As the use of AI increases, so do concerns about its safety, how it is trained, and where its information comes from. After all, the information that an AI model comes up with when you type a question or ask it to do something comes from somewhere.
That “somewhere” is training an AI model. This is the process of feeding the algorithm data, reviewing the results, and making appropriate changes to improve efficiency and accuracy.
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There are many different types of AI training and sources of training data for AI models. These include text, image and audio data, sensor data, simulation data, geospatial data, and more.
Another way experts train AI models is by using synthetic data. Synthetic data refers to data that is artificially generated rather than data collected from real-world sources. This is extremely useful when training AI models, especially when real-world data is limited, expensive to obtain, or contains sensitive information.
Synthetic data is becoming increasingly popular as an AI training source because it offers enhanced safety and privacy compared to real-world data in certain situations.
First, synthetic data can be generated in a way that preserves certain properties of the original data without revealing sensitive information. It also reduces the risk of data breaches, reduces liability, increases flexibility in data use, and increases the likelihood of regulatory compliance.
As synthetic data grows in popularity, MARKETING-INTERACTIVE spoke to two AI experts to explore how synthetic data can address privacy and accuracy concerns surrounding the use of AI platforms.
What is synthetic data?
Synthetic data refers to data that is artificially generated to mimic the characteristics of real-world data.
In contrast to real data obtained from observations of natural phenomena, synthetic data refers to information that is artificially generated. This is the result of employing advanced algorithms trained on real-world datasets through the power of deep learning, Siddharth Janji explained. , Senior Manager of Data Architecture and Engineering (Domain Lead) at Ekimetric
“When you use models to generate data, you can find patterns, structures, correlations, etc. in the actual data and generate entirely new data with the same patterns,” he said.
As new Gen AI text-to-image and text-to-video models emerge, such as Mid Journey and Sora, synthetic data can be created to create new images and videos with similar patterns, he said. added.
How can I train an AI model with synthetic data?
AI models can be trained on different types of data, including real data, synthetic data, and hybrid datasets, Junge explained.
The choice of data type depends on the specific application, actual data availability, privacy considerations, and the level of control and scalability required. he added:
Real data captures the nuances of the real world, and simulated hybrid datasets offer flexibility and privacy benefits.
Junge went on to say that while synthetic data in particular may not fully capture the complexity and variability of real-world data, it can help overcome privacy constraints and generate large amounts of data with desirable properties. said to provide powerful tools.
He explained that he wants to train a text-to-image model like Midjourney that generates images based on a given text description. To train the model, you can use synthetic data by generating artificial text descriptions and corresponding images.
For example, you can create a description such as “A red car on a sunny beach” and generate images that match this description. This synthetic data helps enhance the model's ability to generate images based on text.
What are the pros and cons of training AI models on synthetic data?
Milind, AI Scientist at Mercedes, expresses an independent view Milind, AI Scientist at Mercedes, says the benefits of training AI models on synthetic data include protecting privacy and generating diverse scenarios for training. , said it includes features that can reduce bias that exists in real-world data.
However, he explained that its disadvantages include the risk of not fully representing real-world complexity, leading to potential performance limitations in real-world applications.
“The challenges of using synthetic data include the challenge of accurately capturing the full complexity of real-world data and the need for rigorous validation to ensure its validity.” He said and added:
Synthetic data is not the default because it has inherent limitations in fully replicating the complexity of real data. This can affect model performance in real-world settings.
Junge added to his argument that real data is a more accurate representation of natural phenomena, and that this model may struggle to generalize well to unseen real-world situations. I explained that there is.
For example, if you're training a text-to-video model such as Sora, generating synthetic data may not fully capture the complexity and diversity of real-world video, Junge said. .
He explained that synthetic videos can lack the complexity, randomness, and nuance found in real footage, making it difficult for models to learn and generalize effectively.
“These limitations make synthetic data not the default choice. Synthetic data may not accurately represent the complexity of real data and can lead to suboptimal model performance when applied to real-world scenarios. “There is a potential for a connection,” he said, adding that deals need to be evaluated carefully. Off between synthetic and real data is essential for training effective AI models.
Copyright issues are a top concern for marketers. For marketers using her AI in campaigns, how can synthetic data be used to alleviate these issues?
Using synthetic data can reduce copyright issues for marketers by providing an alternative to using proprietary or sensitive real data, Milind said.
“By generating synthetic data that closely resembles the characteristics of the original without infringing copyright, marketers can use it to train AI models and run campaigns without legal concerns,” he said. I can do it.''
Junge said synthetic data can be used by marketers to create realistic consumer profiles, simulate customer behavior, and generate campaign-related content, making it easier for marketers to use synthetic data to create realistic consumer profiles, simulate customer behavior, and generate campaign-related content. He added that this would reduce the need to rely on
He added that synthetic data is likely to play an important role in the future due to its potential for privacy protection, scalability, and cost efficiency.
“However, its adoption and impact will depend on advances in generating more realistic and representative synthetic data, addressing challenges around bias and interpretability, and ensuring transparency and trust among users and stakeholders. “There will be,” he said.
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