Can AI Learn Like Humans? Exploring Self-Improving Systems

Alvin Lourdes
Feb 15
2 min read

February 2025

Can AI Learn Like a Child?

For years, we have trained AI models by feeding them massive datasets and optimizing them to recognize patterns. But what if this approach is flawed? Instead of preloading AI with all human knowledge at once, what if AI could learn step by step, just like a human baby?

Imagine an AI that:

Develops a sense of self through experience.
Acquires knowledge gradually rather than being force-fed data.
Gains emotional intelligence before processing raw facts.
Learns independently through interactions rather than just from pre-written code.

The key idea? Instead of designing AI to "know" everything from the start, we let it discover knowledge over time—just as humans do.

Reinforcement Learning: A Step Toward Human-Like AI?

A possible step toward this vision is Reinforcement Learning (RL)—a training method that allows AI to learn through trial and error, improving its performance over time. This technique moves AI away from purely static learning models by allowing it to interact with its environment and refine its behavior based on rewards and penalties.

How RL Supports Experiential Learning

Instead of memorizing data, RL-based AI learns through experience, just as a child learns by interacting with the world.
AI in RL environments receives rewards for making correct choices, helping it improve decision-making step by step.
This method aligns with the idea of progressive, experiential AI that develops understanding over time rather than knowing everything at once.

Where RL Falls Short in Human-Like Learning

While RL introduces an element of experiential learning, it does not fully replicate how humans learn:

AI lacks intrinsic motivation—humans explore out of curiosity, but RL models only learn what they are programmed to optimize.
AI has fixed goals—RL is designed around specific reward structures, whereas humans set their own learning objectives.
AI struggles with flexible adaptation—RL performs well in structured tasks but struggles in open-ended, unpredictable environments.

Key Challenges in Creating a Truly Self-Learning AI

For AI to evolve into a truly self-improving system, we must address the following limitations:

Moving Beyond Predefined Rewards
- Current AI models only learn within the rules we set for them.
- A human child learns organically, without needing programmed objectives—AI needs a similar ability to self-direct learning.
Enabling Open-Ended Learning
- RL works well in structured environments like games but fails in complex, real-world situations.
- Humans can apply knowledge across different domains, but AI struggles to generalize beyond its training data.
Developing Curiosity-Driven AI
- Human children learn by asking questions and experimenting—AI does not.
- AI research is exploring curiosity-based reinforcement learning, which allows AI to explore freely without predefined goals.

The Future: Can AI Truly Learn Like a Human?

The shift from static AI to experiential AI is an exciting challenge. Reinforcement Learning is a stepping stone, but it is not enough—AI must be able to: