Flexibility — Key Advantage in Data Annotation Market

 Data Annotation Market Size

The global data annotation market was valued at US$ 695.5 million in 2019 and is projected to reach US$ 6.45 billion by 2027, according to Research And Markets’s report. Expected to grow at a CAGR of 32.54% from 2020 to 2027, the booming data annotation market is witnessing tremendous growth in the forthcoming future.

The data annotation industry is driven by the increasing growth of the AI industry.

Data Annotation Process is Tough

Unlabeled raw data is around us everywhere, such as emails, documents, photos, presentation videos, and speech recordings. The majority of machine learning algorithms today need labeled data in order to learn and get trained by themselves. Data labeling is the process in which annotators manually tag various types of data such as text, video, images, audio via computers or smartphones. Once finished, the manually labeled dataset is fed into a machine-learning algorithm to train an AI model.

However, data annotation itself is a laborious and time-consuming process. There are two choices to do data labeling projects. One way is to do it in-house, which means the company builds or buys labeling tools and hires an in-house labeling team. The other way is to outsource the work to renowned data labeling companies like Appen, Lionbridge.

The booming data annotation market has also stimulated multiple novel players to secure a niche position in the competition. For example, Playment, a data labeling platform for AI, has teamed up with Ouster, a leading LiDAR sensors provider, known for the annotation and calibration of 3D imagery in 2018.

Flexibility is the Key Advantage in Data Labeling Loop

As the high-quality standard, data security, scalability are the most important measurements in labeling service, we may have a look at the rest competitive parts, for example, flexibility and customer service.

In machine learning, in each round of testing, engineers would discover new possibilities to perfect the model performance, therefore, the workflow changes constantly. There are uncertainty and variability in data labeling. The clients need workers who can respond quickly and make changes in workflow, based on the model testing and validation phase.

Therefore, more engagement and control of the labeling loop for clients would be a key competitive advantage as it provides flexible solutions.

Solution

ByteBridge, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.

On ByteBridge’s dashboard, developers can define and start the data labeling projects and get the results back instantly. Clients can set labeling rules directly on the dashboard. In addition, clients can iterate data features, attributes, and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.

As a fully-managed platform, it enables developers to manage and monitor the overall data labeling process and provides API or data transfer. The platform also allows users to get involved in the QC process.

End

“High-quality data is the fuel that keeps the AI engine running smoothly and the machine learning community can’t get enough of it. The more accurate annotation is, the better algorithm performance will be.” said Brian Cheong, founder, and CEO of ByteBridge.

Designed to empower AI and ML industry, ByteBridge promises to usher in a new era for data labeling and accelerates the advent of the smart AI future.

By Typing Captcha, you are Actually Helping AI’s Training

 Living in the Internet age, how occasionally have you come across the tricky CAPTCHA tests while entering a password or filling a form to prove that you’re fully human? For example, typing the letters and numbers of a warped image, rotating objects to certain angles or moving puzzle pieces into position.

What is CAPTCHA and how does it work?

CAPTCHA is also known as Completely Automated Public Turing Test to filter out the overwhelming armies of spambots. Researchers at Carnegie Mellon University developed CAPTCHA in the early 2000s. Initially, the program displayed some garbled, warped, or distorted text that a computer could not read, but a human can. Users were requested to type the text in a box, and have access to the websites.

The program has achieved wild success. CAPTCHA has grown into a ubiquitous part of the internet user experience. Websites need CAPTCHAs to prevent the “bots” of spammers and other computer underworld types. “Anybody can write a program to sign up for millions of accounts, and the idea was to prevent that,” said Luis von Ahn, a pioneer of early CAPTCHA team and founder of Google’s reCAPTCHA, one of the biggest CAPTCHA services. The little puzzles work because computers are not as good as humans at reading distorted text. Google says that people are solving 200 million CAPTCHAs a day.

Over the past years, Google’s reCAPTCHA button saying “I’m not a robot” was followed more complicated scenarios, such as selecting all the traffic lights, crosswalks, and buses in an image grid. Soon the images have turned increasingly obscured to stay ahead of improving optical character recognition programs in the arms race with bot makers and spammers.

CAPTCHA’s potential influence on AI

While used mostly for security reasons, CAPTCHAs also serve as a benchmark task for artificial intelligence technologies. According to CAPTCHA: using hard AI problems for security by Ahn, Blum and Langford, “any program that has high success over a captcha can be used to solve a hard, unsolved Artificial Intelligence (AI) problem. CAPTCHAs have many applications.”

From 2011, reCAPTCHA has digitized the entire Google Books archive and 13million articles from New York Times catalog, dating back to 1851. After finishing the task, it started to select snippets of photos from Google Street View in 2012. It made users recognize door numbers, other signs and symbols. From 2014, the system started training its Artificial Intelligence (AI) engines.

The warped characters users identify and fill in for reCaptcha are for a bigger purpose, as they have unknowingly transcribed texts for Google. It shows the same content to several users across the world and automatically verifies if a word has been transcribed correctly by comparing the results. Clicks on the blurry images can also help identify objects that computing systems fail to manage, and in this process Internet users are actually sorting and clarifying images to train Google’s AI engine.

Through such mechanisms, Google has been able to help users back in recognizing images, giving better Google search results, and Google Maps result.

ByteBridge: an automated data annotation platform to empower AI

Turing Award winner Yann LeCun once expressed that developers need labeled data to train AI models and more quality-labeled data brings more accurate AI systems from the perspective of business and technology.

In the face of AI blue ocean, a large number of data providers have poured in. ByteBridge.io has made a breakthrough with its automated data labeling platform in order to empower data scientists and AI companies in an effective way.

With a completely automated data service system, ByteBridge.io has developed a mature and transparent workflow. In ByteBridge’s dashboard, developers can create the project by themselves, check the ongoing process simultaneously on a pay-per-task model with clear estimated time and price.

ByteBridge.io thinks highly of application scenarios, such as autonomous driving, retail, agriculture and smart households. It is dedicated to providing the best data solutions for AI development and unleashing the real power of data. “We focus on addressing practical issues in different application scenarios for AI development through one-stop, automated data services. Data labeling industry should take technology-driven tool as core competitiveness,” said Brian Cheong, CEO and founder ByteBridge.io.

As a rare and precious social resource, data needs to be collected, cleaned and labeled before it grows into valuable goods. ByteBridge.io has realized the magic power of data and aimed at providing the best data labeling service to accelerate the development of AI.

How Data Training Accelerates the Implementation of AI into Medical Industry

COVID-19 has undoubtedly accelerated the application of AI in healthcare, such as virus surveillance, diagnosis and patient risk assessments. AI-powered drones, robots and digital assistants are improving healthcare industry with better accuracy and efficiency. These have enabled doctors to provide more effective and personalized treatment with real-time data monitoring and analysis.

Garbage in, garbage out

As one of the most popular and promising subsets of AI, machine learning gives algorithms the ability to "learn" from training data so as to identify patterns and make decisions with little human intervention. However, as the saying goes, "garbage in, garbage out," making sure correct data fed into ML algorithms is not an easy work.

According to a report "the Digital Universe Driving Data Growth in Healthcare," published by EMC with research and analysis from IDC, hospitals are producing 50 petabytes of data per year. Almost 90% of this data consists of medical imaging i.e. digital images from scans like MRIs or CTs. However, more than 97% of this data goes unanalyzed or unused.

Unstructured raw data needs to be labelled for computer visions so that when the data is fed into an algorithm to train a ML model, the algorithm can recognize and learn from it. As DJ Patil and Hilary Mason write in Data Driven, "cleaning and labeling the data is often the most taxing part of data science, and is frequently 80% of the work."

Many enterprises wish to apply AI to their business practices. They have a glut of data, such as vast amounts of images from cameras and document texts. The challenge, however, is how to process and label those data in order to make it useful and productive. Many organizations are struggling to get AI and ML projects into production due to data labeling limitations and real-time validation deficiency.

A robust data labeling platform with real-time monitoring and high efficiency

An entire ecosystem of tech startups has emerged to contribute to the data labelling process. Among them, ByteBridge.io, a data labeling platform, solves the data labeling challenge with robust tools for real-time workflow management and automating the data labeling operations. Aiming at increasing flexibility, quality and efficiency for the data labeling industry, it specializes in high volumes, high variance, complex data, and provides full-stack solution for AI companies.

"On the dashboard, users can seamlessly manage all projects with powerful tools in real-time to meet their unique requirements. The automated platform ensures data quality, reduces the challenge of workforce management and lowers the costs with transparent standardized pricings," said Brian Cheong, CEO and founder of ByteBridge.io.

The quality of labeled dataset determines the success of AI projects, making it vital to look for a reliable platform that can help developers to overcome the data labeling challenges. The demands of data labelling will continue to be on the rise with the development of AI programs.

Human beings benefit from the implementation of AI systems into medical industry: from diagnosis to treatment, from drug experiment to generalization. These are all exciting areas for AI developers. But before that, providing high-quality training data lays the cornerstone of making those progress.

The Human-power Behind AI: Machine Learning Needs Annotators

 “The global data collection and labeling market size was valued at USD 1.0 billion in 2019 and is expected to witness a CAGR of 26.0% from 2020 to 2027,” quote from a market analysis report by grand view research.

At present, the application scenes of artificial intelligence are constantly enriched, and applications are changing our lives by providing automated and smart services. Behind the rapid growth of the AI industry, the new profession of data annotator is also expanding. There is a popular saying in the data annotation industry, “more intelligent, more labors”. The data that AI algorithms learn from must be annotated one by one through the human annotators.

These annotation workers don’t need to leave their homes. They can be trained to categorize and annotate data for machine learning from various platforms, such as cloud factories, label box, and Bytebridge.io, which all allow annotators to work remotely without any location requirement. Through these distributed annotators’ hard work, machines can quickly learn and recognize text, pictures, videos, and other content, and finally become “AI trainers.”

Machine learning requires data annotation

AI data annotators are called “the people behind Artificial Intelligence”. “Data is the blood of AI. It can be said that whoever has mastered the data is very likely to do well,” said Brian Cheong, CEO of bytebridge.io, an automated data labeling platform. He explained that the current Artificial Intelligence could also be called data intelligence because how machine learning evolves depends on the quality and quantity of data. “For example, current face recognition system works well on young and middle-aged group people, because young people are more likely to travel and reside on hotels, so their faces can be more easily collected. On the other hand, there are less data on kids and the elderly.”

But at the same time, data alone is useless. For deep learning, data only make sense when it is tagged and used for machines’ learning and evolution. Labeling is a must.

Starting from the data collection, cleaning, labeling to calibration are 100% relying on annotators. The most basic aspect of data annotation will be the image annotation. For example, if the detection target is a car, the annotator needs to mark all the cars on a picture. If the picture frame does not accurately mark the car, the machine may “break down” due to the inaccuracy. Another example is human posture recognition, which includes 18 key points. Only trained annotators can master these key points, so the annotated data can meet the standard for machines to learn.

“We are proud that we provide various functions in our platform. Many platforms only provide few functions, but we are a one-stop solution for AI firm. Everything can be automated with us,” quote from CEO of bytebridge.io.

Different data types require different skill sets to annotators. In addition to the annotation that is relatively simple and can be mastered by training, some annotation require professional background. For example, for medical data, the annotator needs to do the segmentation of medical images and mark tumor areas, which need to be completed by annotators who have a medical background. Another example is local dialects or foreign languages, these also require annotators who master that language.

“We got the annotators globally, we work with people from developed countries to developing areas, since we provide mobile version labeling toolset, our annotators got a very diversified background, which can meet different tasks’ skill requirements. ”

Now AI has entered the stage of technology application to real-world scenarios, including security, finance, home, transportation, and other major industries. In the future, in the data annotation industry, annotators will also enter the market segment chasing stage along with the AI industry.

How an Automated Data Labeling Platform Accelerates AI industry’s Development During COVID-19

 The impact of AI on COVID-19 has been widely reported across the globe, yet the impact of COVID-19 on AI has not received much attention. As a direct result of Covid-19, AI enterprises are enhancing their strategies for digital transformation and business automation.

Data is the core of any AI/ML development. The quality and depth of data determines the level of AI applications. Considering that the better the data that goes into building the ML training model, the better the output. ML teams need to go through proper data preparation such as data collection, cleansing and labeling.

Data labeling is a simple but difficult task

When it comes to data labeling, the essential step to process raw data (images, text files, videos, etc.) for computer vision so that machine learning models can learn from the labeled dataset, some data labeling companies were forced to move to a work-from-home model due to the pandemic, which has posed challenges in terms of communication, data quality and inspection. For example, Google Cloud has officially announced that its data labeling services are limited or unavailable until further notice. Users can only request data labeling tasks through email but cannot start new data labeling tasks through the Cloud Console, Google Cloud SDK, or the API.

Insiders say that data labeling is a simple but difficult task. On one side, as soon as the labeling standard is set, data labelers just need to follow the rules directly with patience and profession. On the other side, however, data labeling is meant to pursue high quality for ML which demands accuracy, efficiency and high cost of labor and time regarding the massive amount of data to be labeled.

A majority of AI organizations said the process of training AI with data has been more difficult than expected, according to a report released from Alegion. Lack of data and data quality issues become their main obstacles to AI application.

An automated data labeling platform aims to transform the industry

To deal with such issues, Bytebridge.io has launched its automated data labeling platform this year. It aims to provide high quality data with efficiency through a real-time workflow management for AI developers so as to free them from the pressure of data preparation.

An autonomous driving company in Korea needs to label roadblocks and 2D bounding boxes for cars. Considering data security, they have built in-house labeling team. However, they ran into a couple of unexpected problems due to improper labeling tools and low efficiency. Upon trying Bytebridge, their project managers are able to improve working efficiency through Bytebridge’s online real-time monitoring function. The number of monthly labeled images has increased from 600k to 750k and they are able to save 60% of budget.

On Bytebridge’s dashboard, developers can upload raw data and create the labeling projects by themselves. They can check the labeling status and quality anytime, even the estimated price and time required. Such an automated and online platform greatly ensures labeling efficiency and quality. Bytebridge’s easy-to-integrate API enables continuous feeding of high-quality data into machine learning systems. Data can be processed 24/7 by the global contractors, in-house experts and the AI technology.

“We want to create an automated data labeling platform that helps AI/ML companies to accelerate their data project and generate high-quality work,” said Brian Cheong, CEO and founder of Bytebridge.io.

An Ultimate Guide to Data Labeling

 What is Data Labeling and Why do We Need It?

Just as cars cannot run without fuel, when it comes to machine learning (ML), data is the fuel. Advanced machine learning requires substantial amounts of data.

However, the current ML algorithms cannot automatically process the huge amount of raw data. Without labelling objects in a photo, pinpointing a specific stuff in an image or highlighting a certain phrase in texts, data is just noise. Through annotation, this “noise” can be transformed into a structured and trained dataset so that the algorithms can understand the right input information easily and clearly.

Therefore, data labeling is the technique of annotating raw data in different formats such as images, texts, and videos. Labeling the data makes it recognizable and comprehensible for computer vision, which further trains the machine Learning models. In short, it is the labeled datasets that trains the machine to think, and behave like human beings. A successful machine learning project often depends on the quality of the labeled dataset and how the trained data is executed.

What Data Can be Labeled?

• Bounding boxes: (the most common kind of data annotation) drawing rectangular boxes to identify objects in image
• Lines and splines:detect and recognize lanes, usually used in self-driving industry
• Landmark and key-point: create dots across the image to identify the object and its shape, frequently used in facial recognitions, identifying body parts, postures, and facial expressions
• Polygonal segmentation: identify complex polygons to determine the shape and location of the object
• Semantic segmentation: a pixel-wise annotation that assigns every pixel of the image to a class (car, truck, road, park, pedestrian, etc.). Each pixel holds a semantic sense.
• 3D cuboids: almost like bounding boxes but with extra information about the depth of the object for 3D environment
• Entity annotation: labeling unstructured sentences with the relevant information understandable by a machine
• Content and text classification

Where to get the labeled dataset?

Developers can’t build a good machine learning model without high quality training data. But building those training datasets is labor-intensive, as it involves labeling thousand and thousands of images, for example. Yes, machine learning industry still requires basic human input even though it aims at liberating manpower.

One optional way to collect such datasets is to visit available open resources such as Google’s Open Images, mldata.org for ML training projects. However, one shortcoming is that those open sources may not be credible enough.It takes the ML team a great deal of time to look into the reliable datasets. If they accidentally collects wrong data from unknown sources, it inevitably reduces the level of accuracy for end-users.

Another popular way is to outsource the task towards data service providers who has rich experiences and knowledge on AI-based projects, which sounds effective as ML team can focus on the modeling and development. Let’s take a deep look into the current outsourcing workflow.

The data service offices recruit data labelers, get them trained on each specific task and distribute the workload to different teams. Or they subcontract the labeling project to smaller data factories that again recruit people to intensively process the divided datasets. The subcontractors or data factories are usually located in India or China due to cheap labor. When the subcontractors complete the first data inspection, they pass on the labeled datasets to the final data service provider who goes through its own data inspection process once again and send the results to ML team.

Complicated, right?

Unlike the AI and ML industry, such traditional working process is inefficient as it takes longer processing time and higher overhead costs, which unfortunately is wasted in secondary and tertiary distribution stages. ML companies are forced to pay high yet the actual small labeling teams could hardly benefit.

How to Solve the Problem?

ByteBridge.io has made a breakthrough on its automated data collection and labeling platform in order to empower data scientists and machine learning companies in an effective and engaged way.

On ByteBridge’s automated platform, also known as dashboard, developers can create the data annotation and collection projects by themselves. Since most of the data processing is completed on the platform, developers can keep track of the project progress, speed, quality issues, and even cost required in real-time, thereby improving work efficiency and risk management in a transparent and dynamic way. Developers can upload data and download processed results through ByteBridge’s dashboard. Via the provided API, all processes such as data transmission, processing, and download can be easily connected with existing programs as well.

To cut the communication and training cost when dealing with complex task flow, ByteBridge.io has built up the consensus algorithm to optimize the labeling system. When dealing with complex tasks, several proposed protocols reduces the task difficulty level by splitting the tasks and then set a consensus index to unify the results through algorithm rules. Before task distribution, set a consensus index, such as 90%. If 90% of labeler’s answer is basically the same, the system will judge that they have reached a consensus and assume the annotation is correct. If customers require higher accuracy of data annotation, ByteBridge.io has “multi-round consensus” to repeat tasks over again to improve the accuracy of final data delivery.

Recently, a Korean pig farm is looking for an AI system to gather information on pigs’ productivity, behavior, and welfare. They hired ByteBridge.io to improve farming efficiency.

“The smart system should be able to reflect each pig’s health condition from tracking its feeding patterns and behaviors. We were looking for a data annotation company to process the data structurally based on machine learning. The tricky part is, we set a very strict time limit for the team. We need the labeling to be done as soon as possible” said the owner of the pig farm.

“Surprisingly, ByteBridge.io perfectly completed the labeling task and improved our system. After handing out millions of images, we received their package even sooner than we expected. We got our 8,000 images labeled within 3 working days. ByteBridge’s speed of data processing is ten times of traditional data labeling companies.”

Data is the foundation of all the AI projects. ByteBridge.io is determined to improve data labeling accuracy and efficiency for ML industry through its premium service.

How to Ensure Data Quality for Machine Learning and AI Projects

 Data quality is an assessment whether the quality of data is fit for the purpose. It’s agreed that data quality is paramount for machine learning (ML) and high-quality training data ensures more accurate algorithms, productivity, and efficiency for machine learning and AI projects.

Why is Data Quality Important?

The power of machine learning is dramatically due to its capability to learn on its own automatically after being fed with huge amount of specific data. In this case, ML systems need to be trained with a set of high-quality data, as poor qualify data would mislead the results.

In his article, “Data Quality in the era of Artificial Intelligence” George Krasadakis, Senior Program Manager at Microsoft, puts it this way:”Data-intensive projects have a single point of failure: data quality.” He mentions that because data quality plays an essential role, his team at Microsoft starts every project with a data quality assessment.

The data quality can be measured from 5 aspects:

* Accuracy: how accurate a dataset is by comparing it against a known, trustworthy reference dataset. Robots, drones, or vehicles rely on accurate data to achieve higher levels of autonomy.

* Consistency: data needs to be consistent when the same data is located in different storage areas

* Completeness: the data should not have missing values or data records

* Timeless: the data should be up to date

* Integrity: high integrity data comforts to the syntax (format, type, range) of its definition provided by data model

Achieving the Data Quality Required for Machine Learning

Traditionally, data quality control mechanisms are based on user experience and data management experts. It is costly and time-consuming since human labor and training time are required to detect, review and intervene in sheer volumes of data.

Bytebridge.io, a blockchain-driven data company, substitutes the traditional model by an innovative and precise consensus algorithm mechanism.

Bytebridge.io, the data training platform, provides high-quality services to collect and annotate different types of data such as text, image, audio and video to accelerate the development of machine learning industry.

In order to reduce data training time and cost when dealing with complicated tasks, Bytebridge.io has built up the consensus algorithm rules to optimize the labelling system: before task distribution, set a consensus index, such as 80%, for a task. If 80% of the labelling’s results are basically the same, the system will consider they have reached a consensus. In this way, the platform can get a large amount of accurate data in a short time. If customers demand a higher accuracy of data annotation, they can use “multi-round consensus” to repeat tasks over again to improve the accuracy of final data delivery.

Consensus algorithm mechanism can not only guarantee the data quality in an efficient way but also save budget through cutting out the middlemen and optimizing the work process with AI technology.

Bytebridge’s easy-to-integrate API enables continuous feeding of high-quality data into machine learning system. Data can be processed 24/7 by the global partners, in-house experts and the AI technology.

Conclusion

In his Harvard Business Review, “If Your Data Is Bad, Your Machine Learning Tools Are Useless,” Thomas C. Redman sums up the current data quality challenge in this way:“Increasingly complex problems demand not just more data, but more diverse, comprehensive data. And with this comes more quality problems.”

Data matters, and it will continue to do so; the same goes for good data quality. Built for developers by developers, Bytebridge.io is dedicated to empowering machine learning revolution through its high-quality data service.