Leads are the driving force of many businesses today. With the advancement of subscription-based business models particularly in the start-up space, the ability to convert leads into paying customers is key to survival. In simple terms, a "lead" represents a potential customer interested in buying your product/service. A significant amount of time, money, and effort is spent by marketing and sales departments on lead management, a concept that we will take to encompass the three key phases of lead generation, qualification, and monetization. Lead generation is the initiation of customer interest or inquiry into the products or services of your business.

While many fintech platforms focus on risk assessment, Brighterion has been solely dedicated to AI-powered decisioning for over 20 years. With a sharp focus on financial irregularities, Brighterion's AI decision-making algorithms provide real-time detection in financial fraud, credit risk, healthcare fraud, waste and abuse, and money laundering (AML). The role of artificial intelligence is taking top billing in the search for software that detects fraud and credit risk. Legacy solutions like rules-based decisioning are hard pressed to stay ahead of bad actors as fraud evolves and becomes more sophisticated. Machine learning rises to the top for its ability to learn from complex and widely varied data.

A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables application in a broad variety of tasks such as zero-shot learning or learning from incomplete data. In this paper, we present the probabilistic rule stacking learner (pRSL) which uses probabilistic propositional logic rules and belief propagation to combine the predictions of several underlying classifiers. We derive algorithms for exact and approximate inference and learning, and show that pRSL reaches state-of-the-art performance on various benchmark datasets. In the process, we introduce a novel multicategorical generalization of the noisy-or gate. Additionally, we report simulation results on the quality of loopy belief propagation algorithms for approximate inference in bipartite noisy-or networks.

The Institutional Analysis and Development (IAD) framework is a conceptual toolbox put forward by Elinor Ostrom and colleagues in an effort to identify and delineate the universal common variables that structure the immense variety of human interactions. The framework identifies rules as one of the core concepts to determine the structure of interactions, and acknowledges their potential to steer a community towards more beneficial and socially desirable outcomes. This work presents the first attempt to turn the IAD framework into a computational model to allow communities of agents to formally perform what-if analysis on a given rule configuration. To do so, we define the Action Situation Language -- or ASL -- whose syntax is hgighly tailored to the components of the IAD framework and that we use to write descriptions of social interactions. ASL is complemented by a game engine that generates its semantics as an extensive-form game. These models, then, can be analyzed with the standard tools of game theory to predict which outcomes are being most incentivized, and evaluated according to their socially relevant properties.

Email management is a bigger challenge every year. In 2019, business email accounted for more than 128.8 billion emails sent and received per day, according to the Radicati Group. Adding to the challenge, many emails never make it to the right business account because they are sent to bulk accounts like info@company.com Regardless of where the emails land, the average full-time worker spends 28% of the workday reading and answering email, according to a McKinsey analysis. That amounts to a staggering 2.6 hours spent each day. Corporate email continues to rule in the business world, but the deluge is impairing productivity, not to mention becoming unmanageable from a corporate perspective.

During IBM's Think 2021 event, the company discussed how hybrid cloud operations and artificial intelligence (AI) are essential to its strategy and the digital transformation of its customers. CEO Arvind Krishna spoke about how these elements are helping customers to emerge from the pandemic as more resilient, agile businesses. The event underlined many aspects of IBM's strategy, including its intention to become a platform company, underpinned by its Red Hat OpenShift and systems infrastructure, Watson AI and automation solutions, and a partner community that brings industry-specific solutions. But AI and automation stood tallest this year, indicating that IBM believes these converging areas represent its biggest prospects for growth in the coming years. Expectedly, AI permeated almost every session, but automation took a more prominent role this year as it forms an increasingly important theme for IBM.

Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time. Blocking all APTs is impossible, security experts caution, hence the importance of research on early detection and damage limitation. Whole-system provenance-tracking and provenance trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur. We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces. Anomalous processes are ranked using both frequent and rare event associations learned from traces. Results are then presented as implications which, since interpretable, help leverage causality in explaining the detected anomalies. When evaluated on Transparent Computing program datasets (DARPA), our method outperformed competing approaches.

A key question in causal inference analyses is how to find subgroups with elevated treatment effects. This paper takes a machine learning approach and introduces a generative model, Causal Rule Sets (CRS), for interpretable subgroup discovery. A CRS model uses a small set of short decision rules to capture a subgroup where the average treatment effect is elevated. We present a Bayesian framework for learning a causal rule set. The Bayesian model consists of a prior that favors simple models for better interpretability as well as avoiding overfitting, and a Bayesian logistic regression that captures the likelihood of data, characterizing the relation between outcomes, attributes, and subgroup membership. The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the \emph{treatment efficient frontier}. We find maximum a posteriori models using iterative discrete Monte Carlo steps in the joint solution space of rules sets and parameters. To improve search efficiency, we provide theoretically grounded heuristics and bounding strategies to prune and confine the search space. Experiments show that the search algorithm can efficiently recover true underlying subgroups. We apply CRS on public and real-world datasets from domains where interpretability is indispensable. We compare CRS with state-of-the-art rule-based subgroup discovery models. Results show that CRS achieved consistently competitive performance on datasets from various domains, represented by high treatment efficient frontiers.

"Software is eating the world," US entrepreneur Marc Andreessen famously wrote in 2011. Fast-forward to today – software is in financial services and healthcare, smartphones and smart homes. Such large volumes of code, however, is a challenge to debug, maintain, and update, especially as enterprises aim to modernize their aging software infrastructure. As a result, we find ourselves in a new age where it's essential to take advantage of today's powerful technologies like artificial intelligence (AI) and hybrid cloud to create new solutions that can modernize processes across the information technologies (IT) pipeline. A large dataset aimed at teaching AI to code, it consists of some 14M code samples and about 500M lines of code in more than 55 different programming languages, from modern ones like C, Java, Python, and Go to legacy languages like COBOL, Pascal, and FORTRAN.

"Artificial intelligence (AI)I will automate everything and put people out of work." "AI is a science-fiction technology." "Robots will take over the world." The hype around AI has produced many myths, in mainstream media, in board meetings and across organizations. Some worry about an "almighty" AI that will take over the world, and some think that AI is nothing more than a buzzword.