According to Krishna Jadhav's academic work, unfortunately, some tech companies are deceiving customers by claiming to apply machine learning (ML) and artificial intelligence (AI) to their goods without disclosing the limitations of their products (Figure 1).
Figure 1: Application of supervised machine learning (ML) depicted in the schematic
What exactly is machine learning?
According to computer scientists and machine learning pioneers, machine learning (ML) is a subfield of artificial intelligence (Hügle et al., 2020). "Machine learning is the study of computer algorithms that allow computer programs to be automatically improved through experience," Tom M. Mitchell said. — We want to enable AI through machine learning (Figure 2). Machine learning uses small to huge data sets, examining and comparing data to discover common patterns and explore complexity.
Figure 2: ML algorithm types
Contribute to artificial intelligence
For example, if you provide a machine learning\model that contains many songs you enjoy and their audio characteristics (dance ability, instrument, rhythm, or genre) (Afsar, Crump, & Far, 2021). Then, based on the supervised machine learning model employed, it should be able to automate and build a recommendation system to provide you with music that you will enjoy in the future (with a high probability rate), similar to what Netflix, Spotify, and others have done.
Figure 3: AI phase lifecycle
Why do IT organizations often use the terms AI and machine learning interchangeably?
Including Allen Newell and Herbert Newell. A group of scholars, including Herbert A. Simon, coined the term "artificial intelligence" in 1956. Since then, the AI industry has experienced countless ups and downs. In the first few decades, there was a lot of hype around the business, with many scientists agreeing that human-level AI was just on the horizon (Figure 3). However, unfulfilled promises have led to widespread dissatisfaction among the public and businesses, leading to an AI winter, during which funding and interest in the topic have declined significantly.
Figure 4: Generative Adversarial Network (GAN) architecture
conclusion
According to the results of Krishna Jadhav's research, groups have moved away from the term artificial intelligence, which has been associated with unsubstantiated hype, and refers to their work under various labels. IBM, for example, refers to Deep Blue as a supercomputer and explicitly emphasizes that it does not use artificial intelligence, although it does (Figure 4). During this time, various additional words, such as big data, predictive analytics, and machine learning\, began to gain momentum and prominence. Machine learning, deep learning, and neural networks made significant progress in 2012 and are being used in various fields. Organizations are starting to use "machine learning" and "deep learning" to advertise their products.