Computer Algorithms

They dominate our lives..

THE WORLD OF ALGORITHMS

INTRODUCTION

Coded algorithms drive our computer world. They are integrated into most computer systems in use today. This article discusses how they have evolved. Without these essential algorithms, our computer systems would find it difficult to function.

DISCUSSION

A Brief History of Algorithms

The word “algorithm” is derived from the name of the Persian mathematician Muhammad ibn Musa al-Khwarizmi, who wrote a treatise on the Hindu-Arabic numeral system in the 9th century. Al-Khwarizmi’s work was later translated into Latin. The algorithmic tradition continued in the West with notable figures like Fibonacci, who popularized Hindu-Arabic numerals in Europe in the 13th century. Algorithms have been used for centuries to solve mathematical problems. In the 19th century, they were used to design mechanical calculators and study mathematics's foundations.

 In the 20th century, algorithms played a crucial role in developing digital computers and the information age. Today, algorithms are used in various ways, from search engines and social media to financial trading and medical diagnosis. They are an essential part of our lives and will only become more so in the future.

What Is an Algorithm? An algorithm is a set of instructions for solving a problem. It can be as simple as a cake recipe or as complex as a set of rules for flying a plane. Algorithms are usually expressed in a formal language, such as pseudocode or a programming language. They can be executed by a computer or implemented in hardware, such as a microprocessor.

Why Are Algorithms Important? Algorithms are essential because they are at the heart of computer science. Without algorithms, there would be no way to solve complex problems, and we rely on computers such as weather forecasting, mapmaking, or stock trading. Algorithms are also crucial because they are vital to artificial intelligence (AI). AI algorithms are used to solve problems that are too difficult for humans, such as playing chess or Go, the ancient game, or to make decisions that are too difficult for humans, such as identifying credit card fraud.

The importance of computer algorithms cannot be overstated and lies in the fact that they provide a way for computers to automate processes that would otherwise be too difficult or time-consuming for humans to do. One of the most critical applications of algorithms is in the field of search engines. When you type a query into a search engine, it uses an algorithm to scour the web for pages that are relevant to your question. Without algorithms, we would have to rely on human beings to manually sift through all the billions of web pages out there, which would be impossible. Algorithms are also used extensively in social media platforms such as Facebook and Twitter. These platforms use algorithms to determine which posts and updates from your friends and followers you see in your news feed. Again, without algorithms, we would be overwhelmed with information and unable to keep up with what’s happening in our networks. Computer algorithms are also behind many of the recommendations we see online these days. For example, if you buy something on Amazon, you will likely see “customers who bought this item also bought…” recommendations on subsequent visits. These recommendations are generated by algorithms that consider your purchase history and the purchase histories of other customers with similar tastes. In short, computer algorithms play a vital role in today’s world by helping us make sense of vast amounts of data and automate repetitive tasks. They are an essential part of how we use computers today, and their importance is only likely to grow as we increasingly rely on computers to help us manage our lives.

PREDICTIVE POLICING – AN EXAMPLE

Predictive policing algorithms are computer programs that analyze data to predict where and when a crime is likely to occur. Law enforcement agencies can use this information to deploy resources more effectively and prevent crime before it happens. There are a variety of predictive policing algorithms in use today, each with its strengths and weaknesses. The most common type of algorithm is the regression model, which uses historical data to identify patterns and trends in criminal activity. This information is then used to generate predictions about future crime. Another type of predictive policing algorithm is the social network analysis, which examines relationships between people involved in criminal activity. This information can be used to identify potential suspects and victims of crime, as well as areas where crime is more likely to occur.

Predictive policing algorithms are effective at reducing crime in several studies. In one study, predictive policing was found to reduce violent crime by 15 percent in New York City. Another study found that predictive policing reduced burglary rates by up to 20 percent in Chicago. While predictive policing algorithms hold great promise for reducing crime, there are also some concerns about their use. One worry is that these algorithms could be biased against certain groups of people, such as minorities or low-income individuals. Another problem is that predictive policing could increase surveillance of innocent people living in high-crime areas. Despite these concerns, predictive policing algorithms are likely to stay due to their proven effectiveness in reducing crime. As these algorithms continue to evolve, they must be

tested thoroughly so that any potential biases can be identified and corrected.

RACIAL BIAS – AN ISSUE

Predictive policing algorithms have been biased against minorities. Studies have shown that these algorithms are more likely to generate false positives for blacks and Latinos than whites. This is because the data used to train the algorithms is often biased. For example, if crime data is disproportionately collected from minority neighborhoods, the algorithm will learn to associate minority groups with a crime. This can lead to innocent people being targeted by police simply because of their skin color or ethnicity. There are several ways to combat this problem. One is to use data that is less likely to be biased in the first place. Another is to design algorithms that are specifically designed to reduce bias. However, it is essential to remember that no algorithm is perfect, and there will always be some degree of bias. The best we can do is try to minimize it as much as possible.