As discussed in Part 1 of this AI discussion, China’s central government plans to achieve AI breakthroughs by 2025 and world AI dominance by 2030. If the DoD’s past acquisition track record doesn’t change, it could be twenty years before significant AI technology is actually deployed to military units. But a more important question is how can AI technology provide significant value to the military? It is always easy to assume a new super hyped technology will add significant value to military warfare, but over time the more important reality becomes how to best make use the technology for current or new deployment strategies.
Because the current state of AI relies primarily on convolutional neural networks, that are heavily reliant on large amounts of use case specific training data, the results are often brittle and return unintended errors. Depending upon how use case errors are detected and diverted, current AI systems can create catastrophic failures. In March of last year a second Tesla driver was killed by driving under a crossing truck, because the autopilot was fooled by early morning sunlight on the truck trailer. While traffic deaths, like these, represent critical AI failures, one can imagine much worse scenarios if AI is allowed to control military force deployments and/or weapon delivery decisions, where almost every situation is significantly different!
Another way to understand AI is to change the way we think about it by being more specific about the names or labels we use to describe the technology. Augmented Intelligence or Intelligence Augmentation (IA) are terms being used to more accurately describe the way we use AI to support or enhance our human intelligence. IA reverses the letters to prevent confusion with the more general AI acronym. Augmented Reality (AR) can be an example of IA and may become the most significant realization of applied IA over the next decade.
Most of us consider our smartphones to be memory upgrades, as they guide us through our daily calendar, contacts, messages, navigation, and internet searches throughout the day. IKEA Place is a new smartphone or tablet application using AR to show a potential buyer how a new piece of furniture will look and fit in an intended room at home. How many of us have realized too late that a purchased piece was too large or too small for the intended room? IKEA combines AI and AR to deliver to customers this powerful improvement to the simple use case of furniture shopping. For that they are predicting significant revenue increases to their bottom line.
Another common successful AI use case is Natural Language Processing (NLP) where research dates back to the 1950’s. Thanks to that research, much of it from DARPA, this is an AI area that is commonly deployed to help us control functions in our cars, homes, and smartphones. As mentioned in Part 1 on AI, NLP is close to delivering universal language translation. One can only guess at the amazing impact NLP will have on global education and learning, as knowledge in any language becomes available to anyone interested.
Virtual Reality (VR), unlike AR, is an immersive experience because the wearer only sees what the headset screen delivers to the user. VR and associated headsets products are a big commercial market for gaming and some use cases, like previewing travel destinations. Alternately, the new commercial hope for IT devices is AR glasses, also called smart glasses. Some will remember that Google introduced Google Glass in 2013 to open this new market. Because of limited uses and limited computer power, they failed to gain a following and were pulled from the market in 2015. Google then introduced Google Glass Enterprise in 2017, as industrial only glasses, designed to augment complex manufacturing, assembly-line, and other industrial AR use cases.
Today, smaller high-power electronics appear to make smart glasses a near term reality. Giants, Apple and Intel, are set to release smart glasses products sometime in 2020. Intel has uncharacteristically decided to enter this commercial market estimating they are better able to optimize the micro size electronics required to make the glasses look normal. Their glasses claim safe delivery of the AR image directly onto the user’s eye retina. Other companies are working on AR contact lenses, which will eventually put all of us into sci-fi movie territory!
Like the heads up AR display in F-35 pilot helmets, one can imagine wearing smart glasses to navigate our daily lives, providing augmented information to help us choose among nearby restaurants, find office locations after parking our car, or letting us know if our health parameters are good or bad at any instance in time. The good news is that augmented IA smart glasses leave the user in charge of their actions and could even help prevent accidents. For the Department of Defense, the challenge continues to be where the best early use cases for AI or IA technologies may lead our Country’s National security?
One answer might be the current research work on Explainable AI, which is an important effort to allow users to understand how AI algorithms or AI models arrive at decisions and/or recommendations. Current convolutional neural network AI/ML algorithms are effectively black boxes that take in inputs and deliver decision outputs without access to the reasoning inside. In warfare, however, even if command & control systems (C2) are using AI to augment decision options for positioning forces, or authorizing the use of weapons, commanders are held accountable for their actions and the results, and must be able to justify how they derive each decision.
One small startup Company, Z Advanced Computing, Inc (ZAC), has been working to develop a form of explainable AI to prevent AI/ML brittle false alarms, and reduce the size of training data sets. ZAC is applying explainable AI to both the commercial market, for searching visual images, like shoes or clothing, and is more recently supporting the the U.S. Air Force to automate finding military information from video and visual images. ZAC’s technology claims to mimics how humans discover, recognize, and learn reusable, modular, cumulative, and scalable knowledge.
Lastly, consider the long standing holy grail of AI’s potential future, Artificial General Intelligence (AGI). AGI is the idea of machines that think and reason with human-like results. Current estimates place effective AGI availability out somewhere between 20 to 200 years from now, meaning that solid estimates are not available. One could imagine a working AGI system to be like the HAL-9000 computer from the infamous 1968 movie, 2001: A Space Odyssey. In the movie HAL, the heart of the spaceship’s control system, kills the ship’s crew and then tries to kill the flight commander, all because HAL had been ordered to tell a lie, an order that broke HAL’s prime directive. Emotional images, like those portrayed in this movie, partly account for our natural fear of AI. Elon Musk has called AI, humanity’s “biggest existential threat.” He and others rationally recommend that our government spend a couple of years, better understanding AI, before it becomes deployed in ways that could make dangerous societal mistakes.
Until explainable AI or AGI technologies become more widely available, current IA is being explored to help augment C2 warfare functions, such as language translation using NLP, and AI/ML data tracking to improve situation assessment. Done properly, these functions can augment C2 support to the warfare commanders without relieving them of their responsibility to apply experience and knowledge to make good decisions.
…But it could turn out that F-35 like AR and smart glasses will become the more practical military AI capability in the foreseeable future…
