Top Eight Machine Learning Examples in the Real World

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Machine learning is an AI domain that has already solved hundreds of problems and challenges people happen to face every day. Increasingly more companies are thinking of integrating machine learning when starting their technology business to gain a competitive edge among their competitors. 

However, for some, machine learning remains a complicated scientific term that’s hard to grasp. It definitely shouldn’t be one with so many applications in the real world! In this article, we are sharing eight use cases of machine learning that have changed our lives.

1. Diagnosing a disease 

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

A good example of machine learning in use is diagnosing Alzheimer’s disease. By analyzing a patient’s speech, especially focussing on the characteristics such as pauses between words, frequency, amplitude, and pronunciation, the machine learning model can identify patterns pointing at the high possibility of the disease being in place. In this case, patients’ audio tapes are analyzed.

2. Helping write better content

For content marketers, machine learning has become a useful tool to automate a big deal of research on keywords, user intent, backlink profile, and more – all this to make their next content piece appear on the first page of Google search.

Search engine optimization (SEO) marketers can benefit from adding AI to SEO activities.  Machine learning can offer a recipe for a high-performing blog article or a landing page that for many companies translates to an increase in leads, sales, and, eventually, revenue.

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

3. Predicting a fraudulent transaction

With machine learning, it’s getting easier for analysts to identify fraudulent transactions. Thanks to predictive analytics and collecting previous history of transactions, algorithms can predict suspicious behavior on a user’s bank account.

For example, PayPal has already integrated machine learning in their tech solution. 

As millions of people are using PayPal to send and receive money, the service has attracted hackers aiming to break into poorly protected accounts and clean them.

PayPal is collecting huge amounts of data and it fuels machine learning models that, over time, become even more sophisticated and targeted at identifying suspicious behavior. 

4. Preventing ransomware attacks

Ransom is asked for in the digital space as well these days, with data becoming a target for cybercriminals. No wonder as it involves a much lower risk for hackers who can easily cover the traces of a cyber crime and scale ransomware fast, infiltrating thousands of computers at once. 

Wannacry cyberattack is a good example of this type of ransomware attacks. Wannacry is a malware that was induced in Microsoft Windows to block access to data until the ransom was paid. In 2017, over 300,000 computers got infected. Nissan, Telefonica, FedEx, British NHS and German Deutsche Bahn were also affected. Before the attacks were contained, hackers managed to receive over 79 thousand USD in ransom.

Machine learning is being used to prevent such attacks from spreading. Thanks to machine learning models, companies can get better at running security risk assessment and securing their systems before damage is inflicted.

5. Improving employee retention

Employee retention is one of the biggest challenges high-growth companies face these days, especially in terms of hiring tech talent. For bigger companies it is more difficult to keep a close look at new hires and take care of their onboarding on a personal level. HR managers and heads of departments are aiming to automate onboarding, while hiring an increasingly bigger number of 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 around specific needs and preferences of new hires. 

When it comes to the assessment of the onboarding process, machine learning can identify the patterns in employee responses and give HR professionals insights into what needs to be improved to make the onboarding process better.

Machine learning can also be used to predict employee churn – based on the historical data, models can be trained to identify candidates that are most likely to churn in the first months of work, thus, helping HR specialists choose the candidates that are going to stay with the company for longer.

6. Speech recognition

You have probably heard of the superpowers of Alexa, Cortana, Siri, or Google Assistant. There are increasingly more services that base their functioning on voice recognition – just look at push to talk apps.

There are multiple ways AI and machine learning can make our lives more convenient with speech recognition. You can think of the benefits in terms of telling a question and finding an answer to it without typing a single letter or making a phone call without looking through 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 lectures.

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 integrate data sets. With machine learning, a big chunk of 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 AIOps efficacy in action. With machine learning, IT operations can identify problems analyzing anomalies and deviations. Another use case can be evaluation of server health based on multiple metrics.

8. Provide better answers via chat

Instead of hiring more customer support agents, some companies are relying on chatbots. While traditional chatbots have a limited number of responses, failing to cover unusual questions, NLP-powered chatbots can understand the context behind questions better.  They can also “learn” fast using the data infused via Docusense – the way it is done in Engati bot.

By using this type of solutions, businesses can improve customer experience and increase the availability of customer support. As chatbots receive more data such as previously answered tickets or the answers to new questions, their responses become more accurate. With companies collecting more data across customer support channels, it will become easier to train machine learning algorithms. As the line between a response of a customer support agent and a chatbot becomes more blurred, it’s harder to see a difference between the two.

Bottom line

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

Machine learning is not only a complicated piece of technology only tech giants and progressive startups can nail. AI and machine learning at its forefront become a tool for making people’s lives easier, safer, and longer. 

Starting with diagnosing lethal diseases early enough to undergo a treatment and finishing with making IT infrastructure more secure and reliable for a safer use of the Internet, machine learning is just getting started with solving the toughest challenges of mankind. 

 

 



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