Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-RNN-GANs), where both the generator and the discriminator modules are based on contextual history (modeled as RNNs) and the adversarial discriminator guides the generator to produce realistic images for the particular time step in the image sequence. We evaluate the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning, where it performs competitively with 10th-grade human performance but there is still scope for interesting improvements as compared to college-grade human performance. We also evaluate our model on a standard video next-frame prediction task, achieving improved performance over comparable state-of-the-art.
@cite_31 introduced IQ Tests to measure the success of an individual at adapting to a specific situation under a specific condition. Visual problems in intelligence tests have been among the earliest and continuously researched problems in AI. It has been looked at in terms of propositional logic beginning with @cite_7 and more recently by @cite_30 . Recently, there has also been significant interest in building systems that compete with humans on a variety of tasks such as geometry-based problems @cite_5 , physics-based problems @cite_22 , repetition and symmetry detection @cite_22 , visual question answering @cite_40 , and verbal reasoning and analogy @cite_36 @cite_23 .
@cite_30 @cite_22 @cite_7 @cite_36 @cite_40 @cite_23 @cite_5 @cite_31
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Among the diverse processes at work in human cognition, the ability to establish analogies plays a crucial role and is often evaluated via IQ tests where an incomplete sequence has to be completed with a suitable item. This has motivated the AI community for developing various computational models of analogy-making. A Boolean logic view of analogical proportions (a basic form of analogical statements of the form "a is to b as c is to d") has been recently proposed and extended to another logical proportion, namely paralogical proportion (stating that "what a and b have in common, c and d have it also"). When used in combination, these 2 proportions provide an enhanced power to complete IQ tests. This Boolean modeling essentially relies on the assessment of the differences and similarities between the items involved, and in the case of analogy, satisfies the expected properties of an analogical proportion. An extension to multiple-valued features has also been defined, reinforcing their scope of applications. It is then possible to complete, in a deterministic manner, some incomplete proportions where the last item d is missing. In this paper, we show how this can be the basis of a simple inference paradigm that provides a rigorous way to solve representative analogy-based IQ tests by computing the missing items rather than by choosing in a list of options. The result of the analogical paralogical inference depends on the way the items are represented. The paper discusses how this approach can be used in analogy-making for both determining missing items in proportions and laying bare the relation linking the components of such proportions. The novelty of the approach is stressed w.r.t. other proposals existing in the literature. Solving physics problems usually requires geometric reasoning; a computer problem solver must use a representation that is in some respects equivalent to the use of diagrams by human problems solves. In this paper, we review the uses of diagrams and geometric reasoning in physics problem solving programs. Next, we consider the many roles that diagrams seem to play in human problem solving, the relative strengths and weaknesses of computers and human problem solvers for reasoning with diagram-like representations, and ways in which some of the uses of diagrams by humans might be implemented in computer programs. 13,072. Brandenberger, J. E. May 31. 1913, [Convention date]. Compound fabrics and sheet materials.-Films for photographic and like purposes consist of two previously prepared outer films of regenerated cellulose enclosing between them a perfectly uniform layer of plastic material, such as gelatine, resin, paraffin, rubber, or mixtures containing oil. In preparing the film, the cellulose films are unwound from rolls D, D over cylinders A, A placed close together and heated by steam &c., the distance between the cylinders being adjustable. The plastic material is fed into the gap E between the films, and the composite film passes over a series of cylinders B, which may be heated when heat is necessary to set the plastic material. One of the two outer films may be glued to a strip of paper, fabric, met al, or other thin sheet material, and powder, fibres, blades, spangles, and other solid substances may be mixed with the plastic material. We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing 0.25M images, 0.76M questions, and 10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV (this http URL). Intelligence Quotient (IQ) Test is a set of standardized questions designed to evaluate human intelligence. Verbal comprehension questions appear very frequently in IQ tests, which measure human's verbal ability including the understanding of the words with multiple senses, the synonyms and antonyms, and the analogies among words. In this work, we explore whether such tests can be solved automatically by artificial intelligence technologies, especially the deep learning technologies that are recently developed and successfully applied in a number of fields. However, we found that the task was quite challenging, and simply applying existing technologies (e.g., word embedding) could not achieve a good performance, mainly due to the multiple senses of words and the complex relations among words. To tackle these challenges, we propose a novel framework consisting of three components. First, we build a classifier to recognize the specific type of a verbal question (e.g., analogy, classification, synonym, or antonym). Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries. Third, for each type of questions, we propose a specific solver based on the obtained distributed word representations and relation representations. Experimental results have shown that the proposed framework can not only outperform existing methods for solving verbal comprehension questions but also exceed the average performance of the Amazon Mechanical Turk workers involved in the study. The results indicate that with appropriate uses of the deep learning technologies we might be a further step closer to the human intelligence. This paper introduces GEOS, the first automated system to solve unaltered SAT geometry questions by combining text understanding and diagram interpretation. We model the problem of understanding geometry questions as submodular optimization, and identify a formal problem description likely to be compatible with both the question text and diagram. GEOS then feeds the description to a geometric solver that attempts to determine the correct answer. In our experiments, GEOS achieves a 49 score on official SAT questions, and a score of 61 on practice questions. 1 Finally, we show that by integrating textual and visual information, GEOS boosts the accuracy of dependency and semantic parsing of the question text.