Artificial general intelligence is able to comprehend feedback from concepts, experiences and instruction, indicating human-level capabilities
OpenAI’s artificial intelligence system GPT-4 has shown “sparks of artificial general intelligence (AGI),” displaying abilities in a wide range of knowledge domains with a performance that is almost at the “human-level,” according to a paper by Microsoft Research.
An AGI would be able to understand the world as human beings do and have a similar capacity to learn how to carry out various tasks. An early version of GPT-4 tested by Microsoft researchers showed “more general intelligence than previous AI models,” according to the March 22 paper (pdf). “Beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology, and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4’s performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT.”
“Given the breadth and depth of GPT-4’s capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.”
GPT-4 was found to be able to code at a “very high level” both in terms of writing code from instructions as well as understanding existing code. Though the AI is “not perfect in coding yet” since it generates semantically incorrect or syntactically invalid code, GPT-4 is able to improve the code by responding to human feedback.
When it came to math, GPT-4 was able to express mathematical concepts and solve related problems. The AI was able to apply quantitative reasoning when faced with problems that required mathematical thinking and model building. However, GPT-4 is still “quite far from the level of experts” as it does not have the ability to conduct mathematical research, the study admitted.
A key aspect of intelligence is interactivity, which is basically the ability of an intelligent entity to communicate and respond to feedback from other entities, environments, and tools. “Interactivity requires an agent to comprehend complex ideas, learn quickly, and learn from experience, and thus it is closely tied to our definition of intelligence.”
