History of Artificial Intelligence

Artificial Intelligence: Birth, Applications, and Future Trends
For those who don't use it daily, artificial intelligence seems like a concept from blockbuster movies or science fiction books. But the truth is that it's a set of almost century-old concepts that are increasingly prevalent and to which we often resort without realizing it. Discover what artificial intelligence is, what it's used for, what its risks and challenges are, and what we expect from it in the future.

Artificial intelligence has become a broad and revolutionary tool with countless applications in our daily lives. Capable of creating robots that respond with human-like responses and responding to voice requests with practical features on mobile phones and speakers, artificial intelligence has attracted the attention of Information and Communications Technology (ICT) companies around the world and is considered the Fourth Technological Revolution following the proliferation of mobile and cloud platforms. Despite the innovation it brings to our lives, its history is a long process of technological advancement.
Definition and Origins of Artificial Intelligence

When we talk about “intelligence” in a technological context, we often refer to a system's ability to use available information, learn from it, make decisions, and adapt to new situations. It implies the ability to solve problems effectively, given existing circumstances and limitations. The term “artificial” means that the intelligence in question is not inherent to living beings, but is created through the programming and design of computer systems.

As a result, the concept of “artificial intelligence” (AI) refers to the simulation of human intelligence processes by machines and computer programs. These systems are developed to perform tasks that, if performed by humans, would require the use of intelligence, such as learning, decision-making, pattern recognition, and problem-solving. For example, managing vast amounts of statistical data, detecting trends, and making recommendations based on them, or even implementing them.

Currently, AI is not about creating new knowledge, but rather about collecting and processing data to make the most of it when making decisions. It is based on three basic pillars:

Data. This is the collected and organized information on which we want to automate tasks. It can be numbers, text, images, etc.
Hardware. This is the computing power that allows us to process data faster and with greater precision to make software possible.
Software. It consists of a set of instructions and calculations that allow us to train systems that receive data, establish patterns, and can generate new information.

But what are AI algorithms? This is the name given to the rules that provide instructions for the machine. The main AI algorithms can be those that use logic, based on the rational principles of human thought, and those that combine logic or intuition (deep learning), which use the working patterns of people's brains so that the machine learns just as they would.
How was artificial intelligence born?

The idea of creating machines that mimic human intelligence was present even in ancient times, with myths and legends about automata and thinking machines. However, it wasn't until the mid-20th century that their true potential was explored, after the development of the first electronic computers.

In 1943, Warren McCulloch and Walter Pitts presented their model of artificial neurons, considered the first artificial intelligence, even though the term didn't yet exist. Subsequently, in 1950, British mathematician Alan Turing published an article entitled "Computing Machinery and Intelligence" in the journal Mind, in which he asked the question: Can machines think? He proposed an experiment that came to be known as the Turing Test and which, according to the author, would determine whether the machine could exhibit intelligent behavior similar to that of a human or indistinguishable from one.

John McCarthy coined the term "artificial intelligence" in 1956 and drove the development of the first AI programming language, LISP, in the 1960s. Early AI systems focused on rules, which led to the development of more complex systems in the 1970s and 1980s, along with a surge in funding.

Currently, AI has experienced a renaissance thanks to advances in algorithms, hardware, and machine learning techniques.

As early as the 1990s, advances in computing power and the availability of large amounts of data allowed researchers to develop learning algorithms and consolidate the foundations of today's AI. In recent years, this technology has experienced exponential growth, driven largely by the development of deep learning, which leverages multi-layered artificial neural networks to process and interpret complex data structures. This advancement has revolutionized AI applications, including image and speech recognition, natural language processing, and autonomous systems.
Source