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.

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 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.

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.

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.