Types of Artificial Intelligence: ANI, AGI, and ASI
Artificial intelligence, or simply AI, is one of the hottest topics of our time. Virtual assistants, algorithms on Netflix, and even robot vacuums—AI is already everywhere, although we rarely realize it. But what really lies behind these two letters? Can a machine think like a human? And what if one day AI gains consciousness? These are questions that scientists, philosophers, and science fiction writers have been asking themselves for decades. The truth is that what was once a crazy idea is now becoming a reality. It is worth learning about the types of artificial intelligence, as this is crucial to further discussion.
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History of Artificial Intelligence.
Before computers became commonplace, thinking about artificial intelligence began in literature. In 1921, Czech playwright Karel Čapek coined the word “robot” in his play, initiating a public discussion about “artificial people.” Just a few years later, in 1929, Japanese professor Makoto Nishimura built Gakutensoku, the first Japanese robot capable of expressing facial expressions. At that time, steam-powered machines embodied dreams of automated assistants.
The real revolution began in the mid-20th century. In 1950, Alan Turing, a legendary mathematician, published a groundbreaking paper on “machines that think.” He asked whether a machine could conduct a conversation in a manner indistinguishable from that of a human, creating the famous Turing test, which remains a milestone in the field of AI to this day. Shortly afterward, in 1956, a new discipline was born at the Dartmouth conference. It was there that John McCarthy first used the term “artificial intelligence.”
The early years were full of optimism. The first programs that learned to play checkers on their own were developed, along with the LISP programming language, dedicated to AI research. In 1966, Joseph Weizenbaum created ELIZA, the first chatbot to simulate conversations with people, which made a tremendous impression. But the enthusiasm quickly faded, and the “AI winter” arrived in the 1970s and 1980s. It turned out that machines were not as intelligent as promised.
The revival came in the 1990s. In 1997, the world held its breath as the IBM Deep Blue computer defeated world chess champion Garry Kasparov. It was a symbolic victory – the machine had surpassed humans in strategic thinking. Another breakthrough was IBM’s Watson supercomputer, which won the quiz show “Jeopardy!” in 2011 by answering questions in real time. That same year, the virtual assistant Siri reached millions of iPhones, bringing AI into everyday life.
The last decade has seen a true explosion of technology. From 2015 to 2017, Google DeepMind’s AlphaGo program defeated Go masters in a game that is far more complex than chess. But the real revolution was brought about by generative models. In 2020, OpenAI introduced GPT-3, a language model capable of generating text that is difficult to distinguish from human-written text. DALL-E followed, generating images, and in 2022, the public release of ChatGPT brought AI into the mainstream. Meanwhile, Polish researchers created the Bielik model, while the largest technology companies, such as Google and Meta AI, continue to compete in developing new, more powerful models.
Types of intelligence: specialist, genius, and superhuman
To understand where we are with AI, we need to distinguish its three main types.
1. Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence is the AI we encounter every day. ANI, also known as “weak AI,” is designed for one specific task. It is a digital specialist that knows everything about its field, but nothing beyond it.
Real-Life Examples:
- Voice assistants (Siri, Alexa) understand commands, but only those they have been programmed to understand.
- Algorithms on Netflix analyze what you have watched to recommend your next series.
- Facial recognition systems on phones.
- Chatbots, even the most advanced ones like ChatGPT, although they may seem versatile, operate within the framework of their algorithms and are not self-aware.
Although ANI is incredibly useful, it lacks flexibility. It cannot learn from mistakes like humans do, nor think abstractly beyond its field of operation.
I believe it is worth dividing ANI into two types, such as fixed and variable, or narrow and very narrow. This distinction is particularly important from the user’s perspective. ChatGPT, which can perform certain tasks, differs significantly in this respect from voice assistants such as Siri or Alexa (which can mainly perform simple, pre-programmed tasks), or, for example, AI-based filters for changing the appearance of photos.
2. Artificial General Intelligence (AGI)
It is the “Holy Grail” of AI research and the goal pursued by the biggest players in the industry. AGI, known as “strong AI,” would have human-level or superior intelligence and be able to perform any intellectual task that a human can. Think of AGI as a digital genius who can not only play chess, but also attend university, solve a puzzle, and even plan how to brew coffee.
The Path to AGI: Scientists are working on various approaches. One of them is machine learning, which enables algorithms to learn and adapt on their own. Another, more futuristic approach is the emulation of the entire human brain. This involves precisely copying all neural connections and recreating their operation in a computer simulation, which would require enormous computing power—on the order of 500 petabytes of memory. Another idea is to use a global network of supercomputers, the first of which are expected to start operating as early as this year, which is intended to accelerate work on AGI.
When will AGI emerge? Opinions are divided. Sam Altman, the head of OpenAI, believes it is a matter of “a few thousand days.” Meanwhile, Nvidia CEO Jensen Huang predicts that AGI will emerge by 2028. Regardless of the date, one thing is certain—AGI would change the world beyond recognition.
3. Artificial Superintelligence (ASI)
This is a purely theoretical vision of the future. ASI is a hypothetical form of AI that would surpass the human mind in every respect. It would be so powerful that it could solve problems that are impossible for us today. Some speculate that ASI might even possess emotions and the ability to form relationships. It is precisely this concept that leads to questions about the “technological singularity”—the point at which technological development becomes uncontrollable and unpredictable.
The Greatest Mystery: Can AI Be Conscious?
This is a question that has occupied the minds of philosophers for centuries. As St. Augustine put it, “What, then, is time? If no one asks me, I know. If I try to explain it to someone who asks, I do not know.” The same is true of consciousness. It is “thinking that knows it is thinking.” Consciousness is not merely knowledge, but “knowledge that we know.” It is a deeper level that allows us to experience the world and feel pain, taste, or color. In philosophy, we call these subjective, qualitative experiences qualia. The problem is that “consciousness cannot be grasped like a stone; it cannot be weighed or dissected.”
The debate on this topic boils down to two issues, which the philosopher David Chalmers summed up perfectly:
- Easy problems: These are questions about how the brain processes information, integrates it, or generates behavior. These are issues that science can solve.
- The hard problem of consciousness: This is the question of how and why physical processes in the brain lead to subjective experience. What is it like to be something? Chalmers believes that this is something more than mere physics and that it cannot be explained even by knowing everything about the brain.
The most famous argument against conscious AI is John Searle’s thought experiment “The Chinese Room.” Imagine that you are in a room and someone outside passes you Chinese characters. You do not know Chinese, but you have instructions telling you how to manipulate these characters in order to generate a response. An outside observer will think that you understand Chinese because your answers are correct. But you know that you are simply following rules, without understanding a single word. Searle argues that a computer is like you in that room: it manipulates symbols (data), but does not understand their meaning.
This argument, however, has its critics. Some claim that although you do not understand, the entire system—you, the instructions, and the symbols—does. Others add that genuine understanding requires interaction with the environment, which Searle rejects, saying that the robot still merely reacts based on its program.
However, although AI cannot currently experience emotions, some researchers believe that consciousness could be recreated in a machine if the brain’s functional organization could be copied. This is the main aim of the computational neuroscience theory of consciousness. There is also the theory of panpsychism, which suggests that consciousness is a fundamental feature of reality and exists in some form in everything that exists.
The Dark Side and Hopes: What Awaits Us?
The development of AI, especially toward AGI, poses serious challenges that require immediate attention.
Risks and Concerns
- Labour market: Will robots take our jobs? This is one of the greatest concerns. Automation may lead to rising unemployment and widening social inequalities.
- Manipulation and disinformation: AI can be used to create false content, such as deepfakes, which threatens public order and democratic debate.
- Increased crime and surveillance: AI can be used for cybercrime or mass surveillance, threatening our freedom and privacy.
- The “control problem”: What if a superintelligent machine escapes our control and its goals cease to align with ours? Experts acknowledge that this is a serious existential risk requiring an urgent solution.
Opportunities and Hopes
On the other hand, AI promises enormous benefits. It can help solve global problems such as climate change and accelerate progress in medicine and engineering. Properly managed AGI can benefit all of humanity, provided that we approach it responsibly.
In response to these challenges, governments, including the European Union, are creating new laws, such as the EU AI Act, which aims to classify AI systems according to their level of risk and ensure their transparency.
Are Large Language Models (LLMs) the Best Path to AGI? Or Are They a Dead End?
Currently, when we think about conscious artificial intelligence, or AGI, we mainly think of language models. The reason is simple: messages written by ChatGPT look, at first glance, as though they were written by someone conscious. Communication through words is what we most strongly associate with consciousness. There is also a certain versatility here: language models can operate in agent mode. That is why Sam Altman long claimed that ChatGPT 5 would be an AGI-class model, and now claims that it was a significant step toward AGI.
Increasingly often, however, the question arises: What if language models are a dead end?
There are a few issues here. The limitation may not lie in the model’s “linguistic” capabilities themselves, but rather in how it operates. It is possible that in a model based on predicting tokens through statistics and probability calculations, consciousness cannot “emerge.” However, it is possible that consciousness could emerge in models that generate text not by predicting the next word or syllable, but by creating noise. This noise is then processed into text.
Another approach argues that language models as a whole are a dead end because they are not task-oriented models. Perhaps consciousness has a greater chance of emerging where artificial intelligence must remember on its own to monitor certain tasks and analyze efficiency. What if AGI were to emerge as a program for managing traffic, or, for example, crab farming?
The Future Is Being Written Before Our Eyes
Artificial intelligence is constantly evolving. ANI is already part of our everyday lives, AGI is undergoing intensive research, and ASI remains a distant vision. Regardless of whether machines gain consciousness or remain merely sophisticated tools, one thing is certain: AI is changing our civilization.
Ensuring that this transformation serves the good of all requires the involvement not only of programmers and scientists, but also of philosophers, lawyers, and each of us. It is a shared effort that will determine whether, in the future, AI will be a partner and ally in the pursuit of wisdom, or a source of new threats.