U.S. Companies Introduce Robots on a Large Scale

5 min readMar 25, 2022

From April 2021, the United States has seen a “big wave of resignations,” with the highest record since 2000. The number of resignations in August exceeded 4.27 million, and the resignation rate reached a record of 2.9%.

The U.S. Bureau of Labor Statistics said that many people left their old jobs but did not find new ones. As a result, the number of job vacancies in the United States also broke the record, exceeding 10 million. At the same time, however, the wave of resignations has led to a surge in robot orders. According to data from the Association For Advancement Automation (A3), in the first nine months, total robot sales were up to USD 1.48 billion, a year-on-year increase of 34.86%.

Most of the orders are industrial robots, which are usually applied to assemble parts or transport heavy materials in a production environment. According to A3, the companies ordered 9,928 robots in the third quarter, of which 6,302 were from non-automotive industries.

The fastest-growing non-automotive orders are from the metal industry, which has nearly doubled. Then, the food and consumer goods industry has increased by 40%. The growth rate of semiconductors and electronics is 26%. Jeff Burnstein, the president of A3 said: “With labor shortages in manufacturing, logistics, and almost in all industries, the companies of all sizes are increasingly turning to robotics and automation to maintain productivity and competitiveness.”

At present, due to labor shortages across the United States, many American companies have difficulty finding workers, and some warehouses have turned to order robots to help with daily operations. For example, FedEx Corp. is using robots to help sort out the packages. Researchers and companies are also working together on advancing AI systems so that robots can collaborate with a broader range of objects.

Artificial intelligence robots will also impact future work and life. The University of Oxford published a research report that the probability of the following occupations being replaced by AI in 2033 is as follows:

(1) Telemarketer： 99%

(2) Cashier：97%

(3) Fast food chef： 96%

(4) Paralegal： 94%

(5) Tour guide： 91%

(6) Bus and taxi drivers： 89%

(7) Security personnel： 84%

(8) Archivist： 76%

Why the High-Quality Training Data is so Important to Industrial Robots?

Data, Algorithms, and Processing are Three indispensable Elements of AI. Data is the starting point.

At present, the research community is doing unsupervised, small-sample deep learning work. Through three-dimensional synthetic data, the machine is trained with synthetic data, so as to minimize the data collection and labeling process. In this way, the machine can learn and evolve independently. However, as there is a lack of theoretical technology breakthroughs, although the technology is growing fast, the overall level is still relatively low. The current deep learning still relies on the big data model based on statistical significance, which requires scalable data.

The current artificial intelligence is also called data intelligence. At this stage of development, the more layers of the neural network, the larger amount of labeled data is needed. High-quality training data will maximize the efficiency of artificial intelligence, while low-quality AI data will be not only impossible to improve efficiency, but also will hinder the evolution of artificial intelligence to a certain extent.

Common Labeling Tools in Robotics

Common Labeling Types in Robotics

Object Recognition
Object Tracking in Video
Industrial Robot Navigation
Robot Arm Guidance

① Robotic sorting and material handling, like Package Delivery, Warehouse Inventory Handling

② QA Check, Maintenance

Semantic Segmentation of street, sidewalk, and landscape scenery

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

Aware of those challenges, ByteBridge moves a big step forward through its automated data collection and labeling platform. It provides high-quality training data for the machine learning industry.

Accuracy

ML-assisted capacity can help reduce human errors by automatically pre-labeling
The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy
Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output
All results are thoroughly assessed and verified by a human workforce and machine

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.

Communication Cost Saving

On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.

ByteBridge Labeling Instruction Template

Flexibility: More Engaged in the 2D Images Labeling Loop

On ByteBridg’s dashboard, developers can set labeling rules directly, check the ongoing process simultaneously on a pay-per-task model with a clear estimated time and price.