Most business process automation is still developed using traditional automation technologies such as workflow engines. These systems provide domain specific languages that require both business knowledge and programming skills to effectively use. As such, business users often lack adequate programming skills to fully leverage these code oriented environments. We propose a paradigm for the construction of business automations using natural language. The approach applies a large language model to translate business rules and automations described in natural language, into a domain specific language interpretable by a business rule engine. We compare the performance of various language model configurations, across various target domains, and explore the use of constrained decoding to ensure syntactically correct generation of output.

Protecting private patient data is critical for any healthcare organization. From securing systems from outside hackers to monitoring and controlling internal access, there are a multitude of steps any organization can take to better protect PHI and EMR data. See Also: OnDemand Fireside Chat Zero Tolerance: Controlling The Landscape Where You'll Meet Your Adversaries One of the recent developments in this arena is machine learning driven access monitoring. Unlike traditional, rules-based access monitoring, this new, more adaptive technology is changing how organizations monitor, assess, and control access. Access monitoring is exactly what it sounds like -- monitoring user access to network resources, critical data, and high-risk access points.

Asian security experts have welcomed the victory by Prime Minister Fumio Kishida's ruling coalition in Sunday's House of Councilors election, saying Japan's political stability and leadership are vital for shoring up a rules-based international order being challenged by countries such as China and Russia. Citing Russia's aggression in Ukraine, China's increased assertiveness in the Indo-Pacific and North Korea's nuclear and missile development, the experts either expressed support for or did not oppose Kishida's calls for boosting Japan's defense capabilities and amending the Constitution, including adding a reference to the Self-Defense Forces in the war-renouncing Article 9. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.

This primer is an attempt to provide a detailed summary of the different facets of lifelong learning. We start with Chapter 2 which provides a high-level overview of lifelong learning systems. In this chapter, we discuss prominent scenarios in lifelong learning (Section 2.4), provide 8 Introduction a high-level organization of different lifelong learning approaches (Section 2.5), enumerate the desiderata for an ideal lifelong learning system (Section 2.6), discuss how lifelong learning is related to other learning paradigms (Section 2.7), describe common metrics used to evaluate lifelong learning systems (Section 2.8). This chapter is more useful for readers who are new to lifelong learning and want to get introduced to the field without focusing on specific approaches or benchmarks. The remaining chapters focus on specific aspects (either learning algorithms or benchmarks) and are more useful for readers who are looking for specific approaches or benchmarks. Chapter 3 focuses on regularization-based approaches that do not assume access to any data from previous tasks. Chapter 4 discusses memory-based approaches that typically use a replay buffer or an episodic memory to save subset of data across different tasks. Chapter 5 focuses on different architecture families (and their instantiations) that have been proposed for training lifelong learning systems. Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.

Within asset management particularly, modern-day computational modelling can identify trends and ... [ ] collect data points in the stock market within seconds, meaning traders can map out parameters for a more objective approach to trading. Family offices know better than most that we are in the throes of a technological revolution across the entire industry. Within asset management particularly, modern-day computational modelling and portfolio management software can identify trends and collect data points in the stock market within seconds, meaning traders can map out parameters for a more objective approach to trading. These parameters help define the rules guiding an entire portfolio's operation, in sharp contrast to traditional, reactive investing which largely uses numbers to quantify an asset's performance in a portfolio. More first-time investors are taking an interest in understanding portfolio management, and while it might sound like the robots have taken over, there's still very much a place for IRL thinking here.

WNS (Holdings) Limited a leading provider of global Business Process Management (BPM) services, today announced it has acquired Vuram, a global leader in enterprise automation services. Vuram helps companies accelerate digital transformation by aligning, automating, and optimizing processes using a combination of low-code software applications and intelligent automation platforms. By integrating these technologies into core business operations, Vuram is able to drive end-to-end enterprise automation and the creation of custom, scalable BPM solutions. These solutions include the ability to extract, collect, and categorize data using OCR and AI-based document processing, develop rule-based processing engines and ML-based augmentation, and leverage advanced analytics to improve decision-making. Vuram has also created customizable, low-code, "plug and play" solutions across front, middle, and back-office functions, including industry-specific solutions for the Banking/Financial Services, Insurance, and Healthcare verticals.

Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lead to unexpectedly different explanations, even when applied to simple models for which we would expect qualitative coincidence. To quantify this effect, we propose a new approach to compare explanations produced by different methods.

We implement a divide-and-concur iterative projection approach to context-free grammar inference. Unlike most state-of-the-art models of natural language processing, our method requires a relatively small number of discrete parameters, making the inferred grammar directly interpretable -- one can read off from a solution how to construct grammatically valid sentences. Another advantage of our approach is the ability to infer meaningful grammatical rules from just a few sentences, compared to the hundreds of gigabytes of training data many other models employ. We demonstrate several ways of applying our approach: classifying words and inferring a grammar from scratch, taking an existing grammar and refining its categories and rules, and taking an existing grammar and expanding its lexicon as it encounters new words in new data.

Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lead to unexpectedly different explanations, even when applied to simple models for which we would expect qualitative coincidence. To quantify this effect, we propose a new approach to compare explanations produced by different methods.

In socio-technical settings, operators are increasingly assisted by decision support systems. By employing these, important properties of socio-technical systems such as self-adaptation and self-optimization are expected to improve further. To be accepted by and engage efficiently with operators, decision support systems need to be able to provide explanations regarding the reasoning behind specific decisions. In this paper, we propose the usage of Learning Classifier Systems, a family of rule-based machine learning methods, to facilitate transparent decision making and highlight some techniques to improve that. We then present a template of seven questions to assess application-specific explainability needs and demonstrate their usage in an interview-based case study for a manufacturing scenario. We find that the answers received did yield useful insights for a well-designed LCS model and requirements to have stakeholders actively engage with an intelligent agent.