Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images. However, empirical studies have shown that variational autoencoders (VAE) and generative adversarial networks (GAN) lack the generalization ability that occurs naturally in human perception. For example, humans can visualize a woman smiling after only seeing a smiling man. On the contrary, the standard conditional VAE (cVAE) is unable to generate unseen attribute combinations. To this end, we extend cVAE by introducing a multilinear latent conditioning framework that captures the multiplicative interactions between the attributes. We implement two variants of our model and demonstrate their efficacy on MNIST, Fashion-MNIST and CelebA. Altogether, we design a novel conditioning framework that can be used with any architecture to synthesize unseen attribute combinations.

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.

Unmanned Aerial Systems (UAS) are being increasingly deployed for commercial, civilian, and military applications. The current UAS state-of-the-art still depends on a remote human controller with robust wireless links to perform several of these applications. The lack of autonomy restricts the domains of application and tasks for which a UAS can be deployed. Enabling autonomy and intelligence to the UAS will help overcome this hurdle and expand its use improving safety and efficiency. The exponential increase in computing resources and the availability of large amount of data in this digital era has led to the resurgence of machine learning from its last winter. Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS. We first begin motivating this chapter by discussing the application, challenges, and opportunities of the current UAS in the introductory section. We then provide an overview of some of the key deep learning and reinforcement learning techniques discussed throughout this chapter. A key area of focus that will be essential to enable autonomy to UAS is computer vision. Accordingly, we discuss how deep learning approaches have been used to accomplish some of the basic tasks that contribute to providing UAS autonomy. Then we discuss how reinforcement learning is explored for using this information to provide autonomous control and navigation for UAS. Next, we provide the reader with directions to choose appropriate simulation suites and hardware platforms that will help to rapidly prototype novel machine learning based solutions for UAS. We additionally discuss the open problems and challenges pertaining to each aspect of developing autonomous UAS solutions to shine light on potential research areas.

Central University of Technology, South Africa, is introducing an Artificial Intelligence university programme powered by Microsoft. To firstly skill employees with the in-demand skill and secondly address the demand for the skill in the province and South Africa in general. The Artificial Intelligence university programme is developed by Microsoft and will be delivered by Microsoft Partner Gijima. The initiative is also in partnership with the Free State Provincial Government. It will comprise of a 12-month blended learning model of self-study, online learning, classroom instructor-led training and a flipped classroom.

Africa is on the rise. Mobile and Internet penetration continue to rise, according to the latest ITU data. The continent's entrepreneurs are taking advantage of this growth in connectivity, leading to a rapid growth of tech hubs across the continent in recent years, fueling fresh innovation. But as the world moves deeper into the Fourth Industrial Revolution, what is next for Africa, especially regarding Artificial Intelligence (AI)? "AI happens in Africa!" said Alex Tsado, Co-Founder and Board Chair at Alliance4ai, during the recent AI-driving digital divide and the future of African economies AI for Good webinar.

Humans are the most advanced form of Artificial Intelligence (AI), with an ability to reproduce. Artificial Intelligence (AI) is no longer a theory but is part of our everyday life. Services like TikTok, Netflix, YouTube, Uber, Google Home Mini, and Amazon Echo are just a few instances of AI in our daily life. This field of knowledge always attracted me in strange ways. I have been an avid reader and I read a variety of subjects of non-fiction nature. I love to watch movies – not particularly sci-fi, but I liked Innerspace, Flubber, Robocop, Terminator, Avatar, Ex Machina, and Chappie. When I think of Artificial Intelligence, I see it from a lay perspective. I do not have an IT background.

We consider the problem of identifying, from its first $m$ noisy moments, a probability distribution on $[0,1]$ of support $k<\infty$. This is equivalent to the problem of learning a distribution on $m$ observable binary random variables $X_1,X_2,\dots,X_m$ that are iid conditional on a hidden random variable $U$ taking values in $\{1,2,\dots,k\}$. Our focus is on accomplishing this with $m=2k$, which is the minimum $m$ for which verifying that the source is a $k$-mixture is possible (even with exact statistics). This problem, so simply stated, is quite useful: e.g., by a known reduction, any algorithm for it lifts to an algorithm for learning pure topic models. We give an algorithm for identifying a $k$-mixture using samples of $m=2k$ iid binary random variables using a sample of size $\left(1/w_{\min}\right)^2 \cdot\left(1/\zeta\right)^{O(k)}$ and post-sampling runtime of only $O(k^{2+o(1)})$ arithmetic operations. Here $w_{\min}$ is the minimum probability of an outcome of $U$, and $\zeta$ is the minimum separation between the distinct success probabilities of the $X_i$s. Stated in terms of the moment problem, it suffices to know the moments to additive accuracy $w_{\min}\cdot\zeta^{O(k)}$. It is known that the sample complexity of any solution to the identification problem must be at least exponential in $k$. Previous results demonstrated either worse sample complexity and worse $O(k^c)$ runtime for some $c$ substantially larger than $2$, or similar sample complexity and much worse $k^{O(k^2)}$ runtime.

Deepfakes may have innocent and fun applications -- companies like RefaceAI and Morphin enable users to swap their faces with those of popular celebrities in a GIF or digital content format. But like a double-edged sword, the more realistic the content looks, the greater the potential for deception. Deepfakes have been ranked by experts as one of the most serious artificial intelligence (AI) crime threats based on the wide array of applications it can be used for criminal activities and terrorism. A study by University College London (UCL) identified 20 ways AI can be deployed for the greater evil and these emerging technologies were ranked in order of concern in accordance with the severity of the crime, the profit gained, and the difficulty in combating their threats. When the term was first coined, the idea of deepfakes triggered widespread concern mostly centered around the misuse of the technology in spreading misinformation, especially in politics.

A bus waits at a stop in Dubai. Dubai's Roads and Transport Authority (RTA) has started experimenting the use of artificial intelligence (AI) technologies (machine learning algorithms) in plotting bus routes in Dubai, based on the extent of usage throughout the day. The step is part of RTA's endeavours to apply technology in saving the time and effort of all parties and improving the experience of public transport riders. "The use of artificial intelligence technology, such as machine learning algorithms, in planning the routes of public buses aims to revamp the planning of 150 routes used by 2,158 buses all over Dubai. During a trial period, RTA experimented the use of technology on 10 routes where nol card data was employed to figure out all-day busy bus stops, stops used during peak hours, and rarely used stops," said Ahmed Mahboub, Executive Director of Smart Services, Corporate Technology Support Services Sector, RTA.