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Artificial intelligence: short history, present developments, and future outlook, final report

Summary

The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the AI field is evolving so rapidly, the study scope was to look at the recent past and ongoing developments to lead to a set of findings and recommendations. It was important to begin with a short AI history and a lay-of-the-land on representative developments across the Department of Defense (DoD), intelligence communities (IC), and Homeland Security. These areas are addressed in more detail within the report. A main deliverable from the study was to formulate an end-to-end AI canonical architecture that was suitable for a range of applications. The AI canonical architecture, formulated in the study, serves as the guiding framework for all the sections in this report. Even though the study primarily focused on cyber security and information sciences, the enabling technologies are broadly applicable to many other areas. Therefore, we dedicate a full section on enabling technologies in Section 3. The discussion on enabling technologies helps the reader clarify the distinction among AI, machine learning algorithms, and specific techniques to make an end-to-end AI system viable. In order to understand what is the lay-of-the-land in AI, study participants performed a fairly wide reach within MIT LL and external to the Laboratory (government, commercial companies, defense industrial base, peers, academia, and AI centers). In addition to the study participants (shown in the next section under acknowledgements), we also assembled an internal review team (IRT). The IRT was extremely helpful in providing feedback and in helping with the formulation of the study briefings, as we transitioned from datagathering mode to the study synthesis. The format followed throughout the study was to highlight relevant content that substantiates the study findings, and identify a set of recommendations. An important finding is the significant AI investment by the so-called "big 6" commercial companies. These major commercial companies are Google, Amazon, Facebook, Microsoft, Apple, and IBM. They dominate in the AI ecosystem research and development (R&D) investments within the U.S. According to a recent McKinsey Global Institute report, cumulative R&D investment in AI amounts to about $30 billion per year. This amount is substantially higher than the R&D investment within the DoD, IC, and Homeland Security. Therefore, the DoD will need to be very strategic about investing where needed, while at the same time leveraging the technologies already developed and available from a wide range of commercial applications. As we will discuss in Section 1 as part of the AI history, MIT LL has been instrumental in developing advanced AI capabilities. For example, MIT LL has a long history in the development of human language technologies (HLT) by successfully applying machine learning algorithms to difficult problems in speech recognition, machine translation, and speech understanding. Section 4 elaborates on prior applications of these technologies, as well as newer applications in the context of multi-modalities (e.g., speech, text, images, and video). An end-to-end AI system is very well suited to enhancing the capabilities of human language analysis. Section 5 discusses AI's nascent role in cyber security. There have been cases where AI has already provided important benefits. However, much more research is needed in both the application of AI to cyber security and the associated vulnerability to the so-called adversarial AI. Adversarial AI is an area very critical to the DoD, IC, and Homeland Security, where malicious adversaries can disrupt AI systems and make them untrusted in operational environments. This report concludes with specific recommendations by formulating the way forward for Division 5 and a discussion of S&T challenges and opportunities. The S&T challenges and opportunities are centered on the key elements of the AI canonical architecture to strengthen the AI capabilities across the DoD, IC, and Homeland Security in support of national security.
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Summary

The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the...

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Classifier performance estimation with unbalanced, partially labeled data

Published in:
Proc. Machine Learning Research, Vol. 88, 2018, pp. 4-16.

Summary

Class imbalance and lack of ground truth are two significant problems in modern machine learning research. These problems are especially pressing in operational contexts where the total number of data points is extremely large and the cost of obtaining labels is very high. In the face of these issues, accurate estimation of the performance of a detection or classification system is crucial to inform decisions based on the observations. This paper presents a framework for estimating performance of a binary classifier in such a context. We focus on the scenario where each set of measurements has been reduced to a score, and the operator only investigates data when the score exceeds a threshold. The operator is blind to the number of missed detections, so performance estimation targets two quantities: recall and the derivative of precision with respect to recall. Measuring with respect to error in these two metrics, simulations in this context demonstrate that labeling outliers not only outperforms random labeling, but often matches performance of an adaptive method that attempts to choose the optimal data for labeling. Application to real anomaly detection data confirms the utility of the approach, and suggests direction for future work.
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Summary

Class imbalance and lack of ground truth are two significant problems in modern machine learning research. These problems are especially pressing in operational contexts where the total number of data points is extremely large and the cost of obtaining labels is very high. In the face of these issues, accurate...

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Cyber network mission dependencies

Published in:
MIT Lincoln Laboratory Report TR-1189

Summary

Cyber assets are critical to mission success in every arena of the Department of Defense. Because all DoD missions depend on cyber infrastructure, failure to secure network assets and assure the capabilities they enable will pose a fundamental risk to any defense mission. The impact of a cyber attack is not well understood by warfighters or leadership. It is critical that the DoD develop better cognizance of Cyber Network Mission Dependencies (CNMD). This report identifies the major drivers for mapping missions to network assets, introduces existing technologies in the mission-mapping landscape, and proposes directions for future development.
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Summary

Cyber assets are critical to mission success in every arena of the Department of Defense. Because all DoD missions depend on cyber infrastructure, failure to secure network assets and assure the capabilities they enable will pose a fundamental risk to any defense mission. The impact of a cyber attack is...

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