What is machine learning and what is machine learning?
What is machine learning and what is machine learning? Will computers and robots soon replace humans?
What is the relationship between machine learning and artificial intelligence?
Overarching machine learning models
What is machine learning and what is machine learning? Other machine learning models
What is machine learning and what is machine learning? Will computers and robots soon replace humans?
Technological progress is speeding up year after year at an impressive pace. Artificial intelligence products are used on a daily basis not only by entrepreneurs at work, but also by most of us in everyday life. In this article, we will explain what “machine learning” is, because it is a term closely coupled with the development of modern technologies. We translate “machine learning” as a subfield of artificial intelligence (AI). It is about educating robots and computers on how to use data and improving these skills.
We emphasize that we do not program any improvements, but we train the algorithms in such a way as to find the best possible patterns and interactions. Based on such tests, we can make the best decisions and make certain predictions regarding the results of the analyzes carried out. Systems that use machine learning become more and more precise over time. The likelihood of this accuracy increases when we give it access to the data. We find machine learning technology on various levels of everyday life. An example may be the use of multimedia and various applications. What is machine learning and what is machine learning? What exactly does this have to do with artificial intelligence?
What is the relationship between machine learning and artificial intelligence?
We consider machine learning and all its technology to be one of the overlapping sub-fields of artificial intelligence. The processes of data processing and analyzing statistics are also elements of artificial intelligence. Thanks to the above activities, we can formulate some conclusions or even anticipate the consequences of certain actions. One thing is certain: we consider artificial intelligence to be a superior field, and through precise machine learning algorithms, artificial intelligence can additionally develop. We do not need to use additional software for this. We distinguish three subsets of artificial intelligence:
- machine learning;
- deep learning; we call this subdomain of learning “deep” because it involves many layers of the neural network. Then we use large amounts of unstructured data, which we gradually verify at an increasingly advanced level of accuracy. If we were to illustrate this on the basis of an example, let us recall the moment of trying to identify a plant. By putting the mobile device to the plant, thanks to artificial intelligence, we gradually learn:
- that we are dealing with a plant;
- later that it is, for example, a flower;
- then, thanks to another layer of the neural network, we learn that we have a daisy in front of us;
- in the final stage of elimination, thanks to artificial intelligence, we learn that it is exactly a species of common daisy.
The type of learning we discuss is used in voice recognition and in all pharmaceutical analysis and in the classification of various images. Within deep learning, we distinguish neural networks – (ang. artificial neural network, ANN). They imitate the functioning of human brain neurons. In an imitative approach, we call them nodes, which we can group into numerous correlating layers. They function on the principle of transmitting numerical signals that are processed into information. It is passed on to the next neurons. Note that the human brain works similarly. The stronger the reinforcement of neurons, the greater the chance that we will accumulate a lot of professional knowledge, which we will then use in practice.
Overarching machine learning models
If we want to illustrate the principles of machine learning in more detail, we need to extract its basic models and specific algorithmic techniques related to them. Let us point out that depending on various types of data and expected analysis results, we use one of the possible machine learning schemes. So, let’s list and approximate these models:
- supervised – in the case of this model, we base learning in algorithms on examples. Here we include specific pairs of input and final data. We mark the final ones with the desired value. To explain this, let’s use an example: our goal is to teach the system to distinguish between two species of flowers – crocus and pasque flower. We have a pair of inputs that contain images of both kinds of flowers. The result we expect for this pair is a pasque flower, so the reflection of a pasque flower is considered the correct answer. As for the algorithm, the system saves the data that we have implemented and over time it starts to “see” the differences and common features, along with logical elements. In addition, we can predict which of the above plant images will be marked as a pasque flower. To simplify the discussed process, let’s introduce the situation when we give a small child a set of tasks with an answer key. Then ask the child to present his results and to present the method of action. Supervised learning patterns can be found in many everyday situations. We can list here product recommendations and traffic analysis applications, thanks to which we can determine the fastest route at any time of the daył
- unsupervised – for this model, we do not have a clear answer key. In practice, this means that we examine the input data with a specific system, which is mostly ordered. We then begin to recognize specific patterns and dependencies based on the use of any relevant data. If we were to illustrate this machine learning model, let’s compare it to one of the ways we observe the world around us. Usually, we are guided by experience and intuition and we associate various phenomena in cause-and-effect sequences. When it comes to machine learning, they also learn in a similar way. We define it based on the amount of data we give them to process. An example of the use of the discussed machine learning model are the following activities that our mobile device can perform for us: facial recognition, market research, analysis of individual gene sequences, various cyber security activities.
What is machine learning and what is machine learning? Other machine learning models
Among the machine learning schemes, we also distinguish models:
partially supervised – this model of machine learning works well in the modern world, given the large amount of unstructured and “dry” data. They accompany us every day. The algorithm of this type of machine learning supposedly recommends the system to process them in the context of their interdependent properties. The drawback that we must emphasize when discussing a semi-supervised machine learning model is that the system accepts and processes incorrect information. In other words, it is data that has been mislabeled. Those companies where we use the discussed model diligently prepare protocols. We base them on only proven methods. We often use this method of learning when analyzing speech and language. We also use this method to conduct advanced medical research and to expose fraud.
with reinforcement – based on this machine learning model, we take into account the answers of the so-called key answers. We use a whole set of different rules and legal actions at the same time. If the goal of the algorithm we want is certain, we can learn from examples. Unfortunately, if we expect a variable solution, we must rely on the use of experience. In the case of the machine learning model in question, the so-called the prize is mathematical and we are dealing with programming in the algorithm in such a way that the system strives to collect it. If we were to compare the discussed machine learning model to something, we can apply it to a game of chess. It would certainly not be possible to show a novice player all possible moves. However, we can easily explain the rules of this game. so-called the reward is here not only in the form of victory, but also in the gradual capturing of the opponent’s pieces. A vivid example of the use of the discussed machine learning model is, for example, the development of computer games, playing on the stock exchange. If we submit automated price offers, we can also talk about the use of the discussed machine learning scheme.
W każdym z powyższych modeli stosujemy co najmniej jedną z algorytmicznych technik. Uzależniamy to od tego, na jakich zbiorach danych pracujemy oraz od tego jakie planujemy wyniki. Wspomniane algorytmy używamy pojedynczo lub je łączymy, jeśli chcemy uzyskać jak najprecyzyjniejsze wyniki, zwłaszcza, gdy mamy do czynienia z mało przewidywalnymi i złożonymi danymi.
