How Data Annotation Makes or Breaks AI

Data annotation doesn’t operate on the logic of just labeling desired objects/parts/sections to train algorithms. Rather it fuels machine learning models with perfect inputs, thereby helping them develop the required intelligence.

Reaching 100% accuracy in their speech recognition models became possible for a cloud communication platform only after standardizing the data annotation process to build perfect transcription datasets.

For AI-led decision making, data annotation is the new oil and companies need smart tooling options to consistently improve the efficiency of this fuel. AI algorithms demand quality datasets to ignite and train and develop the intelligence that characterizes any AI/ML model. Data annotation gives this ability to machine learning and AI models.

Data annotation is not a one-time process of building training datasets, instead, it is an ongoing process of refining the training datasets. The sheer importance of the concept lies in the fact that even the slightest error proves disastrous for ML model accuracy. Practically, without data annotation, no ML and AI models can ever be built.

Let’s check out how data annotation, when executed with precision, can make or break the capable AI models.

What is data annotation?

In Artificial Intelligence, data annotation is a process that tags, labels, or transcribes the objects of interest so that a machine learning or computer vision model can interpret the contexts accurately. By building refined datasets, data annotation helps AI systems progressively enhance their prediction efficiency.

Since data exists in a variety of forms — image, audio, video, text, etc. — data annotation also works as a class of annotation mechanisms. Each of these techniques helps differentiate significant elements from the data. These elements are crucial features to fast train machine, learning models.

For instance:

How data annotation helps businesses to implement AI

Quality data annotation is a mark of a high-performing data environment and it also helps to build successful AI ecosystems.

1. Implements systematic workflows that offer structured training

Training a model is an iterative process, and depending on the type of data, data annotators apply techniques to allow machine learning models to enhance their prediction accuracy. Data annotation relies on data annotation specialists who are experts in identifying the target elements. As the most important component in the data annotation framework, data annotators, therefore, add significant value to the objects. In short, the process works as:

· Data annotation team collaborates with machine learning experts to understand the assignment.

· Identifying the annotation technique, data annotators finalize the techniques to categorize and feed each target object into the algorithms.

· With evolving machine learning models, data annotation, too, evolves, thereby progressively enhancing the prediction precision.

2. Builds performance-driven annotated datasets

Real-life datasets exist as a mix of structured and unstructured data which must be properly categorized to address use-case-specific requirements. By deploying a systematic sequence, data annotation consistently adds comprehensive tags and leads to the formation of rich metadata that defines or categorizes data in the form of code snippets.

To give an example, companies use NLP to filter abusive content from their social media accounts/handles. The process begins with data annotation, where human annotators label and separate hate speeches, abuses, and obscene visuals/multimedia. With such labeled datasets as input, the model develops the ability to classify content as objectionable.

3. Acts as a medium of ground truth

An annotated dataset is the most valuable commodity for artificial intelligence and machine learning models. And why not, for it denotes the accuracy of the supervised learning model’s training set. Better the data annotation, better the classification accuracy of the model.

Supervised learning models need data with labels, and when the training sets have accurate labels, it allows the models to predict for unforeseen elements. For instance, to develop automated disorder detection systems, all underlying medical conditions in the image have to be labeled. With this rich corpus of input data, the computer vision models provide radiologists accurate insight into a given health condition.

4. Drives contextual application to improves model accuracy

Invariably, a machine learning model’s accuracy is regulated by the accuracy of data annotation. The selection of the right data annotation technique for a given data type decides the accurate formation of annotated datasets. It is the single most important factor in the functioning of the entire machine learning execution.

Image annotation technique like polygon annotation proves useful in identifying objects of no standard shape. In crop classification, the techniques take into consideration variables like shape, pattern, colors, etc. of the crop to build computer vision models for crop classification. On the other hand, simple annotation techniques like line annotation and point annotation are used to studying traffic patterns, lane violations, and analyzing facial expressions in sentiment analysis respectively.

5. Works as a data cleansing agent

Mckinsey Global Institutes’ findings have confirmed that computer systems cannot be trained to perform intelligent functions and improve their prediction accuracy, unless and until they are backed with clean and well-trained datasets. By building practically useful datasets, data annotation drives consistent data cleansing and eliminates the bias, thereby improving the entire training lifecycle.

The Waymo Open Dataset, one of the largest datasets, comprises thousands of images that have been labeled with over 12 million 2D and 3D bounding boxes, polygons, lines, etc. Regular and improved annotation has been a crucial part of Waymo’s database management strategy to consistently cleanse its continuously growing database.

Read more: How outsourcing data annotation can help ML Companies

Conclusion

According to a leading market intelligence firm, International Data Corporation (IDC) has shared an insight showing that 50% of data professionals find data quality as the most important challenge in deploying truly functional AI systems.

The sole way to address quality issues in machine learning development is to have perfect data annotation process. Remember, a mere implementation of data annotation leads to a catastrophe when executed poorly, while the right data annotation application reduces bias and false positives.

Playing a pivotal role, the accuracy of data annotation, therefore, justifies your investment in AI adoption and can make or break your AI systems