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The Real Difference Between Machine Learning and Artificial Intelligence Featured

The Real Difference Between Machine Learning and Artificial Intelligence "The Eccentric"

Artificial Intelligence (AI) and Machine Learning (ML) are some of the closely linked terms in the field of computer science. They are among the most trending technologies used when talking about intelligent systems and big data. These terms are often confusing, and most people interchangeably use them.

Artificial intelligence

Artificial intelligence (AI) is a technology that allows machines to simulate human behaviour. It is a subset of AI that allows machines to learn from past data without or with little human intervention. It intends to make a smart computer system like humans that can solve complex problems. AI systems do not require to be pre-programmed. Rather, they use algorithms that can use their intelligence to learn. Some algorithms include machine learning algorithms like reinforcement learning and deep neural networks (DNN). AI is currently used in various places and platforms like Google, Siri, websites, smartphones, and AlphaGo.

AI can be classified into three main types based on their capabilities. They can be:

  • Strong AI
  • Weak AI
  • General AI

Currently, there are weak and general AI. However, the world is heading towards Strong AI, which is expected to be more intelligent than humans. AI systems do not require to be pre-programmed. Rather, they use algorithms that can work through their own intelligence. The machine learning algorithms like reinforcement learning and deep learning enable such learning.

Machine learning

Machine learning entails extracting knowledge from collected data. It is another subfield of AI that enables machines to learn from the historical data or without necessarily being programmed explicitly. It enables computer systems to make predictions or decisions using past data. It uses vast amounts of structured and semi-structured data to enable machine learning models to generate accurate results or offer predictions based on that data.

Machine learning consists of algorithms that work on their own. However, it uses specific domains in its work. Currently, machine learning is used by giving machines access to data and letting them learn by themselves. This is called the training of models. ML is used in various areas such as online recommender systems, mainly used by e-commerce companies, spam filtering in emails, Facebook Auto-tagging suggestion systems and Google search algorithms. Machine learning can be divided into three types:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Unlike AI, which prioritizes making intelligent systems perform like humans, machine learning teaches machines through data how to carry out certain tasks. The tasks they carry out are the specific ones to which they are trained to perform. On the other hand, it is concerned with accuracy and patterns while performing these tasks. Machine learning enables computer systems to make predictions or make decisions using semi-structured and structured data to generate accurate results or make predictions based on this data. For example, machine learning systems can read a text and determine what the person who wrote it is saying. Furthermore, they can also listen to music and decide if it can make a person happy or sad or work out the possible mood that the said music can trigger. Machine learning algorithms can also compose music and express the themes that the listeners can appreciate.

Some key similarities between AI and ML are that they both include learning and self-correction. Furthermore, they both deal with semi-structured and structured data. However, unlike machine learning, artificial intelligence, on top of self-correction and learning, also includes reasoning when introduced with new data. Furthermore, while machine learning deals with structured and semi-structured data, AI deals with structured, semi-structured and unstructured data. This is a critical difference between the two closely related technologies.

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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