Artificial Intelligence (AI) has facilitated innovation across a wide range of industries. As much as we don’t realize it, AI is a big part of our society. Nevertheless, integrating AI into our current technology hasn’t been easy due to a bunch of reasons.
Data overload is a huge problem with AI integration, as we’re producing 2.5 quintillion bytes of data every day. Even though we have created machines to organize and study data for us, we need more effective measures to prepare such a large quantity of data before computers use it.
There’s only one viable solution to this problem: preprocess the data so that automated systems can go through it much faster and find patterns within. The goal of this article is to take a look at how data is collected and turned into a readable form with the help of data annotation,
What is Data Annotation?
Data annotation is the process of labeling and categorizing data in order to enable a model to make better decisions and take swift action. The annotated data can then be used to perform a number of actions, such as training speech recognition platforms, autonomous vehicles, and translation systems.
When you are building any model based on AI, you often start with a massive amount of data that is not labeled. Data annotation converts this unlabeled data into training data so that AI models can recognize patterns and produce the desired outcome.
Different Types of Data Annotation
Data needs to be expertly annotated so that machines know what they should be learning and what they should ignore. There are lots of different mediums that can undergo data annotation, including:
- Image Annotation
It is a method in which machine learning engineers preprogram labels. These labels enable visual perception-based AI models to recognize objects in still images. Image annotation is usually human-powered as different annotators are assigned the task of labeling images to pinpoint accuracy.
- Video Annotation
Unlike image annotation, where still pictures are labeled, video annotation labels moving pictures. Since you have to annotate images frame by frame in video annotation, precision is a must. Data for self-driving cars or autonomous cars is usually trained with this technique.
- Text Annotation
Text annotation is used to improve the search relevance and train chatbots by assigning predefined categories and tags to documents. It makes it easier for machines to fetch relatable data and then communicate with humans.
- Audio Annotation
Audio annotation can be used to develop and improve voice-enabled applications. Annotators analyze different audio clips and then classify them into their appropriate categories. NLP-based speech recognition models are then used to promote interaction between virtual assistants and humans more meaningful.
Light Detection and Ranging or LiDAR is a technique in which 3D point cloud annotation is used to visualize and detect an object with precision so that it can be accurately classified. It is mainly used in autonomous vehicles to judge their surroundings and understand their environment using 3D cuboids as the main technique for classification.
Benefits and Application of Data Annotation in AI Development
- One of the most interesting uses of data annotation in AI can be seen through its role in autonomous driving. Recently, Volkswagen unveiled their level 5 autonomous car designed through AI concepts.
- The retail and e-commerce industry is also reaping the benefits of AI by improving their online customer experience. Different features such as AR can be included on top of AI-powered chatbots, which make use of data annotation.