Juba recently proposed a formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. The main shortcoming of this formulation of the task is that it assumes access to full-information (i.e., fully specified) examples; relatedly, it offers no role for declarative background knowledge, as such knowledge is rendered redundant in the abduction task by complete information. In this work we extend the formulation to utilize such partially specified examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We also show how to use knowledge in the form of graphical causal models to refine the proposed hypotheses. Finally, we observe that when a small explanation exists, it is possible to obtain a much-improved guarantee in the challenging exception-tolerant setting. Such small, human-understandable explanations are of particular interest for potential applications of the task.

A Gartner study poses that, "By 2019, IT service desks utilising machine learning enhanced technologies will free up to 30% of support capacity." In addition, Edward Carbutt, Executive Director at Marval Africa, believes the features that machine learning introduced will also add a tier of intelligent automation to traditional IT service desks. This will aid decision-making, enhancing staff productivity and opening up a level of smarter self-service for the end user. "The faster networks become, the more data is consumed and generated," says Carbutt. "In today's digital world, with its fast networks and constantly evolving technology, information is being accumulated at a rapid pace. This poses challenges for IT Service Management (ITSM) teams, who are inundated with enormous data streams, often too large to process manually. However, the application of Machine Learning to sift, sort, analyse and manage Big Data could help to simplify the tasks of ITSM."

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and customer ratings, etc. We build a bridge between RBM and tensor network states (TNS) widely used in quantum many-body physics research. We devise efficient algorithms to translate an RBM into the commonly used TNS. Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures. Revealing these general and constructive connections can cross-fertilize both deep learning and quantum many-body physics. Notably, by exploiting the entanglement entropy bound of TNS, we can rigorously quantify the expressive power of RBM on complex data sets. Insights into TNS and its entanglement capacity can guide the design of more powerful deep learning architectures. On the other hand, RBM can represent quantum many-body states with fewer parameters compared to TNS, which may allow more efficient classical simulations.

CAPE TOWN (Reuters) - South Africa's parliament said on Wednesday that President Jacob Zuma will deliver the state-of-the-nation address as planned on Feb 8 despite calls from within the ruling party and the opposition for the scandal-plagued leader.

In that room, Masiyiwa had a short conversation with my colleague deans of law schools of Kenyan universities. Every law school was represented. The deans of the University of Nairobi, Kenyatta University, JKUAT, Mount Kenya, CUEA, Kisii, Nazarene and Daystar were present. Riara, Egerton and Kabarak were not in attendance but they had sent their comments beforehand.

Drones, or unmanned aerial vehicles (UAV), have been used for more than three decades, but in the last few years drones are increasingly being developed and used for commercial purposes. But while inventors and entrepreneurs in Western countries struggle with strict regulations, many African countries are proving very innovative and accepting in terms of drone usage across industries. From Kigali to Khartoum, pioneers are using drones to tackle some of the continent's current challenges. In Rwanda, drones deliver blood to almost half of the country's blood transfusion centres. In Malawi, UAVs deliver HIV test kits to and from remote parts of the country.

When you've got Egyptian heritage and live in the West, something funny happens when you meet another Egyptian. We get giddy, we smile a lot, we act as if we've known each other forever. After some coffee and a quick tour of his California bungalow, the graphic artist known as Ganzeer hands me a stack of some pages from The Solar Grid. The black-and-white pages are crinkled and dried after being soaked in ink. Each panel looks like it may have taken hours.

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Crisis responders are increasingly using social media, data and other digital sources of information to build a situational understanding of a crisis situation in order to design an effective response. However with the increased availability of such data, the challenge of identifying relevant information from it also increases. This paper presents a successful automatic approach to handling this problem. Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts. Informative messages are tagged for actionable data -- for example, people in need, threats to rescue efforts, changes in environment, and so on. In all, eight categories of actionability are identified. The two components -- informativeness and actionability classification -- are packaged together as an openly-available tool called Emina (Emergent Informativeness and Actionability).

We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.