Machine learning – what is it?

Machine learning is an area of research on algorithms that can learn themselves thanks to the analysis of collected data. For example, self-learning machines can gather information about certain behaviors of Internet users and reduce them to general patterns, and on this basis, estimate with high probability how they will behave in the future. So you can say that algorithms learn from their own experience.

Let’s explain it in a slightly simpler way. Suppose we have an algorithm that should distinguish photos of dogs from photos of cats. To teach him this, we give him the so-called test data, i.e. a database of photos of both animals, of course with an indication of which of them is in the photo. The algorithm analyzes these photos and learns which features are characteristic for a cat and which are for a dog. Once he knows it, he can get a completely different photo, from outside the test base, and he will be able to indicate which animal is on it – because experience will tell him.

Of course, however, the algorithm must have a lot of information to avoid making mistakes. If we give him 10 photos of rottweilers and 10 photos of dachshund cats for analysis, he may not be able to correctly indicate what york is. There is simply no data on the basis of which he could conclude that the dog can be smaller, built differently, etc. However, if there are millions of photos of various breeds of dogs and cats in the database of the self-learning machine, its “experience” will be so great that it will be able to catalog subsequent photos with almost no mistakes.

Machine learning in everyday life

Such algorithms surround us in everyday life in many situations. Almost all of us use them, although we often do not even realize it. An example would be e-mail. Every day we get important messages, notifications from applications and websites, as well as completely junk e-mails. It is these machines that, on the basis of a lot of data, determine which messages are to be sent to the main mailbox, which to the community tab, and which simply to spam.

Uber, for example, also uses self-learning machines. By analyzing previous trips in a given time period, day of the week, city, etc., the algorithm predicts where the interest in the service will most likely be the highest in the near future. The system then sends vacant drivers to these places to reduce the waiting time for a ride and increase the number of journeys. Uber also uses algorithms in other aspects, such as estimating arrival time or price for a service.

Another giant that uses machine learning is Facebook. Algorithms based on our behavior in social media show us the ads and posts that we are most likely to be interested in. At the same time, they take into account even those reactions that we may not be aware of ourselves – even a slightly slower scrolling with a specific material.

Where else do self-learning algorithms work?

We also see the operation of self-learning machines on a daily basis, for example on Netflix, when the platform offers us movies and series to watch. Algorithms analyze descriptions of video materials, grouping them into micro-categories, and then, based on our previous choices, propose the next items that are probably the best for us. The recommendations are shown not only on the basis of the productions watched, but also our behavior on other websites – including Facebook (e.g. likes).

American Express, on the other hand, uses machine learning to keep its customers safe. Algorithms are used to analyze the transactions and detect anomalies among them, i.e. fees differing thematically or in terms of amount from others. Whenever a disturbing activity occurs on the account, they report suspected fraudulent activity to prevent any further abuse.

Automatic optimizations in Google Ads

Machine learning is also used by, for example, the Google Ads system to provide customers with the highest possible profit from the campaign. How it’s working? When we set up a campaign, the algorithms learn how the audience behaves, when they are active, which ad texts bring the most conversions. They draw regularities from the collected data and optimize the ads to bring the best effect. Their performance manifests in the use of Enhanced CPC, Smart Bidding Strategies, and Responsive Advertising on the Search Network. For example, if for some reason there is a sudden increase in interest in your product or service, they can adjust their bids almost immediately to maximize your benefit. Likewise, in a responsive ad, algorithms select the best-working headlines and ad text to maximize conversion.

Self-learning machines monitor all your ads non-stop and bring you tons of optimization daily. Each of them, even a very small one, means that the budget is better used, thanks to which more customers can be acquired for the same amount. The more data such a machine has a chance to collect, the better it forecasts future events, and thus refines the campaign. Such algorithms make their own decisions and introduce changes, which is why they give advertisers more time for other promotional activities, not related to AdWords / Google Ads campaigns.

What can you use machine learning for?

Machine learning can be used in business to improve many processes. Using algorithms, you can, for example, track business trends and set up action strategies. On the basis of the collected data, you will find out when your products are gaining popularity and plan your advertising campaigns or other marketing activities according to these trends. An appropriate self-learning system also allows, for example, to create a list of the company’s best customers. Based on the interaction with the website or other communication channels, it is possible to determine to which group of recipients it is best to target sales messages.

Machine learning can also measure how effective the company’s employees are and how changing conditions (such as remote work) affect individual performance. Some entrepreneurs also use artificial intelligence to plan their business development. Based on the collected data, machines can estimate the probability that it will be beneficial for the company before taking a new initiative.

Types of self-learning machines

The example described above with pictures of dogs and cats shows the learning of supervised machines. In this case, the algorithms know what the effect of their actions is supposed to be – for example, that they are to distinguish a cat from a dog. In this model of operation, they check which features of both animals will be useful in comparisons to achieve this goal. This type of machine learning can be used to classify images, recognize speech, or even segment corporate customers.

Another type of machine learning is unsupervised learning. In this case, the algorithms do not get the “goal” from the human serving them, which they should achieve after processing the material. They analyze the collected information and look for relations and patterns between them and draw conclusions based on them. The man taking care of this system is not able to predict the result of the machine’s work, because it simply resembles human observation of the environment. This machine learning system can be used, for example, to detect anomalies or irregularities.

The intermediate version is partially supervised learning, which consists in the fact that machines receive and marked data, assuming what the machine has to learn, and unmarked, where it has to find common elements and draw conclusions from them.

Are the algorithms wrong?

Self-learning algorithms, of course, can make a mistake in their predictions and make a bad decision – just as it can happen to a human. However, this usually only happens at the beginning of the machine operation or when the algorithm has very little data at its disposal. Therefore, it cannot be assumed that the effect of the “young” algorithm will be spectacular in the first phase. The more time it takes to gather information, the better the results, which is easily seen with many applications, websites and search engines on the market.

Algorithms can therefore be unreliable, but less so than a human who has many limitations, such as subjectivism, fatigue or distraction, when analyzing data. So when you think about developing your business nowadays, you cannot ignore the role played by modern self-learning machines – they are an element that may decide about the success or failure of the enterprise.