What is artificial general intelligence (general AI/AGI)?
From ancient mythology to modern science fiction, humans have been dreaming of creating artificial intelligence for millennia. But the endeavor of synthesizing intelligence only began in earnest in the late 1950s, when a dozen scientists gathered in Dartmouth College, NH, for a two-month workshop to create machines that could “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
The workshop marked the official beginning of AI history. But as the two-month effort—and many others that followed—only proved that human intelligence is very complicated, and the complexity becomes more evident as you try to replicate it.
That is why, despite six decades of research and development, we still don’t have AI that rivals the cognitive abilities of a human child, let alone one that can think like an adult. What we do have, however, is a field of science that is split into two different categories: artificial narrow intelligence (ANI), what we have today, and artificial general intelligence (AGI), what we hope to achieve.
Defining artificial general intelligence is very difficult. “General” already implies that it’s a very broad term, and even if we consider human intelligence as the baseline, not all humans are equally intelligent.
But there are several traits that a generally intelligent system should have such as common sense, background knowledge, transfer learning, abstraction, and causality. These are the kind of functions you see in all humans since early age.
The best way to see what a general AI system could do is to provide some challenges:
Challenge 1: What would happen in the following video if you removed the bat from the scene?
This is a challenge that requires the AI to have an understanding of physical dynamics, and causality. It should also be able to reason about counterfactuals, alternative scenarios where you make changes to the scene.
Challenge 2: Consider the following text, mentioned inRebooting AIby Gary Marcus and Ernest Davis: “Elsie tried to reach her aunt on the phone, but she didn’t answer.” Now answer the following questions:
This challenge requires the AI to have basic background knowledge about telephone conversations. Most people know about remote communications and how telephones work, and therefore they can infer many things that are missing in the sentence, such as the unclear antecedent to the pronoun “she.”
Challenge 3: Enter a random house and make a cup of coffee. Add some milk and sugar.
This challenge will require the AI agent to have a general understanding of houses’ structures. It should have basic knowledge such as the following: Food items are usually found in the kitchen. The kitchen is usually located on the first floor of the home. Coffee is stored in the cupboard. Milk has to be kept in the refrigerator. The AI must locate the coffeemaker, and in case there isn’t one, it must be able to improvise. Most humans solve these and dozens of other problems subconsciously.
While very simple and straightforward, solving these challenges in a general way is still beyond today’s AI systems.
Following are two main approaches to AI and why they cannot solve artificial general intelligence problems alone.
The early efforts to create artificial intelligence focused on creating rule-based systems, also known as symbolic AI. Symbolic AI is premised on the fact the human mind manipulates symbols. We have mental representations for objects, persons, concepts, states, actions, etc. and we use these representations (the symbols) to process the information we receive through our senses, to reason about the world around us, form intents, make decisions, etc.
Symbolic AI systems made early progress. Computer programming languages have been created on the basis of symbol manipulation.
