When Humans and Machines Come Together

Some call it human-in-the-loop, we also call it Super Intelligence. The interaction and synergy between humans and machines are crucial when talking about Artificial Intelligence. Bringing these two elements together will sow the seeds of a new era where the speed and efficiency of the machines will complement human critical-thinking capabilities resulting in higher levels of accuracy and better outcomes.

Human-in-the-loop (HITL) is a concept combining both human and machine intelligence in order to create models for Machine Learning algorithms. In this process, humans participate actively in training, tuning and testing data for those algorithms. This mixed approach contributes to making ML more accessible and efficient since it includes two kinds of processes, supervised and unsupervised learning. With supervised learning, we are referring to that process where ML experts adjust dataset parameters to train algorithms. On the other hand, unsupervised learning involves great amounts of data running through an algorithm which shows a structural composition.

A closer union between Machine Learning and HITL has recently become quite popular, not only for big tech giants as Google or Amazon but also for SMEs looking for cost-effective solutions when talking about AI innovations. A good illustration of a well-known company using a human-in-the-loop approach is Pinterest. Their primary challenge was understanding how users were engaging with links. To get into this, they carried out a cluster where data was extracted, transformed and loaded, cluster visualizations were built, and parameters tuned. After that, they added human labels to each cluster which would be then automated and combined with their workflow management system giving rise to more accurate results.

Machine Learning with Humans in the Loop

Generally speaking, a common human-in-the-loop approach tends to follow these three steps:

  • Humans label data helping generate large amounts of high-quality training data.
  • ML algorithms learn to make decisions from this labeled data.
  • Humans tune the outputs through diverse processes (scoring data or classifying).


After this process, some tests must be made to see if a model is properly working, especially when the algorithm must face more complex issues and may be not confident enough to make the right decision on its own. AI algorithms do not work as traditional algorithms. They must be fed with great amounts of data to increase their accuracy. When more complex tasks, as Natural Language Processing, come to the fore, humans are key players for the creation of high-quality training data. 


As Gartner suggests, companies working on AI and ML should employ human-in-the-loop crowdsourcing as an enabler of AI solutions since this approach gives a wider access to problem solving, model training, classification and validation capabilities in comparison with traditional ML processes. Therefore, when rules are too complicated for automation or the ML algorithm cannot get more accurate results is time to resort to humans.


High-quality information would not be that easily achieved without a human input. This human component is an essential part of every product offered by Bitext, ranging from Bot Automation to Customer Feedback Analysis services, including all Core NLP Packages

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