Artificial intelligence and machine learning may be ideal for picking up the day-to-day tasks of running enterprises, but still fall flat when it comes to innovation or reacting to unforeseen or one-off events. While enterprise-grade AI is still a ways off, it's incumbent on business and IT leaders to start piloting and exploring the advantages AI potentially offers. That's the word coming out of a recent report from the MIT Task Force on the Work of the Future, which looked at AI as part of a broad range of changes sweeping the employment scene and workplace. "We are a long way from AI systems that can read the news, re-plan supply chains in response to anticipated events like Brexit or trade disputes, and adapt production tasks to new sources of parts and materials," state the report's authors, David Autor, Ford professor of economics at MIT, along with David Mindell, professor of the history of engineering and manufacturing at MIT, and Elisabeth Reynolds, principal research scientist at MIT. For starters, data – the fuel that propels AI decision-making – is not ready for the leap.

What other trends are shaping the future of energy extraction, refinement, and consumption. These industry insiders provided their takes on the #1 trend shaping energy this year, and into the future. "Some areas where we see nascent AI is in predictive maintenance and asset monitoring. There are a few who are beginning to look at utilizing AI to analyze images from drones for surveillance and also for acoustic listening." "Advances in the'time-series' AI world (as opposed to AI for images or audio) are shaping the energy industry today. These include techniques for time series forecasting, anomaly detection, optimization etc. Specifically, probabilistic techniques and algorithms are showing significant improvements and becoming the driver of the next wave of optimization and value creation. These techniques augment today's unilateral AI predictions with additional information about the confidence in these predictions. This is not unlike the trend shaping the peer to peer transportation industry."

FEI Daily: Can you please describe how CognitiveTax Insight ("CogTax") works and provide some use cases? Andy Gold: CogTax leverages machine learning and smart OCR to ingest both unstructured data (e.g. With the aid of a human who trains the cognitive technology, it applies machine learning technology to that robust amount of data. In the training, an individual will interact with the cognitive technology and "teach" the machine learning taxability determinations based upon the client's facts and transactional information available. Over time, the system learns as it sees more and more transactions that are substantially similar with the goal of providing a highly accurate solution in terms of being able to identify, in real-time, whether a transaction is taxable.

Dr. Benji Maruyama is a Principal Materials Research Engineer in the Air Force Research Laboratory, Materials & Manufacturing Directorate. He is the Leader of the Flexible Materials and Processes Research Team, and leads research on the synthesis and processing science of carbon nanotubes. Dr. Maruyama created and is developing a new method research: Autonomous Research Systems for Materials Development. He is also the point of contact for carbon materials for the Materials and Manufacturing Directorate. His background and interests include carbon nanomaterials, energy storage, field emission, carbon, polymer and metal matrix composites, imaging of complex 3D microstructures and combinatorial experimentation.

The Defense Department today is wrapping up a three-day "hackathon" aimed at using artificial intelligence for aircraft maintenance.

The Air Force has publicly released its strategy for artificial intelligence, building onto the ongoing work at the Pentagon level. The strategy is meant to be an annex to the Department of Defense's AI strategy in support of its Joint AI Center. It will serve as a mechanism to align the Air Force with the larger AI efforts across the department and leverage the JAIC's progress as an AI center of excellence. The technology "is crucial to fielding tomorrow's Air Force faster and smarter, executing multi-domain operations in the high-end fight, confronting threats below the level of open conflict and partnering with our allies around the globe," write acting Secretary Matthew Donovan and Chief of Staff Gen. David Goldfein in the introduction to the dual-signed strategy. AI, they say, will "underpin our ability to compete, deter and win" across all five of the Air Force's missions: air and space superiority; global strike capability; rapid global mobility; intelligence, surveillance and reconnaissance; and command control.

Idemia National Security Solutions (NSS) has announced the appointment of Dr. Mark J. Burge, who is known in the artificial intelligence community for leading federal, industrial, and academic teams developing machine learning programs to address difficult biometric and computer vision challenges, to the newly created position of Chief Artificial Intelligence Officer. Burge brings experience working in academia with ETH Zurich, OSU, USNA, in government for IARPA and the NSF, and in industry for Mitre and Noblis, to the new office, according to the announcement. "As Chief AI Officer, Mark will lend insights and expertise to high-impact government and commercial R&D programs, drive groundbreaking research for AI/ML applications, and strengthen NSS's strategic positioning as the leader of identity intelligence solutions for the national security community," comments Idemia NSS President and CEO Scott Swan. The intricacies of deploying biometric facial recognition to enhance national security were explored by Swan fellow NSS executives B. Scott Swann and James Loudermilk in a guest post for Biometric Update earlier this year.

Despite its role in early 20th-century fiction, AI has been part of the professional conversation for barely 70 years. AI was first studied at a Dartmouth College conference in 1956. The 1960's saw gains in machine translation and analysis. But AI underwent a "winter" from the 1970s through the early '90s. Researchers shelved their work largely because of the problem of "combinatorial explosion." A U.K. professor who first described the AI concept worried that too many variables would make it useless outside of lab settings. In the early '70s, groups like the U.S. Defense Advanced Research Projects Agency pulled funding. Research failures had become the norm. Interest in AI grew during the 1990s and early 2000's. Processing power and data volumes increased. At the same time, data sets grew massively. Algorithms gained more "meat" on which to train. Advances in game theory and data modeling led to new approaches. Today, best-in-class infrastructures can support 100,000 or more computers. Two-and-a-half quintillion bytes of data are now generated every day. Globally, private firms are spending tens of billions of dollars per year researching and improving AI initiatives. In fact, 2018's investment amount is more than 50 percent larger than last year's alone. Add it all up, and AI seems ready for a leap forward unlike any seen in its history. But, after slow decades followed by speedy discoveries, few outside the field feel they truly understand it. A recent Dell Technologies report found that 67 percent of leaders said their companies were struggling to implement AI. A similar two out of three consumers don't even realize they're using it, according to a HubSpot survey. "By far the greatest danger of artificial intelligence is that people conclude too early that they understand it."

Fox News Flash top headlines for Sept. 23 are here. Check out what's clicking on Foxnews.com Drone fighter jets, hypersonic attack planes, artificial intelligence, lasers, electronic warfare and sensors woven into the fuselage of an aircraft are all areas of current technological exploration for the Air Force as it begins early prototyping for a new, 6th-Generation fighter jet to emerge in the 2030s and 2040s. While the initiative, called Next Generation Air Dominance(NGAD), has been largely conceptual for years, Air Force officials say current "prototyping" and "demonstrations" are informing which technologies the service will invest in for the future. "We have completed an analysis of alternatives and our acquisition team is working on the requirements. We are pretty deep into experimenting with hardware and software technologies that will help us control and exploit air power into the future," Gen. James Holmes, Commander, Air Combat Command, told reporters at the Association of the Air Force Air, Space and Cyber Conference.