4. AI and ES

Question 1

AI

Answer 1

Intelligence: The ability to learn and solve problems.
1. Simulation(imitation) Of human intelligence in machines that are programmed to think and act like humans
2. It involves the development of algorithms and computer programmes that can perform tasks that typically require human intelligence such as visual perception speech recognition and decision making and language translation
3. It is to make computers intelligent so that they can act intelligently
4. If computers can somehow solve real world problems by improving on their own from ‘‘past experience” they would be called as intelligent
5. Hence AI systems are more generic than specific that is they can think and are more flexible
6. Intelligence is composed of:
Reasoning
Learning
Problem solving
Linguistic intelligence
Perception
Many tools are used in AI, including versions of search and mathematical optimization, logic, and methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, artificial psychology, and many others.

Question 2

adv
&
disadv

Answer 2

adv:
1. Helping machines to find solutions to complex problems
2. To create expert systems that exhibit intelligent behaviour
3. Improved efficiency
4. Better decision making
5. Enhanced accuracy
6. Personalization
7. Exploration of new frontiers

disadv
1. bias and unfairness
2. Lack of transparency and accountability
3. Job displacement
4. Security and privacy risks
5. Ethical concerns

Question 3

AI vs Natural Intelligence

Answer 3

https://www.geeksforgeeks.org/difference-between-artificial-intelligence-and-human-intelligence/

Question 4

Expert system
ES

Answer 4

1. A computer programme that is designed to solve complex problems and to provide decision making ability like human expert it performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries
2. It is made to support the human experts but not to replace them
3. ES is the part of AI and the first ES was developed in the year 1970 which was the first successful approach to artificial intelligence
4. It solves the complex Problems by extracting the knowledge stored in the knowledge base
   diag:
   https://www.javatpoint.com/expert-systems-in-artificial-intelligence
   Examples of es:
5. Dendral
6. mycin
7. PXDES
8. CaDeT
   characteristics:
9. High performance
10. Understandable
11. reliable
12. highly responsive
    Components of expert system
13. User interface
14. inference
15. knowledge base

1-userinterface
With the help of user Interface, the expert system interact with the user takes the queries and input in readable form and sends it to the inference engine or rules engine
And after getting the response from inference engine, It displays the output to the user
It is the interface that helps a non expert user to communicate with the expert system to find solution

2- Inference engine (rules of the engine)
Known as the brain of the expert system, as it is the main processing unit
- It applies inference rules to the knowledge base to get the information It helps in deriving error free solutions For the questions asked by the user
- With the help of inference engine es system extracts knowledge from the knowledge base
2 type:
Deterministic inference engine
The solutions from this engineer assume to be true It is based on facts and rules
Probabilistic inference engine
Contains uncertainty and conclusions. And it is based on probability

3- Knowledge base
Type of storage that stores knowledge acquired from different experts of the particular domain
- The more the knowledge base, the more precise will be the expert system.
Components of knowledge base:
Factual knowledge
Knowledge based on facts and accepted by knowledge engineers
Heuristic knowledge
Based on practise, the ability to guess, evaluate and experience

Participants in development of expert system
1. Expert
2. Knowledge engineer
3. End user

Question 5

Capabilities of expert system.

Answer 5

1. Helps in advising
2. Provides decision making capabilities
3. can Demonstrate new device
4. Problem solving
5. explaining a problem
6. interpreting the input
7. predicting results
8. diagnosis

Question 6

Advantages of es

Answer 6

1. Highly reproducible
2. They can be used for risky places where human presence is not safe.
3. Error possibilities are less if KB contains correct knowledge
4. The performance of these systems remains steady as it is not affected by emotions, tensions or fatigue
5. They provide a high speed of response to a particular query

Question 7

Limitations of ES

Answer 7

1. The response of es may get wrong if the knowledge base contains the wrong information
2. Ilike a human being, it cannot produce a creative output for different scenarios
3. Maintenance and development costs very high
4. For each domain we require a specific es, which is the biggest limitation
5. It cant learn from itself, and hence requires manual updates.
   eg:
   Alexa
   siri