learning, which is considered a subset of artificial intelligence, is not a new
concept. The term itself originated in the 1950s and was coined by IBM’s Arthur
Samuel, based on his research into computers playing checkers (also called draughts,
it is a thinking game played on a chessboard where players move figures or
tiles diagonally). In the competition between the computer and the American
master of checkers, the computer won, which was astonishing, and the result
opened up an unlimited world of possibilities.
course, machine learning has gone far beyond simple computer games, and we have
been living with it for quite some time, knowingly or unknowingly. It is used,
for example, in financial services, public administration, healthcare, retail,
transport and energy industries; in short, in most industries dealing with
large amounts of data. In the following, we will take a closer look at what
machine learning is, how it works and where it is already being used. So, let’s
look at how “trained” machines already are.
learning studies computers using algorithms to improve through experience and
data automatically. Machine learning algorithms build a model based on sample
data, known as training data so that they can predict or make decisions without
being explicitly programmed to do so.
mathematician, computer scientist and cryptologist Alan Turing, in his seminal
article in 1950, introduced a reference standard for proving machine
intelligence, according to which a machine must be intelligent and responsive
in a way indistinguishable from a human. He wrote: “Machine Learning is an
application of artificial intelligence where a computer/machine learns from
past experiences (input data) and makes future predictions. The performance of
such a system should be at least human level. “
technical definition was given in 1997 by American computer scientist and
professor Tom M. Mitchell: “A computer program is said to learn from experience
E concerning some class of tasks T and performance measure P, if its
performance at tasks in T, as measured by P, improves with experience E.”
handwriting recognition learning problem:
Task T: recognising and
classifying handwritten words within images
Performance measure P: per cent
of words correctly classified, accuracy
Training experience E: a dataset
of handwritten words with given classifications
To perform task T, the system learns from the dataset provided. A dataset
is a collection of many examples. An example is a collection of features.
How does it work?
The American multinational developer of analytics software, SAS Institute, states on its website that machine learning is generally divided into four types: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
algorithms are trained using labelled examples, such as an input where the
desired output is known. The learning algorithm receives a set of inputs and
the corresponding correct outputs, and the algorithm learns by comparing its
actual output with the right outputs to find errors. It then modifies the model
learning is commonly used in applications where historical data predicts likely
future events. For example, the algorithm can anticipate when credit card
transactions are likely to be fraudulent or which insurance customer is expected
to make a claim.
learning: is used
against data that has no historical labels. The system is not told the
“right answer.” The algorithm must figure out what is being shown.
The goal is to explore the data and find some structure within it.
learning works well on transactional data. It can, for example, identify
segments of customers with similar attributes who can then be treated similarly
in marketing campaigns. These algorithms are also used to recommend items and
identify data outliers.
learning: This type
of machine learning uses a certain amount of labelled data and a large amount
of unlabelled data to train the model. The aim of this is to classify some
unlabelled data using labelled data.
used for robotics, gaming, and navigation. The algorithm discovers which
actions yield the most significant rewards through trial and error with
reinforcement learning. This type of learning has three primary components: the
agent (the learner or decision-maker), the environment (everything the agent
interacts with) and actions (what the agent can do). The goal is for the agent
to choose actions that maximise the expected reward over a given amount of
Structured data and unstructured data
are used in machine learning. A
structured data type is predefined, labelled, and well-formatted before being
stored in a data storage. Unstructured data is in native format, and it’s not
processed until it is used.
Abhay Parashar, a computer scientist and software developer, explains on the KD Nuggets website how the development and production of a machine learning model are carried out:
Data Collection: The first stage of any kind of machine learning model. In this stage,
the appropriate data is decided, and then it is collected using some algorithm
Data Processing: In this stage, the data collected in the first stage is pre-processed
by handling all the null values, categorical data, etc. Also, in the same
stage, the data features are made in the same range if they are not already.
Model Building: In this stage, first, we choose appropriate algorithms to create the
model, and then with the use of a specific programme, the model is built.
Model Evaluation: After the model is created, it is evaluated using some techniques of
statistics like accuracy score, z score, accuracy matrix, and more.
Model Saving and Testing: After a successful evaluation of the model, it is saved for future use,
and real-time testing is done using it.
Examples of the machine
learning used in everyday life?
other businesses in the financial industry use machine learning technology
mainly in two areas: to identify important insights in data and to prevent
fraud. The insights can identify investment opportunities or help investors
know when to trade. They can also use data mining to recognise clients with
high-risk profiles or use cyber-surveillance to pinpoint warning signs of
agencies such as public safety and utilities have a particular need for machine
learning. According to the SAS Institute, they have multiple data sources that
can be mined for insights. Data analysis can identify ways to increase
efficiency and save money. Machine learning can also help detect fraud and minimize
health care industry, machine learning can help medical experts analyse data to
identify trends or red flags that may lead to improved diagnoses and treatment.
Retailers rely on machine learning to capture data, analyse it and use it to personalise
a shopping experience, implement a marketing campaign, price optimisation,
merchandise planning, and customer insights. Machine
learning is partly to blame for the fact that annoying ads are displayed everywhere
energy industries, it is used to analyse minerals in the ground, predict
refinery sensor failure, streamline oil distribution to make it more efficient
and cost-effective, etc. The data analysis and modelling aspects of machine
learning are essential tools to delivery companies, public transportation, and
other transportation organisations.
learning technology can offer us a lot, as evidenced by its many applications.
The European Union is also aware of this, as it has included machine learning,
alongside artificial intelligence, in its digital strategy. These technologies
are closely intertwined, along with others that we have already written about. However,
we forgot to mention that there is no concern about the machines conspiring
against people, as humans create the model and set the framework for it.
article is part of joint project of the Wilfried Martens Centre for European
Studies and the Anton Korošec Institute (INAK) Following the path of
digitalization in Slovenia and Europe. This project receives funding from the
information and views set out in this article are those of the author and do
not necessarily reflect the official opinion of the European Union
institutions/Wilfried Martens Centre for European Studies/ Anton Korošec
Institute. Organizations mentioned above assume no responsibility for facts or
opinions expressed in this article or any subsequent use of the information