How Data Labeling Services Are Powering The Next Generation of AI

As technology and AI continue to seep into our everyday lives creating ever-larger amounts of data, data labeling services will continue to have a significant impact on modern society. 

Data is a commodity and just like any other commodity, it needs to be processed and refined from its raw state into something more valuable and useful. Each day, massive amounts of data are used for Machine Learning. Businesses are investing huge amounts of time and money to provide people with the right training and the right tools for data enrichment so that they can be used to teach, validate, and tune AI models. What follows is a guide to the essential elements of this vital but time-consuming work. Here we will explain what exactly data labeling is, the terminology used by the industry, and the applications of the technology to give you a better understanding of what a data labeling service provider can do for your business.

Table of Contents

1. What is data labeling? 

Data labeling, sometimes referred to as data annotation, is the process of identifying raw data (images, text files, audio, videos, etc.) and augmenting it with one or more informative labels to provide meaningful context. For example, a data label might indicate whether a photo contains a car or a bicycle, what type of action is being performed in a video, what topic is being discussed in an audio recording, or whether the subject of a news article is sports or politics. Labeled data is provided by humans reviewing and making judgments on raw data which is then used to help train machine-learning systems to recognize and act on patterns it then discovers in future data sets. For instance, a hospital could use an AI model trained with a particular kind of data set that could help identify a tumor in an X-ray, and businesses can better identify and predict disruptions to the economy and prepare more effectively. 

2. What are the most common types of data labeling?

Computer Vision

Computer vision helps computers to ‘see’ the world around them. It’s an integral part of modernizing the automobile (self-driving cars); manufacturing and utilities (defect detection); and even retail industries.

When building a computer vision system, depending on the visual task that you want the model to perform, you first need to label images, pixels, or key points, or create what’s known as a ‘bounding box’, which fully encloses a digital image, to generate a suitable training dataset. You can then apply this training data to build a computer vision model that can be used to automatically detect, identify, segment, or categorize a single object or multiple objects in a particular image. 

Natural Language Processing

Natural Language Processing (NLP) gives machines the ability to read, understand and derive meaning from languages in much the same way as humans.

NLP is commonly applied to services such as chatbots, speech recognition, automated translation, search engines, auto-correct, and many more. It can also be used to identify the sentiment or intent of a text or news article or classify proper nouns like places and people to ease the locating of relevant or pertinent files in the future. NLP-trained AI is also being used to identify text in images (such as vehicle registration plates), PDFs, and can even interpret signals from the brain of a person thinking about writing with a pen. 

Audio Processing

Audio processing converts all kinds of sounds such as speech, music (the ever-improving Shazam app is a good example), wildlife noises (there are several ‘Shazam for birds’ apps available), and general ‘urban’ sounds (breaking glass, traffic, alarms, etc) into a structured, useable format for use in machine learning. 

3. Why does AI need data labeling? 

The old computer-science adage ‘garbage in, garbage out’ is as true today as it ever was.

Good quality data is essential for Machine Learning algorithms to learn. They discover patterns, develop understanding, find relationships, and make decisions based on the training data they’re given. The quality and quantity of training data directly determine the success of an algorithm and AI can only ever be as good as the data it is trained with. Therefore, the better the training data, the better the model performs.

The harsh truth is, however, most data is messy or incomplete, and ‘Artificial Intelligence’ isn’t actually all that ‘intelligent’. Take a picture of a tree as an example. To a machine, the image is just a series of pixels. Some might be green, some might be brown, but a machine doesn’t know this is a picture of a tree until someone applies a label to it that says this particular collection of pixels is a tree. If a machine sees enough labeled images of a tree, it can start to recognize patterns and understand that when it sees similar groupings of pixels in an unlabeled image in the future, it is, in fact, looking at an image of a tree. Data Labeling Services

That’s why, today, most practical machine learning models utilize supervised learning, where an AI learns from a pre-labeled set of data, to teach machines to make correct decisions. Labeling training data is the first step in the machine learning development process and it starts with humans reviewing, making judgments, and labeling large swathes of unlabeled data. Data Labeling Services

4. Data labeling applications

Data labeling plays an integral part in the development of Machine Learning, so its applications span several industries. In healthcare, data labeling helps AI in the early diagnosis of skin disorders, eye conditions such as glaucoma, and, as mentioned above, cancer. A recent study even showed AI’s ability to outperform doctors in predicting whether or not a patient will develop dementia. One of the biggest uses of data labeling has been to train AI used in search engines to create ranking algorithms. This affects the results you see on the first page of a web search as well as the order in which the results appear.

While AI has proven itself to be problematic in the world of Content Moderation in the past, it can ease the burden on moderators by being able to instantly recognize and delete recurring disturbing images or videos.

Data labeling services continue to also help the development of what is increasingly becoming the ‘everyday’ AI seen in everything from playlist recommendations and intelligent virtual assistants to self-driving vehicles. 

5. Gear Inc provides experts data labeling services

When building an AI model, developers start with a massive amount of unlabeled data. Labeling that data is an integral step in data preparation and preprocessing. 

As previously mentioned, the quality of the AI depends wholly on the quality of the data used to train it, so it’s no surprise that, on average, 80% of the time spent on an AI project is processing, sorting, and refining training data.

Doing this work in-house is a huge investment of time and labor, time better spent focusing on more urgent, strategic initiatives.

Gear Incenables you to access expertly-trained human data labelers to properly annotate your collection of data based upon the most important variables and visual features to train your custom Machine Learning model.

Our services include:

  • Image Classification
  • Text Classification
  • Video Classification Task
  • Video Object Tracking Task
  • Bounding Boxes
  • Polygons
  • Named Entity Recognition

AI can revolutionize the way we do business, and incorporating data labeling services is the first step to building a high-quality AI model. To learn more about outsourcing your data labeling projects and the value that it can bring to your business

Gear Inc.

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