The Importance of High-Accuracy Data For Machine Learning


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When it comes to data annotation services, it is important to have human-annotated data and not simply training data from AI. After all, why would anyone rely completely on artificial intelligence when you can involve human intellect and trained skill? The accuracy and the ability of humans to decipher and classify data is what is the key to accuracy. It is vital to include human-in-the-loop, which means that humans are the backbone of technology. Humans decipher the data for machines to understand.

Data annotation is the process of labeling text, images, and videos to make sure it will be recognized with machine learning. To develop a fully functional and accurate model requires having the right amount and quality of data that typically is a lot of information. One of the most efficient and highly accurate ways to annotate data is to use human-annotated data.

Some things only a human can do well

Humans tend to do a fantastic job in terms of subjectivity, managing, understanding intent and confronting with uncertainty. Even if everything is being fed into an automated system, a human’s insight is vitally important. The human touch is very important in order to identify and annotate image data such as precisely outlining various objects like traffic signs, trees, or other objects in the path.

Another area where human’s insight and expertise is helpful is in developing a search function. Searching for something online requires a search engine that contains extremely large amounts of information. When a search or query is conducted by a user, this information is used to make the quality of search results representative of specific cultures or markets. Some tasks require a human’s input to achieve results in developing a successful machine learning model in regard to on-target and accurate responses and predictions.

Human-annotated data is labeled correctly with the best technique to be recognized by computer visions or machines. The critical thinking ability and personal experience that only humans can know allow for different types of data to be annotated by humans for different scenarios.

Human-annotated data allows for accuracy and reliability

Annotated data for machine learning needs to be extremely accurate to provide a model for machine learning of true scenarios and to be able to predict accurately accordingly. Human-annotated data allows for an accurate and reliable scalable solution.

Compared to sending annotation through an automated service, humans tend to annotate carefully and precisely because of the human factor that only a critically thinking mind can offer. After all, even the best automated system was programmed by somebody. When annotation is done automatically, it tends to have many inaccuracies especially when it comes to quality that only humans can offer. For instance, when annotating irregular shapes, humans can annotate the image data per the object dimension or shape with a lot of accuracy and reliability since they are manually checking and validating everything.

Human Annotation Is An All-In-One Package

While working on a wide range of various types of image and data annotation projects, humans use their experience to annotate pictures of any size or format. This expertise provides a final review and corrections that help models learn for the best results. Humans are often the best one-stop, all-in-one solution for annotation. Humans can annotate a variety of different shapes and sizes of objects in various formats with full customization and flexibility at an affordable cost.

Regina Panergo
Regina is a marketing manager at BUNCH, a modern outsourcing house for high-growth tech companies. A professionally trained content writer and marketing expert, Regina has spent the last 5 years developing strategies for the fastest growing startups in the US and the Philippines. She believes that tech startups shape the future and wants to be a part of advancing a world where machines provide a more enhanced lifestyle to people around the world.


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