Machine learning – characteristics and applications

Machine learning is being called the future of automation. Is it rightly so? Undoubtedly yes. Already the technology is being used extensively by companies, significantly contributing to the efficiency of their operations. It also helps developers, making it easier to build functional, tightly tailored applications. From the article you will learn what machine learning is and how it is used.

What is machine learning?

Machine learning (ML) is a type of artificial intelligence (AI). This technology allows the system to constantly learn from the data provided to it – once the data is received, it is processed, which makes it possible to obtain:

  • concrete numerical results in the case of data analysis, which are available in the form of graphs or reports,
  • ready-made solutions on how to behave in a specific situation to benefit the company’s interests,
  • reliable hints, indicating the optimal way to proceed in the future.

Thanks to ML, the system can acquire knowledge completely without human involvement or with minimal human input. Everything is done automatically, so there is no need to implement time-consuming programming. By working with data sets, the computer is constantly learning, performing various analyses and comparisons to look for patterns and correlations. As a result, it can:

  • predict situations that are yet to happen,
  • determine potential behavior, for example, of application users,
  • determine trends for the next months or years,
  • indicate the effects of actions already taken, such as marketing campaigns.

Machine learning is a part (subset) of artificial intelligence – the two concepts cannot be equated. Unlike AI, machine learning does not lead to the discovery of something new. Instead, it allows an intelligent computer or system to gain experience, allowing it to increasingly mimic the functioning of the human brain.

It is worth realizing that machine learning is not a new concept. The first examples of its application appeared as early as the late 1960s and early 1970s. It was then that Arthur Samuel developed a program designed to learn how to play chess, and at Stanford University the Dendral system was developed, allowing the identification and analysis of molecules of organic compounds. However, it is only now, thanks to the high computing power of computers, that ML can be fully exploited.

What are the types of machine learning?

Machine learning is a constantly developing technology that uses different methods. One can distinguish learning:

  • supervised (supervised learning) – this is the most popular ML algorithm; under it, the user provides the system with data, which is then analyzed, and ready answers on how to solve the given problem; an example is face recognition,
  • unsupervised (unsupervised learning) – in this case the machine does not have ready answers, but has to find them on its own; this type of technology is used, among other things, when detecting spam, segmenting customers or recommending certain products to users,
  • by rein forcement learning – the computer tries to solve a specific problem by trial and error, not having a ready-made set of data.

One of the most advanced types of ML is deep learning, or deep learning. It uses neural networks, which to some extent mimic the human nervous system. They consist of multiple layers, each of which transforms the input data so that it can be used by subsequent layers to perform specific tasks. This allows the machine to learn from its own data processing and perform complex tasks such as speech recognition.

Machine learning is based on a number of techniques. Particularly popular is decision trees learning, which is distinguished by its simplicity of use – possible answers to questions are only „yes” or „no.” In its framework, each node corresponds to a specific test on attribute values. Another technique is learning from examples (instance-based learning), which boils down to finding examples in the database that are most similar to the solution being sought.

For what purposes is machine learning used?

ML is a technology that is extremely useful when doing business. With its help, huge amounts of data can be analyzed. Big Data systems are used for this, thanks to which companies acquire information about:

  • their business, such as financial results or the effectiveness of advertising campaigns,
  • current and potential customers, including how they make purchases on the Internet.

ML algorithms facilitate business decision-making and are helpful when simulating the actions a company should take in the future to achieve its strategic goals. For example, they help determine:

  • what market to expand into to improve product sales,
  • what contractor to choose to guarantee successful cooperation,
  • which target audience to direct marketing messages to achieve the best sales results.

Machine learning is present in the life of almost every modern person. All you have to do is enter any search engine, app or email and you are already dealing with it. In what way? It’s simple – your online activity is constantly monitored, allowing companies to gather useful data on, for example, the ways you prefer to spend your time. Based on what you search for online, machine learning algorithms can indicate:

  • what products you most often look for online,
  • where you usually carry out e-shopping,
  • what kind of food you prefer.

Such examples can be multiplied. They show how broadly applicable ML is. They also prove that the technology is already having a huge impact on the functioning of companies, allowing them to operate more efficiently in the market.

Machine learning in programming

You’re probably wondering, do programmers use machine learning? The answer is clear – of course they are. CosmicWeb works extensively with AI teams when building applications, allowing them to create algorithms that feature:

  • learn independently based on available information,
  • are able to respond effectively when data changes.

Machine learning makes it very easy for us to build applications, allowing us to do extensive data analysis and apply modern functionalities. When we create apps, we can add one or more ML-based elements to them. This includes, for example:

  • detection of people and other objects,
  • image recognition,
  • searching by similar words rather than phrases typed by the user.

Machine learning makes it possible to create simulations, which are useful, for example, in video games, where it is necessary to faithfully reproduce how characters behave. The technology is also useful to build secure applications. It is used, for example, to analyze risks and detect potential security vulnerabilities. ML is also used when chatbots and virtual assistant systems, such as Cortana or Siri, are programmed. They allow voice communication with users. They allow users to quickly schedule tasks, find needed files or check their calendar.

At CosmicWeb, we have a lot of promise for Tiny ML. Until now, one of the main limitations of machine learning was that its use required a lot of CPU power and sizable memory resources, which prevented it from being used in small IoT applications or chips. The situation has changed thanks to the increase in computational efficiency of 32-bit microcontrollers and the continuous improvement of neural networks. As a result, ML technology can now be „encapsulated” in a small battery-powered processor. This is advantageous, for example, from the point of view of cyber security – data does not have to be sent to the cloud. Such a solution opens up completely new possibilities for our team when it comes to application development.

Advantages of machine learning

By using this technology, companies gain many advantages. These include:

  • streamlining the implementation of business processes – with the help of machine learning it is possible to automate a number of activities, including the circulation of documents or the generation of reports and statistics,
  • rapid analysis of huge amounts of data, which makes it possible, for example, to determine the current financial condition of the company and facilitates strategic decision-making,
  • cost reduction – there is no need to employ specialists in the company or use the services of entities that deal with data analysis,
  • time savings – no programming of the system running on the basis of AI is required.

Machine learning is also a convenient way to analyze data on customers, which are collected, for example, in the ERP system. Among other things, you can quickly get an idea of who clicks on ads and what the needs and buying behavior of users are. This makes it easier to design and implement marketing campaigns in terms of the preferences of members of the target group.

Machine learning provides many benefits for app users as well. For example, chatbots make it easier to get information about a company’s offerings – you don’t have to wait for a phone call or email contact, because you can ask a question right away and get an immediate answer. Machine learning is a huge opportunity for us developers as well. It allows us to offer you even better applications to achieve your business goals quickly.