How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?

Tanya Tiwari, Tanuj Tiwari, Sanjay Tiwari


There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.

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Bridge, James P., Sean B. Holden, and Lawrence C. Paulson. "Machine learning for first-order theorem proving." Journal of automated reasoning 53.2 (2014): 141-172.

Loos, Sarah, et al. "Deep Network Guided Proof Search." arXiv preprint arXiv:1701.06972 (2017).

Finnsson, Hilmar, and YngviBjörnsson. "Simulation-Based Approach to General Game Playing." AAAI. Vol. 8. 2008.

Sarikaya, Ruhi, Geoffrey E. Hinton, and AnoopDeoras. "Application of deep belief networks for natural language understanding." IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 22.4 (2014): 778-784.

"AI-based translation to soon reach human levels: industry officials". Yonhap news agency. Retrieved 4 Mar 2017.Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. USA, Massachusetts: MIT Press. ISBN 9780262018258.

Zhong, Sheng-hua; Liu, Yan; Liu, Yang (2011). "Bilinear Deep Learning for Image Classification". Proceedings of the 19th ACM International Conference on Multimedia. MM '11. New York, NY, USA: ACM: 343–352. doi:10.1145/2072298.2072344. ISBN 9781450306164.

Szegedy, Christian; Toshev, Alexander; Erhan, Dumitru (2013). "Deep neural networks for object detection". Advances in Neural Information Processing Systems.

Deng, L.; Yu, D. (2014). "Deep Learning: Methods and Applications" (PDF). Foundations and Trends in Signal Processing. 7 (3–4): 1–199. doi:10.1561/2000000039.



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