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It’s been said that data is the new currency. If you’re in business, you’ll know this to be true firsthand. The better your data management strategy, the more of the market you’ll win.
Today, this phrase has a whole new meaning – one related to the business applications of artificial intelligence and machine learning. These new technologies are expected to drastically change the business landscape, and they’re already doing so. With their help, business teams can spend fewer resources on tedious, repetitive tasks and create more opportunities for people to use their creativity and intelligence.
Both AI and ML rely heavily on data (specifically, labeled data used to train algorithms), so let’s dive right into the most effective approaches used for the foundational stages of any successful ML strategy — data labeling.
Whether you’re new to data labeling or in the intermediary stages of your journey into all things data annotation and machine learning, this article is for you. We’ve outlined the key methods that you’ll use to label data in simple, straightforward terms.
We’ll also delve deeper into the process of data labeling and how to turn raw data into a training dataset for machine learning. Keep scrolling to find out more.
Data labeling, at its core, is a way of categorizing information according to content by determining the tag or label to be assigned. One or more descriptive labels are added to unlabeled raw data — such as images, videos, text, and audio — to provide context for machine learning models, which in turn implement the labeled data to make more accurate predictions.
Natural language processing is an example of where labeled data can be used to train machine learning models to perform tasks such as speech recognition, machine translation, and chatbot interactions. Human annotators might identify figures of speech or colloquialisms that a machine by itself cannot pick up.
Other data labeling tasks fall under categories such as computer vision, which includes image classification, side-by-side comparison, and object detection, and audio processing such as speech or wildlife noises converted into a structured format for machine learning.
As you can see, data labeling is critical in so many areas. So, what are the best strategies and methods? We’ll take a deep dive into this and more.
Human input — in the form of manual labelers of datasets (we call them Tolokers) — is essential to AI and machine learning development. We’ll cover some of the most common examples of where unlabeled data has been turned into high quality training data for machine learning.
As a first example, if you’ve used any voice recognition software such as Siri, and wondered how it knows what you’re saying, it’s because these types of programs have been fed hours upon hours of human speech — which involves audio and text data labeling.
Self-driving cars are another example of where unlabeled image data has been turned into training data for machine learning. With data collected from millions of images by human annotators, such autonomous vehicles become safer for public consumption.
Lastly, search engines are another great example of where human-generated labels have provided a convenient and efficient service for millions of users worldwide. Data labeling for machine learning models plays a key role in helping to narrow down the most relevant search results. In effect, human judgment is used to train, improve, and evaluate machine learning models. The annotators involved determine which search result is the most relevant for a given question and their perceptions are then used to train the search algorithms.
Data labeling can be carried out either manually (by a person) or automatically (by an algorithm). The former can be performed in-house, via outsourcing, or with crowdsourcing (on a platform like Toloka).
Although manual markup is generally considered to be costly and inefficient, crowdsourcing provides a fabulous alternative for labeling data. In fact, many individuals and freelancers sign up on our Toloka platform to perform micro tasks in return for quick, easy payment.
It’s simple and straightforward. AI teams post raw data and labeling tasks, and Tolokers select which ones they’d like to complete. Platforms like Toloka help facilitate rapid and scalable machine learning solutions such as data collection and annotation for model training and monitoring. We even offer customized solutions for companies that would prefer to have their own labeling process built for them. But we’ll dig deeper into this topic later in the article.
Automated data labeling begins with setting up the model to understand, for instance, what is depicted in an image or written in text. Designed to imitate human decision-making, the model must be trained to decipher which tag should be attached to which data unit. This is where human intellect comes into play: an individual teaches a machine to recognize patterns automatically by running learning algorithms for labeled datasets.
Below, we’ve outlined the three types of learning algorithms used to help AI systems analyze and learn from input data autonomously.
The first of our machine learning paradigms, supervised learning relies on a large amount of manually labeled data. (Whereas only a small portion of data is tagged in semi-supervised learning.) Using this method, the machine learning model compares ground truth data with its own predictions to identify errors and discrepancies. Then the model is adapted as needed.
Supervised learning algorithms aim to learn and produce a particular algorithm that maps inputs to outputs. This method can be applied to instances where the available data consists of labeled examples. The model analyzes the training data and produces an inferred algorithm, which can be used to map new examples.
Given that the model is able to predict the likelihood of future events happening, some real-life use cases for supervised learning include anticipating fraudulent credit card transactions and historical data analysis. As a side note, labeling mistakes or input inaccuracies can result in false predictions and erroneous output — so stay aware!
Key takeaway: Following a succession of input, training and output, machine learning is straightforward when it comes to supervised learning. Data with clearly defined output is provided, direct feedback is given, future outcomes are predicted, and any classification and regression issues are resolved.
Leveraging raw or unstructured data, this second method embodies a type of algorithm that identifies patterns in untagged data. Using this approach, a machine can create structure independently and organize data into a group of clusters. In other words, the machine builds a representation of its world and then generates imaginative content from it. This method is applied to more complex processes such as processing customer transactional data for marketing campaigns.
Unlike supervised learning where data is tagged by an expert, unsupervised methods rely on self-organization to identify patterns. In reinforcement learning, which we’ll cover next, the machine is given only a numerical performance score as guidance.
Key takeaway: Through a chain of inputs and outputs, the machine identifies patterns or structures within the data without generating any predictions.
If you’ve had any experience with reward-based learning, you’ll know that the power of positive reinforcement has many benefits across a myriad of daily circumstances — like training your dog or getting your children to behave! The same goes for this third basic machine learning paradigm and data labeling method.
Through a chain of inputs, outputs, and rewards, reinforcement learning encompasses a trial-and-error approach to make predictions within a particular context based on feedback and experience. The AI test system (or intelligent agent) is trained by interacting with its environment from which it acquires feedback while learning to act based on cumulative rewards. Over time it gets better at making predictions and resolving issues.
Key takeaway: The system can perceive its environment, take actions autonomously, and improve its performance independently by analyzing and learning from input data.
Human-handled methods of data labeling also differ when it comes to the data labeling process.
Internal labeling refers to in-house managed data labeling teams that work for a company. Such teams might include data scientists, ML engineers, and labelers who actually perform the data labeling work. The company employs these individuals to perform their data labeling work. The in-house team generally writes their own code and builds a machine learning model and prepares training datasets from scratch, usually by using special data labeling tools.
The main benefit to this approach is control over the process from start to finish. However, maintaining a fully staffed internal team can be costly and time consuming — this involves training, software selection, getting up to speed on quality assurance, data security, and more. That’s where outsourcing data labeling, or crowdsourcing can take some of the pressure off.
Key takeaway: Internal data labeling can be a good way to maintain oversight and control of your projects and get high quality data. You hire your employees, train them as you want, select the software you want, and so on. However, the cost can add up — both financially and timewise. The next two options can help lighten the burden.
External data labeling, on the other hand, is referred to as outsourcing. As an AI product creator, you can outsource to individuals or entire companies to get your data annotation needs met. This usually comprises varying degrees of supervision and project management. Outsourcing involves hiring specialists who have specific skill sets geared toward the relevant model — such as annotating images for computer vision or transcribing speech for natural language processing.
There’s a lot to be said for this type of method, but it can also be expensive and data quality can waver between projects and teams given the involvement of a third-party. You must ask yourself if the pros outweigh the cons.
Key takeaway: You could look at outsourcing as having your own external in-house team of specialists who come with their own set of data annotation tools and who you employ on a temporary basis to get the job done.
As a type of large-scale outsourcing, crowdsourcing brings together an extensive network of data annotators from all around the world.
For example, our Tolokers come from over 100 countries and speak over 40 languages. And we’re mutually better off for it. As a result, we’ve accumulated an inclusive and diverse crowd to label loads of data that reflects the global population.
So, when it comes to crowdsourcing, what does the data labeling process look like in a nutshell? Annotators (Tolokers) from the global crowd select their tasks and decide when and where they want to complete them—and they don’t need to be experts in any field. Tolokers receive instructions on labeling datasets and complete short training sessions. All standard quality control methods apply at every stage and by way of advanced aggregation techniques projects are completed rapidly, accurately, and economically.
Key takeaway: With such a diverse range of skill sets on hand, companies across industries can tap into a wellspring of talent without incurring added costs, training time, or other expenses that come with managing in-house teams. Crowdsourcing allows for freedom and flexibility on multiple fronts.
As you can see, there are a number of methods, approaches, and techniques to choose from when it comes to data labeling — some automated and others manual depending on the needs of an AI product creator or company. Markup tasks also encompass a wide range of online and offline (field) tasks. The former covers a variety of applications and could include text, audio, video or image annotation. With the latter, for example, you might be asked to travel to a specific location in your town or neighborhood for an on-site assignment such as snapping a picture of all the stop signs, coffee shops, or statues in the area. The results you deliver are used to advance services ranging from GPS maps to physical shops.