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Artificial Intelligence aims at developing machines that are capable of thinking and acting like human being. The goal of AI goes beyond the vision of number-crunching machines doing lengthy calculations. An AI machine shall do all the cognitive tasks that humans can do, such as decision making, problem-solving, understanding natural languages, scheduling, planning, reasoning, and learning from past experiences.
The advances in information processing technologies such as faster CPU and GPU, high volume storage devices, and executing multi-processing tasks combined with high-speed communication have realized many of the AI goals in recent times. Few examples of such tasks include optical character recognition, playing competitive games, understanding human speech, and natural language understanding.
Two approaches that have found crucial while realizing machines with human-level intelligence are algorithmic-driven and data-driven.
In algorithmic-driven approaches, machines execute a complex algorithm that mimics the human reasoning process to achieve a task—for example, playing a game like a chess game or finding the shortest path between two locations.
In a data-driven approach, the machine stores a set of learning instances in training data to identify a typical pattern while performing a task. For example, they are diagnosing a disease like Cancer from the size and shape of the tumour. This approach is also known as machine learning. The data-driven approaches are widely adopted while recognizing an object, classifying a given object among multiple categories, predicting values from historical data, and providing recommendation based on a large number of preferences.
The growth of machine learning can be attributed to a large number of data sets available in the public domain. For instance, an image data set like ImageNet spurred machine learning techniques for image processing Similarly the WordNet data set has triggered the development of machine learning techniques for natural language processing.
Machine learning frameworks and languages such as Python Scikit libraries, TensorFlow have also played a significant role in the widespread adoption of machine learning approaches to build intelligent systems.
The developments in AI in ML are influencing our society both positively and negatively. Some of the anticipated impacts are that AI and ML may cause a mass level of unemployment, increased inequalities in society, threats to democratic institutions, restrictions on the free will of its citizens, and biased delivery of justice.
However, AI and ML also have a positive impact on some developmental sectors. In the health sector, AI and ML are increasingly used in diagnosing and analyzing medical images, and it is the underlying technology for precision medicine. Further, AI and ML are used for accurately predicting weather which helping farmers in crop management. In the educational sector, AI and ML are currently used to provide personalized education. In the coming episodes, we describe such use cases of AI and ML in different sustainable development sectors.
