Revisiting AI Project Cycle Class 10 Important Notes

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Revisiting AI Project Cycle Class 10 Important Notes

What is AI Project Cycle?

It is a step-by-step process that a person should follow to develop an AI Project to solve a problem. AI Project Cycle provides us with an appropriate framework which can lead us to achieve our goal.

The AI Project Cycle mainly has 6 stages.

Problem Scoping
Data Acquisition
Data Exploration
Modelling
Evaluation
Deployment

1. Problem Scoping

Identifying a problem and having a vision to solve it, is called Problem Scoping. Scoping a problem is not that easy as we need to have a deeper understanding so that the picture becomes clearer while we are working to solve it. So we use the 4Ws Problem Canvas to understand the problem in a better way.

2. Data Acquisition

This is the second stage of AI Project cycle. According to the term, this stage is about acquiring data for the project. Whenever we want an AI project to be able to predict an output, we need to train it first using data.

For example, If you want to make an Artificially Intelligent system which can predict the salary of any employee based on his previous salaries, you would feed the data of his previous salaries into the machine. The previous salary data here is known as Training Data while the next salary prediction data set is known as the Testing Data.

There can be various ways to collect the data. Some of them are:

Surveys
Web Scraping
Sensors
Cameras
Observations
API (Application Program Interface)

One of the most reliable and authentic sources of information, are the open-sourced websites hosted by the government. Some of the open-sourced Govt. portals are: data.gov.in, india.gov.in

3. Data Exploration

While acquiring data, we must have noticed that the data is a complex entity – it is full of numbers and if anyone wants to make some sense out of it, they have to work some patterns out of it. Thus, to analyse the data, you need to visualise it in some user-friendly format so that you can:

Quickly get a sense of the trends, relationships and patterns contained within the data.
Define strategy for which model to use at a later stage.
Communicate the same to others effectively.

To visualise data, we can use various types of visual representations like Bargraph, Histogram, Line Chart, Pie Chart.

4. Data Modelling

The graphical representation makes the data understandable for humans as we can discover trends and patterns out of it, but machine can analyse the data only when the data is in the most basic form of numbers (which is binary – 0s and 1s). The ability to mathematically describe the relationship between parameters is the heart of every AI model.

Generally, AI models can be classified as follows:

5. Evaluation

Once a model has been made and trained, it needs to go through proper testing so that one can calculate the efficiency and performance of the model. Hence, the model is tested with the help of Testing Data and the efficiency of the model is calculated on the basis of the parameters mentioned below:

Accuracy
Precision
Recall
F1 Score

6. Deployment

After evaluation, the deployment stage is crucial for ensuring the successful integration and operation of AI solutions in real-world environments, enabling them to deliver value and impact to users and stakeholders

Introduction to AI Domains

Artificial Intelligence becomes intelligent according to the training it gets. For training, the machine is fed with datasets. With respect to the type of data fed in the AI model, AI models can be broadly categorized into three domains:

Statistical Data
Computer Vision
Natural Language Processing

1. Statistical Data: It is a domain of AI related to data systems and processes, in which the system collects numerous data, maintains data sets and derives meaning/sense out of them. for example

Price Comparison Website: These websites are being driven by lots and lots of data. PriceGrabber, PriceRunner, Junglee, Shopzilla, DealTime are some examples of price comparison websites. Nowadays, price comparison websites can be found in almost every domain such as technology, hospitality, automobiles, durables, apparel, etc.

2. Computer Vision: Computer Vision, abbreviated as CV, is a domain of AI that depicts the capability of a machine to get and analyse visual information. This extensive processing helps computers to understand any visual content and act on it accordingly. In computer vision, Input to machines can be photographs, videos and pictures from thermal or infrared sensors, indicators and different sources.

The main objective of this domain of AI is to teach machines to collect information from pixels. For Example

Agricultural Monitoring: Computer vision is employed in agriculture for crop monitoring, pest detection, and yield estimation. Drones with cameras capture aerial images of farmland, which are then analysed to assess crop health and optimize farming practices.

Surveillance Systems: Computer vision is used in surveillance systems to monitor public spaces, buildings, and borders. It can detect suspicious activities, track individuals or vehicles, and provide real-time alerts to security personnel.

3. Natural Language Processing: NLP is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. NLP is used to extract information from the spoken and written word using algorithms.
The ultimate objective of NLP is to read, decipher, understand, and make sense of human languages in a valuable manner.

NOTE: Natural language refers to language that is spoken and written by people, and natural language processing

Examples of Natural Language Processing

Email filters: Email filters are one of the most basic and initial applications of NLP online. It started with spam filters, uncovering certain words or phrases that signal a spam message.

Machine Translation: NLP is used in machine translation systems like Google Translate and Microsoft Translator to automatically translate text from one language to another.

Ethical Frameworks for AI

Framework: Frameworks are a set of steps that help us in solving problems. It provides a step-by-step guide for solving problems in an organized manner.

We know that ethics are a set of values or morals which help us separate right from wrong.
Hence, Ethical frameworks are frameworks which help us to ensure that the choices we make do not cause unintended harm.

By utilizing ethical frameworks, organizations can make well- informed decisions that align with their values and promote positive outcomes for all stakeholders involved.

Why do we need Ethical Frameworks for AI?

Ethical frameworks ensure that AI makes morally acceptable choices. If we use ethical frameworks while building our AI solutions, we can avoid unintended outcomes, even before they take place!

Types of Ethical Frameworks

Ethical frameworks for AI can be categorized into two main types: sector-based and value- based frameworks.

Sector-based Frameworks:

These are frameworks tailored to specific sectors or industries. One common sector-based framework is Bioethics, which focuses on ethical considerations in healthcare. It addresses issues such as patient privacy, data security, and the ethical use of AI in medical decision-making. Sector-based ethical frameworks may also apply to domains such as finance, education, transportation, agriculture, governance, and law enforcement.

Value-based Frameworks:

Value-based frameworks guide people to make ethical decisions by thinking about values like honesty, fairness, and responsibility. They can be further classified into three categories:

Rights-based: Prioritizes the protection of human rights and dignity. It emphasizes the importance of respecting individual autonomy, dignity, and freedoms. It ensures that AI systems do not violate human rights or discriminate against certain groups.

Utility-based: It means choosing the action that gives the most benefit and the least harm to the maximum number of people.

Virtue-based: This framework focuses on the character and intentions of the individuals involved in decision-making. It asks whether the actions of individuals or organizations align with virtuous principles such as honesty, compassion, and integrity.

Popular Framework used in the healthcare industry

Bioethics is an ethical framework used in healthcare and life sciences. It deals with ethical issues related to health, medicine, and biological sciences, ensuring that AI applications in healthcare adhere to ethical standards and considerations

Principles of bioethics:

Respect for Autonomy.
Do not harm.
Ensure maximum benefit for all.
Give justice.

1. Respect for Autonomy
Users should clearly understand how the AI works and how decisions are made. Data and predictions should be transparent, accessible, and open to review.

2. Do No Harm (Non-Maleficence)
AI must avoid causing harm. If harm cannot be fully avoided, the least harmful option should be chosen.

3. Maximum Benefit (Beneficence)
AI should actively create positive outcomes, improving well-being for everyone rather than just avoiding harm.

4. Justice (Fairness)
AI systems must treat all individuals fairly, distributing benefits and risks equally.

Key Ethical Principles in Bioethics

1. Non-maleficence means “do not cause harm.” It means we should avoid doing things that can hurt people, society, or the environment. We should always try to reduce harm as much as possible and choose actions which are safe for others.

2. Maleficence refers to the concept of intentionally causing harm or wrongdoing.

3. Beneficence refers to act in ways that help others and promote well-being.

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