Publications

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.

Human trafficking is a form of modern-day slavery affecting an estimated 40 million victims worldwide, primarily through the commercial sexual exploitation of women and children. In the last decade, the advertising of victims has moved from the streets to websites on the Internet, providing greater efficiency and anonymity for sex traffickers. This shift has allowed traffickers to list their victims in multiple geographic areas simultaneously, while also improving operational security by using multiple methods of electronic communication with buyers; complicating the ability of law enforcement to disrupt these illicit organizations. In this paper, we address this issue and present a novel unsupervised and scalable template matching algorithm for analyzing and detecting complex organizations operating on adult service websites. The algorithm uses only the advertisement content to uncover signature patterns in text that are indicative of organized activities and organizational structure. We apply this method to a large corpus of adult service advertisements retrieved from backpage.com, and show that the networks identified through the algorithm match well with surrogate truth data derived from phone number networks in the same corpus. Further exploration of the results show that the proposed method provides deeper insights into the complex structures of sex trafficking organizations, not possible through networks derived from phone numbers alone. This method provides a powerful new capability for law enforcement to more completely identify and gather evidence about trafficking networks and their operations.

We present a new method to bootstrap filter Twitter language ID labels in our dataset for automatic language identification (LID). Our method combines geolocation, original Twitter LID labels, and Amazon Mechanical Turk to resolve missing and unreliable labels. We are the first to compare LID classification performance using the MIRA algorithm and langid.py. We show classifier performance on different versions of our dataset with high accuracy using only Twitter data, without ground truth, and very few training examples. We also show how Platt Scaling can be use to calibrate MIRA classifier output values into a probability distribution over candidate classes, making the output more intuitive. Our method allows for fine-grained distinctions between similar languages and dialects and allows us to rediscover the language composition of our Twitter dataset.

Patent litigation is an expensive and time-consuming process. To minimize its impact on the participants in the patent lifecycle, automatic determination of litigation potential is a compelling machine learning application. In this paper, we consider preliminary methods for the prediction of a patent being involved in litigation using metadata, content, and graph features. Metadata features are top-level easily-extractable features, i.e., assignee, number of claims, etc. The content feature performs lexical analysis of the claims associated to a patent. Graph features use relational learning to summarize patent references. We apply our methods on US patents using a labeled data set. Prior work has focused on metadata-only features, but we show that both graph and content features have significant predictive capability. Additionally, fusing all features results in improved performance. We also perform a preliminary examination of some of the qualitative factors that may have significant importance in patent litigation.

Automatic extraction of intelligent and useful information from data is one of the main goals in data science. Traditional approaches have focused on learning from structured features, i.e., information in a relational database. However, most of the data encountered in practice are unstructured (i.e., social media posts, forums, emails and web logs); they do not have a predefined schema or format. In this work, we examine unsupervised methods for processing unstructured text data, extracting relevant information, and transforming it into structured information that can then be leveraged in various applications such as graph analysis and matching entities across different platforms. Various efforts have been proposed to develop algorithms for processing unstructured text data. At a top level, text can be either summarized by document level features (i.e., language, topic, genre, etc.) or analyzed at a word or sub-word level. Text analytics can be unsupervised, semi-supervised, or supervised. In this work, we focus on word analysis and unsupervised methods. Unsupervised (or semi-supervised) methods require less human annotation and can easily fulfill the role of automatic analysis. For text analysis, we focus on methods for finding relevant words in the text. Specifically, we look at social media data and attempt to predict hashtags for users' posts. The resulting hashtags can be used for downstream processing such as graph analysis. Automatic hashtag annotation is closely related to automatic tag extraction and keyword extraction. Techniques for hashtags extraction include topic analysis, supervised classifiers, machine translation methods, and collaborative filtering. Methods for keyword extraction include graph-based and topical analysis of text.

A new DARPA program called Data Driven Discovery of Models (D3M) aims to develop automated model discovery systems that can be used by researchers with specific subject matter expertise to create empirical models of real, complex processes. Two major goals of this program are to allow experts to create empirical models without the need for data scientists and to increase the productivity of data scientists via automation. Automated model discovery systems developed will be tested on real-world problems that progressively get harder during the course of the program. Toward the end of the program, problems will be both unsolved and underspecified in terms of data and desired outcomes. The program will emphasize creating and leveraging open source technology and architecture. Our presentation reviews the goals and structure of this program which will begin early in 2017. Although the deadline for submitting proposals has past, we welcome suggestions concerning challenge tasks, evaluations, or new open-source data sets to be included for system development and evaluation that would supplement data currently being curated from many sources.

The Offshore Precipitation Capability (OPC) uses machine learning and image processing methods to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar.

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task perform well, we argue that sparse coding should also be built as a holistic learning unit optimizing on the supervised task objectives more explicitly. In this paper, we propose sparse-coded net, a feedforward model that integrates sparse coding and task-driven output layers, and describe training methods in detail. After pretraining a sparse-coded net via semi-supervised learning, we optimize its task-specific performance in a novel backpropagation algorithm that can traverse nonlinear feature pooling operators to update the dictionary. Thus, sparse-coded net can be applied to supervised dictionary learning. We evaluate sparse-coded net with classification problems in sound, image, and text data. The results confirm a significant improvement over semi-supervised learning as well as superior classification performance against deep stacked autoencoder neural network and GMM-SVM pipelines in small to medium-scale settings.

This technical report summarizes work performed by MIT Lincoln Laboratory during the period 1 February 2015 – 30 November 2015 focused on developing and improving algorithms to estimate the impact of convective weather on air traffic flows.

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such as GMM supervectors and sparse RBM. We report the cross-validated classification accuracy and mean average precision of the MED system trained on features learned from our unimodal and multimodal settings for a subset of the TRECVID MED 2014 dataset.