SRI Blog

-By Makarand Tapaswi , Yukun Zhu , Rainer Stiefelhagen  Antonio Torralba , Raquel Urtasun , Sanja Fidler  arlsruhe Institute of Technology,  Massachusetts Institute of Technology,  University of Toronto Paper Link Abstract We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from the simpler “Who” did “What” to “Whom”, to “Why” and “How” certain events occurred. Each question comes with a set of five possible answers; a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information – video clips, plots, subtitles, scripts, and DVS. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is...

-By Sara Sabour, Nicholas Frosst,  Geoffrey E. Hinton  Google Brain Toronto Paper Link Abstract   A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher...

-By Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim Google Brain  University of California Berkeley Paper Link This blog post is the fourth part of the Explainable Artificial Intelligence (XAI) series. Refer previous posts link . The post discusses on Salience Mapping techniques, performance, and metrics. Salience Mapping source: Analytics India Magazine The salience map approach is exemplified by occlusion procedure by Zeiler, where a network is repeatedly tested with portions of the input occluded to create a map showing which parts of the data actually have an influence on the network output. Abstract Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. This paper proposes an actionable methodology to evaluate what kinds of explanations a ...

-By Tameru Hailesilassie Department of Computer Science and Engineering National University of Science and Technology (MISiS) Moscow, Russia Today's blog is the continuation of XAI series. Rule Extraction from Neural Networks Abstract—Despite the highest classification accuracy in wide varieties of application areas, the artificial neural network has one disadvantage. The way this Network comes to a decision is not easily comprehensible. The lack of explanation ability reduces the acceptability of neural network in data mining and decision system. This drawback is the reason why researchers have proposed many rule extraction algorithms to solve the problem. Recently, Deep Neural Network (DNN) is achieving a profound result over the standard neural network for classification and recognition problems. It is a hot machine learning area proven both useful and innovative. This paper has thoroughly reviewed various rule extraction algorithms, considering the classifi...

-By Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin University of Washington  Seattle,  WA 98105, USA Paper Link This is third post in the series of Explainable AI (XAI). Earlier post i shed light on Machine learning impact on models with respect to Biasing. Today's topic is one of the large number of elementary operations , "Linear Proxy Models" (LIME). TRUST I would like to quote Stephen M.R. Covey "THE SPEED OF TRUST" Statements here, which is relevant to Trust. Executive summary link   Simply put, trust means confidence. The opposite of trust — distrust — is suspicion.  Trust always affects two outcomes: speed and cost. When trust goes down, speed goes down and cost goes up. When trust goes up, speed goes up and cost goes down (Strategy x Execution) x Trust = Results Not trusting people is a greater risk. if the users do not trust a model or a prediction, they will not use it.   ...

By - Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai Boston University, 8 Saint Mary’s Street, Boston, MA Microsoft Research New England, 1 Memorial Drive, Cambridge, MA Paper Link Todays topic is part of Explainable ai ( previous blog post ). DeBiasing is one of the aspect that influence Model decision. Abstract The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are...

-By Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter and Lalana Kagal Massachusetts Institute of Technology Cambridge, MA 02139  Paper link Explaining Explanations: An Overview of Interpretability of Machine Learning Explainable AI (XAI), Interpretable AI, or Transparent AI refer to techniques in artificial intelligence (AI) which can be trusted and easily understood by humans. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision. XAI can be used to implement a social right to explanation. Some claim that transparency rarely comes for free and that there are often tradeoffs between how "smart" an AI is and how transparent it is; these tradeoffs are expected to grow larger as AI systems increase in internal complexity. The technical challenge of explaining AI decisions is sometimes known as the interpretability problem. So...