Facebook’s algorithm, self-driving vehicles, and spam filtering: these are all instances of machine-learning technology. Machine learning is a part of Artificial Intelligence (AI) that allows software programs to process massive quantities of data, as well as “learn” to predict outcomes. Why is machine learning so important? Here are nine reasons why:
#1. Machine learning enhances video games
Machine learning can alter the game. Through advanced algorithms, the components of games – such as objects, characters that are not played by players, and even the game’s environment itself – can react and change in response to a player’s actions. The experience of a player will be distinct according to their actions which makes gameplay more exciting. Certain video games (like variations of Chess) already make use of machine learning. However, there’s plenty of room for improvement.
#2. Machine learning is the key to self-driving vehicles
Although many are cautious about autonomous cars currently, these vehicles will soon become more commonplace. The key to this lies in machine learning. The algorithms gather data through cameras and sensors, analyze the data and then decide what the vehicle should do. One group at Boston University recently created a “watch and learn” algorithm that taught self-driving vehicles to steer by watching the other vehicles. In a test conducted in two virtual towns, the neural networks that self-driving themselves had a very low number of accidents and arrived at their destination almost all the time. Research like this shows that machine learning is a viable option for self-driving cars.
#3. Machine learning may take over hazardous jobs
A lot of jobs put lives in danger. Cleaning up nuclear waste is a major one. Scientists in 2021 took part in a team that was focused on the use of AI robots and other robotics for nuclear cleanup. In Chornobyl, perhaps the most well-known nuclear facility, a team of scientists created robots that trained to create a 3D map and to determine radiation. By using computer-aided learning (ML), robots could be taught to detect the different types of radioactive waste. This could help humans to identify and eliminate radioactive waste. Machine learning may also help robots be more efficient in jobs that involve hazardous chemicals, extreme lifting, as well as fires.
#4. Machine learning can help with the protection of the environment
Environmental monitoring is vital in protecting animals, humans, and the natural ecosystem generally. When natural disasters occur, harmful substances from different sources can be mixed with the waterways, which includes the users of the system who depend on drinking. Through machine learning algorithms, regulators can gather data on the industry, location, material use, and much more. With this data, regulators can spot areas at risk and avoid any future issues.
#5. Machine learning is a way to improve elder care
Many struggles to transition into old years. The use of AI and Machine Learning can aid. Remote monitoring of the patient (RPM) is only one instance. Wearable gadgets, RPM collects information like heart rate and blood pressure, oxygen levels, and much more. It’s an excellent method for doctors to monitor patients suffering from chronic diseases without having to bring them to them for frequent visits. RPM can also assist in predicting future health problems. With better health care, seniors can remain more independent and enjoy better health.
#6. Machine learning can help hospitals
Controlling the flow of patients in hospitals is among the major problems that hospitals and healthcare facilities have to deal with. Inconvenient emergency room delays, cancellations, and many other factors affect the outcomes of patients. Machine learning can ease the severity of these problems through the creation of models that are predictive based on real-time information. It can be instrumental in scheduling overtime hours, improving the management of unloading and reducing wait times, and more! This helps save money and allows hospitals to provide better service.
#7. Machine learning helps improve cancer treatment
Because cancer is a complex disease, it is difficult to anticipate drug reactions. A machine-learning model can aid in predicting the likelihood of a patient reacting to first-line treatments. If the model determined that the patient wouldn’t be responding to treatment, it could make accurate predictions on which treatment to use instead.
#8. Machine learning can help improve the efficiency of banking
Banks are complex. Does machine learning help in reducing the complexity of anything? There are many applications for machine learning; however, fraud detection is an important one. Hackers are getting more sophisticated, and banks are having a hard time. Because they can process massive volumes of data in a short time, machine learning algorithms developed to detect fraud can recognize suspicious activity, confirm the identity of the user, and respond quickly to threats. Banks can benefit from this by reducing the chance of cyberattacks and data breaches.
#9. Machine learning enhances cybersecurity
When hackers employ machine learning, every attack – whether successful or not, is a learning opportunity. The AI collects more data, making every attack more intelligent and effective. This is a problem that’s common to technological advances. There always are malicious actors. To defend against these sophisticated threats as well as the more dated (but still risky) attacks, businesses require security measures that are equally efficient. Machine learning can analyze past attacks, react to activities in real time, automate tasks and save money.
Machine learning is directly or indirectly involved in our everyday routine. We have come across a myriad of machine learning software that can be extremely useful in the current technological world. While machine learning is still currently in its development phase, however, it is evolving quickly. The greatest benefit of machine learning is that it provides high-value predictions that allow for better decisions and more efficient actions that are made in real time, with no human intervention. Therefore, at the conclusion of this piece, we can conclude that the field of machine learning is vast, and its value is not restricted to a particular industry or field. It is a universal tool for studying or forecasting the future.
About the Author: Emma Flores
Emma Flores is lucky enough to make her passions into a profession. Editor and proofreader during work days as well as a freelance writer during weekends, mom all day long and loves her time most when she runs in the morning with headphones on. Emma is a part of a college essay service to offer tips on making academic papers that are of high-quality standards.