AI

Question 1

What is Weak AI?

Answer 1

Weak AI refers to artificial intelligence that is designed and trained for a specific task. It is limited to narrow, well-defined functions and lacks general cognitive abilities.

Question 2

What is Strong AI?

Answer 2

Strong AI is more advanced, possessing general cognitive abilities similar to human intelligence. It can understand, learn, and apply knowledge across various domains, similar to a human.

Question 3

What is domain specific AI?

Answer 3

Domain-specific AI is designed for a particular application or field, excelling in tasks related to that specific domain but lacking versatility beyond it.

Question 4

What is AI

Answer 4

AI refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks may include problem-solving, learning, and understanding natural language.

Question 5

What is machine learning?

Answer 5

Machine learning is a subset of AI that focuses on creating systems that can learn from and make decisions based on data. Instead of being explicitly programmed, these systems improve their performance over time through experience.

Question 6

What are neural networks?

Answer 6

Neural networks are a type of machine learning model inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) organized in layers, and they excel at tasks involving pattern recognition.

Question 7

What is deep learning?

Answer 7

Deep learning is a subset of machine learning that involves neural networks with many layers (deep neural networks). It allows the system to automatically learn and represent complex patterns from data.

Question 8

What is pattern recognition?

Question 8

Machine learning and Neural network differences

Answer 8

Neural networks:

• are based on the model of the human brain

-work by creating connections between processing elements
-are used to predict events
- are a subset of machine leaming

-determine outcomes without intervention

-composed of nodes.

Machine learning:

• learns from new data/input
• can perform new tasks after processing the data
• can react to new inputs it has not encountered before
• searches through data for patterns and adjusts accordingly
  -is not explicitly programmed to perform the task.

Question 8

Characteristics of pattern recogntion

Answer 8

Identifies familiar patterns and objects

Recognizes different shapes from different angles

Uses mathematical methods

Is a branch of machine learning

Question 8

Characteristics of propriety software?

Answer 8

Software is copyrighted;
Commercial software;
Requires a subscription or monthly fee;
Has customer support and maintenance;

Question 9

Characteristics of cloud storage?

Answer 9

Data is stored on remote servers accessed from the internet;
Can be accesed from anywhere as long as the user has access to internet;

Question 9

Characteristics of machine learning?

Answer 9

Learns from new data;
Improves on its mistakes;
Searches through data for patterns;

Question 9

Characteristics of neural network?

Answer 9

Mimics the way the brain works;
Uses an iterative process;
Uses significant computer power;
Has the ability to learn, recall and generalize from given data;

Question 9

Benefits of prototypes

Answer 9

Allows failure to be spotted early;
So it can be fixed and costs can be saved;

Allows early feedback from users;
Shows how valuable and usable it is for users;

Question 10

Characteristics of an algorithm

Answer 10

Step-by-step instructions for performing an action;
Has a finite number of steps;
Used by computers to process data;