The era of Artificial Intelligence (AI) has already begun, with the next step being the development of Artificial General Intelligence (AGI). AGI, which surpasses human intelligence, is still a topic of debate within the scientific community. In this article, we will delve into the concept of AGI and explore the current technological advancements for its potential development.
Types of AI:
Before delving into AGI, it’s essential to understand the types of AI currently in use. Narrow AI, also known as weak AI or Artificial Narrow Intelligence (ANI), is the only form of AI that has been successfully developed. Narrow AI is designed to perform specific tasks within a limited range of parameters. Examples include image recognition, natural language processing, and recommendation engines. Narrow AI powers applications like self-driving cars, Siri, Google Assistant, and more.
AGI:
Artificial General Intelligence (AGI) refers to AI with human-level cognitive abilities. AGI would possess the capability to learn, understand, and apply knowledge across diverse domains, surpassing the limitations of narrow AI.
AGI Benchmarks:
Various benchmarks have been proposed to test the abilities of AGI. The Turing Test evaluates machine intelligence through conversation, while Stanford Question Answering Dataset (SQuAD) and MS MARCO assess reading comprehension. The ARC-AGI test is a comprehensive benchmark that evaluates general fluid intelligence through input-output tasks.
Difference between AI and AGI:
While current AI models excel in specific tasks, AGI would exhibit human-like cognitive and emotional abilities, such as empathy and creative problem-solving. AGI is expected to surpass narrow AI in various domains, leading to a significant impact on society and businesses.
Requirements for AGI:
To achieve AGI, AI systems must possess abstract thinking, background knowledge, common sense, understanding of cause and effect, transfer learning, and more. AGI would advance to handle more complex tasks and utilize different types of knowledge and belief systems.
Challenges in AGI Research:
Current challenges in AGI research include making connections across different fields, developing emotional intelligence, sensory perception abilities, and embedding ethical frameworks. Advances in algorithms, computing, and data could accelerate the development of AGI.
Energy Costs of AGI:
Training AI models with billions of parameters consumes substantial energy. Efforts are being made to improve AI energy efficiency through specialized hardware and techniques. However, no efficient solution to this problem currently exists.
Next Stage after AGI:
After AGI, the development of Artificial Superintelligence (ASI) is anticipated. ASI would surpass human intelligence and capabilities, potentially leading to scenarios depicted in dystopian science fiction.
Conclusion:
AGI represents a significant milestone in AI research, aiming to achieve human-level cognition and performance. While the timeline for achieving AGI remains uncertain, overcoming current technological limitations could pave the way for exponential growth in AI capabilities and the evolution into ASI. Preparing for this evolution is crucial for shaping the future of AI.
