State the inputs and outputs of the
fattowing:
a)Rule based approach to Ai
model
b) Learning based approach to
Ai model.
Answers
Answer:
a)Rule based approach to Ai
model
Answer:
Explanation:
‘AI’ is a general term that refers to hardware or software that exhibit behaviour which appears intelligent.
Basic AI has existed since the 1950s, via rules-based programs that display rudimentary intelligence in limited contexts. Early forms of AI included ‘expert systems’ designed to mimic human specialists.
Rules-based systems are limited. Many real-world challenges, from making medical diagnoses to recognising objects in images, are too complex or subtle to be solved by programs that follow sets of rules written by people.
Excitement regarding modern AI relates to a set of techniques called machine learning, where advances have been rapid and significant. Machine learning is a sub-set of AI. All machine learning is AI, but not all AI is machine learning.
Machine learning enables programs to learn through training, instead of being programmed with rules. By processing training data, machine learning systems provide results that improve with experience.
Machine learning can be applied to a wide variety of prediction and optimisation challenges, from determining the probability of a credit card transaction being fraudulent to predicting when an industrial asset is likely to fail.
There are more than 15 approaches to machine learning. Popular methodologies include random forests, Bayesian networks and support vector machines.
Deep learning is a subset of machine learning that is delivering breakthrough results in fields including computer vision and language. All deep learning is machine learning, but not all machine learning is deep learning.
Deep learning emulates the way animals’ brains learn subtle tasks – it models the brain, not the world. Networks of artificial neurons process input data to extract features and optimise variables relevant to a problem, with results improving through training.