The use of artificial intelligence (AI) in writing and editing emails has become a common practice among professionals, with over 75% of them utilizing tools like ChatGPT, Gemini, Copilot, or Claude in their daily work. While these generative AI tools can make writing easier, research reveals that relying on them too heavily can undermine trust between managers and employees.

A study conducted by researchers Anthony Coman and Peter Cardon surveyed 1,100 professionals about their perceptions of emails written with low, medium, and high levels of AI assistance. The results showed a “perception gap” in messages written by managers versus those written by employees. When evaluating their own use of AI, participants tended to rate it similarly across different levels of assistance. However, when rating others’ use, the magnitude of AI assistance became important.

The study found that low levels of AI help, such as grammar or editing, were generally acceptable. However, higher levels of assistance triggered negative perceptions, especially among employees who perceived their managers’ reliance on AI-generated content as laziness or a lack of caring. This perception gap had a substantial impact on trust: only 40% to 52% of employees viewed supervisors as sincere when they used high levels of AI, compared to 83% for low-assistance messages.

The findings suggest that managers should carefully consider message type, level of AI assistance, and relational context before using AI in their writing. While AI may be suitable for informational or routine communications, relationship-oriented messages requiring empathy, praise, congratulations, motivation, or personal feedback are better handled with minimal technological intervention.

In essence, the quiet threat to trust posed by overreliance on AI emails is a reminder that while technology can enhance productivity and efficiency, it cannot replace human touch and emotional intelligence in workplace relationships.

The article you provided showcases a groundbreaking achievement in materials discovery research. A team of scientists has developed an AI-powered laboratory that can collect at least 10 times more data than previous techniques, drastically expediting the process while slashing costs and environmental impact. This self-driving laboratory combines machine learning and automation with chemical and materials sciences to discover materials more quickly.

The innovation lies in the implementation of dynamic flow experiments, where chemical mixtures are continuously varied through the system and monitored in real-time. This approach generates a vast amount of high-quality data, which is then used by the machine-learning algorithm to make smarter, faster decisions, honing in on optimal materials and processes.

The results are staggering: the self-driving lab can identify the best material candidates on its very first try after training, reducing the number of experiments needed and dramatically cutting down on chemical use and waste. This breakthrough has far-reaching implications for sustainable research practices and society’s toughest challenges.

The article highlights the work of Milad Abolhasani, corresponding author of the paper, who emphasizes that this achievement is not just about speed but also about responsible research practices. The future of materials discovery, he says, is not just about how fast we can go, but also about how responsibly we get there.

The paper, “Flow-Driven Data Intensification to Accelerate Autonomous Materials Discovery,” was published in the journal Nature Chemical Engineering and showcases a collaborative effort from multiple researchers and institutions. The work has been supported by the National Science Foundation and the University of North Carolina Research Opportunities Initiative program.

The rapid advancement of Artificial Intelligence (AI) has put an immense strain on our energy resources. In response, researchers are racing to find innovative solutions that can make AI more efficient and sustainable. A groundbreaking discovery in spin wave technology could be the game-changer we’ve been waiting for. A team from the Universities of Münster and Heidelberg, led by physicist Prof. Rudolf Bratschitsch, has successfully developed a novel way to produce waveguides that enable spin waves to travel farther than ever before.

The scientists have created the largest spin waveguide network in history, with 198 nodes connected by high-quality waveguides. This achievement is made possible by using yttrium iron garnet (YIG), a material known for its low attenuation properties. The team employed a precise technique involving a silicon ion beam to inscribe individual spin-wave waveguides into a thin film of YIG, resulting in complex structures that are both flexible and reproducible.

One of the key advantages of this breakthrough is the ability to control the properties of the spin wave transmitted through the waveguide. Researchers were able to accurately alter the wavelength and reflection of the spin wave at specific interfaces, paving the way for more efficient AI processing. This innovation has the potential to revolutionize the field of AI by making it 10 times more efficient.

The study was published in Nature Materials, a prestigious scientific journal. The project received funding from the German Research Foundation (DFG) as part of the Collaborative Research Centre 1459 “Intelligent Matter.” This groundbreaking discovery is poised to take AI to new heights and make our energy resources go further than ever before.