In the information age in which we live, Artificial Intelligence (AI) and Machine Learning (ML) are driving epochal changes across various sectors: from manufacturing to healthcare, including communications and corporate data management. These are two related yet distinct disciplines, yet they are often misunderstood and referred to as synonyms.
In this article, we’ll take a journey through time to explore the incredible advancements that have transformed AI from a mere science-fiction dream into an everyday reality, and we’ll finally clarify the difference between artificial intelligence and machine learning.
Table of contents
- From *The Imitation Game* to Modern Artificial Intelligence
- A Visionary Chat Prototype: The Turing Test
- An Initial Definition of Artificial Intelligence (and a Critique)
- Italy's Contribution and a New Perspective on AI
- Beyond Imperative Programming: Contemporary AI
- Deep Blue and Minimax: The Playing Abilities of Computers
- DeepMind's AlphaGo: The AI That Changed the Game
- So, what's the difference between artificial intelligence and machine learning?
- From Medicine to Email Classification: Real-World Examples of Machine Learning
- AI for Everyone: Toward General-Purpose AI
- Discover the potential of AI with artea.com
From *The Imitation Game* to Modern Artificial Intelligence
The “imitation game” was introduced by the British mathematician and computer scientist Alan Turing in his 1950 essay titled “Computing Machinery and Intelligence.”
Often referred to as the Turing Test, it is a hypothetical scenario designed to assess a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human being.
A Visionary Chat Prototype: The Turing Test
The Turing Test, with its dialogue-based interaction between a human and a machine, can be considered (also) a visionary example of what we would today call a “chat” or an online conversation. In the test, in fact, the examiner communicates via a keyboard and a monitor: a primitive form of textual interaction, similar to what we see in chats.
“The Imitation Game” laid the groundwork for an initial definition of artificial intelligence.
An Initial Definition of Artificial Intelligence (and a Critique)
The first definition of Artificial Intelligence can be summarized as follows: if a machine, during a conversation (the Turing Test), could convince a human judge that it was human, then it would be reasonable to consider it “intelligent.”
One might argue that this definition does not capture the full range of intelligence and that the ability to mimic human conversation does not necessarily imply true understanding or consciousness. The Turing test, however, represents a fundamental starting point in discussions about artificial intelligence and machine learning.
Italy's Contribution and a New Perspective on AI
Over the years, AI has made tremendous strides, and not just in the English-speaking world. In Italy, Marco Somalvico was one of the pioneers in introducing this field. After working at Stanford University, he was one of the founding members of Siri, the Italian Association of Industrial Robotics (1975), and of AI*IA, the Italian Association for Artificial Intelligence (1988). He left a lasting legacy in the training of new Italian researchers and academics.
According to Somalvico’s perspective, artificial intelligence is the discipline dedicated to the development of hardware and software systems capable of emulating or replicating mental processes and behaviors typical of human intelligence: rational thinking, machine learning, natural language recognition, and rational decision-making. It is a discipline that aims to create artificial systems capable of performing complex tasks in a manner similar to humans, using knowledge, reasoning, and learning.
Beyond Imperative Programming: Contemporary AI
The simulation of intelligence, particularly in the field of modern AI, represents a significant step forward compared to classical programming. Classical programming is characterized by its imperative nature; that is, it involves writing a program that provides detailed instructions on how to perform a series of operations on a computer.
These instructions are executed sequentially and deterministically, like a list of steps to follow, often only if certain conditions are met.
Deep Blue and Minimax: The Playing Abilities of Computers
An example of an imperative algorithm—one that does not learn from experience—is called Minimax, and it is used in zero-sum strategy games, such as chess, to make optimal decisions. In this type of game, two players compete against each other, with one trying to maximize their score while the other tries to minimize it.
The goal of the algorithm is to determine the best move for a player in a given game state, taking into account all of the opponent’s possible moves in a game tree according to the rules.
Deep Blue is the computer system developed by IBM that used Minimax to defeat world chess champion Garry Kasparov in 1995. This victory demonstrated the power of AI in solving complex problems and helped further advance research in the field of AI applied to games and beyond. The Minimax algorithm does not learn from experience or data, but operates based on a static evaluation of positions in the game tree.
DeepMind's AlphaGo: The AI That Changed the Game
Modern AI goes beyond imperative programming, in the sense that machines learn from data and improve over time. The simulation of intelligence, through machine learning and the continuous acquisition of knowledge, represents a form of autonomy that approaches human intelligence. Artificial agents thus become capable of making decisions based on past experiences, improving their performance, and adapting to new situations autonomously, without requiring explicit programming for each individual scenario.
AlphaGo is an AI program developed by DeepMind, a Google subsidiary, known for its exceptional success in Go, one of the most complex board games in the world. AlphaGo’s victory over one of the best players, Lee Sedol, in 2016 was a historic event and demonstrated that machines can excel at games that require not only deep strategic insight but also an understanding of context.
This has inspired further research into the application of AI in fields beyond gaming, such as medicine and scientific research.
So, what's the difference between artificial intelligence and machine learning?
This transition is crucial to the evolution of AI and to the goal of creating smarter and more autonomous systems. This brings us to the main point of our article: the difference between artificial intelligence and machine learning.
In a nutshell, we can say that Artificial Intelligence is the broad field aimed at creating intelligent systems, while Machine Learning is one of the techniques within AI that enables systems to learn from data and improve their performance on their own.
Among AI algorithms, the category that relies on statistics and mathematics to extract patterns and insights from data can be defined as statistical learning algorithms. These algorithms are widely used in a variety of applications, including pattern recognition, prediction, classification, and data analysis.
From Medicine to Email Classification: Real-World Examples of Machine Learning
A possible classification of these algorithms might include: linear regression, classification, supervised and unsupervised learning, deep learning, Bayesian learning, and feature engineering.
Specific applications range from medical diagnosis to forecasting product demand, machine translation, financial fraud detection, and email classification.
AI for Everyone: Toward General-Purpose AI
Today, practical applications of AI are available to everyone and range from voice recognition in virtual assistants like Siri or Alexa, to personalized recommendations on audio and video streaming platforms such as Spotify and Netflix, all the way to sensor-based autonomous driving systems.
The next evolutionary step will begeneral-purpose artificial intelligence (GPAI), a class of technologies that dynamically simulate human intelligence and are not limited to a specific domain or application but can adapt to a variety of contexts. Their versatility makes it difficult to predict scenarios for their use and potential impacts, not least the assessment of associated risks (see the ChatGPT and deepfake cases): the AI Act aims to address these aspects as well, as part of a regulatory framework for artificial intelligence in Europe.
Discover the potential of AI with artea.com
If this brief history of the evolution of machine learning and artificial intelligence has piqued your curiosity and fascinated you, why not give us a call so we can explore possible solutions for your company’s needs together with our data scientists?
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