AI Data Set creation, labeling and verification, and its importance for Machine Learning & Artificial Intelligence (AI)

post published October 13, 2020 post modified October 7, 2022

AI Data Set creation

Data scientists continue to work tirelessly to try and replicate human intelligence through the algorithms they create.
Neural networks are systems with autonomous or intelligent behavior. They are able to perform tasks and solve problems independently (so-called artificial intelligence / AI). Before that, the neural algorithms have to be trained using sample data. AI systems learn from these data and can generalize them and apply what has been learned to new tasks.
The more accurate and extensive the amount of AI training data is, the better the first results of AI systems are.

AI Data Set creation for your artificial intelligence systems

What Matters in AI Data Set Creation?

One of the most important tasks in machine learning is the creation of datasets for machine learning. Without data, machines cannot learn. This means that you need enough data to achieve the desired results. However, quantity is only one part of the puzzle. The data set also needs to be diverse enough to provide a variety of input that the machines can use to learn. In addition, quality is the most crucial factor during the AI data set creation. The input needs to be carefully curated to avoid hidden biases so the AI can learn from it.

Simply gathering information is not sufficient when creating an AI data set. The data also has to be classified and labeled to provide the expected output. Without this, the machine cannot learn from it.

Different Kinds of AI Data Set Creation

Depending on what your project is, the AI dataset creation will require different kinds of data. Are you training your machine in facial recognition? Then photo datasets are needed for the training and allow the machine to recognize different facial expressions, people engaged in various activities, or from multiple angles. Are you seeking to train an AI in speech recognition? In that case, you require voice recordings and audio datasets as a starting point. Other possibilities include video dataset recordings for the recognition and evaluation of moving images as well as texts for AI-based text recognition systems.

We at clickworker want you to be able to efficiently advance your research and development work in the field of artificial intelligence (AI), and would be glad to support you in obtaining the AI training data sets you need for this purpose. With our international workforce of more than 4.5 million Clickworkers, we can research, collect, and create thousands of AI training data sets for you in a timely manner, just as you need them. The AI data set creation includes, for example, voice recordings, photos, texts or videos.

Just get in contact with us an learn more about our service AI Dataset Creation!

Editing of training data for your artificial intelligence (AI) systems

We can assist you even if you already have training data, but these are still in a raw state and need to be edited to be used as training data for your AI systems.
Our Clickworkers sort data into categories or tag it quickly and in large quantities. It is also possible to have images electronically marked by our Clickworkers – Image annotation services. They can set keypoints for you or mark individual elements of the images with the help of >polygons or bounding boxes.

Training and testing of your artificial intelligence / AI systems

Our artificial intelligence training data services offer support from top to bottom. Our Clickworkers perform tests on your AI systems, filter through pre-programmed processes, and evaluate the results using human logic.

Comprehensive quality control of training data for your artificial intelligence systems

We put a lot of effort into providing you with a high-quality experience. All of our Clickworkers are thoroughly vetted, and any training data created is tested for quality.
Depending on the project, data sets are proofread or validated using the two-man rule, which requires peer review or majority decision before project completion.



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