Consider that you were mentoring a recently hired intern. This intern is incredibly intelligent and capable of working nonstop for twenty-four hours a day. I mean, this ought to be a dream come true. However, they are completely ignorant of your company. They are unable to distinguish between an urgent consumer complaint and a “thank you” email. They make the most basic mistakes and have no common sense.
This story is likely to resonate with anyone who has ever begun training an AI model for their company. The good news is that AI may be taught to do your most crucial tasks and comprehend your company precisely. However, it requires a lot of data annotation and labor.
The Bottleneck In Data Annotation
In short, data annotation aids AI in comprehending and securely managing the data that powers your company’s operations.
The manual process of adding pertinent classifiers or “labels” to raw data is known as data annotation, or data labeling. In the commercial world, it’s an essential step in training AI models to recognize and react appropriately to trends in your data. For instance, assisting a model in distinguishing between an urgent complaint and a “thank you” email. or assisting it in accurately extracting critical information from a message, such as a customer number or delivery address, which might be essential for numerous useful automations.
Annotation might be considered the new programming. We are increasingly labeling instances for robots to mimic rather than scripting what we want them to accomplish. For those who do it, however, it remains a protracted and tedious process!
Approximately 80% of the time spent on every AI project is spent on data annotation. Subject matter experts (SMEs), who frequently work in teams, would usually label thousands of individual cases over the course of hundreds of hours. Add human error to the mix, however. Unavoidably, some labels will be incorrect, which will affect how the AI interprets the data and likely take even longer to fix.
Because employees are sometimes unwilling to annotate data, many AI initiatives fail to get off the ground. These days, even those who are paid to train AI models are using AI to annotate their data. Furthermore, that isn’t a bad concept at all. After all, removing ourselves from labor we dislike is one of the main reasons AI is used in business.
But there’s a far more efficient and precise method for training AI.
Active Learning: Cheaper, Faster, And Better AI
One of the most often used methods for AI training is supervised learning, which requires data annotation. AI learns from a prelabeled dataset via supervised learning, then applies what it has learnt to handle incoming input in a desired manner. Unsupervised learning, on the other hand, involves providing AI with unlabeled data and letting it find patterns on its own.
Models created by supervised learning behave in a more dependable and consistent manner. It is the only type of model that ought to be applied unsupervised in a real-world commercial setting. Building specialized AI models—which are designed to comprehend and perform a particular task—requires supervised learning. However, compared to models created via unsupervised learning, these models are slower to train and deploy due to the data annotation bottleneck.
However, what if the speed of unsupervised learning could be combined with the precision of supervised learning?
Although active learning is a well-established AI training technique, enterprise AI models have just lately been trained using it. It builds better AI models faster by combining aspects of supervised and unsupervised learning.
Annotated samples are necessary for model training in active learning, much like in supervised learning. However, the model choose what it wants to learn next without supervision, as opposed to merely learning from a dataset.
The SME is then aggressively questioned, but importantly, it only requests that they annotate cases those it is most uncertain about or believes will be most helpful for its training. The model finds patterns on its own and determines what information it needs to learn more effectively, just like in unsupervised learning.
An intelligent approach for annotation is facilitated by active learning. Do you recall the AI intern we used as an example at the start of this blog post? They may finish the majority of the training on their own using active learning, choosing what to learn next and only seeking help when they ran into problems. Active learning requires significantly less work and handholding from the SME and is much more in line with human learning patterns.
Why is active learning so beneficial for companies who are having trouble training their own artificial intelligence? To train a model from start to finish, you require a lot fewer annotated examples. When it comes to training, the AI handles the majority of the work and will collaborate with your SMEs to increase its comprehension as you develop the model and then use and improve it.
Active learning-based AI models can be taught more quickly, with fewer labeled samples, and without compromising performance or accuracy. Another benefit of active learning is that it reduces the likelihood of bias and human error. It is therefore the best way to assist companies in training specialized AI models that are dependable and operationally efficient.
Using AI More Quickly
What is the key component of successful AI? Do you use models for this? Or the number of SMEs and data scientists you employ to teach them?
The ability to “operationalize” the technology quickly is what truly sets the leaders in AI apart from the laggards. how rapidly AI can be implemented in their company and how quickly it begins to benefit them. This has always presented a significant obstacle for intelligent document processing (IDP). It often takes a significant time and effort investment to train AI models to comprehend and interpret documents and messages consistently.
For this reason, leverages our industry-leading AI capabilities for IDP in conjunction with active learning to speed up time to value for clients.
Users may quickly create bespoke AI models that can comprehend and automate documents and business interactions. These Platform features begin training with just a few annotated examples because of active learning. Then, by labeling just the most instructive and useful examples, SMEs and AI collaborate to improve model comprehension.
When paired fully-guided, no-code user interface, our active learning methodology generates precise, high-performing AI models in a matter of hours as opposed to weeks or months. For example, our internal testing show that the addition of active learning has resulted in 80% faster model training. It now only takes a day for models to be prepared for business, compared to a week in the past.
In Brief
Time is the most valuable resource we possess in both business and life. Additionally, data annotation is now consuming an excessive amount of it. applying pressure to our people and extending the time to cherish them. Thankfully, there is a better method available with active learning. Active learning reduces data annotation by utilizing both supervised and unsupervised techniques, allowing it to concentrate on only the most significant cases.
The labeling work required to train and implement precise, high-performing AI that truly understands your business is significantly decreased by active learning. It means a quicker time to value for AI, happier workers, and less labeling.