Let’s face it: training an AI model isn’t much fun.

Imagine you are training an intern who has just joined your company. This intern is extremely bright and can work at lightning speed for 24 hours without a break. It should be a dream come true, right? Plus, they don’t know anything about your business. They can’t tell the difference between a ‘thank you’ email and a quick customer complaint. They lack common sense and misunderstand even the most basic things.

Anyone who has ever started training an AI model for their business can probably relate to this example. The good news is that AI can be trained to accurately understand your business and execute your most important processes. But it takes time and effort, and, in general, a lot of data interpretation.

Data interpretation constraints

Simply put, data interpretation helps AI understand and safely manage the data that drives your business processes.

Data annotation, also known as data labelling, is the manual process of tagging raw data with relevant classifications or ‘labels’. In business, this is an important part of the process of training AI models to accurately recognize patterns in your data and respond appropriately to them. For example, helping the model distinguish between a ‘thank you’ email and a quick complaint. Or helping to correctly extract important data from a message, such as a delivery address or customer number, which can be important for many valuable automations.

You could say that interpretation has become the new programming. Increasingly, instead of programming what we want machines to do, we use examples to replicate them. But that doesn’t stop it from being a long and boring process for those who do!

Uses about data interpretation 80% of the time Dedicated to any AI project. Subject matter experts (SMEs), often involving teams of employees, will typically spend hundreds of collective hours labeling thousands of individual instances. But add human error to the mix. Some labels will inevitably be incorrect, affecting the AI’s understanding of the data and potentially requiring more time to fix the damage.

Many AI projects struggle to get off the ground because employees are often reluctant to interpret data. People even pay to train AI models now. Turning to AI to interpret the data for them. And that’s actually not a bad idea. After all, the main reason we use AI in business is to free ourselves from work we don’t enjoy.

However, there is a much better way to train AI quickly and accurately…

Active learning: Better AI in less time and less cost

Data interpretation is an essential part of supervised learning—one of the most popular AI training methods. In supervised learning, AI learns from a pre-labeled dataset and uses this learned information to process new data in a desired way. This contrasts with unsupervised learning, where the AI ​​is given unlabeled data and left to identify patterns independently.

Supervised learning produces models that perform more consistently and reliably. This is the only type of model that should be used in a real business environment without supervision. Supervised learning is the key to building specialized AI models, designed to understand and perform a specific task. Yet data interpretation constraints mean that these models are slower to train and deploy than unsupervised learning.

But what if we could combine the accuracy of supervised learning with the speed of unsupervised learning?

Active learning is a mature training method for AI, but it has only recently been used to train enterprise AI models. It combines elements of supervised and unsupervised learning to build better AI models in less time.

Similar to supervised learning, active learning requires annotated samples for model training. However, instead of just learning from the dataset, the model makes unsupervised decisions about what it wants to learn next.

It then actively queries the SME but, importantly, only asks them to interpret examples that it is not sure about or thinks are necessary for its training. will be more effective. Just like in unsupervised learning, the model itself identifies patterns and decides what information it needs to learn better.

Active learning helps create an intelligent workflow for annotation. Remember the AI ​​intern from our example at the beginning of this blog post? With active learning they can complete most of the training on their own, deciding what they should learn next and asking for help when they get stuck. Active learning is much closer to human learning patterns, and means much less hand-holding and work from the SME.

What makes active learning useful for businesses struggling to train their AI? You need very few annotated examples to train a model from start to finish. AI takes a lot of weight in terms of training and will work with your SMEs to improve its understanding — both when you build the model and later when you deploy and refine it.

AI models built with active learning can be trained faster, with fewer labeled examples, and without sacrificing accuracy or performance. Another advantage of active learning is that it leaves opportunities to reduce human error and bias. That’s why it’s the ideal way to help entrepreneurs train specialized AI models that are reliable and get up and running quickly.

Putting AI to work — fast

What is the secret of AI success? Are these the models you use? Or how many data scientists and SMEs you hire to train?

What really separates the AI ​​leaders from the laggards is how quickly they can ‘operationalize’ the technology. How quickly they can deploy AI in their business and how quickly it starts creating value for them. Traditionally, this has been a real challenge for Intelligent Document Processing (IDP). Training AI models to reliably understand and process documents and messages typically demands a large investment of time and effort.

That’s why UiPath uses active learning to accelerate time-to-value for users using our leading AI capabilities for IDP.

Understanding UiPath documentation And Communication Mining (both available through the UiPath platform) enable users to rapidly build custom AI models that can understand and automate documents and business communications. Thanks to active learning, these UiPath platform capabilities start training with just a few illustrative examples. The SMEs and AI then work together to improve understanding of the model by labeling only the most informative and valuable instances.

Our active learning approach—combined with the no-code, fully guided user interface of the UiPath platform—generates accurate, high-performing AI models in hours rather than weeks or months. For example, the introduction of active learning in UiPath document understanding has seen 80% faster model training according to our internal tests. Models that used to take a week to train now only need a day before they’re ready for business.

Abstract

In business and in life, time is the most valuable thing we have. And, right now, data interpretation is taking a lot more than that. Taking time for value and putting pressure on your people. Fortunately, active learning offers a better approach. By using both supervised and unsupervised methods, active learning reduces data interpretation to focus only on the most important instances.

Active learning drastically reduces the labeling effort required to train and deploy accurate, high-performing AI that truly understands your business. This means less labeling, happier employees, and faster time to value for AI.

UiPath is a pioneer of active learning for the enterprise, reducing time and effort while increasing the efficiency and accuracy of specialized AI models. To learn more about the latest UiPath developments in active learning, Join the UiPath Insider Program. And try building an advanced document understanding project using the preview capabilities in UiPath Automation Cloud.