Convergence of AI, ML, and DL for Enabling Smart Intelligence: Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things

Introduction

Rapid human growth has created an imperative need of smart technology for effective enhancement of every aspect of urban living in daily life. A lot of data is being generated by smart devices and sensors connected to the internet requiring skills for the analyses and management. Traditional techniques on large datasets can take a considerable amount of time creating an urge to develop an artificial intelligent system to perceive data from the environment and turn into action without any human intervention with optimal precision. In 1950s, Alan Turing, British polymath explored the mathematical possibility of artificial intelligence. Humans use all available information and reason for solving problems and making decisions. A question arises about why machines cannot do the same thing as humans? In seminal paper Computing Machinery and Intelligence, Turing discussed how to build intelligent machines and how to test their intelligence which leads to the evolution of AI and further Machine learning and deep learning as shown in Figure 1.

Artificial intelligence (AI), a mimic of human intelligence, attempts at simulation of human intelligence and produces a new intelligent machine that would have the ability to process information with human consciousness, behavior, and thinking integrated with appropriate algorithms. AI has been applied in many fields, such as Image Analysis, Natural Language Processing (NLP), Robotics, interactive computer games, multiuser virtual environments and Expert Systems. AI can be classified as Narrow AI and Strong AI. Narrow AI is an AI system that is intended for a particular task and a Strong AI with generalized human cognitive abilities, is capable of providing solution to any problem without human intervention even for an unfamiliar task.

Figure 1.

Evolution of Smart Intelligence

Artificial intelligence brings welcome changes in automation of the tasks in all the processes and systems, by making them faster. AI technology enables machines to have cognitive functions for making devices smart and autonomous by learning and adaptation based on the data acquired. Instilling intelligence to make smart machines greatly reduces human efforts and time complexity.ML platforms are used for the achievement of smart intelligence in machines to learn and modify the parameters to improve their performance. ML is excellent for predictive analytics whereas AI prescribes suggestions or decision for reality.

Machine learning (ML) is the core of artificial intelligence and the fundamental approach toward designing intelligent computers. Deep learning (DL) is a subclass of machine learning. In machine learning, the features to be analyzed have to be included by the user, whereas, in deep learning, the system learns to take features of its own based on the application to be performed. Deep learning is time consuming more than machine learning since it has to be accomplished using large data.

Machine Learning

Machine learning is the core of artificial intelligence and the fundamental approach toward designing intelligent computers. The main goal is to reflect human learning activities by machines to discover and acquire knowledge automatically. Machine learning involves a number of disciplines such as the probability theory, statistics, approximation theory and algorithm complexity theory. ML process follows the concepts of training and inference i.e. iterative learning and extraction of input from output. The applications of machine learning span the entire field of artificial intelligence. DL is a machine-learning method based on characterization of data learning and brings machine learning closer to artificial intelligence.

Figure 2.

Classification of Machine Learning