In this article we will learn how artificial intelligence creates synthetic data for machine learning.
Introduction
Today, almost every industry uses AI in one form or the other to harness the advantages it offers. This has piqued interest in AI and many of its related sub-domains. There is an ever-increasing demand for Artificial Intelligence /Machine Learning based applications.
Machine Learning-based applications heavily depend on data to train the machines with a training data set. This data set contains everything from independent or predictor variables to the dependent or predicted outcomes for the device to learn from. These data sets are typically massive. The availability of such data is not as simple as it sounds. This data comes with breach of privacy concerns and sometimes even risks of data theft.
One way you can sidestep this problem is by using artificial data or what is called Synthetic data. AI classes are offered online, from where you can study about AI and ML in detail. In the following sections, we shall explore synthetic data and some techniques used in the industry to generate synthetic data.
Synthetic data- What is it?
Synthetic data is artificially created data and not organically collected data from genuine sources. Often created using purpose-built algorithms, this kind of data has several uses, including product testing, data model validation and Machine Learning/Deep Learning model training. One reason synthetic data is increasing in popularity is the various issues and hurdles in securing genuine data. Issues like privacy and data theft concerns crop up when sourcing data from sources.
Some benefits of using synthetic data in a machine learning setup are:
- Quick and easy data production after the synthetic data model or environment is developed.
- Accuracy in data labeling, which at times is difficult or expensive to get in real-world scenarios.
- The flexibility of data to make any necessary adjustments to the data model.
To give you a perspective, one use case for increasing adoption of synthetic data in Machine Learning and Deep Learning setup is that of Self-driving simulations.
As autonomous driving technology development company Waymo is finding out, real-life experiments are expensive, with it having to create an entire mockup of a city for its self-driving simulations. Another example is self-driving Uber cars causing deadly crashes, dealing with a crippling setback to their operations in Arizona. Some start-ups and businesses are trying to solve this problem by helping with creating synthetic data for their customers using original data. This synthetic data is privacy compliant.
Let’s look at some techniques for generating synthetic data.
Also read: Microlearning 101: What is it and how to use it?
Fitting real data to a known distribution
Businesses can use accurate data to generate synthetic data by determining the optimally fitting distributions for the available data. One such method is the Monte Carlo method to generate synthetic data.
Businesses can also use machine learning models to fit the distributions. ML models such as Decision Trees allow the modeling of non-classical, multi-modal distributions. In other words, data that does not contain common characteristics of familiar distributions. Synthetic data generated using machine learning models tend to correlate with original data highly.
In many cases, some part of the actual data exists. In such situations, a hybrid synthetic data generation model can be used. Here, one part of the data set is generated from theoretical distributions, and the other is generated from real data. For cases where only some real data exists, businesses can also use hybrid synthetic data generation. In this case, analysts generate one part of the dataset from theoretical distributions and generate other parts based on real data.
Generating according to distribution
If there is no real data to model on, but there is enough knowledge of the dataset distribution, a random sample could be generated using such standard distributions. Distributions like Normal, Exponential, Chi-Square are some of the known distributions.
Generating synthetic data using Deep Learning
There are at least two methods for generating synthetic data using Deep Learning, the Variational Auto-encoder method and Generative Adversarial Network.
Variational Autoencoder (VAE)
An unsupervised method, VAE compresses the original data set into a more compact structure before sending it to the decoder. The decoder then generates a representation of the original dataset.
Generative Adversarial Network (GAN)
The GAN model uses two networks called generator and discriminator and employs them iteratively. The generator is supplied with random sample data to generate synthetic data. The discriminator then compares the artificially generated data against the original set based on conditions set before the generation.
Those were some of the theoretical models for generating synthetic data. Let’s look at some python utilities and libraries that implement these theories to generate your synthetic data.
How to generate synthetic data using Python?
Python features three popular libraries to generate synthetic data.
- Scikit-Learn, SymPy, Pydbgen.
- Scikit-Learn can help generate data that are typically used for regression analysis, classification tasks, or clustering tasks.
- SymPy allows users to specify symbolic expressions for synthetic data creation.
- Pydbgen helps users generate random names, email addresses, international phone numbers with just a few lines of code.
Use of Synthetic data in Robotics
Synthetic data is finding its way in every application of machine learning and AI including robotics and automation.
In robotics, testing for real life robotic systems is time consuming and expensive. With synthetic data on hand, robotics applications can run thousands of simulations in quick time. With AI generated synthetic data, you get data cheap and quick. This data is virtually as good as real-world data, thus helping in deploying the solution in the fastest possible way.
Let’s take the example of Nvidia deciding to use synthetic data to train their newly developed robots to pick up objects, simulating a human hand. Nvidia trained its robotic arm using synthetic data to pick up real-world objects. They employed a Convolutional Neural Net system on their Baxter robot to detect, identify and pick up objects with the dexterity that a human hand exhibits.
With a wide array of data that covers aspects like lighting, varying depth of shadows and different positioning of objects, they could train the robot to pick up objects in a variety of environments.
Use of Synthetic data in Automation
Another field that Synthetic data is used is Automation, Testing Automation to be specific. Test data automation is generation of testing data for automated tests of newly developed software. Testing automation is putting software through automated tests along with accurate test data.
Once again, the argument about testing data stops at cost and time for acquisition of real-world data. With AI generated synthetic data, it becomes easier to run test automations and deliver quality software within timelines.
Use of Synthetic data in development of Autonomous mobility
Autonomous mobility, the much-touted future of mobility, needs large data sets of sensor data and live streaming data for the purposes of simulation and machine learning. Generating this data set from a real-world scenario would be close to impossible and can turn out to be quite costly. Synthetic data to the rescue. Synthetic data today can be available instantly through API calls or can be generated internally using AI based algorithms. This data ensures that all possible scenarios are covered and the machine is well trained to go autonomous in the real world.
Conclusion
If you are interested in learning Python code or R library to generate synthetic data sets, there are several courses on GreatLearning.com. GreatLearning also offers ai courses online for those interested to learn about this technology of the future.
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