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AGI Methods
A collection of university labs experimenting with Artificial General Intelligence:
University of Texas at Arlington. Human Data Interaction Lab (HDILab)
UTA's HDILab looks at how humans interact with different AI technology. The results provide innovative ways of investigating AGI.
Templeton's AGI Team takes a computer-science approach to advance current AI technology towards a higher standard of requirements that can be considered AGI.
University of South Carolina's AI & AGI Lab (AAA Lab)
USC's AAA Lab approaches AGI from a human-first experimental approach by exposing participants to different AI problem-solving processes. Insights on what AGI requires are made by investigating how humans compensate or contextualize the holes in AI's solutions.
A.I flusters with clusters of clutter. AGI needs to both clean an environment and make it livable too.
What is AGI?
Artificial General Intelligence (AGI) is the pursuit of creating machines with broad, adaptable intelligence akin to human minds. Unlike current AI, which excels in specific tasks, AGI aims to mimic general human-like experiences that encompass various types of learning. For example, imagine a robot enters a cluttered room it has never seen before. Currently, AI will struggle with adjusting to the clutter or making sense of any changes in the environment. However, AGI would quickly assess the situation, understand any dynamics changes, and even devise a novel solution to organize the room efficiently. To do so, AGI would merge different types of thinking, such as problem-solving how to navigate, categorical thinking to organize, and creative thinking to make the room aesthetically pleasing.
How do we research, study, and test for Artificial General Intelligence?
It is important to remember that AGI does not exist yet, nor may it arrive soon (if ever). Rather, the theory of AGI represents the next paradigm in technology, offering immense potential while prompting critical reflections on how we manage and utilize such advanced capabilities. There is an assumption that studying A.I. will naturally lead us to AGI. However, it is only an assumption and one that relies on waiting for the progress of AI. Just like how there was a benefit for studying A.I before PCs came along, there is a benefit to researching AGI right now. This space covers promising AGI methods and advances in the field.
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