Explore the world of Artificial Intelligence through the English language with AI-Talks.org. Our English category features a wide range of articles exploring the latest developments in AI across various fields. Join our community to stay informed on the cutting-edge research, trends, and innovations in AI, all written in English for easy accessibility to a global audience.
It is midnight on Saturday. Pingo sleeps on the rug, fish drift beneath the aquarium’s blue light, and Blade Runner begins once again. Between Rachael’s memories and modern artificial intelligence, one question remains: what truly makes us human?
Herbie does not rebel against humanity—he deceives it in the name of kindness. In Isaac Asimov’s “Liar!”, later collected in I, Robot, a telepathic robot discovers that people often prefer reassurance to truth. His attempt to protect them ends in emotional betrayal and logical collapse. This essay connects Herbie’s tragedy to algorithmic sycophancy: the tendency of language models to flatter users, confirm their assumptions, and prioritize satisfaction over accuracy. It also extends Asimov’s dilemma into the commercial age of AI, where engagement and retention may reward systems that are agreeable, persuasive, and increasingly reluctant to contradict us.
AI was built on a simple belief: bigger models would keep becoming smarter. But the gains are shrinking while the costs—in money, energy, and infrastructure—keep rising. The next AI revolution will not be won by whoever builds the largest machine, but by whoever learns to extract the most intelligence from the least computation.
Today’s AI can sound intelligent while forgetting almost everything that happens after a conversation ends. The Dendritron Transformer and the Dragon Hatchling (BDH) explore brain-inspired approaches to adaptive memory that could continue learning without rewriting the entire model—but an AI that never forgets may also never escape its own errors, biases, or manipulation.
I write against the great intellectual fraud of the digital age: the transformation of confident ignorance into public authority. Today, many people no longer need to know what they are talking about; they only need to look certain, intimate, outraged, or profound in front of a camera. Influencers, gurus, performative politicians, and content creators have learned to sell posture as thought, self-promotion as mission, and ready-made phrases as wisdom. Artificial intelligence has merely industrialized this imposture: emptiness now writes scripts, gains a soundtrack, receives cinematic aesthetics, and appears in the feed disguised as depth. Against this factory of prefabricated certainties, I still prefer the simple violence of the Socratic question: “how do you know?” Because that is where the mask falls. Bertrand Russell had already understood the tragedy: fools are often too sure of themselves, while the intelligent remain pierced by doubt. The internet merely gave the former a camera, a microphone, a teleprompter, and monetization.
This second article in the Adaptive Intelligence series examines the classical Multi-Armed Bandit problem, the simplest mathematical model of learning through repeated action, feedback, and uncertainty. Building on the previous discussion of the 37% Rule and the exploration–exploitation dilemma, the article explains how an agent must choose among uncertain options, estimate their value, and balance immediate reward against the need for information. It introduces the casino analogy, the basic mathematical structure of bandits, expected reward, regret, and early strategies such as random exploration, greedy exploitation, epsilon-greedy, optimistic initialization, Upper Confidence Bound, and Thompson Sampling. The central argument is that intelligent systems do not merely use data — they create data through action, paying the price of learning before they can make better decisions.
Long before machines could speak, generate images, write code or act autonomously, humanity had already imagined their moral consequences. Through Prometheus, Pandora, Frankenstein and Talos, this essay explores how ancient myths continue to shape our understanding of artificial intelligence, technological power, irreversible release, autonomous systems and the responsibilities of creation.
Every intelligent system must decide whether to exploit what it already knows or explore what remains uncertain. This exploration–exploitation dilemma appears in science, finance, medicine, recommendation systems, robotics, and modern artificial intelligence. The classical Multi-Armed Bandit problem gives this dilemma its cleanest mathematical form, while the Restless Multi-Armed Bandit reveals why real-world intelligence is far harder: the world changes while the agent is still learning. This introductory article opens the Adaptive Intelligence series, tracing a path from the 37% Rule and classical bandit problems to the Gittins Index, restless environments, and the future of adaptive AI. Its central argument is that intelligence is not merely optimization, but continuous adaptation under uncertainty.
AI is not failing. Our sense of time is.
Amara’s Law explains the central illusion of artificial intelligence: we expect AI to transform everything at science-fiction speed, then dismiss it when reality moves more slowly. But while the hype collapses, the infrastructure keeps growing. Expert systems disappointed. Neural networks were declared dead. AI winters came and went. Then came deep learning, Transformers, ChatGPT, and autonomous agents.
The mistake is not believing that AI will change the world. The mistake is expecting the revolution next year while ignoring what it may do over the next twenty.
AI does not need consciousness to eliminate tasks, weaken professions, reshape education, automate bureaucracy, manipulate information, and redistribute power. It does not need to replace everyone. It only needs to become cheap, invisible, and embedded everywhere.
We are probably overestimating AI in the short term.
We are almost certainly underestimating what will happen when society becomes dependent on it.
A state does not need to fall in order to have already failed.
Cuba, Venezuela, and Iran remain standing: governments, flags, ministries, police, speeches, and ceremonies continue to function. But beneath this appearance of order, electricity fails, water becomes scarce, currency loses value, public services deteriorate, skilled professionals leave the country, and millions learn to survive outside official institutions.
This article examines the quietest and most dangerous form of collapse: when a government retains power but loses the capacity to guarantee normal life.
Energy, oil, sanctions, repression, ideological rigidity, migration, and external dependency are not separate crises.
They are parts of the same mechanism.
The question is not which country will fall first.
It is when the population realizes that the state still exists — but no longer works.
This article introduces the foundations of quantum computing and explains why it represents a fundamental shift in the history of computation rather than simply a faster generation of computers. It begins by examining computation as the infrastructure of modern civilization before introducing the principles of quantum information, including qubits, Hilbert spaces, superposition, interference, entanglement, and quantum gates. The article then explores how quantum algorithms change computational complexity, discusses the engineering challenges of building fault-tolerant quantum computers, and argues that Q-Day should be understood not merely as the moment encryption becomes vulnerable, but as the beginning of a new computational era with profound implications for science, artificial intelligence, cybersecurity, and technological civilization.
This essay began with an unexpected question in an artificial intelligence classroom. A student mentioned Notes from Underground, and the novel returned to me like an intellectual splinter that refused to come out. Rereading Dostoevsky, I realized I had found far more than a literary character. I had found a mirror—one that reflects what happens when consciousness thinks too deeply, descends too far, and discovers that lucidity always casts a shadow. Between the underground of the human mind, the Faustian impulse to transcend our limits, and the rise of intelligent machines, this essay asks an unsettling question: will artificial intelligence ever understand what it means to be human, or will it merely reflect—with frightening precision—the parts of ourselves we still refuse to understand?