Tencent, Alibaba, Baidu and JD.com from China are in a global competition with Google/Alphabet, Apple, Facebook, Walmart and Amazon from the USA and SoftBank from Japan. All are agressively searching for talent, intellectual property, market share, logistics and supply chain technology, and presence all around the world. These leading tech-savvy companies have many things in common. Foremost, they are all in pursuit of global growth and the funding, technology and talent to propel that growth. And they all are investing in voice assistance and other forms of AI and robotics.

Amazon confirmed it's rolling out an optional "Brief Mode" that lets Alexa users configure their Echo devices to use chimes and sounds for confirmations, instead of having Alexa respond with her voice. For example, if you ask Alexa to turn on your lights today, she will respond "okay" as she does so. But with Brief Mode enabled, Alexa will instead emit a small chime as she performs the task. The mode would be beneficial to someone who appreciates being able to control their smart home via voice, but doesn't necessarily need to have Alexa verbally confirming that she took action with each command. This is especially helpful for those who have voice-enabled a range of smart home accessories, and have gotten a little tired of hearing Alexa answer back.

It's sad that one of the greatest minds of our time, Professor Stephen Hawking is no more. Nonetheless being one of the most brilliant figures to ever step on planet earth, the world-renowned theoretical physicist and cosmologist had very strong views and concerns about the rise of artificial intelligence. Taking into account Hawking's life history and accomplishments, that is, his discoveries about the four laws of black hole mechanics, the amazing theories about wormholes and time travel, his out of the world wisdom about top-down cosmology, and his books which sold out in millions; we can't afford to ignore his advice on AI. Also known as a fact-based technology dreamer, prof. Hawking voiced his fears that someday in future, a powerful artificial intelligence technology capable of evolving and improving itself could be developed by humans, after which it could turn against the human race.

ORLANDO, Fla. – Speech recognition technologies have improved so much in recent years – thanks to cloud computing and advances in machine learning – that the virtual assistants created by Amazon, Google and Apple have quickly become popular with consumers. So it should come as little surprise that the underlying natural language technology is making inroads at work, too. "I would say that it [enterprise adoption] is in early stages now, but there are certainly basic capabilities here today," Jon Arnold, of J Arnold & Associates, said at the Enterprise Connect conference last week. The main uses for speech recognition in the office will, at least at first, revolve around improving employee productivity and automating workflows. Thanks to advances in artificial intelligence (A.I.) techniques, the accuracy of speech recognition systems has improved significantly, with Google and others passing the 95% accuracy mark. "It has been improving for a long time, but what matters is that it has passed the threshold at which it is on par with human speech recognition," said Arnold, during a keynote presentation at Enterprise Connect.

How do you grow your business while building lasting customer loyalty? Marketers realize this challenge every day. They have access to a tremendous amount of data, yet they struggle with how to optimize and personalize customer experiences. There's always too much to do, and often high-value activities like being analytical and creative get squeezed by recurring tasks that could be simplified with the right tools and assistance. IBM marketing solutions enable marketers to quickly deliver optimized, multi-channel customer journeys with unique AI-powered capabilities such as the Watson Marketing Assistant, automated content tagging and predictive audience identification.

Most of the currently existing voice assistants, like Alexa, Siri, Google Assistant, and Cortana, are generalists. They act as a unifying voice interface to a myriad of controls but rarely support domain-specific expert functionalities. There are efforts to provide more targeted assistant experiences and capabilities around specific areas of applications. In this paper, we discuss several challenges and opportunities in the design of domain-specific voice assistants. We outline a variety of methods to create and utilize an understanding of domain-specific user language and ideas to prototype and study the envisioned user experiences.

Mixed-initiative game design tools combine intelligent agents and human input as collaboration to create novel and interesting content. Traditionally, these systems utilize graphical control-based interfaces. These interfaces can be complex and not reflective of designer intent. Given these issues we propose exploring conversational interfaces for mixed-initiative game design tools. We propose a case-study involving a system for co-creating variations of the game Pong as an initial step towards the exploration of the topic. In addition, we present some of the issues involving the design and implementation of conversational interfaces in mixed-initiative game design tools.

Early detection and routine follow up of dementia are important because it can slow down the progress of the disease. The most common way to detect dementia is based on cognitive tests. The tests are usually done in the clinical setup with the help of a psycho-metrically trained examiner. Revised Hasegawa’sDementia Scale (HDS-R) is one of the prominent screening tests for dementia. We propose a method for early dementia detection by using a Virtual Personal Assistant (VPA) on a computer that has a natural language user interface, such asAmazon Echo, Apple Siri, Google Home, Microsoft Cortana, Soft bank Pepper, Sharp RoBoHon, etc. In our proposal, we consider HDS-R as a guideline to examine dementia. A VPA extracts the necessary features from the verbal and interactive response of the patient to compute the level of dementia.Such implicit checking is physically and mentally much comfortable for old people. We believe the proposed method will be able to contribute future society.

Our work uses the notion of theory of mind to enable an interactive agent to keep track of the state of knowledge, goals and intentions of the human user, and to engage in and initiate sophisticated interactive behaviors using decision-theoretic paradigm of maximizing expected utility. Currently, systems like Google Now and Siri mostly react to user’s requests and commands using hand-crafted responses, but they cannot initiate intelligent communication and plan for longer term interactions. The reason is that they lack a clearly defined general objective of the interaction. Our main premise is that communication and interaction are types of action, so planning for communicative and interactive actions should be based on a unified framework of decisiontheoretic planning. To facilitate this, the system’s state of knowledge (a mental model) about the world has to include probabilistic representation of what is known, what is uncertain, and how things change as different events transpire. Further, the state of user’s knowledge and intentions (the theory of the user’s mind) needs to include precise specification of what the system knows, and how uncertain it is, about the user’s mental model, and about her desires and intentions. The theories of mind may be further nested to form interactive beliefs. Finally, decision-theoretic planning proposes that desirability of possible sequences of interactive and communicative actions be assessed as expected utilities of alternative plans.We describe our preliminary implementation using the Open CYC system, called MARTHA, and illustrate it in action using two simple interactive scenarios.

This article presents a novel approach to monitoring athletes' behavioral changes to predict a decline in motivation. When the system detects such a decline, it refers the athlete to her coach, along with a concise explanation of the detected behavioral changes. The coach thus has all the information needed for a prompt, targeted intervention.