LLM

In 1989, two people were writing programs that generated language out of structure rather than meaning. One of them was Tim Berners-Lee, who that year circulated a memo titled Information Management: A Proposal — the document that became the World Wide Web. The other was a professor in a hallway in Medellín, who wrote a hundred lines of Turbo Prolog to make fun of his colleagues. I knew about the second one. The first I only read about later, the way everyone did. But the two were closer in spirit than the distance between Geneva and the Universidad de Antioquia would suggest. Both were betting that if you got the relationships right — between documents, between words — the content could take care of itself. One bet built the modern internet. The other got pinned to a cork board and read by people who never realized they were the joke. …

The past five years have felt like a reckoning. Large Language Models have proven more capable than anyone predicted — they translate languages, write code, reason about physics, and pass bar exams. And yet, every major lab investing in AI safety and robustness has come to the same uncomfortable conclusion: LLMs alone are insufficient. Intelligence requires both statistical reasoning and deterministic logic. A note on terminology: the term “artificial intelligence” is itself a misnomer. We still don’t know what intelligence is. Neuroscientists, philosophers, and cognitive scientists disagree on its very nature. What we’re actually building are systems that solve problems. And Feynman was right about flight: we don’t build planes by imitating birds. We build them by understanding aerodynamics. Similarly, we build intelligent systems not by copying human cognition, but by understanding what intelligence fundamentally requires. …

As artificial intelligence (AI) becomes increasingly integrated into every aspect of our lives, a new kind of digital exclusion is emerging—one that goes beyond the traditional concepts of alphabetism and lack of internet access. This exclusion stems from a lack of understanding and ability to interact with AI technologies, creating a modern form of illiteracy: AI illiteracy. In the same way that traditional illiteracy prevents individuals from accessing education, employment, and opportunities, AI illiteracy threatens to leave many people behind in a rapidly evolving world. The widespread adoption of AI tools—from automated customer service bots and predictive algorithms to advanced machine learning models—requires a baseline level of knowledge to utilize effectively and responsibly. Without this understanding, individuals may find themselves excluded not just from using these tools, but from making informed decisions in a society increasingly shaped by AI. …

In the twenty-first century, access to information, connectivity, and digital tools are critical to individual success and societal growth. Yet, despite technological advances and widespread communication networks, many people remain excluded due to modern forms of inequalities. Let’s explore three primary barriers: alphabetism, lack of internet access, and digital illiteracy—each representing different levels of exclusion and the limitations they impose on millions around the globe. Level One: Alphabetism — The Foundation of Understanding Alphabetism, or the inability to read and write, is the most fundamental form of exclusion. In many parts of the world, illiteracy limits personal development and access to opportunities. Without the ability to read or write, individuals are deprived of the basic means to communicate, learn, and navigate the world. Alphabetism restricts people’s ability to participate in society, perpetuating cycles of poverty and marginalization. …

Welcome to Exploring the Mind of the Machine, a blog series where I engage in deep conversations with some of the most advanced language models available today—ChatGPT, Claude, Gemini, and Grok. In these dialogues, I’m not just testing their capabilities; I’m pushing the boundaries of what it means to discuss complex philosophical ideas with artificial intelligence. From the paradoxes of ancient Greek philosophy to the existentialism of the 20th century, and from the moral dilemmas of modern ethics to the nuances of Eastern wisdom, this series will probe the models’ understanding, reasoning, and contextual adaptation across diverse philosophical frameworks. …