You must have heard about Machine Learning, right?
This term gained strength with digital transformation technological advances, and the popularisation of another term - almost always linked to it - Artificial Intelligence (AI).
Both technologies are already part of our daily lives, from a product recommendation on our favourite shopping site, or song suggestions on Spotify, to ads that seem to guess what we wish. All this intelligence behind the technology is only possible thanks to Machine Learning.
But anyone who thinks that this learning system is limited to phone apps is wrong. Nowadays, this technology is being absorbed by industries, and as a result, autonomous vehicles have been developed - that's right! This scenario has been causing a fundamental transformation in our consumption pattern, in how we relate to companies (whether digital or not) and in the final products that reach us.
If you want to know how this fascinating technology operates, what types there are and what is the impact of Machine Learning on business, keep reading this article!
First, we need to differentiate two essential concepts to understand the subject: Machine Learning and Artificial Intelligence (AI) mean different things.
Artificial intelligence encompasses a series of processes that result in intelligent systems that allow different applications of this technology in our daily lives - and are not limited to machine learning alone.
AI was conceptualised in 1956 by an American professor named John McCarthy in a study that lasted about two months. In this same period, Arthur Samuel created the first game program, which many believe was the beginning of AI technologies.
However, it was only in the late 50s that Samuel created a method that could surpass him in this game system that he had programmed himself. How? Through Machine Learning.
He defined machine learning as "a field of study that grants computers the ability to learn without being explicitly programmed for that task". Therefore, Machine Learning is a technology under the umbrella of everything that encompasses the concept of AI.
This technology works from specific statistical methods, which make predictions and establish new responses to each new command through patterns found in the immense volume of data we produce.
So, its main feature lies precisely there: machine learning technology does not need any human intervention to operationalise its patterns. By its essence, the system has the ability to learn on its own and execute increasingly precise – and hyper-personalised – commands. It is configured so that, over time, it becomes more and more intelligent and efficient according to the amount of data received.
Machine Learning is a technology that was created to learn from experience. The more this technology needs to work, the more data it will analyse and the easier it will be to predict any scenario. That is, Machine Learning increases your accuracy as your experience increases.
All this smart learning is only possible through algorithms. In short, the algorithms of machine learning technologies are sequences of commands, which, combined with the analysed data, arrive at certain results.
This is how our purchase recommendations work, for example. Upon receiving the data of everything we are researching to buy, the algorithms process this data and, through their sequences of instructions, can accurately suggest similar products or one of our interests.
The most common types that algorithms work to perform this machine learning are supervised modality and unsupervised modality.
In this modality, there is at least some human interaction behind it. This is because the algorithm receives the data already labelled with pre-defined information.
That is, this learning takes place based on the labelled answers and only from there, the algorithms work to find the best combination among the existing "answers".
In any database, for example, the search consists of finding certain combinations and answers that are already available in that database.
In this case, the received data does not carry any labels. Therefore, there is no information on the "answer" or the right combination. This way, the algorithm works precisely to create these patterns by analysing all this data and creating new scenarios, results and filters.
This system is much more complex than the supervised one since it depends on crossing data and all the variables entered.
Both modalities work through methods, which can be: regression, classification, clustering, etc. These methods, in turn, are part of the entire machine learning knowledge process flow that serves to guarantee and achieve the expected result.
Machine Learning is important because it gives companies insight into customer behaviour trends and business operating patterns and supports new product development.
Many of today's leading companies make Machine Learning a central part of their operations, making it a significant competitive differentiator.
Below are some of the main benefits of this technology for business:
This technology, automating several processes that were done by human beings in the past, gives more speed and assertiveness to tasks. Thus, when employees can occupy themselves with other activities of a more strategic nature - automatically, the business's productivity increases.
In the same way that it increases productivity, investing in Machine Learning can help reduce costs. It allows suppliers' previous functions, for example, to be replaced by technology.
By analysing large volumes of data with high complexity through Machine Learning, it is possible to develop algorithms that learn and make predictions. This can help the business predict demand, sales or the company's growth.
Another advantage of Machine Learning for companies is to allow more assertive decision-making. That's because any choice needs to be based on solid information - and analysing data with machine learning can yield valuable insights for the business.
Now that you already know what Machine Learning is and you know the full potential of this important technology for companies check out 4 situations in which it is applied in practice below:
Digital transformation has arrived, making the consumer experience increasingly personalised. And Machine Learning plays a key role in this since, through the algorithm, the Machine observes consumer behaviour and choices in the future, indicating products or information they are likely interested in.
The use of machine learning has been instrumental in preventing fraud in bank transactions and payments, for example. That's because the technology does all the data analysis work in a fraction of the time it would take 100 fraud analysts.
Machines can perform repetitive and tedious tasks 24/7 and only need to escalate decisions to a human when specific insight is needed.
An example of a Machine Learning application in customer service is no lee than the chatbot. That's because it is an artificial intelligence that can constantly learn new interactions, adapt to any situation, and perform rich interactions with customers.
The uses of Machine Learning in search engines are numerous. Google, for example, uses it to detect patterns that help identify spam or duplicate content. Thus, it can automatically filter pages to eliminate low-quality content before a real human has to get involved in the process.
Predictive modelling is the key for a company to increase revenue and remain competitive. Arm of Artificial Intelligence, Machine Learning automates and accelerates customer attraction and retention techniques.
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