5.1 Artificial Intelligence and Machine Learning

This topic introduces Artificial Intelligence (AI) and Machine Learning (ML), explaining how computers can perform human-like tasks and learn from data.

Definition

Artificial Intelligence (AI): Artificial Intelligence (AI) is a branch of computer science to make computer-controlled machines that can intelligently perform human-like tasks. Using AI-based machines is like fixing an intelligent brain in the machines that has decision-making capabilities to solve complex problems.

Machine Learning (ML): Machine learning is a field within Artificial Intelligence that teaches computers to learn from data inputs and experience like humans without direct programming. AI algorithms are based on ML to predict output.

Core Concept

Think of AI as the dream of making machines smart, and Machine Learning as the method to achieve that dream. AI is like wanting a robot that can recognize your face, understand your voice, and make decisions. Machine Learning is how the robot learns to do these things—by showing it thousands of examples and letting it find patterns. Just as a child learns to recognize animals by seeing many pictures, a machine learns by processing data. The more data it sees, the better it gets.

Detailed Explanation

Artificial Intelligence

Artificial Intelligence (AI) is a branch of computer science to make computer-controlled machines that can intelligently perform human-like tasks. Using AI-based machines is like fixing an intelligent brain in the machines that has decision-making capabilities to solve complex problems.

• Branch of computer science.
• Makes machines perform human-like tasks.
• Provides decision-making capabilities.
• Solves complex problems.
• Imitates human actions.

Using AI technology, computers can be trained to perform various tasks by imitating actions of human beings. It is the technology related to making intelligent machines and developing intelligent software. In other words, it is the intelligence shown by machines to help human beings in solving various problems efficiently and with high precision. AI is changing the world by helping us in complex problems what was not possible before.

Machine Learning

Machine learning is a field within Artificial Intelligence that teaches computers to learn from data inputs and experience like humans without direct programming. AI algorithms are based on ML to predict output.

• Field within AI.
• Computers learn from data and experience.
• No direct programming needed.
• AI algorithms based on ML to predict output.
• Improves over time with more data.

Machine learning algorithms facilitate computers to train on data inputs and analyze it to produce accurate output values. It is the technology that recognizes patterns in large databases to build models to automate decision-making. Machine learning algorithms improve their decision-making capabilities over time as more sample data is input in the computers.

• Recognizing images and speech
• Detecting fraud in banking transactions
• Identifying spam and email phishing
• Image and video recognition
• Translation
• Virtual personal assistants
• Google search engine
• Self-driving cars
• Manufacturing
• Medical devices

Types of Machine Learning

There are two types of machine learning:

• Supervised Machine Learning
• Unsupervised Machine Learning

Supervised Machine Learning

Supervised learning algorithms take a set of known input data and known output responses to build a model to make predictions. In supervised learning, the goal is to learn how to do the mapping to match the input to the output. Once the model is built, it is used to make new predictions on unseen data as well.

• Uses labeled data (known input and output).
• Learns mapping from input to output.
• Used for prediction on new data.
• Solves complex problems with large amounts of data.
• No formula to predict output.

• Engine type and power
• Transmission type (manual/automatic)
• Number of seats
• Front-wheel/rear-wheel drive
• Keyless entry
• Push button start
• Safety features
• Country of manufacturing

We have to label the cars with their prices. After entering the data for a large number of cars, we can train the algorithm and create a model. The price of a new car can be predicted based on the input fed in the supervised learning algorithm.

Unsupervised Machine Learning

In unsupervised learning, the training data is unseen and it is un-labeled. The meaning of un-labeled is that the data is not assigned a category or group. The unseen data is fed into the machine learning algorithm to train the model. The trained model tries to search for a pattern and gives the desired result. The unsupervised learning algorithm looks for hidden patterns in the input data. It uses a learning technique known as clustering (grouping).

• Uses unlabeled data (no known output).
• Finds hidden patterns or groupings.
• Uses clustering technique.
• No predefined categories.
• Data is not assigned a category or group.

Example: Loan Repayment Prediction A bank wants to predict how capable an applicant is of repaying a loan. A machine learning system is to be set up to ensure that the loan is given to the applicant who can repay it. In this case, the applicant will provide data about monthly income, debt, credit history, etc., along with personal data such as age, mobile number, gender, etc. Information about age, mobile number, and gender are not important for determining the loan risk. Therefore, age, mobile number, and gender can be eliminated for setting up the machine learning system for predicting the loan risk.

Machine Learning Process

The machine learning process consists of 5 stages:

a) Collection of training data b) Creating algorithm c) Learning process d) Creating training model e) Predicting results

• Training Data
• Training the Machine
• Building a Model
• Predicting Result

AI Algorithms and Errors

AI machine learning algorithms can make errors and produce wrong results as they do not apply formulas like traditional algorithms. It is difficult to find out what went wrong in the AI algorithms as there is no proper breakdown on how the AI algorithm is making decisions. Therefore, sometimes AI algorithms can make wrong decisions.

• AI algorithms can make errors.
• No proper breakdown of decision-making.
• Lack of transparency can create dangerous AI systems.
• Behavior may be unpredictable to developers.
• Inaccuracy can put people in jail, cause business collapse, or even death of a patient.

• Bias: Algorithms developed by humans can introduce bias (ethnicity, gender, age).
• Accidents: Self-driving cars can have accidents.
• Healthcare Errors: AI systems can recommend wrong medicine.
• Cyberattacks: Hackers can develop powerful AI algorithms.
• Job Losses: AI-based automation can lead to job losses.
• Loss of Creativity: Dependence on AI can cause loss of creativity and critical thinking.
• Negative Impact: Unexpected behavior can have negative impact on individuals and society.