Role of Human Input in Machine Learning

In the current landscape of artificial intelligence (AI), self-learning remains a challenge, and AI heavily relies on substantial human input. It’s estimated that approximately 90% of today’s AI and ML applications rely on supervised machine learning, where humans are pivotal.

For instance, think about voice-controlled home assistants. When we give the command “play a song,” it responds correctly because humans have invested extensive hours teaching it how to interpret various commands.

Similarly, machine translation services excel in translating languages because they have undergone training on numerous human-translated texts, possibly numbering in the millions.

So, what constitutes the optimal approach for humans and machine learning algorithms to collaborate effectively in problem-solving?

In the realm of AI and machine learning, the concept of human-in-the-loop (HITL) comes into play.

Human-in-the-loop machine learning (HITL) is an approach in artificial intelligence and machine learning where humans play an integral role in the training, validation, and improvement of machine learning models.