RESEARCH
OVERVIEW
A call for project proposals in 2019 resulted in 10 projects that encompass a total of 15 research workstreams. The projects engage more than 150 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, a project with the United States Space Force, an AI Education Research project, a cyber project, and two additional projects that were spun out of our initial round of projects. AI Accelerator publications can be found on our Google Scholar page.
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.
Team Rus
– Daniela Rus (MIT PI)
– Sanjeev Mohindra (MIT Lincoln Laboratory Lead)
– Ross Allen (MIT Lincoln Laboratory Lead)
– Morgan Mitchell (DAF Liaison)
Fast AI: Data Center & Edge Computing
The AI revolution has been enabled by the availability of vast amounts of labeled data, novel algorithms, and computer performance. But long computer-in-the-loop development cycles inhibit humans from inventing and deploying creative AI solutions. Moreover, the end of Moore’s has curtailed the historical ability of semiconductor technology to deliver performance. AI performance increasingly relies on hardware architecture, software, and algorithms. The Fast AI project focuses on developing a foundation for quickly building AI solutions, enabling performance and portability on both modern and legacy hardware platforms. We innovate in the areas of programming languages, compiler technologies, comprehensive instrumentation, analytical productivity tools, and parallel algorithms.
– Charles E. Leiserson (MIT PI)
– Tao B. Schardl (MIT)
– Neil Thompson (MIT)
– Vijay Gadepally (MIT Lincoln Laboratory Lead)
– Koley Borchard (DAF Liaison)
A core requirement for AI techniques to be successful is high quality data. Preparing systems to be “AI ready” involves collecting and parsing raw data for subsequent ingest, scan, query and analysis. This project will develop ML-enhance database technologies to reduce storing and processing costs while enabling data sharing amongst various database silos. Additionally, we will develop an outlier detection engine to identify temporal anomalies amongst complex event streams from multiple sources.
The AI revolution has been enabled by the availability of vast amounts of labeled data, novel algorithms, and computer performance. But long computer-in-the-loop development cycles inhibit humans from inventing and deploying creative AI solutions. Moreover, the end of Moore’s has curtailed the historical ability of semiconductor technology to deliver performance. AI performance increasingly relies on hardware architecture, software, and algorithms. The Fast AI project focuses on developing a foundation for quickly building AI solutions, enabling performance and portability on both modern and legacy hardware platforms. We innovate in the areas of programming languages, compiler technologies, comprehensive instrumentation, analytical productivity tools, and parallel algorithms.
A core requirement for AI techniques to be successful is high quality data. Preparing systems to be “AI ready” involves collecting and parsing raw data for subsequent ingest, scan, query and analysis. This project will develop ML-enhance database technologies to reduce storing and processing costs while enabling data sharing amongst various database silos. Additionally, we will develop an outlier detection engine to identify temporal anomalies amongst complex event streams from multiple sources.
– Charles E. Leiserson (MIT PI)
– Tao B. Schardl (MIT)
– Neil Thompson (MIT)
– Vijay Gadepally (MIT Lincoln Laboratory Lead)
– Koley Borchard (DAF Liaison)
Transferring Multi-Robot Learning through Virtual & Augmented Reality for Rapid Disaster Response
This project seeks to develop a new framework and class of algorithms that allow unmanned aerial systems to learn complex multi-agent behavior in simulator environments, then seamlessly transfer their knowledge from simulation to real-world field environments. The team envisions a first responder system where a swarm of autonomous aircraft are virtually trained on how to navigate and cooperate in a simulation of a novel disaster area. The system then transfers the learning gained in the simulation to the real autonomous aircraft swarm. An aircraft deploys a large “mothership” ground station which releases these trained autonomous aircraft to automatically perform time-critical, labor-intensive tasks like surveying disaster areas and locating and identifying survivors.
– Sertac Karaman (MIT PI)
– Luca Carlone (MIT Co-PI)
– Daniel Griffith (MIT Lincoln Laboratory Lead)
– Scott Van Broekhoven (MIT Lincoln Laboratory Lead)
– Stephanie Riley (DAF Liaison)
Conversational 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.
– Jim Glass (MIT PI)
– Boris Katz (MIT Co-PI)
– Leslie Shing (MIT Lincoln Laboratory Lead)
– Eric Robinson (DAF Liaison)
Multimodal Vision for Synthetic Aperture Radar
Synthetic Aperture Radar (SAR) is a radar imaging technology capable of producing high-resolution images of landscapes. Due to its ability to produce images in all weather and lighting conditions, SAR imaging has advantages in Humanitarian Assistance and Disaster Relief (HADR) missions compared to optical systems. This project aims to improve human interpretability of SAR images, performance of SAR object detection and Automatic Target Recognition (ATR) by leveraging complementary information from related modalities (e.g., EO/IR, LiDAR, MODIS), simulated data, and physics-based models. Project findings and resulting technologies will be shared across the government enterprise to be beneficial in the HADR problem space where multiple partners across services may be able to exploit developed technology.
Team Isola
– Phillip Isola (MIT PI)
– Taylor Perron (MIT Co-PI)
– Bill Freeman (MIT Co-PI)
– Miriam Cha (MIT Lincon Laboratory Lead)
– Nathaniel Maidel (DAF Liaison)
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.
Team Balakrishnan
– Hamsa Balakrishnan (MIT PI)
– Mike Snyder (MIT Lincoln Laboratory Lead)
– Eric Robinson (DAF Liaison)
The Earth Intelligence Engine
The Earth Intelligence Engine (EIE) includes research and prototype development centered on weather and climate analysis, forecasting, and advanced data visualization that supports rapid, effective decision-making and long-term, strategic planning and operations for USAF. To support these efforts, the EIE also creates benchmark, ML-ready environmental datasets designed to promote AI advancements that benefit the society at large and USAF use cases. The EIE aims to create novel algorithms that improve severe weather identification and forecasting along with climate and subseasonal-to-seasonal (S2S) projections. Advancements in these focus areas will enable improved resource (people, property, and critical infrastructure) protection, enhanced mission support, environmentally informed basing decisions, advanced resource allocation, and foresight into potential environmentally driven humanitarian crises.
Team Newman
– Dava J. Newman (MIT PI)
– Mark Veillette (MIT Lincoln Laboratory Lead)
– Nick Chisler (DAF Liaison)
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.
– Thomas Heldt (MIT PI)
– Tamara Broderick (MIT Co-Investigator)
– Vivienne Sze (MIT Co-Investigator)
– Emilie Cowen (MIT Lincoln Laboratory Lead)
– Jovan Popovich (DAF Liaison)
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.
– Alan Edelman (MIT PI)
– Jonathan Taylor (MIT Lincoln Laboratory Lead)
–Stephanie Riley (DAF Liaison)
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.
Team Wornell
– Gregory W. Wornell (MIT PI)
– Yury Polyanskiy (MIT Co-PI)
– Alexia Schulz (MIT Lincoln Laboratory Lead)
– Binoy Kurien (MIT Lincoln Laboratory Lead)
– Jovan Popovich (DAF Liaison)
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.
– Cynthia Breazeal (MIT PI)
– Katerina Bagiati (MIT Co-PI)
– Andrés Felipe Salazar Gomez (MIT)
– Kathleen Kennedy (MIT)
– Julie Mullen (MIT Lincoln Laboratory Lead)
– Sanjeev Mohinra (MIT Lincoln Laboratory Lead)
– Megan Muniz (DAF Liaison)
Automation in Space Domain Awareness
As space operations become ever more complex and disaggregated and heterogenous space observation data sources proliferate, it is increasingly difficult to separate significant events from routine spaceflight activities and deliver actionable space domain awareness (SDA) to satellite and other operators. AI-based techniques offer the combination of scalability and near-real-time performance to support human-machine teaming paradigms necessary to enable proactive SDA in this increasingly congested, contested, and competitive space. The SDA project leverages AI-based technologies to improve both space domain representation and understanding. Additionally, new AI approaches will be developed to optimize the behaviors of sensors and translate AI reasoning or recommendations into human-interpretable forms. The team also plans to host public challenges intended to render these problems accessible to the SDA expert community and to AI experts outside the space community to accelerate the infusion of the latest AI developments into the project’s approaches. The goal is to provide a common framework and benchmark scenario that enables the comparison of the performances of various AI methods across a set of key SDA-specific problems. A common challenge task framework is also expected to accelerate steps towards the infusion of best-in-breed AI techniques into operational systems by providing a common reference implementation architecture for adaptation.
– Richard Linares (MIT PI)
– Jonathan How (MIT Co-PI)
– Suvendra Dutta (MIT Lincoln Laboratory Lead)
– Jeffrey Price (DAF Liaison)
Better Networks via AI Enabled Hierarchical Connection Science
The costs of adversarial activity on networks are growing at an alarming rate. 90% of Americans are now concerned about cyber-attacks. The Air Force mission places it at the forefront of growing cyber challenges. In the land, sea, undersea, air, and space operating domains observe-pursue-counter (detect-handoff-intercept) walls-out architectures have proven cost effective. Our recent innovations in high performance privacy-preserving network sensing and analysis offer new opportunities for obtaining the required observations to enable such architectures in the cyber domain. Using these network observations to pursue and counter adversarial activity requires the development of novel privacy-preserving hierarchical AI analytics techniques on heavy-tail distributions that explore connections both within and across the layers of the knowledge pyramid from low-level network traffic to high-level social media. This project will explore AI methods fusing diverse data across layers to create an understandable enriched view of network activities, along with appropriate mitigations, and estimated impacts.
– Alex Pentland (MIT PI)
– Stephen Buckley (MIT)
– Jeremy Kepner (MIT Lincoln Laboratory Lead)
– Hayden Jananthan (MIT Lincoln Laboratory)
– Chasen Milner (DAF Liaison)
Trustworthy AI
Current AI models are often brittle, opaque, and fail to live up to performance expectations once deployed in the real world, where conditions are dynamic and uncertain. Achieving Trustworthy AI, i.e., AI that is robust, secure, accountable, fair, privacy-preserving, explainable and reliable, is thus critical to fully leverage AI in settings with strict reliability, safety, legal, and ethical requirements. This project aims to develop foundations of trustworthy AI. Specifically, to design ML-based decision-support systems that are more explainable, resistant to distribution shifts and malicious manipulations, as well as support human-AI teaming in complex, mission-critical environments.
– Aleksander Madry (MIT PI)
– Asu Ozdaglar (MIT Co-PI)
– Arvind Satyanarayan (MIT Co-PI)
– Antonio Torralba (MIT Co-PI)
– Pablo Parrilo (MIT Co-PI)
– Michael Yee (MIT Lincoln Laboratory Lead)
– Vincent Mancuso (MIT Lincoln Laboratory Lead)
– Nick Chisler (DAF Liaison)
Few-Shot and Continual Learning
AI techniques have proven very successful in many critical applications such as object recognition, speech recognition, and others. However, these successes have relied on collecting enormous datasets and careful manual annotations. This process is expensive, time-consuming, and in many scenarios, enough data is not available. Transfer learning offers a solution to these problems by leveraging past data seen by a machine to solve future problems using only few annotated examples. This research focuses on challenges in transfer learning and aims at developing algorithms that can fundamentally learn from multiple heterogeneous tasks, moving beyond low-level task similarity to enable broader transfer across distinct tasks. Such algorithms will find general applicability in several areas, including robotics, computer vision and natural language processing. Furthermore, it will substantially reduce the dependence on large amounts of annotated data and consequently reduce costs and time for deployment and maintenance of AI systems.
– Pulkit Agrawal ( MIT PI)
– Regina Barzilay (MIT Co-PI)
– Marin Soljacic (MIT Co-PI)
– Olga Simek (MIT Lincoln Laboratory Lead)
– Nick Chisler (DAF Liaison)
Research was sponsored by the United States Air Force Research Laboratory and the Department of the Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Department of the Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
