Artificial Intelligence (AI) has seamlessly been integrated into our swiftly evolving modern world. From virtual companions like Siri and Alexa to pivotal applications like facial recognition and medical diagnostics, its influence is undeniable. Yet, despite these impressive feats, AI and Machine Learning (ML) models still depend on large amounts of data for their cognitive abilities and decision making. In fact, 80% of the automation project development time is spent on data collection and preparation. However, this data is not automatically ready to help train AI/ML solutions. The process involves a very crucial step known as data annotation.
Data annotation involves the labeling or marking of data with relevant information, making it usable for training and validating machine learning models. Such datasets allow these AI/ML models to make decisions and take actions similar to humans. For instance, the widely popular chatbots ChatGPT and Bard are the best current examples of conversational AI systems, trained on massive amounts of annotated datasets, which allows them to understand user intent and offer more natural and intuitive interactions through chat.
The applications of data annotation are far-reaching for businesses, like in facial recognition, medical imaging, quality control, chatbots, recommendation engines, etc. Additionally, companies trying to leverage data annotation must either establish a team of experts for the task or (if they wish to save time and keep their internal team focused on core responsibilities) they can outsource data annotation services to a third-party provider.
How does data annotation help in building better AI/ML models
Improved accuracy & quality of output
An AI/ML model can be trained to produce more accurate results with the help of data annotation.
For example, you want to create a machine learning model that can tell cats from dogs based on images. Like a child, the system needs to be told what a cat or dog looks like, except in much more detail. If there is no meaning associated with the images in your massive dataset, the ML model will not be able to interpret them. Annotating data entails providing the unstructured data (in this case, the photos) with structured information.
The images are annotated with labels signifying whether each of them contains a cat or a dog. The ML model is then trained using these annotated images. Various techniques are applied throughout the training phase, enabling the model to identify patterns and traits that distinguish cats from dogs.
The accuracy of the machine learning model’s output increases with the amount of annotated data used to train it. This is so that the model may learn from a wider and more varied sample, leading to a greater knowledge of the particular characteristics of each class (cats and dogs). As a result, the model can give more precise output.
Sequential data refers to data that has a specific order or a sequence of elements. Here each element will have its significance. Understanding sequencing is essential for ML-based systems for the results to be relevant.
Suppose you work for an eCommerce company and you have a dataset of customer reviews for your products. You want to build a sentiment analysis model that predicts whether a particular review is positive, negative, or neutral. Each review is essentially a sequence of words stringed together in a particular order.
By annotating the data with the correct sentiment labels for each review, you can provide the model with the necessary information to learn the relationships between the words and the sentiment expressed in the text.
The term “interpretable ML” refers to designing machine learning models in a way that their decision-making process is understandable and explainable to humans, promoting transparency and accountability. In some domains, such as healthcare and medicine, it is crucial to have transparent models that can provide clear and human-readable justifications for their predictions. Data annotation can facilitate the creation of explainable labels or intermediate representations, making the model’s predictions more interpretable.
Let’s say, there is a model that predicts specific health conditions. By leveraging data annotation, the model can provide more than just a binary label (positive or negative) for the condition. It can also give additional information such as critical symptoms of that particular disease that helps healthcare professionals to understand how that model reached a particular diagnosis. Therefore, in this case data annotation plays a crucial role in improving an AI/ML model’s reasoning abilities and subsequent decision-making.
Continual learning also refers to lifelong learning. It means that a model is trained over time on a sequence of tasks, where each new task is connected to the previous one. Data annotation is imperative in the continual learning process as it allows these models to retain and use knowledge from past tasks, preventing forgetting. An integral facet of this process involves human annotators consistently labeling fresh data, which is subsequently incrementally integrated into the model’s training dataset. By doing so, the AI/ML model becomes adept at autonomously updating itself, obviating the need for extensive retraining cycles.
Say a language translation model learns to translate between multiple language pairs. First, it starts with English to French, in the second training it translates from English to German. Now in the second training, it will not have to start from scratch. It will grasp common linguistic patterns and syntactic structures shared previously and build upon them because of the use of proper data annotation.
After the model is trained, it needs to be evaluated to measure its performance. A thorough human evaluation of the original dataset that has already undergone quality control (QC) is required for the data annotation procedure. This annotated dataset thereafter serves as the ultimate reference, the gold standard, or the ground truth. Any discrepancies or flaws in the training data can be found by comparing the model’s outputs to this ground truth, showing areas that need refinement and additional development.
The accuracy and F1-score (precision & recall score) of the model can be computed by comparing its predictions to these labeled instances.
After training a model to classify emails, for instance, its performance will have to be evaluated. Experts will manually annotate the emails as either spam or not spam and will compare them with the model’s predictions to test their effectiveness and determine their readiness for real-world deployment.
Data annotation undeniably serves as the cornerstone of ML model development, as it produces vital, labeled data required for training and evaluation. As the field of AI continues to further evolve, the continued emphasis on accurate and comprehensive data annotation will be key to unlocking the full potential of machine learning technologies.