Deep learning algorithms have witnessed considerable advances in different sectors. Consequently, these techniques have been commonly deployed for government, mainly to support robotic and autonomous systems. They make intelligent robots, which can replace humans in danger zones or production processes and look and react like humans. The purpose of this chapter is to review the deep learning concept and particularly its applications in governments' working systems. In addition, the authors introduce the robotic field with its importance for governments. Finally, they illustrate this work by two simulated examples of robotic motions based on deep learning algorithms.
TopIntroduction
A remarkable advance in research and development work in artificial intelligence (AI) has recently been recorded. It can replace men by ensuring intelligent tasks, which were once absolutely or relatively unreachable to the human mind .Besides, it can solve the most-complex problems, with an inclusive use in all the governmental sectors in order to achieve much better services and change them for the better. Hence, governments and public sectors elsewhere plan to invest in AI for cost reductions and efficiency benefits. However, still the majority of them have not applied it, yet.
AI is an umbrella term including a wide range of programming constraints, algorithms, optimizers, and machine learning (ML). However, deep learning (DL) is a specialized subset of ML. It is one of the main intelligent approaches with a big neural net, which have been proven very efficient in providing faster, and more accurate data gathering and processing. It is applied to almost every domain, such as virtual Assistants, Image classification and segmentation, Natural language processing, automated predictions, healthcare, entertainment and robotics.
Currently, Applying DL to robotics is a significant area of focus. It is a remarkable step for smart robot creation, which can replace humans in either manufacturing tasks or hazardous missions (Francis 2018), (Donald 2018). Robots may appear, act and possess a level of intelligence like humans. It is a new trend that governments invest in robotics in the sense of diversification and essentially the establishment of robot technologies; investment in new robot technologies has become larger and larger. Additionally, DL is more general than any other learning algorithm. Furthermore, it has been proven that deep networks are capable of thinking and abstracting at a high level, which makes it a perfect solution for robotics in an unregulated environment.
This chapter is organized as follows: in the first section, the authors review deep learning approaches and its applications to governments’ necessities. In the second section, the authors introduce the robotic field and show its importance to governments. For illustrative reasons, two simulated examples of robotic motions based on Deep learning algorithms are shown. Finally, the chapter ends with a brief summary of the main points and their discussion.