Artificial Intelligence (AI) and Machine Learning (ML) are the buzzwords in tech — and any industry that involves automation to enhance or bring about a service. Mobile phones, websites, content recommendation systems, self-driving cars, satellite surveillance, medical devices and systems, and facial recognition are only some of the applications which use AI.
AI and ML are often used interchangeably when describing various technologies, which leads most people to believe that they both are sides to the same coin. This might not exactly be true.
AI is a much broader concept, which includes programming a computer or a model to act more like a human. A result in AI consists of the isolation of a particular information processing problem, the formulation of a computational theory for it, the construction of an algorithm that implements it and a practical demonstration that the algorithm is successful. ML is a type of AI and is used for machines that make decisions based on experience rather than using a condition-based algorithm.
How is AI used?
An AI program is aimed at making a machine simulate human decision making. The algorithms involved may or may not use ML. For example, decision trees or knowledge graphs which use symbolic logic are considered AI programs but not ML models. In these examples, decisions are made by traversing through nodes and reaching the most similar outcome based on the data provided.
An example of AI are virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Google home. These programs use ML algorithms but that is not all, they also contain other logic-based algorithms which work with ML models and contribute to an intelligent experience.
The role of ML
ML is intuitively different from regular computer programming. Usually, a program is fed an input and data, which is processed into an output. ML requires a program or more specifically, a model to be fed input and the output in advance. The model works to “learn” patterns and connections and attempts to understand how the input data is related to an output. So in the future, if the model receives an input, it could give the desired output based on the prior learning experience.
Meanwhile, the data used by the model to learn and gain experience is known as “training data”. When the model has been trained, its efficacy can be tested using “test data”. For instance, ML systems are installed in edge computing use cases that are immediately able to process information and yield results, such as that in medical devices, aviation systems, and fin-tech.
Both AI and ML are closely related but AI has a larger scope, it aims to make intelligent machines that simulate human thinking while ML aims to make better decisions based on learning from data.
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