Differences between Artificial Intelligence and Machine Learning and why it’s important for us
Artificial intelligence (AI) is everywhere around us: image recognition and contacts suggestions on Facebook or Twitter, self-driving cars, software that beats champions in chess and go, books recommendations on Amazon, Google search, Apple Siri and Microsoft Cortana, and so on. It became a very important aspect of our lives. Its importance raises, and we rely on AI more and more every day hoping that it’s going to improve our well-being.
AI is often confused with machine learning (ML), another very important concept tightly related to it.
Having in mind high and the rising importance of AI, as well as ML, this article tries to explain the difference and connection between the two.
What’s Artificial Intelligence?
There are many definitions of AI. John McCarthy defined it in a concise yet comprehensive way here emphasizing the process of making intelligent machines. According to this definition, AI might but doesn’t have to use the methods based on the way human intelligence functions. The same text defines intelligence connecting it with the ability of the real-world objectives fulfillment and concludes that we still lack a definition of intelligence not related to that of humans.
In short, we can think of AI as a concept in which machines and devices (especially computers) are capable to perform different tasks important for humans.
The applications of AI are numerous and increasing: self-driving cars, image recognition, medical diagnosis, natural language processing and translations, spam filtering, smart buildings, predicting energy consumption, finance and banking, military, advertising, art, video games and more.
There are three levels of AI:
Narrow intelligence — limited to a narrow range of tasks and actions,
General intelligence — covering the same range of tasks and actions humans can perform,
Super intelligence — exceeding human intelligence.
AI can use several approaches. For example, it might rely on logic, and the provided set of strict instructions or try to mimic the functionality of the human brain or to. ML is one of such approaches.
What’s Machine Learning?
According to Wikipedia, ML is a subset of artificial intelligence.
It actually is a set of techniques that enable computers to learn from data, by observing relations and patterns, instead of giving them specific instructions on how to perform a task. These techniques rely on calculus, linear algebra, mathematical optimization, probability theory, statistics, etc. ML models can have either deterministic or probabilistic nature.
ML approaches are usually classified into these three categories:
Some of the mostly used ML methods are linear, polynomial and logistic regression, decision trees and random forest, support vector machines, nearest neighbors, Bayesian networks, k-means and hierarchical clustering, frequent itemset mining and association rule learning, etc. Artificial neural networks (ANNs) are a set of models and techniques for supervised and unsupervised learning that try to mimic the functionality of the human brain. They are very popular and effective in solving a wide range of problems.
A significant increase in the computing power and amount of available data, as well as the advances in algorithms, made so-called deep neural networks (DNNs) very effective and promising. DNNs are a special kind of ANNs with additional complexity and capabilities. Deep learning is a subset of machine learning that relies on applying DNNs, recurrent neural networks, and deep belief networks.
Artificial intelligence is everywhere. We rely on it every day. This article explained the relationship between artificial intelligence and machine learning.
In short, artificial intelligence is a broader concept, while machine learning is its subset or one of the approaches used to create artificial intelligence. Deep learning is a powerful and promising kind of machine learning that applies particular types of artificial neural networks, such as deep neural networks, recurrent neural networks, and deep belief networks.