The AI stack: a blueprint for developing and deploying artificial intelligence

This paper provides an abstract technology model called the AI Stack for the development and deployment of Artificial Intelligence, and the strategic investment in research, technology, and organizational resources required to achieve asymmetric capability. Over the past five years, there has been a drastic acceleration in the development of artificial intelligence fueled by exponential increases in computational power and machine learning. This has resulted in corporations, institutions, and nation-states vastly accelerating their investment in AI to (a) perceive and synthesize massive amounts of data, (b) understand the contextual importance of the data and potential tactical/strategic impacts, (c) accelerate and optimize decision-making, and (d) enable human augmentation and deploy autonomous systems. From a national security and defense perspective, AI is a crucial technology to enhance situational awareness and accelerate the realization of timely and actionable intelligence that can save lives. For many current defense applications, this often requires the processing of visual data, images, or full motion video from legacy platforms and sensors designed decades before recent advances in machine learning, computer vision, and AI. The AI Stack - and the fusion of the interdependent technology layers contained within it - provides a streamlined approach to visualize, plan, and prioritize strategic investments in commercial technologies and transformational research to leverage and continuously advance AI across operational domains, and achieve asymmetric capability through human augmentation and autonomous systems. One application of AI for the Department of Defense is to provide automation and human augmentation for analyzing full motion video to drastically enhance the safety of our deployed soldiers by enhancing their situational awareness and enabling them to make faster decisions on more timely information to save lives.

[1]  Vladlen Koltun,et al.  Photographic Image Synthesis with Cascaded Refinement Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[2]  Daniel S. Hoadley,et al.  Artificial Intelligence and National Security , 1986 .

[3]  Andrew Lavin,et al.  Fast Algorithms for Convolutional Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Kris M. Kitani,et al.  Forecasting Interactive Dynamics of Pedestrians with Fictitious Play , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Robin Staffin,et al.  Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD , 2017 .

[6]  Tao Wang,et al.  Deep learning with COTS HPC systems , 2013, ICML.

[7]  Trevor Darrell,et al.  Learning to Segment Every Thing , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Daniel Alderman,et al.  Best Frenemies Forever: Artificial Intelligence, Emerging Technologies, and China–US Strategic Competition , 2017 .

[9]  M. E. Celebi,et al.  Advances in Face Detection and Facial Image Analysis , 2016 .

[10]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[11]  Abhinav Gupta,et al.  The Curious Robot: Learning Visual Representations via Physical Interactions , 2016, ECCV.

[12]  Martial Hebert,et al.  Learning to Learn: Model Regression Networks for Easy Small Sample Learning , 2016, ECCV.

[13]  Tomas Pfister,et al.  Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[15]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[16]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Yaoliang Yu,et al.  Petuum: A New Platform for Distributed Machine Learning on Big Data , 2015, IEEE Trans. Big Data.

[18]  Ulrich W. Eisenecker,et al.  AI: The Tumultuous History of the Search for Artificial Intelligence , 1995 .

[19]  Derek Hoiem,et al.  Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[21]  Martial Hebert,et al.  Learning to Model the Tail , 2017, NIPS.

[22]  Andrea Vedaldi,et al.  Learning multiple visual domains with residual adapters , 2017, NIPS.

[23]  Marcin Andrychowicz,et al.  Learning to learn by gradient descent by gradient descent , 2016, NIPS.

[24]  Frank Keller,et al.  Training Object Class Detectors with Click Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Oriol Vinyals,et al.  Matching Networks for One Shot Learning , 2016, NIPS.

[26]  Tianqi Chen,et al.  Net2Net: Accelerating Learning via Knowledge Transfer , 2015, ICLR.

[27]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[29]  Chairwoman Comstock,et al.  ARTIFICIAL INTELLIGENCE Emerging Opportunities , Challenges , and Implications for Policy and Research , 2018 .

[30]  Derek Hoiem,et al.  Diagnosing Error in Object Detectors , 2012, ECCV.

[31]  Martial Hebert,et al.  Learning robust failure response for autonomous vision based flight , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[32]  Andrew Zisserman,et al.  Multi-task Self-Supervised Visual Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[33]  Jitendra Malik,et al.  Cross Modal Distillation for Supervision Transfer , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Marios Savvides,et al.  A Deep Learning Approach to Joint Face Detection and Segmentation , 2016 .

[35]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[36]  Martial Hebert,et al.  Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs , 2016, NIPS.

[37]  David A. Patterson,et al.  In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).

[38]  H. Zimmermann,et al.  OSI Reference Model - The ISO Model of Architecture for Open Systems Interconnection , 1980, IEEE Transactions on Communications.

[39]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[40]  Ali Farhadi,et al.  Predicting Failures of Vision Systems , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Hugo Larochelle,et al.  Optimization as a Model for Few-Shot Learning , 2016, ICLR.

[42]  Tom Schaul,et al.  Reinforcement Learning with Unsupervised Auxiliary Tasks , 2016, ICLR.

[43]  Jitendra Malik,et al.  Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.