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Vision Guided Robotics & Artificial Intelligence: An Explanation for the Non-Technical


The automation industry is experiencing an explosion of growth and technology capability. To explain complex technology, we use terms such as "artificial intelligence" to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who seeks to understand the technologies you are investing in, this article is for you. What follows is an explanation of vision-guided robotics and deep-learning algorithms. That's right, the article is titled "artificial intelligence" and yet by the end of the first paragraph, we've already switched to deep-learning algorithms!

Meet the New Robots

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General Motors' implementation of flexible robotic automation in Ternstedt, N.J., started U.S. manufacturing on the path to realizing a future depicted in a 1923 play by Karel Capek. In "R.U.R." (Rossum's Universal Robot), Capek's vision was for millions of mechanical workers -- robots (as derived from the Czech words for work or workers). Although U.S. robot numbers are not yet measured in millions, the industrial automatons are nonetheless playing strategic roles in U.S. manufacturing competitiveness, says Jeffrey A. Burnstein, executive vice president, Robotic Industries Association. RIA estimates that more than 171,000 robots are now at work in U.S. factories, placing the U.S. second only to Japan in overall robot use. Worldwide, there are more than a million industrial robots in operation, Burnstein notes.

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Advances in 3D imaging have allowed vision users to overcome some challenging inspection tasks. In the machine vision marketplace, 3D imaging continues to mature, tackling applications 2D imaging cannot. "In a manufacturing setting, the fusion of 2D with 3D is necessary to measure how well components go together into an assembly and assess the product for final fit, finish, and packaging," says Terry Arden, CEO of LMI Technologies. According to David Dechow, Principal Vision Systems Architect at Integro Technologies, a systems integrator specializing in machine vision technologies with broad experience in helping companies implement 3D and 2D imaging for industrial automation, accuracy has improved as well. And with inspection tasks in 3D space, which may include measurement or reconstruction, precision is even more essential than with most tasks in robotic guidance or bin picking.

Computer Vision In Python! Face Detection & Image Processing


Master Python By Implementing Face Recognition & Image Processing In Python Created by Emenwa Global Students also bought Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs Python for Computer Vision with OpenCV and Deep Learning Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Autonomous Cars: Deep Learning and Computer Vision in PythonPreview this course Udemy GET COUPON CODE Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.

Applications (and implications) of artificial intelligence


Advances in artificial intelligence are having a growing impact on sectors as diverse as retail, healthcare and manufacturing. Among the many emerging trends in the technology sector, the rise of artificial intelligence (AI) is likely to be one of the most significant over the coming years. AI refers to the ability of machines to perform tasks that would typically be associated with human cognition such as responding to questions, recognizing faces, playing video games or describing objects. Over recent years, AI capability has improved to such an extent that a range of commercial applications are now possible in areas like consumer electronics, industrial automation and online retail. Technology companies of all sizes and in locations all around the world are developing AI-driven products aimed at reducing operating costs, improving decision-making and enhancing consumer services across a range of client industries.