Part 1 (A), Notes of Artificial Intelligence......

 Role of Machine intelligence in the human life

1. Machine intelligence is the intelligence provided to the particular machine to achieve the goals of the problems in AI.

2. It is defined as the embedding of intelligence in the machine so that the machine can behave like a human.

3. In human life, machine learning solves many problems of daily purpose of the human. 

4. there are many problems which required intelligence such as complex arithmetic which is done by machine very easily.

5. Machine learning plays an important role in the following areas:

i. Learning : Learning means to acquires new things from the set of given knowledge or experiences. It refers to the change in subject's behaviors to a given situation brought by repeated experiences in that situation. 

ii. Reasoning: Reasoning means to infer facts from given facts. Inferences are classified as either deductive or inductive and the resolving is to draw inferences appropriate to the situation.

iii. Problem solving : To solve problem means to move towards the goal. In this, set of rules are defined and a goal is also defined which is to be achieved by using these rules.

iv. Language understanding :  It means to understand natural language meaning. A language is a system of signs having meaning - by - convention is distinctive of language and is very different from natural meaning.

The evolution of artificial intelligence :-

 1. Beginning of AI (1943) :- The concept of AI begin around 1943 AI is not limited to the computer science disciplined, but can be seen in the various other areas. 

2. AI Knowledge based expert system (1970) :- An AI system often uses a rule based system to capture knowledge in the form of if then statements or as a decision trees.

3. Machine learning (1998) :- There are two types of machine learning:-

1. Formal:- The formal type of machine learning is a computer program that Learns from experience in respect to some task and increases performance based on the experience. 

2. Informal:-  The informal involves giving  computers the ability to learn without explicitly programming the capability.

4. Supervised learning (2004):- The supervised learning is based on given the correct answers and having the computer mapping inputs to outputs. For example, 

i. Spam filters:- Software is trained to learn and distinguish between spam and non spam messages (for example, email filter).

ii. Facial recognition:-  It is used by camera to focus and view photo editing software to tag person (for example, Facebook).

5. Unsupervised learning (2010) :- Unsupervised learning is the Reserve of supervised learning where the correct answer are unknown. For example,

i. Clustering algorithm :- Used to find patterns in datasets and then group that data into different coherent clusters. 

ii. Market segmentation :- Targeting customers based on religions, likes, dislikes when the consumer makes purchases etc. This is considered target marketing.

6. Genetic programming (2010) :- Genetic programming is an idea that uses evolutionary process to improve algorithms.

7. Future of AI (2019 onwards) :- There are many challenges in mimicking human intelligence. Human acquire common sense that are intuitive but hard to reason rationally. For example, the color of a blue car is blue.