AR is an important application of artificial intelligence technology
AR (Augmented Reality) is a technology that calculates the position and angle of the camera image in real-time and adds the corresponding image, video, and 3D model. The goal of this technology is to set the virtual world on the screen and interact with the real world.
In simple words, AR is to show virtual information in reality and let you interact with that information. AR can connect reality and virtual information seamlessly, build a three-dimensional scene to show the things that do not exist in reality and connect with real-life through technical tools.
The difference between VR (Virtual Reality) and AR (Augmented Reality)
In short, VR is virtual, and all the characters in the scene are fake; AR brings virtual information into the real world, the scenes and people can be either real or fake.
Apple smartphone is equipped with AR technology which enables you to play the game, experience a more realistic effect.
In fact, many AR applications have appeared on mobile phones in recent years. For example, we often use the “face sticker” function, which can add all kinds of Mickey Mouse nose, cat whiskers, rabbit ears, and so on to our face in our selfie video to create a lovely visual effect. Although most people often use this function, they do not realize that “face sticker” is a typical AI application.
In order to achieve the effect of AR, the system must first be able to perceive the three-dimensional structure of the scene through the camera lens, distinguish the object and position in the space, and be able to accurately place the information that needs to be enhanced to the required place, or replace the part of the scene that needs to be edited and reconstructed. This is a truly advanced application of AI and It is one of the key development directions in the IT science and technology field.
We are on the Eve of the AR Application Explosion
Since Apple launched the AR applications in a high profile, the whole industry is looking forward to the deep combination of AR technology and practical scenarios. Although there is no large-scale application at present, many domestic and foreign manufacturers and e-commerce platforms are constantly launching AR services.
These applications mainly focus on commodity display, trial, and other fields. For example, the AR app launched by IKEA enables users to vividly experience what IKEA’s furniture products will look like when they are placed in their own rooms on the mobile screen. Users can move and rotate furniture freely on the screen to fully observe whether the furniture matches their own hall.
In addition, there are many AR applications such as lipstick trials and clothing trials.
In China, Ali and JD have launched their own e-commerce AR applications, which let users enjoy the fun of virtual shopping.
It is reasonable to believe that with the further maturity of software and hardware technology, AR applications will certainly be popular, and it is likely to become the next wind for mobile Internet applications.
AI Enterprise Layout
International IT giants such as Apple and Google have long been in the AR field. For example, Apple released the ARKit development platform while announcing its AR applications. Google also released the ARCore platform based on the Android system in advance, which enables developers, software, and hardware manufacturers to gather and form a huge AR ecosystem.
While the market of smartphones enters into the saturation state, all major manufacturers are exploring the next growth breakthrough. AR has become a direction that all parties keep their eyes on. Global Software, hardware enterprises, AI enterprises are preparing to seize the commanding position. We are in the night before the AR explosion.
Data, Algorithms, and Processing are Three indispensable Elements of AI.
Data is the starting point.
It makes sense for AI to start with data and take advantage of learning itself. In scalable data and the high-speed era, it is very convenient to use data to train artificial intelligence.
The original data is generally acquired through data collection, and the subsequent data cleaning and data annotation are equivalent to processing the data, and then transferred to the Artificial intelligence algorithm and model for invocation.
If the data used in artificial intelligence training is not sufficiently diverse and unbiased, problems such as artificial “AI bias” may arise.
Labeling Tools in AR Industry
Semantic Understanding, Object Detection, Object Recognition, Video Annotation, Sentiment Recognition
ByteBridge, a data labeling tooling platform with real-time workflow management, provides training data for the machine learning industry.
- 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 work results are completely screened and inspected by the machine and human workforce
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.
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.
Configure Your Own Annotation Project
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.
For example, you can choose a Bounding Box and Classification Template on the dashboard:
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 get involved in the QC process.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
We can provide personalized annotation tools and services according to customer requirements.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
“High-quality data is the fuel that keeps the AI engine running smoothly. The more accurate annotation is, the better algorithm performance will be” said Brian Cheong, founder, and CEO of ByteBridge.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org
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3 Main Applications (Emotional Analysis) in Natural Language Processing Field(NLP) — 3
4 Data Annotation Service and Its Key Advantage — Flexibility
5 What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?