Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this paper, we propose a novel technique for managing process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the identified clusters to detect drifts. VDD complements these features with detailed visualizations and explanations of drifts. Our evaluation, both on synthetic and real-world logs, demonstrates all the aforementioned capabilities of the technique.

Human culture is uniquely cumulative and open-ended. Using a computational model of cultural evolution in which neural network based agents evolve ideas for actions through invention and imitation, we tested the hypothesis that this is due to the capacity for recursive recall. We compared runs in which agents were limited to single-step actions to runs in which they used recursive recall to chain simple actions into complex ones. Chaining resulted in higher cultural diversity, open-ended generation of novelty, and no ceiling on the mean fitness of actions. Both chaining and no-chaining runs exhibited convergence on optimal actions, but without chaining this set was static while with chaining it was ever-changing. Chaining increased the ability to capitalize on the capacity for learning. These findings show that the recursive recall hypothesis provides a computationally plausible explanation of why humans alone have evolved the cultural means to transform this planet.

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%).

All major powers are currently focusing on the development of autonomous weapon systems. Artificial Intelligence (AI) is a technological breakthrough that would render the world unrecognisable as we know it today. Though the idea that machines would possess human-like cognitive capabilities might have sounded like science fiction in the past century, it has now transitioned to reality. Since its inception, Artificial Intelligence has drawn attention from a diverse number of fields and the concerned "researches and developments" are moving at a staggering pace. AI-powered smart assistants to advanced training simulations and even self-driving vehicles are a reality now.

Machine Learning Opens Pathway For Digital Transformation Dave Fellers As companies face exponentially growing amounts of data that can overwhelm individuals' decision-making ability, machine learning provides a powerful method for helping people improve decision-making bandwidth, responsiveness, accuracy, and consistency of results. But what is machine learning? It is the ability of software systems to learn by studying data to detect patterns and/or by applying known rules to the data for processing. Some of the key areas where machine learning can help are: Categorizing and cataloging information like transactions, accounts, companies, people, etc. Predicting likely outcomes and/or deciding on actions by analyzing identified patterns Identifying previously unknown patterns and relationships within the data Detecting new, anomalous, or unexpected behaviors and events from data Machine learning software systems use specialized algorithms to understand the data and actions being handled by relevant processes and to learn how to improve those processes. As new observations of data, events, responses, and changes in the data environment are analyzed by the algorithms, the machine's performance is improved and refined.

What a company chooses, determines the pace of its growth. Processes such as underwriting, claims to process, and policy servicing, bring along with them a plethora of important but mundane and repetitive work, affecting the overall organization's efficiency. This is where the need to automate systems and manual processes arise. Robotic process automation (RPA), with the use of software bots to handle routine processes and time-consuming data entry work, is an objective solution for any organization to drive customer-centric strategies and scale up operations. Why is RPA a Good Fit For the Insurance Sector?

Roundup Hello, here's a few announcements from the world of machine learning beyond what we've already covered this week. AlphaStar is coming out to play: AlphaStar, the StarCraft II-playing bot built by DeepMind researchers, will be facing human players in a series of 1v1 games online. StarCraft II players can enter the open competition league set up by Blizzard Entertainment, the creators of the popular battle strategy game, and opt-in to play against AlphaStar. But nobody will know if they're facing the bot, however, because it'll be entering the matches anonymously. Characters in the StarCraft II are from three species: Terran, Zerg or Protoss.

Let's say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question "Which fruits are red and round?" and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won't be red and round. So I will ask a question "Which fruits have red or yellow colour hints on them? " on red and round fruits and will ask "Which fruits are green and round?" on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition.

AI components of ML, Robotics and NLP play their parts with the support of IoT and 5G technologies. Such business models are ready to be put into practice at the present time. "FIRST 10 PLAYS": How to develop and deploy AI Systems Solutions

With the explosion of'things' that opened access to personal data at scale and intense computing technologies like GPUs, it is no doubt that AI -- the technology that has gone through multiple futile hype cycles in the past -- has a better chance this time in getting traction outside the labs. Most of us consider AI as inorganic intelligence that is still experimental and consumer implications are wide, vague, and in the twilight zone. It is true that AI today can't match the way we humans think and act. But if we look close enough, the implications of such intelligent technologies on us are already being felt.Earlier this year, a swarm AI algorithm created by a company called Unanimous.ai Companies like Scripps Howard have been predicting the final scores for the past 19 years but have got it right only twice.