Research Challenges for the Next Decade Zachary J. Lemnios Victor Zue

Research Challenges for the Next Decade 18 September 2007 Zachary J. Lemnios Victor Zue Chief Technology Officer MIT Lincoln Laboratory Director, MIT Computer Science Artificial Intelligence Laboratory zlemnios@ll.mit.edu zue@csail.mit.edu MIT Lincoln Laboratory HPEC 2007 09/18/2007 Research Challenges for the Next Decade ZJL Page-1 A New World has Emerged in the Last Decade From CAPT Mark Dowd, USN(RC) JIOC/JICPAC HPEC 2007 09/18/2007 Research Challenges for the Next Decade ZJL Page-2 MIT Lincoln Laboratory Page-2 The Four Broad Capability Categories – an OODA-like Loop for the 21st Century 21ST CENTURY STRATEGIC TECHNOLOGY VECTORS Contextual Exploitation Non-linear couplings Ubiquitous Observation everything interacts with everything else Rapidly Tailored Effects Human Terrain Preparation DEFENSE SCIENCE BOARD 2006 SUMMER STUDY 3 Key Technology Enablers for the Evolving Threat Space 21ST CENTURY STRATEGIC TECHNOLOGY VECTORS Preparing Human Terrain Ubiquitous Observation Contextual Exploitation Scalable Effects Delivery DEFENSE SCIENCE BOARD 2006 SUMMER STUDY • Social/cultural dynamics modeling • Automated language processing • Rapid training/learning methods/aids • Day/night all-weather wide area surveillance • Close-in sensor and tagging systems • Soldiers-as-sensors • Mega-scale data management • Situation dependent info extraction • Human/system collaboration • • • • Consequence-modeled decision making Information ops Time critical fires WMD mitigation 4 Key Technology Enablers for the Evolving Threat Space 21ST CENTURY STRATEGIC TECHNOLOGY VECTORS • Social/cultural dynamics modeling • Automated language processing • Rapid training/learning methods/aids Algorithmically • Day/night all-weather wide area surveillance and • Close-in sensor and tagging systems • Soldiers-as-sensors Computationally • Mega-scale data management Rich • Situation dependent info extraction • Human/system collaboration • • • • DEFENSE SCIENCE BOARD 2006 SUMMER STUDY Consequence-modeled decision making Information ops Time critical fires WMD mitigation 5 A Revolution in Capabilities for DoD (from DARPA presentation at HPEC 2002) More Aggressive Threats Adaptive and Intelligent Data-Fused Sensors • Threats are more dynamic and in deeper hide (collapsing time lines) • System performance is outpaced by changing threat environments • Cooperative battle management requires robust information backbone Sensor Data Flow Overwhelming Human Analyst Cognitive Information Exploitation • Sensor bandwidth is increasing faster than processor capability • Target classification has become a multi sensor Computer: Yesterday and Today • Computation of static functions in a static environment, with wellunderstood specification • Adaptive systems operating in environments that are dynamic and uncertain • Computation is its main goal • Single agent • Communication, sensing, and control just as important • Batch processing of text and homogeneous data • Multiple agents that may be cooperative, neutral, adversarial • Stand-alone applications • Stream processing of massive, heterogeneous data • Binary notion of correctness • Interaction with humans is key • Trade off multiple criteria Today’s World MIT Computer Science and Artificial Intelligence Laboratory Ubiquitous communication, cheap computation, overwhelming data, and scarce human resource Page-7 Technology Research Challenges Human-Like Interfaces Robots for Real Smart Autonomous Surveillance Environment High tempo Enormous data loads Civilian clutter Deep hide threats MIT Computer Science and Artificial Intelligence Laboratory Social & Cultural Modeling Robust, Secure & Survivable Networks and Computation Wicked problems Unstructured environments Cultural interaction High consequence Page-8 Challenge 1: Human-like Interfaces Human-Like Human-Like Interfaces Interfaces Robots Robots for Real forReal Smart Smart Autonomous Autonomous Surveillance Surveillance Social Social&& Cultural Cultural Modeling Modeling Robust, Secure Robust,Secure &&Survivable Survivable Computation Computation • Interacting with computation should be as natural as interacting with people. • Human-like interfaces need to be: modality-opportunistic non-distracting mixed-initiative modality-agnostic symmetrically-multimodal multi-lingual MIT Computer Science and Artificial Intelligence Laboratory Page-9 Challenge 2: Operate in Foreign Cultures and Coalitions Cultural data Story understanding & Indexing Human-Like Human-Like Interfaces Interfaces Robots Robots for forReal Real Smart Smart Autonomous Autonomous Surveillance Surveillance Models Of culture Culture sensitive analogical information retrieval Electronic media Recovering topical structure Core natural language processing Universal speech understanding Spoken queries in mixed languages Emotions & motivations Sub-cultures Preferences, value systems Individual decision making Allegiance and desertion MIT Computer Science and Artificial Intelligence Laboratory Spoken dialog based training Social Social&& Cultural Cultural Modeling Modeling Robust, Robust,Secure Secure &&Survivable Survivable Computation Computation Match-making Information push Argument structure capture Analysts Workbench Immersive Language & culture training system Immersive Human like interfaces Page-10 Challenge 2: Operate in Foreign Cultures and Coalitions Cultural data Story understanding & Indexing Human-Like Human-Like Interfaces Interfaces Robots Robots for forReal Real Smart Smart Autonomous Autonomous Surveillance Surveillance Models Of culture Culture sensitive analogical information retrieval Electronic media Recovering topical structure Core natural language processing Universal speech understanding Spoken queries in mixed languages Emotions & motivations Sub-cultures Preferences, value systems Individual decision making Allegiance and desertion MIT Computer Science and Artificial Intelligence Laboratory Spoken dialog based training Social Social&& Cultural Cultural Modeling Modeling Robust, Robust,Secure Secure &&Survivable Survivable Computation Computation Match-making Information push Argument structure capture Analysts Workbench Immersive Language & culture training system Immersive Human like interfaces Page-11 Human-Like Human-Like Interfaces Interfaces Challenge 3: Make Net-Centric Systems Secure and Survivable Capable and dedicated opponents Robots Robots for forReal Real Smart Smart Autonomous Autonomous Surveillance Surveillance Social Social&& Cultural Cultural Modeling Modeling Robust, Robust,Secure Secure &&Survivable Survivable Computation Computation Mobile and distributed components Heterogeneous systems MIT Computer Science and Artificial Intelligence Laboratory Page-12 Challenge 4: Smart Autonomous Surveillance Human-Like Human-Like Interfaces Interfaces Robots Robots for forReal Real Social Social&& Cultural Cultural Modeling Modeling Smart Smart Autonomous Autonomous Surveillance Surveillance Robust, Robust,Secure Secure &&Survivable Survivable Computation Computation Next Generation Intelligence Engine Sensors Communication, storage, computation • Computational cameras • Power and contentaware networking. • Coded aperture sensors • Fusion across modality, time, place, and source • Queuing sensors Analysis • Change detection • anomaly alerts • contextual analysis, integration with historical data, • prediction MIT Computer Science and Artificial Intelligence Laboratory Page-13 Challenge 5: Robotics for Real Human-Like Human-Like Interfaces Interfaces Robots Robots for forReal Real Social Social&& Cultural Cultural Modeling Modeling Smart Smart Autonomous Autonomous Surveillance Surveillance Robust, Robust,Secure Secure &&Survivable Survivable Computation Computation • Military “robots” today lack autonomy – Currently, many soldiers operate one robot – Want few soldiers working with a team of agile robots, to achieve force multiplication even in harsh environments – Put fewer soldiers in harm’s way • Better robots for monitoring RQ1-Predator GCS – Enable soldiers w/ persistent and pervasive ISR, including from hard to reach places (e.g., inside buildings/caves/bunker networks) • Better robots for logistics – Replace soldiers in the supply chain with capable autonomous robots and vehicles MIT Computer Science and Artificial Intelligence Laboratory Supply-chain task Page-14 Human-Like Human-Like Interfaces Interfaces Key Research Elements Robots Robots for forReal Real Social Social&& Cultural Cultural Modeling Modeling Smart Smart Autonomous Autonomous Surveillance Surveillance Logistics: Packing Loading Transportation Perception and Awareness Vision Speech Gesture Localization Surround awareness Robust, Robust,Secure Secure &&Survivable Survivable Computation Computation Monitoring: Surveillance Patrol Observation Planning and Reasoning Manipulation and Control Uncertainty Dynamic world Scale Prediction Grasping Rolling, legged, flying mobility Communication and Coordination Teaming Coordinated motion Enabling Technical Areas MIT Computer Science and Artificial Intelligence Laboratory Page-15 10 year Vision: Exploiting Algorithms and Computation in Human-Like Ways Human-like Interfaces multimodal interaction Robust understanding of causal structures uncontrolled environments learn new vocabulary by example adapting opportunistically to modalities available non-distracting interaction with a teammate Secure and Survivable Systems Social & Cultural Operations Continuously evolving models of culture, values, motivations, preferences Full dialogue Immersive, story and dialogue based interactions Systematic survivability, defense in depth Auditable assurance cases, formal methods and self-checking software and hardware together High confidence that failures and security attacks have not and will not occur MIT Computer Science and Artificial Intelligence Laboratory Autonomous Robotics Autonomous vehicles require minimal supervision, and outperform the best human pilots Robotic supply chain improves efficiency and surge response, greatly reducing the danger to humans Humans interact with robots as partners and capable teammates Smart Autonomous Surveillance Computational cameras Queuing sensors Change detection Power and content-aware networking. Fusion across modality, time, place, and source contextual analysis, integration with historical data prediction Page-16 Summary • We are in a much more challenging threat environment • Success will depend on operating; • in high tempo unstructured environments Robots Social & • against for asymmetric adversaries in deep civilian hide Real Cultural Modeling • A new set of research challenges are before us: Human-Like Human-Like Interfaces Interfaces Robots Robots for forReal Real Technology Research Challenges Smart Smart Autonomous Autonomous Surveillance Surveillance MIT Computer Science and Artificial Intelligence Laboratory Social Social&& Cultural Cultural Modeling Modeling Robust, Robust,Secure Secure &&Survivable Survivable Computation Computation Page-17 The Power of a New Initiative October 9, 1903 “The flying machine which will really fly might be evolved by the combined and continuous efforts of mathematicians and mechanicians in from one million to ten million years” “We started assembly today” Orville Wright’s Diary October 9, 1903 December 17, 1903 MIT Lincoln Laboratory HPEC 2007 09/18/2007 Research Challenges for the Next Decade ZJL Page-18

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