Where Did the Era of Artificial Intelligence Begin?
The human tendency to create algorithms can be traced back to ancient times. Even the ancient Greeks used algorithms to describe mathematical processes, such as the "Sieve of Eratosthenes" for finding prime numbers. In the 9th century, the Persian mathematician Al-Khwarizmi laid the foundations for algorithmic approaches in his book, giving birth to the very term "algorithm."
However, the true era of artificial intelligence began in the early 20th century. In a world shaken by wars and technological breakthroughs, people started asking fundamental questions: can machines think? It was in 1936 when young mathematician Alan Turing introduced a concept that became the foundation for everything we now call artificial intelligence (AI).
Turing's "machine" was ingeniously simple: it could execute any logical operation according to pre-defined instructions. It quickly became clear this was the first step towards creating a "thinking machine." In 1950, Turing formulated his famous "Turing Test": if a human couldn't distinguish between responses from a machine and those from another human, should we consider that machine capable of thought? This test continues to spark heated debates. Interestingly, Turing himself believed machines would pass his test by 2000, a topic still widely debated today.
Simply put, until recently, computer games and virtual assistants appeared intelligent through scripted and pre-written storylines. These scenarios were meticulously crafted like plays, creating the illusion of genuine interaction and behavior. Today, realism is achieved not through scripted algorithms alone but through intricate "prompt engineering," where precise and multi-layered contexts are provided to the AI. This is now an entire discipline in AI development, where engineers meticulously describe scenarios, insert example dialogues, specify tones, emotions, and communication styles. All these efforts help neural networks produce astonishingly natural responses, appearing thoughtful and alive.
Modern neural networks like GPT-4.5 boast massive knowledge bases drawn from billions of texts, articles, books, and internet resources. They can mimic the styles of famous authors, sustain complex multi-level conversations, and even create compelling scripts and storylines for books and games. For instance, AI Dungeon, powered by these neural networks, allows players to immerse themselves in worlds that seem unpredictable and uniquely fresh with every play. Yet, despite their sophistication, even these advanced AIs have not conclusively passed an enhanced Turing Test. The main limitation remains their lack of genuine contextual understanding and self-awareness. Neural networks expertly mimic consciousness, but deeper conversations quickly reveal their thoughts are merely sophisticated data processing, devoid of true awareness of themselves or their surroundings.
From Simple Logic to Deep Neural Networks
Today's AI is built upon simple yet powerful tools: logistic regression and Boolean algebra. Logistic regression is a statistical method allowing machines to predict the probability of an event, answering questions like "will this event occur or not?" For example, this method determines whether an email is spam. Boolean algebra, meanwhile, is based on logical operations like "and," "or," and "not," enabling the creation of complex logical circuits. Think of a simple light switch: "on" represents "yes," and "off" represents "no." Computers use similar logic operations but on a vast scale.
Every action of a machine can be represented as countless simple decisions combined into complex structures called neural networks. These networks mimic the workings of the human brain's neurons. Each "neuron" in the network makes decisions based on data from other neurons, producing intelligent behavior capable of solving intricate tasks, from image recognition to natural language processing.
American cognitive scientist Marvin Minsky, a descendant of Belarusian Jewish emigrants, was among the first to recognize the potential of this approach. He believed: "Machines will think, and nothing will prevent this from happening. The question is just how soon and what the consequences will be." Minsky actively developed the symbolic approach, proposing that AI could not only perform calculations but also manipulate symbols and abstract concepts like "love," "fear," "knowledge," and "understanding." For instance, symbolic AI could explain the concept of "joy" through logical connections with other emotions and situations without genuinely experiencing it. Minsky co-founded the MIT Artificial Intelligence Laboratory, laying the groundwork for modern AI research and development.
Expert Systems and Turning Points
Before the era of deep learning and neural networks, the 1980s saw the first significant breakthrough with expert systems. Computers began advising doctors, financiers, and engineers. However, it soon became clear that despite their effectiveness in specialized areas, expert systems couldn't generalize knowledge or adapt to unexpected situations.
The real breakthrough came with the rise of big data and increased computing power. In the 2010s, the world exploded with deep learning technologies. According to the Stanford AI Index 2025, AI publications rose from 88,000 in 2010 to over 240,000 by 2022, while AI projects on GitHub surged from 845 to 1.8 million. This wasn't just a technological revolution—it was a cultural transformation. This period birthed neural networks that reshaped our understanding of AI, from DeepMind’s AlphaGo, which defeated the world's best Go players, to OpenAI's GPT, capable of generating texts nearly indistinguishable from human writing.
In simple terms, deep learning involves machines "learning" from massive datasets. More data leads to higher accuracy and effectiveness. However, this approach has its dark side: developers often can't fully explain how neural networks arrive at particular decisions. That's why machine learning is frequently described as a "Pandora’s box." When we train neural networks, they can behave unpredictably, revealing outcomes no one anticipated. For instance, neural networks might discover hidden relationships within data nobody noticed before or create unique art beyond initial instructions. This makes AI both astonishing and slightly unsettling, constantly raising new ethical and practical questions.
Narrow AI in Business: Successes and Limitations
Today, AI excels in specialized tasks, from voice assistants like Siri and Alexa to recommendation systems like Netflix and Spotify. As mentioned in our previous article, these systems analyze "your queries, voice, and tone to adapt their responses accordingly."
Businesses actively integrate narrow AI. Amazon boosted sales by 35% thanks to recommendation accuracy. Tesla significantly improved autopilot safety, reducing accidents by 40% due to advanced machine learning. Yet, narrow AI still lacks the ability to generalize knowledge. For example, a medical neural network trained on lung X-rays can't diagnose heart issues, even if provided relevant data. Hence, the pursuit of Artificial General Intelligence (AGI) continues.
General Intelligence—Dream or Inevitability?
Jeffrey Hinton, a pioneer of neural networks, asserts: "Creating general AI isn't just possible—it's inevitable." Yoshua Bengio echoes this, noting, "the question isn't technical feasibility but our readiness for the consequences."
Demis Hassabis, CEO of DeepMind, believes AGI will mark humanity's next evolutionary step: "AGI will be the most significant event since the invention of fire and the wheel." General intelligence (AGI) refers to systems capable of learning and performing any intellectual task a human can. If today's narrow AI is merely a tool, AGI would be an autonomous partner, adapting independently across various tasks and environments.
Next Step: Conscious Machines—Do They Need a Soul?
This raises a fundamental question: for a system to be truly universal, must it possess consciousness? Could a machine have something like a "soul"?
Philosopher David Chalmers calls this "the hard problem of consciousness": how can we determine if a machine experiences subjective awareness? Society and science must reconsider what we mean by life and consciousness.
We will delve into this intriguing topic in our next article: "Conscious AI: Does a Machine Need a Soul?" Are you ready to debate this with a neural network? Write to us with your opinion—and tell us what unusual AI applications you'd like to see soon (please, anything except another AI undresser!).
