Why I think AGI IS right around the corner
The conversation around artificial general intelligence (AGI) often swings between grand predictions and deep skepticism. For example, Dwarkesh Patel recently wrote a blogpost on why he does not think AGI is right around the corner.
Specifically, Patel emphasizes that “models are stuck with abilities you get out of the box” and that “LLMs don’t get better over time the way a human would. The lack of continual learning is a huge huge problem.” Patel’s argument is that "The reason humans are so useful is not mainly their raw intelligence. It’s their ability to build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task."
However, this perception is rapidly being challenged by new research on techniques that teach models to be better! Reinforcement learning, particularly using methods like GRPO, is at the core of teaching models to be better. In fact, it's increasingly understood that RL can be qualitatively similar to how humans learn, by noticing successful or unsuccessful outcomes and efficiently updating.
The technical limitations of reinforcement learning in the past has been that it is fairly hard to set up the reward function and actually go through the mechanics of training. However, new techniques are making this easier and better.
