Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
DETROIT - Ford revealed details of its long-awaited restructuring plan Monday as it prepared for a future of electric and autonomous vehicles by parting ways with 7,000 white-collar workers worldwide, about 10 percent of its global salaried workforce. The major revamp, which had been underway since last year, will save about $600 million per year by eliminating bureaucracy and increasing the number of workers reporting to each manager. In the U.S. about 2,300 jobs will be cut through buyouts and layoffs. About 1,500 have left voluntarily or with buyouts, while another 300 have already been laid off. About 500 workers will be let go starting this week, largely in and around the company's headquarters in Dearborn, Michigan, just outside Detroit.
DIGITAL transformation has become a rallying cry for business and technology strategists. To those charged with mapping the future, it promises a triumphant response to the pressures and potential of disruptive change. Yet all too often, companies anchor their approach on a specific technology advance. To fuel impactful digital transformation, leading organizations combine technology with other catalysts of new opportunities--from emerging ecosystems to human-centered design and the future of work. Because the technology trends that inspire digital transformation efforts don't take place in a vacuum. They cross-pollinate with emerging trends in the physical and social sciences and in business to deliver unexpected outcomes. Developing a systematic approach for identifying and harnessing opportunities born of the intersections of technology, science, and business is an essential first step in demystifying digital transformation, and making it concrete, achievable, and measurable.
Major Japanese nonfinancial companies project a 1.1 percent drop in their group net profit for the year ending in March 2020, a Jiji Press survey has found. The survey covered 243 companies listed on the Tokyo Stock Exchange's first section that have released earnings results for the fiscal year which ended last month. Their combined market value represents 36 percent of the total in the section. Industrial robot maker Fanuc Corp. estimates a 60 percent net profit fall, anticipating continued weakness in capital investment in China. Advantest Corp. forecasts a drop of 50 percent or more in its net profit amid expectations that sluggish memory-chip demand will weigh on sales of its semiconductor testing devices.
Here's the paradox: it takes people to automate. Enterprises are moving aggressively to automate as many of their processes as possible, through artificial intelligence, machine learning and robotic process automation. Automation opens up new types of career opportunities, from programming to training. But it can't simply be inserted into operations without forethought and consideration of the wider impact. That's the word coming out of a survey of 4,000 employees released by Automation Anywhere.
We naturally think of "intelligence" as a trait belonging to individuals. We're all--students, employees, soldiers, artists, athletes--regularly evaluated in terms of personal accomplishment, with "lone hero" narratives prevailing in accounts of scientific discovery, politics, and business. Similarly, artificial intelligence is typically defined as a quest to build individual machines that possess different forms of intelligence, even the kind of general intelligence measured in humans for more than a century. Yet focusing on individual intelligence, whether human or machine, can distract us from the true nature of accomplishment. As Thomas Malone, professor at MIT's Sloan School of Management and director of its Center for Collective Intelligence notes: "Almost everything we humans have ever done has been done not by lone individuals, but by groups of people working together, often across time and space." Malone, the author of 2004's The Future of Work and a pioneering researcher in the field of collective intelligence, is in a singular position to understand the potential of AI technologies to transform workers, workplaces, and societies. In this conversation with Deloitte's Jim Guszcza and Jeff Schwartz, he discusses a vision outlined in his recent book Superminds--a framework for achieving new forms of human-machine collective intelligence and its implications for the future of work. Can you tell us what a "supermind" is, and how you define collective intelligence? Thomas Malone, director, MIT Center for Collective Intelligence: A "supermind" is a group of individuals acting collectively in ways that seem intelligent, and collective intelligence essentially has the same definition. For many years, I defined collective intelligence as groups of individuals acting collectively in ways that seem intelligent.
Aerial filming is becoming more and more popular thanks to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose an intelligent agent which supervises motion planning of a filming drone based on aesthetical values of video shots using deep reinforcement learning. Unlike the current state-of-the-art approaches which mostly require explicit guidance by a human expert, our drone learns how to make favorable shot type selections by experience. We propose a learning scheme which exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both hand-crafted and human reward functions. We deploy the same agent on a real DJI M210 drone in order to test generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shots taken using our method and write comments about them.
This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behaviour of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results.
It's not a perfect measure, but unit sales of industrial robots give some idea of a country's industrial might. The names of the top five buyers in 2017 – China, Japan, South Korea, the US and Germany – shouldn't be too surprising. The global average is 74 per 10,000. One factor in this is the small electronics and automotive sectors here, which are two major drivers of industrial robot investment. The high number of SME and micro-businesses in Australian manufacturing is another.
Silos have always been considered a bad thing for enterprise IT environments, and today's push for artificial intelligence and other cognitive technologies is no exception. A recent survey shows fewer than 50% of enterprises have deployed any of the "intelligent automation technologies" -- such as artificial intelligence (AI) and robotic process automation (RPA). IT leaders participating in the survey say data and applications within their companies are too siloed to make it work. That's the gist of a survey of 500 IT executives, conducted by IDG in partnership with Appian. The majority of executives, 86%, say they seek to achieve high levels of integration between human work, AI, and RPA over the coming year.
Artificial intelligence (AI) in the fintech industry is not about replacing live employees with robots. Instead, it's about using automation to carry out basic or routine tasks in order to let employees handle more complex issues. It's a way of giving employees more responsibility and the chance to work closer with the customers who truly need live help. AI is also ensuring that each transaction is accurate and it's making online transactions safer by automating regulatory compliance. When basic customer tasks are automated, such as simplistic banking transactions like depositing money, checking account balances or cashing checks, employees can have the time and mental energy to handle high-value tasks and troubleshoot difficult problems.