Over the past decades, numerous practical applications of machine learning techniques have shown the potential of data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.

We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work, named PRover (Saha et al., 2020), performs such reasoning by answering a question and also generating a proof graph that explains the answer. However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer. Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases. Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems. In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph. We propose two variants of a proof-set generation model, multiPRover. Our first model, Multilabel-multiPRover, generates a set of proofs via multi-label classification and implicit conditioning between the proofs; while the second model, Iterative-multiPRover, generates proofs iteratively by explicitly conditioning on the previously generated proofs. Experiments on multiple synthetic, zero-shot, and human-paraphrased datasets reveal that both multiPRover models significantly outperform PRover on datasets containing multiple gold proofs. Iterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios where all examples have single correct proofs. It also generalizes better to questions requiring higher depths of reasoning where multiple proofs are more frequent. Our code and models are publicly available at https://github.com/swarnaHub/multiPRover

Algorithmic trading now dominates the derivative, equity, and foreign exchange trading markets. These trading strategies can be complex, but the essentials are straightforward: program a set of rules that takes market data as input and apply basic models (10 -day moving average) to generate an automated trade workflow. Over the years, these strategies have moved beyond simple time-series momentum and mean revision models to more exotic name strategies like snipes, slicers, and boxers. Evolved over decades, algorithm trading has replaced much of the manual trade order flow with faster static rules-based strategies. What was once cutting edge is now an inherent disadvantage.

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.