AI Articles and Analysis in Natural resources

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.

Historically, space has been an industry only governments and heavyweight airspace corporations (such as Boeing) could handle. Uses of AI in space technology might be expected to be even more expensive, but the last decade of innovation has made space more accessible - and thanks to AI - data from space is becoming much more useful for businesses and governments.

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.

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.

Episode Summary: “Artificial intelligence (AI) can be seen as a progression in our scalability of labor.” This quote comes from this week’s guest, Naveen Rao, who received his PhD in Neuroscience from Brown before becoming CEO at Nervana Systems, which works on full stack solutions to help companies solve machine learning (ML) problems at scale. In this week’s episode, Rao speaks about certain domains in industry where he feels optimistic about machine learning (ML) making a difference in the next five to 10 years, providing interesting perspectives that include advances in the areas of agriculture and oil & gas.