What is machine learning? Machine learning and artificial intelligence
Machine learning is a branch of computer science, that can be considered as a close relative of the artificial intelligence. We can explain this concept with two simple words: “automatic learning”. The intent of this science is to teach computers, robots, and machinery of various kinds to perform actions and activities in the most natural way possible, trying to get closer to human behavior. How can this be done? Learning from experience: machine learning algorithms use mathematical-computational models, that allow machines to learn information directly from data and not from predetermined mathematical models.
With this process, the performance is improved adaptively, based on experience and following examples, optimizing the performance thanks to the progressive increase in the examples to learn from. Machine learning can be understood as the ability of computers and technological machinery to arrive at certain knowledge without having been previously programmed to do so. In fact, it is a series of different mechanisms that allow an intelligent machine to improve its capabilities and performance over time.
What is machine learning: how and what do machines learn?
If we analyze the issue from an IT perspective, the machine is provided with data, which are processed through algorithms, that develop their own logic to perform the required activity. The programming code is not written in its entirety, to order the vehicle what to do step by step. We can say that machines also work with the same principle that governs human activity: “learn from your mistakes”.
If we want to better define the concept, machine learning takes into consideration some mechanisms that allow an intelligent machine to improve its performance, capabilities and skills over time. The goal is that it learns to perform certain tasks, improving the functions through experience. A series of different algorithms remain at the basis of machine learning: the processing and learning of operations over time will allow the machine to make an increasingly appropriate decision.
History of machine learning: birth, development and definitions
The ancestor of modern machine learning finds its historical place in the early 1950s. The world was just emerged from the Second World War and an idea took hold within the international community of mathematicians and statisticians: use probabilistic methods to create machines that are able to take into account the probabilities that an event may or may not happen. . Alan Turing is the first big name we can link to machine learning and artificial intelligence theories.
The famous mathematician, considered the great father of computer science, has gone down in history for having created the system that allowed the codes created by the German machine Enigma to be decoded, making an important contribution to the victory of the Allies in the war. The Englishman hypothesized the need to create specific algorithms in order to create machines capable of learning. He was a real pioneer in a very lively historical period, in which studies on artificial intelligence and neural networks were also progressing.
The definition of machine learning: from Samuel to Mitchell
The studies were often interrupted due to the lack of economic subsidies, as well as general skepticism. A rebirth of research, this time decisive and definitive, took place between the 80s and 90s, when a series of new investments in the industry led to the creation of techniques related to statistical and probabilistic elements inherent to machine learning. Machine learning aroused great interest and developed progressively, leading machine learning to be highly demanded in contemporary society.
The term machine learning was coined by the American scientist Arthur Lee Samuel in 1959. The definition of machine learning most accredited by the scientific community, however, is the one given by the American Tom Michael Mitchell, director of the Machine Learning department at Carnegie Mellon University.
How does machine learning work? The different types of learning
Machine learning can be divided into two different sub-categories: supervised learning and unsupervised learning. Supervised learning occurs when a computer is given complete examples to use as directions for performing the required task. Unsupervised learning, on the other hand, takes place when you let a software work without any support or address.
In fact, these are two different learning modes, which differ in the algorithms and in the purpose for which the machines are made. Originally, this subdivision was identified by Arthur Samuel and the learning models are nowadays employed in different ways depending on the machine on which to operate. Mainly, the goal is to achieve the maximum yield and the best result possible. The models are equally effective, the main objective is to adopt the best performance one based on specific needs.
The machines are set up with data and information about the desired results. The goal for the system is to identify a general rule that is able to connect the incoming data with the outgoing data. Basically, the machinery is provided with a series of specific notions, that are models created to build a database of experiences and information.
Machine learning of this type ensures that, when facing with a problem, the machine draws on the experiences already included within the system and decides the best answer based on a series of coded experiences. This kind of machine learning is used medical or voice identification industries.
A famous definition of supervised learning was given by developer Adam Geitgey in his article “Machine Learning is Fun”: “In supervised learning, the solving work is left to the computer. Once you understand the mathematical function that led to solving a specific set of problems, it will be possible to reuse the function to answer any other similar problem ”
In this case, the information is not coded: the machine has the opportunity to draw on a range of information without having had an example of their use. Consequently, the machine itself catalogs all the information, organize it and learn how to use it, learning what the best result is and how to respond to various situations. The machine will identify a logical structure in the various inputs, without having previously labeled them.
The third way: The reinforcement learning
This is probably the most complex way of machine learning. The machine is equipped with systems that can improve their learning and understand the characteristics of the surrounding environment. A very interesting example to explain how it works are the unmanned cars. The system learns from mistakes, improving performance based on previously achieved results, as well as humans do.
Where can machine learning be used? Various applications
The applications and fields of use of machine learning are truly manifold. Machine learning is constantly present in our daily lives. We provide some examples of easily verifiable applications.
- Vocal recognition. In cell phones and home automation applications, machines execute voice commands and learn new vocabulary.
- Tracer advertising. Based on the user preferences and research, advertising proposals are tailored to their interests.
- Search engine. The SERPs (Search Engine Results Pages) are the direct effect of machine learning algorithms, with unsupervised learning.
- Email spam filters: these are machine learning systems that learn to intercept messages deemed suspicious or fraudulent.
- Prevention of fraud, data and identity theft: the algorithms correlate events, user habits and spending preferences to then identify in real time any anomalous behaviors connected to a possible attempt at fraud. Scientific research in the medical field.
- Self-driving cars: thanks to machine learning those cars learn to recognize the surrounding environment and adapt their behavior according to specific situations.
- Conversational AI: machine learning allows to manage human-machine interactions through natural language recognition (NLP) and algorithms that are able to predict the most appropriate responses to customer “intents” or requests.
- Sentiment Analysis and Speech Analytics: thanks to machine learning, it is possible to build models that analyze conversations or reviews to identify the user’s “Mood”, or the topics of greatest interest that require attention. Based on this knowledge, it will then be possible to adopt appropriate marketing actions or in any case improve the services proactively.
XCALLY, when omnichannel meets machine learning
XCALLY allows companies to manage the relationship with their customers in a simple and effective way, through several channels. It is an innovative omnichannel software, already used in over 70 countries and boasting 10,000 active users. It was designed with particular attention to the use of machine learning and artificial intelligence thanks to the expertise of the co-founder Diego Gosmar, author of editorial works in this field. The system is flexible, designed to guarantee speed and efficiency to those who use it, as well as ensuring service continuity and continuous monitoring of performance.
Named “Most Recommended 2021 omni-channel contact center solution”, XCALLY offers fluid experiences across all channels, speeds up processes and improves business performance, keeps everything under control and ensures service continuity.
By exploiting artificial intelligence, it allows you to generate bots to satisfy customer requests by interpreting their intentions. It also offers the possibility to access customer information stored in the CRM system, integrates the help desk processes with the ticketing system and allows you to make the platform even more performing by integrating the services you need.