The Future of the Accelerated Landing of AI Can be Expected
When artificial intelligence (AI) meets medical treatment, how does the cold nature of technology realize the “medical benevolence”? Can AI+ services deeply perceive natural language and realize human-computer interaction?
Today, AI technology is ushering in a wave of landing in many production and life scenarios. AI+ smart city, AI smart manufacturing, AI+smart home, AI+autopilot…AI technology has penetrated into all aspects of people’s production and life. And it is profoundly changing the present and shaping the future.
“Globally, China and the United States are the two giants in the development of AI technology, but China has already run ahead of the United States in terms of application.” Gao Wen, academician of the Chinese Academy of Engineering and chairman of the New Generation Artificial Intelligence Alliance, at the 2021 Global Intelligence Expo, said so.
AI starts smart manufacturing
How much labor is required for a series of processes, from the industrial raw materials storage, the flow of materials in the production workshop, to the products delivery and shipment? At present, this kind of work can be done by an intelligent robot entirely relying on a set of smart handling systems. The intelligent handling system can realize a ‘seamless connection’ between intelligent robots and WCS, WMS, 5G, and other systems to form warehouse information interaction. The entire inventory information realizes visual display and management through the digital twin technology, real-time monitoring and enables intelligent and flexible handling mode.
Of course, AI is more reflected in the production process in the digital transformation of the entire enterprise. However, the role of AI in assisting factory management should not be underestimated. For example, the use of artificial intelligence vision to monitor safe production and check the wearing of helmets and masks has greatly improved the efficiency of factory management.
Hu Guoping, senior vice president of IFLYTEK, said that the current digital wave is sweeping all walks of life. Big data and cloud computing will bring the transformation under the new generation of productivity. He compared the digital transformation empowered by AI to “holding a hammer to find nails.” The focus of this is to create digital applications based on scenarios and then to discover the hidden value of data, build powerful AI capabilities, and improve corporate efficiency.
AI landing applications can be expected in the future
With the continuous expansion of the application field and application depth of AI technology, many new products, applications, and models have been spawned. However, the innovative breakthroughs and industrial landing of artificial intelligence are still important issues at this stage. According to IDC statistics, it is estimated that by 2025, the total global AI application market will reach 127 billion U.S. dollars, of which the medical industry will account for 20% of the total scale.
How can AI be truly implemented and industrialized
The real success of artificial intelligence must meet three conditions. There are tangible and visible practical application cases, standardized promotion of related products, and application effects proven by statistical data. The landing of artificial intelligence technology must be combined with application scenarios. The technology transformation can be realised through cooperation between the research institute and local enterprises. Artificial intelligence technology can be truly implemented and industrialized through such a cooperation model of production, education, and research.
High demend of labeled data
At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, and finance, etc. Each field has requirements for subdivision and specialization.
Among them, in particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.
In the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.
ByteBridge.io, a Human-Powered and ML-powered Data Annotation Platform
- 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%.
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.
Configure Your Own 2D Images Annotation Project
- Developers can control the labeling project from setting labeling instructions to output review on a pay-per-task model with a clear estimated time and price
- Real-time management and monitoring of project
- Real-time Outputs: clients can get real-time output results through API. (We support JSON, XML, CSV, etc. And we can provide customizable datatype to meet your needs)
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.
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