In general an AGI would be based on a reinforcement learning framework. Its main skill would be to observe the world, judge the situation and perform actions. These three processes are run in a continuous loop. It would receive a reward signal by which it would learn behavior. It would have to be embedded in a world where it can move about and act upon. If it has all these ingredients, it can become a general intelligence, as long as the reward signal is leading it to do that.
Memorizing is just one of the actions such an agent is able to perform. Another mental action besides memory would be attention. It would also need to be able to simulate the world, people and systems it is interacting with (to know how they behave) in order to be able to do reasoning and planning.
In short, an AGI would need: sensing (deep neural nets for vision, audio and other modalities), attention, memory, estimating the desirability and effects of various actions (a kind of imagination), an extensive database of common known facts, and the ability to act (for example by speech and movement).
Many of these systems have been demonstrated. Sensing, attention and memory are common place in ML papers. Creativity is demonstrated in generative models that can write text, music and paint. Ability to predict the future and reason about it was demonstrated in AlphaGo. Speech and motor control are under development. We have most of the necessary blocks, but nobody has put them together to form a functioning general AI yet.