Artificial intelligence (AI) is the potential of a computer to perform functions and reasoning typical of the human mind.
Machine Learning: What Is It
Machine learning is defined as the ability of machines, designed as a computer, to learn. In practice, it does not tell the computer to perform a specific action, but you ask him to learn knowledge from experience, thanks to which you can then execute actions.
Therefore, there is learning when the program’s performance improves after carrying out a task or completing an action, even if it is wrong, starting from the assumption that the principle “making mistakes you learn” applies.
In more IT terms, programmers usually write a code through which the machine is told what to do: every time you click on a point on the screen with the mouse, a command is automatically generated that makes the computer respond in a certain predetermined way. . For example, if you click on the Word icon, a new document opens automatically, because someone created some automatic reaction to our click action.
This does not happen when it comes to machine learning: the programmer, in this case, creates algorithms in which data sets are inserted, and from this data, the computer tries to understand what it is and respond accordingly.
Imagine the cash desk in a canteen, a motorway restaurant, or a fast-food restaurant. Each time a customer fills his tray, the cashier looks at the dishes and types in the prices for each of them. The human brain immediately recognizes if there is pie, meat, or tiramisu in a word. It has learned with years and years of experience that what it sees in the tray is precisely pie, meat, or tiramisu.
The learning algorithms don’t tell the program “if it has four equal sides, then it’s a mess”, but through the data, they say to the computer that there is something and that this something has four sides, it is yellow, it has something dark red on top. , etc.
The task of the machine, in this example, will be to find a model that is general enough to be able to describe the figures present in each tray that it will have to read, in such a way as to be able to establish, in the presence of new examples that it has never seen, if one it is a pie, meat, tiramisu or other.
In doing so, the computer will try to understand why what it is “seeing” is a mess and not something else.
Likewise, he will distinguish apples from pears and never give them the same price because they will have learned that they are different fruits.
So, in summary, in machine learning, you pass examples to the machine. From these examples, the device builds the rules that describe the criteria and will understand by itself whether or not a new case responds to the power it has derived.
How Machine Learning Works: Learning Methods
To date, there is no single way machines learn.
We have just said that machine learning algorithms start from the analysis of examples to extract rules that describe them; these examples consist of data that can be structured or unstructured and can be defined as the input of the analysis process.
Structured data is organized in tables in which columns represent a variable and rows its value; instead, unstructured data do not have this direct variable-value association since they are, for example, audio, text, video, or images.
These data are manipulated, analyzed, and transformed thanks to the learning algorithm and provide results, giving the output.
Depending on the type of “knowledge” we expect, the algorithms can use four learning methodologies :
- machine-learning with supervised learning ;
- machine-learning with unsupervised learning ;
- machine-learning with semi-supervised learning ;
- machine-learning with reinforcement learning.
Machine Learning With Supervised Learning
With this approach, data is supplied to the computer as input and information relating to the desired results with the aim that the system identifies a general rule that connects the incoming data with the outgoing data.
In practice, they are given examples of inputs and outputs so that the machine finds a link between them to identify a rule to reuse for other similar tasks.
This type of learning is mainly used for classification problems: an example of application is the anti-spam systems of emails. When a new email arrives, the system can decide whether it should be tagged as spam or not.
Machine Learning With Unsupervised Learning
In this case, only data sets are provided to the system and not information relating to the desired result, not knowing which category they belong to. Therefore the machine is asked to find a rule that groups the cases presented according to characteristics derived from the data themselves.
The aim is to “go back” to hidden schemes and models, to identify a logical structure in the inputs without these being previously labeled.
Unsupervised learning is often used in medical or biology disciplines for diagnostic analysis or the identification of genetic groups; moreover, it finds great application today in Big Data when it is necessary to correlate different data and extract unknown information.
Machine Learning With Semi-Supervised Learning
This approach could be defined as a “hybrid” model since the computer is provided with a set of data, as in supervised learning, but incomplete. Some examples also have the output and are labeled; others not.
This approach is used to improve the predictions made by the machine on unlabeled data and usually requires the intervention of an expert/analyst.
The approach is mainly used in classification and clustering problems or the description of cause-effect relationships between variables.
Machine Learning With Reinforcement Learning
In this case, the system must interact with a dynamic environment in which all the inputs must achieve a goal.
A learning routine determines the behavior and performance of the system based on reward and punishment:
once achieved, it receives a sort of reward, also learning from mistakes, identified through “punishments.”
With such a model, the computer knows, for example, to beat an opponent in a game or to drive a vehicle, concentrating efforts on carrying out a specific task and aiming to reach the maximum value of the reward.
In other words, the system learns by playing or driving and by making mistakes, improving its performance according to the results achieved previously.
Reinforcement learning is used in all fields where the machine must respond to changes in the environment. For this reason, it is frequently used in robotics to control the movements of automata and driverless driving (the so-called driverless cars). It also finds application in industrial fields in production and quality control and various other sectors.
Machine Learning , Data Mining, Deep Learning: Common Goals, Different Approaches
Often there is confusion around the use of the terms: some media use “machine learning”, “deep learning,” and “data mining” as synonyms when in reality, these are not at all.
While there is a common goal of extracting information, patterns, and relationships that can be used to make decisions, the three have different approaches and skills.
Data mining can be considered as a super assortment of many different methods.
It involves many different areas, including machine learning. It applies different methods, such as statistical algorithms, text analytics, time series analysis, and other analytics, to extract information and include the study and training on data storage and manipulation.
Deep learning (or deep learning, sometimes translated with depth learning) is, however, that particular case of feature learning, a branch of machine learning, supervised or less, which characterizes artificial neural networks equipped with two or more layers capable of processing information in a non-linear way.
Deep learning combines increasingly powerful computers with particular neuronal systems to learn the complicated patterns of large volumes of data.
Researchers are trying to apply successful examples of pattern recognition to more complex tasks such as machine language translation, medical diagnoses, and countless other social and business problems.
Machine Learning: Examples In The Daily
Machine learning applications are now prevalent and are firmly part of our daily life.
Let’s think about the use of search engines: anyone today writes a word on Google. Once registered in the search bar, the engine returns lists of results that are nothing more than the result of unsupervised machine learning algorithms.
And in addition to everyday life, this technology is already used by half of the companies and actively contributes to daily work: this is the data that emerges from research commissioned by ServiceNow to Oxford Economics, entitled “The Global CIO Point of View”, which involved 500 CIOs worldwide, 318 of which in Europe.
It was also found that sectors that work with large volumes of data see improvements in the workplace both in terms of efficiency and competitive advantage since they use machine learning.
This is the case of Mastercard, a leader in digital payment solutions, which uses it to automate what it calls “fatigue,” or repetitive and manual activities, and free humans to use them in other activities that increase productivity.
The American company also uses this technology to increase change management across its ecosystem of products and services. For example, machine learning tools help determine which changes are least risky and require additional controls.
In addition, it is used to detect anomalies in your system that suggest hacker attempts at intrusion. When suspicious behavior is detected, switches are activated that protect the network: the systems assign a score to the scams and constantly observe the transactions to update this score and evaluate the subsequent transactions.
But that of Mastercard is undoubtedly not the only example of application in companies:
– UPS, the international express courier company, has applied Machine learning in the logistics sector, implementing the so-called intelligent navigation that has led to the optimization of routes. In this way, UPS was able to identify that, in some American cities, the right turn is always the most convenient one, the one that saves the most time to get to your destination;
– Amazon is experimenting with ‘Amazon Go,’ a type of shop without cashiers: the cameras can recognize the products purchased, charging the customer the amount once he arrives at the exit;
– in the insurance sector, this technology makes it possible to recognize any frauds: the companies are always presented with the same photographs of accidents, and the analysis makes it possible to identify fraud attempts;
– in the oil and gas sector, machine learning is used to find new energy resources, analyze minerals in the soil, predict sensor failure in the refinery, streamline oil distribution to make it more efficient and profitable.
And again, machine learning has made it possible:
– the real-time check of the quality of the products on the store shelves and reporting to the employees of the items to be replaced ;
– the analysis of the behavior of specific subjects for terrorism prevention activities and
– the creation of medical devices with wearable sensors that use the data to check in real-time the state of a patient’s health.
Why Is Machine Learning Important?
The use of data is essential to interpret the past and monitor the present and extract value from it for the future.
The ability of machine learning to significantly reduce decision-making times and improve the efficiency of our choices may now seem like something less than sensational. Still, it is precisely technologies like these that change our way of life.
Companies that harness the power of Machine Learning will make progress in a short time, thanks to the speed and efficiency of the decision-making processes that it allows. Nobody can afford to wait: whoever does will be left behind.
Also, because the beauty of Machine Learning is that its uses are almost unlimited, where there is value in quickly analyzing and deriving insight from the data or where there is value in identifying trends or anomalies in large amounts of data, it can have a transformative effect and has a role to play.
To all this, beanTech can give an added value: combining theory with practice.
From the statistical modeling aspects to the computational performances allowed by new technologies (cloud and not), as well as to the integration of Machine Learning outputs to existing business processes: beanTech is the technological partner with matured skills and experience, able to create solutions customized for the customer’s business.