publive-image

The top 10 AI and Machine Learning Use Cases That Everyone Should Be Aware Of in 2023

Machine learning is a hot topic in the technology world right now, and with good reason: it represents a significant advancement in how computers can learn. A machine learning algorithm is given a "teaching set" of data and then asked to use that data to answer a question.

1. Data Security

Malware is a massive and growing issue. Kaspersky Lab claimed in 2014 that it detected 325,000 new malware files every day. According to Deep Instinct, an institutional intelligence firm, each piece of new malware has nearly the same code as previous versions only between 2 and 10% of the files change from iteration to iteration. Their learning model is unaffected by the 2-10% variations and can accurately predict which files are malware. In other cases, machine learning algorithms can detect patterns in how cloud data is accessed and report anomalies that could lead to security breaches.

2. Personal Security

If you've recently flown on an airplane or attended a large public event, you've almost certainly encountered lengthy security screening lines. However, machine learning is proving to be useful in reducing false alarms and detecting items that human screeners may miss during security screenings at airports, stadiums, concerts, and other venues. This can significantly speed up the process while also ensuring safer events.

3. Financial Trading

For obvious reasons, many people want to be able to predict what the stock markets will do on any given day. Machine learning algorithms, on the other hand, are getting closer all the time. Many high-profile trading firms use proprietary systems to predict and execute trades at high speeds and volumes. Many of these are based on probabilities, but even a trade with a low probability, executed at a high enough volume or speed, can generate enormous profits for the firms. And humans cannot compete with machines in terms of consuming massive amounts of data or the speed with which they can execute a trade.

4. Healthcare

Machine learning algorithms can process more data and identify more patterns than humans. One study used computer-assisted diagnosis (CAD) to review early mammography scans of women later diagnosed with breast cancer, and the computer detected 52% of the cancers up to a year before the women were officially diagnosed. Furthermore, machine learning can be used to understand disease risk factors in large populations. The company Medecision created an algorithm that identified eight variables that could predict avoidable hospitalizations in diabetic patients.

5. Marketing Personalization

The more you know about your customers, the better you can serve them and sell to them. That is the basis for marketing personalization. Perhaps you've had the experience of visiting an online store and looking at a product but not purchasing it only to see digital ads for that exact product for days afterward. This is just the tip of the iceberg in terms of marketing personalization. Companies can personalize which emails a customer receives, which direct mailings or coupons they receive, which offers they see, which products appear as "recommended," and so on, all with the goal of more reliably leading the consumer to a sale.

6. Fraud Detection

Machine learning is becoming increasingly adept at detecting potential cases of fraud in a wide range of fields. PayPal, for example, is combating money laundering with machine learning. The company has tools that can distinguish between legitimate and fraudulent transactions between buyers and sellers by comparing millions of transactions.

7. Recommendations

If you use services like Amazon or Netflix, you're probably familiar with this usage. Intelligent machine learning algorithms analyze your activity and compare it to the activity of millions of other users to determine what you should buy or watch next. These recommendations are becoming smarter all the time, recognizing, for example, that you may buy certain items as gifts (but not want the item yourself) or that different family members may have different TV preferences.

8. Online Search

Google, perhaps the most well-known application of machine learning, and its competitors are constantly improving what the search engine understands. Every time you conduct a Google search, the program monitors how you respond to the results. If you click the top result and stay on that page, we can assume you found what you were looking for and that your search was successful. If, on the other hand, you click to the second page of results or type in a new search string without clicking any of the results, we can conclude that the search engine did not provide the results you desired and the program can learn from that mistake to provide a better result in the future.

9. Natural Language Processing (NLP)

NLP is being used in a wide range of exciting applications across multiple disciplines. Customer service agents can be replaced by machine learning algorithms that use natural language to route customers to the information they require more quickly. It's being used to translate obscure legalese in contracts into plain language and to assist attorneys in sorting through large amounts of data in preparation for a case.

10. Smart Cars

According to a recent IBM survey of top auto executives, 74% believe that smart cars will be on the road by 2025. A smart car would not only connect to the Internet of Things, but it would also learn about its owner and its surroundings. It may automatically adjust the internal settings temperature, audio, seat position, and so on based on the driver, report and even fix problems, drive itself, and provide real-time traffic and road condition advice.