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ai research – thoughts, theories & studies on artificial intelligence (ai) research skip to content thoughts, theories & studies on artificial intelligence (ai) research menu and widgets blog articles research papers talks books people & groups dev resources academic programs search ai research search for: welcome! ai research [.com] is a collection of global thoughts, theories and studies on the subject of artificial intelligence. got something to add? please submit it here . overcoming priors for visual question answering abstract a number of studies have found that today’s visual question answering (vqa) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. to encourage development of models geared towards the latter, we propose a new setting for vqa where for every question type, train and test sets have different prior distributions of answers. specifically, we present new splits of the vqa v1 and vqa v2 datasets, which we call visual question answering under changing priors (vqa-cp v1 and vqa-cp v2 respectively). first, we evaluate several existing vqa models under this new setting and show that their performance degrades significantly compared to the original vqa setting. second, we propose a novel grounded visual question answering model (gvqa) that contains inductive biases and restrictions in the architecture specifically designed to prevent the model from ‘cheating’ by primarily relying on priors in the training data. specifically, gvqa explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, enabling the model to more robustly generalize across different distributions of answers. gvqa is built off an existing vqa model – stacked attention networks (san). our experiments demonstrate that gvqa significantly outperforms san on both vqa-cp v1 and vqa-cp v2 datasets. interestingly, it also outperforms more powerful vqa models such as multimodal compact bilinear pooling (mcb) in several cases. gvqa offers strengths complementary to san when trained and evaluated on the original vqa v1 and vqa v2 datasets. finally, gvqa is more transparent and interpretable than existing vqa models. aishwarya agrawal , dhruv batra , devi parikh , aniruddha kembhavi [facebook ai research] download the full paper here posted on june 18, 2018 categories research papers tags aishwarya agrawal , aniruddha kembhavi , devi parikh , dhruv batra , facebook ai research leave a comment on overcoming priors for visual question answering the importance of single directions for generalization abstract despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance. however, the differences between the learned solutions of networks which generalize and those which do not remain unclear. additionally, the tuning properties of single directions (defined as the activation of a single unit or some linear combination of units in response to some input) have been highlighted, but their importance has not been evaluated. here, we connect these lines of inquiry to demonstrate that a network’s reliance on single directions is a good predictor of its generalization performance, across networks trained on datasets with different fractions of corrupted labels, across ensembles of networks trained on datasets with unmodified labels, across different hyperparameters, and over the course of training. while dropout only regularizes this quantity up to a point, batch normalization implicitly discourages single direction reliance, in part by decreasing the class selectivity of individual units. finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance. ari s. morcos , david g.t. barrett , neil c. rabinowitz , & matthew botvinick @deepmind download the full paper here posted on march 26, 2018 april 3, 2018 categories research papers tags ari s. morcos , david g.t. barrett , deepmind , google deepmind , matthew botvinick , neil c. rabinowitz leave a comment on the importance of single directions for generalization singularitynet: a decentralized, open market and inter-network for ais abstract the value and power of artificial intelligence is growing dramatically every year, and will soon dominate the internet – and the economy as a whole. however, ai tools today are fragmented by a closed development environment; most are developed by one company to perform one task, and there is no way to plug two tools together. singularitynet aims to become the key protocol for networking ai and machine learning tools to form a coordinated artificial general intelligence. singularitynet is an open-source protocol and collection of smart contracts for a decentralized market of coordinated ai services. within this framework, the benefits of ai become a global commons infrastructure for the benefit of all; anyone can access ai tech or become a stakeholder in its development. anyone can add an ai/machine learning service to singularitynet for use by the network, and receive network payment tokens in exchange. singularitynet is backed by the singularitynet foundation, which operates on a belief that the benefits of ai should not be dominated by any small set of powerful institutions, but shared by all. a key goal of singularitynet is to ensure the technology is benevolent according to human standards, and the network is designed to incentivize and reward beneficial players. ben goertzel download the full paper here posted on december 25, 2017 december 25, 2017 categories research papers tags ai , ben goertzel , blockchain , singularity , singularitynet leave a comment on singularitynet: a decentralized, open market and inter-network for ais what is offline ai? [originally posted by jason hadjioannou on medium – 30th june 2017] offline ai refers to artificial intelligence programs that run on-device, as opposed to server-side apis that run programs to perform ai tasks remotely. why is this a thing? well there are three big benefits to using offline ai. speed the first is operation speed. if a device has all the data it needs and possess the ability to perform intelligent tasks such as image recognition and natural language processing without needing to send/receive data processed on a remote server somewhere, then the speed of the operation is greatly improved due to the lack of reliance on network connectivity and/or server hardware performance. an on-device ai program can run trained machine learning models and neural networks using nothing but the device hardware and software. not having to rely on network connectivity greatly improves the speed of operation and has a positive impact on user experience. ( core ml for macos and ios is a framework by apple that will allow such programs to run on a mac or an ios device. i’ll be talking more about core ml in future posts). cost the second benefit to using offline ai comes hand in hand with the first. if online is false then network costs are zero! to give you an example of how much of an impact this can have on an ai business.. the company i work for is an artificial intelligence and augmented reality company with a consumer facing mobile app, and it’s not uncommon for even medium-sized tech companies to spend millions of dollars per month on the server-side technology needed to perform the ai tasks that make an app’s feature-set possible. the majority of this huge cost goes towards server hosting and data bandwidth fees that occur whenever the app sends image data from a user’s device camera and up to our online neural nets for processing. if you want really fast image-recognition performance for exam
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