Alphabet's AI research company DeepMind has released the next generation of its language model that almost has the reading comprehension of a high schooler.

In order to develop artificial general intelligence (AGI), the sort of all-encompassing AI that we see in science fiction, we might need to merely sit back and let a simple algorithm develop on its own. Reinforcement learning, a kind of gamified AI architecture in which an algorithm "learns" to complete a task by seeking out preprogrammed rewards, could theoretically grow and learn so much that it breaks the theoretical barrier to AGI without any new technological developments, according to research published by the Google-owned DeepMind last month in the journal Artificial Intelligence and spotted by VentureBeat. While reinforcement learning is often overhyped within the AI field, it's interesting to consider that engineers could have already built all the tech needed for AGI and now simply need to let it loose and watch it grow. The kind of artificial intelligence that we encounter every day of our lives, whether it's machine learning or reinforcement learning, is narrow AI: an algorithm designed to accomplish a very specific task like predicting your Google search, spotting objects in a video feed, or mastering a video game. By contrast, AGI -- sometimes called human-level AI intelligence -- would be more along the lines of C-3PO from "Star Wars," in the sense that it could understand context, subtext, and social cues.