AI is remarkable. It can outplay humans in chess, solve complex mathematical equations
and generate working code. But it does not develop or demonstrate all skills at the same
rate. Some skills take flight and others tumble after several iterations. This phenomenon in
the field of AI research is called the “reinforcement gap.”
Reinforcement gap occurs as AI performs well at tasks that are scalable and identifiable
problems with structured definitions. The crux of how AI learns is through reinforcement
learning, which put in simpler terms is nothing but super-charging trial-and-error learning. AI
tries something, receives feedback in terms of reward or punishment, changes its approach,
and tries again. In the domains of coding, riddles, and math equation solving, the feedback
that it receives during each iteration of learning is explicitly defined and received in real-time.
As a result, the AI system learns quickly and begins to perform better exponentially in
comparison to the human learner.
When the actions have subjective, fuzzy or context-specific factors, the AI’s development
and performance slows dramatically. In artistic creative writing tasks, emotional reasoning
and judgement, and wind high stakes decision making, there are no definitive “right or
wrong” answers and without a sharp benchmark or explicitly measurable reward, it becomes
more challenging for an AI mechanism to determine its own performance and improve
incrementally and in some cases plateau.

This gap in reinforcement alludes to a basic truth about AI: it excels in circumstances where
success can be measured and quantified but falters in contexts that require judgement,
intuition, or creativity. Even the most advanced systems require human intuition to
understand ambiguous, complex, or socially-laden situations.
This distinction is useful for students and aspiring technologists. Though AI can allow for
efficiency and speed in taking on problems (in areas that are also well-understood), this
leaves humans with a crucial advantage in areas that involve subtlety, empathy, and
skepticism.

In a nutshell, while it is likely that AI will outperform human capacity in some quantifiable
areas, when imagination is required, human judgement cannot be replaced. Recognizing the
reinforcement gap not only helps make sense of why AI develops at such an uneven pace
but also helps clarify how unbelievably valuable human judgement will continue to be in a
more automated world.

– Divyansh Jai Purohit