We’ve been hearing about Artificial Intelligence (AI) a lot over the past decade. From robot assistants to industrial processes powered by automation, this technology has made many jobs and lives easier.
One of AI’s major powers is using data to create and train AI algorithms. This means that you can create an AI-based algorithm to process vast amounts of data and turn it into valuable insights.
However, that’s only half the battle—to make data actionable, it must be labeled so that the computer can comprehend it.
Data labeling is the process of adding tags to your data points in order to train the machine learning algorithm. Yes, machine learning is here to automate data processing, but you need to set the rules first.
In this guide to data labeling, we’ll look into:
- What is data labeling, and why is it important?
- The most common types of data labeling
- Unlabeled data vs. labeled data
- The challenges of data labeling
- How to label your data
- How to automate data labeling
Businesses are embracing AI technology to automate processes and capitalize on new business prospects. However, according to McKinsey, data annotation is one of the most difficult barriers to AI adoption in the business.
Let’s change that.
What is data labeling?
Data labeling—or data annotation—refers to the practice of adding tags or labels to raw information such as photographs, videos, text, and audio.
These tags describe the data's entity type, referring to various attributes and characteristics of the data point. This allows a machine learning model to learn to recognize that type of object when it’s met in data without a label.
To train AI and machine learning algorithms to understand and learn from your data, you need a well-streamlined and high-quality process of data labeling.
Your labeling has to be as precise as possible, whether it's labeled by class, subject, theme, or any other category. With comprehensive data labeling, the AI system performs better and delivers more accurate results.
Most common types of data labeling
Data can be structured or unstructured. Structured data is typically quantitative and numbers-based, whereas unstructured data is typically qualitative and can’t be analyzed using conventional data analytics tools.
Data labeling can be done for different types of data with the help of different AI sub-technologies.
Computer Vision for images
Computer vision (CV) is an AI subset that allows machines to recognize objects in an image. This means that computers can “see” what’s in the image and name it, just like the human eye would but without the same time investment.
To do this, computers need:
- Image annotation: the process of adding labels to images
- Video annotation: the process of adding labels to videos
When we create a digital perimeter around items in an image for computer vision—also known as a bounding box—the computer can separate the different sections of the picture for categorization.
This training data can then be leveraged to create a computer vision model that’s able to categorize pictures, recognize the position of objects, identify important spots in an image, and fragment an image.
For example, many e-commerce stores have implemented computer vision tools to recognize objects on product images and tag them. These tags help website visitors easily find what they’re looking for.
Let’s say you have an online fashion store. A computer vision tool can process thousands of your product images containing fashion items. If there’s a red skirt on the image, the tool will add tags like red, velvet, A-line, pleated, etc. With these tags added, anyone who comes to the page and searches for a “red velvet skirt” will be able to easily find what they’re looking for.
No code computer vision allows people of all levels of technical knowledge to improve the efficiency of their business operations. Using computer vision, you can label your image and video data in a simple automated process rather than hiring someone with a highly qualified skill set to build an in-house solution.
Natural language processing (NLP) for text
Natural language processing, or NLP, is a branch of artificial intelligence that allows computers to understand human speech. NLP combines languages, statistics, and machine learning to investigate the form and laws of language to develop intelligent systems that can understand text and speech. Essentially, you are teaching the machine to comprehend language.
Natural Language Processing is the technology used to aid computers to understand natural human language.
To create your training dataset for NLP, you must first manually choose relevant chunks of text or tag the text with particular labels. Sentiment analysis and Named Entity Recognition (NER) are done using NLP models.
NLP can help you automate business processes and gain actionable insights from them. Any text-related process can be used in NLP, such as social media analysis.
Audio processing for speech recognition
Audio processing turns various types of sounds into a structured format that can be used in machine learning, such as recognizing speech, animal noises, and construction sounds.
Audio processing often requires you to first transcribe the audio file into written language. By adding tags and classifying the audio, you can provide more information about it.
Very often, speech recognition and NLP will be linked together. After the audio file has been transcribed into a written format, it is NLP that would come into play to understand the content of the text.
Why is data labeling important for AI?
Data labeling is important for AI because it helps train your model to understand and categorize incoming data. Data labeling allows computers to accurately grasp real-world settings, which opens up new potential for a wide range of industries.
Consider the following scenario: you wish to train a sentiment analysis model.
To do this, you will have to provide the AI model instances of positive, negative, and neutral emotions that have been categorized so that it can begin to differentiate between the three.
You'll also need to add phrases that reflect the natural human language, such as sarcasm, humor, and irony.
If your labels are inaccurate or unspecific, your AI model’s prediction will be directly affected by this. That’s why it’s important to make sure you have enough data points and that they are labeled correctly before automating a process with AI.
The quality of the training data determines the success of your AI model—it needs to be meaningful and targeted at what you’re looking to understand. Once you've organized your training data and labels, you'll be able to use them to make your everyday activities easier.
Labeled vs. unlabeled data
A data point that contains a tag, such as a name, a type, or a number, is referred to as labeled data.
Data that hasn't been assigned a label is referred to as unlabeled data.
To understand the difference between labeled data and unlabeled data, we’ll go through the three types of machine learning that we can use. Each type of machine learning requires a different type of data.
Supervised machine learning
This type of machine learning requires labeled data to learn. Supervised learning models are trained with labeled data and then used to forecast future outcomes.
This training dataset contains both inputs (the data point, e.g.: image) and outputs (the label, e.g.: living room). These help the model improve over time by knowing exactly the data points that come in and the information that should come out as a result (the label).
Supervised learning can do classification, meaning it can sort data into categories (e.g.: is this a car, bike, or train?), and regression, which refers to its ability to discover continuous correlations between dependent and independent variables (e.g.: based on input data, predicting the cost of a house based on an image of it).
Predicting real-estate prices or labeling real estate images are examples of supervised learning. For an algorithm to be able to predict this, it needs current and past prices. It also needs data about the number of rooms, size, the year it was built, and so on. The result is a prediction of the house price based on past and current data.
Unsupervised machine learning
Unsupervised learning requires unlabeled data. The model runs without being aware of any labels that the input data may contain. This learning method is suitable for problems where we have little or no idea what our results should look like.
These algorithms uncover hidden patterns or data clusters. It’s the best option for exploratory data analysis, cross-selling techniques, consumer segmentation, and picture identification because of its capacity to detect similarities and contrasts with no human instruction.
Semi-supervised machine learning
Semi-supervised machine learning requires a combination of labeled and unlabeled data.
It guides categorization and extraction of features from a larger, unlabeled data set using a smaller labeled data set during training. Put simply, semi-supervised learning uses labeled data as an example to tag the unlabeled data.
Semi-supervised learning can overcome the problem of not having sufficient labeled data to train a supervised learning algorithm.
For more accurate results, you could also use the human-in-the-loop (HITL) approach.
What is “human in the loop?”
The phrase 'human in the loop' refers to the process of monitoring and validating the AI model's output by including human review. When using HITL, you can set a threshold for the accuracy confidence of your model, and any prediction that has a value lower than the one you have set will be reviewed by a human.
As an AI model’s knowledge is mostly based on statistical data—which excludes the idea of complete certainty—they can't generate 100% confident predictions.
Some AI solutions enable people to engage with them directly to mitigate this underlying unpredictability.
Humans cooperate with machines when:
- They revise and label the training data that the model is uncertain about.
- They train and validate the model as data scientists and use findings to improve it.
However, data labeling can be more complex than it sounds, especially if we do it manually. Read on for more on the challenges of data labeling.
Challenges of data labeling
The data labeling process is typically a manual process. Thus, it can bring many challenges. Let’s take a look at some of them.
Data labeling takes a lot of time and resources
Finding large amounts of data, especially for some smaller industries or segments, can be complex and lengthy.
Once you find your data, you need to ensure it’s clean and prepare it for labeling. By clean, we mean consistent and standardized, which also needs to be done manually.
The initial manual process of data labeling can potentially take quite some time. In reality, data-related tasks such as labeling consume the majority of AI project time. Depending on different factors, you may need a team of people to dedicate their time to labeling data, but once this is complete, the automated part of the process can begin.
Data labeling can be inconsistent
When numerous people are involved in the labeling process of the dataset, the accuracy of the outcome is usually higher. However, because people typically have varying degrees of experience, labeling standards and ideas may differ, which adds to the list of challenges.
Two or more professionals may disagree on some tags, making for inconsistent data labeling.
Data labeling can lead to errors
Manual labeling is prone to human mistakes, regardless of how vigilant you are during the labeling process.
Tagging large amounts of data can lead to discrepancies and mistakes, simply through human error.
Data labeling can require domain knowledge
Domain knowledge is required for many industries, especially healthcare or engineering. To add labels, you may have to recruit domain specialists.
Underqualified annotators, for example, will be under-equipped to properly recognize conditions in medical records.
Despite these challenges, data labeling remains a critical component of most machine learning initiatives. So, let's have a look at how this process works and how you can avoid the hang-ups of data labeling.
How to efficiently label your data
There are a number of ways that teams can approach data labeling:
- In-house data labeling
Let’s see what the typical process looks like.
The starting point is always to gather a large amount of raw data. Depending on the industry, each company uses different sources for data collection. Some could gather data internally, while others buy data from industry researchers.
Whatever the case, at this point, the data is typically disorganized and cluttered. To prepare it for tagging, it needs to be cleaned. For a model to deliver more accurate findings, it should include a large amount of different data.
Now it’s time for the data labelers to go through all the data and add tags.
These labels provide relevant context for the algorithm to use as ground truth, which involves the input data points and the end result you need your model to contain. For example, if you're creating a fashion image recognition tool, you’ll need to label the different clothing pieces included in your dataset.
Labeling quality assurance
The machine learning model should only use high-quality, reliable data.
The precision with which labels are applied to each data point determines the reliability of the predictions you get. Continuous quality assurance tests allow you to verify label correctness and improve it as necessary.
This is one of the most important steps when labeling your data, as the quality of your AI model’s predictions could be severely affected by poor-quality or inaccurate labels.
Checking whether your model works by trying it out on an unlabeled data set is a standard part of the model training process. You'll choose confidence ratings or accuracy levels based on the use case.
You can, for example, determine that the model has been effectively trained if the accuracy is 90% or above.
The above four steps detail the manual data labeling process. However, this can be executed in a lot less time and with less effort.
How? The answer is—automated data labeling. Let’s see how it works.
Manual vs. automated data labeling
Yes, manual data labeling for AI data takes time. However, after you've done your initial data tagging, you can delegate this job to computers.
With automated data labeling, you can:
- Save a lot of time and resources: by using a system that can start immediately instead of having to hire an entire in-house team.
- Improve data accuracy: with an automated labeling process that works according to the rules you’ve set.
- Focus on growing your business: instead of dealing with repetitive manual tasks, you’ll have more time to focus on growth-oriented activities.
It is very important to make sure all your data is labeled correctly before training your AI model - but thanks to Levity’s highly intuitive interface this process is now simpler than ever. AI-powered workflow automation can be accessible to people of all levels of technical knowledge. Sign up to Levity here to receive a personal onboarding and support with your automation needs.
Data labeling is a key step in your AI model training. However, doing it manually is energy-consuming and takes up a lot of valuable time that a fast-paced business simply can’t afford to lose. What is more, it’s an error-prone method that doesn’t guarantee high accuracy.
The good news is—it doesn’t have to be so hard. Humans and machines can now work together to produce accurate and effective data for various machine learning applications using today's data labeling tools.