Page 156 - AI Computer 10

u AI Project Cycle

A step-by-step procedure to plan, oragnise, evaluate, implement, and maintain an AI system.
u Problem Scoping
The process of understanding a problem with the aim of solving it in an efficient way using AI.
u Data Acquisition
The process of identifying and gathering all the data requirements for an AI project.

u Data Exploration
The process of understanding the nature of data in terms of quality and characteristics.
u Evaluation

Testing the created AI model on different datasets to measure its efficiency across the required parameters.
u Deployment
The process of integrating the trained AI model into an existing system or environment where it can
operate effectively to solve practical problems.
u AI Ethics
AI Rules and principles addressing complex ethical issues arising from advancing AI capabilities.
u Ethical Framework

A structured approach that helps individuals and organisations make decisions about what is right and
wrong.

u Bioethics
An ethical framework that provides a structured approach to addressing ethical issues and dilemmas that
arise in the practice of medicine and healthcare.

In a Nutshell
In a Nutshell

● The steps involved in an AI project life cycle are: Problem scoping, Data Acquisition, Data Exploration,
Modelling, Evaluation, and Deployment.
● Problem Scoping is the process of understanding a problem in terms of its nature, complexity, and boundaries.
● Data Acquisition is the process of identifying and gathering all the data requirements for an AI project.
● Some common sources of data are surveys, web scrapings, sensors, cameras, observations, and APIs.
● Data Exploration is the process of understanding the nature of data in terms of quality, characteristics etc.
to ensure effectiveness of the collected data.
● Data can be visualised in graphical or pictorial way to recognise patterns and trends in data.
● Modelling is the stage in which the AI model is developed and trained on the basis of selected datasets.
● Evaluation involves testing the created model on different datasets to measure its efficiency across the
required parameters.
● Deployment involves integrating the AI model in existing real-world situations.
● Data Science, Computer Vision, and Natural Language Processing are three major domains of AI.
● Data Science deals with analysis of large amount of data to derive insights, patterns, and trends
● Computer Vision is a field of AI includes obtaining, screening, analysing, identifying, and extracting
information from images.
● The main objective of NLP is to read, interpret, understand, and interact in natural language.

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