AI, fire of Prometheus or fruit of the Tree of Knowledge of good and evil?
This article was writen in English and translated with ChatGPT and made the final revision myself.
AI (Artificial Intelligence) has emerged into our world at one of the most tumultuous moments in recent history—when democratic values, which some nations began to establish in the 20th century, are under siege; while a cultural war in these countries is clear evidence of a deep and critical fracture… when, over the last 40 years, radical Islamist movements have fostered terrorism with the aim of destroying democratic nations through acts of extreme barbarity and unassimilated mass migration, both in America and Europe. And to top off this grim scenario, China—an openly totalitarian nation—has become an economic and technological powerhouse, enforcing social control through AI with relentless precision. Its ambitions for global expansion are deeply embedded in its ideological and political foundations.
AI amplifies fears and fuels extremist positions. It is a tool that seemingly offers infinite organizational and analytical power, enabling humans to apply knowledge with the intent and direction of whoever controls it—whether for scientific and technological advancement or for terrorism and authoritarianism. It is a superpower in the hands of individuals and organizations that have yet to fully acknowledge the consequences of their actions.
The goal of this article series is both focused and deliberate: I will provide a brief history of how AI has entered our world, explain what it is and how it works, explore how to use it, and discuss some of the risks it poses to those who engage with it. As in all my writings, my primary objective is to highlight the responsibility that comes with our decisions and to uncover the deeper motivations behind our ambitions—so that we may advance with AI more wisely.
Why Do I Have Any Merit to Be ‘Heard’?
A few months ago, I discovered ChatGPT. At first, I resisted using it—I had no temptation to become a better writer through this tool or to enhance any synthetic or cybernetic skill. I simply had no ‘urgent need’ that would compel me to try it.
Then, something unexpected happened: ‘I found out that in just two days, I would have an interview for a commercial leadership position overseeing all of Spain, with the potential to expand into other European countries.’ The biggest challenge? It was an industry I knew absolutely nothing about.
I asked a friend if he knew anyone in that market, and he replied: —Why don’t you ask ChatGPT?
With the right questions, I was able to get close to ‘sounding’ like an expert in that industry—without pretending to be one. In just seconds, ChatGPT provided all the summarized information I needed to gain a solid understanding and a clear vision of what had previously been an entirely unknown field to me.
From that moment on, I became deeply interested in understanding how this incredible tool worked, where it came from, and the fascinating history behind its development. I was amazed at how it is shaping the future in fields like medicine, logistics, marketing, e-commerce, human resources, project management, research, and nearly every area of human knowledge and expertise.
Since then, I have participated in several online selection processes to join AI training teams. —I know it sounds extraordinary, but it’s far less glamorous than it seems. —Currently, I work remotely with three companies in ‘data annotation’ and ‘AI training’, which has given me a unique perspective on how AI operates and the personal risks associated with using this incredible tool we have created.
There are several angles from which to explore this story. On one hand, there’s the technological development (hardware) that increases “FLOPS” (Floating Point Operations Per Second), a fascinating topic that determines computing power. On the other, there's the study of how humans learn—fundamentally connected to “neural networks” (NN), which are circuits of interconnected nodes that mimic the brain’s neural networks and its learning processes, enabling artificial intelligence to come to life.
Before diving deeper, let’s clarify something: What Is Intelligence?
One of NASA’s objectives in sending the “Rover robots” to Mars was to find evidence of past intelligence on the planet—such as ‘a group of stones arranged by size or shape.’ Pattern recognition is one of the brain’s core abilities, and our capacity to predict the outcome of an action on a pattern we’ve never encountered before is “intelligence”—in other words, the ability to solve new problems by drawing from past experiences that, at first glance, seem unrelated to the new situation.
Keep this definition in mind—it will be useful!
A brief history of AI
I have explored countless theories about when interest in artificial intelligence first emerged, with various proposals dating back to the mid-20th century. However, in my view, one figure stands out as an indisputable starting point—both practically and conceptually: Alan Turing.
In the early 1940s, World War II was in a dire state for Britain and the rest of Europe. The Germans were using a secret encryption system generated by the ENIGMA electromechanical machine, allowing them to communicate freely and coordinate attacks. The brilliant mathematician Alan Turing was recruited by British intelligence as a cryptographer. In less than a year, he developed the “Bombe machine”, which worked by systematically testing encrypted message combinations, generating a chain of logical deductions. The machine could detect contradictions and discard incorrect combinations, allowing it to crack the Enigma code. (Alan Turing, The Secret Weapon of the Allies, National Geographic). This was essentially the recognition of contradictions in patterns within encrypted messages.
With this breakthrough, Turing successfully deciphered Enigma communications, an achievement that, according to Winston Churchill, "shortened the war by two to four years and saved over 16 million lives." (La Razón).
Bombe Machine developed by Alan Turing (The Imitation Game film depiction)
Following this monumental success, in October 1950, Turing published a scientific paper titled Computing Machinery and Intelligence, in which he posed a groundbreaking question: "Can machines think?" In the article, he introduced the "Imitation Game," later known as the Turing Test. The experiment involved three participants: a human interrogator, a human respondent, and an automated respondent. The goal was for the interrogator to determine, through text-only communication, which of the two respondents was human and which was a machine. Turing proposed that if the interrogator could not reliably distinguish between the two, the machine was exhibiting human-like behavior—thus, it could be said to "think."
Turing further suggested that "Instead of trying to produce a program to simulate the adult mind (with fixed rules for every possible scenario), why not rather try to produce one which simulates the child?" He emphasized learning through trial and error, stating: "The machine might be thought of as analogous to the brain of a child." However, such a system required hardware that would not be invented until 1967—multilayer neural networks.
Yet, despite early theoretical progress, the 1970s, 1980s, and 1990s saw AI research enter what became known as the "AI Winter." This period of stagnation was due to a combination of factors: limited computational power, skepticism from both scientists and the media, and diminished investment in the field.
A turning point came in 1986, when Geoffrey Hinton published a groundbreaking paper on Deep Learning, presenting a method for training neural networks—effectively implementing Turing’s idea of trial-and-error learning. Hinton’s research involved training machines through the analysis and segmentation of images. While promising, progress remained constrained by hardware limitations.
In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov, marking a milestone in AI’s ability to surpass human strategic thinking—not through intuition, but via raw computational power, evaluating millions of board positions per second (which are, essentially, patterns). (The Technology Behind Deep Blue by Iván Rivera Jofré).
By the early 2000s, computational power had increased 4.4 times beyond Deep Blue’s capabilities, ushering in a new era of AI. Around 2005, the term Big Data gained prominence, and social media platforms began processing vast amounts of user data, analyzing browsing and consumption habits, and refining algorithms to maximize screen time.
A fascinating side note: at the beginning of the 21st century, advancements in statistical modeling and mathematics allowed for highly accurate weather forecasting, an early example of AI’s predictive capabilities in real-world applications.
The Dawn of AI in the 21st Century
Through technology and human ingenuity, we have managed to probe the depths of our own intelligence, creating a simulation of the human brain through neural networks. This has given birth to artificial intelligence—one that is now breaking free from the constraints that once defined it. In the next article, we will explore how AI has developed processes we never explicitly taught it, seemingly taking on a life of its own as it learns and reacts.
But this tool forces us to ask a crucial question: Is AI the fire that Prometheus stole from the gods to benefit humanity, or is it the forbidden fruit from the Tree of Knowledge—enticing us with the illusion of divinity, only to expose our deepest vulnerabilities and shortcomings?
Without a doubt, AI is an extraordinary tool—a power tool of immense capability. And like any power tool, it comes with a warning:
"For use by responsible adults only. Keep out of reach of children."
And here, dear reader, age has nothing to do with it... 😉
Here I share what I believe is the best video that analyses the history of AI
Next Week…
How Does AI Work?
There are incredible AI tools like ChatGPT (OpenAI), Copilot (Microsoft), Bard (Google), and Claude, which takes a more ethics-focused approach to artificial intelligence… how did these extraordinary tools learn to draft letters, suggest diets, and even provide strategies for mastering personal skills?
In the next article, we’ll dive into the fascinating process that allows AI to understand, generate, and refine human-like responses—exploring the mechanisms behind its learning and reasoning capabilities.
Stay tuned! 🚀