Future Trends of AI .

This article discusses the future trends of AI including deep learning theory, capsule networks, deep reinforcement learning, lean and augmented data learning, and hybrid learning models. It also provides insights into the use of artificial intelligence in production management and fault diagnosis of rotating machinery. The article concludes with a discussion on the state of the art and future trends of Industry 4.0.

Running head: Future trends of AI
Future trends of AI
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2References
Future trends in AI
There have been various future use of the artificial Intelligence as mentioned below:
Deep learning theory: Deep Neural networks have been compared with a human brain
that might help in demonstrating ability to learn from audio, image and text data. Deep learning
theory helps in enabling greater development of human brain applications in the networks (Xu et
al., 2018). The use of deep learning methods have been helping in maintaining an inventory
control in the digital media. The use of the artificial intelligence theory in the deep learning
methods might help in exploring different deep neural networks and its designs.
Capsule networks: Capsule network are new type of deep neural network that help in
processing visual information as same as brain (Liu et al., 2018). Therefore, there might be a
hierarchical relationships within networks.
Deep Reinforcement learning: This is a type of neural network that helps in learning with
the help of environment by observations, actions and rewards. Deep Reinforcement learning
(DRL) has been used for learning various gaming strategies including Atari and Go.
Lean and Augmented data learning: The largest issue in the Machine learning has been
availability of huge volumes of labeled data and training to the system (Lu et al., 2018).
Therefore, two broad techniques are used including synthesizing new data and transferring a new
model for relating with one another. However, using these techniques, a variety of problems
including historical data might be addressed.
Hybrid learning models: There are different types of deep neural networks including
GANs and GRL that have been providing better performance with wide applications of data

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(Burggräf, Wagner & Koke, 2018). Hybrid learning models have been a combination of two
approaches including Bayesian deep learning, Bayesian GANs and Bayesian conditional GANs.
Figure 1: Artificial Neural Network
(Source: Created by author)
References
Burggräf, P., Wagner, J., & Koke, B. (2018, January). Artificial intelligence in production
management: A review of the current state of affairs and research trends in academia.
In Information Management and Processing (ICIMP), 2018 International Conference
on (pp. 82-88). IEEE.
Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of
rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33-47.
Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: go beyond artificial
intelligence. Mobile Networks and Applications, 23(2), 368-375.

4References
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future
trends. International Journal of Production Research, 56(8), 2941-2962.