Despite the recent success in data-driven fault diagnosis of rotating machines, there are still remaining challenges in this field. Among the issues to be addressed, is the lack of information about variety of faults the system may encounter in the field. In this paper, we assume a partial knowledge of the system faults and use the corresponding data to train a convolutional neural network. A combination of t-SNE method and clustering techniques is then employed to detect novel faults. Upon detection, the network is augmented using the new data. Finally, a test setup is used to validate this two-stage methodology on a centrifugal pump and experimental results show high accuracy in detecting novel faults.

Like runaway climate change, the rapid development of self-learning artificial intelligence is an unprecedented existential threat to humanity, where past experience will be no guide to our future prospects (AI will end the west's weak productivity and low growth. But who exactly will benefit?, 7 April). This is especially true when AI links to either super- or quantum-computing power. Complex systems like these give rise to emergent properties, and circumstances where the whole becomes greater than the sum of its parts. Previously "dumb" neural networks like ChatGPT, by drawing on large language models, have already led to increasingly sophisticated and adaptable generative AI.

Medical coding audits are complex, time-consuming, and costly processes that historically have required significant investment of manual resources to complete, but, more recently, advances in artificial intelligence have made audits more efficient, accurate, and cost-effective. Due to the volume and complexity of clinical documentation that providers must manage each day in support of coding, the Medical Group Management Association (MGMA) recommends that medical practices perform coding audits on a regular basis. Coding audits are an important tool to ensure that a practice's processes are functioning appropriately and are imperative for effective risk mitigation and revenue cycle optimization, according to MGMA. Coding audits ensure accuracy and enable practice leadership to understand the level of complexity in the coding and billing within the practice. They can also identify educational opportunities for coders – whether outside vendors or in-house staff – to protect the practice and maintain compliance.

The capabilities of automation tools have not gone unnoticed in the cybersecurity market. Here, Tim Wallen, LogPoint Regional Director, UK&I, discusses what is needed for AI, Machine Learning and RPA to transform cybersecurity. Unfortunately, this trend was not bucked in 2021. Indeed, the threat landscape continued to intensify, driven by a growth in attacks and the increasing sophistication of techniques used by perpetrators, adding additional pressures into a profession that – in the eyes of many – is nearing breaking point. Reports that a record US$40 million ransomware payment was paid by a US insurance company after its network was brought down and data stolen, merely scratches the surface of the cybercriminal events of last year.

Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs. To achieve the promising benefits, the crucial I-UAV networking issue should be tackled. This article argues that I-UAV networking can be classified into three categories, quality-of-service (QoS) driven networking, quality-of-experience (QoE) driven networking, and situation aware networking. Each category of networking poses emerging challenges which have severe effects on the safe and efficient accomplishment of I-UAV missions. This article elaborately analyzes these challenges and expounds on the corresponding intelligent approaches to tackle the I-UAV networking issue. Besides, considering the uplifting effect of extending the scalability of I-UAV networks through cooperating with high altitude platforms (HAPs), this article gives an overview of the integrated HAP and I-UAV networks and presents the corresponding networking challenges and intelligent approaches.

Business automation is the way forward for accelerating growth. Businesses can grow in one of two ways; either increasing revenue, winning new clients and innovating, or increasing efficiencies. When a company does both, long-term profitability and sustainability are more secure. With business automation, productivity increases and staff who were doing manual tasks can focus on more useful work that generates higher-value results. However, it's no longer enough to use software to automate a few processes.

Trust, transparency and technology: helping your employees embrace AI. If we look at today's workforce like a traditional family unit, employees can feel like the proverbial middle child, often overlooked in order to respond to the louder demands of the younger child and never given the same trusted responsibilities of an elder child. Business leaders, like busy parents, can tend to focus on keeping their key stakeholders, investors and customers happy while considering their employees as an afterthought. However, with the rapid digital transformation of today's workplace, employers need to focus more on reassuring their employees as they deal with change and uncertainty. This was reinforced in a recent survey of employers by Genesys, revealing a belief that more than half of employees (52 per cent) have concerns their jobs may be eliminated as a result of advanced technologies, like artificial intelligence (AI) and bots.

Ensuring that companies make ethical decisions is clearly a boardroom matter. A new study by the IBM Institute for Business Value reported that more than half of the 1,250 executives surveyed believe AI actually can improve their companies' ethical decisions. Yet while 8 out of 10 directors believe the ethical questions raised by the deployment of AI are board-level issues, only 45 percent feel fully prepared to oversee them. AI is different from other technology innovations, affecting how boards approach oversight. Boards are seeking concrete guidance on how to protect the interests of their companies and stakeholders impacted by AI.

BOSTON – A few years ago, toward the end of his life, my father battled severe depression. As a physician and professor, he did not lack access to mental-health care. But he had grown up in a society that stigmatized mental illness, and he was unwilling to seek professional help. As a son, it was devastating to watch my father suffer. As a public-health researcher, I gained a new awareness of the myriad systemic failures in the provision of care.

European spending on artificial intelligence (AI) is estimated to reach more than $7bn in 2019 and to almost triple over the next four years, according to market intelligence firm IDC. The latest IDC Worldwide Semiannual Artificial Intelligence Spending Guide forecasts AI spending to grow at 32% per year, topping $21bn in Europe in 2023. Industries with the highest AI spend will be banking, retail, and discrete manufacturing, accounting for 39% of total spending. The report found that healthcare remains an attractive market for AI. Many hospitals are piloting projects and are increasing investments in AI-enabled diagnosis and treatment systems which will support a 38% increase in spending across the health sector over the next five years.