Author:
Jiang Yuchen,Li Xiang,Luo Hao,Yin Shen,Kaynak Okyay
Abstract
AbstractThe study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do more than just labor-intensive work; they can develop human-like intelligence. Being aware or not, AI has penetrated into our daily lives, playing novel roles in industry, healthcare, transportation, education, and many more areas that are close to the general public. AI is believed to be one of the major drives to change socio-economical lives. In another aspect, AI contributes to the advancement of state-of-the-art technologies in many fields of study, as helpful tools for groundbreaking research. However, the prosperity of AI as we witness today was not established smoothly. During the past decades, AI has struggled through historical stages with several winters. Therefore, at this juncture, to enlighten future development, it is time to discuss the past, present, and have an outlook on AI. In this article, we will discuss from a historical perspective how challenges were faced on the path of revolution of both the AI tools and the AI systems. Especially, in addition to the technical development of AI in the short to mid-term, thoughts and insights are also presented regarding the symbiotic relationship of AI and humans in the long run.
Publisher
Springer Science and Business Media LLC
Reference144 articles.
1. Kaynak O. The golden age of Artificial Intelligence. Discov Artif Intell. 2021;1:1. https://doi.org/10.1007/s44163-021-00009-x.
2. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence. Stanford: AI Magazine; 1995.
3. Market research report, markets and markets, report code: TC 7894, May 2021. https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html.
4. Hinton G, Deng L, Yu D, Dahl GE, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82–97.
5. Graves A, Mohamed AR, Hinton G. Speech recognition with deep recurrent neural networks. In: IEEE international conference on acoustics, speech and signal processing. IEEE: Piscataway; 2013. p. 6645–9.
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