Abstract
There have been many improvements on neural networks and neural nets in order to come up with a program that is as intelligent as humans. Recent improvements in natural language processing and machine learning techniques in the field of artificial intelligence has proved that the near future will be very exciting with artificial intelligent robots around us. The BornBaby model proposed is a whole new dimension for Artificial Intelligence and opens up array of possibilities for researchers to come up with natural solutions for problems. Attempts are being made to come up with a software synthesis program, which is able to write programs on its own, like human programmers. BornBaby model is a structured model which defines how a program must learn in order to come up with solutions. Though there are models like Neural networks which emphasizes on the implementation of program to simulate working of human brain, BornBaby model emphasizes on how the program must learn the data based on natural concepts of living beings and the implementation is generic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hu, Y., Chen, J., Rong, Z., Mei, L., Xie, K.: A neural networks approach for software risk analysis. In: Proceedings of the Sixth IEEE International Conference on Data Mining Workshops, pp. 722–725. Hong Kong. Washington DC: IEEE Computer Society (2006)
Goldman, S., Zhou, Y.: Enhancing Supervised Learning with Unlabeled Data. Department of Computer Science, Washington University, MO, USA
Caruana, R., Niculescu- Mizil, A.: An Empirical Comparison of Supervised Learning Algorithms. Department of Computer Science, Cornell University, Ithaca, NY, USA
Lavesson, N.: Evaluation and Analysis of Supervised Learning Algorithms and Classifiers. Blekinge Institute of Technology Licentiate Dissertation Series No 2006:04. ISSN 1650-2140, ISBN 91-7295-083-8
Lavesson, N.: Evaluation and Analysis of Supervised Learning Algorithms and Classifiers. Blekinge Institute of Technology Licentiate Dissertation Series No 2006:04. ISSN 1650-2140, ISBN 91-7295-083-8
Ng, A.: Deep Learning And Unsupervised, “Genetic Learning Algorithms”, “Reinforcement Learning and Control. Department of Computer Science, Stanford University, CA, USA
Liu, B.: Supervised Learning. Department of Computer Science, University of Illinois at Chicago (UIC), Chicago
Caruana R., Niculescu-Mizil, A.: An Empirical Comparison of Supervised Learning Algorithms. Department of Computer Science, Cornell University, Ithaca, NY, USA
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gururaj, H.L., Ramesh, B. (2019). BornBaby Model for Software Synthesis. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_33
Download citation
DOI: https://doi.org/10.1007/978-981-13-2514-4_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2513-7
Online ISBN: 978-981-13-2514-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)