It listens to the sound waves bouncing around the room, then adjusts the output for pristine audio. Chat interactions are a bit limited for now, but Siri's an ace DJ. The AI can play songs or podcasts from Apple Music and control connected outlets and smart LED bulbs. The Max puts out a beastly thump, with enough low end to make the neighbors think Janelle Monáe has moved in. This article appears in the May issue.

In 2016, after four years of running a health and fitness app that lets customers track their daily food and workout routines, health-tech startup HealthifyMe found that it was sitting on millions of data points about its users' lifestyle habits. The Bengaluru-based company's customers were using the app to not only log and track their health regimes, but also talk to nutritionists or fitness coaches. This meant that HealthifyMe had data on everything from users' food and workout logs to the questions they asked the nutritionists and the responses they received. So the company, which is backed by IDG Ventures India, Inventus Capital, and Blume Ventures, among others, decided to feed all this information to a machine-learning algorithm and help fitness coaches respond better to app users. That project has now developed into a customer-facing programme where an AI-driven bot talks to over 25,000 of HealthifyMe's paid subscribers, similar to how Google Assistant or Amazon's Alexa operate.

The technology is being used for a wide array of purposes in many industries. Amazon's Alexa, Apple's Siri, Google Assistant and many more, are examples of AI being used to make our lives easier. But what exactly is Artificial Intelligence, and how can it help you increase your website's conversions? Put simply, it's the idea that a machine (or more specifically, a computer program) can complete tasks in a very similar way to a human. And the main difference between how a human and a conventional computer program approach solving a problem is that a human can improvise, learn from mistakes and get better with experience.

It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder. Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don't want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise.

Amazon is encouraging us to put listening devices in every room of the house with executives from Amazon saying that Echo assistants don't listen to private conversations, they say the device will start listening to conversations only if the word Alexa was used, this is not always the case as a story from a user in Portland highlights. An Alexa user from Portland, Oregon has installed Echo and Smart bulbs in every room of their house thinking that nothing bad will happen, however when asking Amazon to investigate an issue about Alexa recording a private conversation between her and her husband that was sent to a random number in her address book without her consent. She didn't believe her friend at first, however when her he explained the conversation between her husband she finally believed them. "You sat there talking about hardwood floors." Danielle realised the colleague must have heard everything.

It was a week of near misses and closer hits than the tech industry probably would have wanted. Amazon's Alexa "accidentally" recorded more than a few customers' private conversations, Apple's iPhones turned out to be bendier than anticipated, and that PUBG chicken dinner of yours wound up being harder fought than anybody had previously thought. Nobody: The number of people who saw that coming. Kidding, of course, even the greenest of horned labor attorney could tell you that Tesla CEO Elon Musk was simultaneously talking out of his tailpipe and putting his entire automotive empire in legal jeopardy when he started running his mouth on Twitter the other night. This is why you don't let the guy who owns the business do the ads for said business.

Ask around, and you'll find a surprising number of people have a smart speaker in their homes. As of January, 1 in 6 Americans own a voice-activated speaker, but Gartner predicts 75 percent of U.S. households will have one by 2020. With a broad gamut of capabilities--streaming news and music, answering questions, issuing reminders, and controlling connected home products--they can offer a good value proposition, particularly when paired with an attractive price point. But just because our Echos, Google Homes, and HomePods can do all sorts of things doesn't mean we're taking advantage of every single one of their features. Many of us are content to rely on our digital assistants for just one, or a handful, of specific tasks.

The challenge of controlling and protecting data is a big one, but the bigger question is how to make people more productive with corporate information while maintaining standards of compliance and governance for broad access and use in the age of digital business. Applying AI and Natural Language Processing within the various stages of data analytics is a key way to democratize data and build in safeguards for broad use. Below is a Q&A with Ayush Parashar, a Co-Founder and Vice President of Engineering with Unifi Software. Often the quest for security can eclipse data usability. How can applying AI be used to both discover data and ensure information is not being seen or used by those that shouldn't have access to certain kinds of data?

From Daniel Eran Dilger's More companies need to temper their Artificial Intelligence with authentic ethics: When Apple outlined that its new HomePod didn't initiate phone calls on its own, nobody jumped to the conclusion that this was because having a device in your home that anyone's voice could use to place a telephone call from your personal mobile number might be a bad idea. Instead, the company was generally lambasted for "again" failing to match one of the many features of Amazon's Alexa Echo always-listening appliances. This week, Alexa got famous for recording a private conversation and automatically sending it to a random contact of the owner. That's something HomePod doesn't do, not because Apple doesn't know how, but because Apple chose not to rush to make it possible to do things that might not be a good idea in the long run. My take: Does HomePod really know how to initiate a phone call?

Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent users, person- alization plays an important role. In many large portals such as Taobao and Amazon, there are a bunch of di erent types of search and recommendation tasks operating simultaneously for person- alization. However, most of current techniques address each task separately. This is suboptimal as no information about users shared across di erent tasks. In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization. In partic- ular, user behavior sequences (e.g., click, bookmark or purchase of products) are modeled by LSTM and attention mechanism by integrating all the corresponding content, behavior and temporal information. User representations are shared and learned in an end-to-end setting across multiple tasks. Bene ting from better information utilization of multiple tasks, the user representations are more e ective to re ect their interests and are more general to be transferred to new tasks. We refer this work as Deep User Perception Network (DUPN) and conduct an extensive set of o ine and online experiments. Across all tested ve di erent tasks, our DUPN consistently achieves better results by giving more e ective user representations. Moreover, we deploy DUPN in large scale operational tasks in Taobao. Detailed implementations, e.g., incre- mental model updating, are also provided to address the practical issues for the real world applications.