Skip to content
Related Articles

Related Articles

Difference between AI and Soft Computing
  • Difficulty Level : Hard
  • Last Updated : 25 Mar, 2020
GeeksforGeeks - Summer Carnival Banner

Artificial Intelligence:
AI manages more comprehensive issues of automating a system. This computerization should be possible by utilizing any field such as image processing, cognitive science, neural systems, machine learning etc. AI manages the making of machines, frameworks and different gadgets savvy by enabling them to think and do errands as all people generally do.

Soft Computing:
Soft Computing could be a computing model evolved to resolve the non-linear issues that involve unsure, imprecise and approximate solutions of a tangle. These sorts of issues square measure thought of as real-life issues wherever the human-like intelligence is needed to resolve it.

Difference between AI and Soft Computing:



S.NO.A.I.SOFT COMPUTING
1Artificial Intelligence is the art and science of developing intelligent machines.Soft Computing aims to exploit tolerance for uncertainty, imprecision, and partial truth.
2AI plays a fundamental role in finding missing pieces between the interesting real world problems.Soft Computing comprises techniques which are inspired by human reasoning and have the potential in handling imprecision, uncertainty and partial truth.
3Branches of AI :
1. Reasoning
2. Perception
3. Natural language processing  
Branches of soft computing :
1. Fuzzy systems
2. Evolutionary computation
3. Artificial neural computing  
4AI has countless applications in healthcare and widely used in analyzing complicated medical data.They are used in science and engineering disciplines such as data mining, electronics, automotive, etc.
5Goal is to stimulate human-level intelligence in machines.It aims at accommodation with the pervasive imprecision of the real world.
6They require programs to be written.They not require all programs to be written, they can evolve its own programs.
7They require exact input sample.They can deal with ambiguous and noisy data.

machine-learning

My Personal Notes arrow_drop_up
Recommended Articles
Page :