Algorithms are step-by-step instructions or rules to solve a problem or perform a task efficiently. Contrary to popular belief, algorithms are not purely used in programming. In fact, we heavily rely on algorithms in our day-to-day lives. When we follow a step-to-step process, like putting on our clothes, we are following an algorithm. The current shift in technological advancement, however, urges the need to understand how technologies such as AI function; you’ve guessed it, it’s all based on algorithms.
This article will be your introduction to what algorithms are, how they work, and their different characteristics.
What’s an Algorithm?
The word algorithm has a fun origin that dates back to the ninth century as it is derived from a Persian mathematician named, al’Khwārizmī. The Latinized version of the name became “Algoritmi”, meaning the decimal number system was later used for centuries up until the modernized version that we all use today, Algorithm.
In the realm of computer science, they form the basis of computer programming. An Algorithm is a mathematical process for solving a problem using a defined number of steps. Algorithms are basically the catalysts of how computers work. They instruct the computer to perform a particular task or make a certain decision. Algorithms therefore solve equations, find patterns in data sets, classify objects, and find solutions to complex problems.
Let’s better understand what algorithms are by visualizing an example. Let’s say you encountered a problem where your TV wouldn’t work. You’d go over a list of steps before making a decision. Consider the visuals below:
This entire process you did just there is actually an algorithm. You followed steps in a particular order and as a result, reached the desired outcome of fixing your tv. So then we would have a task, a starting situation, steps based on decisions, and a desired outcome.
The task: Fix the TV
The starting situation: TV won’t turn on
- Step (conditional) 1: Check if the TV is plugged in properly, if:
- Yes: Proceed to step
- No: Plug in the TV
- Step (conditional) 2: Check if there are batteries in the remote, if:
- Yes: Return TV to store
- No: Put the batteries in the remote
How Do They Work?
So, how does it work exactly? Here’s an overview of how they work:
- Input: Algorithms start with initial input data that could come in various forms, such as numbers, images, text, or any other type of information.
- Step-by-Step Instructions: They consist of a series of step-by-step instructions that describe how to manipulate and process the input data to achieve the desired output.
- Control Flow: They control the execution of instructions by applying conditionals (such as if statements), loops (such as for and while), and branches. These control structures determine the order in which instructions are to be executed.
- Data Manipulation: Algorithms can perform a wide range of operations on the input data, including mathematical calculations, comparisons, sorting, searching, and data transformations.
- Output: They produce an output or result based on the processed input data and the instructions provided. The output can be in the form of numbers, text, graphics, or any other relevant format.
- Efficiency: Good algorithms are designed to be as efficient as possible, minimizing the use of computational resources like time and memory.
- Implementation: Once an algorithm is designed and tested, it can be implemented in a programming language of choice to create a computer program or software application that can be used to solve real-world problems.
Let’s apply the same logic of the previous example of fixing a tv to a new task: calculate the grades of students. The below example is illustrated by a flowchart. A flowchart is a way to illustrate algorithms graphically.
Now if we want to apply it to computations, such as performing computational calculations to determine whether number N is even or odd.
Characteristics of Algorithms
So what makes an algorithm an algorithm? There are many characteristics that define what it is:
- Finiteness: It should have a finite number of steps. That means that it should eventually stop after a certain number of operations.
- Unambiguous: Each step of the algorithm should be clear, unambiguous, and easy to understand. There should be no room for confusion.
- Clear Input: It should have well-defined inputs.
- Clear Output: It should produce at least one output, which is the result of processing the input according to the defined instructions.
- Effectiveness: It must be effective, meaning that it should solve the problem or accomplish the task for which it was designed.
- Language Independent: It isdesigned in a language-neutral way, allowing them to be implemented in various programming languages.
Algorithms have a wide range of use cases across various domains and industries due to their ability to solve specific problems or perform tasks in a systematic and efficient manner. Here are some of their common use cases:
- Computer Science: Algorithms are fundamental to computer science, powering data manipulation, problem-solving, machine learning, and efficient resource utilization.
- Mathematics: Algorithms solve complex mathematical problems such as finding solutions to a system of linear equations.
- Artificial Intelligence: Algorithms form the basis of artificial intelligence and machine learning. Machine learning including decision trees, neural networks, and k-means clustering, enable pattern recognition, predictive modeling, and data classification.
- Data Science: In data science, algorithms drive the extraction of insights from vast datasets, enabling pattern recognition, predictive modeling, and data-driven decision-making.
- Natural Language Processing: Algorithms in natural language processing (NLP) enable machines to understand and generate human language, revolutionizing applications like chatbots, translation, and sentiment analysis.
Artificial Intelligence and Algorithms
In artificial intelligence, algorithms are the setting stone. AI algorithms such as machine learning and deep learning stimulate intelligence and decision-making. They enable machines to learn from data, make predictions, and adapt to changes in circumstances.
Machine learning algorithms including linear regression and decision trees allow AI systems to recognize patterns in data. Deep learning algorithms such as neural networks excel at tasks such as image and speech recognition.
So, as you can see, they are vital as they provide systematic solutions to problems. AI algorithms, in particular, are crucial in diverse applications, from autonomous vehicles and natural language processing to healthcare and finance.