A new anonymous texting bot program is aimed at helping children, teenagers and young adults quit using Juuls and other e-cigarettes. Developed by the large anti-smoking and public health non-profit the Truth Initiative, the text-based coaching is being integrated into the group's This Is Quitting and BecomeAnEx online quitting aids. The need for adolescent-focused programs is dire, according to a slew of recent studies and declarations of a youth vaping'epidemic' from the Food and Drug Administration (FDA) and the Surgeon General. FDA Commissioner Gottlieb called for cessation drugs for teens last month, but in the mean time the Truth Initiative's program hopes to help fill that void with text-coaching designed with teens in mind. Truth Initiative's new program texts encouragement geared toward teenagers who are trying to quit vaping in general - but focuses on the addictive Juul in particular Between 2017 and 2018, the number of high school students using e-cigarettes double, according to a University of Michigan study published in December.
The crucial importance of metrics in machine learning algorithms has led to an increasing interest in optimizing distance and similarity functions, an area of research known as metric learning. When data consist of feature vectors, a large body of work has focused on learning a Mahalanobis distance. Less work has been devoted to metric learning from structured objects (such as strings or trees), most of it focusing on optimizing a notion of edit distance. We identify two important limitations of current metric learning approaches. First, they allow to improve the performance of local algorithms such as k-nearest neighbors, but metric learning for global algorithms (such as linear classifiers) has not been studied so far. Second, the question of the generalization ability of metric learning methods has been largely ignored. In this thesis, we propose theoretical and algorithmic contributions that address these limitations. Our first contribution is the derivation of a new kernel function built from learned edit probabilities. Our second contribution is a novel framework for learning string and tree edit similarities inspired by the recent theory of (e,g,t)-good similarity functions. Using uniform stability arguments, we establish theoretical guarantees for the learned similarity that give a bound on the generalization error of a linear classifier built from that similarity. In our third contribution, we extend these ideas to metric learning from feature vectors by proposing a bilinear similarity learning method that efficiently optimizes the (e,g,t)-goodness. Generalization guarantees are derived for our approach, highlighting that our method minimizes a tighter bound on the generalization error of the classifier. Our last contribution is a framework for establishing generalization bounds for a large class of existing metric learning algorithms based on a notion of algorithmic robustness.
Traditional control methods are inadequate in many deployment settings involving control of Cyber-Physical Systems (CPS). In such settings, CPS controllers must operate and respond to unpredictable interactions, conditions, or failure modes. Dealing with such unpredictability requires the use of executive and cognitive control functions that allow for planning and reasoning. Motivated by the sport of drone racing, this dissertation addresses these concerns for state-of-the-art flight control by investigating the use of deep neural networks to bring essential elements of higher-level cognition for constructing low level flight controllers. This thesis reports on the development and release of an open source, full solution stack for building neuro-flight controllers. This stack consists of the methodology for constructing a multicopter digital twin for synthesize the flight controller unique to a specific aircraft, a tuning framework for implementing training environments (GymFC), and a firmware for the world's first neural network supported flight controller (Neuroflight). GymFC's novel approach fuses together the digital twinning paradigm for flight control training to provide seamless transfer to hardware. Additionally, this thesis examines alternative reward system functions as well as changes to the software environment to bridge the gap between the simulation and real world deployment environments. Work summarized in this thesis demonstrates that reinforcement learning is able to be leveraged for training neural network controllers capable, not only of maintaining stable flight, but also precision aerobatic maneuvers in real world settings. As such, this work provides a foundation for developing the next generation of flight control systems.
Chaudhri, Vinay K. (SRI International) | Cheng, Britte (SRI International) | Overtholtzer, Adam (SRI International) | Roschelle, Jeremy (SRI International) | Spaulding, Aaron (SRI International) | Clark, Peter (Vulcan Inc.) | Greaves, Mark (Pacific Northwest National Laboratory) | Gunning, Dave (Palo Alto Research Center)
Inquire Biology is a prototype of a new kind of intelligent textbook — one that answers students’ questions, engages their interest, and improves their understanding. Inquire Biology provides unique capabilities via a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students’ questions. Students ask questions by typing free-form natural language queries or by selecting passages of text. The system then attempts to answer the question and also generates suggested questions related to the query or selection. The questions supported by the system were chosen to be educationally useful, for example: what is the structure of X? compare X and Y? how does X relate to Y? In user studies, students found this question-answering capability to be extremely useful while reading and while doing problem solving. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hardcopy or conventional E-book version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the initial prototype clearly demonstrates the promise of applying knowledge representation and question-answering technology to electronic textbooks.
Gunning, David (Vulcan, Inc.) | Chaudhri, Vinay K. (SRI International) | Clark, Peter E. (Boeing Research and Technology) | Barker, Ken (University of Texas at Austin) | Chaw, Shaw-Yi (University of Texas at Austin) | Greaves, Mark (Vulcan, Inc.) | Grosof, Benjamin (Vulcan, Inc.) | Leung, Alice (Raytheon BBN Technologies Corporation) | McDonald, David D. (Raytheon BBN Technologies Corporation) | Mishra, Sunil (SRI International) | Pacheco, John (SRI International) | Porter, Bruce (University of Texas at Austin) | Spaulding, Aaron (SRI International) | Tecuci, Dan (University of Texas at Austin) | Tien, Jing (SRI International)
In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.