The great promise of AI is increased productivity and accomplishing bigger, better things. The great risk is that productivity comes at the expense of critical thinking and long-term growth.
You might feel this in your work. How sharp is your syntax? How often are you having novel ideas? We’re producing more, but it’s not clear that we’re getting better.
The solution is not to reject AI. It’s to use AI in ways that develop our capabilities and measure progress by something other than our outputs.
To do that, we need to understand different barriers to progress.
Sometimes, we’re on a plateau and need to keep going. Others, we’ve reached a local maximum and need to change direction. In reinforcement learning, this resembles the trade-off between exploitation and exploration.
Plateaus
A plateau is a period of little progress. Mathematically, it resembles a region where the slope is near-zero: continued movement produces little change.
The underlying direction is still correct and effort eventually breaks the plateau.
In the above image, increasing voltage eventually results in a steeper curve. If the underlying strategy is sound, we escape plateaus by continuing to exploit existing strategies (increase voltage).
However, not all stalls are plateaus - we can’t always brute force our way out of problems.
Local maxima
Local maxima are suboptimal points that may appear to be optimal. You might find yourself in a local maximum if you’ve ever tried to do a hard thing: You’re better off than when you started, but progress eventually halts.
In typing, hunt-and-peck is a local maximum. It is faster initially than switching to touch typing, but it imposes a substantially lower ceiling. You’re in a local maximum that requires intervention to continue progress. Exploiting hunt-and-peck is a losing game.
Mathematically, a local maximum refers to a solution or state that is better than its immediate neighbors, but not globally optimal. An algorithm that finds a local maximum might say, “Yeah, this is good enough,” and not properly seek out alternatives.
To escape the local maximum, we have to explore alternative strategies for continued progress.