Meta CEO Mark Zuckerberg has announced plans to develop Artificial General Intelligence (AGI) and intends to responsibly open-source it for public use. 

In a video on Instagram, Mr Zuckerberg emphasised the need for AGI in advancing AI chatbots and assistants. The company aims to build an open-source AGI for widespread benefit.  

“It has become clearer that the next generation of services requires building full general intelligence. Building the best AI assistants, AIs for creators, AIs for businesses and more,” Mr Zuckerberg said in the video. “Our long-term vision is to build general intelligence, open-source it responsibly, and make it widely available so everyone can benefit,” he added. 

This move follows similar investments by OpenAI, Google, and Meta in generative AI, with recent models like Google’s Gemini and Meta’s Llama 2 contributing to applications like AI chatbots and smart glasses. 

Difference between AI and AGI

Definition

AI (Artificial Intelligence): AI refers to machines or computer systems designed to perform tasks that typically require human intelligence. These tasks can include actions like problem-solving, learning from experience, understanding natural language, and recognizing patterns.

AGI (Artificial General Intelligence): AGI takes AI a step further. While AI is task-specific, AGI aims to possess intelligence that can be applied across a wide range of tasks, similar to human intelligence. In essence, AGI would be a machine with the ability to understand, learn, and apply knowledge in diverse domains, much like a human being.

Scope of Tasks

AI: AI is designed for specific tasks. For example, image recognition, voice assistants, and recommendation systems are all examples of AI applications. These systems excel in their designated tasks but lack the versatility to perform tasks outside their programmed scope.

AGI: The key differentiator here is the range of tasks. AGI would be capable of seamlessly transitioning from one task to another, displaying a level of adaptability and generalisation that AI lacks.

Learning and Adaptation

AI: AI systems are generally trained for a specific task using large datasets and predefined algorithms. They operate within the boundaries of their programming and lack the ability to generalise their learning to other unrelated tasks.

AGI: AGI, on the other hand, would possess the ability to learn from one task and apply that knowledge to perform new, unseen tasks. This reflects the human capacity to learn from various experiences and apply that knowledge across different aspects of life.

Understanding Context

AI: AI systems may struggle to grasp the nuances of context. For instance, a voice recognition AI might misinterpret a command if it lacks contextual understanding.

AGI: AGI would ideally comprehend context and make decisions with a depth of understanding comparable to human cognition. This contextual awareness is vital for AGI to navigate the complexity of diverse tasks.

Human-Like Reasoning

AI: AI operates on predefined rules and patterns, lacking the ability for independent, human-like reasoning. It excels within its specific domain but doesn’t possess the cognitive flexibility of a human mind.

AGI: The aspiration for AGI is to emulate human-like reasoning, enabling it to tackle a myriad of tasks with a level of cognitive flexibility and problem-solving akin to human intelligence.

While AI represents a powerful tool for performing specific tasks, AGI is the next frontier – a form of artificial intelligence capable of versatile learning, understanding context, and applying knowledge across a broad spectrum of activities. While AGI remains a theoretical concept, advancements in AI continue to push the boundaries, bringing us closer to the realisation of more intelligent and adaptive machines. 

The use of AGI by the public comes with its share of risks and rewards.  On the positive side, it might accelerate AGI development through collaborative efforts and bring diverse perspectives into play. Transparency could also increase, as users gain insight into the technology. However, security risks could arise, leading to misuse, and ethical concerns might become more challenging to address. The uncontrolled evolution of AGI, competitive disadvantages for companies, and regulatory challenges are also potential downsides.