Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Cognitive intelligence as a service


The field of cognitive intelligence is vast and exciting since we are trying to follow an intangible entity, the human mind. As our understanding of how human cognition works, we can implement similar behaviors in Cognitive Systems. At a high level, the cognitive intelligence based human decision process has four basic components as follows:

Figure 12.5: Basic components of cognitive intelligence based human decision process

We observe the environment and the various inputs simultaneously through the sensory organs. The inputs are interpreted within the context of environmental state. During the interpretation stage, we refer to the historical data as well as the intended goal for the process. Once the interpretation is done, various options based on the past experiences and future rewards are evaluated and the best option is selected, which maximizes the overall gain. The decision making is also based on a reinforcement learning process, which we have seen...