Abstract
It is widely accepted in Artificial Intelligence (AI) that different tasks require different learning methods. The same is true for different sensory modalities. However, auto-programming for general purposes seems to require a learning engine that is task-independent and modality-independent. We provided the Developmental Network (DN) as such an engine to all contestants of the AI Machine Learning Contest 2016 for learning three well-recognized bottleneck problems in AI—vision, audition, and natural languages. For vision, the network learned abstract visual concepts and their hierarchy with invariant properties and autonomous attention. For audition, sparse and dense actions jointly serve as auditory contexts. For natural languages, the network acquires two natural languages, English and French, conjunctively in a bilingual environment (i.e., patterns of text as inputs). All the three sensory modalities used the same DN learning engine, but each had a different body (sensors and effectors). The contestants independently verified the DN’s base performance, and competed to add (hinted) autonomous attention for better performance. This seems to be the first task-independent and modality-independent learning engine, which was also verified by independent contestants. Much remains to be done in the learner-age related sophistication of learned tasks.
Keywords
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Daw, N.D., Kakade, S., Dayan, P.: Opponent interactions between serotonin and dopamine. Neural Netw. 15(4–6), 603–616 (2002)
Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991)
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980)
Gomes, L.: Machine-learning maestro Michael Jordan on the delusions of big data and other huge engineering efforts. IEEE Spectrum (Online article posted 20 Oct 2014)
Graves, A., et al.: Hybrid computing using a neural network with dynamic external memory. Nature 538, 471–476 (2016)
Graves, A., Wayne, G., Danihelka, I.: Neural Turing machines. Technical report, Google DeepMind, London, UK 10 December 2014. arXiv:1410.5401
Guo, Q., Wu, X., Weng, J.: Cross-domain and within-domain synaptic maintenance for autonomous development of visual areas. In: Proceedings of the Fifth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, Providence, RI, pp. 1–6, 13–16 August 2015
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Holm, E.A.: In defense of the black box. Science 364(6435), 26–27 (2019)
Ji, Z., Weng, J., Prokhorov, D.: Where-what network 1: “Where” and “What” assist each other through top-down connections. In: Proceedings of IEEE International Conference on Development and Learning, Monterey, CA, pp. 61–66, 9–12 August 2008
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015)
Kakade, S., Dayan, P.: Dopamine: generalization and bonuses. Neural Netw. 15, 549–559 (2002)
LeCun, Y., Bengio, L., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Minsky, M.: Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Mag. 12(2), 34–51 (1991)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Moran, J., Desimone, R.: Selective attention gates visual processing in the extrastrate cortex. Science 229(4715), 782–784 (1985)
Olshausen, B.A., Anderson, C.H., Van Essen, D.C.: A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. J. Neurosci. 13(11), 4700–4719 (1993)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Scriven, R., Amiot-Cadey, G.: Collins: Collins French grammar. HarperCollins, Glasgow (2011)
Sharma, J., Angelucci, A., Sur, M.: Induction of visual orientation modules in auditory cortex. Nature 404, 841–847 (2000)
Solgi, M., Weng, J.: WWN-8: incremental online stereo with shape-from-x using life-long big data from multiple modalities. In: Proceedings of INNS Conference on Big Data, San Francisco, CA, pp. 316–326, 8–10 August 2015
Sutton, R.S., Barto, A.: Reinforcement Learning. MIT Press, Cambridge (1998)
Treisman, A.M.: A feature-integration theory of attention. Cogn. Sci. 12(1), 97–136 (1980)
Tsotsos, J.K.: A ‘complexity level’ analysis of immediate vision. Int. J. Comput. Vis. 1(4), 303–320 (1988)
Voss, P.: Sensitive and critical periods in visual sensory deprivation. Front. Psychol. 4, 664 (2013). https://doi.org/10.3389/fpsyg.2013.00664
Wang, Y., Wu, X., Weng, J.: Synapse maintenance in the where-what network. In: Proceedings of International Joint Conference on Neural Networks, San Jose, CA, pp. 2823–2829, 31 July–5 August 2011
Weng, J.: Natural and Artificial Intelligence: Introduction to Computational Brain-Mind. BMI Press, Okemos (2012)
Weng, J.: Brain as an emergent finite automaton: a theory and three theorems. Int. J. Intell. Sci. 5(2), 112–131 (2015). Received Nov. 3, 2014 and accepted by Dec. 5, 2014
Weng, J., Ahuja, N., Huang, T.S.: Learning recognition and segmentation of 3-D objects from 2-D images. In: Proceedings of IEEE 4th International Conference Computer Vision, pp. 121–128, May 1993
Weng, J., Ahuja, N., Huang, T.S.: Learning recognition and segmentation using the Cresceptron. Int. J. Comput. Vis. 25(2), 109–143 (1997)
Weng, J., et al.: Autonomous mental development by robots and animals. Science 291(5504), 599–600 (2001)
Yu, A.J., Dayan, P.: Uncertainty, neuromodulation, and attention. Neuron 46, 681–692 (2005)
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Weng, J., Castro-Garcia, J., Zheng, Z., Wu, X. (2019). Task-Nonspecific and Modality-Nonspecific AI. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_10
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