With the rapid advancement in technology, it seems like experts are about to realize the full potential of AI. But that is far from the truth. AI needs massive amounts of smart data to keep learning and recognizing patterns that people can’t. The quality of the data is a key factor in AI projects.
The reality is that there is insufficient smart data to enable the speedy advancement of AI technology. Experts acknowledge that transforming raw data into smart data can be difficult.
However, a procedure helps add important information to raw data, hence giving structure to the data. That process is called data annotation. Keep reading to explore facts about data annotation and frequently asked questions.
Data Annotation Explained
Before learning about facts in data annotation, one must first understand what the term means. Data annotation is the procedure of labeling data in different formats to allow machines to understand it. Data annotation is also called data labeling. It is crucial to ensure that AI and machine learning projects have the correct information.
Data annotation supplies a machine learning model with what it requires to understand inputs and generate precise outputs. When annotated and tagged data gets added using an algorithm, the result is a model that will get smarter. The more people utilize annotated data in training, the smarter the model becomes.
But it all begins with people. Individuals must first identify and annotate data so that machines can learn and categorize information. If people don’t work on the labeling, an algorithm will find it challenging to compute any required attributes.
Properly annotated data is well structured and labeled. Data scientists need such data to train machine learning models to study, understand, predict and reach conclusions in given situations.
Facts on Data Annotation
It is essential to learn about data annotation as it will ensure that people are contributing to the space in a more adequate way.
Different Types of Data Annotation
Experts use different types of data annotation methods based on the machine learning model. Types of data annotation include:
Text annotation teaches machines to understand the text more accurately. Take a chatbot, for example. It recognizes user requests using keywords taught to it and then provides solutions. In case the annotations are wrong, it can’t give beneficial solutions.
Text annotations are further categorized into sentiment, intent, and semantic annotation. Sentiment annotation tags emotions in the text. It helps machines identify human emotions using words.
Intent annotation assesses the need behind the text and classifies it. Semantic annotation refers to the procedure of tagging documents with relevant concepts. It makes unstructured content easier to navigate.
Audio annotation involves the transcription of speech data and annotating the resultant text. It goes as far as transcribing particular intonation and pronunciation. It also includes identifying the dialect and demographics of the speaker.
Image annotation involves labeling images to teach an AI or machine learning model. The number of labels on an image may rise based on what experts need the data for.
Video annotation involves labeling video clips for use in training models to detect objects. It is different from image annotation because it includes a frame-by-frame annotation of objects.
Data Annotation Faces a Variety of Challenges
As per McKinsey, data annotation is among firms’ leading challenges in AI implementation. That is because annotating data is complex and thus requires a lot of time and expertise. Other challenges of annotating data include the cost and possibility of human error.
Machine learning and AI technology cannot learn and understand situations without data annotation. Hopefully, the facts about data annotation – frequently asked questions about data annotation provided here, help advance the understanding of the subject.