No Bias Labeled Data — the New Bottleneck in Machine Learning

 

The Performance of an AI System Depends More on the Training Data Than the Code

Over the last few years, there has been a burst of excitement for AI-based applications through businesses, governments, and the academic community. For example, computer vision and natural language processing (NLP) where output values are high-dimensional and high-variance. In these areas, machine learning techniques are highly helpful.

Indeed, AI depends more on the training data than the code. “The current generations of AI are what we call machine learning (ML) — in the sense that we’re not just programming computers, but we’re training and teaching them with data,” said Michael Chui, Mckinsey global institute partner in a podcast speech.

AI feeds heavily on data. Andrew Ng, former AI head of Google and Baidu, states data is the rocket fuel needed to power the ML rocket ship. Andrew also mentions that companies and organizations which are taking AI seriously are eager to acquire the correct and useful data. Moreover, as the number of parameters and the complexity of problems increases, the need for high-quality data at scale grows exponentially.

Data Ranks the Second-Highest Obstacle in AI Adoption

An Alegion survey reports that nearly 8 out of 10 enterprises currently engaged in AI and ML projects have stalled. The research also reveals that 81% of the respondents admit the process of training AI with data is more difficult than they expected before.

It is not a unique case. According to a 2019 report by O’Reilly, the issue of data ranks the second-highest obstacle in AI adoption. Gartner predicted that 85% of AI projects will deliver erroneous outcomes due to bias in labeled data, algorithms, the R&D team’s management, etc.

The data limitations in machine learning include but not limited to:

Data Collection: Issues such as inaccurate data, insufficient representatives, biased views, loopholes, and data ambiguity affect ML’s decision and precision. Especially during Covid-19, certain data has not been available for some AI enterprises.

Data Quality: Since most machine learning algorithms use supervised approaches, ML engineers need consistent, reliable data in order to create, validate, and maintain production for high-performing machine learning models. Low-quality labeled data can actually backfire twice: during the training model building process and future decision-making.

Efficiency: In the process of machine learning project development, 25% of the time is used for data annotation. Only 5% of the time is spent on training algorithms. The reasons for spending a lot of time on data labeling are as follows:

• The algorithm engineer needs to go through repeated tests to determine which label data is more suitable for the training algorithm.
• Training a model needs tens of thousands or even millions of training data, which takes a lot of time. For example, an in-house team composed of 10 labelers and 3 QA inspectors can complete around 10,000 automatic driving lane image labeling in 8 days.

How to avoid sample bias while obtaining large scale data?

Solution

Accuracy

Dealing with complex tasks, the task is automatically transformed into tiny component to make the quality as high as possible as well as maintain consistency.

All work results are completely screened and inspected by the machine and the human workforce.

Efficiency

The real-time QA and QC are integrated into the labeling workflow.

ByteBridge takes full advantage of the platform’s consensus mechanism which greatly improves the data labeling efficiency and gets a large amount of accurate data labeled in a short time.

Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.

Ease of use

The easy-to-integrate API enables the continuous feeding of high-quality data into a new application system.

End

“We have streamlined data collection and labeling process to relieve machine learning engineers from data preparation. The vision behind ByteBridge is to enable engineers to focus on their ML projects and get the value out of data,” said Brian Cheong, CEO of ByteBridge.

Both the quality and quantity of data matters for the success of AI outcome. Designed to power AI and ML industry, ByteBridge promises to usher in a new era for data labeling and collection, and accelerates the advent of the smart AI future.

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.

Data Annotation Market sees tremendous growth in the forthcoming future

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 ResearchAndMarkets’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.

This is not a surprising trend. The rapid growth of data labeling industry can boil down to the rising integration of machine learning into various industries.

                   (source: statista)

Unlabeled raw data exits around us everywhere, for example, emails, document, photos, presentation videos and speech recordings. The majority of machine learning algorithms today need data labeled so as to learn and train themselves. Data labeling is the process in which annotators manually tag various types of data such as text, video, images, audio through computers or smartphones.Once finished, the manually labeled dataset would be packaged and fed into algorithms for machine learning's model training.

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 is to outsource the work to renowned data labeling companies like Appen and LionBridge, who can handle product scale and guarantee quality.

The booming of 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, for annotation and calibration of 3D imagery captured by its sensors in 2018.

ByteBridge.io, a data labeling platform, has innovated the industry through its robust tools for real-time workflow management. On ByteBridge’s platform, developers can define and start the data labeling projects and get the results back instantly. It not only improves the efficiency dramatically but also allows clients to customize their task based on their needs. As a fully-managed platform, it enables developers to manage and monitor the overall data labeling process and provides API for data transfer. The platform also allows users to audit the data quality.

“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 better the annotation, the more accurate the algorithm’s results become. Properly annotating data paves the way for efficient machine learning,” said Brian Cheong, founder and CEO of ByteBridge.io.

Designed to empower AI and ML industry, ByteBridge.io 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.

Can AI Address Real World Issues, such as Agriculture?

 To quote a classic paper titled “Machine Learning that Matters” by NASA computer scientist Kiri Wagstaff: “Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society.”

Ordinary people who are not familiar with AI and ML may consider them as fictional, but their applications are stepping out of the science community to address real life issues.

According to UN Food and Agriculture Organization (FAO), the worldwide population will increase to 10 billion by 2050. However, only 4% additional land will come under cultivation by then, let along the threat from climate change and increasing sea level. Traditional methods are not enough to handle those tough problems. AI is steadily emerging as one of the innovative approaches to agriculture. AI-powered solutions should not only enable farmers to produce more with less resources, but also improve food quality and security for consumer market.

AI’s Booming in Agriculture

The global AI in agriculture market size is expected to grow at a CAGR of 24.8% from 2020 to 2030, based on an industrial report. At this rate, the market size would rise from $852.2 million in 2019 to $8,379.5 million in 2030. At present, AI in agriculture is commonly used for precision farming, crop monitoring, soil management, agricultural robots and it has more to come.

Take precision farming as an example, the comprehensive application of AI technologies, such as machine learning, computer vision, and predictive analytics tools. It comprises farm-related data collection and analysis in order to help farmers make accurate decisions and increase the productivity of farmlands.

Dr. Yiannis Ampatzidis, an Assistant Professor of precision agriculture and machine learning at University of Florida (UF), mentions ML applications are already at work in agriculture including imaging, robotics, and big data analysis.

“In precision agriculture, AI is used for detecting plant diseases and pests, plant nutrition, and water management,” Ampatzidis says. He and his team at UF have developed the AgroView cloud-based technology that uses AI algorithms to process, analyze, and visualize data being collected from aerial- and ground-based platforms.

“The amount of these data is huge, and it’s very difficult for a human brain to process and analyze them,” he says. “AI algorithms can detect patterns in these data that can help growers make ‘smart’ decisions. For example, Agroview can detect and count citrus trees, estimate tree height and measure plant nutrient levels.” Ampatzidis believes AI holds great potential in the analytics of agricultural big data. AI is the key to unlocking the power of the massive amounts of data being generated from farms and agricultural research.

Stepping Stone: Labeled Data

A weeding robot makes real-time decisions to identify crops and weeds through close cooperation of built-in cameras, computer vision, machine learning and robotics technology. As the machine drives through the field, high-resolution cameras collect images at a high frame rate. A neural network analyzes each frame and produces a pixel-accurate map of where the crops and weeds are. Once the plants are identified, each weed and crop is mapped to field locations, and the robot sprays only the weeds. The entire process occurs just in milliseconds.

It is challenging to train the neural network models as many weeds look similar to crops. Traditionally, it is agronomists and weed scientists who label millions of field images. However, data labeling is arduous and time-intensive. ML models need to be fed with labeled data in high quality and quantity to get trained or “smarter”automatically and constantly.

The high cost of gathering labeled data restrains AI’s application in agriculture. Aware of such a dilemma, some data service companies, such as ByteBridge.io, provide premium quality data collection and labeling service to empower AI applications to practical industries such as agriculture.

ByteBridge.io has made a breakthrough on its automated data collection and labeling platform where agricultural researchers can create the data annotation and collection projects by themselves. Since most of the data processing is completed on the platform, researchers 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. They can upload raw data and download processed results through ByteBridge’s dashboard. Not only that, ByteBridge.io has utilized blockchain technology to make sure the labeled data service is cost-effective and productive.

Data is powerful, but labeling data makes it useful. Labeled data can be used to train machine learning models effectively. Furthermore, automated, AI-driven labeling platforms, such as ByteBridge.io can help to speed up the data labeling process and accelerate the development of AI industry which aims to address real world issues such as agriculture.

How Data Labeling Contributes to the War against Covid-19

 Healthcare industry is under enormous pressure, especially in the midst of Covid-19 period. The unexpected global pandemic has presented overwhelming challenges on human beings. Scientist, medical experts, doctors and nurses across the globe have undertaken their responsibility to fight against the disease. However, with a shortage of healthcare labor force, we still cannot deny how limited the current medical capacity is.

On December 30 of 2019, Healthmap, an artificial intelligence (AI) data-driven system that scans data sources for disease outbreak signs, detected an unusual activity about a new type of pneumonia burst in China. One day later, BlueDot, an AI outbreak risk software, raised a similar alarm after scanning thousands of Chinese news reports through its machine learning algorithms.

There’s no doubt that Covid-19 has been a catalyst for strengthening the increasing connection and cooperation between AI and healthcare industry.

Medical image diagnosis for future healthcare

AI and ML can be powerful methods for everything in healthcare: medicine research, diagnosis, disease prevention and control, patient treatment, even administrative and personnel management. AI/ML-enabled systems improve their capabilities and effectiveness by automating the most repetitive and homogenous activities. It is currently moving out of the labs and into real-world applications in the health sector.

When it comes to medical images, ML’s applications can cover the entire cycle from image creation and reconstruction to diagnosis and outcome prediction. AI-backed Machines use the computer vision to detect patterns that human eye can’t catch and correlate them with similar medical image data to identify possible diseases and prepare reports after analysis. X-ray, computed tomography (CT) scan, magnetic resonance imaging (MRI) and other image-based test reports can be easily screened to predict various illness in an automated, accurate, and fast way.

Some healthcare companies are now using ML technology to detect organ anomalies, such as identifying tumors from an MRI scan of the brain, along with millions of labeled medical images to show the affected area and to train ML algorithms to detect such diseases. For example, AI semantic segmentation can be used in liver and brain diagnosis; polygon annotation can be used in dentistry; bounding box in kidney stone; annotation detection in cancer cells, and etc. Medical image annotations provide results of greater accuracy in the early detection, diagnostics and treatment of disease as well as understanding the normal. The medical imaging diagnosis is seen as a powerful method for future applications in the health sector.

Bottlenecks of medical image labeling

High-quality training data is the key to building ML models and help to improve medical image-based diagnosis. However, a great challenge in this field is the lack of high quality data and annotation. Specifically, medical imaging annotations have to be performed by clinical specialists, which is costly and time-consuming.

As DJ Patil and Hilary Mason write in Data Driven, “Cleaning the data is often the most taxing part of data science, and is frequently 80% of the work.” The lack of precise and high quality data presents an overwhelming challenge for machine learning industry, limiting their ability to provide the “right data” to answer specific questions. Currently, most medical research organizations have limited access to data samples from a certain geographic areas.

The hardest part of building AI products is not the AI or algorithms but data preparation and labeling. For example, retinal images are used to develop automated diagnostic systems for conditions, such as diabetic retinopathy, age-related macular degeneration. In order to do that millions of medical images need to be labeled by various conditions structurally. This is laborious as it requires identification of very small structures and usually takes hours for experts to annotate them carefully.

Turning points

Aware of those challenges, ByteBridge.io moves a big step forward through its automated data collection and labeling platform. It allows researchers to have access to high-quality labeled datasets related to health care and public health.

ByteBridge’s innovative data training platform empowers healthcare researchers and ML medical companies to use data cost-effectively and improve healthcare outcomes. From data collection, to data labeling, to machine learning applications, ByteBridge.io provides professional data annotation service on medical images with the highest quality and maximum accuracy.

Different with traditional data labeling companies, in ByteBridge’s dashboard, researchers can create the data project by themselves, upload raw data, download processed results as well as check ongoing labeling progress simultaneously on a pay-per-task model with clear estimated time and more control over the project status.

Compared to existing Western companies for data annotation outsourcing, Bytebridge.io charges 90% lower. It offers 50% cheaper price than its competitors in China and India. More than that, ByteBridge’s data processing speed is more than 10 times faster than the current data annotation company.

“I believe that we can achieve great innovation in this field based on our product development capabilities and underlying blockchain-based technology. ByteBridge.io is aimed at accelerating the development of ML industry and seamlessly transforming it into other essential areas such as healthcare,” said Brian Cheong, CEO of ByteBridge.io.

Imagine one day, patients can simply go through a fast AI scan as diagnosis; smart wearable devices, such as Apple Watch, can analyze physical data, note abnormality and generate an alarm before you are about to have a heart attack or a stroke; medical detection and prediction can be fully automated and supervised with little human intervention. Such scenes can definitely be realized in the coming future, thanks to ML and AI technology.

Machine Learning has achieved unprecedented success in computer vision and other industries so far. And now it is drastically revolutionizing healthcare area with indispensable support from automated data labeling service.