data and apply them to particular problems. The iterative aspect of machine learning is important because when models are exposed to new data, they are able to independently adapt. They learn from previous computations and predictions to produce reliable, repeatable decisions and results with minimal or no human intervention.

In the age of a big data and breathtaking advances of artificial intelligence, social infrastructure promotes digital engagement and active presence. Digital democracy propagates the participation of a growing number of users to interact with institutions and services, ensuring that decisions made by AI-powered digital tools reflect human values. Immersed in automation, many choices we make include some form of a computationally-modeled process. This transformation from manual to programmed behavior has started with the introduction of recommendation systems to find similar products according to users' preferences. However, today's AI systems go beyond imposing suggestions and know pretty well what we do and what we want.