Artificial General Intelligence or AGI refers to artificial intelligence (AI) systems that possess human-like general intelligence and can adapt to a wide range of cognitive tasks.
In other words, the goal of AGI is essentially to create the most human-like AI possible. This will be an AI that can teach itself to essentially operate in an autonomous manner.
Paul Ferguson, AI consultant and founder of Clearlead AI Consulting, says AGI would be capable of understanding, learning, and applying knowledge across diverse domains.
“The key advantage of AGI would be its ability to transfer learning from one domain to another, solve novel problems, and exhibit creativity and reasoning comparable to human intelligence,” says Ferguson.
In simpler terms, Ghazenfer Monsoor, founder and CEO of Technology Rivers says unlike today’s AI, which is so good at specialized functions like facial recognition or voice translation, AGI can do almost anything you ask it to do.
His company develops healthcare software that uses AI to perform specific tasks. It can help doctors diagnose diseases based on medical data. “But [AGI] goes beyond that,” says Monsoor. “It can provide new treatments, analyze many studies, and predict health problems, in ways we never imagined.
State of AI
Before we can understand AGI, we must first understand what intelligence is, says Sertac Karaman, Associate Professor of Aeronautics and Astronautics at MIT.
He says intelligence is what differentiates us humans from any other species on the planet. It has several attributes. But most importantly, it involves the ability to reason, chain thoughts together, and come to conclusions that are not obvious from the start.
He says there are glimpses of such “intelligence” that were demonstrated since the early days of computing; as early as the mid-1960s. However, most of these demonstrated intelligence in a narrow set of fields and conversations and did not seem to generalize to all human conversation.
“Now, artificial general intelligence would be an “intelligence” that is not naturally evolved (hence, artificial) and covers all human endeavors and conversations (hence, general),” explains Karaman. “An AGI system would be able to reason and chain thoughts, similar to us humans.”
He says the tasks that we can do with AI today are typically limited to non-autonomous tasks. While AI today is already very capable, its main role is to gather information from astronomically-sized datasets and present it in a more human-like, natural manner.
It is also able to correlate existing data with other key information you provide, says Karaman. For instance, you tell AI what you have in your fridge and what food you like, and it can tell you a few recipes. “In principle, how AI writes code with/for software engineers is not a very different process, albeit technically more involved,” he says.
Sarah Hoffman, AI evangelist at AlphaSense explains that while AI today can outperform humans in specific tasks like playing chess, it lacks the versatility to transfer its knowledge to unrelated tasks.
“Consider DeepMind’s AlphaGo that, in 2016, outperformed human champions at the game of Go but couldn’t play other games, even simpler ones,” says Hoffman.
How does AGI defer from AI?
Karaman says AGI, on the other hand, will feature reasoning and chain of thought. This will enable more autonomy and agency. Instead of presenting us with information, AGI will be able to go do a task end to end. That would be the key difference between AI and AGI, points out Karaman.
Ferguson too believes it’s crucial to distinguish between true AGI and the current state of AI. Today’s AI systems, he says, including large language models (LLMs), are essentially sophisticated pattern-matching systems trained on vast amounts of data.
“While they’ve become increasingly flexible and can be applied in various settings, they’re still far from exhibiting genuine general intelligence,” says Ferguson.
AI’s influence on AGI
Karaman believes AGI is not so much of a one-train stop, but more like new reasoning capabilities coming online with increasing capability. He thinks related technologies will continue to come and transform our lives and our economies at an unprecedented pace.
Ferguson also thinks the pursuit of more general and flexible AI systems is already yielding significant commercial benefits. In his work with businesses across various sectors, Ferguson has observed that the real impact of AI lies in its integration into existing workflows and decision-making processes.
“The advancements we’re seeing in AI, particularly in making systems more adaptable and “general,” are opening up new possibilities for businesses,” says Ferguson. For instance, he says, LLMs are being used in a variety of settings beyond just content generation.
Hoffman credits this advancement to increased investment and research in AI technology. This is paving the way for more powerful and versatile AI systems, which are transforming industries even without being AGI.
How far are we from true AGI?
Despite the media hype and claims from some large tech companies about being on the brink of AGI, Ferguson believes we’re still very far from achieving true AGI.
“In my professional opinion, we’re likely decades away from this level of artificial intelligence,” he says. “While we’ve made significant strides in narrow AI applications and seen impressive advancements in the flexibility of AI systems, particularly LLMs, the leap to general intelligence presents numerous technical and conceptual challenges.”
Despite estimates for AGI varying widely among experts, Hoffman also believes we are far from true AGI.
“While today’s generative tools are compelling, and more sophisticated and helpful than previous AI tools, the gap between what even our most advanced AIs can do and human intelligence is vast and will remain so for the foreseeable future,” she says.
That said, she says the advancements made by today’s AI systems are already driving innovation and efficiency in industries like healthcare and finance. AGI however has the potential to unlock even greater advancements across industries.
Ferguson explains that the path to AGI involves overcoming complex hurdles in areas like common-sense reasoning, transfer learning, and consciousness simulation.
He believes the focus for commercial applications in the near to medium term should be to think more logically, improve their reliability, and seamlessly integrate into human workflows.
“This is where I see AI having the greatest impact in the coming years, rather than in the form of a fully realized AGI,” says Ferguson. “For now, I see AGI primarily as an academic exercise and a long-term research goal rather than an imminent reality.”