AI Articles and Analysis in Heavy industry

Our cities, streets, homes, and businesses are built for beings that walk on two legs (biped). From stairs to the shape of hallways to the placement of kitchen cabinets, all have been designed for bipeds. The fact that a society of two-legged creatures designed everything around them for bipeds is so obvious most people don’t even think about it, but it becomes a serious issue when talking about the future of robotics.

Major companies including GE, Siemens, Intel, Funac, Kuka, Bosch, NVIDIA and Microsoft are all making significant investments in machine learning-powered approaches to improve all aspects of manufacturing. The technology is being used to bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed.

It would be great if instead of having our car break down - could have them fixed as soon as the underlying problem began. It would be great if instead of having to diagnose a malfunctioning piece of mechanical equipment - would could have the right "fix" presented to us immediately. As it turns out, artificial intelligence may be working its way to accomplish both of those goals in the not-so-distant future.

While self-driving trucks and self-driving cars make use of much of the same technology to power their AI systems, it would be a mistake to think the expected roll out date of both developments to would be identical. Their similarities can easily mask their significant differences.

Episode summary: Unlike the field of self-driving cars, the fields of construction, mining, agriculture, and other classes of “heavy industry” involve a huge variety of equipment and use-cases that go beyond traveling from A to B. The heavy industry leaders of today are no farther behind automakers in their understanding that AI and automation will be essential for the future of their companies. In this episode, guest Dr. Sam Kherat discusses the applications of AI in heavy industry, including: What type of capabilities and functions are automate-able, and at what level. He also shines a light on how AI might affect the future of the industry within the next 2-3 years, and in what ways we can expect large equipment to become more autonomous.

High costs and technical limitations kept the uses of drones relatively limited until recently. After significant excitement starting around 2012, the FAA's 2016 adoption of regulations - combined with the drop in price - has made drones an economically viable option for a broad range of commercial functions.

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time.

The rise of AI industrial robotics experienced record double-digit expansion in various countries in 2014 and 2015, but such large scale segments i.e. 'industrial' versus 'medical' or 'military', were more or less one amalgam of parts a couple of decades ago. Examples of medical and military applications can be found in our updated machine learning in robotics guide. There was a time before the early 1980s when it was possible for AI researchers to keep up with all that was going on in the AI and the robotics industry as a whole, but it seems the tides had changed by 1982.