Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the correct answer without using the image information. As a result, they suffer from a huge drop in performance when evaluated on data outside their training set distribution. This critical issue makes them unsuitable for real-world settings. We propose RUBi, a new learning strategy to reduce biases in any VQA model.

Multiple-choice machine reading comprehension is difficult task as its required machines to select the correct option from a set of candidate or possible options using the given passage and question.Reading Comprehension with Multiple Choice Questions task,required a human (or machine) to read a given passage, question pair and select the best one option from n given options. There are two different ways to select the correct answer from the given passage. Either by selecting the best match answer to by eliminating the worst match answer. Here we proposed GenNet model, a neural network-based model. In this model first we will generate the answer of the question from the passage and then will matched the generated answer with given answer, the best matched option will be our answer. For answer generation we used S-net (Tan et al., 2017) model trained on SQuAD and to evaluate our model we used Large-scale RACE (ReAding Comprehension Dataset From Examinations) (Lai et al.,2017).

Many financial institutions are rapidly developing and adopting AI models. They're using the models to achieve new competitive advantages such as being able to make faster and more successful underwriting decisions. However, AI models introduce new risks. In a previous post, I describe why AI models increase risk exposure compared to the more traditional, rule-based models that have been in use for decades. In short, if AI models have been trained on biased data, lack explainability, or perform inadequately, they can expose organizations to as much as seven-figure losses or fines.

IBM recently announced several new Watson technologies designed to help organizations identify, understand, and analyze some of the most challenging aspects of the English language with greater clarity and insights. These new features are considered the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater. There is a new advanced sentiment analysis feature defined to identify and analyze idioms and colloquialisms for the first time. So it can recognize phrases such as "hardly helpful" or "hot under the collar." Phrases like those have been challenging for artificial intelligence systems since they are difficult for algorithms to spot.

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In previous posts we explored what analysts want to discover about their virtual assistant and some building blocks for building analytics. In this post I will demonstrate some common recipes tailored to Watson Assistant logs. First we extract raw log events and store on the file system. This requires the apikey and URL for your skill. For a single-skill assistant you will also need the workspace ID (extractable from the "Legacy v1 Workspace URL"), for a multi-skill assistant there are other IDs you can use to filter on (described in the Watson Assistant list log events API).

Artificial intelligence researchers at IBM have introduced a major upgrade to the famed Watson computer, allowing it to understand idioms and colloquialisms for the first time. IBM says the update makes it the first commercial AI system capable of identifying, understanding and analysing some of the most challenging aspects of the English language. Phrases like "hardly helpful" and "hot under the collar" are tricky for algorithms to spot, meaning AI is unable to debate complex topics or have nuanced conversations with humans. "Language is a tool for expressing thought and opinion, as much as it is a tool for information," said Rob Thomas, a general manager at IBM Data and AI. "This is why we believe that advancing our ability to capture, analyse, and understand more from language with NLP will help transform how businesses utilise their intellectual capital that is codified in data." Russia has launched a humanoid robot into space on a rocket bound for the International Space Station (ISS).

IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights. The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like'hardly helpful,' or'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operation.

In this paper,we propose a novel task of Manipulation Question Answering(MQA),a class of Question Answering (QA) task, where the robot is required to find the answer to the question by actively interacting with the environment via manipulation. Considering the tabletop scenario, a heatmap of the scene is generated to facilitate the robot to have a semantic understanding of the scene and an imitation learning approach with semantic understanding metric is proposed to generate manipulation actions which guide the manipulator to explore the tabletop to find the answer to the question. Besides, a novel dataset which contains a variety of tabletop scenarios and corresponding question-answer pairs is established. Extensive experiments have been conducted to validate the effectiveness of the proposed framework.

Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology? To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer. Our work makes the first attempt to build such a dataset. Different from creating general-domain VQA datasets where the images are widely accessible and there are many crowdsourcing workers available and capable of generating question-answer pairs, developing a medical VQA dataset is much more challenging. First, due to privacy concerns, pathology images are usually not publicly available. Second, only well-trained pathologists can understand pathology images, but they barely have time to help create datasets for AI research. To address these challenges, we resort to pathology textbooks and online digital libraries. We develop a semi-automated pipeline to extract pathology images and captions from textbooks and generate question-answer pairs from captions using natural language processing. We collect 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness. To our best knowledge, this is the first dataset for pathology VQA. Our dataset will be released publicly to promote research in medical VQA.