Reactive Machines

- Overview

Reactive machines are the most basic type of artificial intelligence (AI). They are task-specific, have no memory, and always respond to the same input in the same way. Reactive machines are unable to learn actions or conceive of past or future. 

Reactive machines and limited memory AI are the most common types today. They're both a form of narrow intelligence because they can't perform beyond programmed capabilities.

Machine learning (ML) models are often reactive machines. They use customer data, such as search or purchase history, to provide recommendations to the same customers. 

The advantage of Reactive Machines like Deep Blue lies in their ability to perform specific tasks at speeds and scales that humans cannot match. They can process vast amounts of data and make decisions in fractions of a second.

- Reactive Machines: The Most Common Type of AI Systems

These are the oldest forms of AI systems that have extremely limited capability. They emulate the human mind’s ability to respond to different kinds of stimuli. 

These machines do not have memory-based functionality. This means such machines cannot use previously gained experiences to inform their present actions, i.e., these machines do not have the ability to “learn.” 

These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same. 

Reactive machines have no concept of the world and therefore cannot function beyond the simple tasks for which they are programmed. A characteristic of reactive machines is that no matter the time or place, these machines will always behave the way they were programmed. 

These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same.  

Reactive machines are basic in that they do not store ‘memories’ or use past experiences to determine future actions. They simply perceive the world and react to it. They cannot refer to any of its prior experiences, and cannot improve with practice. There is no growth with reactive machines, only stagnation in recurring actions and behaviors. 

A popular example of a reactive AI machine is IBM’s Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997. Google's AlphaGo is also an example of reactive machines.

- Examples of Reactive Machine

Reactive machines are a type of AI system that lack memory and operate within specific tasks. They provide consistent outputs based on given inputs, and always respond to identical situations in the exact same way every time. Reactive machines are not able to learn or conceive of the past or future. 

Here are some examples of reactive machines: 

• IBM Deep Blue: A chess-playing supercomputer created by IBM in the mid-1980s. Deep Blue can understand the rules of chess, recognize all the pieces on the chessboard, and know how each of them moves.
• Spam filters: An example of reactive AI.
• Netflix recommendation engine: An example of reactive AI.
• Machine learning models: Often fall into this category, utilizing customer data to deliver tailored recommendations.