Ask most UX design professionals why they opt for female voices, and they'll usually cite practicality. They'll say that user research proves that everyone wants to get information from a woman, but the data doesn't actually bear that out. Or they'll argue that men and women hear female voices more clearly--again, a view that the numbers do not support. They may throw their hands up in frustration, claiming that the voice's gender is simply a matter of stakeholder preference they have to design around. While stakeholder preference might sound like a perfectly good reason at first, it hides an ugly reality.

The airline is spending about $43 million to test new equipment for loading bags onto planes that carries bags farther into the cargo hold. That means less heavy lifting for workers, which should reduce overuse injuries, but it should also speed up the process of loading and unloading bags, DeGiovanni said. For travelers, that would mean faster turnarounds between flights and hopefully fewer delays.

In honor of Amazon Prime Day, let's take a look at the inner workings of this company that is pushing the bounds of innovation, not only with Amazon Prime, but the many other cutting-edge management strategies. The company that sets the tone for so many aspects of customer experience is breaking down internal barriers and showing how other companies can do the same. Amazon, a leader in customer experience innovation, has taken things to the next level by reorganizing the company around its AI and machine learning efforts. Amazon's approach to AI is called a flywheel. In engineering terms, a flywheel is a deceptively simple tool designed to efficiently store rotational energy.

The speaker orders Google Assistant to book him a hair-cutting appointment. Google Assistant places a call to a nearby salon. The reception picks the call. Google Assistant talked its way out with the receptionist and in a very human manner asked it to book an appointment, which she obediently did. All this happened during Google's annual keynote event I/O 2018 in the front of thousands of people and the speaker was none another than Google's CEO Sundar Pichai.

Google recently repitched Duplex to explicitly reveal to restaurant hosts and salon staff that they are speaking with the Google Assistant and are being recorded. Google omitted this small but important detail when it first introduced Duplex at its I/O developer conference in May. A media backlash ensued, and critics renewed old fears about the implications of unleashing AI agents that can impersonate the behavior of humans in indistinguishable ways. By tweaking Duplex, Google puts to rest some of that criticism. But why is it so important that companies be transparent about the identity of their AI agents?

Artificial intelligence (AI) is playing an increasingly influential role in the modern world, powering more of the technology that impacts people's daily lives. For digital marketers, it allows for more sophisticated online advertising, content creation, translations, email campaigns, web design and conversion optimization. Outside the marketing industry, AI underpins some of the tools and sites that people use every day. It is behind the personal virtual assistants in the latest iPhone, Google Home, and Amazon Echo. It is used to recommend what films you watch on Netflix or what songs you listen to on Spotify, steers conversations you have with your favorite retailers, and powers self-driving cars and trucks that are set to become commonplace on roads around the world.

Mobile apps have been at the center of the technological revolution because of rising consumer demand for instantaneous, on-the-go access to content. With U.S. users spending over 5 hours a day on mobile apps, it's no wonder apps are one of the main sources for how users access the internet. And because mobile apps have become such a fundamental part of our day-to-day technological experience, there's a sense of urgency among tech companies to discover and distribute something that's new and exciting. The biggest trends that are taking shape in app creation involve the integration of artificial intelligence, virtual reality and augmented reality. Consumers first began to see the application of this advanced tech with the introduction of Niantic's hit gaming app, Pokemon Go.

AI-enhanced computer programs able to hold audio or text-based conversations that simulate convincingly how a human would interact… Whatever you want to call these code-based dialoguers, they are taking the enterprise by storm. From marketing, customer service, and e-commerce bots helping people purchase everything from flowers to flights, to workforce productivity'manager' bots delivering reminders, deadlines, and tips tailored to individual employees… companies are deploying bots to serve all manner of objectives. Such initiatives are also a common entry point for enterprise adoption of machine learning, but they are also just the tip of the iceberg when it comes to the opportunities and the risks of and artificial intelligence (AI). What follows are three areas companies often overlook when deploying chatbots. Chatbots are now being used by brands for service interactions, including simple outreach, education, feedback and survey collection, questions and answers (Q&A), tips and advice, etc.

Google Assistant wants to help you get your day started. The search giant said Tuesday it's launching a new'visual snapshot' feature in the Google Assistant app that will give users a rundown of all their important meetings, restaurant or movie suggestions, commute times, upcoming bills and more. It's now available on the app for both Android and iOS devices in all languages supported by the Google Assistant app. Google said it's launching a new'visual snapshot' feature in the Assistant app that will give users a rundown of meetings, movie suggestions, commute times, upcoming bills and more Google says the app will provide'curated, helpful information' depending on what time of day it is, where you are and your recent interactions with Google Assistant. Users are encouraged to check back throughout the day for updates.

An important task for a recommender system to provide interpretable explanations for the user. This is important for the credibility of the system. Current interpretable recommender systems tend to focus on certain features known to be important to the user and offer their explanations in a structured form. It is well known that user generated reviews and feedback from reviewers have strong leverage over the users' decisions. On the other hand, recent text generation works have been shown to generate text of similar quality to human written text, and we aim to show that generated text can be successfully used to explain recommendations. In this paper, we propose a framework consisting of popular review-oriented generation models aiming to create personalised explanations for recommendations. The interpretations are generated at both character and word levels. We build a dataset containing reviewers' feedback from the Amazon books review dataset. Our cross-domain experiments are designed to bridge from natural language processing to the recommender system domain. Besides language model evaluation methods, we employ DeepCoNN, a novel review-oriented recommender system using a deep neural network, to evaluate the recommendation performance of generated reviews by root mean square error (RMSE). We demonstrate that the synthetic personalised reviews have better recommendation performance than human written reviews. To our knowledge, this presents the first machine-generated natural language explanations for rating prediction.