{"id":8744,"date":"2018-04-04T21:35:11","date_gmt":"2018-04-04T21:35:11","guid":{"rendered":"http:\/\/manufacturinglounge.com\/?p=8744"},"modified":"2024-07-11T16:10:49","modified_gmt":"2024-07-11T23:10:49","slug":"3-advances-changing-future-artificial-intelligence-manufacturing","status":"publish","type":"post","link":"https:\/\/www.autodesk.com\/products\/fusion-360\/blog\/3-advances-changing-future-artificial-intelligence-manufacturing\/","title":{"rendered":"3 Advances Changing the Future of Artificial Intelligence in Manufacturing"},"content":{"rendered":"
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Today\u2019s factories are easy to envision as futuristic-seeming hives of automation, where industrial robots mimic the movements and, seemingly, the intentionality of human workers.<\/p>\n\n\n\n

Today\u2019s robots are not only working faster and more reliably than their human counterparts but also performing tasks beyond human capability altogether, such as microscopically precise assembly<\/a>. But many of those robots are dumber than they look. That is, they may be more dexterous than humans, but they are programmed to perform a limited range of tasks. Many robots can\u2019t safely work in close proximity to humans and literally have to be caged or regulated<\/a> in ways that safeguard human coworkers.<\/p>\n\n\n\n

Artificial intelligence (AI) is just now finding its niche in manufacturing, as the technology matures and costs drop\u2014and as manufacturers discover applications for which AI algorithms can make complex decisions. And as it becomes ubiquitous, the future of artificial intelligence in manufacturing is already becoming feasible in emerging markets; showcasing better sensory capabilities; and, off the factory floor, predicting what will be needed and when.<\/p>\n\n\n\n

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1. The Rise of AI in New Markets<\/strong><\/h2>\n\n\n

In manufacturing, capital investments are high and profit margins are often thin. Those conditions helped to drive a lot of manufacturing to low-wage countries<\/a>, where the human-resource costs have been so low that the capital investment in AI and related automation was hard to justify. But rising living standards and wages in places like India<\/a> have made AI an easier sell. In fact, China is already making significant investments<\/a> in AI for manufacturing and e-commerce.<\/p>\n\n\n\n

And just as US workers have lamented loss of jobs to automation, the same is now happening in Chinese factories<\/a>. Although many workers will be replaced by robots in the short term, the end game will be to retrain those workers to perform higher-level design, programming, or maintenance tasks. The real driver, however, will be to develop applications for AI that don\u2019t just automate tasks, but make entirely new business processes feasible\u2014for example, custom configuration of products to individual customer requirements.<\/p>\n\n\n

2. Better Machine Senses Mean Safer Workplaces<\/strong><\/h2>\n\n\n

AI has its roots in the 1950s but only found broad acceptance with the development of machine-learning algorithms that could be loosed on a body of data to discover meaningful patterns\u2014without deliberate programming. \u201cWithout flexible algorithms, computers can only do what we tell them,\u201d says Michael Mendelson, a curriculum developer at the NVIDIA Deep Learning Institute<\/a>. \u201cMany tasks, especially those involving perception, can\u2019t be translated into rule-based instructions. In a manufacturing context, some of the more immediately interesting applications will involve perception.\u201d This would make factory robots more capable and better able to interact with\u2014and take instructions from\u2014humans.<\/p>\n\n\n\n

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Machine vision is one of these applications. Devising cameras many times more sensitive than the human eye has been the easy part. What AI adds is the increasingly useful ability to make sense of the images. Landing.ai<\/a>, a startup formed by Silicon Valley veteran Andrew Ng, focuses on manufacturing problems such as precise quality analysis. It has developed machine-vision tools to find microscopic defects in products such as circuit boards at resolutions well beyond human vision, using a machine-learning algorithm trained on remarkably small volumes of sample images.<\/p>\n\n\n\n

That\u2019s a microlevel challenge. A macrolevel problem is training a robot to sense<\/a> what is going on around it so that it can avoid disruptions or danger. This is analogous to the self-driving-vehicle problem, which is nearing real-world adoption. There is a likely role in factories<\/a> for smart, self-driving forklifts and conveyors to move materials and finished goods around.<\/p>\n\n\n\n