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.

Tinder now lets you see exactly what your matches are posting to social media. The dating app says its new'feed'is'an exciting new way to see more of what someone is all about by giving you a true glimpse into their world.' It shows all of their posts on Instagram, recent photos uploaded to Tinder and even what they are listening to on Spotify. The new'Feed' tab shows all of their posts on Instagram, recent photos uploaded to Tinder and even what they are listening to on Spotify. To access it, users tap on'Feed' from their Match List.

Artificial intelligence (AI) has been widely considered as the buzzword of the tech industry in 2017. The UK government has even dedicated £75m of funding for AI, including up to £45m to build AI capability and knowledge by increasing the number of AI PhD students to 200 a year. With governments launching dedicated funding and organisations from all walks of life racing to reposition themselves as tech companies - from launching chatbots to deploying big data analysis and deep learning - AI is transforming the way we work and how we interact with brands. In today's economic climate, organisations are seeking alterative methods and technologies to serve a larger volume of people, keeping customer service standards up and ensuring costs are managed. It is in this area that the explosion of AI-powered chatbots has disrupted the very meaning of customer service.

In the original Blade Runner--set in 2019--technologists had already developed replicant humans and flying cars. Although I'm doubtful we'll hit that mark in real life, I'd venture we're not too far off. Indeed, artificial intelligence (AI) has already become an everyday part of life for most humans in 2017. We use it at work and at home--often without even realizing. As we close out the year, I'd say there isn't much artificial intelligence hasn't touched.

If you're watching the impact of artificial intelligence on the IT organization, your interest probably starts with your own job. Can robots do what you do? But more importantly, you want to skate where the puck is headed. What emerging IT roles will AI create? We talked to AI and IT career experts to get a look at some emerging roles that will be valuable in the age of AI.

Netflix values the recommendation engine powering its content suggestions at $1 billion per year and Amazon says its system drives a 20-35% lift in sales annually. What makes these systems so valuable? The answer lies in the data science that powers them.