Synthetic data can help test exceptions in software design or software response when scaling.
It’s impossible to understand what’s going on in the enterprise technology space without first understanding data and how data is driving innovation. Synthetic data can be employed in a privacy-safe environment, meaning that users and developers can access it without any concerns over disclosing sensitive information. The main artificial intelligence methods used to create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders or generative adversarial networks (GANs). A 2018 Deloitte survey, for instance, found that “data issues” such as privacy, accessing, and integration was considered the biggest challenges in implementing AI and data analytics initiatives. By generating non-identifiable datasets, however, synthetic data generation can be a vital privacy-enhancing technology that does not carry the regulatory or legal burdens associated with disclosing personal data.
What is synthetic data?
Synthetic data is data that you can create at any scale, whenever and wherever you need it. Crucially, synthetic data mirrors the balance and composition of real data, making it ideal for fueling machine learning models.
What makes synthetic data special is that data scientists, developers, and engineers are in complete control. There’s no need to put your faith in unreliable, incomplete data, or struggle to find enough data for machine learning at the scale you need. Just create it for yourself.
What is Deepfake?
Deepfake technology is used in synthetic media to create falsified content, replace or synthesizing faces, and speech, and manipulate emotions. It is used to digitally imitate an action by a person that he or she did not commit.
Advantages of deepfakes:
Bringing Back the Loved Ones!
Deepfakes have a lot of potential users in the movie industry. You can bring back a decedent actor or actress. It can be debated from an ethical perspective, but it is possible and super easy if we do not think about ethics! And also, probably way cheaper than other options.
Chance of Getting Education from its Masters
Just imagine a world where you can get physics classes from Albert Einstein anytime, anywhere! Deepfake makes impossible things possible. Learning topics from its masters is a way motivational tool. You can increase the efficiency, but it still has a very long way to go.
Can Synthetic Data bring the best in Artificial Intelligence (AI) and Data Analytics?
In this technology-driven world, the need for training data is constantly increasing. Synthetic data can help meet these demands. For an AI and data analytics system, there is no ‘real’ or ‘synthetic’; there’s only data that we feed it to understand. Synthetic data creation platforms for AI training can generate the thousands of high-quality images needed in a couple of days instead of months. And because the data is computer-generated through this method, there are no privacy concerns.
At the same time, biases that exist in real-world visual data can be easily tackled and eliminated. Furthermore, these computer-generated datasets come automatically labeled and can deliberately include rare but crucial corner cases, even better than real-world data. According to Gartner, 60 percent of the data used for AI and data analytics projects will be synthetic by 2024. By 2030, synthetic data and deepfakes will have completely overtaken real data in AI models.
Use Cases for Synthetic Data
There are a number of business use cases where one or more of these techniques apply, including:
- Software testing: Synthetic data can help test exceptions in software design or software response when scaling.
- User-behavior: Private, non-shareable user data can be simulated and used to create vector-based recommendation systems and see how they respond to scaling.
- Marketing: By using multi-agent systems, it is possible to simulate individual user behavior and have a better estimate of how marketing campaigns will perform in their customer reach.
- Art: By using GAN neural networks, AI is capable of generating art that is highly appreciated by the collector community.
- Simulate production data: Synthetic data can be used in a production environment for testing purposes, from the resilience of data pipelines to strict policy compliance. The data can be modeled depending on the needs of each individual.
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