Key Differences Between Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)

Key Differences Between Artificial Intelligence, Machine Learning and Deep Learning

Artificial intelligence (AI), as defined until recently by Professor Andrew Moore, is the science and engineering of enabling computers to improve their behavior in the same way as human intelligence does.

These include:

? Computer Vision

? Language Processing

? Creativity

? Summarizing

Professor Tom Mitchell defines machine learning as "a scientific field of artificial intelligence devoted to the study of computer algorithms that enable computer systems to learn automatically via experience."

These include:

? Classification

? Neural Network

? Clustering

Artificial intelligence is the general concept for systems that make predictions or make decisions, whether or not they involve machine learning. Artificial intelligence, contrary to popular opinion, can be implemented without the use of machine learning or deep learning techniques. Until the advent of machine learning algorithms, artificial intelligence research was centered on a framework known as "hard-coded," in which the programmer coded all logical and mathematical processes himself. For example, the first chess player’s artificial intelligence algorithms were exactly like that. This type of artificial intelligence is called symbolic artificial intelligence.

Algorithms in machine learning learn purely from data. Machine learning is distinguished from hard-coded symbolic artificial intelligence systems by the fact that it learns entirely from data. Naturally, the risky aspects of ML algorithms learning from data may come to mind. If immoral and racist statements are frequently asked to the software machine learning, the software can eventually become perverted and racist.

According to the structure of the data, the deep learning model determines which parameters to prioritize. Although the deep learning method does data-based learning, it does so using calculations created in a structure similar to network diagrams described as a neural network, rather than a single mathematical model as in typical machine learning algorithms.

Every deep learning algorithm is a machine learning algorithm because it learns from data. However, not every machine learning algorithm is a deep learning algorithm, as deep learning is a specific type of machine learning. Deep learning is a subset of machine learning where layered neural networks with high computing power and large datasets can create powerful mathematical models.

?Disciplines forming the backbone of machine learning:

· Machine learning is closely related to statistical computing as it focuses on making predictions.

· Mathematical optimization work contributes to the field of machine learning by providing methods, theory, and application areas.

· Data mining is a similar field of study that focuses on unsupervised learning for exploratory data analysis.

· ?Applied Mathematics

· ?Computer systems and software

Machine learning methods are utilized in a wide range of filtering and functions with variable coefficients applications when developing standard algorithms to fulfill the needed tasks is difficult or impossible.

Data mining is a similar branch of research that focuses on unsupervised learning for exploratory data analysis. Machine learning is the process of figuring out how computers can do jobs without having to be explicitly programmed. It contains algorithms that learn to do specific jobs based on the data presented. Machine learning employs a range of techniques to learn how to complete jobs for which no totally suitable solution exists.

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