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AIA: Artificial intelligence for art

Electronic Visualisation and the Arts (EVA)

Electronic Visualisation and the Arts

9 - 13 July 2018

Artificial intelligence, Recurrent neural network, Reinforcement learning

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      Abstract

      There are limits to state-of-the-art AI that separate it from human-like intelligence. Today’s AI algorithms are limited in how much previous knowledge they are able to keep through each new training phase and how much they can reuse. There is domain called AGI where will be possible to find solutions for this problems. Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action. "General" means that one AI program realises number of different tasks and the same code can be used in many applications.

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      Most cited references 10

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      Long short-term memory.

      Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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        Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

         G E Dahl,  Dong Yu,  Li Deng (2012)
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          Image net classification with deep convolutional neural networks

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            Author and article information

            Affiliations
            Institute for Research in Science and Art

            Enschede, Netherlands
            Contributors
            Conference
            July 2018
            July 2018
            : 26-31
            10.14236/ewic/EVA2018.5
            © Lisek. Published by BCS Learning and Development Ltd. Proceedings of EVA London 2018, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Electronic Visualisation and the Arts
            EVA
            London, UK
            9 - 13 July 2018
            Electronic Workshops in Computing (eWiC)
            Electronic Visualisation and the Arts
            Product
            Product Information: 1477-9358 BCS Learning & Development
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

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