Synthetic ‘AI’ vs Generative ‘AI’: Which one to use to strengthen data engineering in machine learning

Sufficient data is foundational for building reliable, accurate, and effective machine learning models. When training an ML model, data is the raw material used to learn patterns, make predictions, and perform tasks. The patterns in data, their characteristics, quality, etc., directly influence the performance and capabilities of AI models.

Two prominent concepts have emerged and are already making waves, reshaping various industries and creative processes: Synthetic AI and Generative AI. In this blog, we will delve into the nuances of Synthetic AI and Generative AI, highlighting their distinctions and potential applications.

Synthetic AI

Synthetic AI is used to generate synthetic data that imitate real-world data, created using statistical or ML techniques and aims to learn the statistical properties and structure of real-world data. It involves the replication or synthesis of existing data, content, or media through the use of artificial intelligence algorithms.

When real-world data is scarce, expensive, or difficult to obtain, it can easily be substituted with synthetic data. It can also augment existing data or generate data for training and testing AI/ ML models without compromising the privacy or security of the original data. By mimicking real-world scenarios, researchers or data analysts can avoid violating data protection regulations and minimize the risk of data leaks or privacy breaches. Here are some key advantages of Synthetic AI:

• Improves model accuracy and efficiency: Real-world data is usually scarce, complex and not easily accessible. Synthetic data can serve as a preliminary dataset for model development and testing and increases the diversity of the dataset, helping improve model generalization.
• Privacy Protection: Synthetic data allows organizations to share or distribute data without revealing sensitive information. It can be used to maintain privacy compliance while still allowing researchers and analysts to work with realistic data.
• Model Development and Testing: In machine learning, synthetic data can serve as a preliminary dataset for model development and testing. This is especially useful when real data is scarce or unavailable.
• Mitigating bias: The Bias issue in AI models arises from underlying bias in training data. Organizations can use synthetic data to reduce bias by creating more diverse and inclusive training data.
• Handling Imbalance: In classification tasks with imbalanced classes, synthetic data can be generated to balance class distributions, enhancing the model's ability to learn from minority classes.
• Scalability: When dealing with applications requiring large data, generating synthetic data can be more scalable and cost-effective than collecting and storing real data.

Synthetic data facilitates research, model training, security testing, and more while overcoming limitations associated with real data availability and privacy concerns.

Generative AI

Generative AI, on the other hand, involves the creation of entirely new content that is not directly based on existing data. It refers to a class of artificial intelligence models and techniques that aim to create new content or generate new data samples that resemble the patterns or distribution of the input data. The system can generate text, images, or other media in response to prompts. Generative models learn the underlying structure and characteristics of the data and use this knowledge to generate new examples that capture the essence of the input.

OpenAI's conversational chatbot ChatGPT and the AI image generator DALL-E are creating a lot of buzz. Google has two large language models, Palm, a multimodal model, and Bard, a pure language model. AlphaCode by DeepMind, GitHub Copilot developed by OpenAI and GitHub are some some notable examples of LLMs available today. The tools like ChatGPT are being used to create new content within seconds - codes, essays, emails, Excel formulas, social media captions, poems, and more!

Here are some common applications of generative AI:

• Text generation: Generative AI can be used in content creation, such as producing blog posts, news articles, and social media content. AI-generated text, such as chatbots and virtual assistants, benefits customer support by providing automated assistance that improves response times and satisfaction.
• Art and Design: Generative AI can create unique pieces of visual art, designs, and even architecture.
• Video Content: Generative AI can create video content, including animations and special effects.
• Music Composition: Creating music that resonates with human emotions requires creativity.
• Text-to-speech and Speech-to-speech generation: In audio-related AI applications, generative AI can produce realistic speech audio from user-written text and generate new voices using existing audio files.

Why do you need synthetic data?

• Data Preprocessing: Generative AI models often require extensive and high-quality training data. Synthetic AI can help with data preprocessing by generating data points that match the distribution of real data, creating a more balanced and representative training dataset.
• Content Augmentation: Synthetic AI can be used to augment generative AI processes. For example, If you're training a generative model to create realistic human conversations, synthetic AI can help by generating additional training data by replicating or modifying existing conversations. This enhances the diversity and richness of the data available for training the generative model.
• Content Variation and Diversity: Generative AI can sometimes produce similar outputs or converge to specific patterns. By incorporating synthetic data that introduces variations and diversity, you can enhance the uniqueness of the generated content.
• Customization and Personalization: Synthetic AI can assist generative AI models in producing personalized content. Generative models can create content that resonates more with specific users by generating synthetic examples that reflect individual preferences or traits.
• Enhanced Creativity: Combining synthetic AI with generative AI can boost creative workflows. Synthetic AI can provide initial drafts, outlines, or concepts, which generative AI can then expand upon and refine into fully developed creative pieces.

Applications of synthetic data

When it comes to generating synthetic data, researchers use these techniques interchangeably based on the use case, data type, training data availability etc. Synthetic data has a wide range of applications across domains:

LLMs tuning:

Using synthetic data improves the learning efficiency of LLMs for code as they provide clear, self-contained, instructive, and balanced examples of coding concepts and skills. For niche areas, it allows customization of datasets tailored precisely to the specific task, domain, or use case to achieve impressive results. Synthetic data introduces diversity by incorporating a wide range of scenarios and edge cases, thereby enhancing the robustness and adaptability of LLMs. When fine-tuning LLMs, synthetic data can speed up the prototyping process, allowing researchers and developers to iterate and experiment with different scenarios quickly. 

Autonomous cars:

Synthetic data can provide a more comprehensive way to test the effectiveness of safety features, edge cases, and anomaly detection without exposing real-world risks. Along with its flexibility in simulating crash scenarios, synthetic data facilitates rapid prototyping, precise data labelling, fault diagnosis, and scalability for targeted challenges. This ensures autonomous vehicles are well-prepared for the complex and dynamic nature of real-world driving, enhancing their safety, reliability, and adaptability.

Protein structure design:

Synthetic data holds immense potential in protein structure design by offering diverse, customizable, and rapidly accessible protein structures for research and development. It aids in generating novel protein variants, especially those challenging to obtain experimentally, and accelerates the iterative design process.

Fraud detection:

Synthetic data provides a wealth of diverse fraudulent scenarios, improving the capability of machine learning models to recognize various forms of fraud, including rare and complex patterns. By balancing the dataset, the model can detect fraud cases more efficiently. Additionally, synthetic data enables rigorous testing of models against extreme and evolving fraud tactics, promotes early detection, and offers cost-effective alternatives to collecting extensive real-world data. 

Data privacy protection:

Simply anonymizing data is no longer sufficient to ensure data privacy. Synthetic data safeguards sensitive customer information, addressing privacy and compliance concerns. It enables organizations to share, analyze, and test datasets without exposing sensitive or personally identifiable information (PII). Since it's not subject to existing privacy regulations, it is an efficient way to address privacy and compliance concerns.

Beyond these use cases, there are various additional domains where synthetic data can be valuable, such as Healthcare and Medical Imaging, Retail and Customer Behavior Analysis, Climate Modeling, Agriculture and Precision Farming, and many more. 

In this blog we briefly discussed introduction to Generative AI and Synthetic AI, how they work in general terms, applications across industries and how Synthetic AI compliments generative AI.

Generative AI and Synthetic AI are helping us solve complex problems at speed. The quality of these models has also increased dramatically, creating an exciting immediate future for Artificial Intelligence and Machine learning.