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

Results pyramid


The quality of human life is a factor of all the decisions we make. According to Partners in Leadership, the results we get (positive, negative, good, or bad) are a result of our actions, our actions are a result of the beliefs we hold, and the beliefs we hold are a result of our experiences. This is represented as a results pyramid as follows:

At the core of the results pyramid theory is the fact that it is certain that we cannot achieve better or different results with the same actions. Take an example of an organization that is unable to meets its goals and has diverted from its vision for a few quarters. This is a result of certain actions that the management and employees are taking. If the team continues to have same beliefs, which translate to similar actions, the company cannot see noticeable changes in its outcomes. In order to achieve the set goals, there needs to be a fundamental change in day-to-day actions for the team, which is only possible with a new set of beliefs. This means a cultural overhaul for the organization.

Similarly, at the core of computing evolution, man-made machines cannot evolve to be more effective and useful with the same outcomes (actions), models (beliefs), and data (experiences) that we have access to traditionally. We can evolve for the better if human intelligence and machine power start complementing each other.