AI
➽➽➽ AI is now trending technology in this era. It creates enormous vacancies in Computer Stream. Even because of leading technology, it have high pay salary. So, this is the BIG Opportunity for Techies.
➽➽ Below mentioned questions and answers may be helpful for AI Interview. So, read it once.
1. What is AI?
AI means Artificial Intelligence, which making various kinds of intelligence machines. It is based on task of using computers to understand human intelligence. It is important in human life.
2. What are the examples of applications of AI?
AI is now spreading worldwide and make useful applications like....
▪︎ Google Search Engine
▪︎ Chatbots
▪︎ Intelligent Cybersecurity
▪︎ Improve in workplace Communication
▪︎ AI in healthcare
▪︎ In HR management
and
Other applications
3. Which kind of AI Technology available?
Basically, there are 2 types of categories in AI,
1. Based on Functionality
▪︎ Narrow AI
▪︎ General AI
▪︎ Strong AI
2. Based on Capability
▪︎ Reactive Machine
▪︎ Theory of Mind
▪︎ Self Awareness
▪︎ Limited Memory
4. In which department AI is used?
AI is widespread, which is used in many other domains...
▪︎ Deep Learning
▪︎ Robotics
▪︎ Speech Recognition
▪︎ Neural Network
▪︎ Machine Learning
▪︎ Expert System
▪︎ Fuzzy Logic
▪︎ Natural Language Processing
And many other departments...
5. Which programming language is used for AI?
Mainly, 5 programming language is used to build AI..
1. Python
2. Java
3. Prolog
4. R
5. Lisp
Among above languages, python is widely used in AI development.
6. What is Tensorflow?
Tensorflow is an open-source framework which is used by ML. It is comprehensive and highly adaptable ecosystem of libraries, tools and community resources that helps developer build and deploy ML powered solutions.
7. What are the different types of AI?
There are mainly 7 kind of AI :-
1. Reactive Machines AI
2. Theory of mind AI
3. Limited Memory AI
4. Artificial Narrow Intelligence (ANI)
5. Artificial General Intelligence (AGI)
6. Artificial Superhuman Intelligence (ASI)
7. Self aware AI
8. What is Q-Learning?
The Q-learning is a Reinforcement Learning Algorithm, where an agent try to learn the optimal policy. Agents learns these optimal policies from past experience. Q is used to represent the quality of the actions at each state, and goal of the agent is to maximize the value of Q.
9. What is an agent in Artificial Intelligent?
In AI, Agents have sensors which allows them to understand their settings. They also have actuators for acting on the environment. They have specified goals, and they can learn and use their knowledge to achieve their goals.
10. What is the Turing Test?
Turing Test is popular intelligence test in Artificial Intelligence. This test is mainly used for machine that it think like a human or not?
In this test, 3 preferences(players) involved, first player is a computer, the second player is a human responder and the third player is human interrogator needs to find which response is from the machine on the basis of questions and answers.
11. What are the various techniques of knowledge representation in AI?
Below mentioned techniques :-
➽ Logical Representation
➽ Frame Representation
➽ Semantic Network Representation
➽ Production Rules
13. What is Fuzzy Logic?
Fuzzy Logic is a method of reasoning applied to the AI. 'FUZZY' means the situations where it is difficult to decide if the state is true or false. It contains all the possibilities that occur between YES and NO.
14. What is the use of computer vision in AI?
Computer vision is a field of Artificial Intelligence that is used to train the computers, so they can interpret and obtain information. Hence, Computer vision uses AI technology to solve complex issues, such as image processing, Object Detections, etc..
15. What is Deep Learning? and what are the application of it?
Deep Learning is a subset of Machine Learning, which is inspired by the human brain cells called neurons and work on the concept of neural network to solve complex real-word problems. It is also known as the deep neural network or deep neural learning.
These are the application of it :-
➽ Computer Vision
➽ Text Generation
➽ Deep-learning Robots
➽ Colors Generation (Transformation)
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