Understand synthetic data in the development of AI?
In the field of artificial intelligence (AI), the technology of synthetic data has become a major concept known to most Data Scientists and model specialists. As fuel for AI models, quality data is important. However, they are often rare or sensitive. Synthetic data is a promising solution - it's artificial information generated by computer to mimic real world data. This advance means that developers can train AI systems more effectively and ethically without compromising individual privacy, in particular.
Let's dive and explore how synthetic data is an important driver for the development of AI and why they are an almost indispensable tool for your future AI developments.
Why is Innovatiana interested in this subject? This may seem counterintuitive to you, since Innovatiana is a specialist in the manual and human annotation of data. However, one of our goals is to accelerate the development of AI products, by focusing on quality data. It therefore seems essential to us to emphasize this concept which, combined with data generated manually, can significantly improve the efficiency and accuracy of AI models. By combining human expertise and advanced techniques such as synthetic data, Innovatiana aims to Optimize the process of training AI models while ensuring the relevance and authenticity of the data processed.
🤯 BREAKING NEWS (17.09.2024) - Argilla has just published ”DataCraft“, an interface using Distilabel to create synthetic datasets! You can test the tool at this address (https://huggingface.co/spaces/argilla/distilabel-datacraft) and if you want to review, enrich or complete your dataset with manual reviews, do not hesitate to contact Innovatiana ! If you want to know more about Argilla, do not hesitate to consult our article.
How do you define synthetic data?
Synthetic data is like a original data clones. Think of them as a copy that is not real, but looks and acts almost like a real entity. This type of artificial data is made using a computer program that understands how the original data used in the real world appears and works.
This computer program creates new data that has the same patterns and behaviors as the original object that was copied. It's a bit like how video games create worlds that look real but are actually made and generated by a computer.
The particularity of creating synthetic data is that it can be used to test and train AI without touching sensitive or private data belonging to “real” people. This allows sensitive information to be preserved. For example, in healthcare, AI can learn from synthetic data that is similar to real patient data, but without any risk of revealing personal information about an individual's health.
Synthetic data is used in Computer Vision and computer simulation! This fake data can be manufactured in large quantities, and the AI needs a very large volume of data (synthetic or real) to learn well as part of the training process. Using synthetic data allows AI to become “smarter.” And with better AI... we can get useful information more effectively, like better predicting the weather, building smarter robots, or even helping doctors determine the best treatments for their patients.
Why is synthetic data important?
Synthetic data is very important because it helps us solve big problems in AI. Remember that AI needs to learn from large data sets. Without sufficient data, AI cannot improve. Sometimes we can't use real data because it's private, like people's medical records or their personal information.
That's where synthetic data comes in. It's fictional data that AI can use to learn. With synthetic data, we don't have to worry about the safety of real data because the AI doesn't use any of it in the training process.
This means we can create huge amounts of synthetic data and allow AI to learn from it without putting anyone's privacy at risk. With synthetic data, AI can train again and again, since another AI will be able to generate training data on demand, or almost. In short, synthetic data is a powerful tool for AI.
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For what purposes should synthetic data be used?
Synthetic data is used to generate data for many things, especially in AI. They are also used as training data to produce original data on demand! Here's how:
Training AI models
We use synthetic data as training data to teach AI. It's like giving the AI a manual full of examples so it can learn how to do things by itself.
Testing AI systems
Before the AI is ready to actually work, it needs to train. Synthetic data is ideal for testing because it is not likely to use real sensitive data.
Accelerating research
Scientists and engineers can use synthetic data to create AI more quickly because they don't have to wait for real data.
Protection of privacy
This means that AI does not need to use private details like names or health information to generate synthetic data. The fake data produced preserves the privacy of individuals, since they are generated randomly.
Data availability
Sometimes, for a lot of use cases, we don't have enough real data. Synthetic data fills this gap, giving AI bigger and more accessible data.
Cost reduction
Gathering and managing real data can be expensive. Synthetic data reduces the costs of collecting and researching data, making the AI development cycle less time-consuming and less expensive!
💡 By using synthetic data, we ensure that our AIs learn from lots of good examples, without putting real people's private information at risk or spending a fortune. It's a smart way to teach AI to do useful things while using known and responsibly produced data.
How does synthetic data help in the development of AI?
Synthetic data aims to generate data to train AI models and generate data based on real scenarios (even if this data itself cannot be described as “real”). Synthetically generated data is important in building advanced AI models. They are also useful for labeling data and providing operational data to make the AI model smarter.
Let's take a look at how relevant data or synthetic data sets help in the development of AI!
Making AI smarter without risks
Synthetic data makes AI smarter, much like regular race training makes you more likely to participate in an Iron Man, or how regular review sessions make you perform better on exams. AI uses synthetic data to learn how to do things before they do them in the real world. In this way, the AI becomes proficient without making mistakes that could hurt people. It's a bit like a pilot who will learn to fly an Airbus A320 on a flight simulator, before flying a real plane.
Safe and solid learning
Since synthetic data is not real, using it means that real private information stays safe. Imagine teaching AI about health without using real patient information - that's what synthetic data allows, in some cases. No real names, no real faces, just machine learning models without any danger of revealing secrets or compromising an individual's safety.
Globally inexpensive data that is easy to obtain
Real data can be hard to find, but AI needs a lot of that data to learn well. Synthetic data can be created at any time, in any quantity, as long as you have the right tools.
Save time and money
Getting real data takes time and money. You should be careful not to break the laws, depending on the nature of the data you use or the jurisdiction where you operate. Producing synthetic data is faster and cheaper. Data is the “raw material” of AI: with synthetic data, you have access to raw material of reasonable quality at a low cost, allowing you to start building your AI model very quickly.
💡 By using synthetic data in AI, we teach models in a safe and effective way. We give AI plenty of examples to learn from, and because it's inexpensive and risk-free, we can use synthetic data to make AI proficient in a lot of jobs, at a lower cost. This is beneficial for everyone, making life easier and safer.
How do I generate synthetic data for machine learning models?
Artificially generated data or synthetic data can be generated through comprehensive planning and meaningful data refinement practices. Data Scientists use synthetic data to produce original data for better machine learning models. Here is an overview of the process used to turn unstructured data into complete synthetic data that can be used to train models!
Start with a plan
Before creating synthetic test data, decide what you want your AI to learn. Think about real data and try to copy its important parts. This means that your fake synthetic test data should have the same types of information as the real ones.
Choose your tools
Use special computer programs to create synthetic images or data using natural language processing.
Some programs are called 'generative models' and they are very good at producing synthetic data that completely surpasses real data. A popular choice is' GAN 'or Antagonistic Generative Network.
Create the data
Now, start creating data with your tool. The program will look at the actual data points used and try to create new used data points that are similar. We create mathematical models and then train them to produce original data for machine learning!
Test and improve
After creating the synthetic data, test it to see if the AI can learn from it. If the AI is not doing well, change the generation of artificially generated synthetic data a bit.
Keep testing and improving until AI can learn from artificially generated synthetic data as if it were real. To validate mathematical models, it is important to do comprehensive tests!
Use a lot of data
Remember, AI needs a lot of synthetic training data to learn well.
Be sure to create a large amount of synthetic training data, so that the AI can practice. It's like giving someone lots of books to read, and reading goals (for example: read 10 books in 1 month) so they can learn and make progress.
Control your synthetic data... for more security
Ensure that the synthetic data generated does not contain any real private information. This helps to avoid possible security problems.
👉 By following these steps, you can produce a true synthetic data vault. You can create great synthetic data that helps AI models learn safely and quickly. This saves time and money, and is an approach that protects people's privacy, and ensures that data is produced ethically.
Synthetic data vs real world data: what's the difference?
Synthetic data sets and real world data are like two flavors for the same ice cream. Both are tasty, can be combined, but they are not the same. Let's look at how they differ:
Synthetic data sets
It's like a robot creating designs of cats that have never been seen before. It is a synthetic data vault that is designed to be similar to real data. However, this data is not from the real world. This means that there are no real people or real situations, and that a face used, even if it looks like a well-known person, was entirely produced by a computer.
Real data sets:
This data is extracted directly from daily life, encompassing names and images of real people. For example, the image of a photographer who captures the essence of urban life through shots of cats in neighborhoods. Data science experts describe this process as an attempt to immerse artificial intelligence in the complexity and diversity of the real world. This approach carries risks, as it sometimes involves the use of data relating to real individuals, thus requiring particular attention to the protection of confidentiality and privacy.
Acquiring this data can be expensive, as it requires a meticulous process of verification and validation to ensure its legitimacy and ethical compliance. In addition, the quantity of data available is limited by the collection capacities and the permissions required for their use. This poses unique challenges for researchers and developers looking to integrate this data into artificial intelligence applications, while maintaining ethical and legal standards.
Why do Data Scientists and Data Managers need synthetic data generation tools?
Data Scientists and Data Managers need tools to create synthetic data, as this is essential for training AI safely and without privacy concerns. These tools help them produce large amounts of synthetic data quickly and cheaply. They don't have to worry about violating privacy policies because synthetic data doesn't come from real people. Also, real data can be limited or difficult to obtain, but with synthetic data, you can create as much as you need. This means that AI can learn and become very efficient in its tasks, for many use cases, without using real data.
Another reason why these tools are valuable is that they create synthetic data sets to help avoid bias in AI training. Real-world data can sometimes be unfair or may not include everyone equally. By creating a synthetic data set, we can create a balanced set of examples for AI to learn. It's like making sure a teacher has books on all sorts of topics for their students.
Synthetic data generation tools use techniques like GaNs (Generative Adversarial Networks) that are very effective in creating synthetic data anonymously, that is, something that looks real but is not. This is perfect for generating synthetic data and test data, allowing AI to be tested and improved, making it ready for the real world without any risk.
For example, in healthcare, synthetic data can simulate patient information to train AI without using real patient details. This keeps patient information safe while allowing AI to learn how to help doctors before being used in a real world situation. Likewise, in finance, AI can learn about fraud detection systems without the need for real transactions that could be regulated, or sensitive data.
In short, these tools give data experts the power to harness sensitive customer data to form smarter, more ethical AI systems. This is important because AI is everywhere, helping us in daily life, and it needs to be as efficient and fair as possible!
Final Thoughts
At the end of the day, synthetic data is extremely useful for the AI training process. They are safe, economical and respect everyone's privacy. Plus, they're great at making AI fair for everyone. We would love to hear about your own experiences with synthetic data! Have you used them? How did they work for your AI projects? Share your stories and continue to explore this exciting technology further. Let's keep learning and growing together!
