ARTIFICIAL INTELLIGENCE

​AI Definition:

Artificial intelligence is a branch of Computer Science dealing with the simulation of intelligent behaviour by a computer. This is often referred to as the cognitive functions of the human brain (The mental process of acquiring knowledge and undestanding through thought, experience and the five senses). All these functions can be replicated in a machine, and they can be measured against human benchmarks such as reasoning, speach and sight.

THE COGNITIVE FUNCTIONS OF THE HUMAN BRAIN : "REASONING"

REASONING : is the ability to draw reasoned conclusions based on given data/situations.

DEDUCTIVE REASONING: is where a number of correct facts are built up to form a set of rules which can then be applied to other problems.

​CHARACTERISTICS OF AI

​Essencialy, AI is really just a collection of rules and data, and the ability to reason, learn and adapt to external stimuli. They divide in 3 categories:

​Narrow AI : this occurs when a machine has superior perfromance toa human when performing a specific task:

​General AI : this occurs when a machine is similar (not superior) in its performance to a humen when performing a task.

​Strong AI : this occurs when a machine has a superior performance to a human in many tasks.

​Examples of AI include:

• ​News generation based on live news feeds.

• Smart home devices for example Amazon Alexa, Google Now, Apple Siri and Microsoft Cortana.

• ChatBots for example Chat GPT, Luzia etc.

• Autonomous Cars.

• Facial expression recognition.

​ARTIFICIAL INTELLIGENCE STRUCTURE

​EXPERT SYSTEMS

​DEFINITION: Expert Systems are a form of AI that has been developed to mimic human knowledge and experiences. They use knowledge and inference to solve problems or answer questions that will normally require a human expert.

​TYPICAL EXPERT SYSTEM STRUCTURE

​KNOWLEDGE BASE

• ​The knowledge base is a repository of facts

• It stores all the knowledge about an area of expertise obtained from a number of expert resources.

• It is bassically a collection of OBJECTS and ATTRIBUTES.

  EXAMPLES:

​RULES BASE

​Machine Learning

​Is a sub-set of artifical intelligence (AI), in which algorithms are trained and learn from their past experiences and examples. IT is possible for the system to make predictions or even take decisions based on previous scenarios. They can offer fast and accurate outcomes due to very powerful processing capabilities. One of the key factors is the ability to manage and analyse cosiderable volumes of complex data.

Example: The most significant search engines.

​Differences between AI and Machine Learning

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