The Air Force released the Artificial Intelligence Strategy Sept. 12, highlighting the importance of artificial intelligence capabilities to 21st century missions. The strategy provides definition, context and purpose for artificial intelligence in the Air Force, and is the service's annex to the Department of Defense Artificial Intelligence Strategy. "The Air Force is charged to provide the nation with Air and Space Superiority, Global Strike, Rapid Global Mobility, Intelligence, Surveillance and Reconnaissance and Command Control," said Acting Secretary of the Air Force Matthew Donovan and Air Force Chief of Staff General David Goldfein in the dual-signed annex. "Al is a capability that will underpin our ability to compete, deter and win across all five of these diverse missions. It 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." The strategy serves as the framework for aligning Air Force efforts with the National Defense Strategy and the Department of Defense Artificial Intelligence Strategy as executed by the Joint Artificial Intelligence Center.

Summary: Now that we've detailed the four main AI-first strategies: Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it's time to pick. Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup. In our last several articles we've taken a tour of the four major strategies for creating a successful AI-first company. So which one is best? Since we're going to offer a side-by-side comparison you may want to refer first to the foundation articles on the four strategies: There is wide agreement that controlling a unique data set is the most effective way to create a defensible moat.

Summary: The fourth and final AI strategy we'll review is Systems of Intelligence (SOI). This is getting nearly as much attention as the Vertical strategy we previously reviewed. It's appealing because it seems to offer the financial advantages of a Horizontal strategy but its ability to create a defensible moat requires some fine tuning. In the last several articles we've been looking at different strategies for successful AI companies. We described Data Dominance, and the Vertical and Horizontal strategies.

The rise of machine intelligence has prompted policy wonks to weigh in with a list of caveats along with recommendations for preserving the American technology lead in AI and machine learning while initiating the process of managing future risks. The respected Center for Strategic and International Studies (CSIS) released a report on Thursday (March 1) calling for a national machine intelligence strategy. Underwritten by U.S. technology contractor Booz Allen Hamilton (NYSE: BAH), the study takes a "look-before-you-leap" approach to machine intelligence development while advocating steps for maintaining the current U.S. lead. The report recommends "safe and responsible" development of machine intelligence by funding long-term R&D in areas the private sector has little incentive to invest. Along with risky, long term research, the government should focus on national security applications and "systems of ethics and control" akin to government agencies that help referee disputes over technology standards.

In every prediction about the future of work, artificial intelligence appears pretty close to the top of technology trends for businesses to prepare for. Google and other technology giants are developing algorithms which can learn from human inputs, meaning that they can accelerate their learning in a particular area at an incredibly rapid rate. However, before diving into a new artificial intelligence strategy for business, it's worth taking a look at what the capabilities of AI actually are right now, because the media presents a confusing picture. The first type of AI is highly achievable, and probably shouldn't be called AI at all – it's just a good algorithm. More successful types of this AI are the'recommendation engines' – characterised by you-watched-this-movie so you-may-like-this-TV-show. These are extremely helpful for customer engagement, and bringing people personalised recommendations to make them come back to your product.