The acquisition of new skills and the transfer of knowledge acquired by others was essential to the development of human civilization. Without it, we would not have been able to build cities, cure diseases and certainly would not be able to go into space.
Since the twentieth century, there has been a drastic development in the research of artificial intelligence. A new discipline, machine learning, became independent of it.
What does learning mean?
Everyday vocabulary is mostly seeing it as the acquisition of some new knowledge. The school is closely related to this. This well-known institution is designed to put knowledge into students’ memory by pushing them out of their comfort zone. Learning is essential to the survival of human civilization. Broadly speaking, this includes everyday actions such as when a child learns to tie his shoes, how to behave in company, or even how to talk.
How does one study?
A human relies on his/her memory to develop abilities. We remember how we implemented the sample and at best we also have an idea of what it should be like. Repeat the pattern many times to perfect the ability. This is exactly the process that takes place when we learn to ride a bike. The first few attempts still end in a fall, but we are getting more and more into the sense of balance and there are those who are already doing this as a professional sport.
Learning, of course, is not just an ability applied by humans, animals also change their patterns of behavior based on their past experiences. It is an important element of evolution and vital to survival. General, basic mechanisms of learning include classical conditioning. The father of this mechanism was the Russian physiologist Ivan Pavlov. Everyone is familiar with the experiment in which saliva formation was induced in the dog by sound stimuli.
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Conditioning is also a learning path for people. In the event that the pattern of behavior practiced results in an experience that is unsatisfactory, we change the pattern of behavior. If the change already produces the desired result, we will continue to repeat it, which will later result in the formation of a new habit. Through a concrete example: if we reach into the hot water and burn our hands, next time we will beware of this act. So, we learned how to avoid burns when encountering hot water.
What is machine learning?
We are already confident that humans and animals can learn. But what about the machines? Computers, unlike the previous two categories, are non-living beings. They are not the result of evolution, but of human work. Machine learning is a kind of artificial intelligence. Unlike traditional algorithms, they have the incredible novelty of being able to learn from their past experiences. They seek a solution to the problem assigned to them by following several possible paths and moving forward with the result that gave the most successful result.
Machine learning learns data based on a predictive model and predicts what will be the next data point. The more data you work with, the more accurate the forecast will be.
Reinforcement learning is a branch of machine learning. Feedback is an essential element of reinforcement learning. We give the system a task, for example, to recognize some patterns. Recognize whether we see an apple in the picture or not. If we give an image of a pear, the system will give an answer. If it answers incorrectly, so if it identifies the pear as an apple, we will give it negative feedback. The more such tests we perform on it, the more accurate its predictions will be.
Reinforcement learning is an area that is already very similar to the learning used by living creatures and the conditioning mentioned earlier. Although Pavlov’s dog did not try to distinguish between apples, he was able to associate the sound of the bell with the food.
How long has it existed?
The term was published as early as 1959, and a book on the application of machine learning to pattern recognition was made in the 1960s. However, it only became known to the general public in the 21st century. Artificial intelligence researchers were very preoccupied with the question of how machines could learn from data and saw machine learning as a viable path through which true “artificial intelligence” could be born. Later, the two sciences separated and in the 1990s, machine learning was reborn as a separate field. The new goal of ML was already a purely problem solution, for which it borrowed models from statistics and probability theory.
Where do we use it?
Machine learning can be used in many areas and it is quite certain that the wonderful potential of it is recognized in even more places. The essence of so-called precision agriculture is that producers try to determine more precisely how much they need for optimal production. Every farm needs water, chemicals and seeds. Machine learning helps to determine the exact amount needed. This not just reduces waste, which is not only cost-effective and also good for the planet but increases production capacity as well. We can go beyond that. If machine learning increases productivity and reduces waste, we can be sure that it will also play an important role in overcoming famine.
The emergence of agriculture also marks the beginning of human civilization. In contrast, cryptocurrencies are young assets. This is not to say that machine learning cannot be used here as well. True, only a few companies have attempted to do so. The fintech startup b-cube.ai in Paris uses quantitative models and machine learning to forecast the cryptocurrency market. It works like this: their technology sends signals (forecasts) to the trader and predicts where the market is more likely to move. The signal is then automatically executed by a Bot in the market. It took many unsuccessful attempts at machine learning to develop an algorithm that can already produce nice results:
Where is the industry going?
According to the forecasting company Tractica, there is a bright future ahead for machine learning and artificial intelligence in general. The market is growing at a rapid pace and its revenues are expected to double every two years.
This, of course, is just a prediction that we know is not always accurate and cannot be accurate. In any case, we can be sure that the opportunities facing the market exist, that development is consistent and that growth is guaranteed.