National Academy of Artificial Intelligence Criteria and Scheme for Accreditation of Artificial Intelligence Engineers
1、 Recognition criteria
Technical Capability:
The system is able to master and flexibly apply the basic theoretical knowledge and professional technical knowledge of artificial intelligence, grasp the current status and development trends of artificial intelligence technology, and have the ability to track the forefront level of artificial intelligence technology development.
Has a profound academic background and rich practical experience in the field of artificial intelligence, especially with significant achievements in machine learning, natural language processing, computer vision, robotics technology, intelligent systems, and other areas.
Having strong research capabilities, able to lead high-level and high-value research projects and topics in the field of artificial intelligence, and producing technical reports that have been evaluated by peer experts and are at the world's advanced level, achieving good economic and social benefits.
Project management capability:
Capable of independently managing projects, including project planning, execution, and summarization, with extensive project management experience.
Ability to coordinate team members, ensure smooth project progress, and play key roles in the team.
Innovation capability:
In the field of artificial intelligence, there is innovative thinking that can propose novel solutions and promote the innovation and application of artificial intelligence technology.
Significant breakthroughs have been made in technological innovation, introduction, and promotion of new technologies, resulting in significant economic and social benefits.
Academic contributions:
As the first author or corresponding author, publish professional papers with significant academic value in key journals, or as an important person in charge, complete invention patents, technical reports, research reports, design documents, technical standards, monographs, etc. that have a significant impact in the industry.
Industry recognition:
It has a high industry recognition in the field of artificial intelligence and can play an important role in guiding and cultivating young and middle-aged academic and technical backbones.
2、 Certification Scheme
Application process:
The applicant submits application materials, including personal resume, research achievements, project experience, academic papers, patent certificates, etc.
The Academy of Sciences organizes experts to review the application materials and, if necessary, conduct interviews or on-site inspections.
Based on the review results, the Academy of Sciences decides whether to recognize the applicant as a junior/senior engineer in artificial intelligence.
Evaluation criteria:
The reviewing experts evaluate the applicant's technical ability, project management ability, innovation ability, academic contribution, and industry recognition based on the recognition criteria.
During the evaluation process, emphasis is placed on the applicant's practical experience and achievements, as well as their influence and contribution in the field of artificial intelligence.
Subsequent management:
Individuals recognized as senior engineers in artificial intelligence are required to regularly submit work reports to the Academy of Sciences, reporting on their research progress and achievements in the field of artificial intelligence.
The Academy of Sciences will conduct regular reviews of the recognized individuals to ensure that they continue to meet the recognition criteria.
For personnel who no longer meet the recognition criteria, the Academy of Sciences will revoke their engineer recognition qualifications.
Incentive mechanism:
To encourage the development of talents in the field of artificial intelligence, the Academy of Sciences will give certain honors and rewards to personnel recognized as junior/senior engineers in artificial intelligence.
At the same time, the Academy of Sciences will actively recommend recognized individuals to participate in academic exchanges and collaborations both domestically and internationally, providing a broader stage for their career development.
Through the above recognition criteria and programs, the National Academy of Artificial Intelligence in the United States aims to select and recognize junior/senior engineers with outstanding abilities and contributions in the field of artificial intelligence, promote innovation and application of artificial intelligence technology, and promote the healthy development of the artificial intelligence field.
3、 Recognized fees
Official pricing for single confirmation: 1500RMB/200 USD
NAAI Artificial Intelligence Engineer Testing Room Collaboration Authorization:
50000 RMB (7000 USD) per year
Engineer certification exam room cooperation contact information:
secretary@thenaai.org
The recognized majors include:
Artificial Intelligence Algorithm Major
The professional direction of artificial intelligence algorithms includes machine learning, pattern recognition, data mining, computational intelligence, natural language processing, knowledge representation and processing, big data intelligence, cross media intelligence, swarm intelligence, brain like computing, human-machine hybrid intelligence, computer vision, speech recognition and synthesis, multi-agent systems, autonomous intelligent unmanned systems, virtual reality and augmented reality, artificial intelligence security and other artificial intelligence algorithms, as well as related basic software design, development and optimization technical positions.
Artificial Intelligence Hardware Specialty
The professional direction of artificial intelligence hardware includes research and development, deployment, and optimization technology positions for artificial intelligence hardware such as artificial intelligence chips, intelligent sensors, intelligent controllers, computing platforms, edge and end devices, brain machine devices, intelligent robots, and intelligent terminals.
Artificial Intelligence Application Major
The professional direction of AI application includes the design, development, testing, optimization, operation and maintenance, service and other technical posts that combine AI algorithms and related technologies with the needs of industries such as manufacturing, medical care, transportation, home furnishing, finance, commerce, agriculture, education, government affairs, security, logistics, energy, and the Internet to achieve the engineering landing of related software and hardware platforms.
Send relevant resume materials to email: secretary@thenaai.org
Email indicating the recognized junior/senior engineer and professional direction
NAAI official website link: https://thenaai.org/index/index/newsdata2/id/355.shtml
Click on the application recognition form to download:
NAAI Application Form.pdf
The National Academy of Artificial Intelligence (NAAI) in the United States recognizes the qualifications of engineers engaged in the fields of computer and artificial intelligence worldwide. This recognition represents NAAI's recognition of the professional level of practitioners in the artificial intelligence industry. NAAI has rigorous and high standards for certification work, and engineers must strictly comply with NAAI certification standards in order to be awarded the title of engineer. NAAI academic committee organizes authoritative academicians in the field of artificial intelligence in various countries to carry out the accreditation of NAAI engineers. Therefore, the NAAI engineer certification is not only a strict recognition of the high technical level of engineers in the field of artificial intelligence, but also a highly honorable recognition.
Interpretation and Introduction of NAAI Engineer Certification Testing System
NAAI engineer certification testing roughly examines the following aspects
1. Programming skills: Programming is a fundamental skill for artificial intelligence engineers. Mastering programming languages such as Python, R, Java, and C++is key to building and implementing AI models. Python has become the preferred language in the field of AI due to its simplicity, readability, powerful functionality, and rich ecosystem.
2. Fundamentals of Mathematics and Statistics: Mastering mathematical knowledge such as linear algebra, probability theory, statistics, and calculus is the foundation for understanding and implementing machine learning algorithms. These knowledge are crucial in data analysis and model construction, as they can help optimize the performance of machine learning models.
3. Machine learning and deep learning frameworks: Familiar with common machine learning algorithms such as linear regression KNN、 Naive Bayes, support vector machines, etc., and can use frameworks such as PyTorch, TensorFlow, Keras for model training and optimization. In terms of deep learning, understand common neural network structures and their applications such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN).
4. Data processing and analysis: Master database, data cleaning techniques, and data visualization tools such as Tableau, Matplotlib, Seaborn, etc. to present data analysis results more intuitively. Understanding data mining techniques can help extract valuable information from complex data and provide high-quality data for model training.
5. Domain knowledge: Master relevant professional knowledge based on the area of interest (such as natural language processing, computer vision, etc.). For example, natural language processing involves techniques such as text preprocessing, word segmentation, part of speech tagging, syntactic analysis, semantic understanding, etc; Computer vision involves techniques such as image preprocessing, object detection, and image classification.
6. Project management and communication skills: In addition to technical skills, project management and good communication skills are also required to collaborate and effectively communicate technical solutions within the team.
NAAI Artificial Intelligence Engineer Certification
After certification, engineers can check their personal homepage on the NAAI official website, Engineer
NAAI Engineer Certification Junior Engineer Test Sample Question:
Test objective: To cover basic theoretical knowledge, programming skills, algorithm understanding, and practical application abilities. This test is divided into four parts: Fundamentals, Programming and Algorithms, Deep Learning and Machine Learning, and Projects and Practices.
Part One: Basic Knowledge
1. Describe the relationship and differences between artificial intelligence, machine learning, and deep learning.
2. Explain the basic concepts of supervised learning, unsupervised learning, semi supervised learning, and reinforcement learning, and provide examples of their respective application scenarios.
3. What is overfitting? What methods can prevent overfitting?
4. Explain the gradient descent method and its working principle, including the differences between batch gradient descent, random gradient descent, and small batch gradient descent.
4. Briefly describe the role of activation functions and list three common activation functions and their characteristics.
Part 2: Programming and Algorithms
1. Please implement a simple linear regression model in Python, including data preprocessing, model training, and prediction.
2. How to use Pandas library for data cleaning on a dataset containing missing values?
3. Explain what time complexity and space complexity are, and provide examples to illustrate each.
4. Describe a sorting algorithm (such as quicksort) and write its Python implementation.
5. Implement multiplication of two matrices using NumPy.
Part Three: Deep Learning and Machine Learning
1. Explain the basic structure and working principle of Convolutional Neural Networks (CNNs), as well as their applications in image recognition.
2. What is Recurrent Neural Network (RNN)? How does it handle sequential data? Please briefly describe the difference between LSTM and GRU.
3. Explain what a Generative Adversarial Network (GAN) is and its basic components.
4. What is Dropout in deep learning? How does it help improve the generalization ability of the model?
5. Describe a deep learning framework that you are familiar with, such as TensorFlow or PyTorch, and explain its main features and advantages.
Part Four: Projects and Practices
1. Describe an AI project you have participated in, including project background, role you have undertaken, technology stack used, challenges encountered, and solutions.
2. Assuming you need to build a recommendation system for an e-commerce website, which algorithm or model would you choose? Why? Please briefly describe your design concept.
3. How to process text data to improve model performance when performing natural language processing (NLP) tasks? Please list and explain at least three methods.
4. Provide a practical scenario, design and implement a simple AI solution, including the entire process of data collection, model selection, training, evaluation, and deployment.
5. Discuss the importance of data privacy and ethical issues in AI projects, and propose at least two protective measures.
The NAAI engineer confirms that the sample test questions are sourced from the NAAI official website: thenaai.org