Future Trends in AI: Deep Learning, Reinforcement, and Hybrid Models

This essay explores the future trends of artificial intelligence, focusing on several key areas. It begins with deep learning theory, highlighting how deep neural networks mimic the human brain's ability to learn from various data types. The essay then discusses capsule networks, a new type of deep neural network designed to process visual information similarly to the brain, establishing hierarchical relationships within networks. Deep reinforcement learning (DRL) is also examined, noting its capacity to learn from environmental interactions through observations, actions, and rewards, with applications in gaming strategies. The challenges of data availability in machine learning are addressed through lean and augmented data learning techniques, which involve synthesizing new data and transferring models. Finally, the essay introduces hybrid learning models, combining approaches like Bayesian deep learning and Bayesian GANs to enhance performance and broaden data applications. The essay concludes by referencing an artificial neural network diagram.

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

3References
(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.