Digital technologies and AI offer a new wave of opportunities to turn data into actionable insights – creating a balance between social, environmental, and economic opportunities. In 2018, it's safe to say that the Internet, the World Wide Web, and the myriad of technologies derived from their development are all here to stay. With the ceaseless amalgamation of these various innovations, engineers are creating a cyber-physical world where pervasively interconnected objects, things, and processes can potentially unlock a breadth of unprecedented opportunities. However, I should point out that encapsulating the entire medley of possibilities afforded by these technologies is a considerable endeavour requiring a far longer and more comprehensive overview – perhaps in the form of a book, or three – than this article can offer in isolation. As such, I'll concentrate on something closer to my own work: smart cities.

Corporate mobility is often based on fixed assignments of vehicles to employees. Relaxing these fixed assignments while including alternatives such as public transportation, bike sharing, and taxis for the employees' business and private trips could increase fleet utilization, foster the use of battery electric vehicles, and lower the costs for the companies' transportation needs. A system in which all employees specify their mobility demands gives rise to optimization problems concerning the assignment of company cars or alternative modes of transport to satisfy the needs of the users. In this work we introduce the NP-hard mobility offer allocation problem which has similarities to interval scheduling problems. We propose an integer linear programming model and heuristic solution approaches based on large neighborhood search. The efficiency of these methods is based on the usage of suitable conflict graphs. In a computational study, the approaches are evaluated and it is demonstrated that, depending on instances and run-time requirements, either solving the model exactly using a general purpose integer linear programming solver, fast greedy heuristics, or the adaptive large neighborhood search outperforms the others.

Entity resolution seeks to merge databases as to remove duplicate entries where unique identifiers are typically unknown. We review modern blocking approaches for entity resolution, focusing on those based upon locality sensitive hashing (LSH). First, we introduce $k$-means locality sensitive hashing (KLSH), which is based upon the information retrieval literature and clusters similar records into blocks using a vector-space representation and projections. Second, we introduce a subquadratic variant of LSH to the literature, known as Densified One Permutation Hashing (DOPH). Third, we propose a weighted variant of DOPH. We illustrate each method on an application to a subset of the ongoing Syrian conflict, giving a discussion of each method.

Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training. An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation.

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.

Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

The recent practical successes [26] of Artificial Intelligence (AI) programs of the Reinforcement Learning and Deep Learning varieties in game playing, natural language processing and image classification, are now calling attention to the envisioned pitfalls of their hypothetical extension to wider domains of human behavior. Several voices from the industry and academia are now routinely raising concerns over the advances [49] of often heavily media-covered representatives of this new generation of programs such as Deep Blue, Watson, Google Translate, AlphaGo and AlphaZero. Most of these cutting-edge algorithms generally fall under the class of supervised learning, a branch of the still evolving taxonomy of Machine Learning techniques in AI research. In most cases the implementation choice is artificial neural networks software, the workhorse of the Connectionism school of thought in both AI and Cognitive Psychology. Confronting the current wave of connectionist architectures, critics usually raise issues of interpretability (Can the remarkable predictive capabilities be 1 trusted in real-life tasks? Are these capabilities transferable to unfamiliar situations or to different tasks altogether? How informative are the results about the real world; about human cognition?

The SA Innovation Summit as an annual flagship event on the South African Innovation Calendar, is a platform for nurturing, developing and showcasing African innovation, as well as facilitating innovation thought-leadership. Created to support and promote innovation and facilitate collaboration within its own eco-system, the initiative brings together corporates, thought leaders, inventors, entrepreneurs, academia and policy makers to amplify South Africa's renowned competitive edge and to inspire sustained economic growth across the continent of Africa. The outcomes achieved by the Summit, is a powerful platform to bring together thought leaders and accelerate innovation in South Africa, and into the African continent as whole. MIIA ill also be represented at the South African Innovation Summit and invitethe MIIA community to also join the 48-hour hackathon being held in Cape Town Stadium from 5 - 7 September 2017.

Text-to-speech technology has advanced greatly over the past two decades. Once defined by the robotic sounding voices that they produced, text-to-speech voices today can sound just as lifelike as an actual human. Today, making a natural sounding text-to-speech voice is labor intensive and expensive. The two most popular methods, HMM and USS, require hours of recordings from a voice actor. Then, computer programmers with an understanding of linguistics must break down all of that audio into the tiniest possible pieces, called phonemes, and appropriately tag them and define the rules for when each individual unit of speech should be used.

Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterized by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data. This paper presents a comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing "smart". The evolvement of deep learning technologies and their advantages over traditional machine learning are firstly discussed.