The eight best examples of machine learning in the real world

Machine learning is one area of ​​AI that has already solved hundreds of issues and challenges that people face every day. More and more companies are considering integrating machine learning when they start their tech business to gain a competitive advantage among their competitors.

However, for some, machine learning remains a complicated and difficult scientific term to grasp. It certainly shouldn’t be one with so many real-world apps! In this article, we share eight machine learning use cases that have changed our lives.

1. Diagnose a disease

With machine learning, medical staff can use models based on the analysis of various variables associated with a disease. As clinics collect huge amounts of data on patients’ symptoms, it can be used to train algorithms, find and track similarities in medical conditions. As a result, it can be predicted whether a patient’s symptom is associated with a certain disease. In addition, open source data such as information available on known symptoms and diseases can be used to improve models.

The diagnosis of Alzheimer’s disease is a good example of machine learning. By analyzing a patient’s speech, particularly focusing on characteristics such as pauses between words, frequency, amplitude, and pronunciation, the machine learning model can identify patterns indicating the strong possibility that the disease is in place. In this case, the audio tapes of the patients are analyzed.

2. Help write better content

For content marketers, machine learning has become a useful tool to automate a lot of research on keywords, user intent, backlink profile, etc.

Search Engine Optimization (SEO) Marketers May Benefit From add AI to SEO activities. Machine learning can offer a recipe for a successful blog post or landing page that for many businesses translates into increased lead count, Sales, and, possibly, income.

By integrating machine learning into their writing process, marketers can get on the right track with the right keywords to include in their article, learn more about optimal length of the article, article and the correct number of images they should use in the article. All of this is available to them by typing a few seed words into the tool, powered by machine learning.

3. Anticipate a fraudulent transaction

With machine learning, it is increasingly easier for analysts to identify fraudulent transactions. Using predictive analytics and collecting transaction history, algorithms can predict suspicious behavior on a user’s bank account.

For example, PayPal has already integrated machine learning into its technology solution.

While millions of people use PayPal to send and receive money, the service has attracted hackers to break into and clean poorly protected accounts.

PayPal collects huge amounts of data and powers machine learning models that over time become even more sophisticated and targeted to identify suspicious behavior.

4. Prevent ransomware attacks

Ransom is also demanded in the digital space these days as data becomes a target for cybercriminals. No wonder, because it involves a much lower risk for hackers who can easily cover the trail of cybercrime and rapidly scale ransomware, infiltrating thousands of computers at once.

The Wannacry cyber attack is a good example of this type of ransomware attack. Wannacry is malware that has been tricked into Microsoft Windows to block access to data until the ransom is paid. In 2017, more than 300,000 computers were infected. Nissan, Telefonica, FedEx, the UK NHS and German Deutsche Bahn have also been affected. Before the attacks were contained, the hackers managed to receive over $ 79,000 in ransom.

Machine learning is used to prevent such attacks from spreading. Using Machine Learning Models, Businesses Can Improve How They Operate security risk assessment and secure their systems before damage is done.

5. Improve employee retention

Retaining employees is one of the biggest challenges facing high growth companies these days, especially in terms of hiring tech talent. For larger companies, it is more difficult to keep a close watch on new hires and take care of their onboarding on a personal level. HR managers and department heads aim to automate onboarding, while hiring more and more people.

Some companies are already experimenting with using AI, machine learning, and robotic process automation to improve onboarding and retention of newly hired employees. With machine learning, more effective training programs can be designed, also based on the specific needs and preferences of new hires.

When it comes to evaluating the onboarding process, machine learning can identify patterns of employee responses and give HR professionals insight into what needs to be improved to improve the onboarding process.

Machine learning can also be used to predict employee attrition – based on historical data, models can be trained to identify candidates most likely to lose time in the first few months of the job, thereby helping HR specialists to choose candidates who will stay with the company longer.

6. Voice recognition

You’ve probably heard of the superpowers of Alexa, Cortana, Siri, or Google Assistant. There are more and more services that base their operation on speech recognition – just look push to talk apps.

There are many ways AI and machine learning can make our lives more convenient through speech recognition. You can think of the benefits in terms of asking a question and finding an answer without typing a single letter or making a phone call without consulting a contact book.

There are obviously many other ways machine learning can automate repetitive tasks. For example, you can schedule meetings, translate from one language to another, or transcribe video conferences.

7. Make IT operations smarter

Artificial intelligence for IT operations (AIOps) tools are used to make IT operations smarter and they involve big data, automation technology as well as data set integration. With machine learning, much of the tedious IT tasks can be automated, leaving more time for strategic decisions and more human-centric processes and tasks.

There are many use cases that show the effectiveness of AIOps in action. With machine learning, IT operations can identify problems by analyzing anomalies and deviations. Another use case might be to assess server health based on multiple metrics.

8. Provide better answers via chat

Instead of hiring more customer support agents, some companies rely on chatbots. While traditional chatbots have a limited number of answers, not answering unusual questions, NLP-powered chatbots can better understand the context behind the questions. They can also “learn” quickly using the data infused via Docusense – as is done in Engati bot.

By using these types of solutions, companies can improve the customer experience and increase the availability of customer support. As chatbots receive more data such as previously answered tickets or answers to new questions, their answers become more precise. As companies collect more data on customer support channels, it will become easier to train machine learning algorithms. As the line between a response from a customer support agent and a chatbot becomes blurred, it’s harder to see a difference between the two.

At the end of the line

Among technology companies, machine learning has already become an important element in web application development. It is a way to stand out with the product, to solve market problems more effectively and to gain competitive advantage.

Machine learning isn’t just a complicated technology that only tech giants and progressive startups can master. Cutting-edge AI and machine learning are becoming a tool to make people’s lives easier, safer and longer.

Starting with diagnosing deadly diseases early enough to undergo treatment and ending with making IT infrastructure safer and more reliable for safer internet use, machine learning has only just begun with solving the most challenging challenges. most difficult of humanity.

About Shirley L. Kreger

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