Are you aware that even the most technically advanced algorithms cannot solve a problem without relevant data? That is why having a massive amount of data won’t completely serve the purpose, you must have access to data with a learnable ‘signal’ which is consistently added at a massive scale. Data annotators effectively bridge the gap between human expertise and combined with machine learning to aid brands across the globe in solving complex data challenges and improve operational efficiency at lower costs. 

The Emergence of Artificial Intelligence

Artificial Intelligence is everywhere, and it has left its footprint in almost all sectors. From logistics to healthcare, AI aims to redefine how data is gathered, analyzed, and integrated. Also, it empowers you with informed insights to deliver better results. Automated systems easily learn and process data quicker than a human, therefore data is equally crucial. Artificial Intelligence utilizes massive amounts of data to identify and learn patterns which humans can’t. It’s the ability of AI in processing data and making informed decisions that often lead to be the game-changer in the industries that rely on massive volumes of data.

The Enterprises require to develop their AI strategy to integrate, link, and supplement their data so they could have an accurate foundation to build and train their machine learning algorithms as algorithms powering artificial intelligence which depend on the quality of data. 

According to an MIT Technology Review, insufficient data quality is one of the biggest challenges for employing AI. Hence, companies should think about AI and machine learning as engines that will help them achieve what they want to accomplish. But, an engine needs fuel to run, and here data is being the fuel.

How Data Annotation is linked to Artificial Intelligence? 

Data annotation is for ensuring that if your artificial intelligence and machine learning projects can scale. It offers the initial setup for training a machine learning model with a proper understanding and differentiation between the various inputs and outputs.

There are different types of data annotation models based on the type of data such as image and video annotation, semantic annotation, text and content categorization. But, humans should identify and annotate specific data so that machines can learn and recognize the data information. Without proper labels, machine learning models would face difficulties in computing necessary attributes. 

Artificial Intelligence Use Cases while performing an annotation task

– Sequence: Time or text series starts from left boundary and ends at right boundary, and the label (ML model) could easily recognize a name in text or identify a paragraph. 

– Categorization: Categorize according to BISAC ontology or categorize an image as offensive or proper. 

– Mapping: Map full text to a summary, map one language to another, map raw data to normalized data, and more. 

– Segmentation: Identifies the paragraph splits, finds an object in a picture, etc. 

Usually, data annotation powered with AI allows you to solve complex problems in sequence or a combination of tasks. For instance, when you unlock your mobile phone with your face, machine learning is utilized to spot your facial features like eyes and nose segmentation etc, and categorize the object if it’s a human or not. Hence, AI and Machine Learning go hand-in-hand to annotate data and offer perfect solutions to your problems.

How Data Labeler can help you with Robust Data Annotation Services? 

Data Labeler brings you the most relevant, accurate, convenient, customized, and quality-labeled datasets for AI and Machine Learning initiatives. It will help you achieve competitive advantage with unlimited support and growth. 

We at Data Labeler offer highly accurate labeled data, guidance on labeling instructions, options on real-time labeling, and more. 

Collaborate with us for seamless data labeling and data annotation solutions. Contact Now – Sales@DataLabeler.com