10 Reasons Why Machine Learning Is Important

Facebook’s algorithm, self-driving cars, and spam filtering: these are all examples of machine learning technology. Machine learning is a subset of artificial intelligence (AI) that lets software applications process huge amounts of data and “learn” to predict outcomes. Why is machine learning important? Here are ten reasons:

#1. Machine learning improves video games

Machine learning could transform gaming. With advanced algorithms, elements of a game – including objects, non-player characters, and the game world itself – could react and change based on a player’s actions. A player’s experience would be unique based on their choices, making gameplay more engaging. Some video games (like versions of chess) already use machine learning a bit, but there’s still lots of room for advancement. Considering that around 3.1 billion people play video games, this would affect a lot of people.

#2. Machine learning is essential for self-driving cars

While many are rightfully wary of self-driving cars right now, these cars will become more common. The secret is machine learning. The algorithms collect data via sensors and cameras, analyze the data, and decide what the car should do. One team at Boston University recently created a “watch and learn” algorithm that taught self-driving cars to drive by watching other cars. In a test set in two virtual towns, the self-driving neural networks got into very few accidents and reached their destinations 92% of the time. Studies like this show the potential of machine learning in self-driving cars.

#3. Machine learning could take over dangerous jobs

Many jobs put human life at risk. Nuclear cleanup is a big one. In 2021, scientists participated in a consortium focused on using AI and robotics in nuclear environments. At Chernobyl, arguably the most famous nuclear site, the team trained robots to create a 3D map and measure radiation. Using machine learning, robots can be trained to recognize the differences between radioactive waste types. This would help humans safely identify and get rid of nuclear waste. Machine learning could also make robots very effective at jobs involving dangerous chemicals, extreme heavy lifting, and fires.

#4. Machine learning could help with environmental protections

Environmental monitoring is essential to protecting animals, humans, and the environment in general. When storms and other natural disasters strike, toxic materials from a variety of facilities can mix with waterways, including the systems people depend on for drinking. With machine learning algorithms, regulators can collect data by industry, location, material usage, and more. With this information, regulators can identify high-risk areas and prevent future problems.

#5. Machine learning can improve elder care

Many people struggle with transitioning into older age. Machine learning and AI could help. Remote patient monitoring (RPM) is just one example. From wearable devices, RPM collects information like heart rate, oxygen levels, blood pressure, and more. It’s a great way for clinicians to monitor patients with chronic diseases without them needing to come in for constant in-person visits. RPM can also help predict future health issues. With better healthcare, older people can stay independent longer and enjoy better health.

#6. Machine learning can help hospitals

Managing hospital patient flow is one of the biggest issues hospitals and other healthcare systems deal with. Overcrowded emergency rooms, delays, cancellations, and more all affect patient outcomes. Machine learning can help reduce many of these issues by creating predictive models based on real-time data. It can play a part in scheduling overtime, improving unloading management, reducing waiting times, and so on! This saves money and lets hospitals provide better care.

#7. Machine learning improves cancer treatment

Because cancer is so complex, it’s hard to predict drug responses. A machine-learning model could help predict the chances of a patient responding to first-line therapies. If the model found that they wouldn’t respond, it could make good predictions about which drug to try instead. In 2021, the Georgia Institute of Technology and Ovarian Cancer Institute used machine learning algorithms to create predictive models for 15 distinct cancer types. When compared to a clinical dataset, the model ended up showing an overall predictive accuracy of 91%.

#8. Machine learning can improve banking

The banking industry is complicated. Can machine learning streamline anything? It has many uses, but fraud detection is a noteworthy one. Hackers are becoming more advanced and banks are struggling. Thanks to their ability to process huge volumes of data very quickly, machine learning algorithms designed for fraud detection can identify malicious activity, verify user identity, and respond immediately to attacks. For banks, this reduces the risk of data breaches and cyberattacks.

#9. Machine learning can both threaten and improve cybersecurity

When hackers use machine learning, each attack – successful or not – becomes a learning experience. The AI gathers more and more information, making each attack smarter and more effective. It’s a common problem with technological advancements: there are always malicious actors. To combat these advanced threats and more outdated (but still dangerous) attacks, organizations need defenses that are just as effective. Machine learning can analyze past attacks, respond to activity in real-time, automate tasks, and help save money.

#10. Machine learning has a dark side

While machine learning has many promising uses, it has significant issues. “Algorithmic fairness” is one of them. If algorithms are created and used without considering fairness, discrimination that affects peoples’ lives can easily follow. As an example, ProPublica found that a criminal justice algorithm used in a Florida county mislabeled African-American defendants as “high risk” at twice the rate it mislabeled white defendants. When biased machine learning ends up widely used in courtrooms, social welfare, healthcare systems, banking systems, and more, the consequences will be devastating. It’s essential that society recognizes bias and deploys machine learning responsibly and ethically.

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Emmaline Soken-Huberty is a freelance writer specializing in a variety of topics from healthy living to human rights and cookbooks. She lives in Oregon with her husband. When not working, she enjoys reading, baking, and exploring nature.