AI and automation are becoming integral parts of today’s business environment. But it’s only as good as the data it’s trained with. Here, we look at ‘data labeling’, a method employed by businesses to improve their AI and automation capabilities.
1. The definition of data labeling
Data labeling is the process of reviewing raw data samples and adding meaningful and informative labels to them. ‘Data’, in this context, can be any type of data, such as images, videos, audio, and text. A data label, or tag, therefore, is simply an identifying element that explains what a piece of data is. It’s the first step in developing a machine learning model or AI. Labeling data provides context so that the model can learn from it.
For instance, if you want to train a model that can identify insects, you must label examples with tags such as beetles, ants, and termites in the image dataset. Data labeling can tell an AI model that a particular image is of a person, tree, or car. This is particularly useful in training AI for autonomous vehicles, such as self-driving cars, that need to be able to tell the difference between objects to process the external world and ensure a safe journey for all. Data labeling can help AI identify which words were uttered in an audio recording or what action is being performed in a video.
The process begins with manual data labeling. Humans generate highly accurate labels for a collection of data that you can then use in your machine learning models. In data labeling companies, this process is known as ‘annotation’. Annotation teaches the AI to recognize patterns according to the task or target. The AI then learns by example, leading to predictable and accurate labels of new unlabeled data from the model. A properly labeled dataset provides what’s known as a ‘ground truth’ that the model uses to check its predictions for accuracy and continue refining its algorithm.
2. How can data labeling be applied to my companies?
Unfettered global trade and huge advancements in communications technology have made for an intensely competitive business environment. It’s becoming increasingly difficult to find the necessary edge against the competition. Many businesses are conscious of how the convenience and speed that machine learning offers can help make their business operations more productive. They want AI to help automate business processes and facilitate faster more efficient decision-making. But machine learning is not magic. Like any machine, it needs fuel to work. The higher the grade of fuel, the better the performance and for machine learning models, that fuel is ‘labeled data’.
2.1 Is data labeling necessary?
As the volume of data generated by business grows, obtaining suitably annotated and labeled data to train machine learning models is becoming an ever more challenging prospect. In fact, it’s widely estimated that on average, 80% of the time spent on an AI project is contending with training data and data labeling. So, is it worth the time and effort? Today’s successful business leaders understand the importance of accuracy in the data labeling process. A well-trained machine learning algorithm is able to find patterns in the new datasets that you feed into it and build complex forecasting models. Companies with more accurately trained models are more likely to have an advantage when it comes to winning new business, capitalizing on opportunities, and foreseeing threats.
3. Should We keep my data labeling in-house, crowdsource, or outsource?
As AI models require a large quantity of annotated information prior to going live, many companies looking to develop their machine learning algorithms will have a choice to make very early on. That is: whether to create an in-house team, utilize crowdsourcing, or work with an established outsourcing partner.
Some think that setting up a data labeling team in-house can offer advantages such as direct oversight, more security, and better protection for their IP. However, the process of creating the training data necessary to build AI models is often prohibitively expensive, complicated, and time-consuming. Not many companies can redirect the necessary time and resources needed to hire, train, and manage a professional team of data labelers. Take into account the extra office space needed and the requirement to develop the right software and tools, and costs can swiftly spiral. Furthermore, data-labeling work is often done on a project-to-project basis, so there will be a high rate of staff turnover to contend with. This means a fresh round of hiring and training for each project.
Crowdsourcing is an approach that hands your data-labeling service requirements over to a large number of people via the internet. If cost, rather than the quality of data, is the biggest concern for your company, then crowdsourcing is an option. However, in order to produce a high-quality algorithm, the labels used to identify data features must be informative and accurate. Crowdsourced solutions are proven to be less accurate than in-house or outsourced teams with management oversight. According to a recent study, crowdsourced workers operate with an average 4-8% error rate in basic transcription tasks. The error rate for managed workers (in-house and outsourced) is under 1%. So crowdsourcing can result in over four to eight times the error rate when compared to a dedicated team. Errors in data labeling impair the quality of the training dataset and therefore the performance of any predictive models it’s used for. There is also little to no confidentiality.
3.3 Outsourcing to data labeling companies
For a ‘best of both worlds’ approach, many businesses choose to work with an external, specialized, data-annotation service. Working with an established and reputable partner can help companies save money without sacrificing quality. In any particular data labeling company, these specialists employ trained, professional annotators who are able to quickly adapt to any demand and are familiar with the most up-to-date and sophisticated annotation tools. Outsourcing allows you to form long-term relationships with your partner which can be particularly useful if you know you’ll be coming back with new batches of data over time. If you’re anticipating a seasonal surge and require to scale up the workforce, your third-party partner can simply reassign some of their staff to your account. This avoids the need for conducting a laborious hiring and training process, only to lay people off once the demand drops.
4. The Different Types of Data Labeling to Outsource?
Manual data labeling helps computer models to ‘see’ specific objects, but the vision systems of an AI require a considerable amount of training. Data labelers use software that allows them to draw around objects in an image (such as a person, flower, or cat) and label them so that the model can understand and eventually recognize them in an unlabeled image in the future.
Similar to image annotation, video annotation involves adding bounding boxes, polygons, or key points on a frame-by-frame basis. This helps the AI’s vision system track the movement of an annotated object in the video. When training a computer vision model, humans are needed to identify and annotate the data by outlining all the pixels containing, for example, faces or car license plates, in an image.
Digital voice assistants, such as Alexa and Siri, are very real applications of artificial intelligence that are becoming increasingly integral to our daily lives. Many more businesses are training their own virtual assistants to understand voice communication to operate in their specific industry. They all rely on natural language generation and processing in order to effectively respond to any spoken question or request. This requires transcribing thousands of hours of audio recordings and transferring the data to the model to help it understand the intent of the speaker and provide a relevant response. The large data sets required by the machine learning models used to train the AI to make this a challenging task.
It’s easy to overlook, as we use computers every day for emailing, texting, and creating documents but AI has a difficult time understanding unstructured text data. Data labeling for text projects can include training a chatbot for a website, image recognition models used to read labels on packaging, or document management systems. Annotation of text involves identifying words and phrases and training the model to understand synonyms and paraphrasing. This helps, for instance, the chatbot to respond appropriately to a customer’s question or help AI document management accurately search for files containing information on a specific topic.
3. Pure Moderation – a data labeling company
Labeling data takes a great deal of skill and attention to detail. Data labelers must sustain focus and work consistently, so choosing the right partner to work with is a key decision. As an established BPO vendor specializing in a range of services, Pure Moderation provides manual data labeling services that will improve the performance and ability of your machine learning algorithms. We understand that every business is unique and has specialized needs. Therefore we offer bespoke services to suit any industry and size of the organization and offer the ability to quickly scale up or down as needed to suit changes in business needs and goals. Our clients benefit from our capability to quickly deliver large volumes of high-quality data across multiple data types, including image, video, speech, audio, and text for your specific AI program needs.
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