Written by Vratislav Holub
Nowadays, machine learning is becoming more and more popular. Almost everyone in IT talks about this subset of Artificial Intelligence (AI), which builds mathematical models based on data by its algorithms. Machine learning builds a model based on which computers can take decisions on our behalf. The reliability of machine learning is based on a specific kind of training, which is a direct parallel to the human world. For example, this technology can be used for autonomous vehicles. Imagine the situation, when a car wants to drive to another traffic lane. Thanks to a lot of training, the model can be used to calculate if it is safe to change lanes
As we mentioned above, machine learning is the way we can teach computers to perform any kind of tasks, where they do not have to be explicitly programmed. But how does it work in the end? If we want to build a system with machine learning to decide, say, whether a given drink is beer or wine, we would need to buy some beverages and then collect their data, such as color and the proportion of alcohol. Thanks to this, we can tell our system what kind of drink we tested and this part is called training. Training is responsible for building more and more accurate models, which are the main part of making decisions. After some time and a lot of training, our system can be as accurate as can be, but we cannot fully trust it, because machine learning is not able to create a 100% accurate model.
Machine learning is the art and science of technology. But why do people always talk about machine learning and nobody talks about people learning? People learning is not only important from a technological point of view, but it can be helpful for entire teams and companies.
We can say that people learning is almost exactly the same as machine learning. As an example, we can mention a driving school. When you are there for the first time, you do not know what to do, nor, for instance, which pedal does what. But when we successfully finish the driving school, we should be prepared to drive a car in traffic. But what will happen after one year of driving on a daily basis? We should become skilled drivers and we should not be afraid for example of driving during rush hour. As I mentioned above, this situation is identical to what happens with machine learning. Just as we need a lot of training, the same applies to machines.
But why is machine learning more popular than people learning? Machine learning is something relatively new, and a lot of people still don’t know much about it. So it’s likely that curiosity sparks many questions and therefore discussions. On the other hand, people learning is somehow written to our DNA: it’s something we take for granted as natural, and which we do pretty much our whole lives, so we’re always prepared for it. The ability to learn is one of the most important things for us and we need to take it with responsibility. Without skills, achieved through people learning, we would not be able to create something like machine learning.
People learning is important in every possible part of our lives. When we take a look at companies, they first need to train their employees to become productive in their own way. Without skilled employees, a company is basically doomed.
At a more personal level, what we learn is something intangible which becomes part of ourselves. When we learn, we open up to new possibilities and points of view, we become more confident and more complete as human beings. The main difference between machine learning and people learning is that we, human beings, can rely on our intuition or some experience from the past, while machines can’t.
The importance of people learning is extremely high and everyone should be aware of that. “People learning” created “machine learning”, so the latter will not replace the first, at least for a long time still.
At Newired, we care about our staff and their learning, so we try to be a step ahead in every possible way. Thanks to a good amount of training/learning, we can accomplish bigger things, together.
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