Artificial intelligence And Machine Learning Differences and Uses

Artificial intelligence (AI) and machine learning (ML) are often treated interchangeably as if they were technological synonyms. In reality, these are two very different definitions, which refer to as many meanings. There is no shortage of areas of overlap, especially in the field of application, and it is in all likelihood this aspect that causes them to be alternated in a rather inappropriate manner.

It is therefore necessary to clarify what is meant by artificial intelligence and machine learning, in data science and more generally with regards to their applications in the field.

Artificial intelligence and machine learning are two emerging technologies that are used above all in the field of big data analytics, to generate value thanks to machine learning based on the digital data that companies acquire every day from IoT systems and communication channels.

Artificial intelligence and machine learning are increasingly widespread both in the context of enterprise applications and in the consumer sector, in an increasingly transparent manner, to the point that we often use them in an absolutely unaware manner.

It is not an exaggeration to say that AI and ML are now indispensable technologies for processing and analysing large quantities of data to obtain insights capable of supporting business decisions and making the processes in which they are implemented increasingly efficient.

Let’s see what they consist of and what the substantial differences are between artificial intelligence and machine learning, to understand how they fit into the technological ecosystems that companies implement during their digital transformation journey.

What is artificial intelligence or AI?

There is not, and probably will never be, a single definition of artificial intelligence, given that there are so many facets that influence it. According to the “humanistic” definition offered by Stanford University in its special AI 100, artificial intelligence is: “ That activity dedicated to making machines intelligent, and intelligence is that quality that allows an entity to function in an appropriate and with foresight in one’s environment .”

In other words, artificial intelligence is conventionally associated with all techniques capable of emulating human intelligence using computer systems, to address and solve specific or generalist problems with various approaches, exactly as the human brain would try to do.

According to the definition offered by the Oxford Reference, in fact, artificial intelligence coincides with: ” The theory and development of computer systems capable of performing tasks that normally require human intelligence, such as visual perception, recognition of spoken language, decision making and translation between languages ”.

In light of this definition, it seems vitally important to underline how artificial intelligence is often identified as a unitary system when in reality it is an umbrella term that includes multiple technologies, the combination of which allows learning and reasoning to solve complex problems.

What is machine learning or ML?

Machine learning is a sub-branch of artificial intelligence specialising in the development of algorithms and mathematical models capable of progressively learning from datasets, to improve their analytical performance over time.

Machine learning allows computers to learn from past experiences to improve the quality and relevance of the contextual information they can generate thanks to their applications.

The term machine learning first appeared in 1959, when IBM researcher Arthur Samuel developed software for playing checkers with an automatic learning process.

Given its progressive diffusion since the 1980s, machine learning is now treated as an autonomous discipline, and no longer as a simple subset of artificial intelligence.

It is important to note how machine learning definitely distances itself from expert systems and AI techniques that base their operations on a series of predetermined rules. In fact, ML uses algorithms that learn thanks to the analysis of large quantities of data, to carry out descriptive and predictive analyses useful for consciously supporting companies’ decision-making processes.

Although its roots date back well before, machine learning has accelerated the terms of its application popularity thanks to the data-driven culture, which has spread in companies since the 1990s. The current democratization of ML has been favoured by the technological maturity of the computers needed to meet the computational needs of its more complex algorithms. A dream come true thanks to cloud computing.

To clarify what is meant by machine learning, it is appropriate to define the two currently most widespread typologies: supervised learning and unsupervised learning.

In the case of supervised learning, ML algorithms solve problems using already labelled data values ​​as inputs and outputs, thanks to the discovery of their correlations. In the case of unsupervised learning, the outputs are not necessarily known, so the algorithms are called to do more exploratory work, to discover hidden models starting from unlabeled data sets.

In addition to being a sub-branch of artificial intelligence, machine learning employs various algorithms, which in turn lead to additional subsets. This includes the well-known deep learning, which is characterised by the presence of a neural network comprising three or more levels.

The differences between artificial intelligence and machine learning

The main difference between artificial intelligence and machine learning is implicit in their respective definitions, which see them directly connected but at the same time referring to applications and objectives that are placed on a different level of interpretation.

Artificial intelligence refers to very broad topics, using various techniques to simulate the reasoning of the human brain. These include machine learning, whose algorithms can learn autonomously from a data set relating to a specific context, progressively improving knowledge of the context itself and the ability to define increasingly accurate models.

Artificial intelligence constitutes the umbrella term of a technological paradigm, which in addition to machine learning is currently made up of many other subsets: cognitive computing, expert systems, natural language processing, computer vision, and generative AI, just to mention some of the most popular and currently widespread techniques on the market.

This clarification allows us to understand how, in the broad context of artificial intelligence, for complex applications, machine learning is used in combination with the other techniques mentioned. This overlap is one of the main reasons why AI and ML are often associated, sometimes interchangeably.

To better identify the differences between AI and ML, it’s useful to outline a non-exhaustive summary of their main functionalities.

Artificial intelligence

• – It is openly inspired by how the human brain carries out its decision-making processes
• – Simulates human intelligence to solve generic problems
• – Develop systems capable of performing very complex analyses on enormous quantities and varieties of data
• – Use various techniques to analyse all types of digital data available: structured, semi-structured, and unstructured

Machine learning

• – Develop computer processing systems capable of learning autonomously from a historical data set
• – Uses machine learning algorithms to generate predictive models (output).
• – Machine learning is aimed at improving the learning process over time and consequently, the quality of the analyses carried out
• – Performs activities in specific contexts and the data used for training must be of sufficient quality to describe them accurately, to avoid biases that are too important compared to the real situation to which it refers
• – Use structured and semi-structured data

Artificial intelligence adopts a generalist approach, typical of human intelligence when it approaches a problem by carrying out reasoning based on logic. In this sense, AI presents itself as a synthetic brain, while machine learning by definition narrows the reference context, to optimise and make increasingly accurate an analysis aimed at solving a specific case.

How companies can leverage AI and ML

The combined and simultaneous use of AI and ML generates important benefits in many application areas. Today, artificial intelligence, in its broadest definition, is used both in the consumer context, typical of everyday activities involving ordinary people, and in enterprise applications, by public and private companies of any size in all business sectors.

The ability of artificial intelligence and machine learning to automate processes is proving to be increasingly strategic in generating value, allowing companies to make more informed and conscious decisions.

Organizations can leverage artificial intelligence and machine learning in various ways, with various technological approaches and solutions, to make their business processes more efficient, as well as enable new ones.

Among the most recurring application areas of AI and ML in corporate contexts in all business sectors, we find:

• – Data analytics: Artificial intelligence and machine learning are currently used in business intelligence and business analytics to analyse large quantities and varieties of data. Among the main objectives we find the recognition of patterns that allow us to understand market trends and make targeted forecasts on demand.
• – Process automation: AI and ML provide various applications to facilitate the automation of business processes, including document management, supply chain, logistics, invoicing, and accounting.
• – Marketing: AI and ML are frequently utilised to enhance the degree of personalisation in marketing campaigns. Analysing customer behaviour and transactions allows companies to profile them with greater accuracy compared to traditional systems, enabling the design of personalised marketing campaigns capable of anticipating customer needs
• – Customer service: Artificial intelligence is used in various contexts as part of customer care activities. In fact, chatbots powered by generative AI (Gen-AI) are increasingly widespread allowing you to answer customer questions quickly and efficiently, as well as suggest content and initiatives to interact with them in the most natural way possible.

Human resource management: AI and ML are also used with increasing frequency in HR activities. For example, companies can use AI-based applications to select candidates based on their affinity for the positions sought, monitor employee performance, and suggest personalised training courses based on interests and/or professional roles.