Annotated data is the powerful foundation behind ML and AI algorithms, creating a highly accurate source of information that directly impacts the performance of the developed algorithms. Properly labeled data is vital for ML and AI models to detect and understand input data accurately. People continue to rely on smart equipment and enjoy the convenience of intelligent life. ML and AI models need tons of data for these devices and equipment to function. 

Annotating data for ML and AI

Data annotation is connecting the data to AI and Machine learning to ensure that ML or AI projects remain scalable. Data annotation used to be a human task. Humans identify and label particular data, videos, and images to enable machines to identify, classify, and decide, similar to what humans do. However, with tech innovations and developments, combining the innate skills of humans and machine intelligence speeds up the production of datasets with optimal accuracy, allowing clients to deploy their programs faster. 

Data collection and preparation is a continuous loop, as the learning models need a constant supply of quality data to maintain and increase their level of accuracy. Because datasets can quickly accumulate, the efficiency of labeling and productivity becomes critical. Therefore, minimizing wasted hours due to human fatigue is necessary, as this could lead to human errors. With an annotation platform, the machines can pre-annotate data before humans can label them. This combination can save the time spent sorting every data batch.

Benefits of human/machine Annotated data for AI-based machine learning

A data annotation platform combining human skills and machine intelligence ensures the accuracy and quality of the data that human annotators feed to computers for AI and machine learning. It provides benefits to both the annotators and clients.  

• Improved search relevance for various markets. Search engines use AI and machine learning to ensure their system can provide the best response and predictions for all search queries based on global market trends and culture. Annotators label the data sets using the right techniques to make the information recognizable to machines and computer visions. The human annotations on different scenarios allow the machine models to learn a wide range of data to help them make precise predictions according to the behavior and pattern they learned during the machine model’s training.
• Reliability and accuracy. With machines using powerful algorithms to prepare the data for humans to annotate, the combination of humans and machines, and the use of applicable techniques and tools, will ensure that data annotation will be more reliable and accurate. For example, images and video elements have different shapes. It is difficult for automatic annotators to define irregular shapes, whereas humans can annotate the image according to its shape and dimension.
• The human-machine combo provides a one-stop annotation solution. An automated tool or dedicated software is not enough because it limits the types of data it can annotate, particularly when working on specific objects or images in different shapes and sizes. The collaboration of machine and humans make the data annotation service faster and more affordable.

Annotated data is challenging, but it is the path to the future. As the demand increases, service providers continuously create innovations to ensure that the project turnaround time is shortened while ensuring data accuracy and reliability.

The post Why Machine Learning Requires Human and Machine Annotated Data appeared first on Technology Moon.