Have you ever thought that artificial intelligence is chasing you but you don’t know why? And when it seems as though you’ve reached an optimum level of understanding, it takes off again and you no longer know exactly where you stand?
Have you ever, without realising, started a conversation where machine learning and deep learning, along with other terms like big and small data, neural networks, algorithms, the cloud and many more gain importance, meaning the only thing you feel like doing is returning to management systems that are closer to your comfort zone and far removed from what can often be identified with science fiction?
Forums, seminars, conferences and multiple contents are all around us today, more than ever, with a new reality that never ceases to amaze, despite everything.
This is the age of the democratisation of artificial intelligence.
On a daily basis, companies, organisations and even end users enjoy the advantages of what has come to be known as machine learning, or automatic learning. It enables us to figure out future behaviour patterns and even avoid undesired emails.
But what is machine learning?
ITS ORIGINS: “Beyond the 50s…”
The birth of machine learning was relatively recent. It took its first steps in the 50s towards the age of smart machines. The pioneer in this field was Alan Turing, who determined whether a machine was really intelligent by being capable of displaying behaviour akin to human behaviour in its capacity as a computer through his “Turing Test”.
Later, it was the computer scientist Arthur Samuel who created a programme to play draughts, using a simple algorithm to discover the best moves to win. In 1958, Frank Rosenblatt designed “The Perceptron”, the first artificial neural network.
THE CONCEPT: “What point have we reached?”
Machine learning enables us to create systems that learn automatically through the programming of algorithms capable of predicting future behaviour. These algorithms are classified in two broad categories: supervised learning and unsupervised learning.
In the former, we have prior knowledge (data history) that will enable an understanding of the new data the company obtains, facilitating decision making and allowing predictions to be outlined.
An example of this is Google’s spam control system. The user labels unwanted emails, which are then identified by Gmail and redirected to a specific folder.
In the latter case, artificial intelligence does not have prior experience in data analysis (unlabelled data), meaning it focuses more on seeking patterns. Its usefulness is based on the segmentation of clients, among other aspects.
For example, unsupervised learning is key when establishing up-selling and cross-selling strategies, two old friends in sales techniques that enable the conversion rate to be improved through service and product recommendations.
Nowadays, ecommerce sites access a relatively high amount of data, including average prices, geolocation, number of visits, devices, and more. Smart algorithms are charged with detecting behaviour patterns that enable predictive marketing, following the example of companies like Amazon and eBay.
THE TOOLS: “But I’m not Amazon or eBay.”
The belief that this type of tools are solely for large companies and multinationals is perhaps widespread. The idea that “this is not for me” can simply be the fruit of a lack of knowledge. At the end of the day, whether actively or passively, they’ve told us “what they’re talking about,” however, they don’t often state what the real cost of implementation is and what its specific application is, when business figures or the volume of data of organisations like Spotify are not considered.
Today, there are a multitude of tools that include machine learning algorithms and are prepared to offer real solutions in real time.
It is now possible to develop automatic learning models through infrastructure managed and integrated in the cloud, despite the fact we aren’t Netflix.
Such is the case with Amazon Machine Learning, which is a managed service that allows machine learning models to be created without the need for vast prior knowledge in automatic learning techniques. Another example is Google Cloud Machine Learning, a managed and scalable service used to easily develop automatic learning models.
You see, there are many options. You don’t need to have an inside-out knowledge of machine learning. Talk to us here at Data Seekers and we’ll help you predict, categorise and improve your sales figure.
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