Machine learning is a useful and effective tool to have when it comes to building prediction models or to build a useful data structure from an avalanche of data. Many ML algorithms are in use today for a variety of real-world use cases. Given a sample dataset, a machine learning model can give predictions with only certain accuracy, which largely depends on the quality of the training data fed to it. Is there a way to increase the prediction accuracy by somehow involving humans in the process? The answer is yes, and the solution is called as ‘Interactive Machine Learning’.
Why we need interactive machine learning
As we already discussed above, a model can give predictions only as good as the quality of the training data fed to it. If the quality of the training data is not good enough, the model might:
- Take more time to learn and then give accurate predictions
- Quality of predictions will be very poor
This challenge can be overcome by involving humans in the machine learning process. By incorporating human feedback in the model training process, it can be trained faster and more efficiently to give more accurate predictions.
In the widely adopted machine learning approaches, including supervised and unsupervised learning or even active learning for that matter, there is no way to include human feedback in the training process to improve the accuracy of predictions. In case of supervised learning, for example, the data is already pre-labelled and is used without any actual inputs from the human during the training process. For this reason alone, the concept of interactive machine learning is seen by many machine learning and AI experts as a breakthrough.
How interactive machine learning works
Machine Learning Researchers Teng Lee, James Johnson and Steve Cheng have suggested a novel way to include human inputs to improve the performance and predictions of the machine learning model. It has been called as the ‘Transparent Boosting Tree’ algorithm, which is a very interesting approach to combine the advantages of machine learning and human inputs in the final decision making process.
The Transparent Boosting Tree, or TBT in short, is an algorithm that would visualize the model and the prediction details of each step in the machine learning process to the user, take his/her feedback, and incorporate it into the learning process. The ML model is in charge of updating the assigned weights to the inputs, and filtering the information shown to the user for his/her feedback. Once the feedback is received, it can be incorporated by the ML model as a part of the learning process, thus improving it.
A basic flowchart of the interactive machine learning process is as shown:
Interactive Machine Learning
More in-depth information on how interactive machine learning works can be found in their paper.
What can Interactive machine learning do for businesses
With the rising popularity and applications of AI across all industry verticals, humans may have a key role to play in the learning process of an algorithm, apart from just coding it. While observing the algorithm’s own outputs or evaluations in the form of visualizations or plain predictions, humans can suggest way to to improve that prediction by giving feedback in the form of inputs such as labels, corrections or rankings. This helps the models in two ways:
- Increases the prediction accuracy
- Time taken for the algorithm to learn is shortened considerably
Both the advantages can be invaluable to businesses, as they look to incorporate AI and machine learning in their processes, and look for faster and more accurate predictions.
Interactive Machine Learning is still in its nascent stage and we can expect more developments in the domain to surface in the coming days. Once production-ready, it will undoubtedly be a game-changer.
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