Multilingual Data Provisioning
Welcome to our International Translating Company’s Multilingual AI Data Provisioning Service! We offer a range of services to companies who build or maintain AI and machine learning platforms. We have several teams who are trained and certified in data collection, annotation and validation. We also provide training and certification to other companies offering data provisioning. Our teams are flexible, and provide these services in many different provisioning platforms.
Our team of native speakers, located all around the globe, are trained in the following areas:
- Data Collection for AI
- Data Annotation
- Data Validation
- Machine Learning
Going well beyond the most common languages, our data provisioning services are offered in approximately 230 languages.
Benefits
Each of these areas within our data provisioning services umbrella offer several benefits. The following explains some of the benefits our clients have seen with these services.
Data Collection Benefits
Involving humans in your data collection efforts is sometimes critical, but always beneficial to help improve Accuracy, Context and Costs. The following explains several benefits to using humans for data collection:
- Accuracy: Humans are often more accurate than AI systems at collecting data, especially when the data is complex or subtle. For example, a human might be able to recognize and transcribe spoken language more accurately than an AI system.
- Context: Humans can bring valuable context and understanding to the data collection process. For example, a human data collector might be able to identify and collect data that is relevant to a specific task or industry because they have a deeper understanding of the context in which the data is being collected.
- Cost: In some cases, using humans for data collection can be more cost-effective than using AI systems, especially when the data is complex or the task is particularly time-consuming.
- Flexibility: Humans are generally more flexible than AI systems when it comes to data collection. They can adapt to changing circumstances or requirements more easily and can be trained to collect data in a variety of different formats or from a range of different sources.
Overall, using humans for data collection can help to improve the accuracy, relevance, and effectiveness of AI systems, making them more reliable and valuable for businesses and organizations.
Data Annotation Benefits
There are several benefits to data annotation and more specifically using humans for all or a part of your data annotation:
- Accuracy: Humans are often more accurate than AI systems at labeling and categorizing data, especially when the data is complex or subtle. For example, a human might be able to recognize the tone of an article or the sentiment of a spoken language more accurately than an AI system.
- Consistency: Humans can be more consistent than AI systems when it comes to labeling and categorizing data. This can be especially important when the data is being used to train machine learning models, as inconsistent labels can lead to poor model performance.
- Context: Humans can bring valuable context and understanding to the data annotation process. For example, a human data annotator might be able to identify and label objects in an image more accurately because they have a deeper understanding of the context in which the image was taken.
- Cost: In some cases, using humans for data annotation can be more cost-effective than using AI systems, especially when the data is complex or the task is particularly time-consuming.
Overall, using humans for data annotation can help to improve the accuracy, consistency, and effectiveness of AI systems, making them more reliable and valuable for businesses and organizations.
Data Validation Benefits
Data validation is an important process that helps to ensure the quality and accuracy of data used to train and improve AI systems. Some of the benefits of data validation for AI include:
- Improved accuracy: By verifying the quality and accuracy of data, data validation can help to improve the accuracy of AI systems. This is especially important when the data is being used to train machine learning models, as poor quality data can lead to poor model performance.
- Increased reliability: Data validation can help to ensure that AI systems are built on a solid foundation of reliable data. This can help to increase the overall reliability and trustworthiness of the systems.
- Reduced errors: By catching errors and inconsistencies in the data, data validation can help to reduce the number of errors and problems that occur in AI systems. This can save time and resources that might otherwise be spent debugging and fixing issues.
- Enhanced performance: By ensuring that the data used to train and improve AI systems is of high quality, data validation can help to enhance the overall performance of the systems. This can lead to better results and a more positive user experience.
Machine Learning Benefits
There are several benefits to using humans in the development and implementation of machine learning models.
- Expertise: Humans can bring valuable expertise and knowledge to the machine learning process. For example, a human data scientist might be able to design and implement a more effective machine learning model because they have a deeper understanding of the problem being solved and the available data.
- Creativity: Humans can bring creativity and ingenuity to the machine learning process. For example, a human might be able to come up with new ideas for features or algorithms that can improve the performance of a machine learning model.
- Interpretability: Humans can help to interpret and explain the results of machine learning models in a way that is easier for others to understand. This can be especially important in cases where the models are being used to make important decisions or to inform business strategy.
- Cost: In some cases, using humans for machine learning tasks can be more cost-effective than using AI systems, especially when the task is particularly complex or time-consuming.
Overall, using humans in the machine learning process can help to improve the accuracy, effectiveness, and interpretability of machine learning models, making the machine learning more valuable and useful for businesses and organizations.
Definitions and Use Cases
If you would like to learn more about these services, below we have included some definitions and use cases that will help you understand how our teams add value to the efforts of our data provisioning clients.
Human Data Collection for AI
Definition: The process of gathering data for use in training and improving AI systems. This data can come from a variety of sources, including sensors, databases, and human input.
Use Cases: A company that is building a chatbot will often use human data collectors to gather a large dataset of conversation logs to be used to train the chatbot. These conversation logs might be collected through a variety of methods, such as customer service interactions, online forums, or social media platforms.
Another example is a company that is building an image recognition system for a medical application. In this case, human data collectors can be responsible for gathering and labeling images of medical conditions, such as X-ray scans or dermatological images. These labeled images could then be used to train an AI system to recognize and classify different medical conditions.
Human data collectors can be particularly useful in cases where it is difficult for AI systems to accurately gather and interpret data on their own, or where the data is sensitive or complex and requires a human touch.
Data Annotation
Definition: The process of labeling and categorizing data to improve the accuracy of AI models. This can involve adding tags to data points, drawing bounding boxes around objects in images, or transcribing audio into text.
Use Cases: A company that is building a system to automatically transcribe and translate spoken language might use data annotation to label and categorize audio recordings. This could involve transcribing the audio into text and adding labels to identify different speakers, accents, or languages.
Another example is an organization that is building a machine learning model to classify and categorize news articles might use data annotation to label and categorize the articles. This could involve adding tags to identify the topic, tone, or target audience of each article.
A company that is building a system to recognize and classify different types of vehicles in traffic camera footage might use data annotation to label and categorize images of vehicles. This could involve drawing bounding boxes around the vehicles and adding labels to identify the make, model, and type of each vehicle.
Data Validation
Definition: The process of verifying the quality and accuracy of data to ensure that it can be used effectively by AI systems. This can include checks for missing or incorrect values, as well as more thorough testing to ensure the data is fit for its intended use.
Use Cases: A company that is building a machine learning model to predict credit risk might use data validation to ensure that the data used to train the model is accurate and appropriate. This might involve checking the data for errors or inconsistencies, or manually reviewing a sample of the data to ensure that it is fit for use.
A company that is building an image recognition system might use data validation to ensure that the data used to train the system is of high quality. This might involve manually reviewing a sample of the images to ensure that they are clear and properly labeled.
A company that is building a natural language processing (NLP) system might use data validation to ensure that the data used to train the system is accurate and appropriate. This might involve checking the data for errors or inconsistencies, or manually reviewing a sample of the data to ensure that it is fit for use.
Machine Learning
Definition: A subfield of AI that focuses on the development of algorithms that allow systems to learn and adapt over time, without being explicitly programmed. This involves training machine learning models on large amounts of data and using those models to make predictions or take actions based on new inputs.
Use Cases: In summary, when it comes to machine learning or AI, there are many reasons to make sure you have the right team helping you. Here are a few:
- Image recognition: Humans can be used to label and categorize images that are used to train machine learning models for image recognition. This can involve drawing bounding boxes around objects in images and adding labels to identify the objects.
- Speech recognition: Humans can be used to transcribe and label audio data that is used to train machine learning models for speech recognition. This can involve transcribing spoken language into text and adding labels to identify different speakers, accents, or languages.
- Text classification: Humans can be used to label and categorize text data that is used to train machine learning models for text classification. This can involve adding tags or labels to identify the topic, tone, or sentiment of each piece of text.
- Translation: Humans can be used to label and categorize data that is used to train machine learning models for translation. This can involve transcribing and translating spoken language into text and adding labels to identify different languages.
- Medical diagnosis: Humans can be used to label and categorize medical data that is used to train machine learning models for diagnosis. This can involve adding labels to identify different medical conditions, symptoms, or treatments.
Our services are available in more than 230 different languages, making us the go-to choice for companies looking to build and maintain AI and machine learning platforms on a global scale. Contact us today to learn more about how we can help your company succeed in the exciting world of AI!
