6.3.1 | ARTIFICIAL INTELLIGENCE

ON THIS PAGE
6.3.1 Understand what is meant by artificial intelligence (AI)

• AI is a branch of computer science dealing  with the simulation of intelligent behaviours by computers 

ALSO IN THE TOPIC
 6.1.1 SENSORS, MICROPROCESSORS AND ACTUATORS
6.1.2 AUTOMATED SYSTEMS IN ACTION
6.2.1 ROBOTICS
6.2.2 CHARACTERISTICS OF A ROBOT
 6.2.3 ROBOT ROLES
YOU ARE HERE | 6.3.1 WHAT IS AI
​6.3.2 CHARACTERISTICS OF AI
​6.3.3 OPERATIONS AND CONTROLS OF AI
AUTOMATED SYSTEMS TERMINOLOGY
AUTOMATED SYSTEMS ANSWERS

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ARTIFICIAL INTELLIGENCE

SECTION 1 | WHAT IS AI

Artificial Intelligence (AI) is a field of computer science that aims to create systems capable of performing tasks that would normally require human intelligence. These tasks include learning from experience, understanding natural language, recognizing patterns, solving problems, and making decisions. AI is designed to mimic human thought processes, often by learning or being trained through exposure to data. It’s used in a wide range of applications, from voice assistants and recommendation systems to autonomous vehicles and medical diagnosis.

SECTION 2 | THE TURING TEST

The Turing test is not part of this curriculum but it has been a significant benchmark of the classification of AI for many year and may be of interest to know.

Proposed by Alan Turing in 1950, the Turing Test is a measure of a machine’s ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.

• Criteria | If a human judge cannot reliably tell whether they are interacting with a human or a machine, the machine passes the test.
• Significance | It was one of the first and most well-known criteria for determining machine intelligence, focusing on the machine's ability to exhibit human-like conversational abilities.

SECTION 3 | KEY AI CONCEPTS

​Here are some key aspects and concepts related to AI:

Learning
Machine Learning (ML) | A subset of AI that enables machines to improve at tasks with experience.
Deep Learning | A subset of ML that utilizes neural networks with three or more layers.

Reasoning
Expert Systems | Applications that solve specific problems in a particular domain.
Decision Trees | A model that makes decisions based on asking a series of questions.

Self-correction
Feedback Loops | Systems that get better over time through iterations and corrections.
Reinforcement Learning | Learning method that is focused on maximizing a reward signal.

Problem-solving
Algorithms | A set of rules or procedures for solving problems.
Heuristics | A practical approach not guaranteed to be optimal or perfect.

Perception
Computer Vision | Allows machines to interpret and make decisions based on visual data.
Speech Recognition | Translates spoken words into text.

Interaction
Natural Language Processing (NLP) |  Enables machines to understand and respond to human language.
Chatbots |  Programs that can have a conversation with human users.

Automation
 Robotic Process Automation (RPA) | Using AI to automate repetitive tasks.
Autonomous Vehicles | Vehicles capable of sensing its environment and operating without human involvement. 

ALSO IN THIS TOPIC

 6.1.1 SENSORS, MICROPROCESSORS AND ACTUATORS
6.1.2 AUTOMATED SYSTEMS IN ACTION
6.2.1 ROBOTICS
6.2.2 CHARACTERISTICS OF A ROBOT
 6.2.3 ROBOT ROLES
 6.3.1 WHAT IS AI
​6.3.2 CHARACTERISTICS OF AI
6.3.3 OPERATIONS AND CONTROLS OF AI
AUTOMATED SYSTEMS TERMINOLOGY
AUTOMATED SYSTEMS ANSWERS