The three stages of AI: ANI, AGI, ASI

Artificial intelligence is evolving at almost unstoppable rates. From limited AI to the search for the AGI and a hypothetical ASI, there are currently 3 stages of AI (artificial intelligence) that you should know about.

Artificial intelligence has undergone revolutionary advances in recent decades, profoundly transforming the way technology is interacted with and opening up new possibilities in a wide range of industries.

With the goal of simulating human intelligence, AI has become a constantly changing and improving discipline that seeks to develop systems capable of learning, reasoning, and making autonomous decisions.

As this technology advances almost unstoppably, one could move from lower to higher intelligence, which means that human-like characteristics, such as emotions and certain cognitive processes, would emerge.

Currently —because who knows if a new variety may be born— there are 3 types of artificial intelligence models, each with a different ability to perform tasks. Although there is no need to be alarmed or fall into cheap talk, it is crucial to think about the future and establish an ethical and moral framework to control AI, considering its possible consequences.

stages of AI, ANI, AGI, ASI
Artificial Intelligence Types

Stages of AI: ANI, AGI, ASI

Weak Artificial Intelligence (ANI)

This stage represents the current level of most AI applications. Weak AI, or narrow AI, as it is also known, focuses on specific and limited tasks, such as speech recognition or recommendation systems.

The ANI is present in many aspects of life even if you don’t imagine it, like Google Translate and Siri. 

These tools are designed to perform specific tasks efficiently but lack understanding and self-awareness. They cannot generalize knowledge or learn independently beyond their areas of expertise, and therefore human intervention is necessary.

ANI systems can be classified into 2 categories: supervised learning systems and unsupervised learning systems. Supervised learning systems are trained on labeled data sets that allow the system to learn the relationship between the input data and the desired output. 

These applications are considered weak or limited because they cannot match human intelligence, since ANI is neither sensitive nor aware and, as in the aforementioned cases, its use is intended for a single function.

General Artificial Intelligence (AGI)

General AI refers to artificial intelligence systems that can understand, learn, and apply knowledge in a similar way to humans. 

These systems would have expanded reasoning and cognitive abilities, being able to tackle different tasks and learn new skills on their own. The AGI would be able to overcome the limitations of weak AI and could carry out intellectual tasks in a human-like manner.

The AGI does not pretend to have general cognitive abilities, that is, they are programs designed to solve a single problem and, therefore, they do not experience consciousness, they only seek to imitate it. This is far beyond the capabilities of AI – as it is currently known – and some scientists worry that it will lead to a dystopian future.

Some examples are autonomous vehicles and IBM’s Watson supercomputer. That being said, AGI in computing is envisioned as an intelligent system with comprehensive or complete knowledge and cognitive computing capabilities.

Artificial Super Intelligence (ASI)

Super intelligence implies an AI that significantly exceeds human intelligence in all aspects. It is a type of hypothetical AI, that is, it has not been possible to achieve at present but it is known what will happen if this happens. 

These systems would be capable of understanding and solving complex problems in a wide range of domains, even those that are beyond the scope of human comprehension. Super intelligence could lead to revolutionary advances in science, technology, and other fields, but it also poses significant challenges and dangers.

With super intelligence, machines can think of possible abstractions/interpretations that are simply impossible for humans to think of. This is because the human brain has a limit to the ability to think that is restricted to the billions of neurons.

The ASI would be much better in everything that the human does, be it in mathematics, sciences, arts, sports, medicine… and would have a greater memory with a faster capacity to process and analyze situations, data and feelings. 

Due to this fact, it can be assured that the decision-making and problem-solving capabilities of super-intelligent machines would be far superior and accurate compared to those of human beings.

The problem of this last stage lies precisely in what hypothetically the AI ​​would be able to do. One of the biggest risks lies in the lack of proper control and oversight over an AGI. If effective measures are not put in place, there are fears that it could become uncontrollable and act in ways that are detrimental to humans. 

In addition, the risk of malicious use of the AGI is added, since it could be used by governments or organizations for malicious purposes, such as the development of autonomous weapons or advanced cyber attacks.

Despite all this and knowing that there is still a long way to go, it is important to emphasize that AI is not the solution to all the world’s problems. It cannot completely replace human labor, nor can it solve all social and economic problems. 

However, if developed and used responsibly and with a long-term view, artificial intelligence can be a powerful tool in addressing many of the most complex challenges of today—and tomorrow.

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