Workshop Objectives
AI workshops, conferences, and publications over the past few years have served to raise awareness of the challenges, responsibilities, opportunities, and concerns in applying AI to national defense. This workshop will extend this conversation by focusing at a deeper technical level on recent state-of-the-art national security AI applications. The workshop will showcase several examples of significant progress in applying AI and will provide a glimpse into exciting future directions that promise to have a profound impact on national security.
Target Audience
National security professionals interested in understanding the state of AI research and development (R&D) and expert practitioners interested in applying the latest AI techniques and technologies.
Workshop Highlights
Sessions
- Artificial Intelligence Landscape:
State of the art in key areas of AI from commercial, military, and university perspectives - AI Applied to National Security:
R&D enabling transformational capabilities for specific national security applications - AI Future Directions:
Vanguard topics with specific relevance to applications of national security
Posters, Demonstrations, and Tours
Visual representations of applied AI, interactive demonstrations showcasing AI in motion, and organized tours of R&D facilities
AI Introductory Course
Half-day course open to all interested registrants, 12 November 2019
National Security Applications
Radar, electronic warfare, intelligence, surveillance and reconnaissance, cyber, homeland protection, communications, biosecurity and life sciences, device/system engineering, law enforcement
Key Areas of AI
Deep learning, graph-based learning, data and graph analytics, datasets, adversarial AI, counter AI, compute capabilities, human-machine teaming, autonomy, test and evaluation
Presentations
Friday, November 15, 2019
- AI Accelerator: Producing innovative, disruptive, technology to rapidly field AI into DoD systems COL Randy “Laz” Gordon, USAF/MIT (PDF 2.2Mb)
- Session Introduction: Enabling the Future of AI Dr. Robert Freking, MIT LL (PDF 20.2 Mb)
- AI in DARPA I2O Dr. Bruce Draper, PM, DARPA I20 (PPTX 19.9 Mb)
- Accelerating Artificial Intelligence with Silicon Photonics Dr. Nicholas C. Harris, CEO, Lightmatter Inc. (PDF 8.4 Mb)
- Fast AI Prof. Charles E. Leiserson, MIT CSAIL (PPTX 17.2 Mb)
- Pragmatic AI: Enabling the Future of Autonomy and Assured AI Ms. Paula Ward, MIT LL (PPTX 117.6 Mb)
- Session Introduction: Vanguard Topics Dr. Vijay Gadepally, MIT LL (PPTX 9.8 Mb)
- Towards AI You Can Rely On Prof. Aleksander Madry, MIT CSAIL (PDF 32.9 Mb)
- Robust Neuro-Symbolic AI Dr. Vikash Mansinghka, MIT CSAIL (PDF 6.5 Mb)
- Decision Uncertainty in Deep Learning and its Applications Dr. Theodoros Tsiligkaridis, MIT LL (PPTX 18.3 Mb)
- Synthetic Data Augmentation for AI Dr. Pooya Khorrami, MIT LL (PPTX 22.6 Mb)
- Visual ReasoningDr. Ryan Soklaski, MIT LL (PDF 27.1 Mb)
Demonstrations
Thursday and Friday, November 14-15, 2019
- Exploiting Risk Taking in Group Operations Ross Allen, MIT LL (PPTX 176.3 Mb)
- AI-Guided Vascular Access for the Battlefield Laura Brattain, MIT LL (PPTX 101 Mb)
- Decentralized Multi-Agent Coordination Dan Griffith, MIT LL (PPTX 106.9 Mb)
- Visual Inertial Odometry for non-GPS AI Applications Nathan Hughes, MIT LL (PPTX 72.7 Mb)
- Automated Volumetric Segmentation for Dense Axon Tracing Tzofi Klinghoffer MIT LL (PPTX 34.9 Mb)
- Robust and Intelligent Mobile Manipulators Micah Fry, MIT LL (PPTX (91.7 Mb)
- Virtual–Physical Environment for Autonomy Research Kevin Leahy, MIT LL (PPTX 396 Mb)
- Neural Control of Exoskeletons Ho Chit Siu, MIT LL (PPTX 21.8 Mb)
- A Brain–Computer Interface for Hearing Enhancement Christopher Smalt, MIT LL (PPTX 286 Mb)
- Global Synthetic Weather Radar Matk Veillette, MIT LL (PPTX 25.2 Mb)
Courses
Tuesday, 12 November, 2019
Track 1 – Autonomy: AI for Autonomous Systems with RACECAR
- AI for Autonomous Systems with RACECAR Ms. Tate DeWeese, MIT LL (PPTX 283.2 Mb)
- Imitation Learning Lab (URL .RTF)
Track 2 – Computer Vision: Introduction to Deep-Learning Computer Vision Applications in GEOINT
- Machine Learning Overview Dr. Ryan Soklaski, MIT LL (PDF 7.1 Mb)
- Image-Based Geolocalization Dr. Ryan Soklaski, Mr. Greg Angelides, MIT LL (PDF 3.8 Mb)
- Classification and Detection in Imagery Dr. Ryan Soklaski, Mr. Greg Angelides, MIT LL (PDF 3.8 Mb)
Track 3 – Natural Language Processing: AI for Text Analytics
- Deep Learning for Semantic Representation Dr. Charlie Dagli, MIT LL (PPTX 3.8 Mb)
- Natural Language Processing: Overview and Applications Dr. Olga Simek, Dr. Charlie Dagli, Dr. Lin Li, MIT LL (PPTX 27.1 Mb)
- Natural Language Processing: Applications for National Security Dr. Lin Li, MIT LL (PPTX 127.4 Mb)
- Topic Modeling Dr. Olga Simek, MIT LL (PPTX 10.8 Mb)
- Topic Modeling (IPYNB 181 Kb)