In this tutorial, we will cover Unit 1: AI Reflection, Project Cycle, and Ethics of PART B – Subject Specific Skills.

Unit 1: AI Reflection, Project Cycle, and Ethics

In the table below, we have detailed the division of Unit 1 into subunits and topic descriptions.

1.1 Understanding AI: Domains and Applications

The first step is to understand AI, its various domains, and applications, using simple examples and some games.

• What is Artificial Intelligence (AI)?

What do you understand by the term “Artificial Intelligence”?

Any system that is not intelligent by default but, when trained to possesses the ability to mimic human traits, i.e., make decisions, predict the future, learn and improve on its own, can be called an artificial intelligence system.

The core idea behind AI is to build machines that work with data using algorithms and make them capable of performing computational tasks, just like the human brain functions, and help in solving real-world problems.

Artificial Intelligence has many applications in daily life and covers a broad range of domains that are expected to impact every field in the future.

• Applications of AI in Everyday Life

We are using AI in our everyday lives, knowingly and unknowingly. Here are some applications of AI:

1. Smartphones & Virtual Assistants

• Three Domains of AI and their Applications

Three major domains of AI are used in everyday life.

1. Data Science

Data Science is an AI domain where the focus is on statistical techniques to analyse, interpret, learn, draw insights, predict, and make decisions from data.

Try and have fun!!

Try the Rock, Paper, Scissors game online from the link below:

https://next.rockpaperscissors.ai/

This game is based on Data for AI, where the machine tries to predict the next move of the participant. It is a replica of a basic rock, paper, and scissors game where the machine tries to win by learning from the participant’s previous moves.

2. Computer Vision

Computer Vision is an AI domain that works with images and videos, enabling machines to interpret, predict, and understand visual data.

Try and have fun!!

Try the game Quick Draw to understand a computer vision application using the link below:

https://quickdraw.withgoogle.com/

Quick Draw game is based on Computer Vision developed by Google. It challenges players to draw a picture of an object or idea, and then use an artificial intelligence neural network to guess what the drawing represents.

3. Natural Language Processing

Natural Language Processing (NLP) is an AI domain focused on textual data, enabling machines to comprehend, interpret, and generate human language.

ChatGPT is the most hyped example of an AI application that is based on NLP, extensively used by students these days for their assignments and educational content.

Try and have fun!!

Try the Semantris game to experience the NLP domain using the link below:

https://research.google.com/semantris/

Semantris’ game is based on Natural Language Processing is a set of word association games powered by machine-learned, natural language understanding technology. Each time you enter a clue, the AI looks at all the words in play and chooses the ones it thinks are most related.

1.2 The AI Project Cycle

The AI project cycle is the cyclical process followed to complete an AI project. It consists of different steps to be completed to create AI projects successfully. Let’s understand the complete AI project cycle process and learn in detail.

• Explain the AI Project Cycle

The AI project cycle consists of 6 stages. Let’s understand the AI project cycle in detail with the help of an analogy of ‘Making a Pizza’

• Problem Scoping

The first step is to define and understand the scope of the problem that you want to solve with the help of AI.

Analogy: Decide which pizza to make and ingredients required, like cheese pizza, with toppings mushrooms, onions, capsicum, olives, and corn.

• Data Acquisition

The next step is to acquire all the data needed and make sure the data is authentic, cleaned, accurate, and reliable to ensure the efficiency of the AI system.

Analogy: At this step, collect all the ingredients required, like flour, water, tomato, cheese, mushrooms, onion, olive, capsicum, corn, oil, and baking soda.

• Data Exploration

Explore the data which is collected at the previous stage, explore it, pre-process it, and get it ready for the next step.

Analogy: At this step, check all the ingredients, check their quality, wash them, cut them into the required size pieces, prepare pizza sauce, and prepare the dough for the pizza base.

• Modelling

Now is the time to implement the idea, look for the various Machine learning algorithms, and see what suits your needs best, and implement it for modelling.

Analogy: It’s time to cook the pizza. Roll the dough, put the sauce and the ingredients together with cheese, bake the pizza, and adjust the temperature.

• Evaluation

Evaluation is the time to test your model, its efficiency, and the results it delivers with different algorithms.

Analogy: Now, the tasting time, check if the Pizza is baked well and if the spices are right or not.

• Deployment

Deployment is the last stage where we deploy our AI-based solution on the required system, which can be used by the users.

Analogy: After tasting and ensuring the pizza is perfect, you place it on the table for your family members to eat and enjoy it.

• Importance of the AI Project Cycle.

We need an AI project cycle for various reasons:

1. Efficiency

The AI project cycle can make things faster and efficient, with better solutions and less effort.

2. Modularity

The AI project cycle breaks the complete process into small parts so that in case any problem arises in one stage, that problem can be fixed without restarting the entire project.

3. Organised Workflows

It helps in organising the entire workflow by breaking down complex AI projects into manageable steps without skipping important stages.

4. Scalability

The AI project cycle helps in improving, retraining, and scaling the AI system for future needs.

• Learn to Structure the AI Problem Statement with the AI Project Cycle

If you want to learn the structure of the AI problem statement with the AI project cycle in detail, click here.

1.3 The AI Ethics

We have learnt the complete AI cycle and how it works. But, these AI solutions must be built with some AI Ethics to make them secure and ethical for the common user.

• Difference between Ethics and Morality.

Let’s first learn the difference between Ethics and morality in a tabular format.

• Ethical scenarios faced while building AI Solutions

• Ethical Principles for Safer AI

Four principles in AI Ethics affect the quality of AI solutions and make them better.

1. Human Rights

The AI solution must prioritize human rights, including freedom, privacy, non-discrimination, and employment.

2. Bias

The AI solutions must not be biased towards any category, gender, or organisation. Training data must be collected carefully, as bias in the training data is reflected in the results.

3. Privacy

Privacy and human safety need to be taken care of while building AI solutions. Personal data should not be leaked, and individual privacy should not be breached.

4. Inclusion

AI must not discriminate against a particular group or person. It should be inclusive to every kind of population, rich and poor, or male and female, without causing any kind of disadvantage to any of them.

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