Applications of Intelligent Robots in Different Fields — Part1
The era of algorithms has arrived
The self-service robots developed by IT giants such as Google, Amazon, and Apple shocked the world. These robots can answer users’ questions, solve complex problems, and provide users with necessary advice when needed. These robots are becoming more and more popular. And most industries, such as finance, healthcare, aviation, software, food and restaurants, and entertainment, have all transformed due to the application of robots.
Chatbots are the most common artificial intelligence application widely used in many organizations. It dramatically reduces the pressure on the customer service team and increases availability when needed, thereby saving the organization’s resources to a large extent.
The development of chatbots has changed how these institutions operate, whether it is government agencies, financial institutions, healthcare industry, recruitment industry, or catering industry. For example, in government agencies, there are many tax declarations, legislation, judicial-related issues, and other public inquiries that may take days or even weeks to resolve. In this case, chatbots will bring flexibility, transparency, and efficiency to serve the public and internal employees.
The chatbot can quickly answer some simple questions, such as “How much does it cost?” or “My payment was unsuccessful, but it was deducted from my bank account, how do I file an appeal?”. It can help reduce the staff workload and allow them to solve more complex problems that robots cannot solve technically. Some government agencies have begun to use chatbots to perform their operational tasks, such as the city hall personalized robot used by the Los Angeles local government or the Emma chatbot used by the U.S. Department of Homeland Security for U.S. citizens and immigration services.
Healthcare is one of the earliest areas where robots have a long-term impact. These robots have built-in artificial intelligence medical functions, including full-featured symptom checkers, medical content from trusted sources, and language understanding models developed to understand medical and clinical terminology. Today, patients would like to have quick and easy access to information and want to use self-service, making them feel interactive regardless of their location, equipment, or time. These robots can enhance the digital medical experience by providing patients with relevant information, solving problems, and accessing health records in real-time.
The first healthcare robot, ELIZA, came out in the 1960s. It served as a psychotherapist to facilitate communication with patients. The robot is so powerful that many people mistakenly think it is a real human therapist. In addition, according to data from Discover, Ada is the number one medical application in 130 countries/regions. Since its global launch in 2016, it has been evaluated more than 15 million times. Through a simple conversation with Ada, you can accurately assess your condition and get valuable suggestions. Such healthcare robots can also perform various tasks, like connecting patients with corresponding doctors based on observed symptoms, answering complex medical questions, organizing reports, and so on.
With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.
If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.
ByteBridge, a human-powered and ML-powered data labeling tooling 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 results are thoroughly assessed and verified by a human workforce and machine
In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.
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These labeling tools are available: Image Classification, 2D Boxing, Polygon, Cuboid.
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We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).
Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.
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