Overview

Rather than relying on exit interviews and their comparisons to occasional employee surveys to determine engagement, organizations can turn instead to big data and advanced analytics to identify those workers at greatest risk of quitting. A new Harvard Business Review article outlines how applying machine learning algorithms to turnover data and employee information can provide a much more accurate picture of workplace satisfaction. This measure of "turnover propensity" comprised two main indicators: turnover shocks, which are organizational and personal events that cause workers to reconsider their jobs, and job embeddedness, which describes an employee's social ties in their workplace and interest in the work they do. Though achieving this kind of "proactive anticipation" will require a sizable investment of time and effort to develop the necessary data and algorithms, the payoff will likely be worth it: "Leaders can proactively engage valued employees at risk of leaving through interviews, to better understand how the firm can increase the odds that they stay," per HBR. More articles on leadership and management: Can your anesthesia department handle NORA?

Following the World Intellectual Property Organization (WIPO) report in early 2019, a new report from the UK Intellectual Property Office (UKIPO) now identifies the growth in terms of published AI patent applications. This insight provides an overview of the UKIPO findings and considerations for technology businesses in this space. AI is the use of technology to perform tasks that would usually require some intelligence, if done by humans. A patent is a registered intellectual property right, which seeks to create a monopoly over the exploitation of an invention. Patents historically can take years to process from application, publication to grant.

Current research is aligned with the need of rescue workers but robustness and ease of use remain significant barriers to adoption, NCCR Robotics researchers find after reviewing the field and consulting with field operators. Robots for search and rescue are developing at an impressive pace, but they must become more robust and easier to use in order to be widely adopted, and researchers in the field must devote more effort to these aspects in the future. This is one of the main findings by a group of NCCR Robotics researchers who focus on search-and-rescue applications. After reviewing the recent developments in technology and interviewing rescue workers, they have found that the work by the robotics research community is well aligned with the needs of those who work in the field. Consequently, although current adoption of state-of-the-art robotics in disaster response is still limited, it is expected to grow quickly in the future.

Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline.

The insurance industry, after the trade market, is another sector where it is hard to predict the next big paradigm shift. Given the tentative stability and natural catastrophes, insurance companies often stand on a trembling ground and confront massive challenges, even when i comes to adopting seamless and intuitive digital solutions such as Artificial Intelligence in Insurance. Research N Reports has added a report, titled "Global Artificial Intelligence in Insurtech Market Size, Status and Forecast 2025," which provides an overview of the various factors enabling growth in the market. The statistical report offers a prime wellspring of applicable information for global business progress. What will be the market size and growth rate in the forecast year?

Recent advances in supervised machine learning have improved diagnostic accuracy and prediction of treatment outcomes, in some cases surpassing the performance of clinicians.1 In supervised machine learning, a mathematical function is constructed via automated analysis of training data, which consists of input features (such as retinal images) and output labels (such as the grade of macular edema). With large training data sets and minimal human guidance, a computer learns to generalize from the information contained in the training data. The result is a mathematical function, a model, that can be used to map a new record to the corresponding diagnosis, such as an image to grade macular edema. Although machine learning–based models for classification or for predicting a future health state are being developed for diverse clinical applications, evidence is lacking that deployment of these models has improved care and patient outcomes.2 One barrier to demonstrating such improvement is the basis used to assess the performance of a model.

Last month's announcement by Amazon that it plans to spend $700 million (£569 million) over six years to retrain a third of its US workforce was eye-catching for many reasons. One was the price tag: even for the world's second most valuable company, spending three-quarters of a billion dollars over half a decade to retrain 100,000 workers is a huge undertaking. Also noteworthy was the firm's reasoning. Amazon explicitly attributed its move to the rise of automation, machine learning and other technology: the so-called fourth industrial revolution. There was a sense that the pioneer of online retailing, famed for its use of automation, was merely an early accepter of an inescapable truth that all employers will soon have to face: that the skills of their existing workforces will no longer have any market value as their old roles are taken by machines and new roles are created. The company reportedly has 20,000 current vacancies.

Some of the techniques that can be utilized to drive artificial intelligence (AI) innovation are lean and design thinking. Both techniques, or a modified combination, are useful to effectively innovate. In this article, I will provide an overview to both to enable you to be a practitioner, especially to employ design thinking without any tools whatsoever. Lean is the elimination of waste, where "waste" refers to work that adds no value or limited value to a process. Lean is about organizing work activities.

The research and development of neural networks is flourishing thanks to recent advancements in computational power, the discovery of new algorithms, and an increase in labelled data. Before the current explosion of activity in the space, the practical applications of neural networks were limited. Much of the recent research has allowed for broad application, the heavy computational requirements for machine learning models still restrain it from truly entering the mainstream. Now, emerging algorithms are on the cusp of pushing neural networks into more conventional applications through exponentially increased efficiency. Neural networks are a prominent focal point in the current state of computer science research.

Imagine you're completing a mission in a computer game. Maybe you're going through a military depot to find a secret weapon. You get points for the right actions (killing an enemy) and lose them for the wrong ones (falling into a pit or getting hit). If you're playing on high difficulty, you might not conclude this task in just one attempt. Try after try, you learn which consecutive actions are needed to get out of a location safe, armed, and equipped with bonuses like extra health points or small artifacts in your bag.