What Is Artificial General Intelligence and How Does It Work?

Artificial General Intelligence (AGI) hasn’t become real yet. However, research into this type of artificial intelligence where machines think and learn as people continues in different parts of the world. The idea behind AGI is to have machines develop self-awareness and consciousness. These developments have already started manifesting in innovations like self-driving cars. Once developed fully, AGI can potentially blur the intellectual differences that currently exist between machines and humans.

Although it’s still too early to tell whether machines can simulate human intellectual capabilities fully, the concept of AGI is fascinating. In this article, we explore AGI further to help you understand how it differs from artificial intelligence (AI) and the technologies behind it.

Artificial General Intelligence is a theoretical form of AI that can learn, understand, and apply knowledge to perform intellectual tasks like humans. Although AGI isn’t a reality yet, its design incorporates adaptability, flexibility, and problem-solving skills. These skills will enable it to perform any intellectual task that a human can, or in some instances, outperform human abilities.

AGI is designed to address gaps in current AI systems. Currently, AI systems have limited scope. They cannot self-teach or complete tasks they are not trained to perform. AGI promises complete AI systems that utilize generalized human cognitive abilities to perform complex tasks across different domains. Artificial general intelligence examples already exist in self-driving cars.

In decades past, computer scientists advanced machine intelligence to a point where machines perform specific tasks. For instance, AI text-to-speech tools use deep learning models to establish the link between linguistic elements and their acoustic features. These machine-learning models learn from huge volumes of audio and text data and then generate AI speech and voice patterns.

Today, AI systems are designed to perform specific tasks. They can’t be repurposed to work in other domains. Their computing algorithms and specifications are limited and they rely on real-time data for decision-making. This form of machine intelligence is considered narrow or weak AI.

AGI seeks to advance current AI capabilities. It seeks to diversify the tasks that machines can perform to enable them to solve problems in multiple domains instead of one. This makes AGI a hypothetical representation of a strong, full-fledged AI. Such AI will have general cognitive abilities that enable it to solve complex tasks, just like humans.

The concept of AGI is based on the theory of mind that underpins the AI framework. This theory focuses on training machines to understand consciousness and learning as fundamental aspects of human behavior. Besides applying algorithms, AGI will incorporate logic into machine learning and AI processes to mirror human learning and development.

With a solid AI foundation, AGI is expected to learn cognitive abilities, make judgments, integrate learned knowledge in decision-making, manage uncertain situations, and even plan. General artificial intelligence will also facilitate machines to conduct imaginative, innovative, and creative tasks.

The concept of AGI is still in the theoretical stage. Research on its viability and efforts to create AGI systems continue in different parts of the world. The following are the emerging technologies that will most likely characterize AGI:

This is an engineering discipline that involves the creation of mechanical systems that automate physical tasks. In AGI, robotics facilitate the physical manifestation of machine intelligence. Robotics plays an important role in supporting the physical manipulation ability and sensory perception required by AGI systems.

This AI branch enables machines to generate and understand human language. NLP systems convert language data into representations known as tokens using machine learning and computational linguistics.

It’s an AI discipline that involves training multiple layers of neural networks to understand and extract complex relationships from raw data. Deep learning can be used to create systems that understand different types of information like audio, text, video, and images.

A technology that supports extraction, analysis, and comprehension of spatial data from visual data. For instance, self-driving cars rely on computer vision models to analyze camera feeds in real time for safe navigation. Computer vision relies on deep learning technologies to automate object classification, recognition, and tracking among other image-processing tasks.

A subset of deep learning, this technology enables AI systems to generate realistic and unique content from knowledge learned. Generative AI models use huge datasets to train, which enables them to answer questions from humans in text, visuals, and audio formats that resemble natural human creations.

If it becomes a reality, there is no doubt artificial general intelligence will change how we work and live. But the journey to making it work isn’t smooth. In developing this emerging technology, computer scientists must find ways to make AGI models connect between domains the way humans do. Another challenge that needs to be overcome relates to emotional intelligence.

Neural networks cannot replicate the emotional thinking required to drive creativity and imagination. Humans respond to situations and conversations depending on how they feel. Considering the logic embedded in current AI models, replicating this ability and improving sensory perceptions to enable machines to respond and perceive the world the way humans do remains an uphill task.