What is Artificial General Intelligence (AGI)?

While AGI remains a distant goal for computer scientists, advancements in generative AI, natural language processing, deep learning, and computer vision continue to propel research in this area. Rodney Brooks, a roboticist at MIT and co-founder of iRobot predicts that AGI may not emerge until the year 2300, as reported by McKinsey.

One of the primary challenges researchers face today in developing artificial general intelligence (AGI) is replicating human emotional intelligence. Creativity in AI systems necessitates emotional reasoning, which current neural network architectures cannot adequately emulate. 

Furthermore, the capacity of existing AI models to make connections is restricted to specific domains and application areas. Additionally, sensory perception presents another barrier, as AGI needs to engage physically with the external environment and perceive the world like humans.

Even though we do not have cross-domain mastered AI systems that can transfer knowledge between unrelated tasks, such as applying chess strategies to logistics, we definitely see task-specific autonomy in many of the applications of software-defined autonomous vehicles and healthcare that require adaptive learning but within the same domain. 

Difference between AGI and Agentic AI

In the previous article, we explored the concept of agentic AI systems. This section will shift our focus to the distinctions between artificial general intelligence (AGI) and agentic AI.

1. Scope: AGI fundamentally refers to the capability of performing intellectual tasks across various domains, whereas agentic AI concentrates on executing autonomous actions within specific environments.
2. Autonomy: Although both AI systems are designed to operate independently without human intervention, AGI exhibits a broader autonomy that spans multiple domains and tasks.
3. Learning and Adaptability: Artificial general intelligence possesses the ability to learn and adapt its knowledge across different fields, while agentic AI is typically trained with a focus on particular applications and industry-specific contexts.
4. Use Cases: Hypothetical applications for AGI may include areas such as medical research, global policy formulation, and artistic innovation. In contrast, agentic AI currently demonstrates practical applications in fields like logistics (e.g., route optimization), healthcare, and customer service.

So, even though an AGI would inherently need to be agentic, not all agentic AI systems are AGI.

Is AGI here?

While some scientists may argue that advanced large language models (LLMs) such as Meta’s Llama, OpenAI’s GPT, Anthropic’s Claude, and the recently open-sourced DeepSeek have already achieved artificial general intelligence (AGI), critics contend that such claims are misguided. They believe that while these models can understand and correlate a wide range of topics, perform various tasks, and process multimodal inputs, this does not equate to true AGI.

Meta’s chief AI scientist, Yann LeCun, stated in an interview with TIME that while LLMs perform impressively when trained at scale, they still have significant limitations. He says, “We see today that these systems hallucinate; they don’t truly understand the real world. They require enormous amounts of data to attain a level of intelligence that ultimately is not very high.”

The ongoing debate surrounding AI continues, yet the significant and rapid advancements in this field can only be described as a revolution. Although the speed of innovation is seen, we may eventually reach a saturation point where growth slows, similar to what we observed with smartphones in the early 2010s and 2020s. It will be fascinating to see how research and adoption unfold over the next decade, particularly in light of the ethical considerations associated with these AI systems.

Article Topics

agentic AI  |  AGI  |  AI  |  AI/ML  |  Artificial General Intelligence  |  edge AI  |  LLM