A call for project proposals in 2019 resulted in 10 projects that encompass a total of 15 research workstreams. The projects engage more than 140 faculty, researchers, and students, who are affiliated with more than 20 different organizational units across MIT Campus and MIT Lincoln Laboratory. All project teams involve Air Force personnel, who are embedded in the research teams and serve as liaisons between projects and Department of Defense stakeholders. The projects started in January 2020 and advance AI research in a broad range of areas, including weather modeling and visualization, optimization of training schedules, and enhancement of autonomy for augmenting and amplifying human decision-making. The research activities of the AI Accelerator have been successfully expanding, including seed research projects in collaboration with the Naval Postgraduate School (NPS) and an AI Education Research project that started in January 2021.
AI Accelerator Projects
Guardian Autonomy for Safe Decision Making
Air Guardian aims to advance AI and autonomy by developing algorithms and tools for augmenting and amplifying human decision making. The AI Guardian assists humans by suggesting actions using data from the past and fusing inputs from sensors and information sources. Support from an AI Guardian system is especially useful in the presence of surprises and complex situations. Guardian’s end-to-end machine learning algorithms learn from experts how to respond with common sense reasoning in highly dynamic and surprising situations. Our goal is to enable an agent to perceive its environment, identify short-term risks, reason about intentions and behaviors of its operator, and other cooperative and adversarial agents to determine the best course of action. This will lead to Guardian autonomy systems capable of anticipating potential hazardous situations in the future.
Transferring Multi-Robot Learning through Virtual & Augmented Reality for Rapid Disaster Response
Multimodal Vision for Synthetic Aperture Radar
AI-Assisted Optimization of Training Schedules
In order to improve the immensely complex and time-consuming process of manually scheduling aircraft flights, this project aims at automating aircraft flight scheduling to improve scheduling efficiency and robustness in the presence of uncertainty. This will optimize training flight schedules while providing explainability and removing silos in decision-making. This technology enables schedulers to quickly and effectively re-build schedules in the presence of rapidly changing circumstances, vastly accelerating planning and decision cycles. While initially focused on aircraft flight scheduling, this technology applies to all complex resource-allocation tasks across many sectors.
Fast AI: Data Center & Edge Computing
Fast AI: Quick Development of Portable High-Performance AI Applications
ML-Enhanced Data Collection, Integration & Outlier Detection
Conversational Interaction for Unstructured Information Access and Language Learning
Conversations Interaction for Unstructured Information Access
The AI Accelerator Natural Language Processing project aims to advance conversational agents, knowledge representation, and prediction algorithms on flat/text image data and on Air Force missions. As the field of artificial intelligence advances, as we memorialize more of our work in data, and find more devices in our homes, it’s crucial that people are able to interact with the technology in meaningful ways – as humans, language matters – especially in discovering information on digital systems. The goal is to advance the AI community with conversational interaction and knowledge extraction for open-domain conversation and into unstructured information.
AI for Personalized Foreign Language Education
The AI Accelerator Natural Language Processing for foreign language project focuses on building personalized foreign language education framework, which includes a model of the language knowledge to be acquired. This work leverages current foreign language, tailored to the expected level of knowledge by the learner at various stages of coursework, and sets to standardized proficiency test measures. The model will help personalize the learning experience and illuminate when and/or where learning outcomes are unfavorable to the student.
The Earth Intelligence Engine
The Earth Intelligence Engine
Explainable Machine Learning
– Aleksander Madry (MIT PI)
– Arvind Satyanarayan (MIT Co-PI)
– Antonio Torralba (MIT Co-PI)
– Steven Gomez (MIT Lincoln Laboratory Lead)
– Theo Tsiligkardis (MIT Lincoln Laboratory Lead)
– Kevin Nam (MIT Lincoln Laboratory Lead)
– John Radovan (Air Force Liaison)
– Lindsey McEvoy (Air Force Liaison)
– Allan Vanterpool (Air Force Liaison)
Continual and Few-Shot Learning
Robust AI Development Environment
Objective Performance Prediction & Optimization Using Physiological and Cognitive Metrics
This project brings together experts in biomedical instrumentation, signal processing, neurophysiology, psychophysics, computer vision, Artificial Intelligence (AI), and Machine Learning (ML), as well as Air Force pilots, to develop and test AI-based, multi-modal physiologic sensor fusion approaches for objective performance prediction and optimization. The project will leverage immersive virtual environments to train pilots and unobtrusively measure predictors of performance. A series of Challenge Datasets developed from the program will be used to engage the community. Partnering with multiple governmental research efforts and Air Education and Training Command’s myriad pilot training units, the team seeks to provide proof-of-concept by demonstrably accelerating pilot training timelines, producing “better pilots faster.” These methods for accelerating training can then be transferred to all modes of learning across many disciplines and any task requiring significant cognitive effort.
Robust Neural Differential Models for Navigation and Beyond
There are several different GPS-alternatives being researched across the DoD and civilian sectors to address a GPS alternative; however, each alternative comes with additional costs and use cases. Magnetic Navigation presents an alternative GPS system that relies on magnetic resonance of the Earth – a system that is largely known and unchanging – to navigate. Some of the current problems with magnetic navigation involve 1) reducing excess noise on the system, such as magnetic outputs from the Aircraft itself, 2) determining position at a real-time pace or speeds consistent with military systems, and 3) combining with other systems to present a full-alternative GPS system. The present project looks into using robust neural differential models to solve magnetic navigation shortcomings and provide a viable alternative to GPS.
AI-Enhanced Spectral Awareness and Interference Rejection
This project seeks to apply AI to enhance the USAF’s ability to detect, identify, and geolocate unknown radiofrequency (RF) signals, while providing tools for adaptive interference mitigation and smart spectrum analysis. These capabilities enhance Air Force Intelligence Surveillance and Reconnaissance (ISR) missions, communications, signals intelligence (SIGINT), and electronic warfare. Results will increase bandwidth utilization efficiency and spectrum sharing, improve Air Force communications performance in high interference environments, produce higher-quality RF signals intelligence, and improve system robustness to adversarial attacks and interference.
AI Education Research: Know-Apply-Lead (KAL)
KAL is an exploratory research project that aims to advance educational research activities that promote maximum learning outcomes at scale for learners with diverse roles and educational backgrounds, ranging from Air Force and Department of Defense (DoD) personnel to the general public. The project team will research and evaluate various pedagogical practices and learning benefits associated with training Air Force personnel in AI topics over a variety of existing courses, map out the landscape of educational needs and competencies, and pilot experimental learning experiences with the goal of outlining early prototypes for innovative technology-enabled training and learning. The research is expected to provide insights that will benefit AI learners across the nation while supporting the DoD’s objective to develop elite and world-class AI-ready services.