Introduction to the AI Landscape

“AI sees the invisible and reaches the unreachable.”

Artificial Intelligence (AI) is one of the fundamental concepts that evolved well before computers existed on every desk in homes and offices across the world. Today, AI is applied across various domains to optimize processes and address issues where human abilities and outreach do not provide a feasible solution. In this chapter, we will briefly examine the history of AI, its classifications, and the applications of AI in enterprises.

This chapter provides a detailed overview of the AI landscape, covering the following key topics:

• AI – key concepts
• Types of AI
• Forms of AI and approaches
• AI in digital transformation
• AI platforms and tools

Technical requirements

This chapter assumes that you are aware of a few basic concepts of AI in various forms, with some knowledge of how AI is impacting daily life. This chapter explains the fundamental concepts for beginners, so technical know-how is not a mandatory requirement.

AI – key concepts

AI has many definitions based on the nature of its techniques, its usage, and also the timeline of its research. However, the most common definition is as follows—AI is the intelligence and capability exhibited by a computer to perceive, learn, and solve problems, with minimal probability of failure.

The ability of AI to compute and achieve results within a shorter period of time than humans has made computers the cornerstone of automation across various industries. The computational work of humans is often prone to errors, is time-consuming, and exhibits diminishing accuracy as the problem gets harder to solve. However, computers have been able to fill this role for a long time, from the early beginnings of automation that can be observed in many passive forms in our daily life. One of the best examples of such automation is the introduction of Optical Character Recognition (OCR), which converts embedded text in an image or document into a text source...

Types of AI

There are several forms of AI, each conceived to solve different problems. AI can be categorized and classified by various different criteria, including the theoretical approach used to design it and the application domain for which it is intended to be used.

Efforts at categorization were directly influenced by some parameters such as the ability to learn a particular task without supervision, obtaining cognitive abilities, and the ability to perform reasoning similar to humans. Based on these and a complex set of expectations, we will look into the three basic types of AI.

Weak AI

Also generally known as narrow AI, weak AI can be used to execute narrow and repetitive tasks. Weak AI functions are based on a preexisting combination of logic and data. User inputs are processed based on the same logic, and hence, weak AI lacks self-consciousness and aggressive learning abilities. Some prominent examples of weak AI implementations are voice assistants, chatbots, and linguistic...

Forms of AI and approaches

Implementations of AI have come in various forms due to the varying nature of the intended application and the technology available for the solution. Hence, AI has been manifested in code in various forms, utilized by a wide range of developers in different domains for respective problems.

In the following Venn diagram, we can see various forms of AI:

Fig 2.2: Relationships between forms of AI

In the preceding diagram, I have mentioned all the major forms of AI categorized into three major manifestations. Each form is explained in detail in the following section, broken down into expert systems, machine learning, and neural networks.

We will now explore these primary approaches and forms of AI with brief introductions to their backgrounds and applications.

Statistical and expert systems

Statistical systems were one of the most primitive forms of AI, dating back to the late 1960s. As the name suggests, statistical approaches used a huge amount of data to arrive...

AI in digital transformation

Many organizations have already prepared for the next wave of digital transformation. While a few digital solutions have adopted AI techniques successfully and are reaping the benefits, a significant portion of the digital solution space is busy preparing for the upcoming leaps in AI. We will briefly observe some of the key milestones where AI can enable future digital transformation programs and address major challenges.

We'll begin by observing some of the key milestones involved in a digital transformation project enabled by AI in the following diagram:

Fig 2.6: Important milestones in digital transformation using AI

The preceding diagram represents all the important milestones of an AI-led digital transformation project. The diagram also represents the flow connecting one milestone to another. Each milestone is elaborated in the following sections.

Data extraction

Before AI can be used to fuel a digital transformation project, essential information...

AI platforms and tools

One of the major signs of maturity in an AI ecosystem is identified by the application of new tools and frameworks. Other indicators such as innovation, interoperability, and accuracy also play a major role in identifying the maturity of an AI ecosystem and its tools. In the following sections, we can see a short list of a few AI platforms and tools that are leveraged by many engineers around the world.

TensorFlow

TensorFlow is an open source project anchored by internet giant Google. It is a sophisticated framework used for machine learning projects. It offers a wide ecosystem with comprehensive toolkits, libraries, and documentation that enable researchers and developers to easily design, build, and deploy machine learning-powered applications. Find out more about the tools at https://www.tensorflow.org.

Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit (CNTK) is an open source toolkit for commercial-grade distributed deep learning. It describes neural...

Summary

AI has been at the cutting edge of innovation, with the majority of business applications adopting it across all domains. In this chapter, we were able to observe the key concepts, brief history, and progress of AI so far. We also covered different approaches used in the AI landscape and understood multiple implementations and categories within AI with the help of a diagram. We then observed the steps required to apply AI in a digital transformation program to achieve the best results. Finally, we concluded the chapter with an introduction to some of the latest tools and platforms used proactively by the AI community globally.

In the next chapter, we will explore some interesting applications of AI and blockchain, along with some detailed case studies of real-world scenarios.