Artificial intelligence (AI) is no longer Science Fiction, nor is it the future; on the contrary, it is the present, part of our daily lives, and is increasingly fulfilling more significant technological functions at a global level. It is, in short, the result of a true revolution.

And although it has made significant progress, capturing the attention of investors around the world, there are still few who know and understand it in depth.

Hence the idea of this sequence of AI and its relationship and benefits from the financial world, addressing different aspects to understand how it works. This, in order to provide more tools to investors for decision making and the search for opportunities beyond the "Big Tech".

From Science Fiction to reality

The first steps in artificial intelligence date back to the 1950s, with pioneers such as Alan Turing and John McCarthy, who -among others- laid the foundations of this field.

Turing, with his "Turing Test," proposed that machines could exhibit intelligent human-like behavior. McCarthy, for his part, coined the term "artificial intelligence" and organized the Dartmouth conference in 1956, a crucial event for the formal study of this technology.

Despite initial enthusiasm, advances in AI were limited for several decades, due to the lack of computational power and the inability of algorithms to solve complex problems. Although there were some successes, progress was intermittent, and its funding and interest declined due to slow funding and interest declined due to the slow pace of progress.

It was not until the late 1990s and early 2000s that artificial intelligence began to re-emerge, driven by the availability of large volumes of data, improvements in computational capacity and advances in machine learning algorithms.

This renaissance allowed AI to begin generating successful commercial applications in sectors such as finance, healthcare, manufacturing and e-commerce. Understanding its evolution is key to recognizing how a speculative technology has become a tangible, commercial tool.

Growth catalysts

Several factors have catalyzed its progress and evolution, opening new frontiers in the investment world, which is important to consider:

• Computational capacity. The development of hardware specialized -such as graphics processing units (GPU)- has been essential for processing large volumes of data and training deep neural networks, driving areas such as image recognition and autonomous driving.driving areas such as image recognition and autonomous driving.

• Big Data. In the digital era, every interaction generates data. The large volume of information is key to train AI models and make them more accurate.

• Progress in Algorithms. Although the concepts behind neural networks have been around for decades, recent advances in deep learning ( deep learning (deep learning) have enabled machines to interpret unstructured data, enabling applications such as facial recognition.

• Investments and business adoption. Investments in artificial intelligence have grown exponentially, with technology companies-Google, Amazon and Microsoft, to name a few-leading the way. However, other sectors, such as banking, retail retail and healthcare, have been adopting this technology to improve efficiency, optimize supply chains and personalize products.

Fundamentals of AI

To understand how it works, it is crucial to become familiar with some of the concepts that are transforming industries and opening up new opportunities.

• Machine Learning (Machine Learning, ML). A branch of artificial intelligence that allows machines to learn from data without being programmed with specific rules.. It is highly relevant in sectors such as finance, where accurate predictions can transform the industry.

• Deep Learning (Deep Learning). Subdiscipline of ML that uses artificial neural networks to analyze complex data, such as images or audio. This advance has been fundamental in the development of technologies such as voice recognition and conversational AI.

• Big DataBig Data: AI relies on large volumes of data to train predictive models. The growth of digital storage has made more information available to feed these models.

• Intelligent automation. Unlike traditional automation, which is based on fixed rules, AI-assisted automation allows systems to make decisions in real time, adapting to new conditions. This capability has applications in sectors such as manufacturing, where it can improve efficiency and reduce costs.

Technology is growing by leaps and bounds, and artificial intelligence is not far behind.It has not only delivered on its initial promises, but has also established itself as a commercial insight applicable to a variety of industries. Understanding these factors is critical for investors to evaluate the benefits and risks of AI and explore opportunities beyond "Big Tech".

In future editions, we will delve into the benefits, risks and use cases of this new technology and its relationship with the financial markets..

Tomas Fernandez

Discretionary Portfolio Analyst