Skip to main content
SpringerLink
Log in

Artificial Intelligence: State of the Art

  • Chapter
  • First Online:
Recent Trends and Advances in Artificial Intelligence and Internet of Things

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 172))

Abstract

Artificial Intelligence (AI) is the most fascinating and discussed technology in the current decade for its nature of mimic human intelligence. As John McCarthy defines it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. AI simply means the study of building machines with human like sense (perceiving), analysis or understand and response. Precisely, it’s the Weak AI, the AI systems are capable to do a specific kind of job for which it is trained. Even, the journey of AI was started back in 1950s, it become popular and started using in recent years for three reasons. First, the availability of big data; the gigantic amount of data generated by the e-commerce, social networks and businesses, second the machine learning algorithms are improved and more reliable, third the cloud and high-performance computer systems become cheap. The AI is changing the personal, social, and business landscape with every new day. It is used to develop products ranging from general to specific, such as playing music, playing chess, Painting, self-driving cars, proving theorems, etc. AI is widely used in automobile, logistic, healthcare, stock-trading, robotics, finance, transport, education like industries. This chapter starts with defining AI and its relationship with machine learning and deep learning followed by a brief time-line of the evaluation of AI, advantages and challenges of AI in today’s world. Then discuss about the three fundamental techniques problem solving, knowledge and reasoning, and learning, artificial neural networks and natural language processing (NLP) are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: Ordering Points to Identify the Clustering Structure, pp. 49–60. ACM Press (1999)

    Google Scholar 

  2. Block, H.D.: A review of perceptrons: an introduction to computational geometry? Inf. Control 17(5), 501–522 (1970)

    Article  Google Scholar 

  3. Bundy, A., Wallen, L.: Bidirectional Search, p. 9. Springer, Berlin, Heidelberg (1984)

    Google Scholar 

  4. Clark, A.: Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford University Press (2015)

    Google Scholar 

  5. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  6. Delling, D., Sanders, P., Schultes, D., Wagner, D.: Engineering Route Planning Algorithms, pp. 117–139. Springer, Berlin, Heidelberg (2009)

    Google Scholar 

  7. Felner, A.: Position paper: Dijkstra’s algorithm versus uniform cost search or a case against Dijkstra’s algorithm. In: Fourth Annual Symposium on Combinatorial Search (2011)

    Google Scholar 

  8. Filipovych, R., Resnick, S.M., Davatzikos, C.: Semi-supervised cluster analysis of imaging data. NeuroImage 54(3), 2185–2197 (2011)

    Article  Google Scholar 

  9. Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference (2002)

    Google Scholar 

  10. Freedman, D.A.: Statistical Models: Theory and Practice. Cambridge University Press (2009)

    Google Scholar 

  11. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)

    Google Scholar 

  12. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press (2016)

    Google Scholar 

  13. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  14. Grosan, C., Abraham, A.: Informed (Heuristic) Search, pp. 53–81. Springer, Berlin, Heidelberg (2011)

    Google Scholar 

  15. Hayes. P.J.: On semantic nets, frames and associations. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, IJCAI’77, San Francisco, CA, USA, vol. 1, pp. 99–107. Morgan Kaufmann Publishers Inc. (1977)

    Google Scholar 

  16. Hoy, Matthew B.: Alexa, siri, cortana, and more: an introduction to voice assistants. Med. Ref. Serv. Q. 37(1), 81–88 (2018). PMID: 29327988

    Article  Google Scholar 

  17. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discovery 2(3), 283–304 (1998)

    Article  Google Scholar 

  18. Jungnickel, D.: The Greedy Algorithm, pp. 129–153. Springer, Berlin, Heidelberg (1999)

    Google Scholar 

  19. Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 1st edn. Prentice Hall PTR, Upper Saddle River, NJ, USA (2000)

    Google Scholar 

  20. Kao, A., Poteet, S.R.: Natural Language Processing and Text Mining. Springer, Incorporated (2006)

    MATH  Google Scholar 

  21. Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E.: RoboCup: the robot world cup initiative. (1995)

    Google Scholar 

  22. Korf, R.E.: Depth-first iterative-deepening: an optimal admissible tree search. Artif. Intell. 27(1), 97–109 (1985)

    Google Scholar 

  23. Kozen, D.C.: Depth-First and Breadth-First Search, pp. 19–24. Springer, New York, NY (1992)

    Google Scholar 

  24. Krotkov, E., Blitch, J.: The Defense Advanced Research Projects Agency (DARPA) tactical mobile robotics program. Int. J. Robot. Res. 18(7), 769–776 (1999)

    Article  Google Scholar 

  25. Lark, J.W., White, C.C., Syverson, K.: A best-first search algorithm guided by a set-valued heuristic. IEEE Trans. Syst. Man Cybern. 25(7), 1097–1101 (1995)

    Article  MathSciNet  Google Scholar 

  26. LaValle, S.M.: Planning Algorithms. Cambridge University Press (2006)

    Google Scholar 

  27. McCarthy, J., Levin, M.I.: LISP 1.5 Programmer’s Manual. MIT Press (1965)

    Google Scholar 

  28. McCarthy, J.: Some expert systems need common sense. Ann. N. Y. Acad. Sci. 426, 129–137 (1984)

    Article  Google Scholar 

  29. McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag. 27(4), 12 (2006)

    Google Scholar 

  30. McClelland, J.L., Rumelhart, D.E.: Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises. Computational Models of Cognition and Perception. MIT Press (1988)

    Google Scholar 

  31. McCulloch, W.S., Pitts, Walter: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)

    Article  MathSciNet  Google Scholar 

  32. Minsky, M.: Steps toward artificial intelligence. In: Computers and Thought, pp. 406–450. McGraw-Hill (1961)

    Google Scholar 

  33. Mondal, B., Choudhury, J.P.: A comparative study on k means and PAM algorithm using physical characters of different varieties of mango in India. Int. J. Comput. Appl. 78(5), 21–24 (2013)

    Google Scholar 

  34. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press (2012)

    Google Scholar 

  35. Newell, A., Shaw, J.C., Simon, H.A.: Report on a general problem-solving program. In: Proceedings of the International Conference on Information Processing, pp. 256–264 (1959)

    Google Scholar 

  36. Nisbet, R., Miner, G., Yale, K.: Chapter 9—Classification. In: Nisbet, R., Miner, G., Yale, K. (eds.) Handbook of Statistical Analysis and Data Mining Applications, 2nd edn., pp. 169–186. Academic Press, Boston (2018)

    Google Scholar 

  37. Pinker, S., Mehler, J.: Connections and Symbols. A Bradford Book. Elsevier (1988)

    Google Scholar 

  38. Pollock, J.L.: Planning Agents, pp. 53–79. Springer, Dordrecht (1999)

    Google Scholar 

  39. Ramanujan, R., Sabharwal, A., Selman, B.: Understanding sampling style adversarial search methods (2012). arXiv preprint. arXiv:1203.4011

  40. Shortliffe, E.H., Buchanan, B.G.: A model of inexact reasoning in medicine. Math. Biosci. 23(3), 351–379 (1975)

    Article  MathSciNet  Google Scholar 

  41. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (2018)

    Google Scholar 

  42. Turing, A.M.: I. Computing machinery and intelligence. Mind LIX(236), 433–460 (1950)

    Google Scholar 

  43. Turing, A.M.: Computing machinery and intelligence. In: Parsing the Turing Test, pp. 23–65. Springer (2009)

    Google Scholar 

  44. van Harmelen, F., van Harmelen, F., Lifschitz, V., Porter, B.: Handbook of Knowledge Representation. Elsevier Science, San Diego, USA (2007)

    MATH  Google Scholar 

  45. Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Xavier School of Computer Science & Engineering, Xavier University Bhubaneswar, Bhubaneswar, Orissa, India

    Bhaskar Mondal

Authors
  1. Bhaskar Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bhaskar Mondal .

Editor information

Editors and Affiliations

  1. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  2. Department of Computer Science and Engineering, LNCT Group of Colleges, Jabalpur, Madhya Pradesh, India

    Raghvendra Kumar

  3. Department of Computer Science and Engineering, DIT University, Dehradun, Uttarakhand, India

    Rajshree Srivastava

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mondal, B. (2020). Artificial Intelligence: State of the Art. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_32

Download citation

Publish with us

Policies and ethics

Search

Navigation