For decades, science fiction writers have painted pictures of worlds where artificial intelligence (AI) and robots play a crucial role in daily life. There have been plenty of false starts and “AI winters” in AI research, which seems to follow a “boom and bust” cycle.
Things seem different now. Technology has matured and evolved to a point where AI deployments are practical. AI is ubiquitous, solving real-world problems in business, manufacturing, customer service, medicine, and even people’s daily lives.
Along with improvements in processor technology and internet infrastructure, one other factor has played a critical role here – the availability of big data and data labeling services. In this blog post, we will explore the role played by data labeling services in creating better AI, and shaping the future.
This post will cover the following key topics:
- What is Data Labeling
- History of Data Labeling
- Main Uses of Data Labeling
- Different Approaches to Data Labeling
- Future of Data Labeling Services
What is Data Labeling?
AI development is a data-intensive process. Researchers try to mimic the human process of learning when creating AI. There is an entire sub-field in AI science dedicated to this process called Machine Learning.
The process is rather labor-intensive as the quality of the data used can make a huge impact. Instead of raw and unstructured data, supervised machine learning models require processed and properly organized data.
Data annotation and data labeling are, for all practical purposes, interchangeable. They both refer to the same process of labeling text, video, and photos using image annotation tools and special text annotation software.
Data labeling is the process of making raw data suitable for AI development. It involves adding meaningful tags and labels to the data, using specialized software tools. The type of data used can vary depending on the AI use case – text, images, and videos can all be improved through proper data labeling.
AI for self-driving cars needs accurately labeled images and videos of traffic situations. Data labelers tag important areas like cars, pedestrians, traffic signals, and road signs with appropriate labels. Once an AI has been fed thousands of such processed data, it can accurately recognize and identify these entities in the real world.
Labeled vs Unlabeled Data
A significant portion of machine learning is about teaching AI to recognize patterns in data. To do this properly, developers need large amounts of processed and labeled data. Raw data is like crude oil – it needs to be organized, labeled, and annotated to convert it into usable data sets.
This requires intensive human intervention. During the data labeling process, a human will use his/her judgment, knowledge, and experience to mark parts of the data that are important for the AI use case.
In the self-driving AI example, this would involve accurately tagging and labeling all images for any humans, road signs, other vehicles, and so on. Having fully labeled data speeds up the process of training the AI and ensures that errors are kept to a minimum.
The accuracy and overall efficiency of an AI depend heavily on the quality of the data sets used to train it. While it is possible to train AI using raw data (unsupervised deep machine learning), it has not reached a stage where it can surpass supervised learning using processed data.
The quality of data used for AI model training is critical. In AI data science, “ground truth” is the data set that reflects the real world. It is the basis of training a future AI platform. If the ground truth is flawed or inaccurate, it will affect all future workflows of the AI.
This is why developers put a lot of effort into the selection and curation of training data. In fact, an estimated 80% of the work involved in an AI project revolves around the collection and curation of training data.
A Brief History of Data Labeling
The curve of AI development is not a smooth upward curve. The field has a “boom-bust” cycle, with numerous false starts and “AI winters.” The evolution of data labeling has largely stayed parallel to the developments in AI research over the decades.
To be more precise, data labeling slowly became more important in AI research with the evolution of machine learning. While the term was coined by pioneering computer scientist Arthur Samuel in 1952, it didn’t truly take off until the 1990s.
This coincided with a shift from the traditional “knowledge-driven” approach to the modern “data-driven” approach in the field. AI specialists shifted their attention to creating programs that could analyze vast amounts of data and “learn” new things through this process.
Naturally, this created an increased demand for processed and structured data. And thus, the field of data labeling was born. But it evolved into a bonafide industry only after the evolution of the internet and “big data.”
Strides made by AI and ML research into sub-fields like deep learning, neural networks, computer vision, and natural language processing (NLP) have accelerated the demand for labeled data in recent years.
“Human In The Loop”
Over the decades, one thing has remained constant in AI research – the “Human in the Loop” paradigm (HITL). One of the main narratives behind the evolution of AI is its potential to replace humans in tedious, repetitive, and dangerous tasks.
One of the ironies of AI development is that some key aspects of it are incredibly labor-intensive. Data labeling is the main example. To create more efficient and error-free algorithms, you need high-quality data sets.
This requires human intervention, often at the granular level, harnessing the right kind of tools. Data labeling experts have to spend hours painstakingly poring over images/text/videos, marking and tagging pertinent objects like keywords, physical objects, faces, and so on.
Apart from data labeling, HITL can also happen in the following ways:
- Designing and creating the machine learning algorithm
- Training the ML algorithm
Out of the three, data labeling is the most labor-intensive, slowest, and most expensive process. An effort is underway to streamline the process, or remove the human intervention altogether – via approaches like unsupervised machine learning, and using deep learning algorithms.
However, when accuracy is a high priority, human intervention is essential. Automated machine learning processes may be faster, but the margin for error can increase substantially. Introducing humans into the process of data labeling and validation can increase the reliability of training data.
Main uses of Data labeling – Computer Vision and NLP
Data labeling can be effectively deployed to improve machine learning algorithms in a wide range of industries and use cases. However, two main areas have emerged as the main beneficiaries of data labeling services within AI research – Computer Vision and Natural Language Processing.
Computer Vision
Computer vision is a sub-field in AI research with far-reaching implications. As the name suggests this is the field specializing in creating AI that derives actionable information from visual data – images, videos, and other visual inputs. It can effectively enable an AI to “see” things and take decisions based on the inputs received.
The ultimate aim of computer vision is to recreate something close to human sight in AI software – our abilities to recognize objects instantly, calculate distance, identify defects, etc. The real-world utility of an AI armed with computer vision is quite vast indeed. The automotive sector, manufacturing, retail, utilities, energy/minerals can all benefit from computer vision.
Data labeling services are essential to “teach” algorithms to identify specific objects. To create training data sets, professionals use various advanced techniques:
- Bounding boxes
- Polygons
- Cuboids
- Semantic Segmentation
- Video annotation
In most of these processes, the data labeler draws digital outlines around an object in an image/video and tags it appropriately so that the AI can learn to identify that object in the future. Since objects like cars can come in various shapes, sizes, and colors, the AI needs many different samples of tagged data to improve its ability to accurately identify the object without any tags in the future.
Natural Language Processing (NLP)
Instead of images, this branch of AI research focuses on teaching AI to recognize text, speech, and other linguistic cues. AI transcription software, virtual assistants with voice search, and chatbots all benefit from advances in NLP. The basic process remains the same here – tagging words and phrases in text or audio so that the AI can do the following:
- Named entity recognition – identify person names, place names, objects, locations, etc
- Sentiment analysis – identify the emotional tone behind the text/audio
- Optical character recognition – for scanning printed text and converting it into digital data
While the process of labeling visual data is relatively simple in most basic scenarios, the same is not true about NLP data labeling. Human language and speech are incredibly complex, with subtle nuances that an algorithm can find quite difficult to identify properly.
Accents, contextual words, phrases, homonyms (words with the same spelling/pronunciation, but different meaning) present a formidable challenge for AI developers. Two main approaches are used in NLP to create effective training data sets through text classification and annotation – syntax techniques and semantic techniques.
Syntax techniques focus primarily on the grammar and sentence structure in the raw text/audio data. Semantic techniques look at the more subtle aspects like identifying the context, categorizing named entities, etc.
Computer Vision & NLP Use Cases
Progress in computer vision and NLP has dramatically expanded the capabilities of AI in the last decade. While one approach essentially gives software eyes to see things, the other enables it to understand human speech and text. Together, they have already impacted modern human life in profound ways.
Self-driving cars
Advances in computer vision have enabled engineers to create AI through ML models that can drive cars on real roads. Thanks to visual data labeling services, algorithms can safely negotiate traffic, identifying cars, obstacles, traffic signals, and other visual inputs. In the future, AI could replace human drivers in many industries.
Medical Diagnostics
With data labeled by trained medical experts, AI can be taught to check Xrays and CT scans and accurately identify tumors and cancers. In a pandemic situation where health systems are overwhelmed, the software has the potential to speed up diagnostics and result in quicker access to medical treatment.
Traffic Management
Visual data from traffic cameras can be used by algorithms to accurately gauge the flow of vehicles on the roads. In high-density areas, this can be leveraged to create faster, more efficient traffic management systems where the AI makes decisions faster than humans can.
Manufacturing Automation
There is immense potential for AI in future manufacturing. Computer vision can be used to detect defects in finished products, improve the accuracy of manufacturing robots on the production line, and improve packaging and labeling standards. It also allows firms to use predictive maintenance – using cameras and sensors to identify early signs of damage/wear in machinery.
Agriculture
Crop monitoring, yield estimation, early warning systems for insects and parasites, livestock health monitoring, weather forecasting, and soil analysis are just some of the areas where computer vision is making a difference for farmers around the globe.
Online Retail/eCommerce
Smarter AI chatbots created using NLP and machine learning techniques are improving the quality and efficiency of online customer service for many firms. It has helped reduce the burden on customer executives, allowing them to focus on advanced queries that require more attention.
Virtual Assistants
Siri, Google Assistant, and Alexa all use voice and speech recognition algorithms to deliver better service in our daily lives. They can answer full-length questions, search for content online, and perform various other activities based purely on voice commands.
Data Mining
Many businesses have access to vast troves of business data. But the vast majority of this data is in a raw, unusable format. Using data mining AI, firms can create actionable insights including customer sentiment, market trends, and more.
Fraud Detection
Financial institutions increasingly rely on AI to detect suspicious emails and analyze financial statements for errors/signs of fraudulent data. Studies have revealed that fraudulent text usually has certain specific identifiers and patterns that the AI can detect better than humans.
As you can see, AI armed with computer vision and NLP has near limitless potential in real-life use cases across business and our daily lives. And in the vast majority of these deployments, the algorithms were trained using labeled data sets, at least in the early stages.
How AI Companies Approach Data Labeling
While data labeling is essential for machine learning, it is also incredibly labor-intensive. For developers, especially those will limited resources, getting access to high-quality training data can be quite challenging. Over the years, AI experts have developed multiple approaches to data labeling:
In-house Departments
Only the biggest corporations can afford to maintain large teams of data labelers on their payroll. While undoubtedly expensive, this approach also gives you maximum control over the entire process, along with security advantages like optimal confidentiality and privacy. In AI development for military and defense applications, governments prefer to use in-house data labeling departments.
Programmatic Labeling
This approach tries to use automation to reduce expenses in data labeling. It leverages custom scripts to automatically label raw data. While it is faster than human labelers, it suffers from a lack of accuracy. A compromise solution involves using programs for the initial labeling, with a quality assurance team in place to verify the data sets. This technology is still in its early stages.
Crowdsourcing
The cheapest form of data labeling involves using free or paid volunteers. Platforms like Amazon Turk have thousands of freelancers for hire on smaller labeling tasks. While cost-effective, this approach suffers from a lack of adequate quality controls, lack of accuracy, and privacy/data security issues.
Data labeling services
Due to the ever-increasing demand for high-quality training data sets, an entire industry dedicated to professional data labeling services has sprung up over the last decade. Firms like iMerit employ trained and dedicated data labeling professionals who provide highly customized services to AI firms across multiple sectors.
Outsourcing has numerous advantages for AI firms. They get guaranteed Quality Control, a feedback loop to improve the data sets, and the freedom to deploy vital resources to their core focus areas. At present, this approach has maximum utility for AI startups and SMEs who do not have the budget or HR to develop in-house labeling capabilities.
Synthetic Labeling
Several attempts have been made over the years to reduce or remove the role of humans in the machine learning loop. Synthetic labeling attempts to use advanced AI to create artificial data sets, based on existing ones. While it is quite exciting, the approach is cutting-edge and requires large amounts of processing power, which is incredibly expensive.
The Future of Data Labeling Services in 2022
In 2021, the market for data labeling services reached the milestone of a $1 billion valuation. According to Grand View Research, the market is expected to expand at a rate of close to 30% in this decade alone. In the next 5 years, it could grow by a factor of 700%, reaching $7 billion in 2027.
Given the acceleration in demand for AI solutions in recent years, those numbers don’t sound exaggerated. Gartner predicts that the AI market itself will touch $62 billion in 2022, spurred by autonomous vehicles, virtual assistants, knowledge management, and digital workplaces. It is safe to say that 2022 will be the year of ML data ops.
And alternatives like synthetic/programmed labeling are not mature enough to replace the human in the loop in the immediate future. Data labeling services have a bright future ahead, especially since they play a positive role in generating employment opportunities.
Except for niche sectors like medical diagnostics, the vast majority of data labeling projects can be handled by low-skilled workers. As sectors like retail hospitality shrink in a hostile economic environment, new sectors like data labeling offer furloughed workers beneficial employment.
Conclusion
Human civilization faces a multitude of challenges in the 21st century. Climate change, new pandemics, economic recession, and political/social instability are all rearing their heads. Proponents of AI argue that the new technology is vital to solving some of the most pressing problems facing our society today.
For AI to deliver on such lofty promises, data labeling services are essential. In the future, we may develop “strong” AI or “AI general” – with the ability to learn from raw data without the need for labeling. But the path to such an AI will be built on the efforts of dedicated data labeling service providers.
If you are an AI startup or SME with a need for high-quality training data sets, iMerit has the services you seek. Our data labeling experts have thousands of hours of experience working on projects related to computer vision, NLP, and content services, serving medical, financial, geospatial, and autonomous driving sectors.
