When I tell my daughters, ages 6 and 9, that I have a new robot to show them, they perk up. I then take Professor Einstein out of the box. My wife walks into the room: "Ahhh!" This very expressive, very wacky robotic character is a creation of Hanson Robotics, which calls it "your personal genius." Professor Einstein can chat about science, tell jokes, check on the weather, and, naturally, quote Einstein himself.

That's because this was the fourth and final meetup for participants of "Program N," a project where volunteers tested a hands-free open-ear audio device (called, appropriately enough, Concept N) for Sony for almost a year. Aside from paying their own money for the hardware, Program N participants were invited to attend several meetups throughout the year, where they interacted with Sony engineers directly. Future Lab also attended the Silicon Valley Bike Festival and Bike To Work Day SF in order to talk to cyclists and bike commuters about how N could improve their experience. "The first version of N didn't have a calling function," said Okamoto, adding that most people in Tokyo don't use headsets to make calls, so it was an afterthought.

Did you ever stop to wonder: What is Amazon not doing with technology? These days, you'd be hard-pressed to answer that question, given the company's incessant schedule for announcing updates and new products. The Seattle-based e-commerce giant is seemingly everywhere--whether it's the latest cloud offerings in AWS, new entertainment shows on Prime, automated retail stores, leased fleets of Boeing jets, smart speakers, payment systems, autonomous cars and trucks, freight forwarding companies, or airborne warehouses. Amazon also happens to have warehouses within 20 miles of 44% of the population of the United States, according to Piper Jaffray analyst Gene Munster. In many of the company's recent announcements, Amazon's voice assistant Alexa plays a central role.

But striking a balance between these two extremes remains a significant challenge for voice interaction designers, and raises important questions about what people really want from a virtual assistant. The ability to intonate will make digital assistants capable of similarly nuanced expression. You've probably heard the design maxim form should follow function. Amazon's efforts to make Alexa sound as human as possible suggest that users expect their artificially intelligent sidekicks to do more than turn on their lights or provide a weather forecast.

Artificial intelligence is becoming a defining characteristic in the smartphone market, powering personalization, virtual assistants, and even battery life. But AI takes a lot of computing power. To make up for that, companies like Apple and Huawei are adding additional chips into smartphones to handle such tasks. These are complementary to the existing CPU and GPU chips already in phones, and configured to be faster for one specific purpose--AI--at the expense of being able to do anything else. They also keep AI tasks from draining phone batteries as fast. Apple has dubbed theirs the Neural Engine, located inside the A11 Bionic chip, while Huawei's is called the Kirin 970.

If you've tried to conduct a conversation with Amazon Alexa, you know just how stilted it can be. "Alexa, will you..." followed by "I don't understand what you mean." The Holy Grail, of course, would be to carry on a multi-faceted conversation, with Alexa responding to a command like "play me some music" with a question of which kind, how loud, etc. Such a voice-driven future is just that, however: The future. Well, it turns out that Alexa plays dumb on purpose.

The introduction of voice-activated smart home solutions – like Amazon Echo and Dot, Google Home, and Apple's HomePod – have brought with them the dream of convenient Star Trek-like interfaces where a user's spoken wish is their command. But at the same time, these devices have served as a Trojan Horse, increasingly inviting in security issues and unintended consequences. The greatest security vulnerabilities created by these products are due to the fact that, while they prominently feature advanced voice recognition, they cannot really tell who's talking. The dangers this presents are compounded when the devices feature the ability to make purchases (with few safeguards under default settings), as well as control smart home features (lights, thermostats, locks, etc.) that users do not want malicious actors to be able to manipulate. These factors have contributed to a number of actual events, which land somewhere between fascinating and frightening as to the level of harm they represent – but all should certainly provoke concern.

The current work characterizes the users of a VoD streaming space through user-personas based on a tenure timeline and temporal behavioral features in the absence of explicit user profiles. A combination of tenure timeline and temporal characteristics caters to business needs of understanding the evolution and phases of user behavior as their accounts age. The personas constructed in this work successfully represent both dominant and niche characterizations while providing insightful maturation of user behavior in the system. The two major highlights of our personas are demonstration of stability along tenure timelines on a population level, while exhibiting interesting migrations between labels on an individual granularity and clear interpretability of user labels. Finally, we show a trade-off between an indispensable trio of guarantees, relevance-scalability-interpretability by using summary information from personas in a CTR (Click through rate) predictive model. The proposed method of uncovering latent personas, consequent insights from these and application of information from personas to predictive models are broadly applicable to other streaming based products.

The iPhone 8 will pack a new 4.7-inch Retina HD display, while the iPhone 8 Plus has 5.5-inch Retina HD display -- what's new here is the addition of Apple's True Tone tech. Apple has embedded Qi inductive wireless charging to mean that both phones will charge on compatible pucks and and surfaces when they launch. Qi charging surfaces are already on sale pretty much everywhere.

In recent years, enterprise machine learning has revolved around crunching numbers: analyzing datasets or tracking customer behavior. But what organizations will soon realize is that applying machine learning to content--physical documents, images, presentations and even conversational UIs--removes the cap on who machine learning impacts, and how far its value extends across the enterprise. Tracking down lost documents and images, or drafting abstracts and case studies only to realize they've already been written are just a few of the daily frustrations that we typically consider unavoidable. But as it turns out, it's these issues specifically--content discovery, tagging and classification--where machine learning is in a strategic position to make a substantial impact. Numbers-driven algorithms have informed strategy for years now, but applying machine learning to content will likely have a similar, if not greater, impact on the enterprise.