Top Machine Learning Applications in Business and Beyond

Modern artificial intelligence technology is developing at an unreal rate. It is entrenched in almost every sphere of our lives, even if you overlook it.

Machine learning is one of the subspecies of artificial intelligence. It is based on the ability of the computer to study by performing multiple tasks of the same type. It creates an algorithm for an optimal solution for the given task.

You don’t even think about it, but we use machine learning applications in business, healthcare, marketing, computer games, etc.

The world is going online and taking advantage of all the benefits of machine learning.

Even the formation of real paystubs is now available online with paystubs generators. It is the result of the automation of the activity using artificial intelligence.

Here are some examples of the most common machine learning applications.

1. Analysis of the text

Here the computer solves the problem of recognition of text data: definition of the topic, extraction of keywords, determination of the feedback tonality about the product, etc.

It is necessary for almost every field.

To start retrieving features from the text, the latter goes through several stages of preparation.

First of all, the text is cleared of punctuation and stop words. Words in the text are divested of suffixes and prefixes, translated into the nominative. In this way, the source will be prepared for machine processing algorithms.

Second, you turn words into numbers. It is called vectorization. Then it is already possible to process such text.

This technology can be used, for example, when analyzing feedback or news about a particular product.

Text analysis functions are also widely used to extract keywords from the text. The incoming material for it will be the whole text (for example, an article). At the output, we will have only a list of keywords.

2. Chatbots

Chatbots are essential for automating the company’s business processes. It can save you 90% of your human and time resources.

It can communicate with speech or text. It has a prearranged list of questions and an algorithm of actions, providing more variability of answers. They work based on a limited list of keywords. But there are also self-learning options. It is more challenging to create.

The algorithm of the chatbot is as follows:

• receiving a request from the customer;

• recognition of the client’s speech and intentions;

• generate a response according to a predefined scenario;

• saving Data;

• sending a response to the client.

3. Facial recognition system.

This technology allows you to localize a person’s face in an image or video to identify him if the necessary databases are available.

This system exists because each person has unique features of the face.  Unfortunately, this machine learning application has several weaknesses. Thus, it will not be possible to achieve the desired result in the case of poor lighting or improper tilting of the head.

Recognition here also occurs in several stages:

• face detection

• person analysis

• creating a numeric code

• match search.

Law enforcement agencies, banks, and mobile apps actively use this technology.

4. Car without driver

Not so long ago, it was impossible to imagine the development of the computer industry to the level of automatic driving. We are sure that such cars will soon seize the automobile market.

This technology is based on recognizing real-time objects in a second. The capabilities of radar, GPS, computer vision, and various sensors are used.

It’s a sophisticated technological process that relies on computer self-study.

Surprisingly, these cars even learn to recognize human gestures, as they are integral to traffic rules.

5. Speech recognition

I probably don't need to explain the meaning of speech recognition technology today. We meet it in mobile apps, smartwatches, and game consoles. It can recognize our commands and execute them. We can use scripted text or voice commands.

6. Road traffic forecast

The most popular application that effectively uses machine learning to predict the situation on the road is Google maps.

Forecasting takes place based on the data obtained earlier. Such information repeats periodically. The annex concludes with information on the vehicle's movement during a specific daytime and on particular weekdays. The technology can display the traffic in real-time and make predictions for the future. This data will be correct unless a new, unforeseen circumstance has occurred (for example, construction, repair, etc.).

GDPR Compliant

Data collection is the most crucial stage in the machine learning model's life cycle. Internet pages, social networks, and other online sources contain a wealth of information to collect and process. The biggest problem is the legal possession of such data.

Disclosure of personal data is unfortunately not related to security. Someone can use groundlessly transmitted data against you.

In this connection, the European Union developed and implemented the EU's data protection law, called the General Data Protection Regulation or GDPR.

It is pretty new and, without exaggeration, the most formidable piece of legislation that regulates the sphere of collection and protection of personal data. It provides enormous fines for violations of the rules.

The security of confidential data is a natural right that is subject to special protection.

The Act applies to cases where data is collected and processed automatically or semi-automatically.

General Data Protection Regulation and the USA

In my mind immediately arises the question of the extension of GDPR to the territory of the USA.

The European Union has adopted the regulation that also applies to processing EU citizens' data.

It means that the American website, which Europeans can use, is subject to the requirements of the Act.

 The concept of personal data in GDPR is the same as personally identifiable information (PII) in American law.

Data processing principles

GDPR provides the following principles to observe while working with confidential information:

• Legality, fairness, and transparency;

• target restriction;

• minimizing data;

• accuracy;

• storage limit;

• integrity and confidentiality;

• responsibility.

The most significant number of disputes relates to the failure to respect the principle of legality and transparency. Today, the work of some GDPR institutes, especially supervisory bodies, raises a lot of questions. Thus, the dispute settlement procedure was too long and often did not serve its purpose. Moreover, there was confusion about the territorial jurisdiction of specific categories of cases. Therefore, none ensures the adequate judicial protection provided by law.

The reviewed cases immediately became famous because of the significant fines imposed (20 000 000 euros or 4% of the annual turnover in the world, whichever is larger).

When deciding whether to impose a fine in each case, the seriousness and duration of the violation, the intentional or negligent nature of the act, the category of personal data breached, the number of damages suffered, and any other factors that could affect the severity of the breach.

Conclusion

Machine learning applications speed up technological development and automate business and personal processes. However, it would be best to use it with caution, given the regulatory requirements.