Artificial Intelligence Terminology Database
1.0.0
A comprehensive mapping database of English to Chinese technical vocabulary in the artistic intelligence domain.
This term library currently has about 2442 professional terms and 2 specialized fields, mainly basic concepts and terms in the field of artificial intelligence.
The first two versions of this term library mainly record the professional terms encountered by Machine Heart in the process of compiling technical articles and papers. I hope it will help you review and translate it. At the same time, I hope you can actively point out the inappropriate compilation of our To jointly promote the efficient and wide dissemination of knowledge.
Since the third edition, in addition to the accumulation of daily compilation work, we will work with field experts to further expand and improve this warehouse to provide the community with unified AI and related fields in Chinese and English terms translations. Comparative reference.
Heart of Machine will continue to improve the inclusion of terms and the construction of extended reading in three aspects:
①The first stage of the Machine Heart will continue to improve the construction of basic terms, that is, extracting common terms through authoritative textbooks or other credible materials;
②The second stage of the Machine Heart will continue to update the uncommon terms that appear in compiled papers or other materials to the glossary;
③The third stage of Machine Heart will join forces with more experts in the field to build a glossary of special fields.
| index | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 |
|---|---|---|---|---|---|---|---|---|---|---|
| U1 | - Q - | - W - | - E - | - R - | - T - | - Y - | - U - | - I - | - O - | - P - |
| U2 | - A - | - S - | - D - | - F - | - G - | - H - | - J - | - K - | - L - | |
| U3 | - Z - | - X - | - C - | - V - | - B - | - N - | - M - |
Readers can view the professional terms they want to know through the above alphabet or different fields. Among them, the organizational form of the term is:
All English professional terms in this project compare Chinese from articles compiled by Machine Heart and a series of machine learning textbooks (such as Professor Zhou Zhihua's "Machine Learning", Dr. Li Hang's "Statistical Learning Methods", Professor Qiu Xipeng's "Neural Networks and Deep Learning, Dr. Li Mu's "Hand-On Deep Learning", Professor Li Hongyi's "Mechanical Learning Methods" and Ian Goodfellow's "Deep Learning" Chinese translation, etc.), we strive to retain the most commonly used versions while providing accurate translations. form. At the same time, in order to ensure the accuracy of term translation, we open source this project to readers and hope to iterate the accuracy of terminology with readers.
Because many terms in this project are accumulated by the articles compiled by Machine Heart, we first need to explain to readers the standards for Machine Heart Term Compilation.
Compiling common terms of Machine Heart first ensures the correctness of the terms, and then considers the spread of the terms. Machine Heart does not retain English and will not explain further when compiling common terms.
Machine Heart often encounters uncommon terms when compiling technical articles or papers. Because articles like papers are written in a standardized manner in a specific field to solve specific problems, there are more uncommon terms. When the Heart of Machines compiles uncommon terms, the only criterion is accuracy, and we usually retain English. Because uncommon terms are usually professional terms in fields such as mathematics, neuroscience, and physics, Machine Heart will learn from translations and meanings in other fields as much as possible to determine how to compile. For example, fixed-point theorem, in the case of reference mathematics, we will prefer to translate it as the fixed point theorem, and fixed-point is translated as fixed point rather than fixed point.
There are many other terms that are actually ambiguity, and for this type of terms, the compilation standards of Machine Heart will be determined based on semantics, so there will be some errors. For example, bias can be translated as bias terms when describing neural network hierarchy units, while bias can be translated as bias terms when describing the relationship or learning curve between training error and cross-validation error. There are many other examples, such as Stationary can be translated as Stationary Distribution in Markov models, Stationary Point in optimization problems, and involve game theory or adversarial training. When it is static, it may also be expressed as static.
The above is the standard for machine heart to compile terms. Although our error rate is relatively low in compilation of commonly used terms, there are still some errors in uncommon terms and ambiguity terms. Especially when compiling uncommon terms, it is easy to cause errors in compilation without specific background knowledge. Therefore, we hope to work with readers to enhance the compilation quality of terms.
In addition to the term base accumulated by the Heart of Machines, this project also contains terms from special fields, and the quality of special fields will be higher. The terms compilation standards for special fields are as follows:
The participation of specialist specialists ensures that the terms we collect are professional. For example, in the Machine Learning article, we not only completed the terminology of this field under the guidance of authoritative experts in this field, such as Professor Zhou Zhihua, Dr. Li Hang, Professor Qiu Xipeng, Dr. Li Mu, Dr. Aston Zhang, and other authors of classic textbooks, and not only completed the terminology of this field, under the guidance of authoritative experts in this field, as well as authors of classic textbooks. The coverage, accuracy, professionalism and credibility of the terms Chinese translation usage included, and the work of building a set of credible fields for the community to unify the reference materials for the Chinese translation usage in fields.
The Heart of Machine will verify and summarize the glossary collected from authoritative textbooks and papers and other credible sources, and will select some controversial and untranslated terms for field experts to discuss to confirm translation. Unified translation. During the discussion with experts, different experts will also have differences in the translation of the same word. In this case, experts will start from their own experience and understanding and help other experts understand a background knowledge of their translations like this. After continuous discussion among experts, the translation of a word will eventually be close to or reach a consensus.
In the process, we also recorded some interesting results. For example, "Robust" is often translated as "robust", and experts believe that this translation lacks the beauty of Chinese. In addition, in cybernetics, "Robust" is translated into "strong". After expert discussion, we use "robust" as the recommended translation of "Robust"; "Dropout" has never been a good translation, several people According to the expression "temporarily removing" in the original Dropout text, experts agreed that the "temporarily withdrawal method" is a more appropriate translation; "Zero/Few/One-shot Learning is generally translated as "zero/few/single sample learning", but experts believe that "sample" is not rigorous because it is not really "zero/few/single" samples, but is used after establishing a mapping. A small number of samples are transferred. If translated into samples, it is easy to be confused with the real "small sample learning" in learning theory. In particular, "shot" itself does not have the meaning of "sample", but the meaning of "snapshot" is closer. When determining the Chinese translation, it is thought that "have a shot" means "try it", so experts tend to translate it as "zero/less/single-single-try-study".
Commonly used terms such as Accuracy and Recall will be used in different fields, so the same term will appear in different special fields.
If you find any errors in the process of using the glossary, or want to expand the content of the term base, discuss the translation of specific terms, etc., you are very welcome to discuss with us and readers. At the same time, readers are also very welcome to conduct Fork and Pull Request to jointly strengthen the compilation quality of terms and expand the scale of the term database.
Readers’ feedback and update suggestions will be carried out throughout the entire phase, and we will also show readers who have played a positive role in the project on the project acknowledge page. We want the term to be updated with more accuracy and confidence, so we want readers to attach the source and extended address of the term. In this way, we can update the term more objectively and attach trusted sources and extensions.
Thank you to the following personnel for participating in the work including but not limited to term provision, proofreading, translation discussion, etc., which has expanded the coverage of terminology in the special field and improved the accuracy, professionalism and credibility of the Chinese translation usage of terminology. Thank you to the teacher Our hard work.
Ranking will be sorted by A~Z:
Special thanks for Machine Learning
Special thanks for AI for Science
Basic work is completed by many project team members of the Heart of Machine
In addition to the currently included machine learning and AI for Science, this project will further produce more special field chapters. Field experts who are interested in our project and are willing to support this project together can contact us at [email protected].
We invite more friends who are interested in participating in the "AITD" project to join the "Machine Heart Analyst Network" to join the "AITD" project.
Scan the QR code to follow the "Sota Model of Machine Heart" service account and participate in the "AITD" project.
This work is licensed under the Creative Commons Attribution-Non-Commercial-Share 4.0 International License.
