Machine Learning Based on Cloud Solutions

• Authors: CHMIELECKI PRZEMYSŁAW
• Conference: Edukacja-technika-Informatyka
• Languages of publication: PL EN

Abstracts

EN

Cognition is a domain of thinking creatures, isn't it? Based on that computers cannot learn anything more than was in the initial data feed. In this article, I just want to defend that nowadays technical solutions can break this rule. The aim of this article is to provide a short technical overview what Machine Learning (ML), Artificial Intelligence (AI) and Neural Networks (NN) were before in the area of standalone gigantic servers, and how do they look now in Cloud Computing (CC) times. The ML paradigm is not any more reserved for big enterprises only but now is available for single internet user. I just want to present AWS and Azure, as the biggest CC providers, functionalities and potential usage of such Cognitive Services (CS) in current internet services. The great example is for instance bot usage instead of diving deep in the FAQ on the company website or digging into the corporate wiki. Another big area is graphics analysis and sound or text recognition. Those are only examples of predefined CC functions ready for use right now in the public cloud.

Keywords

EN

Machine Learning; Artificial Intelligence; Neural Networks; Cloud Computing; Cognitive Services; AWS; Azure

• Publisher: Uniwersytet Rzeszowski
• Journal: Edukacja-Technika-Informatyka
• Year: 2019
• Volume: 10
• Issue: 1
• Pages: 132-138

Dates

published

2019

Contributors

author

CHMIELECKI PRZEMYSŁAW

• PhD Eng., Higher Baptist Theological Seminary, Institute of Systematic Theology, Department of Philosophy, Poland

References

• Accenture (2017). Technology Vision 2017. Technology for the People. The Era of the Intelligent Enterprise. Retrieved from: https://www.accenture.com/t20170530T164033Z__w__/us-en/_acnmedia/Accenture/next-gen-4/tech-vision-2017/pdf/Accenture-TV17-Full.pdf (15.03.2019).
• Barga, R., Fontama, V., Hyong Tok, W. (2015). Predictive Analytics with Microsoft Azure Machine Learning. Second Edition. New York: Apress.
• Bekkerman, R., Bilenko, M., Langford, J. (2012). Scaling Up Machine Learning. Parallel and Distributed Approaches. New York: Cambridge University Press.
• Bell, J. (2015). Machine Learning. Hand-on for Developers and Technical Professionals. Indianapolis: John Wiley & Sons, Inc.
• Brink, H., Richards, J.W., Fetherolf, M. (2017). Real-Word Machine Learning. Shelter Island: Manning Publications Co.
• Cassimatis, N.L. (2012). Artificial Intelligence and Cognitive Modeling Have the Same Problem.. In: P. Wang, B. Goertzel (eds.), Theoretical Fundamentals of Artificial Intelligence (pp. 11-24). Paris: Atlantis Press.
• Feigenbaum, E.A., McCorduck, P. (1983). The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World. Massachusetts: Addison-Wesley Publishing Company.
• Feigenbaum, E.A., McCorduck, P. (1983). The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World. Massachusetts: Addison-Wesley Publishing Company.
• Goertzel, B., Pennachin, C. (2007). Artificial General Intelligence. New York: Springer.
• https://aws.amazon.com/machine-learning/ (15.03.2019).
• https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/ (15.03.2019).
• https://azure.microsoft.com/en-us/services/cognitive-services/custom-speech-service/ (15.03.2019).
• Nandy, A., Biswas, M. (2018). Reinforcement Learning With Open AI, Tensor Flow and Keras Using Python. New York: Apress.
• Newell, A., Simon, H.A. (1963). GPS, a Program that Simulates Human Thought. In: E.A. Feigenbaum, J. Feldman (eds.), Computers and Thought (pp. 279-293). New York: McGraw-Hill.
• Watt, J., Borhani, R., Katsaggelos, A.K. (2016). Machine Learning Redefined. Foundations, Algorithms, and Applications. Cambridge: Cambridge University Press.