Abstract
Artificial General Intelligence will not be general without computer vision. Biologically inspired adaptive vision models have started to outperform traditional pre-programmed methods: our fast deep / recurrent neural networks recently collected a string of 1st ranks in many important visual pattern recognition benchmarks: IJCNN traffic sign competition, NORB, CIFAR10, MNIST, three ICDAR handwriting competitions. We greatly profit from recent advances in computing hardware, complementing recent progress in the AGI theory of mathematically optimal universal problem solvers. © 2011 Springer-Verlag Berlin Heidelberg.
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Schmidhuber, J., Cireşan, D., Meier, U., Masci, J., & Graves, A. (2011). On fast deep nets for AGI vision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6830 LNAI, pp. 243–246). https://doi.org/10.1007/978-3-642-22887-2_25
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