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.