"Computer systems can automatically detect and interpret what is happening on video surveillance cameras; Siri allows anyone to have a personal assistant in their pocket; Watson has beaten two former champions on Jeopardy and Google driverless cars have driven over 500 000km accident-free. Modern technology is increasingly intelligent," says Suren Govender, Accenture Analytics MD. With the growing availability of sensors, better algorithms for data analytics and growing computational power, these intelligent technologies are becoming more prevalent and are being incorporated in everyday life and business. "The cognitive era is about thinking itself – how we gather information, access it and make decisions," notes Hamilton Ratshefola, country GM at IBM South Africa. Cognitive analytics engines have the ability to build knowledge and learn, they understand natural language, reason and interact more naturally with human beings than traditional programmable systems.

A big claim was made at a conference in London on Thursday. Dave Coplin, chief envisioning officer at Microsoft UK, told an audience of business leaders at an AI conference that AI is "the most important technology that anybody on the planet is working on today." Before we go any further, it's worth putting that claim into perspective. There are a number of consumer-facing AI products already out there on the market that are getting better all the time -- Microsoft's Cortana, Amazon's Alexa, and Apple's Siri. But AI also has the potential to support crucial scientific research into everything from autonomous cars to cancer research.

POI recommendation provides users personalized location recommendation. It helps users to explore new locations and filter uninteresting places that do not match with their interests. Multiple factors influence users to choose a POI, such as user's categorical preferences, temporal activities and location preferences as well as popularity of a POI. In this work, we define a unified framework that takes all these factors into consideration. None of the previous POI recommendation systems consider all four factors: Personal preferences, spatial (location) preferences, temporal influences and POI popularity. This method aims to provide users with a list of recommendation of POIs within a geo-spatial range that should match with their temporal activities and categorical preferences. Experimental results on real-world data show that the proposed recommendation framework outperforms the baseline approaches.

Learners’ skills decay during gaps in instruction, since they lack the structure and motivation to continue studying. To meet this challenge, the PAL3 system was designed to accompany a learner throughout their career and mentor them to build and maintain skills through: 1) the use of an embodied pedagogical agent (Pal), 2) a persistent learning record that drives a student model which estimates forgetting, 3) an adaptive recommendation engine linking to both intelligent tutors and traditional learning resources, and 4) game-like mechanisms to promote engagement (e.g., leaderboards, effort-based point rewards, unlocking customizations). The design process for PAL3 is discussed, from the perspective of insights and revisions based on a series of formative feedback and evaluation sessions.

We present an approach to creating conversational agents that are capable of returning appropriate responses to natural language input. The approach described consists of a genetic algorithm used as a feature selection technique to evolve a subset of random features towards a set of features that are more relevant to the language used in the domain; therefore improving the conversational agent's ability to return appropriate responses. The results show that over multiple iterations of the evolutionary process the genetic algorithm was able to filter out unfit features. After the evolutionary process the features that were found to be relevant were tested on an unseen test set and the algorithm achieved an accuracy of 72.678%

Studies of online social behaviour indicate that users often fail to specify privacy settings that match their privacy behaviour. This issue has caused a dilemma whether to use publicly available data for targeted advertisement and personalization. As a possible approach to manage this dilemma, we propose a collaborative filtering method that exploits homophily to build a probabilistic model. Such a model can indicate the likelihood that a given public profile is meant to be private. Here, we provide the results of an analysis of a set of observable variables to be used in a neighbourhood-based manner. In addition, we establish a social graph augmented with privacy information. Users in the graph are then transformed into a set of latent features, uncovering informative factors to infer privacy preferences.

Regularisation is typically applied to the optimisation objective of matrix factorisation methods in order to avoid over-fitting. In this paper, we explore the use of regularisation to enhance the diversity of the recommendations produced by these methods. Given a matrix of pairwise item distances, we add regularisation terms dependent on the item distances to the accuracy objective of a learning to rank matrix factorisation formulation. We examine the impact of these regularisers on the latent factors produced by the algorithm and show that such regularisation does indeed promote diversity. The regularisation comes at a cost of performance in terms of accuracy and ultimately the approach cannot greatly enhance diversity without a consequent fall-off in accuracy.

Recommender systems face several challenges, e.g., recommending novel and diverse items and generating helpful explanations. Where algorithms struggle, people may excel. We therefore designed CrowdLens to explore different workflows for incorporating people into the recommendation process. We did an online experiment, finding that: compared to a state-of-the-art algorithm, crowdsourcing workflows produced more diverse and novel recommendations favored by human judges;some crowdworkers produced high-quality explanations for their recommendations, and we created an accurate model for identifying high-quality explanations;volunteers from an online community generally performed better than paid crowdworkers, but appropriate algorithmic support erased this gap. We conclude by reflecting on lessons of our work for those considering a crowdsourcing approach and identifying several fundamental issues for future work.

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.

Gary Grossman is a futurist and public relations and communications marketing executive with Edelman. It's easy to see a rainbow when it's in the distance, but more difficult to discern when you are in its midst. Klaus Schwab, the founder of the World Economic Forum, says the impending "transformation will be unlike anything humankind has experienced before." Digital technologies now surround us, with many people having multiple devices for business and personal use. When combined with the Internet of Things and its assortment of embedded sensors and connected devices in the home, the enterprise and the world at large, we will have created a digital intelligence network that transcends all that has gone before.