Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises given training examples. In contrast to most forms of machine learning, ILP can learn human-readable hypotheses from small amounts of data. As ILP approaches 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main ILP learning settings. We describe the main building blocks of an ILP system. We compare several ILP systems on several dimensions. We describe in detail four systems (Aleph, TILDE, ASPAL, and Metagol).
Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.
Akelius buys, upgrades and manages residential properties. The company owns 47,000 apartments in Sweden, Denmark, Germany, France, Canada, England and the United States. We are a rapidly growing international company more than eight hundred employees around the world. An integral part of our company is the Technology department. The Development team consists of more than one hundred employees mostly based in Berlin.
Almost half of UK grocery retail directors say replenishment is still driven by gut feel, according to research by Blue Yonder, which supplies predictive applications of retail. It said that interviews with 750 grocery managers and directors in the USA, UK, Germany and France, showed that despite a rise in accurate machine learning algorithms for automated replenishment and demand planning, 46 per cent of surveyed directors in the UK say replenishment is still an entirely manual process. Some 85 per cent of respondents identified automation as a key tool for making the fast decisions needed to meet customer demand. The research also identified that 31 per cent of directors in the UK feel there are now too many decisions to be made manually, with the same number stating that gut feel is slowing them down. The research also found that 62 per cent of UK directors say they have invested in replenishment optimisation in the last two years; 31 per cent say they will be investing further in replenishment optimisation in the next two years.
A global study of 750 grocery decision-makers reveals key challenges in meeting customer expectations. Grocery retailers face many challenges and barriers to delivering on their customer experience promises of'any time, anywhere'. To understand these challenges, we commissioned a global survey to discover how grocery retail managers and directors feel about their customer experience delivery. The research also uncovered thoughts on how to overcome the hurdles of delivering the best customer experience. The findings are the result of interviews with 750 grocery managers and directors across the globe, in the UK, USA, Germany and France.
The efficiency of current cargo screening processes at sea and air ports is largely unknown as few benchmarks exists against which they could be measured. Some manufacturers provide benchmarks for individual sensors but we found no benchmarks that take a holistic view of the overall screening procedures and no benchmarks that take operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. Our aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right commodity-threat combination in order to maximise detection rates. In this paper we present our ideas for developing such a system and highlight the research challenges we have identified. Then we introduce our first case study and report on the progress we have made so far.