For millions of people with swallowing disorders, preventing potentially deadly aspiration pneumonia requires following prescribed safe eating strategies. But adherence is poor, and caregivers’ ability to encourage adherence is limited by the onerous and socially aversive need to monitoring another’s eating. We have developed an early prototype for an intelligent assistant that monitors adherence and provides feedback to the patient, and tested monitoring precision with healthy subjects for one strategy called a “chin tuck.” Results indicate that adaptations of current generation machine vision and personal assistant technologies could effectively monitor chin tuck adherence, and suggest the feasibility of a more general assistant that encourages adherence to a wide range of safe eating strategies.

The problem of designing recommender systems for scholarly article citations has been actively researched with more than 200 publications appearing in the last two decades. In spite of this, no definitive results are available about what approaches work best. Arguably the most important reason for this lack of consensus is the dearth of standardised test collections and evaluation protocols, such as those provided by TREC-like forums. CiteSeerX, a "scholarly big dataset" has recently become available. However, this collection provides only the raw material that is yet to be moulded into Cranfield style test collections. In this paper, we discuss the limitations of test collections used in earlier work, and describe how we used CiteSeerX to design a test collection with a well-defined evaluation protocol. The collection consists of over 600,000 research papers and over 2,500 queries. We report some preliminary experimental results using this collection, which are indicative of the performance of elementary content-based techniques. These experiments also made us aware of some shortcomings of CiteSeerX itself.

An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggregation technique outperforms Yelp's proprietary filter, as well as baseline techniques from recommender systems.

In this paper, we propose an intelligent dialogue agent for the IoT home. The goal of the proposed system is to efficiently control IoT devices with natural spoken dialogue. This system is made up of the following components: Spoken Language Understanding for analyzing textual input and understanding user intention, Dialogue Management with a State Manager that consists of dialogue policies, Context Manager for understanding the environment, Action Planner responsible for generating a sequence of actions to achieve user intention, Things Manager for observing and controlling IoT devices, and Natural Language Generation that generates natural language from computer-based representation. This system is fully implemented in software and is evaluated in a real IoT home environment.

By now, you've probably met Siri. You speak to her, give her directions, and even hold conversations with her; basically you've made the most of her capabilities as a handy, smartphone-based digital assistant. The person behind this novel addition to smartphone technology is Dag Kittlaus. Since co-founding Siri, Kittlaus has gone on to become the director of iPhone Apps for Apple, heading the Siri and speech recognition division. From there, he left Apple to work on his vision of what the next generation of AI should be.

Silicon Valley is seeking poets, writers, comedians, and other artistic people to help humanize the personalities of artificial intelligence tools. Demand for chatting virtual assistants and other artificial intelligence (AI) products is creating favorable job prospects for writers, poets, comedians, and other people of artistic persuasion in Silicon Valley. The industry is tapping them to engineer the personalities of AI tools to make them capable of seamless interaction with people. AI writers are tasked with imbuing the AIs with natural-seeming conversational capabilities. Writers for virtual assistants must typically concoct a backstory for these assistants, and inject personality quirks into even the most mundane operations.

It's almost inevitable, given dire warnings about the threat to the human race by some prominent commentators, including a billionaire and visionary entrepreneur, and one of the world's most prominent physicists, that you would associate Artificial Intelligence (AI) with the rise of the machines, aka, Skynet, aka the Terminator. Futurists predict that it's only a matter of time before we create a computer that's smarter than the human brain, and after that the very smart machines can create even smarter machines, eventually leapfrogging the capacity of human intelligence. It doesn't take a Hollywood imagination to predict the doomsday scenario of super smart computers creating machines with capabilities vastly beyond the human ken – after all, even the lowliest computer today can out-compute, at vastly superior speeds, the average human. Humans however, have always been able to think, and therefore outsmart fast, efficient computers programmed to do whatever they're programmed to do. Cognitive computing, artificial intelligence, machine learning systems, call it what you will, is on the cusp of a technology explosion.

Technology advances not so much when it exhibits innovation, but when it becomes truly practical for everyday people. In 2016, we'll see an acceleration of that shift of technologies from the drawing board and geek-only curiosities to consumer devices that change our lives in ways small and big. Here are a handful of technologies that are on the cusp of major action in the coming year. For decades, artificial intelligence was a thing best understood by sci-fi fanatics and screenwriters. That started to change n 2011 with Apple's Siri voice assistant, but 2015 turned out to be a watershed year for computer algorithms that could ape human thought and interaction.

Hitachi Ltd. is offering new competition to SoftBank Group Corp.'s humanoid robot Pepper in the market for robot assistants. Hitachi's Emiew3, revealed last week, can be controlled through centralized software that uses a network camera to detect people in need of help. The system can dispatch the humanoids for assistance. They can travel at 3.7 miles per hour–or about three times the speed of SoftBank's Pepper. The Emiew3 can communicate with humans and answer their questions in four languages, including English and Chinese, the company said.

Learn Amazon's recommendation technique and enhance your sales. Learn a new Machine Learning or Data Scientist skill and boost your Resume. Show the appropriate video to people visiting your website and hook them. Learn which news or blog post to display to your website's visitors. Learn how to choose the best technique for a certain recommendation task.