The spread and rapid development of AI-related technologies are influencing many aspects of our daily lives, from social to educational, including the labour market. Many researchers have been highlighting the key role AI and technologies play in reshaping jobs and their related tasks, either by automating or enhancing human capabilities in the workplace. Can we estimate if, and to what extent, jobs and related tasks are exposed to the risk of being automatized by state-of-the-art AI-related technologies? Our work tackles this question through a data-driven approach: (i) developing a reproducible framework that exploits a battery of open-source Large Language Models to assess current AI and robotics' capabilities in performing job-related tasks; (ii) formalising and computing an AI exposure measure by occupation, namely the teai (Task Exposure to AI) index. Our results show that about one-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs (aka, white collars). This exposure correlates positively with employment and wage growth from 2019 to 2023, indicating a beneficial impact of AI on productivity. The source codes and results are publicly available, enabling the whole community to benchmark and track AI and technology capabilities over time.

Instead of elaborately programming a solution, neural networks and deep learning algorithms teach an image processing system to see, recognize and verify objects - in this case smoke. Heat waves caused by climate change are currently also increasing strongly across Europe and the associated risk of forest fires is rising immensely. Fires in natural areas are increasingly getting out of control due to drought or wind, and the risk of danger to people, animals, nature and infrastructure is growing. But how can fires be detected and localized at an early stage in order to minimize or even avoid serious damage? With image processing and artificial intelligence, even such challenges can be mastered.

This book shows you how to build real-time image processing systems all the way through to house automation. Find out how you can develop a system based on small 32-bit ARM processors that gives you complete control through voice commands. Real-time image processing systems are utilized in a wide variety of applications, such as in traffic monitoring systems, medical image processing, and biometric security systems. In Real-Time IoT Imaging with Deep Neural Networks, you will learn how to make use of the best DNN models to detect objects in images using Java and a wrapper for OpenCV. Take a closer look at how Java scripting works on the Raspberry Pi while preparing your Visual Studio code for remote programming.

President and CEO: Yoshihito Yamada) has developed a unique defect extraction AI technology that recognizes defects by reproducing "human sensibility" and "expert experience" in order to automate the appearance inspection at the manufacturing site. By providing stable detection of defects that up to now have been difficult to detect with machines, it enables further automation of appearance inspections that currently rely on human vision. This AI functionality will be added to the existing OMRON image processing system "FH Series" and will be released in the spring of 2020. In recent years, the shortage of skilled technicians and rising labor costs have become more critical, and in the manufacturing industry there is a tremendous reliance on human experience and human senses. Therefore automation of the transporting, assembly, and inspection processes that depend on people has become an urgent task for businesses.