Artificial General Intelligence (AGI) refers to AI systems that demonstrate human-level general intelligence and possess the ability to perform a wide array of cognitive tasks.

At its core, AGI aims to create an AI that mimics human capabilities, potentially operating autonomously and learning to perform a variety of functions. Paul Ferguson, an AI consultant and founder of Clearlead AI Consulting, emphasizes that AGI would have the capacity to comprehend, learn, and apply knowledge across multiple fields.

"The key advantage of AGI would be its ability to transfer knowledge from one domain to another, solve new challenges, and exhibit creativity and reasoning on par with human intellect," Ferguson explains.

In simpler terms, Ghazenfer Monsoor, founder and CEO of Technology Rivers, contrasts AGI with current AI. Unlike today’s AI, which excels at specific tasks such as facial recognition or voice translation, AGI could tackle nearly any task. Monsoor elaborates, “Our healthcare software, for instance, leverages AI to assist doctors in diagnosing diseases based on medical data, but AGI would transcend that. It could recommend new treatments, analyze studies, and forecast health issues in ways we've never thought possible.”

The Current State of AI

Before diving into AGI, it's important to first grasp the concept of intelligence itself, says Sertac Karaman, an Associate Professor of Aeronautics and Astronautics at MIT. Karaman explains that intelligence is what sets humans apart from other species. It includes several traits, notably the ability to reason, link thoughts together, and draw conclusions that are not immediately obvious.

Although glimpses of such intelligence have been evident since the 1960s, these early examples typically demonstrated intelligence within narrow domains and couldn't generalize across diverse human interactions.

“AGI, in contrast, would be an intelligence that is artificially created, rather than naturally evolved, and it would apply to all human endeavors,” Karaman notes. “An AGI system would be able to reason and link thoughts in ways similar to human thinking.”

He adds that current AI tasks are usually non-autonomous. While AI systems today are proficient at processing large datasets and presenting information in a more intuitive manner, they still lack the autonomy and broad capabilities of AGI. AI can correlate data across different areas, such as suggesting recipes based on the contents of your fridge, but its capabilities remain constrained.

Sarah Hoffman, an AI advocate at AlphaSense, points out that while AI can excel in specific tasks, such as playing chess, it cannot transfer knowledge to unrelated fields. She gives the example of DeepMind’s AlphaGo, which, while successful in Go, was unable to perform well in other games.

How Does AGI Differ from AI?

Karaman asserts that AGI will possess reasoning and the ability to chain thoughts, which will allow it to operate autonomously. Unlike today's AI, which only provides information, AGI will have the capacity to complete tasks from start to finish. This autonomy is the key distinction between AGI and the AI we currently use.

Ferguson agrees, stressing the importance of distinguishing between true AGI and the AI systems of today. He explains that AI, including large language models (LLMs), are sophisticated pattern-matching tools trained on vast amounts of data. While they are increasingly flexible, they are still far from achieving true general intelligence.

AI's Role in Advancing AGI

Karaman envisions AGI not as a singular milestone, but rather as a gradual evolution in reasoning capabilities. He believes that advancements in AI will continue to shape industries and transform economies at an accelerating pace.

Ferguson concurs, noting that the push for more adaptable AI systems is already providing considerable commercial value. In his work with businesses, Ferguson has observed that the real impact of AI is in its integration into existing workflows and decision-making.

“The advancements we’re seeing in AI, especially in making systems more flexible and general, are opening up new possibilities for businesses,” Ferguson explains. For example, LLMs are already being employed beyond content creation.

Hoffman attributes this progress to increased investment and research, which is leading to more powerful AI systems. Though AGI may still be a distant goal, the innovations driven by current AI are already transforming industries.

How Close Are We to True AGI?

Despite some tech companies claiming they’re on the verge of AGI, Ferguson believes we’re still a long way from achieving it.

“I estimate we are decades away from true AGI,” he states. “Although we’ve made substantial progress in narrow AI applications and seen impressive developments in the flexibility of AI systems, the leap to general intelligence comes with many technical and conceptual hurdles.”

Hoffman also shares the view that true AGI is still a long way off. She acknowledges that today’s generative tools are more advanced and useful than their predecessors, but the gap between AI capabilities and human intelligence remains vast and will likely persist for the foreseeable future.

That said, she notes that the progress made by current AI systems is already driving innovation in sectors like healthcare and finance. AGI, however, holds the potential to unlock even greater advances across multiple industries.

Ferguson highlights that the road to AGI involves tackling challenges in areas such as common-sense reasoning, transfer learning, and simulating consciousness. In the near future, he believes businesses should focus on making AI systems more logical, reliable, and seamlessly integrated into human workflows.

"Right now, I see AI having the greatest impact in its current form rather than as a fully realized AGI," Ferguson concludes. "For now, AGI remains an academic pursuit and a long-term research goal rather than an immediate reality."