For years, we've been told the future belongs to tech jobs, coding boot camps and college degrees that leave young Americans saddled with debt. But while artificial intelligence is shaking up white-collar professions, there's one sector AI won't be replacing anytime soon: blue-collar skilled trades. Let's face it, when your septic system blows up are you the one who is going to clean up the mess? That's right -- while office workers worry about ChatGPT taking their jobs, the demand for electricians, plumbers, welders, and mechanics is skyrocketing. Companies are desperate for skilled workers, wages are soaring, and many of these careers offer six-figure salaries without the need for a four-year degree.

Lightweight, durable textile-based inflatable soft actuators are widely used in soft robotics, particularly for wearable robots in rehabilitation and in enhancing human performance in demanding jobs. Fabricating these actuators typically involves multiple steps: heat-sealable fabrics are fused with a heat press, and non-stick masking layers define internal chambers. These layers must be carefully removed post-fabrication, often making the process labor-intensive and prone to errors. To address these challenges and improve the accuracy and performance of inflatable actuators, we introduce the Weld n'Cut platform-an open-source, automated manufacturing process that combines ultrasonic welding for fusing textile layers with an oscillating knife for precise cuts, enabling the creation of complex inflatable structures. We demonstrate the machine's performance across various materials and designs with arbitrarily complex geometries.

The industry of the future, also known as Industry 5.0, aims to modernize production tools, digitize workshops, and cultivate the invaluable human capital within the company. Industry 5.0 can't be done without fostering a workforce that is not only technologically adept but also has enhanced skills and knowledge. Specifically, collaborative robotics plays a key role in automating strenuous or repetitive tasks, enabling human cognitive functions to contribute to quality and innovation. In manual manufacturing, however, some of these tasks remain challenging to automate without sacrificing quality. In certain situations, these tasks require operators to dynamically organize their mental, perceptual, and gestural activities. In other words, skills that are not yet adequately explained and digitally modeled to allow a machine in an industrial context to reproduce them, even in an approximate manner. Some tasks in welding serve as a perfect example. Drawing from the knowledge of cognitive and developmental psychology, professional didactics, and collaborative robotics research, our work aims to find a way to digitally model manual manufacturing skills to enhance the automation of tasks that are still challenging to robotize. Using welding as an example, we seek to develop, test, and deploy a methodology transferable to other domains. The purpose of this article is to present the experimental setup used to achieve these objectives.

The number of American workers who quit their jobs during the pandemic--over a fifth of the workforce--may constitute one of the largest American labor movements in recent history. Workers demanded higher pay and better conditions, spurred by rising inflation and the pandemic realization that employers expected them to risk their lives for low wages, mediocre benefits, and few protections from abusive customers--often while corporate stock prices soared. At the same time, automation has become cheaper and smarter than ever. Robot adoption hit record highs in 2021. This wasn't a surprise, given prior trends in robotics, but it was likely accelerated by pandemic-related worker shortages and Covid-19 safety requirements.

Two things can be said about human beings: we like building machines, and we tend to freak out about the machines we build. The Luddites of 19th-century England, an oath-based secret society, looked to the industrial era and saw not liberation but destitution. The most radical among them formed paramilitary groups to raid textile factories and destroy knitting machines and mechanical looms -- devices that would replace workers. Their political descendants include the lamplighters of early-20th-century New York who went on strike to protest the advent of electric streetlights, and the switchboard operators of Bloomington-Normal, Illinois, who in the 1930s took action against the rotary dial system. Did predictions of automation and mass joblessness come true?

VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. The 2020 LinkedIn U.S. Emerging Jobs Report identified the top 15 jobs over the previous five years and emphasized that "artificial intelligence and data science roles continue to proliferate across nearly every industry." Artificial intelligence specialist (No. 1) showed 74% annual growth, and data scientist (No. 3) and data engineer (No. 8) followed with 37% and 33% annual growth. But the problem is: we don't have enough skilled talent to fill these jobs, and it's a national imperative that we change the way we imagine, educate, recruit and upskill our technical workforce. We must abandon the flawed idea that AI jobs are only for people with master's degrees or PhDs with decades of experience.

Safety Controller Synthesis for Collaborative Robots Mario Gleirscher, Radu Calinescu Assuring Autonomy International Programme, University of Y ork, Y ork, UK Department of Computer Science, University of Y ork, Y ork, UK mario.gleirscher,radu.calinescu@york.ac.uk Abstract --In human-robot collaboration (HRC), software-based automatic safety controllers (ASCs) are used in various forms (e.g. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to ASC developers and certification authorities. Key among these challenges is the need to assure the correctness of ASCs under reasonably weak assumptions. T o address this need, we introduce and evaluate a tool-supported ASC synthesis method for HRC in manufacturing. Our ASC synthesis is: (i) informed by the manufacturing process, risk analysis, and regulations; (ii) formally verified against correctness criteria; and (iii) selected from a design space of feasible controllers according to a set of optimality criteria. The synthesised ASC can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task. I NTRODUCTION An effective collaboration between industrial robot systems (IRSs) and humans [1], [2] can leverage their complementary skills, but is difficult to achieve because of uncontrolled hazards and unexploited sensing, tracking, and safety measures [3]. Such hazards have been studied since the 1970s, resulting in elaborate risk taxonomies based on workspaces, tasks, and human body regions [2], [4]-[10]. The majority are impact hazards (e.g. Addressing these hazards involves the examination of each mode of operation (e.g. I, a variety of safety measures [3] can prevent or mitigate hazards and accidents by reducing the probability of their occurrence and the severity of their consequences . There are functional measures using electronic equipment (e.g.

In the era of Industry 4.0, the focus is on the minimization of human element and maximizing the automation in almost all the industrial and manufacturing establishments. These establishments contain numerous processing systems, which can execute a number of tasks, in parallel with minimum number of human beings. This parallel execution of tasks is done in accordance to a scheduling policy. However, the minimization of human element beyond a certain point is difficult. In fact, the expertise and experience of a group of humans, called the experts, becomes imminent to design a fruitful scheduling policy. The aim of the scheduling policy is to achieve the optimal value of an objective, like production time, cost, etc. In real-life situations, there are more often than not, multiple objectives in any parallel processing scenario. Furthermore, the experts generally provide their opinions, about various scheduling criteria (pertaining to the scheduling policies) in linguistic terms or words. Word semantics are best modeled using fuzzy sets (FSs). Thus, all these factors have motivated us to model the parallel processing scenario as a multi-objective linguistic optimization problem (MOLOP) and use the novel perceptual reasoning (PR) based methodology for solving it. We have also compared the results of the PR based solution methodology with those obtained from the 2-tuple based solution methodology. PR based solution methodology offers three main advantages viz., it generates unique recommendations, here the linguistic recommendations match a codebook word, and also the word model comes before the word. 2-tuple based solution methodology fails to give all these advantages. Thus, we feel that our work is novel and will provide directions for the future research.

Throughout each industrial era, the companies best able to embrace change have become the most likely to succeed. In The Future Computed: AI and Manufacturing, Microsoft Senior Director Greg Shaw explores how AI, automation and the internet of things (IoT) present new challenges and opportunities. Here are some of the manufacturers already demonstrating how the latest tech advances are changing the way they work. A collaboration between Thyssenkrupp and Microsoft has led to the development of the elevator industry's first real-time, cloud-based predictive maintenance system. This means an elevator can accurately predict when it is about to fail and summon an engineer, making it far less likely that people could get trapped inside.

While claiming to address the "shortage" of skilled welders, a company called Hirebotics has now engineered the first "robotic welding solution for hire". The automated welder is paid by the hour, just like skilled manual labor, and is far less likely to need a bathroom break and/or file a grievance with the local union. Claiming that traditional robotic welding automation has been a "poor solution" for most fabricators, the company is now offering a product called the BotX welder, which it says can accelerate business growth while eliminating the "headaches" of finding skilled welders. The company, which was started with the goal of helping manufacturers, says that the BotX Welder bridges the gap between inflexible, expensive and higher productive traditional automation and manual welding with skilled welders. It says that the BotX can be used both as a tool for a company's manual welders to get more done each day, and also as a robotic welder that is set up by a skilled welder, but then run by an untrained operator, via app.