The present article comprises of 3 sections where section 1 refers to the classification of AI followed by section 2 where areas in key capabilities are derived from. In section 3 development roadmap is outlined considering both freshers and developer community.

1. Classification of Artificial Intelligence: Artificial Intelligence (AI) tasks refer to simulation of a human brain by machine. Considering the extent to which these simulations can be achieved, AI is classified as General Artificial Intelligence and Narrow Artificial Intelligence.

The General class of AI focus predominantly on developing the systems that can mimic humans. These tasks are difficult because of challenges in translation of implicit hard coded logic into the real time adaptive learning. This class is emerging quite rapidly in recent past say for example designing self driving cars.

In Narrow AI, the tasks aim at accomplishing single specific activity. Chat bot applications can be considered as an example for this class of AI.

The primary challenge of developing applications in this class lies in the fact that a small variation in the input might degrade the overall user experience.

For example, chat bot applications can be considered under Narrow AI. The complexity and performance of these bot applications vary based on domain and the complexity of the tasks under execution. Natural language processing tasks are quite natural and implicit in developing such applications[1]. Considering the nature of tasks and associated complexities we can look further classification as shown in Figure 1.

Figure 1: High level tasks in Artificial Intelligence, Data Science and Machine Learning

2. Areas of competencies: There are several competencies that are required in order to perform various tasks pertaining to the roles in this discipline. Figure 2 below, describes broad areas that are involved in above three classes (AI, Data Science and Machine Learning).

However the degree of involvement of each of these areas varies from class to class. Among these 5 broad areas of influence in AI, Domain is one area where the necessary skills and competencies vary from each specific domain category. For example the kind of use cases we encounter in retail can be quite different from that of insurance. So is the class of problems we deal with in data discipline. Table1 below highlights specific techniques and tools that are required to perform various tasks in the data discipline.

Skill Development RoadMap:

1. https://towardsdatascience.com/artificial-intelligence-vs-machine-learning-vs-data-science-2d5b57cb025b
2. NPTEL invited talk at IIT Madras: https://www.youtube.com/watch?v=PJixBTQINXU
3. https://www.analyticsvidhya.com/blog/2020/05/art-storytelling-analytics-data-science/
4. https://ieeexplore.ieee.org/document/8049066

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