How Does Artificial Intelligence Work?

Artificial intelligence “works” by combining several approaches to problem-solving from mathematics, computational statistics, machine learning, and predictive analytics.

A typical artificial intelligence system will take in a large dataset as input and quickly process the data using intelligent algorithms that learn and improve each time a new dataset is processed.

In order to fully understand how an artificial intelligence system quickly and “intelligently” process new data, it is helpful to understand some of the main tools and approaches that AI systems use to solve problems. Below are the most common techniques used in artificial intelligence systems today:

Neural Networks

Neural networks  – or more specifically, artificial neural networks – are computing systems that progressively improve their ability to complete a task without specific programming on the task. The approach that these artificial neural networks use is based on the method that actual biological neural networks in human brains use to solve problems. Read more about artificial neural networks.

Statistical Learning and Classification

A classifier is a function that uses pattern recognition and pattern matching to identify the closest match. In supervised learning, the classifier will attempt to match the pattern out of a limited set of options. In unsupervised learning, there is no predefined pattern that the classification function needs to be used with.

Classifiers are ideal for artificial intelligence applications because their predictive models are adjusted and improved as they process more new data. Read more about classifiers and statistical learning.

Optimized Search Tactics

Typically exhaustively scanning through every possible solution is not a very efficient way to solve a problem, especially in artificial intelligence applications where speed may be very important. However, it is possible to apply rules of thumb or heuristics to prioritize possible solutions and complete the problem-solving process more quickly.

Some search algorithms will also use mathematical optimization to solve problems. A mathematical optimization is an approach that involves taking the best guess to the solution based on limited information, and then evaluating “nearby” solutions until the best answer is reached. This can be thought of as using “blind hill climbing” as an approach to reach the solution, or “top of the hill.”

There are many other approaches to search optimization, including beam search, simulated annealing, random optimization, and evolutionary computation, which more specifically includes various swarm intelligence algorithms and evolutionary algorithms.

Other Artificial Intelligence Techniques

Various approaches to artificial intelligence design and programming have been taken from concepts in logic programming and automated reasoning. These techniques allow programs to “reason” through problems.

There have also been many models and approaches designed for situations where information is uncertain or incomplete. Some of these tools include Bayesian networks, hidden Markov models, Kalman filters, decision theory and analysis, and Markov decision processes. Even certain programming languages, like Prolog, have been adapted to be used in artificial intelligence applications.


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