If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots — automated player simulations based on models of players with varying sets of skills — to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.
Partlan, Nathan (Northeastern University) | Carstensdottir, Elin (Northeastern University) | Snodgrass, Sam (Northeastern University) | Kleinman, Erica (Northeastern University) | Smith, Gillian (Worcester Polytechnic Institute) | Harteveld, Casper (Northeastern University) | El-Nasr, Magy Seif (Northeastern University)
Analysis of interactive narrative is a complex undertaking, requiring understanding of the narrative's design, its affordances, and its impact on players. Analysis is often performed by an expert, but this is expensive and difficult for complex interactive narratives. Automated analysis of structure, the organization of interaction elements, could help augment an expert's analysis. For this purpose we developed a model consisting of a set of metrics to analyze interactive narrative structure, enabled by a novel multi-graph representation. We implemented this model for an interactive scenario authoring tool called StudyCrafter and analyzed 20 student-designed scenarios. We show that the model illuminates the structures and groupings of the scenarios. This work provides insight for manual analysis of attributes of interactive narratives and a starting point for automated design assistance.
Journalists act as gatekeepers to the scientific world, controlling what information reaches the public eye and how it is presented. Analyzing the kinds of research that typically receive more media attention is vital to understanding issues such as the “science of science communication” (National Academies of Sciences, Engineering, and Medicine 2017), patterns of misinformation, and the “cycle of hype.” We track the coverage of 91,997 scientific articles published in 2016 across various disciplines, publishers, and news outlets using metadata and text data from a leading tracker of scientific coverage in social and traditional media, Altmetric. We approach the problem as one of ranking each day’s, or week’s, papers by their likely level of media attention, using the learning-to-rank model lambdaMART (Burges 2010). We find that ngram features from the title, abstract and press release significantly improve performance over the metadata features journal, publisher, and subjects.
Sparse and low rank coding has widely received much attention in machine learning, multimedia and computer vision. Unfortunately, expensive inference restricts the power of coding models in real-world applications, e.g., compressed sensing and image deblurring. In order to avoid the expensive inference, we propose a predictive coding machine (PCM) which aims to train a deep neural network (DNN) encoder to approximate the codes. By this means, a test sample can be fast approximated by the well-trained DNN. However, DNN leads PCM to be a non-convex and non-smooth optimization problem, which is extremely hard to solve. To address this challenge, we extend accelerated proximal gradient for PCM by steering gradient descent of DNN. To the best of our knowledge, we are the first to propose a gradient descent algorithm guided by accelerated proximal gradient for solving the PCM problem. Besides, a sufficient condition is provided to ensure the convergence to a critical point. Moreover, when the coding models are convex in PCM, the convergence rate O (1/( m 2 √ t )) can be held in which m is the iteration number of accelerated proximal gradient, and t is the epoch of training DNN. Numerical results verify the promising advantages of PCM in terms of effectiveness, efficiency and robustness.
Wang, Yugang (University of Electronic Science and Technology of China) | Elhamifar, Ehsan (Northeastern University)
We address the problem of high-rank matrix completion with side information. In contrast to existing work dealing with side information, which assume that the data matrix is low-rank, we consider the more general scenario where the columns of the data matrix are drawn from a union of low-dimensional subspaces, which can lead to a high rank matrix. Our goal is to complete the matrix while taking advantage of the side information. To do so, we use the self-expressive property of the data, searching for a sparse representation of each column of matrix as a combination of a few other columns. More specifically, we propose a factorization of the data matrix as the product of side information matrices with an unknown interaction matrix, under which each column of the data matrix can be reconstructed using a sparse combination of other columns. As our proposed optimization, searching for missing entries and sparse coefficients, is non-convex and NP-hard, we propose a lifting framework, where we couple sparse coefficients and missing values and define an equivalent optimization that is amenable to convex relaxation. We also propose a fast implementation of our convex framework using a Linearized Alternating Direction Method. By extensive experiments on both synthetic and real data, and, in particular, by studying the problem of multi-label learning, we demonstrate that our method outperforms existing techniques in both low-rank and high-rank data regimes.
Wang, Yanzhi (Syracuse University) | Ding, Caiwen (Syracuse University) | Li, Zhe (Syracuse University) | Yuan, Geng (Syracuse University) | Liao, Siyu (City University of New York) | Ma, Xiaolong (Syracuse University) | Yuan, Bo (City University of New York) | Qian, Xuehai (University of Southern California) | Tang, Jian (Syracuse University) | Qiu, Qinru (Syracuse University) | Lin, Xue (Northeastern University)
Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and performance for hardware implementations of deep neural networks (DNNs). An algorithm-hardware co-optimization framework is developed, which is applicable to different DNN types, sizes, and application scenarios. The algorithm part adopts the general block-circulant matrices to achieve a fine-grained tradeoff of accuracy and compression ratio. It applies to both fully-connected and convolutional layers and contains a mathematically rigorous proof of the effectiveness of the method. The proposed algorithm reduces computational complexity per layer from O(n 2 ) to O(n log n) and storage complexity from O(n 2 ) to O(n), both for training and inference. The hardware part consists of highly efficient Field Programmable Gate Array (FPGA)-based implementations using effective reconfiguration, batch processing, deep pipelining, resource re-using, and hierarchical control. Experimental results demonstrate that the proposed framework achieves at least 152X speedup and 71X energy efficiency gain compared with IBM TrueNorth processor under the same test accuracy. It achieves at least 31X energy efficiency gain compared with the reference FPGA-based work.
Action prediction based on video is an important problem in computer vision field with many applications, such as preventing accidents and criminal activities. It's challenging to predict actions at the early stage because of the large variations between early observed videos and complete ones. Besides, intra-class variations cause confusions to the predictors as well. In this paper, we propose a mem-LSTM model to predict actions in the early stage, in which a memory module is introduced to record several "hard-to-predict" samples and a variety of early observations. Our method uses Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) to model partial observed video input. We augment LSTM with a memory module to remember challenging video instances. With the memory module, our mem-LSTM model not only achieves impressive performance in the early stage but also makes predictions without the prior knowledge of observation ratio. Information in future frames is also utilized using a bi-directional layer of LSTM. Experiments on UCF-101 and Sports-1M datasets show that our method outperforms state-of-the-art methods.
Neller, Todd W. (Gettysburg College) | Butler, Zack (Rochester Institute of Technology) | Derbinsky, Nate (Northeastern University) | Furey, Heidi (Manhattan College) | Martin, Fred (University of Massachusetts Lowell) | Guerzhoy, Michael (University of Toronto) | Anders, Ariel (Massachusetts Institute of Technology) | Eckroth, Joshua (Stetson University)
The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning ex- perience, we here present abstracts of seven AI assign- ments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instruc- tor needs.
Assessing the veracity of claims made on the Internet is an important, challenging, and timely problem. While automated fact-checking models have potential to help people better assess what they read, we argue such models must be explainable, accurate, and fast to be useful in practice; while prediction accuracy is clearly important, model transparency is critical in order for users to trust the system and integrate their own knowledge with model predictions. To achieve this, we propose a novel probabilistic graphical model (PGM) which combines machine learning with crowd annotations. Nodes in our model correspond to claim veracity, article stance regarding claims, reputation of news sources, and annotator reliabilities. We introduce a fast variational method for parameter estimation. Evaluation across two real-world datasets and three scenarios shows that: (1) joint modeling of sources, claims and crowd annotators in a PGM improves the predictive performance and interpretability for predicting claim veracity; and (2) our variational inference method achieves scalably fast parameter estimation, with only modest degradation in performance compared to Gibbs sampling. Regarding model transparency, we designed and deployed a prototype fact-checker Web tool, including a visual interface for explaining model predictions. Results of a small user study indicate that model explanations improve user satisfaction and trust in model predictions. We share our web demo, model source code, and the 13K crowd labels we collected.
Identifying multi-view outliers is challenging because of the complex data distributions across different views. Existing methods cope this problem by exploiting pairwise constraints across different views to obtain new feature representations,based on which certain outlier score measurements are defined. Due to the use of pairwise constraint, it is complicated and time-consuming for existing methods to detect outliers from three or more views. In this paper, we propose a novel method capable of detecting outliers from any number of dataviews. Our method first learns latent discriminant representations for all view data and defines a novel outlier score function based on the latent discriminant representations. Specifically, we represent multi-view data by a global low-rank representation shared by all views and residual representations specific to each view. Through analyzing the view-specific residual representations of all views, we can get the outlier score for every sample. Moreover, we raise the problem of detectinga third type of multi-view outliers which are neglected by existing methods. Experiments on six datasets show our method outperforms the existing ones in identifying all types of multi-view outliers, often by large margins.