How an Automated Data Labeling Platform Fuels Self-driving Industry

 “I’m extremely confident that self-driving cars or essentially complete autonomy will happen, and I think it will happen very quickly,” Tesla CEO Elon Musk said in a virtual speech to the World Artificial Intelligence Conference in July, 2020. Musk mentioned Tesla will have basic functionality for level-five complete autonomy this year.

The self-driving vehicles are not just hot in Silicon Valley. In China, the largest automobile market worldwide, companies are also getting on board to develop autonomous driving technology, including China’s internet search tycoon Baidu, also referred to as the “Google of China.” Baidu has been developing the autonomous driving technology through its “Apollo” project (also known as open-source Apollo platform) launched in April 2017. Now the company announced the world’s first production-ready compute platform specifically for autonomous vehicles is ready for application.

Behind the self-driving: Machine learning and Data annotation

Before we talk about the feasibility about self-driving and autonomous technology, let’s make one question clear: how is self-driving possible?

In a nutshell, a self-driving car should be able to sense its environment and navigate without human intervention. Self-driving vehicles depend on hardware and software to drive down the road. The hardware collects data and software processes it through machine learning algorithms that have been trained in real-world scenarios. Simply put it, it is machine learning technology that plays a vital role in the self-driving industry. Machine learning algorithms, sensors and graphics processing devices have integrated into a smart driving neural network, or “brain.”

First and foremost, the smart “brain” needs to learn image verification and classification, object detection and recognition, as well as traffic rules, weather conditions. Engineers “teach” these situations by feeding the machine learning models millions of labeled images to make it adept at analyzing dynamic situations and acting on their decisions.

With the tremendous amount of raw data required for machine learning algorithms, and the need for accuracy, high quality data annotation is crucial to ensure that autonomous vehicles are safe to use for public.

Going back to Tesla, this company uses cameras for visual detection, each car equipped with 8 surround cameras. If a Tesla user drives one hour a day on average, considering more than 750,000 Tesla cars around the world, about 180 million hours of video can be generated per month.

Tesla Autopilot project has included 300 engineers plus more than 500 skilled data annotators. The company plans to enlarge the data annotation team to 1,000 people to support the data process. Elon Musk admits during an interview that data annotation is a tedious job, and it requires skills and training, especially when it comes to 4D (3D plus time series).

A new solution for data annotation market

It’s becoming challenging for the machine learning and AI companies to internally meet the burgeoning demand of high-quality data annotation.

ByteBridge.io has provided an innovative solution to empower the machine learning revolution through its automated and integrated data annotation platform. Built by developers for developers, ByteBridge.io has applied blockchain technology to the data processing platform where developers can create the project by themselves, highlight specific requirements, check the ongoing process simultaneously on a pay-per-task model with clear estimated time and price.

In order to reduce data training time and cost when dealing with complicated tasks, ByteBridge.io has also built up the consensus algorithm rules to optimize the labelling system and improve the accuracy of final data delivery.

Self-driving technology is going to transform the transportation industry, social and daily lives. It’s hard to know when that day will arrive. But one thing for sure is that with top data service companies, such as ByteBridge.io, to fuel the machine learning and autonomous industry, the intelligent future is edging closer into reality.

Invisible Workforce of the AI era

The Surging Demand for Data Labelling Services

Thirty years ago computer vision systems could hardly recognize hand-written digits. But now AI-powered machines are able to facilitate self-driving vehicles, detect malignant tumors in medical imaging, and review legal contracts. Along with advanced algorithms and powerful compute resources, labeled datasets help to fuel AI’s development.

AI runs on data. The unstructured raw data need to be firstly labeled in the dataset so that the machine learning algorithms can understand it. Given the rapid expansion of digital transformation progress, there is a surging demand for high quality data labeling services. According to Fractovia, data annotation tools market was valued at $ 65O million in 2019 and is projected to surpass $5billion by 2026. The expected market growth refers to the increasing transition of raw unlabeled data into useful Business Intelligence (BI) by machine learning skills with human guidance.

AI’s new workforce

Data labelers are referred as “AI’s new workforce” or “invisible workers of the AI era”. They annotate tremendous amount of raw datasets for model training that enables the public to enjoy machine learning empowered goods and services. Along with the hugely lucrative market, there is more than one way for the data labelling industry to organize their workforce.

In-house

The data labelling enterprises hire part-time or full-time data labelling teams with direct oversight of the whole tagging process. When the annotation projects are quite specific, the team can adjust to changes of the particular needs. As a rule of thumb, it is more common to have an in-house team for long-term AI projects, where data flow is continuous during the prolonged periods of time.

The cons of in-house data labeling team are quite obvious. It’s expensive to hire and train a professional labeling team, develop a software with the right tools and maintain a secured working environment.

Outsourcing

Hiring a third-party annotation service can be another option. Outsourced companies have experienced annotators who finish tasks with higher speed and efficiency. Specialized labelers can proceed with a large volume of datasets within a shorter period.

On the other hand, outsourcing results in less control over the project process and the communication cost is comparably high. A clear set of instructions is necessary for the labeling team to understand what the task is about and make annotations correctly. Tasks may also change as developers optimize their models. Besides that, it takes extra time to check the quality of the completed tasks.

Crowdsourcing

Crowdsourcing means sending data labelling tasks to individual labelers all at once. It breaks down large and complex projects into smaller and simpler parts for a large distributed workforce. A crowdsourcing labelling platform also implies the lowest cost. It is always the top choice when facing a tight budget constraint.

While Crowdsourcing is considerably lower priced than other approaches, its biggest challenge, as we can imagine, is the accuracy level of the tasks. According to a report studying the quality of crowdsourced workers, the error rate of the task is significantly related to data annotation types. In the case of basic description task, crowdsource workers’ error rate is around 6%, which is much lower than sentiment analysis task with 40%.

A turning point during COVID-19

Crowdsourcing has been proven beneficial during the COVID-19 crisis as in-house and outsourced data labelers are affected by the lockdown. Meanwhile, people stuck indoors are now turning to more flexible jobs. Millions of unemployed or part-time workers are starting the crowdsourcing labelling tasks from anywhere with internet.

Bytebridge.io, a tech startup for data service, has also seen the workforce as well. It provides high quality and cost-effective data labeling service for AI companies and job opportunities for labelers who can work without any limit on time and place.

Bytebridge.io employs consensus mechanism to optimize the labelling system. Before distributing individual tasks for labelers, the system firstly sets a consensus index, such as 90%. If 90% of labeling results are basically the same for the same part of the task, the system would judge that they have reached a consensus and move onto the next part of the task. If the machine learning model requires higher accuracy for data annotation, the platform can adjust to “multi-round consensus” to repeat tasks over again to improve the accuracy of final data delivery.

Developers can create their own projects on Bytebridge’s dashboard. The automated platform allows developers to write down their specific requirements for the labeling projects, upload raw dataset and control the labeling process in a transparent and dynamic way. Developers can check the processed data, speed, estimated price and time, even though working at home.

By cutting down the intermediary costs and time, Bytebridge.io charges 90% cheaper than Google and any other in Silicon Valley, shows 10 times or more rapid data processing speed. Bytebridge.io is devoted to gearing up the AI revolution and digital transformation through its premium data processing service, automated data platform and connection of the cost-effective international fragmented labor force.

Bytebridge.io Brings Brand New Data Annotation Service for Machine Learning Industries

Brand New Data labeling service platform “ByteBridge.io” launched to better support machine learning industry

 Bytebridge.io, an automated data service provider to collect, manage, and process data sets for machine learning applications, was recently introduced into the AI industry.

It is a self-service platform to manage and monitor the overall data processing, specialized in data collection and labeling services for organizations, and to provide convenient toolkits for machine learning companies.

Bytebridge.io is backed by many world-class known investment companies, such as KIP, Union Partner, SoftBank Global Star Fund, and Ameba Capital.

Traditional data service providers use limited workers to finish multiple tasks at the same time, which would cause problems such as low-efficiency and long-waiting task delivery period to the clients. Bytebridge.io has come to a better solution. With close to million task partners across the globe, it supports workers in different regions to work at the same task at the same time on its platform, which allows works to work on tasks 24hr non-stop. It not only improves the efficiency dramatically but also allows clients to customize their task based on their needs by themselves.

Currently, Bytebridge.io has already been working with a few tech companies around the globe, helping them to build a machine learning system much faster through its automated data labeling process. With a handful of experiences, Bytebridge.io is confident that they could supply the best product and service to the AI industry.

Empowering data science developers to build a great machine learning product, Bytebridge is designed to build a strong data labeling infrastructure to the machine learning team with powerful automation, collaboration, and developer-friendly features.

“We are well-positioned to fuel the industrialization of machine learning across many sectors, we have a handful experiences in this industry and we understand the pain of developers are facing. Our goal is to relieve AI companies from the burden of machine learning data preparation and management and accelerate the machine learning development cycle, allowing them to build better AI in a shorter time,” said Brian Cheong, the Founder of Bytebridge.io.

About Bytebridge.io:

Bytebridge.io is an automated platform designed to accelerate the machine learning process It aims to power the machine learning industry with high-quality trained data.

ByteBridge.io Provides Language Opportunity Across Globe to Help Local Economy Against Covid-19

 ByteBridge.io, an automated service provider for collecting, managing, and processing datasets for AI and machine learning industries, has partnered with over 10 different language speaking communities across the globe, aiming to help local economies against Covid-19 through providing language job opportunities and the best quality data services to its clients meanwhile minimizing the effect that Covid-19 brought to the local economy.

ByteBridge.io now provides language services covering Asia, Europe to South America regions, such as Chinese, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Spanish, and more. With the close partnership with these communities, it highly improves its data quality in different languages meanwhile expand its service scope to a wider range.

“We are honored to have support from these different communities, during this global pandemic. We believe the non-English speaking populations are part of the most vulnerable ones. By providing language services, we can leverage the expertise of our global network to help them expand their opportunities they serve,” said Brian Cheong, founder of ByteBridge.io.

Providing services in over 10 languages, ByteBridge.io is dedicated to improving the quality of its data service, and by partnering with local communities in different native languages, it could ensure data service quality through the help of thousands of workers across these regions and the processing time to finish tasks will also be shortened.

Currently, anyone can use ByteBridge.io for free. Clients will only be charged once they hit a certain usage threshold, and once the free credits run out, clients are only charged based on the volume of data that clients upload and the breadth of services they use.

About ByteBridge.io :

ByteBridge.io is a self-service platform to manage and monitor the overall data processing, to provide data collecting and labeling services for organizations and provide convenient toolkits for machine learning companies to initiate tasks, manage the data they are receiving, and ensuring the quality of the data meets their requirements.

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.