Despite the indisputable benefits of Continuous Integration (CI) pipelines (or builds), CI still presents significant challenges regarding long durations, failures, and flakiness. Prior studies addressed CI challenges in isolation, yet these issues are interrelated and require a holistic approach for effective optimization. To bridge this gap, this paper proposes a novel idea of developing Digital Twins (DTs) of build processes to enable global and continuous improvement. To support such an idea, we introduce the CI Build process Digital Twin (CBDT) framework as a minimum viable product. This framework offers digital shadowing functionalities, including real-time build data acquisition and continuous monitoring of build process performance metrics. Furthermore, we discuss guidelines and challenges in the practical implementation of CBDTs, including (1) modeling different aspects of the build process using Machine Learning, (2) exploring what-if scenarios based on historical patterns, and (3) implementing prescriptive services such as automated failure and performance repair to continuously improve build processes.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.

From reducing the burden of back-office admin to offering life-saving potential in diagnoses and treatment recommendations, artificial intelligence (AI) has massive potential to advance healthcare as more and more data becomes available. This new frontier offers a global market expected to be worth US$19.25 billion by 2026. But it's also being approached with a good dose of caution. As organizations strive to leverage the potentially huge benefits of AI in healthcare, the risks of underestimating how to implement these tools safely and effectively alongside human medical professionals remains a key challenge in tapping its full potential. In some cases filling spots where experience, knowledge, even gut-instinct of a medical professional would have served, the decisions made by what can be opaque AI algorithms can be met with suspicion, doubt, or confusion.

The Covid-19 pandemic has made RoI on AI redundant. The worldwide adoption of AI has busted the myth that AI requires intensive investment on infrastructure, process changes, and manpower. It has been seen that AI-powered solutions have become the determining factor of an enterprise's survival. Enterprises have realigned their priorities to survive the pandemic. Several start-ups sprang into action and created AI-powered solutions that helped every sector.

A new report from the Duke-Margolis Center for Health Policy explores some of the policy changes that should be made to enable safer and more effective deployment of artificial intelligence in healthcare. As AI and machine learning become de facto ingredients in many key clinical technologies, a better understanding of how they can best be leveraged for optimal analytics and decision support is the goal of the study, "Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care." WHY IT MATTERS The Duke report takes stock of the existing legal and regulatory landscape for algorithm-based CDS and diagnostic support software, and lays out some essential priorities to work toward in the years ahead to ensure safe deployment of AI in clinical settings. AI and ML are making inroads all over healthcare, of course, and current legislation and regulatory policy – whether it's the massive 21st Century Cures Act or FDA's new updates to the Software Pre-Cert Pilot Program – are adequate but still not optimal for a future that promises to evolve at a dizzying pace. The Duke-Margolis paper, meant as a "resource for developers, regulators, clinicians, policy makers, and other stakeholders as they strive to effectively, ethically, and safely incorporate AI as a fundamental component in diagnostic error prevention and other types of CDS," looks at some of the major challenges and opportunities facing AI in the years ahead.

As healthcare systems strive to realize IOM's vision for continuous improvement in care delivery, many are recognizing that they have outgrown their data management and reporting capacity. Those that have turned to new machine-learning approaches have found they can expand capacity and capabilities while reducing administrative burden on clinicians. Here's an example of how one health system used machine-learning tools to improve care delivery for intestinal surgery: Until recently, the health system's surgical services team used traditional methods of hospital data analysis to inform their creation of order sets, protocols, and provider and patient education materials spanning the pre-op, intraoperative and post-op phases of care. Then they applied a "machine intelligence" platform that pairs machine learning algorithms with topological data analysis (TDA)--a mathematical process that uses shape as an organizing principal for understanding complex data. By giving visible form to their data, the health system was able to replicate and validate years of analytical insights in a matter of days.

Autodesk announced recently the availability of a shape-based search capability in A360. A blog article titled How Machine Learning Will Transform 3D Engineering describes the new capability, called Design Graph, as a "Google search-like functionality for the world of 3D models." Google search functionality is probably the wrong metaphor for 3D search. Web search is fundamentally text based, whereas searching for a part or a design requires a combination of textual and geometric terms and attributes, and sufficiently deep domain semantics. In fact, the blog article makes the very same argument later, describing Design Graph's purpose to "identify and understand designs based on their inherent characteristics--their shape and structure--rather than by any labeling (tags) or metadata" (i.e. Design Graph is not the first attempt to offer the engineering community a geometry-based search tool.

The factory floor is a marvel of automation. With a press of a button, the whole place can seem to run itself. But although today's factories use automated workflows, process change is still mostly manual. When demands arise in an industrial environment, managers and engineers must interrupt the automation to update the processes that make the machines go. Now, thanks to machine learning algorithms, it's becoming possible for smart software to scrutinize data from a variety of sources -- sensors on machines or changes in supply chains, for instance -- and redesign processes in real time.

In the early 1990s, executives and managers welcomed information technology -- databases, PC workstations, and automated systems -- into their offices. They saw the potential for significant business gains. Computers wouldn't just speed up processes or automate certain tasks -- they could upset nearly all business processes and allow executives to rethink operations from the ground up. And so the reengineering movement was born. Powerful machine-learning algorithms that adapt through experience and evolve in intelligence with exposure to data are driving changes in businesses that would have been impossible to imagine just five years ago.