Active Learning for Artificial Intelligence

As data generation by businesses and society continues to grow exponentially, it becomes increasingly difficult to build models from small training sets. The labelling process becomes a significant bottleneck, with limited subject-matter experts available for the task.

Reinforcement learning offers a solution, enabling AI to iteratively test and improve outputs in real-world scenarios. A notable example of this technique is its implementation in the AlphaGo project.

However, in many cases, such as doctors labelling millions of MRI scans, this approach is not feasible. So, how can we help machines learn more effectively?

For big data problems where traditional methods fall short, Active Learning (AL) emerges as a powerful solution. AL, defined as learning by asking questions, operates much like a child's learning process. While supervised learning involves learning from observations and reinforcement learning relies on trial and error, AL is akin to learning from a teacher by asking questions.

To make AI a viable alternative or significant support for human decision-making processes, its learning capabilities must be enhanced. This requires an efficient and intelligent questioning process, allowing AI to extract maximum information with minimal questions, saving both time and money.

Active Learning focuses on empowering AI to ask the right questions and accelerate its learning process.

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