It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.

A time series can have different properties depending on the generating process and how the process is measured. There are many properties that describe a time series: 1) stationary 2) continuous 3) random 4) periodic. I'll focus on non-random signals for this post, but I would recommend Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems By Vincent Granville, Ph.D. to anyone interested in random processes. Most data scientist working with B2B or B2C time series are primarily working with non-continuous, or discrete, processes. Discrete means that the data are collected at fixed time intervals.

In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modelling assumptions. The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data. This is achieved by automatically gauging the models' ability to generate data that is similar to the observed data. Importantly, the criterion follows from the model class itself and is therefore directly applicable to a broad range of inference problems with varying data types. The proposed data consistency criterion is illustrated and evaluated using three synthetic and two real data sets.

Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide new opportunities for transgender people to overcome these barriers by sharing their personal health experiences. Our research employs a computational framework to collect tweets from self-identified transgender users, detect those that are health-related, and identify their information needs. This framework is significant because it provides a macro-scale perspective on an issue that lacks investigation at national or demographic levels. Our findings identified 54 distinct health-related topics that we grouped into 7 broader categories. Further, we found both linguistic and topical differences in the health-related information shared by transgender men (TM) as com-pared to transgender women (TW). These findings can help inform medical and policy-based strategies for health interventions within transgender communities. Also, our proposed approach can inform the development of computational strategies to identify the health-related information needs of other marginalized populations.

Much of the debate over how artificial intelligence (AI) will affect geopolitics focuses on the emerging arms race between Washington and Beijing, as well as investments by major military powers like Russia. And to be sure, breakthroughs are happening at a rapid pace in the United States and China. But while an arms race between superpowers is riveting, AI development outside of the major powers, even where advances are less pronounced, could also have a profound impact on our world. The way smaller countries choose to use and invest in AI will affect their own power and status in the international system. Middle powers--countries like Australia, France, Singapore, and South Korea--are generally prosperous and technologically advanced, with small-to-medium-sized populations.

The Herald spoke to industry leaders Mahmood Hikmet, R&D Coordinator, Ohmio; Matthew Ensor, Business Director - Advisory, Beca Diane Edwards, General Manager People, Systems and Technology, Ports of Auckland; Ben Reid, Executive Director, AI Forum NZ and Coby Duggan, General Manager, Volvo New Zealand to understand the AI opportunities for New Zealand, and what it could mean for the future of our roads and infrastructure. It's already out there and has been for decades! There is so much artificial intelligence already around you and not just on the roads. Ensor: It is hard to prepare when there is so much uncertainty around what changes artificial intelligence will create. We need to wait before making bets on which emerging technologies will dominate.

If I wanted to show my distaste for the direction the US is going by boycotting American PC hardware, software and services, could it be done? You could certainly eliminate a lot of American products, but you might be giving up features without getting any ethical benefits. For example, more than a billion people already manage without a lot of American technology because they live in China or Russia. While I share your distaste for the Trump regime, Xi Jinping and Vladimir Putin are not exactly choirboys. And while Trump is scapegoating immigrants, more than half of America's top technology companies were co-founded by immigrants or the children of immigrants.

People are using AI-powered tools everyday, without even knowing it. Think of the smartphone's autocorrect (if error prone) feature, or your email provider's spam filter. But should people balk when they realise AI is being used not only to help diagnose a condition, but also replace or inform a clinician's clinical decision? This is a theme Alin Ungureanu, former CIO, health sector consultant and now CEO of Chelmer Limited, explores in a paper for his masters in health informatics at AUT. His paper identifies the complexity of the subject, looks at current trends and paints a possible future.

What will happen when we've built machines as intelligent as us? According to the experts this incredible feat will be achieved in the year 2062 – a mere 44 years away – which certainly begs the question: what will the world, our jobs, the economy, politics, war, and everyday life and death, look like then? Fortunately, Toby Walsh, Scientia Professor of Artificial Intelligence (AI) at UNSW has done the research for us. An avid sci-fi fan from childhood, Walsh, who also leads the Algorithmic Decision Theory group at Data61 – Australia's Centre of Excellence for ICT Research, has long been fascinated by robots, machines and the future. In 2017, he published his first book, It's Alive!, in which he tells the story of AI and how it is already affecting our societies, economies and interactions.

Following a particular news story online is an important but difficult task, as the relevant information is often scattered across different domains/sources (e.g., news articles, blogs, comments, tweets), presented in various formats and language styles, and may overlap with thousands of other stories. In this work we join the areas of topic tracking and entity disambiguation, and propose a framework named Story Disambiguation - a cross-domain story tracking approach that builds on real-time entity disambiguation and a learning-to-rank framework to represent and update the rich semantic structure of news stories. Given a target news story, specified by a seed set of documents, the goal is to effectively select new story-relevant documents from an incoming document stream. We represent stories as entity graphs and we model the story tracking problem as a learning-to-rank task. This enables us to track content with high accuracy, from multiple domains, in real-time. We study a range of text, entity and graph based features to understand which type of features are most effective for representing stories. We further propose new semi-supervised learning techniques to automatically update the story representation over time. Our empirical study shows that we outperform the accuracy of state-of-the-art methods for tracking mixed-domain document streams, while requiring fewer labeled data to seed the tracked stories. This is particularly the case for local news stories that are easily over shadowed by other trending stories, and for complex news stories with ambiguous content in noisy stream environments.