Siri, Alexa, and the other personal assistants that so many of us have in our homes, cars, and pockets are designed to learn from us. They familiarize themselves with facets of our lives – what routes we take to work, what musicians we like to listen to – so that they can perform more efficiently and improve a world of artificial intelligence-powered devices. But what can we learn from the AI in order to improve our world? Turns out there's a lot.

As artificial intelligence is entering all spheres of our lives, a lot of concern is arising about the possible white bias and patriarchy of the impending AI world. Moreover, research shows women are much more skeptical of and averse to innovation in comparison to men who embrace and triumph it. This fear of technological innovation has to do with the fact that society often views the role of women as replaceable by AI, which is visible in the abundance of women robots and female personal assistants, such as Alexa and Cortana. If we're coming to the point when most jobs are automated and robots become everyday reality of our lives we'd better make sure those algorithms are beneficial for most people, be it an Afro-American woman or a Chinese man. As of today, 85% of the machine learning workforce is male.

In a remarkable new app, Google has addressed one of the most pervasive grievances of our impersonal times: voice mail jail, or its technical name IVR (Interactive Voice Response). Although IVR's multiple menu options, long waits, and tinny music are a bother, they pale by comparison with the annoyance of the technology's characterless robotic voices. This is where a new feature of Google Assistant, called Google Duplex, a technology for automating for "real world" telephone tasks, has come to the rescue. And it does so by going against the grain by including the bane of all speakers, the dreaded "um" and "ah" words, also called filler words. Listen to the audio clip in this Google AI Blog post marked "Duplex calling a restaurant," where an automated male voice calls to make a reservation with a live receptionist. You'll hear him say "um" four times in 50 seconds.

Ditching a TV remote for simple voice commands sounds great in a lot of circumstances. And, with a little help from Alexa (and some other digital assistants down the road), the Sonos Beam comes as close to executing that as anyone has so far. And while the Beam won't let you toss your clicker just yet, it's still one of the best home audio devices around, even with its $400 price tag. The Beam is the latest in the Sonos series of speakers meant to pull double duty, handling both music as well as the sound that comes from your home entertainment center and TV. It's the smallest and cheapest option in the Sonos lineup to handle that kind of task, sliding in under both the Playbar and the Playbase.

In recent years, artificial intelligence algorithms have become very good at recommending content to users -- a bit too good, you might say. Tech companies use AI to optimize their recommendations based on how users react to content. This is good for the companies serving content, since it results in users spending more time on their applications and generating more revenue. But what's good for companies is not necessarily good for the users. Often, what we want to see is not necessarily what we should see.

Here's how John Brandon sees the future less than 15 years from now: augmented reality will be mainstream, digital assistants will guide our every move, everything will be translated on the fly, we'll be using digital scrolls, and some of our employees will not be human but robots. The list follows questions to futurists and smart thinkers about what business tech will be like in 2025. Future robots won't look like us but these smart machines will replace us on mundane tasks such as flipping hamburgers at a local eatery, diagnosing our illness at the doctor, and acting as our digital avatars at meetings. And like an advanced version of Siri, digital assistants will know our schedules, read our email, and store all of our photos and documents. More importantly, they'll know where we need to be and why.

Completing a data matrix X has become an ubiquitous problem in modern data science, with applications in recommender systems, computer vision, and networks inference, to name a few. One typical assumption is that X is low-rank. A more general model assumes that each column of X corresponds to one of several low-rank matrices. This paper generalizes these models to what we call mixture matrix completion (MMC): the case where each entry of X corresponds to one of several low-rank matrices. MMC is a more accurate model for recommender systems, and brings more flexibility to other completion and clustering problems. We make four fundamental contributions about this new model. First, we show that MMC is theoretically possible (well-posed). Second, we give its precise information-theoretic identifiability conditions. Third, we derive the sample complexity of MMC. Finally, we give a practical algorithm for MMC with performance comparable to the state-of-the-art for simpler related problems, both on synthetic and real data.

Google is working on some nifty new features for Google Maps, including a short list of your favorite places, the possibility of a'virtual positioning system' and more. Assistant is coming to Google Maps in a big way, with a ton of new shortcuts, as well as the ability for the digital assistant to text your friend when you're on your way. Google is rolling out a tool called'Your Match', which uses machine learning to determine your location and interests, serving up targeted suggestions for new businesses opening up in your area and more.

In January of 2018, Annette Zimmermann, vice president of research at Gartner, proclaimed: "By 2022, your personal device will know more about your emotional state than your own family." Just two months later, a landmark study from the University of Ohio claimed that their algorithm was now better at detecting emotions than people are. AI systems and devices will soon recognize, interpret, process, and simulate human emotions. With companies like Affectiva, BeyondVerbal and Sensay providing plug-and-play sentiment analysis software, the affective computing market is estimated to grow to $41 billion by 2022, as firms like Amazon, Google, Facebook, and Apple race to decode their users' emotions. Emotional inputs will create a shift from data-driven IQ-heavy interactions to deep EQ-guided experiences, giving brands the opportunity to connect to customers on a much deeper, more personal level.

Today, Google shows a list of hits for every search, from which the user can choose a suggestion. Voice assistants do not work the same way: They offer recommendations. In two years, half of consumers will no longer be searching the web manually, but with their voices. They will be using voice assistants like Alexa, Siri, and Cortana. The risks this new behavior poses for brands are enormous. Become conscious of what your brand stands for.