This article is based on a presentation at the 2017 AI Applications Summit conference in Boston, entitled “Artificial Intelligence in the Hospital Setting” (slide deck embedded below) delivered by Emerj CEO Daniel Faggella - updated with recent insights and interviews from Emerj's podcast and AI Opportunity Landscape data.
The idea of using artificial intelligence (AI) in the military scares many people in the US, especially when it comes to the Army. The US Army typically operates on the ground, and so it may be uncomfortably closer to home for some people.
The Chinese military, or People's Liberation Army, is focusing heavily on artificial intelligence. However, China's race to develop "smarter," cheaper AI technology for the military is not linear, but instead a many-pronged strategy that involved the central government, domestic companies, and international trade. Gregory Allen of the Center for a New American Security published a report on China's AI strategy, in which he said:
Chinese military leaders increasingly refer to intelligent or “intelligentized” military technology as their confident expectation for the future basis of warfare. Use of the term “intelligentized” is meant to signify a new stage of military technology beyond the current stage based on information technology.
He also reported that “total Chinese national and local government spending on AI to implement these plans is not publicly disclosed, but it is clearly in the tens of billions of dollars.”
Government contracts are notoriously hard to acquire. It takes many months of painstaking work to get through the gamut of regulations that weigh about 8.5 lb when printed out. Startups and small companies, including those developing artificial intelligence, often do not have the resources to compete for a share of this market. The US Air Force is changing that by streamlining the process in what it calls Pitch Days, the first of which was on March 6 and 7, 2019 in New York.
Logistics in the military encompasses more functions than most people realize. In modern warfare, that means large quantities of data to sift through in order to make decisions regarding supply, transport, communications, and so on. Using artificial intelligence (AI) and machine learning (ML) in one or more areas in logistics could help speed up that process and make it more agile.
The Department of Homeland Security (DHS) routinely handles large amounts of data. Its mandate is to “keep America safe,” and that encompasses many fronts. This includes anything to do with potential threats to the nation, ranging from border security to cybersecurity, so “big data” would be an understatement. Using machine learning and artificial intelligence technology for Homeland Security was inevitable.
Large volumes of data, managed properly, are a boon for many industries, including the military. It would not be possible to mount effective military operations without knowing the when, where, and what in deploying resources. Military big data, therefore, helps defense leaders make better decisions, provided it is not “dark data.”
The Federal Bureau of Investigation has figured in many works of fiction as well as documentaries because what they do fascinates people. The representations have not always been favorable, but most people agree they have spearheaded many advances in science and technology in law enforcement.
The military has always been at the forefront of advanced technology. Some of the most important applications we use every day, such as the Internet, were developed by or for military use. That said, the military is adopting predictive analytics at what seems to be a slower pace than industry, although there are likely applications for the technology that they choose not to publicize.
The US Department of Defense's DARPA has a plan to invest as much as $2 billion in artificial intelligence research and development in the next 5 years. This is on top of the $2 billion the federal government has already spent on AI-based technology R&D.
The focus of the National Aeronautics and Space Administration (NASA) is to provide information to civilian institutions to help them solve scientific problems at home and in space. This requires a continuous stream of raw data under a constantly shifting environment. According to a 2017 interview with Kevin Murphy, Earth Science Data Systems Program Executive at NASA, the biggest challenge now is not going where no man has gone before, but managing the data.
IT services firms are doing their best to keep up with the changing landscape as AI begins to dominate the tech conversation. Many IT services firms have recently started branding themselves as AI companies without having the requisite talent to back up their claims. In reality, many of the older IT services companies are struggling to hire PhD graduates in machine learning who would rather use their skillset at global AI firms like Google and Amazon.
Although AI has broader applications in the travel and tourism industry, facial recognition kiosks at airports have been one of the most prominent applications in the public discussion about AI. Their promise: to increase security and potentially speed up passenger boarding.
Militaries around the world are starting to make increasing investments in artificial intelligence and machine learning capabilities. The top military defense contractors in the US, Europe, and Israel are all working on AI software to sell into the defense sector. That said, the military adoption of AI is as of right now slow in comparison to that of contractors.
According to Accenture, approximately 66% of A&D executives polled indicated they are looking at investing in AI for 2019, particularly on security, production, and R&D. 80% believe that AI-based decisions will have a direct impact on the workforce by 2021.
Deloitte estimated the size of the global travel and tourism industry at around $1.6 trillion in 2017. When adding in the indirect and induced economic contributions of related activities, the travel and tourism industry accounts for 10.4% of the world’s gross domestic product (GDP).