Another significant challenge raised by the video is shortage of natural resources. Here AI can play a dual role of more efficiently managing the resources available and accelerating the cultivation of new ones.
DeepMind recently demonstrated that using "machine learning [in] Google data centres [can] reduce the amount of energy we use for cooling by up to 40 percent." (DeepMind)
Another RL example comes from Castelletti. They propose that reinforcement learning (Q-learning) can be used to model optimal water reservoir operations with an increased number of state variables (while gaining computational efficiency over prevailing Stochastic Dynamic Programming).
Behmann, et al. survey successful applications of supervised and unsupervised methods used for "early detection of weeds, plant diseases and insect pests in crops."
Another area of focus is, what happens to improvised infrastructure like "makeshift power grids" particularly in the face of natural disasters?
An interesting development in this area is the concept of "transiently-powered computing" that are "networked autonomous devices, collections of tens to thousands of nodes organized into cooperative networks." (Balsamo)
Balsamo "presents a new power-neutral paradigm to tackle the power supply challenge for the transient computing system whose operation is solely based on harvested power. Such a paradigm enforces the well match between the instantaneous power consumption and the harvested power through using an innovative control algorithm exploring dynamic frequency scaling" (Hu)
While there are many promising research areas, we should not underestimate the scale and the complexity of the challenges that the Pentagon video highlights.
We at Adversarial.AI (and our parent Startup.ML) are taking some small steps to:
Balsamo, Domenico, et al. "Graceful Performance Modulation for Power-Neutral Transient Computing Systems." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35.5 (2016): 738-749.
Behmann, Jan, et al. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture 16.3 (2015): 239-260.
Castelletti, A., et al. Treebased reinforcement learning for optimal water reservoir operation. Water Resources Research 46.9 (2010).
DeepMind Blog. DeepMind AI Reduces Google Data Centre Cooling Bill by 40%
Hu, Shiyan, Xiaobo Sharon Hu, and Albert Y. Zomaya. Guest Editorial Leveraging Design Automation Techniques for Cyber-Physical System Design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35.5 (2016): 697-698.
Ilievski, Ilija, Sonja Gievska, and Ognen Marina. Discovering Patterns of Urban Development.(2013).
Juan Carlos Asensio