Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and occurrence of bad debt. With the advancements in data science; machine learning techniques can be used to build models to accurately predict a customer's propensity-to-pay. Creating effective machine learning models without access to large and detailed datasets presents some significant challenges. This paper presents a case-study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models that are able to predict residential customers entering financial hardship which then reduces their ability to pay energy bills. Incorrect predictions can result in inefficient resource allocation and vulnerable customers not being proactively identified. This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. Seven models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.

Along with a dedicated team of students, Engineer installs air sensors on top of buildings and the backs of motorbike taxis ― known as boda bodas and one of the city's most common forms of transportation ― to collect pollution data from all over the city. The team then uses cloud-based AI software to analyze air particle data in real-time and predict local pollution. These forecasts offer Kampala's communities a way to reduce their risk of exposure and are being used by government agencies to improve air quality on the ground. Engineer and the team at Makerere University are one of 20 organizations selected from more than 2,600 applicants to receive a grant through the Google AI Impact Challenge: the Google.org Through this program, the Makerere team also received coaching and mentorship from Google and DeepMind AI experts over the course of a 9-month AI accelerator.

From stock trades to credit card purchases, the financial services sector has always been a powerhouse of data generation. Today, many financial institutions are harnessing insights from this data--using artificial intelligence and machine-learning tools to serve customers in new, innovative ways and quickly expand their service offerings. According to a 2020 survey of over 150 financial services firms by the Cambridge Centre for Alternative Finance and the World Economic Forum, artificial intelligence is expected to become an essential business driver across the industry, with 77% of respondents anticipating AI will "possess high or very high overall importance to their businesses within two years." The same survey revealed the range of applications of AI and machine learning, from risk management and product development to customer service and client acquisition. So it's no secret that AI and machine learning are playing a foundational role in financial services.

Several years ago, I packed up my life in Cairo, Egypt, and moved to the UK to pursue my PhD – thousands of miles away from everyone I knew and loved. As I settled into my new life, I found myself spending more hours with my laptop than with any other human being. I felt isolated and incredibly homesick. Chatting online with my family back home, I was often in tears, but they had no idea how I was feeling behind my screen (with the exception of a sad face emoticon that I would send). I realised then that our technology and devices – which we consider to be "smart", and helpful in many aspects of our lives – are emotion blind.

Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters required to be calibrated grows. This leads to the ABMS equivalent of the \say{curse of dimensionality}. We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration. We show that surrogate assisted methods perform better than the standard sampling methods. In addition, we show that the XGBoost and Decision Tree SMs are most optimal overall with regards to our analysis.

Increasingly, organizations across many industries and geographies are building and deploying machine learning models and incorporating artificial intelligence into a variety of their different products and offerings. However, as they put AI capabilities into systems that we interact with on a daily basis, it becomes increasingly important to make sure these systems are behaving in a way that's beneficial to the public. When creating AI systems organizations should also consider the ethical and moral implications to make sure that AI is being created for good intentions. Policymakers that want to understand and leverage AI's potential and impact need to take a holistic view of the issues. This includes things like intentions behind AI systems, as well as potential unintended consequences and actions of AI systems.

A burst of technology in the 1960s--the Green Revolution--raised agricultural output significantly across developing economies. Since then, rising incomes have boosted protein consumption worldwide, and elevated new challenges: greenhouse-gas emissions from agriculture are increasing (more than a fifth of all emissions worldwide), while a host of practices, from waste to overfishing, threaten the sustainability of food supplies. The COVID-19 pandemic has brought these concerns to the fore: the disease has disrupted supply chains and demand, perversely increasing the amount of food waste in farms and fields while threatening food security for many. As agriculture gradually regains its footing, participants and stakeholders should be casting an eye ahead, to safeguarding food supplies against the potentially greater and more disruptive effects of climate change. Once again, innovation and advanced technologies could make a powerful contribution to secure and sustainable food production. For example, digital and biotechnologies could improve the health of ruminant livestock, requiring fewer methane-producing animals to meet the world's protein needs. Genetic technologies could play a supporting role by enabling the breeding of animals that produce less methane. Meanwhile, AI and sensors could help food processors sort better and slash waste, and other smart technologies could identify inedible by-products for reprocessing. Data and advanced analytics also could help authorities better monitor and manage the seas to limit overfishing--while enabling boat crews to target and find fish with less effort and waste.

Abstract-- In highway scenarios, an alert human driver will typically anticipate early cutin and cutout maneuvers of surrounding vehicles using only visual cues. Different sizes of the regions around the vehicles are analyzed, evaluating the importance of the interaction between vehicles and the context information in the performance. I. INTRODUCTION One of the closest and most plausible scenarios in the To deal with lane-change prediction of surrounding vehicles, adoption of the autonomous vehicles is autonomous navigation in this paper we pose the problem as an action at SAE L3 (chauffeur) or L4 (autopilot) on highways, recognition problem using visual information from cameras. The most advanced The idea behind our proposal is to use the same source of information automation systems to date are the Highway Chauffeur (visual cues) and the same type of approach (action (HC) and the Highway Autopilot (HA), which includes the recognition) that drivers use to anticipate these maneuvers. HC is mostly considered as L3 and HA as L4[1].

Formal Concept Analysis (FCA) has seen application in different knowledge areas, including Social Network Analysis (SNA). In turn, research has also shown the applicability of SNA in assessing team sports. In this project, to uncover frequent passing sequences of a soccer team, an FCA-based approach is introduced. The approach relies on a minimum cover of implications, the Duquenne-Guigues (DG) basis and the notion that a soccer team's passes describe a social network.

The importance of algorithmic fairness grows with the increasing impact machine learning has on people's lives. Recent work on fairness metrics shows the need for causal reasoning in fairness constraints. In this work, a practical method named FairTrade is proposed for creating flexible prediction models which integrate fairness constraints on sensitive causal paths. The method uses recent advances in variational inference in order to account for unobserved confounders. Further, a method outline is proposed which uses the causal mechanism estimates to audit black box models. Experiments are conducted on simulated data and on a real dataset in the context of detecting unlawful social welfare. This research aims to contribute to machine learning techniques which honour our ethical and legal boundaries.