Try to put yourself in the shoes of an intern who has just started working at your company. This intern is incredibly talented and can work nonstop for eight hours straight. This is going to be the stuff of dreams, isn’t it? The problem is that they are completely unaware of your company. Their inability to distinguish between a “thank you” message and a critical consumer complaint is quite concerning. They are completely illogical and fail to understand even the most fundamental concepts.
This is a perfect example for anyone who has ever begun training an AI model for their company. The bright side is that AI can be programmed to fully grasp your company and execute your critical procedures. However, it requires dedication and, in most cases, a large amount of data annotation.
One Of The Obstacles To Data Annotation
Annotating data makes it easier for AI to comprehend and responsibly manage the data that powers your company’s operations.
The act of manually assigning appropriate classifiers or “labels” to raw data is known as data annotation or data labeling. For businesses, it’s an essential step in teaching AI models to spot patterns in data and act accordingly. A good example would be training a model to distinguish between a “thank you” message and a critical complaint. Alternatively, you may guide it to accurately extract critical data from messages, such as a customer number or delivery address, which are essential for numerous useful automations.
Annotation might be considered the new programming language. Labeling instances for machines to replicate is becoming more common as an alternative to scripting their actions. That being said, it is still a tedious and time-consuming process for all involved!
About 80% of the time spent on every AI project is devoted to data annotation. In most cases, teams of professionals working as subject matter experts (SMEs) will spend hundreds of hours classifying thousands of unique examples. It becomes even more complicated when you include the possibility of human mistake. The AI’s comprehension of the data will be affected and it will likely take much more time to fix the damage caused by incorrect labels.
Staff members’ reluctance to annotate data is a common reason why artificial intelligence initiatives fail to launch. The practice of using AI to annotate data has even extended to individuals who are compensated to train AI models. In fact, that’s not a terrible suggestion. Indeed, the ability to delegate tasks that we dislike is a major motivation for utilizing AI in the corporate world.
But there’s a far more efficient method for teaching AI…
Active learning: efficient AI with reduced time and cost
One of the most common ways to train artificial intelligence systems is data annotation. In supervised learning, artificial intelligence (AI) takes what it has learnt from a previously labeled dataset and applies it to new data in a way that the user specifies. On the other hand, unsupervised learning involves presenting AI with unlabeled data and letting it figure out patterns on its own.
Models trained with supervision are more likely to exhibit consistent and dependable behavior. It’s the only model that can function well in an unsupervised corporate setting. Specialized AI models, developed to comprehend and complete a particular task, rely heavily on supervised learning. Training and deploying these models is slower than unsupervised learning models due to the data annotation bottleneck.
But what if unsupervised learning were faster and more accurate than supervised learning?
Enterprise AI models have only lately begun to be trained using active learning, despite the fact that it is a mature AI training method. In order to build more accurate AI models faster, it integrates supervised and unsupervised learning techniques.
Active learning, similar to supervised learning, uses annotated examples to train models. But the model doesn’t just take in data from a dataset; it decides what it wants to learn on its own.
After that, it actively asks the SME questions, but, critically, it only asks them to annotate cases that it is either really confused about or that it believes will be most beneficial for its training. The model determines what data it needs to learn better and finds patterns on its own, just like in unsupervised learning.
The development of an intelligent annotation workflow can be facilitated by active learning. Do you recall the AI intern we introduced at the outset of this piece? They could learn most of the material themselves with active learning, choosing what to study next and only needing help when they were stuck. With active learning, the SME isn’t required to micromanage as much, and the process is more in line with how people actually learn.
For companies who are having trouble training their own AI, what is the benefit of active learning? Training a model from scratch requires significantly less annotated instances. While you construct the model and then consume and refine it, the AI will conduct the bulk of the training, and it will collaborate with your SMEs to increase its understanding.
Active learning allows for the construction of AI models that may be trained more quickly with less labelled samples while maintaining accuracy and performance. There are fewer room for bias and human error in active learning, which is another benefit. That’s why it’s the best way for organizations to train trustworthy specialized AI models that can start working right away.
Applying AI—More Efficiently
How can artificial intelligence be successful? Does your use of models play a role? Another consideration is the number of data scientists and subject matter experts you employ for their training.
A company’s ability to “operationalize” its AI technology quickly distinguishes it from its competitors. How soon they can use AI into their company and see a return on investment. Intelligent document processing (IDP) has never had it easy with this. It often takes a lot of time and effort to train AI models to accurately read and process communications and documents.
UiPath’s active learning approach leverages our industry-leading AI capabilities for IDP to expedite the time to value for our customers.
Users can automate the understanding and processing of business communications and documents with the use of UiPath’s Document Understanding and Communications Mining tools, which are accessible through the UiPath Platform. These UiPath Platform features can begin training with few annotated examples because of active learning. Afterwards, SMEs and AI collaborate to improve model knowledge by assigning labels to the most instructive and beneficial cases.
By combining our active learning method with the UiPath Platform’s no-code, fully-guided user interface, we can build high-performing AI models in hours instead of weeks or months. For example, our internal tests have shown that UiPath Document Understanding’s model training is now 80% faster since active learning was introduced. A day is all it takes to get a model ready for usage, whereas a week was previously required for training.
Summary
Time is of the essence in all aspects of life and business. Additionally, data annotation is now consuming an excessive amount of it. We are extending time to value our staff while also putting pressure on them. A more effective method is provided by active learning, which is a relief. Active learning reduces data annotation to zero in on the most relevant examples by combining supervised and unsupervised methods.
With active learning, you can train and deploy high-performing AI with a deep understanding of your business with a fraction of the labeling effort. The result will be a quicker time to value for AI, happier workers, and less labeling.