Until one and a half years ago, Devidas Lonkar from Chakan town of Pune district had to depend on local fertiliser and pesticide sellers to resolve diseases and fungal issues in his crops. Hailing from an agrarian background, the 26-year-old farmer grows sugarcane, cabbage, cauliflower as well as beetroot and groundnuts across a 7-acre plot. "I would describe the symptoms of fungus or disease to the shopkeeper, to which he would then suggest various pesticides and add-ons. It took me a while before realising that these shopkeepers only suggested chemicals with short-lived efficiency that would inevitably bring farmers back to them within a couple of months," he says. "This app ended up saving me a lot of money as well as time. Sitting at home, I can now diagnose plant diseases and have already saved about Rs 1-1.5 lakh in a year that I would otherwise spend on fertilisers," he mentions.

The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient information of the target distribution, it can explore the state space much more efficiently than the random-walk proposals. However, probabilistic inference involving multi-modal distributions is very difficult for standard HMC method, especially when the modes are far away from each other. Sampling algorithms are then often incapable of traveling across the places of low probability. In this paper, we propose a novel MCMC algorithm which aims to sample from multi-modal distributions effectively. The method improves Hamiltonian dynamics to reduce the autocorrelation of the samples and uses a variational distribution to explore the phase space and find new modes. A formal proof is provided which shows that the proposed method can converge to target distributions. Both synthetic and real datasets are used to evaluate its properties and performance. The experimental results verify the theory and show superior performance in multi-modal sampling.

The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and targeted policymaking, where in some cases legislative or regulatory frameworks for fairness exist and define specific protected classes. In this paper we study a fundamental challenge to assessing disparate impacts in practice: protected class membership is often not observed in the data. This is particularly a problem in lending and healthcare. We consider the use of an auxiliary dataset, such as the US census, that includes class labels but not decisions or outcomes. We show that a variety of common disparity measures are generally unidentifiable aside for some unrealistic cases, providing a new perspective on the documented biases of popular proxy-based methods. We provide exact characterizations of the sharpest-possible partial identification set of disparities either under no assumptions or when we incorporate mild smoothness constraints. We further provide optimization-based algorithms for computing and visualizing these sets, which enables reliable and robust assessments -- an important tool when disparity assessment can have far-reaching policy implications. We demonstrate this in two case studies with real data: mortgage lending and personalized medicine dosing.

Attention networks show promise for both vision and language tasks, by emphasizing relationships between constituent elements through appropriate weighting functions. Such elements could be regions in an image output by a region proposal network, or words in a sentence, represented by word embedding. Thus far, however, the learning of attention weights has been driven solely by the minimization of task specific loss functions. We here introduce a method of learning attention weights to better emphasize informative pair-wise relations between entities. The key idea is to use a novel center-mass cross entropy loss, which can be applied in conjunction with the task specific ones. We then introduce a focused attention backbone to learn these attention weights for general tasks. We demonstrate that the focused attention module leads to a new state-of-the-art for the recovery of relations in a relationship proposal task. Our experiments show that it also boosts performance for diverse vision and language tasks, including object detection, scene categorization and document classification.

Recommender systems have become ubiquitous, transforming user interactions with products, services and content in a wide variety of domains. In content recommendation, recommenders generally surface relevant and/or novel personalized content based on learned models of user preferences (e.g., as in collaborative filtering [Breese et al., 1998, Konstan et al., 1997, Srebro et al., 2004, Salakhutdinov and Mnih, 2007]) or predictive models of user responses to specific recommendations. Well-known applications of recommender systems include video recommendations on YouTube [Covington et al., 2016], movie recommendations on Netflix [Gomez-Uribe and Hunt, 2016] and playlist construction on Spotify [Jacobson et al., 2016]. It is increasingly common to train deep neural networks (DNNs) [van den Oord et al., 2013, Wang et al., 2015, Covington et al., 2016, Cheng et al., 2016] to predict user responses (e.g., click-through rates, content engagement, ratings, likes) to generate, score and serve candidate recommendations. Practical recommender systems largely focus on myopic prediction--estimating a user's immediate response to a recommendation--without considering the long-term impact on subsequent user behavior. This can be limiting: modeling a recommendation's stochastic impact on the future affords opportunities to trade off user engagement in the near-term for longer-term benefit (e.g., by probing a user's interests, or improving satisfaction).

This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels. The net effect of this is to transform the underlying problem from an ordinal regression task to a (structured) classification task which we solve with conditional random fields, thereby achieving a coherent and probabilistic model in which all model parameters are jointly learnt. Importantly, we show that although we have cast ordinal regression to classification, our method still fall within the class of decomposition methods in the ordinal regression ontology. This is an important link since our experience is that many applications of machine learning to healthcare ignores completely the important nature of the label ordering, and hence these approaches should considered naive in this ontology. We also show that our model is flexible both in how it adapts to data manifolds and in terms of the operations that are available for practitioner to execute. Our empirical evaluation demonstrates that the proposed approach overwhelmingly produces superior and often statistically significant results over baseline approaches on forty popular ordinal regression models, and demonstrate that the proposed model significantly out-performs baselines on synthetic and real datasets. Our implementation, together with scripts to reproduce the results of this work, will be available on a public GitHub repository.

Digital Archaeoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines - computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. - to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project.

The invention of religion is a big bang in human history. Gods and spirits helped explain the unexplainable, and religious belief gave meaning and purpose to people struggling to survive. But what if everything we thought we knew about religion was wrong? What if belief in the supernatural is window dressing on what really matters--elaborate rituals that foster group cohesion, creating personal bonds that people are willing to die for. Anthropologist Harvey Whitehouse thinks too much talk about religion is based on loose conjecture and simplistic explanations. Whitehouse directs the Institute of Cognitive and Evolutionary Anthropology at Oxford University. For years he's been collaborating with scholars around the world to build a massive body of data that grounds the study of religion in science. Whitehouse draws on an array of disciplines--archeology, ethnography, history, evolutionary psychology, cognitive science--to construct a profile of religious practices. Whitehouse's fascination with religion goes back to his own groundbreaking field study of traditional beliefs in Papua New Guinea in the 1980s.

In his latest novel, Machines Like Me, Ian McEwan reimagines what 1980s Britain might have looked like if certain iconic events had swung a different way: Argentina winning the Falklands war; Margaret Thatcher battling Tony Benn for power. And then there's the catalyst for the book – what might have happened if Alan Turing had achieved a major breakthrough in the field of artificial intelligence. As with most visions of AI's future – or hypothetical past – the author speculates a dystopia in which sentient, anthropomorphous robots are embedded into our everyday lives. The pros and cons of AI are debated at an ethical and moral level; with less credence given to the practicality or the technological constraints. Whilst undoubtedly a necessity for a science fiction novel, in the real world, this lack of practical consideration for AI and the related constraints has been responsible for a huge amount of hyperbole in the enterprise technology market; especially in relation to CRM.

We investigate the data complexity of answering queries mediated by metric temporal logic ontologies under the event-based semantics assuming that data instances are finite timed words timestamped with binary fractions. We identify classes of ontology-mediated queries answering which can be done in AC0, NC1, L, NL, P, and coNP for data complexity, provide their rewritings to first-order logic and its extensions with primitive recursion, transitive closure or datalog, and establish lower complexity bounds.