Leverage your AI with synthetic Data
As data-hungry machine learning models demand increasing amounts of information, the market for synthetic data continues to grow. Why is it the real deal?
What exactly is synthetic data?
The most recent photo identification system from Meta was trained using one billion photos, which is an astounding illustration of the current appetite for data. Another option for businesses without access to social media sites like Instagram and Facebook is synthetic data.
In contrast to data that is gathered from the real world, synthetic data is manufactured artificially by a computer. The software that makes these computer-generated images has the ability to automatically annotate them. Annotation, a crucial component of AI training, is the process of labeling significant elements in an image, such as people or objects, to assist machine learning models in understanding what the image represents. They also avoid any compliance or privacy-related difficulties because they are original images without real individuals in them.
By 2024, 60% of the data required to construct AI and analytics projects will be generated synthetically, according to Gartner’s prediction, making it “the future of AI,” in the words of the consulting company
Synthetic data will be the main form of data used in AI.
Source: Gartner, "Maverick Research: Forget About Your Real Data - Synthetic Data Is the Future of AI", Leinar Ramos, Jitendra Subramanyam, 24. Juni 2021
No data privacy concerns
Such technology saves businesses from having to find and gather thousands of actual photographs, as well as from dealing with GDPR, copyright, and privacy concerns.
The lack of real-world data that complies with privacy laws. Even a basic image recognition application requires up to 100,000 training photographs, each of which must be precisely annotated by a human and adhere to privacy laws. Gathering, annotating, and cleansing real-world data is a gigantic task that might take up to 80% of a data scientist’s work.
Data-centric approach
In the past, creating an AI model required gathering data, training it, testing it, making any necessary adjustments, and then testing it again. With this approach is that them used data remains the same.
The performance boost you receive from this model-centric approach, is quite minor. You need to alter your thinking in order to truly see a noticeable improvement in the functionality of your AI algorithms. You should iterate on the data itself rather than the model’s parameters.
What we do at Synthetic Future
At Synthetic Future, we either build the computer-generated data ourselves or use 3D data from our customers to produce it. We produce 3D renderings using varying camera angles, lighting conditions, and object locations based on this 3D data. We are able to create millions of images with the greatest variety. To ensure the highest level of data quality, we test our data using several computer vision models after it has been generated. Curious? Test it in real time on our BETA-Platform !
