This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Overview of Automotive Artificial Intelligence: Potential of Adapting Deep Thinking and Quick Learning Paradigm from Gaming Domain
Technical Paper
2019-01-5009
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Event:
Automotive Technical Papers
Language:
English
Abstract
Artificial intelligence (AI) has witnessed significant attention from both research and industry in recent years. AI is not a new area of research, and research in this field has been reported over decades since 1950 on a continuous basis. Renewed interest in this stream of computing has been primarily due to development of pathbreaking methodologies which have potential application in various industries including automotive. The buildup of research in the area of AI over the past 60 years and the general viability of application of the past and ongoing research especially in the area of automotive are the motivation behind this work. However there are still important gaps that need to the bridged to make it possible to develop truly general as well as adaptive intelligent machines with application to the automotive sector. The article is an effort to point out that a meaningfully useful general learning machine must not only be “general” that it is able to learn and solve various types of problems encountered in the automotive domain but also be able to learn quickly in an constantly changing, chaotic environment, for example, general traffic in India. The article reviews the present limitations of industry-accepted AI-based learning methodologies which have the potential to fill the gaps. A paradigm related to deep thinking and deep learning is discussed that has the potential to give a future direction to research to put forth truly general and adaptive intelligent machines.
Recommended Content
Authors
Citation
Krishna, S., Mathew, P., and Kumar, P., "Overview of Automotive Artificial Intelligence: Potential of Adapting Deep Thinking and Quick Learning Paradigm from Gaming Domain," SAE Technical Paper 2019-01-5009, 2019, https://doi.org/10.4271/2019-01-5009.Also In
References
- http://standards.sae.org/j3016_201401/
- ISO 26262-1:2011 https://www.iso.org/standard/43464.html
- IEC 61508-1:2010 https://webstore.iec.ch/publication/5515
- McCarthy , J. Comments Mechanization of Thought Processes: Proceedings of a Symposium 1958 464
- Winograd , T. A Procedural Model of Language Understanding Computer Models of Thought and Language Schank , R.C. and Colby , K.M. San Francisco W. H. Freeman and Company 1973 152 186
- Minsky , M. and Papert , S. Perceptrons Cambridge, MA MIT Press 1969
- Hanson , A.R. and Riseman , E.M. Computer Vision Systems New York Academic Press 1978
- Rumelhart D. E. , McClelland J. L. , and the PDP Research Group Parallel Distributed Processing: Exploration in the Microstructure of Cognition Cambridge, MA MIT Press 1986
- LeCun , Y. , Bengio , Y. , and Hinton , G.E. Deep Learning Nature 521 436 444 2015
- Sutton , R.S. and Barto , A.G. Reinforcement Learning: An Introduction Cambridge, MA MIT Press 1998
- Silver , D. et al. Mastering the Game of Go with Deep Neural Networks and Tree Search Nature 529 484 489 2016
- Deng , L. and Yu , D. Deep Learning Methods and Applications Netherlands Now Publishers 2014
- Cheng , H. Autonomous Intelligent Vehicles: Theory, Algorithms, and Implementation New York Springer 2011
- Mnih , V. et al. Human-Level Control through Deep Reinforcement Learning Nature 518 529 533 2015 10.1038/nature14236
- Lake , B.M. , Salakhutdinov , R. , and Tenenbaum , J.B. Human-Level Concept Learning through Probabilistic Program Induction Science 350 1332 1338 2015
- Teng , T.-H. , Tan , A.-H. , Ong , W.-S. , and Lee , K.-L. Adaptive CGF for Pilots Training in Air Combat Simulation Proceedings of the 15th International Conference on Information Fusion 2012 2263 2270
- Ho , S.-B. Deep Thinking and Quick Learning for Viable AI Proceeding of the FTC 2016 - Future Technologies Conference 2016
- Greenspan , J. Coyotes in the Crosswalks? Fuggedaboutit! Scientific American 309 4 17 2013
- Ho , S.-B. Principles of Noology: Toward a Theory and Science of Intelligence Cham, Switzerland Springer 2016
- Pearl , C. Models, Reasoning, and Inference Second Cambridge Cambridge University Press 2009
- Cheng , P.W. From Covariation to Causation: A Causal Power Theory Psychological Review 104 2 367 405 1997
- Ho , S.-B. On Effective Causal Learning Proceedings of the 7th International Conference on Artificial General Intelligence Berlin Springer Verlag 2014 43 52
- Fire , A. and Zhu , S.-C. Learning Perceptual Causality from Video ACM Transactions on Intelligent Systems and Technology 7 2 2016 10.1145/2809782
- Hart , P.E. , Nilsson , N.J. , and Raphael , B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths IEEE Transactions on Systems Science and Cybernetics SSC4 4 2 100 107 1968
- Ho , S.-B. and Liausvia , F. Rapid Learning and Problem Solving Proceedings of the IEEE Symposium Series on Computational Intelligence for Human-Like Intelligence Piscataway, NJ IEEE Press 2014 110 117
- Ho , S.-B. Cognitive Realistic Problem Solving through Causal Learning Proceeding of the 18th International Conference on Artificial Intelligence Las Vegas CSREA Press 2016 115 121
- Russell , S. and Norvig , P. Artificial Intelligence: A Modern Approach Upper Saddle River, NJ Pearson Education, Inc. 2010
- The Arcade Learning Environment https://github.com/mgbellemare/Arcade-Learning-Environment
- Bellemare , M.G. , Veness , J. , and Bowling , M. Investigating Contingency Awareness Using Atari 2600 Games Proceedings of the 26th AAAI Conference on Artificial Intelligence 2012 864 871
- https://www.intelnervana.com/intel-nervana-neural-network-processors-nnp-redefine-ai-silicon/