Chapter 1, Big Data and Artificial Intelligence Systems, will set the context for the convergence of human intelligence and machine intelligence at the onset of a data revolution. We have the ability to consume and process volumes of data that were never possible before. We will understand how our quality of life is the result of our decisive power and actions and how it translates into the machine world. We will understand the paradigm of big data along with its core attributes before diving into the basics of AI. We will conceptualize the big data frameworks and see how they can be leveraged for building intelligence into machines. The chapter will end with some of the exciting applications of Big Data and AI.
Chapter 2, Ontology for Big Data, introduces semantic representation of data into knowledge assets. A semantic and standardized view of the world is essential if we want to implement artificial intelligence, which fundamentally derives knowledge from data and utilizes contextual knowledge for insights and meaningful actions in order to augment human capabilities. This semantic view of the world is expressed as ontologies.
Chapter 3, Learning from Big Data, shows broad categories of machine learning as supervised and unsupervised learning, and we understand some of the fundamental algorithms that are very widely used. In the end, we will have an overview of the Spark programming model and Spark's Machine Learning library (Spark MLlib).
Chapter 4, Neural Networks for Big Data, explores neural networks and how they have evolved with the increase in computing power with distributed computing frameworks. Neural networks get their inspiration from the human brain and help us solve some very complex problems that are not feasible with traditional mathematical models.
Chapter 5, Deep Big Data Analytics, takes our understanding of neural networks to the next level by exploring deep neural networks and the building blocks of deep learning: gradient descent and backpropagation. We will review how to build data preparation pipelines, the implementation of neural network architectures, and hyperparameter tuning. We will also explore distributed computing for deep neural networks with examples using the DL4J library.
Chapter 6, Natural Language Processing, introduces some of the fundamentals of Natural Language Processing (NLP). As we build intelligent machines, it is imperative that the interface with the machines should be as natural as possible, like day-to-day human interactions. NLP is one of the important steps towards that. We will be learning about text preprocessing, techniques for extraction of relevant features from natural language text, application of NLP techniques, and the implementation of sentiment analysis with NLP.
Chapter 7, Fuzzy Systems, explains that a level of fuzziness is essential if we want to build intelligent machines. In the real-world scenarios, we cannot depend on exact mathematical and quantitative inputs for our systems to work with, although our models (deep neural networks, for example) require actual inputs. The uncertainties are more frequent and, due to the nature of real-world scenarios, are amplified by incompleteness of contextual information, characteristic randomness, and ignorance of data. Human reasoning are capable enough to deal with these attributes of the real world. A similar level of fuzziness is essential for building intelligent machines that can complement human capabilities in a real sense. In this chapter, we are going to understand the fundamentals of fuzzy logic, its mathematical representation, and some practical implementations of fuzzy systems.
Chapter 8, Genetic Programming, big data mining tools need to be empowered by computationally efficient techniques to increase the degree of efficiency. Genetic algorithms over data mining create great, robust, computationally efficient, and adaptive systems. In fact, with the exponential explosion of data, data analytics techniques go on to take more time and inversely affect the throughput. Also due to their static nature, complex hidden patterns are often left out. In this chapter, we want to show how to use genes to mine data with great efficiency. To achieve this objective, we’ll introduce the basics of genetic programming and the fundamental algorithms.
Chapter 9, Swarm Intelligence, analyzes the potential of swarm intelligence for solving big data analytics problems. Based on the combination of swarm intelligence and data mining techniques, we can have a better understanding of the big data analytics problems and design more effective algorithms to solve real-world big data analytics problems. In this chapter, we’ll show how to use these algorithms in big data applications. The basic theory and some programming frameworks will be also explained.
Chapter 10, Reinforcement Learning, covers reinforcement learning as one of the categories of machine learning. With reinforcement learning, the intelligent agent learns the right behavior based on the reward it receives as per the actions it takes within a specific environmental context. We will understand the fundamentals of reinforcement learning, along with mathematical theory and some of the commonly used techniques for reinforcement learning.
Chapter 11, Cyber Security, analyzes the cybersecurity problem for critical infrastructure. Data centers, data base factories, and information system factories are continuously under attack. Online analysis can detect potential attacks to ensure infrastructure security. This chapter also explains Security Information and Event Management (SIEM). It emphasizes the importance of managing log files and explains how they can bring benefits. Subsequently, Splunk and ArcSight ESM systems are introduced.
Chapter 12, Cognitive Computing, introduces cognitive computing as the next level in the development of artificial intelligence. By leveraging the five primary human senses along with mind as the sixth sense, a new era of cognitive systems can begin. We will see the stages of AI and the natural progression towards strong AI, along with the key enablers for achieving strong AI. We will take a look at the history of cognitive systems and see how that growth is accelerated with the availability of big data, which brings large data volumes and processing power in a distributed computing framework.
