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Appendix D: Capability Technology Tables
Pages 53-58

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From page 53... ... AlphaGo) • Automating architecture learning • Hyperparameter search • Improving generative models • Memory-based networks and neural Turing machines • Semi-supervised learning • Mix-and-match architectures • Privacy-preserving models continued 53 Read the entire page →
From page 54... ... Cognitive models of users/students in long-term tutoring with capabilities for induction/ • fine-tuning of student models User modeling and personalization is a mature field for well-defined tasks/domains • Models of joint interdependent activity in non-learning tasks (e.g., human-robot • interaction) Interactive task learning for constrained robot tasks and contexts • Programming by demonstration • Mobile phone digital assistants • Chris Callison-Burch, University of Recent emergence of start-ups focused on annotation such as CrowdFlower, which is • Pennsylvania being leveraged by the Department of Defense and the intelligence community Mark Riedl, Georgia Institute of Value alignment is an unsolved problem -- no examples of aligned agents except in toy • Technology worlds Virtually no understanding of sociocultural conventions that reduce human–human • conflict Can learn commonsense knowledge on curated corpora in limited a priori known • contexts Currently data is bottleneck employing curated corpora -- deep learning is needed to • learn human values/conventions from stories on the Internet Curated data sets do not currently exist, and researchers still do not know how to solve • these problems using deep neural networks Panel on Evaluation of Machine- Robust activity from Intelligence Advanced Research Projects Activity in creating data • Generated Products; Anthony Hoogs, challenges Panel Moderator Widespread public awareness of and participation in data notation and labeling (e.g., • Mechanical Turk) Read the entire page →
From page 55... ... Capabilities Tom Dietterich, Oregon State Open category classification • University Automatic detection of biased and untrusted sources • Anomaly detection on time-varying and network data • Initial methods for validation and system monitoring • Joseph Mundy, Vision Systems, Inc. The rapid growth of web-linked data sources based on semantic ontologies such as • geographic and functional knowledge should be exploited to label training data and to provide context for test time • major effort in incorporating theoretical knowledge into data-driven decision making A Using machine learning to drive logical and grammatical structure formation is a • promising first step Extend current deep learning three-dimensional modeling algorithms to four dimensions • and combine with object recognition networks to achieve functional designs such as building information models Rama Chellappa, University of Handle pose, illumination, and low-resolution challenges in unconstrained scenarios • Maryland, College Park Handle full motion videos • Sharing/transfer learning of features for different but somewhat related tasks • Model/understand context and occlusion • Handle "reasonable" amount of noise in data • Limited robustness to adversarial data • Vision and language • Other multi-modal data • Anthony Hoogs, Kitware, Inc. Read the entire page →
From page 56... ... • Explainable artificial intelligence for visual analytics and simulated drone operations • Interactive task learning for usable soft bots for well-defined tasks (clear goal, well • defined constraints, clear operators) Improved visual analytics for machine learning programming • Chris Callison-Burch, University of Tighter integration of crowdsourcing with machine learning • Pennsylvania -- Correct/confirm output from models -- Active learning -- Domain adaptation New crowdsourcing platform for natural language processing • -- Remove hassles of Mechanical Turk -- Cultivate groups of language experts -- Create standing pools of language workers -- Deployable on inside of the intelligence community Mark Riedl, Georgia Institute of Agents expected to start using commonsense knowledge and world knowledge to • Technology address human needs Conversational agents + expectation; need to engage in longer conversations and rely • on computational imagination Agents expected to differentiate behavior based on cultural context • Computational creativity as part of mainstream content creation • Panel on Evaluation of Machine- Open source tools for efficient annotation • Generated Products; Anthony Hoogs, Semi-automated labeling to efficiently fuse computed labels with manual adjudication • Moderator Large-scale annotation of operationally representative data sets in domains of interest, • made available to researchers, particularly multi-modal data sets NOTE: IARPA, Intelligence Advanced Research Projects Activity. Read the entire page →
From page 57... ... Structure learning of deep networks on a large scale • Human-level accuracy for action and complex activity recognition in surveillance video • -- DVA data Super-human performance in video search and retrieval for complex activities and • abstractions in internet (consumer) videos -- Cataloguing of all objects, scene elements, and events -- Similar accuracy to humans but much faster -- Free-form text-based queries with open syntax and vocabulary Large graphical processing unit farms required to keep up with video generation • -- Computation speed increases offset by data growth Kathy McKeown, Columbia • Interpretation, analysis, and generation from informal text (social media, online University discussion, online narrative) Read the entire page →
From page 58... ... Sponsor work on world knowledge induction and situational awareness • Collaborate on ameliorating data availability issues • -- Privacy-preserving methods for mining Peter Pirolli, Institute for Human and Foundational science of Human-Autonomy Collaboration • Machine Cognition -- Transition from "programming" to "learning to work together" -- Sensemaking, autonomous vehicles, precision behavioral medicine, workforce -- Education, pervasive personalized assistants -- Multi-level models of sensemaking that include dynamically adapting humans and machine learning in joint activity Continuous in-the-loop explainable artificial intelligence and interactive task learning • -- Usable, scalable, generalizable, dynamic interactive task learning Evaluation frameworks on joint human–autonomy tasks • -- Move beyond current machine learning-centric optimization metrics Open source data and code for tasks that have some relevance and validity to the • intelligence community Mark Riedl, Georgia Institute of Increasing presence of autonomous systems in social contexts • Technology -- Chicken-and-egg problem; the problem of humans understanding artificial intelligence and artificial intelligence understanding humans will be far from solved -- Explainable artificial intelligence Broader artificial intelligence safety concerns and "big red button problem" • Use of military teammates could become more common • Computational creativity: Autonomous creation of images and video that are difficult to • detect Read the entire page →

From page 53...

... AlphaGo) • Automating architecture learning • Hyperparameter search • Improving generative models • Memory-based networks and neural Turing machines • Semi-supervised learning • Mix-and-match architectures • Privacy-preserving models continued 53

Read the entire page →

From page 54...

... Cognitive models of users/students in long-term tutoring with capabilities for induction/ • fine-tuning of student models User modeling and personalization is a mature field for well-defined tasks/domains • Models of joint interdependent activity in non-learning tasks (e.g., human-robot • interaction) Interactive task learning for constrained robot tasks and contexts • Programming by demonstration • Mobile phone digital assistants • Chris Callison-Burch, University of Recent emergence of start-ups focused on annotation such as CrowdFlower, which is • Pennsylvania being leveraged by the Department of Defense and the intelligence community Mark Riedl, Georgia Institute of Value alignment is an unsolved problem -- no examples of aligned agents except in toy • Technology worlds Virtually no understanding of sociocultural conventions that reduce human–human • conflict Can learn commonsense knowledge on curated corpora in limited a priori known • contexts Currently data is bottleneck employing curated corpora -- deep learning is needed to • learn human values/conventions from stories on the Internet Curated data sets do not currently exist, and researchers still do not know how to solve • these problems using deep neural networks Panel on Evaluation of Machine- Robust activity from Intelligence Advanced Research Projects Activity in creating data • Generated Products; Anthony Hoogs, challenges Panel Moderator Widespread public awareness of and participation in data notation and labeling (e.g., • Mechanical Turk)

Read the entire page →

From page 55...

... Capabilities Tom Dietterich, Oregon State Open category classification • University Automatic detection of biased and untrusted sources • Anomaly detection on time-varying and network data • Initial methods for validation and system monitoring • Joseph Mundy, Vision Systems, Inc. The rapid growth of web-linked data sources based on semantic ontologies such as • geographic and functional knowledge should be exploited to label training data and to provide context for test time • major effort in incorporating theoretical knowledge into data-driven decision making A Using machine learning to drive logical and grammatical structure formation is a • promising first step Extend current deep learning three-dimensional modeling algorithms to four dimensions • and combine with object recognition networks to achieve functional designs such as building information models Rama Chellappa, University of Handle pose, illumination, and low-resolution challenges in unconstrained scenarios • Maryland, College Park Handle full motion videos • Sharing/transfer learning of features for different but somewhat related tasks • Model/understand context and occlusion • Handle "reasonable" amount of noise in data • Limited robustness to adversarial data • Vision and language • Other multi-modal data • Anthony Hoogs, Kitware, Inc.

Read the entire page →

From page 56...

... • Explainable artificial intelligence for visual analytics and simulated drone operations • Interactive task learning for usable soft bots for well-defined tasks (clear goal, well • defined constraints, clear operators) Improved visual analytics for machine learning programming • Chris Callison-Burch, University of Tighter integration of crowdsourcing with machine learning • Pennsylvania -- Correct/confirm output from models -- Active learning -- Domain adaptation New crowdsourcing platform for natural language processing • -- Remove hassles of Mechanical Turk -- Cultivate groups of language experts -- Create standing pools of language workers -- Deployable on inside of the intelligence community Mark Riedl, Georgia Institute of Agents expected to start using commonsense knowledge and world knowledge to • Technology address human needs Conversational agents + expectation; need to engage in longer conversations and rely • on computational imagination Agents expected to differentiate behavior based on cultural context • Computational creativity as part of mainstream content creation • Panel on Evaluation of Machine- Open source tools for efficient annotation • Generated Products; Anthony Hoogs, Semi-automated labeling to efficiently fuse computed labels with manual adjudication • Moderator Large-scale annotation of operationally representative data sets in domains of interest, • made available to researchers, particularly multi-modal data sets NOTE: IARPA, Intelligence Advanced Research Projects Activity.

Read the entire page →

From page 57...

... Structure learning of deep networks on a large scale • Human-level accuracy for action and complex activity recognition in surveillance video • -- DVA data Super-human performance in video search and retrieval for complex activities and • abstractions in internet (consumer) videos -- Cataloguing of all objects, scene elements, and events -- Similar accuracy to humans but much faster -- Free-form text-based queries with open syntax and vocabulary Large graphical processing unit farms required to keep up with video generation • -- Computation speed increases offset by data growth Kathy McKeown, Columbia • Interpretation, analysis, and generation from informal text (social media, online University discussion, online narrative)

Read the entire page →

From page 58...

... Sponsor work on world knowledge induction and situational awareness • Collaborate on ameliorating data availability issues • -- Privacy-preserving methods for mining Peter Pirolli, Institute for Human and Foundational science of Human-Autonomy Collaboration • Machine Cognition -- Transition from "programming" to "learning to work together" -- Sensemaking, autonomous vehicles, precision behavioral medicine, workforce -- Education, pervasive personalized assistants -- Multi-level models of sensemaking that include dynamically adapting humans and machine learning in joint activity Continuous in-the-loop explainable artificial intelligence and interactive task learning • -- Usable, scalable, generalizable, dynamic interactive task learning Evaluation frameworks on joint human–autonomy tasks • -- Move beyond current machine learning-centric optimization metrics Open source data and code for tasks that have some relevance and validity to the • intelligence community Mark Riedl, Georgia Institute of Increasing presence of autonomous systems in social contexts • Technology -- Chicken-and-egg problem; the problem of humans understanding artificial intelligence and artificial intelligence understanding humans will be far from solved -- Explainable artificial intelligence Broader artificial intelligence safety concerns and "big red button problem" • Use of military teammates could become more common • Computational creativity: Autonomous creation of images and video that are difficult to • detect

Read the entire page →

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Appendix D: Capability Technology Tables Pages 53-58

Appendix D: Capability Technology Tables
Pages 53-58