Human-in-the-loop machine learning is when human and machine intelligence are combined to create effective machine learning algorithms. It’s a blend of supervised machine learning and active learning. Humans are involved in both the training and testing stages of building an algorithm, which creates a continuous feedback loop that allows the algorithm to produce better results each time.
In the training stage, humans label the original AI training data, which includes both input data and the corresponding expected output. Then, humans feed the AI training data to the machine. This process of providing the algorithm with known quantities to support future judgments is called supervised machine learning, and can include algorithms as linear and logistic regression, multi-class classification, and support vector machines. Based on this “ground truth” data, the algorithm can learn how to apply technologies such as neural networks to learn and produce sophisticated results and uncover insights, patterns, and relationships in the training dataset. The ultimate goal is for the algorithm to be able to make accurate decisions when later presented with new data.
In the testing and evaluation stage, the human’s role is to correct any inaccurate results that the machine produced. At this stage, humans should focus on correcting results where the algorithm is unconfident about a judgment. This process of having humans handle low confidence units and feeding those back into the algorithm is called active learning. Another instance that requires human attention is when the algorithm is overly confident about a wrong result. The goal in the testing stage is to further train the machine to make better decisions, so that next time it can reach the correct result without human intervention.
When all of this is put together, it creates a continuous feedback loop between human and machine. As the humans continue to fine-tune the model’s responses to various edge cases, it becomes more accurate and more consistent. It can even begin to analyze its own performance, identify areas where it isn’t effective, and send that data to the human annotators. In this way, both model and annotators can work in harmony to ensure that improvements have a big impact.
When should you use human-in-the-loop machine learning?
Human-in-the-loop can be used for any deep learning AI projects including natural language processing (NLP), computer vision, and transcription. It can be especially useful in the following situations:
- When there is a steep cost of algorithmic errors, such as for machine algorithms for medical diagnosis, prognosis, and therapy.
- When there is a lack of currently available data, humans generally make better judgments than machines. Once a certain amount of training and testing data becomes available, then machines can take over and make better judgments.
- When the data that you’re looking for is rare, such as using image recognition to find a photo of a particular person’s face. In this situation, machine intelligence can save significant time and costs by eliminating images that do not depict that person with confidence and narrowing down the pool of images. Then, the task can be passed on the humans in the final stage to sort through dozens of images of similar faces, to find the correct one.
Why Lionbridge AI?
AI is transforming the way almost every industry works, but we still need humans in the loop, especially to create and feed training data to algorithms in the beginning. Lionbridge AI can provide multilingual AI training data in over 300 languages, for various purposes including image recognition, natural language process (NLP), and much more. We have over 500,000 qualified professionals working on our platform in all major time zones and almost every country, so we can keep up with your requests for high-volume training datasets with a fast turnaround.