AI Articles and Analysis about Automation and 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.

Oil remains one of the most highly valued commodities in the energy sector. Estimates of total energy investment in 2016 tip the scale at approximately $1.7 trillion which represents 2.2 percent of the global GDP. However, as concerns over the environmental impact of energy production and consumption persist, oil and gas companies are actively seeking innovative approaches to achieving their business goals while reducing environmental impact.

The market for military robotics is massive, and many developments can be observed in public competitions, university campuses, and DARPA's own announcements.

The precipitous drop in the price of drones and the intelligent guidance systems that power them has created a broad range of commercial applications (we covered industrial drone applications in a previous article). One use-case that has captured popular attention and large scale investment from a variety of retailers and start ups is autonomous delivery vehicles.

The applications of AI in pharma are building momentum, but we wanted to look beyond the hype and find the underlying trends that matter in business.

It’s no secret that e-tailer Amazon has an army of robots at its warehouses to fulfill orders; currently its robot detail is 45,000 strong. The company also wants to put a fleet of airborne drones into service for fast deliveries. The question that traditional retailers face is whether or not investing in robots to operate inside their stores can actually improve business and help them stay relevant with consumers.

Episode summary: Machine learning currently faces a number of obstacles which prevent it from advancing as quickly as it might. How might these obstacles be overcome and what impact would this have on the machine learning across different industries in the coming decade? In this episode we talk to Dr. Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about the developments in core machine learning technology that need to be made, and that researchers and scientists are working, on to further the application of machine learning in autonomous vehicles.

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.