Active Learning: How to Accelerate AI Model Training

Let’s be honest: training an AI model can be quite a chore.

Picture an intern who has just joined your company. This intern is incredibly sharp and capable of working around the clock without pause. Sounds perfect, right? However, they lack any knowledge about your business, struggle to differentiate between a 'thank you' email and a critical customer complaint, and often make basic mistakes due to a lack of common sense.

Anyone who has begun training an AI model for their business can relate to this analogy. The positive news is that AI can indeed be trained to understand your business and execute crucial tasks accurately. Yet, this process demands considerable time, effort, and usually a lot of data annotation.

The Data Annotation Bottleneck

Data annotation is essential for AI to comprehend and effectively manage the data driving your business processes.

Also known as data labeling, data annotation involves manually tagging raw data with relevant labels or classifiers. This step is critical in training AI models to accurately identify and respond to patterns in your data. For instance, it helps an AI model distinguish between a 'thank you' email and an urgent complaint or extract vital information from a message, such as a delivery address or customer number, which is important for various automations.

In many ways, annotation has become the new programming. Instead of coding machine behavior directly, we now provide labeled examples for AI to learn from. Despite this shift, the process remains tedious and monotonous for those involved.

Data annotation typically consumes about 80% of the time invested in any AI project. Subject matter experts (SMEs), often working in teams, spend countless hours labeling thousands of examples. Human error is inevitable, leading to incorrect labels that can disrupt the AI’s understanding and necessitate additional time to correct.

Many AI projects falter due to employees’ reluctance to perform data annotation. Even those who are paid to train AI models are now leveraging AI to handle data labeling. This approach is not necessarily a bad thing, as one of the primary reasons for employing AI in business is to alleviate undesirable tasks.

However, there is a more efficient way to train AI quickly and accurately...

Active Learning: Faster, More Efficient AI Training at Lower Costs

Data annotation is a crucial aspect of supervised learning, one of the most prevalent AI training methods. In supervised learning, AI is trained using a pre-labeled dataset, which it then uses to process new data accurately. This is in contrast to unsupervised learning, where AI analyzes unlabeled data to identify patterns on its own.

Supervised learning typically yields models that operate with greater consistency and reliability, making them suitable for real-world business environments without the need for constant oversight. It is essential for developing Specialized AI models, which are designed to perform specific tasks. However, the data annotation process can slow down the training and deployment of these models compared to those created through unsupervised learning.

But what if we could merge the precision of supervised learning with the efficiency of unsupervised learning?

Active learning offers a solution. Although it’s been around for some time, its use in training enterprise AI models has become more common recently. Active learning integrates aspects of both supervised and unsupervised learning to develop superior AI models more quickly.

In active learning, the model requires annotated examples to begin training, similar to supervised learning. However, it doesn’t just passively learn from the dataset. Instead, it actively determines what it needs to learn next by making unsupervised decisions.

The model then queries subject matter experts (SMEs) only for annotation on the data it is most uncertain about or finds most beneficial for training. This process mirrors unsupervised learning, where the model independently identifies patterns and decides on the most relevant information to enhance its learning.

Active learning streamlines the annotation process by allowing the AI to manage most of the training autonomously. Recall the AI intern from the previous example? With active learning, this intern could independently navigate the training process, seeking guidance only when necessary. This approach aligns more closely with human learning patterns, reducing the need for constant SME involvement.

For businesses struggling with AI training, active learning offers significant advantages. It reduces the number of annotated examples required to train a model from start to finish. The AI handles much of the training workload and collaborates with SMEs to enhance its understanding, both during the model's development and subsequent refinement.

AI models trained with active learning can be developed faster, with fewer labeled examples, and without compromising accuracy or performance. Additionally, active learning minimizes the potential for human error and bias. This makes it an ideal method for training Specialized AI models that are both reliable and quickly operational.

Accelerating AI Deployment

What’s the key to successful AI implementation? Is it the models themselves or the number of data scientists and SMEs involved in training them?

What truly distinguishes AI leaders from followers is their ability to rapidly operationalize the technology—how quickly they can integrate AI into their business and start seeing tangible results. For intelligent document processing (IDP), this has historically been a major challenge. Training AI models to reliably interpret and process documents and communications has often required a substantial investment of time and effort.

UiPath addresses this challenge by leveraging active learning to speed up the value realization for customers using our advanced AI capabilities for IDP.

UiPath Document Understanding and Communications Mining (both available through the UiPath Platform) allow users to swiftly create custom AI models that can automate document and business communication processes. With active learning, these capabilities begin training with just a few annotated examples. SMEs and AI collaborate to refine the model’s understanding by labeling only the most informative and valuable examples.

Our active learning methodology, combined with the no-code, fully-guided interface of the UiPath Platform, enables the development of precise, high-performing AI models in hours rather than weeks or months. For example, implementing active learning in UiPath Document Understanding has resulted in model training being 80% faster according to our internal tests. Models that previously took a week to train are now ready in just one day.

Summary

Time is an invaluable resource in both business and life, and data annotation can consume too much of it, creating delays and stress. Active learning presents a more efficient alternative by combining supervised and unsupervised techniques to minimize data annotation and focus on the most critical examples.

Active learning significantly reduces the effort required to label data, resulting in accurate, high-performing AI that genuinely comprehends your business. This leads to less labeling work, more satisfied employees, and quicker AI value realization.