AGI regularly appears in AI discussions. Sometimes it sounds like a clear destination that is just around the corner. But the meaning attached to AGI makes a significant difference to that claim.
What does it mean?
AGI stands for Artificial General Intelligence: the idea of AI performing at a high level across a broad range of tasks. That leaves open which tasks count, how reliable performance must be and how much human help is allowed.
In “Levels of AGI”, the authors propose distinguishing the breadth and level of performance. Autonomy also deserves separate attention. Doing well on a task and running an entire process without supervision are different capabilities.
“Ordinary AI only does one thing” is too simple
A strict split is not very helpful when assessing the tools you encounter. Look at the actual application. A system may write useful text yet draw the wrong conclusion from a document. A good demonstration does not show how consistently it will work in daily use.
For a business, I would choose a few real tasks, define a good result in advance and test difficult exceptions. That produces more useful evidence than a label.
What does a predicted date tell you?
A date is only meaningful if the intended achievement is defined. Ask about the definition, test and conditions. Otherwise, people can easily talk past one another.
I follow this topic because of its possible impact on work and systems. For an actual assignment, I focus on demonstrated capability, failure cases and the checks needed.
Sources and further reading
Explanation and commentary based on the sources below. Examples are illustrative.