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

What the human brain does best


While the machines are catching up fast in the quest for intelligence, nothing can come close to some of the capabilities that the human brain has.

Sensory input

The human brain has an incredible capability to gather sensory input using all the senses in parallel. We can see, hear, touch, taste, and smell at the same time, and process the input in real time. In terms of computer terminology, these are various data sources that stream information, and the brain has the capacity to process the data and convert it into information and knowledge. There is a level of sophistication and intelligence within the human brain to generate different responses to this input based on the situational context.

For example, if the outside temperature is very high and it is sensed by the skin, the brain generates triggers within the lymphatic system to generate sweat and bring the body temperature under control. Many of these responses are triggered in real time and without the need for conscious action.

Storage

The information collected from the sensory organs is stored consciously and subconsciously. The brain is very efficient at filtering out the information that is non-critical for survival. Although there is no confirmed value of the storage capacity in the human brain, it is believed that the storage capacity is similar to terabytes in computers. The brain's information retrieval mechanism is also highly sophisticated and efficient. The brain can retrieve relevant and related information based on context. It is understood that the brain stores information in the form of linked lists, where the objects are linked to each other by a relationship, which is one of the reasons for the availability of data as information and knowledge, to be used as and when required.

Processing power

The human brain can read sensory input, use previously stored information, and make decisions within a fraction of a millisecond. This is possible due to a network of neurons and their interconnections. The human brain possesses about 100 billion neurons with one quadrillion connections known as synapses wiring these cells together. It coordinates hundreds of thousands of the body's internal and external processes in response to contextual information.

Low energy consumption

The human brain requires far less energy for sensing, storing, and processing information. The power requirement in calories (or watts) is insignificant compared to the equivalent power requirements for electronic machines. With growing amounts of data, along with the increasing requirement of processing power for artificial machines, we need to consider modeling energy utilization on the human brain. The computational model needs to fundamentally change towards quantum computing and eventually to bio-computing.