Artificial intelligence (AI) is a technology with the true potential to change how we humans solve problems and generally get work done. While the ongoing hype around AI is real, few understand the sheer scale of work involved in developing one.
By nature, computers are good at one thing – following instructions, which is what traditional programming involves. The gulf between that and relatively autonomous programs is massive.
AI developers need massive tranches of specially prepared data to “teach” the software to make autonomous decisions. And quite ironically, this is a herculean task – to make software capable of taking over repetitive tasks, humans need to first perform a ton of repetitive tasks!
AI developers rely on a variety of avenues to gain access to data for machine learning. One of the most promising routes is through data annotation companies. In this blog post, we will assess the current state of data annotation and explore how data annotation companies fare against other training data solutions for AI research.
Why Data Annotation Services?
Smart AI has numerous real-world applications – autonomous driving, weather forecasting, medical diagnostics, smart assistants, web search optimization, navigation, and so on. In all these scenarios, humans make decisions based on the input they receive.
This input could be images, text, or audio fragments. From childhood, we are trained to identify and “label” these inputs to arrive at appropriate solutions. AI software does not have this wealth of life experience.
Instead, researchers have to replicate that learning process by supplying the program/algorithms with vast quantities of curated data sets and machine learning models. For example, a self-driving car AI should be capable of identifying humans, traffic signals, and other incoming traffic as they are.
Data annotation is the process that makes it happen. It involves painstakingly labeling relevant data points so that the machine can accurately identify the important objects in real life. It is usually a very time-consuming process where experts spend hours marking individual pixels on an image.
In the image annotation of road traffic, the data annotator will tag and label all relevant areas – cars, humans, and so on. By going through hundreds of thousands of images covering a wide range of possible traffic scenarios, the AI can quickly identify patterns of objects seen on their live video feed in real-time.
While the process of data annotation can be automated – for maximum accuracy, you need actual humans to spend hours labeling as many images/text/video as possible.
Some data sets can be annotated by ordinary individuals with basic educational qualifications. These involve daily objects like fruits, pets, text fragments related to daily conversation, and so on.
But in several scenarios like medical diagnostics, the annotator should have relevant expertise in the field.
Data annotation services involve working with different types of data. Images, audio files, and video files are frequently processed by skilled data annotators. It involves sub-fields like video annotation, image segmentation, semantic segmentation, text annotation, and named entity recognition.
Service providers use techniques like natural language processing (NLP) and computer vision to process raw data and create curated machine learning models. Data scientists deploy these sets of high-quality training data to develop deep learning AI algorithms.
The Importance of Curated Data
When it comes to annotated and curated data sets for machine learning, quantity and quality are equally important. Poor quality of training data sets can significantly compromise the AI’s ability to make accurate and appropriate decisions down the line.
Depending on the task at hand, the after-effects will vary. In online search/chatbots, poor quality data will result in a sub-par customer experience. This has the potential to drive your consumers to rival companies offering “smarter” data.
But in other scenarios, it can affect the lives and wellbeing of other humans. Self-driving cars are the simplest example of this. If the data sets are not properly curated the autonomous vehicle AI may make mistakes that result in fatal accidents.
In an era where there is growing skepticism towards the evolution of AI, developers are acutely aware of the consequences of using poorly annotated data. Cutting corners here is simply not an option. This is why specialized data annotation companies like iMerit are important in the current market.
Different Approaches to Data Annotation
Data annotation companies are just one of the sources of curated data for AI developers. There are multiple ways to peel the proverbial data annotation banana. The selection of a particular route hinges on the following key factors:
- The scale and complexity of the project
- The available budget
- Data quality thresholds
Take facial image recognition as an example. For a small firm developing a casual app to apply fun filters to photos posted on Instagram etc, the data sets don’t need to be of a very high accuracy or fidelity. The budget will also be rather limited in these circumstances.
On the other end of the extreme is a government project for suspect identification, to be used for defense and law enforcement purposes. This is a far more complex project with significant security implications, large volumes of data, and a massive budget.
The approach to data annotation in this kind of project will be vastly different from the one for a social media app. In 2022, AI companies and government agencies have access to the following sources of annotated and curated data:
Crowdsourcing
Since the early days of computing in the internet era, some forms of crowdsourcing have been employed by researchers. Many volunteers supplied idle processing power from their personal computers to solve complex equations in fields like microbiology and astrophysics.
With the rise in demand for annotated data in AI research, crowdsourcing has emerged as one way to gain access to data. The main appeal of this mode is that you can get the data without spending too much money.
Companies pay freelancers and volunteers money for short-term data annotation projects. Even big corporations like Apple rely on freelancers for their data labeling requirements. While affordability is always a welcome advantage, this approach does have some serious flaws.
Micro-job platforms like Amazon Turk have emerged as a popular source of cheap data annotators for hire. But despite its many touted advantages, recent reports indicate issues like low pay and the rampant appearance of forced data bias.
The quality of work output will vary significantly depending on the freelancer. And due to the short-term contracts, there are no mechanisms in place for future improvements and feedback. And finally, if data privacy and security are major concerns, crowdsourcing is NOT a good option.
Captchas
This is one aspect of daily online life many people don’t realize – by filling up captcha forms during sign-up, users are actually providing free data annotation services! Google is the main beneficiary of this approach. Users trying to prove they are not a robot provide free labeling services for objects like cars, trains, and boats, as well as traffic signs.
This process of training the Google AI on the sly started way back in 2014. The images are sourced from Google street view. The garbled text captchas are also quite valuable in training AI to identify written text and improve its transcription accuracy. Obviously, reCAPTCHA is more of an option for major internet giants like Google.
In-House Departments
Major corporations and government agencies often take this route because of privacy concerns and data security needs. While it allows you to create data sets that are in perfect sync with your organization’s needs, the costs are often prohibitively high.
Human labor is not cheap, and data annotation can take many hours on a single image for advanced use-cases. Corporations can often circumvent the HR costs by deploying data annotation divisions overseas in regions where skilled labor is plentiful and available at a lower cost.
Open-Source Data Sets
For basic AI projects on a limited budget and lower complexity, firms may settle for open-source data sets. These are usually free but come with numerous disadvantages. It is not easy to find curated sets that perfectly match your AI needs. The data quality may also be quite suspect since you are getting it for free. But if your firm’s budget cannot handle data labeling services, this is often a viable compromise.
Automated Data Annotation
This is an approach that is still in its relative infancy. Given the steep costs associated with manual data annotation, there is considerable investor interest in the development of automated solutions that speed up the process and cut down on costs.
But ultimately, we cannot rule out the role of human agency in creating accurate data sets. Automation cannot provide a silver bullet to all AI data annotation needs. Once properly developed, it may provide cost-effective solutions for basic data annotation.
In the meanwhile, professionals armed with data annotation tools represent the best option to gain access to training data for machine learning AI models. And in most aspects, data annotation companies are your best bet to gain access to seasoned data specialists.
Data Annotation Companies – Main Advantages?
As a response to the ever-increasing demand for annotated data, companies specializing in data curation have created a thriving market. In keeping with the modern trend of outsourcing, these firms are located overseas in markets where the cost of living is much lower than in Western nations.
India, Southeast Asia, and Africa have emerged as hotspots for data annotation companies. Firms like iMerit employ trained and well-educated professionals for a wide range of data annotation services including segmentation, bounding boxes, landmarking, polylines, and tracking.
A data annotation company has some clear advantages against the other approaches to data annotation, including:
Professional Expertise
Markets like India have an excellent supply of well-educated and motivated labor force. Apart from generalists capable of tackling basic annotation tasks, these companies also have specially trained departments to handle sectors like medical diagnostics and geospatial analysis.
Accuracy and Customization
Partnering with a competent data annotation company gives an AI startup several advantages. The skilled and experienced workforce ensures that the data sets are of high quality. And since there is a clear feedback loop, adding tweaks and customizations is easy.
Competitive Pricing and Compliance
Thanks to outsourcing, data annotation companies can provide more economical services, which can be a major factor for SMEs and startups in the current economic climate. Reputed firms like iMerit are committed to compliance and provide highly competitive services to their clients while offering a rewarding career for skilled job seekers.
Data Annotation Companies Outlook in 2022
Spurred by strong demand from AI developers, data annotation is witnessing robust growth in recent years. Industry trends indicate that the sector was worth $1 billion in 2020, with an annual growth rate (CAGR) of 30% at least until 2027.
Along with marketing and facial recognition, medical AI is fast emerging as a major engine for data annotation company demands in 2022. With the global pandemic stretching healthcare infrastructure to its limits across the globe, there is a high demand for medical AI tools.
Not surprisingly, the United States and EU have emerged as the main markets witnessing increased demand for affordable data annotation services and machine learning models. In the US alone, there are an estimated 11,000+ AI-based startups as of November 2021.
And that number is constantly growing, increasing the demand for data annotation companies. On the supply side, emerging markets like India, South East Asia, and Africa are becoming the main source of manual data annotation labor.
While new developments are expected in automated annotation platforms, the market will still be dominated by manual annotation. At the present stage of AI evolution, there is simply no substitute for skilled human data annotation professionals. They are expected to cater to 76% of the data annotation demand for the immediate future.
Conclusion
Given the current state of the AI sector and the global economy, it is quite safe to assume that data annotation companies have a bright future ahead in the coming decade. We are still far away from peak demand for machine learning data sets.
With the evolution of strong AI at least several decades away, human data annotators will continue to perform the bulk of the industry’s curation tasks. In the remote future, automated platforms may eat some of the market shares from data annotation companies.
But the demand for highly customized data sets curated by trained professionals will never fade away. Data annotation companies still have a valuable role to play in the evolution of AI. They are the ones responsible for providing the foundation for what could be humanity’s greatest ever achievement!
iMerit is a vastly experienced provider of customized data annotation services with clients spread across sectors like agriculture, medical diagnostics, and geospatial analysis. To learn how iMerit’s team of data annotation experts can fulfill your machine learning needs, contact the sales team here.
