Publications
Artificial intelligence: short history, present developments, and future outlook, final report
- Artificial Intelligence Technology and Systems
- Homeland Sensors and Analytics
- Lincoln Laboratory Supercomputing Center
- Cyber System Assessments
- Cyber Operations and Analysis Technology
- Cyber-Physical Systems
- Artificial Intelligence Software Architectures and Algorithms
- Space Systems Analysis and Test
Summary
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...
Detecting food safety risks and human trafficking using interpretable machine learning methods
Summary
Summary
Black box machine learning methods have allowed researchers to design accurate models using large amounts of data at the cost of interpretability. Model interpretability not only improves user buy-in, but in many cases provides users with important information. Especially in the case of the classification problems addressed in this thesis...
Detection and characterization of human trafficking networks using unsupervised scalable text template matching
Summary
Summary
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...
Implicitly-defined neural networks for sequence labeling
Summary
Summary
In this work, we propose a novel, implicitly defined neural network architecture and describe a method to compute its components. The proposed architecture forgoes the causality assumption previously used to formulate recurrent neural networks and allow the hidden states of the network to coupled together, allowing potential improvement on problems...
Twitter language identification of similar languages and dialects without ground truth
Summary
Summary
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...
Approaches for language identification in mismatched environments
Summary
Summary
In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system. The evaluation is performed on a 9-language set that includes data in...
Multi-lingual deep neural networks for language recognition
Summary
Summary
Multi-lingual feature extraction using bottleneck layers in deep neural networks (BN-DNNs) has been proven to be an effective technique for low resource speech recognition and more recently for language recognition. In this work we investigate the impact on language recognition performance of the multi-lingual BN-DNN architecture and training configurations for...
LLTools: machine learning for human language processing
Summary
Summary
Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components...
Predicting and analyzing factors in patent litigation
Summary
Summary
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...
Making #sense of #unstructured text data
Summary
Summary
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...