There are few electronic devices with which you cannot order a Domino's pizza. When the craving hits, you can place an order via Twitter, Slack, Facebook Messenger, SMS, your tablet, your smartwatch, your smart TV, and even your app-enabled Ford. This year, the pizza monger added another ordering tool: If your home is one of the 20 million with a voice assistant, you can place a regular order through Alexa or Google Home. Just ask for a large extra-cheese within earshot, and voila--your pizza is in the works. Sign up to get Backchannel's weekly newsletter, and follow us on Facebook, Twitter, and Instagram.

Many in Russia, in fact, seem to prefer their AI helpers with sass and a dark sense of humor. The Moscow-based tech giant Yandex launched a Russian-speaking personal assistant called Alice this October (pictured above, sans snark). And unlike Siri or Alexa, the program relies less on scripted responses than on what it's learned by consuming conversational data mined from the Web, news articles, and even a little Russian literature. As a result, Alice can respond to a much wider range of queries. However, some of program's responses can be a bit surprising.

His artificially intelligent assistant greets him of the key news items, events of the day, priority messages, meetings, and urgent action items. My important items are addressed through a natural language interface while I get ready for work and prepare for my hour long commute. Upon entering my self driving car, I Initiate a 6G video call through a hologram projected on the dashboard. Via HD virtual reality I engage in a discussion re out upcoming sales conference. My virtual assistant records notes and key action from the meeting, presenting the team with a summary and recording next steps in our meetingspace for later review.

In the business intelligence (BI) world, more and more companies are talking about machine learning (ML) being leveraged in their software. However, try to talk to them about it and there is silence. Infrastructure vendors, IBM, NVIDIA, Intel, Oracle (remember, that's where Sun went), Qualcomm and more are talking up their chips for ML. Again, try to talk with them about a real business case study, a customer who has implemented a system, and, if you get back anything, you get back anonymous companies described in a paragraph or even just a sentence. On the other hand, the success of Apple Siri and Amazon Echo, the continued growth of Microsoft Cortana Echo, and the entrance of Google Now show that voice recognition is rapidly becoming mainstream in the consumer world.

In Estonia, where the digital state was invented, the government is hard at work on the legal status of robots. The question is: do artificial intelligences deserve "human" rights? This may seem like a particularly lame way for EU bureaucrats to kill some time and spend taxpayer cash, slightly ahead of counting angels on pins, and just behind dictating rules around who can make cheese, or what wines qualify as "Burgundy." Because in a time when AI is advancing and commerce will move largely to AI-driven voice-powered systems, you will soon be in command of intelligent systems. You'll be able to request that systems buy things, reserve tickets, and purchase commodities for you.

Cloud-based platforms that employees and managers can access without help from HR personnel are a fact of work life for companies large and small. In the not-so-distant future, people could bea using voice-based assistants such as Amazon's Alexa and Google Home to look up payroll, benefits and other work-related apps.

As artificial intelligence (AI) and machine learning are woven into banking's fold, their potential is almost too vast to predict. The real benefit is in financial institutions' ability to understand where and how it makes sense to apply these tools first, and where they can derive the greatest value in the fastest way. While a few industry leaders do get it, many discussions around artificial intelligence (AI) in banking shows that the industry at large still views AI in very abstract terms. While banks seem to be thinking about AI more and more, there still seems to be a consistent struggle in understanding when or where to apply this analytic tool. This struggle often leads to hesitation to actually testing and implementing the benefits of AI at financial institutions.

Affectiva, one in a series of companies to come out of MIT's Media Lab whose work revolves around affective computing, used to be best known for sensing emotion in videos. It recently expanded into emotion detection in audio with the Speech API for companies making robots and AI assistants. Affective computing, the use of machines to understand and respond to human emotion, has many practical uses. In addition to Affectiva, Media Lab nurtured Koko, a bot that detects words used on chat apps like Kik to recognize people who need emotional support, and Cogito, whose AI is used by the U.S. Department of Veteran Affairs to analyze the voices of military veterans with PTSD to determine if they need immediate help. Then there's Jibo, a home robot that mimics human emotion on its five-inch LED face that Time magazine recently declared one of the best inventions of 2017. Instead of natural language processing, the Speech API private beta uses voice to recognize things like laughing, anger, and various forms of arousal, alongside voice volume, tone, speed, and pauses.

Many problems in computer vision and recommender systems involve low-rank matrices. In this work, we study the problem of finding the maximum entry of a stochastic low-rank matrix from sequential observations. At each step, a learning agent chooses pairs of row and column arms, and receives the noisy product of their latent values as a reward. The main challenge is that the latent values are unobserved. We identify a class of non-negative matrices whose maximum entry can be found statistically efficiently and propose an algorithm for finding them, which we call LowRankElim. We derive a $\DeclareMathOperator{\poly}{poly} O((K + L) \poly(d) \Delta^{-1} \log n)$ upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the number of columns, $d$ is the rank of the matrix, and $\Delta$ is the minimum gap. The bound depends on other problem-specific constants that clearly do not depend $K L$. To the best of our knowledge, this is the first such result in the literature.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.