Soft computing solves real world problems using similar tools. AI is an older term and soft computing a refinement that puts the focus on solving problems not on being intelligent or human like. AI tends to solve human like problems such as games, perception or conversation !

Your answer will be found here

https://www.researchgate.net/post/What_is_the_difference_and_the_link_between_artificial_intelligence_and_soft_computing

In my point of view, they have more similarities tan differences. Both relate approaches to process incomplete, uncertain and some times contradictory information

Machine Learning could be considered a part of AI, however I would classify Machine Learning as the study of creation of semantic models and adaptive behavior with AI being the overall science of systems that intelligent-seeming behavior.

Most of what goes as "AI" is rather simplistic, but highly effective, such as heuristics and the like.

Soft Computing doesn't fell like it has many ML and AI components as it is more about analysis of complex systems. I could be wrong though. As with most things in computer science, the deeper you dig, the more you discover that it's all related.

refer below this link getting some ideas related to AI ans SC

https://pdfs.semanticscholar.org/b87f/b44313c75780814b9b86888ba89498ebfc58.pdf

Hi,

Both techniques are relevant.it differ from the various progress depends upon the situation. AI is an older term and soft computing a refinement that puts the focus on solving problems not on being intelligent or human like. Soft computing solves real world problems using similar tools. AI tends to solve human like problems such as games, perception or conversation. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think.

Dear sir both technique are more or less same.It will differ from application and computational process

In my opinion, both techniques are quite relevant while they aim to solve real world problems that have featured (tolerance for imprecision, uncertainty and partial truth to achieve tractability) using similar methodologies and tools.

Soft computing solves real world problems using similar tools. AI is an older term and soft computing a refinement that puts the focus on solving problems not on being intelligent or human like. AI tends to solve human like problems such as games, perception or conversation !

Machine learning and AI can be considered almost same. The basic purpose of AI is to provide intelligence to machines so that they can behave like human beings. One of the aspects of intelligence is learning. So, Machine learning is a topic under artificial intelligence.

AI may not be dealing with imprecision always.Where as soft computing has no special standing unless uncertainty is involved in the process.

Artificial Intelligence is a wide concept that includes many disciplines. Soft computing is a part of Artificial Intelligence.

I agree with you Hayder Naser Khraibet Al-Behadili both techniques are quite relevant topics.

AI is one of the technique of soft computing. In addition to AI soft computing includes fuzzy, genetic algorithm or may be combination of two or more techniques together.

AI is a study of computer science that attempt to model and apply the intelligence of human mind, it is also branch of computer system dealing with simulation of the intelligence behavior while soft computing is one the technique use in machine learning.

”The OECD AI Principles

The Recommendation identifies five complementary values-based principles for the responsible stewardship of trustworthy AI:

- AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being.

- AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards – for example, enabling human intervention where necessary – to ensure a fair and just society.

- There should be transparency and responsible disclosure around AI systems to ensure that people understand AI-based outcomes and can challenge them.

- AI systems must function in a robust, secure and safe way throughout their life cycles and potential risks should be continually assessed and managed. ”( OECD, 2019).

As mentioned by the OECD representatives, we believe that "Organizations and individuals who develop, implement, or operate intellectual intelligence systems should be held accountable for their proper functioning in accordance with the above principles."