AI-Generated Synthetic Data Playbook For Developing AI

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In today’s Deep Dive…

• No data? No problem! Unlock the power of AI-generated synthetic data

• Top use cases of synthetic data you can't ignore

• Generative AI for enterprises: How to develop GenAI programs using synthetic data

• Reverse engineering GPT-4V's ability to convert screenshots to code

Integrating synthetic data into various team workflows can revolutionize business operations by enhancing efficiency, security, and innovation. Let's explore how it can boost your business.

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DEEP DIVE OF THE WEEK

Shatter AI Barriers, Not Privacy – Synthetic Data is the Key

This dilemma is at the heart of data privacy concerns in AI development.

For many enterprises, the data required to train AI models may include confidential customer information, trade secrets, or other proprietary knowledge. 

Using such data directly in training not only risks breaches of privacy and confidentiality but also potential legal and reputational damage. 

One effective solution to this is synthetic data, which is artificially generated data that mimics the statistical properties of real-world data. 

Synthetic data can be a powerful tool, especially when real data is scarce, too sensitive to use, or if collecting real data is impractical or expensive.

💰 Best Use Cases + Impact On Your Business

Synthetic data can enhance several core functions in the workplace, making operations more efficient, secure, and innovative.

Here are some key areas where synthetic data can be particularly impactful:

1. Data Privacy and Security

• ✅ Synthetic data can replace sensitive real-world data, significantly reducing the risk of privacy breaches.

• ✅ It can help you with strict data protection laws like GDPR, as it can be used without the same legal constraints that apply to real data.

2. Machine Learning and AI Training

• ✅ AI models can be trained on diverse and extensive synthetic datasets, leading to better performance and reduced bias.

• ✅ More accurate models lead to smarter business decisions, be it in sales, customer service, or streamlining operations.

• ✅ Adjusting synthetic data quickly for different scenarios means faster training times for your AI models.

3. Software Testing and Quality Assurance

• ✅ With synthetic data ready at the click of a button, testing new software becomes a breeze, cutting down the time you'd normally spend gathering and prepping data.

• ✅ More in-depth testing means fewer bugs and glitches later on, which ramps up productivity.

• ✅ Fewer data breaches and privacy headaches can mean big savings in legal costs.

4. Risk Management and Compliance

• ✅ Synthetic data can simulate various risk scenarios, including rare events, for comprehensive risk analysis.

• ✅ Less chance of data breaches means lower risks and costs.

• ✅ Getting data for use inside and outside the company gets quicker, sidestepping privacy concerns.

🏆 Golden Nuggets

So, why use synthetic data?

• Synthetic data is more accessible than real data. Synthetic versions of your real data can be shared without privacy concerns. It does not contain any personal data and is privacy-safe.

• Synthetic data is more flexible than real data. It allows you to easily manipulate the data. 

• Real data always comes with significant limitations, while synthetic data generation provides higher privacy and high utility.

• Synthetic data helps solve privacy problems and enable wider and better data sharing. Makes data more secure and accessible.

How to Build GenAI Programs for Enterprises Using Synthetic Data

⚒️ Actionable Steps

Enterprises need a generative AI program to effectively harness the power of GenAI for creating data, insights, and solutions that are beyond the scope of traditional analytics. 

John Myers, CTO and co-founder of synthetic data company Gretel, explained how synthetic data enables enterprises to build a generative AI program without compromising data privacy or relying on hard-to-source real-world data.

Step 1: Identify the Types of Data

Think about the various kinds of data your business might be dealing with. It’s crucial to do this first when creating a multi-modal generative AI program without risking real data. Let's look at a few common types:

• Tubular: This is data in tables, like what you see in Excel. Commonly used for generating synthetic customer and patient data for safe analysis.

• Relational databases: These are more complex. They involve multiple tables linked together. For example, one table might have customer information, and another might have their order details. This setup is fantastic for complex data handling, where you need to test out a whole database without messing with the real one.

• Natural language:  This refers to anything written in human language – from tweets to customer reviews to emails. Super useful for training chatbots or figuring out what people think in surveys.

• Time-series data: Data that tracks changes over time, like stock prices or weather. Vital in forecasting and anomaly detection in various domains like stock market analysis, weather forecasting, and IoT sensor data analysis.

• Images: This one’s self-explanatory (photos, drawings, or any digital image). With synthetic data, GenAI can create and use artificial visual content that can safely replace real images for sensitive or resource-intensive tasks.

Step 2: Match Modalities with Needs

Now, it's about linking these data types to what your business actually needs. Here's how you might think about it:

• For Customer Insights: If you're looking to understand your customers better, tabular data from sales records or customer feedback in natural language can be key.

• For Operational Efficiency: If your focus is on streamlining operations, relational database data can help you see how different parts of your business interact.

• For Market Trends Analysis: Time series data is your go-to for understanding trends and patterns, like how your product sales have changed over time.

• For Product Development: If you're developing a product, especially in tech or manufacturing, images can be crucial for training AI models to recognize certain patterns or defects.

The idea is to first figure out what kinds of data you have or can access and then connect that to what your business is trying to achieve or improve. Each type of data has its own strengths and can help in different aspects of your business.

Step 3: Data Cleaning 

Clean and preprocess your existing data. This step is crucial as the quality of synthetic data depends on the quality of real data used for training.

Read next: How These AI Data Automation Tech & Tactics Are Saving Companies Millions 

Step 4: Pick a Deployment Model

This step is about deciding how you're going to set up and run your generative AI and synthetic data systems. 

Choose between open-source, cloud-hosted, or hybrid models based on factors like data privacy, resource availability, and integration needs.

Think of it like choosing where to build a house - each option has its pros and cons:

• Open-Source Models: Customizable and often free, but you need the skills to put it all together and maintain it.

• Fully-Hosted SaaS Models: A company hosts your AI system on their servers. It's easy to start, and you don't need much technical know-how, but you'll pay a subscription fee, and you're trusting someone else with your data.

• Hybrid Models: This is a mix of both. You might use some open-source tools but also pay for certain cloud services. It's like owning a home but hiring some services for maintenance. This gives you control and flexibility, but it requires careful planning to get the right balance.

Each model impacts your data's security. You also have to consider how much effort and money you’re willing to invest. Keep these two things in mind:

1. Data Privacy: If your data is sensitive (like customer information), you need to think about how safe it is with each model. 

• With open-source, you have more control over security, but it's all on you.

• Cloud-hosted services handle security, but you're entrusting your data to someone else. 

• Hybrid models offer a balance, but they require you to be vigilant about where and how your data is stored and processed.

2. Resource Allocation: This includes both the money and the time you'll spend.

• Open-source might be free or cheap, but can cost you more in time and effort.

• Cloud-hosted services are simpler but can get expensive, especially as you scale up.

• Hybrid models can be cost-effective, but they require careful planning to avoid overspending.

Step 5: Train, Then Validate 

Use your cleaned real data to train the synthetic data model. This usually involves setting parameters in your chosen tool and running the data generation process. With some platforms like MOSTLY AI, the process could be as easy as:

• Grabbing your real dataset.

• Upload your dataset. 

• Download your new synthetic dataset.

Compare the synthetic data against your real data to ensure it preserves key statistical properties without replicating sensitive information.

Then test the synthetic data in your specific use case. For example, if it's for machine learning, check the model's performance with the synthetic data.

Step 6: Integrate Your Workflow

Once validated, start integrating synthetic data into your machine-learning operation processes.

You can also create specific workflows for each use case, detailing how synthetic data will be generated, integrated, and used.

Implement Synthetic Data Into Your ML Ops Process

In this part, we’ll walk you through two workflows leveraging synthetic data to enhance the quality and effectiveness of ML models. 

One is for correcting imbalances in existing datasets, and the other is for kickstarting ML initiatives in environments that lack sufficient training data.

Workflow #1: Correcting Imbalances in Existing ML Training Sets

This workflow is designed for situations where machine learning (ML) training datasets already exist but suffer from imbalances. 

For example, in a dataset of animal pictures, you might have lots of cat and dog photos but very few images of lions. This imbalance can lead your AI to be great at recognizing cats and dogs but pretty bad at spotting lions.

Here’s how your ML Ops team can use synthetic data to balance out your company’s datasets:

1. Have your team review your existing ML datasets for imbalances or underrepresented categories. For instance, if you have a dataset for facial recognition, check if all age groups or ethnicities are adequately represented.

2. Pinpoint specific gaps or areas where data is lacking. For example, if your data on customer interactions is heavily skewed towards positive feedback, you're missing insights from negative or neutral feedback.

3. Generate synthetic data using a synthetic data generation tool (like MOSTLY AI, Hazy, or Gretel) to add variety to your training sets. 

4. Add this synthetic data to your training sets to enable it to learn and replicate the patterns and trends within your data.

5. Use the trained model to generate new data that specifically addresses the identified gaps. For instance, if you need more data from a particular location, instruct the model to create data from that area.

6. Add this newly generated synthetic data into your existing training set to create a more balanced dataset.

In more advanced situations, synthetic data has the ability to allow folks to augment their data sets with new records. This is called “conditioning,” which allows you to either increase the number of training samples or create training samples of a certain class within the data set.

Workflow #2: Building ML Training Sets in Data-Limited Environments

This approach is ideal for companies looking to start using machine learning but don't have training sets yet. 

Synthetic data plays a key role in supplementing these initial datasets, especially where real data may be limited or sensitive. 

With the help of synthetic data platforms, here’s what you can do to solve that headache and build new ML training sets:

1. Begin by looking at data you're already collecting in your business (like sales data, customer info, etc.).

2. Since some of your real business data might be private or sensitive, create a smaller, anonymous version of this data. This way, you can use it without risking any sensitive information.

3. Then, use these subsets to start building your first set of data for training your AI. Determine the kind of queries needed to extract useful data. 

4. In cases where the available real data is either too scarce or too sensitive to use directly, synthetic data becomes invaluable. It helps fill in the gaps and adds depth to these preliminary ML datasets.

5. Once you have a base dataset (possibly a single table of combined data), use synthetic data generation to enhance this dataset. This helps in creating a more comprehensive training set for your ML models.

6. Use the newly synthesized dataset in your ML operations platform to start training your models.

In a nutshell, if you're new to ML and don't have much data, you start by using a bit of what you have, add some made-up data to make it better, and then use this mix to teach your AI.

MOST IMPACTFUL OF THIS WEEK

How to reverse engineer GPT-4V's sketch-to-code function with synthetic data

Check out this tweet by NVDIA research scientist Jim Fan.

In this post, he dissected GPT-4V's uncanny ability to transform visual inputs like screenshots and sketches into functional code.

The secret to this impressive functionality lies partly in the strategic use of synthetic data.

This could be a game-changer for developers and innovators looking to harness similar technologies in their projects.

🏆 Golden Nuggets

• Synthetic data allows for massive scaling in AI training, making complex tasks like converting images to code more manageable and efficient.

• GPT-4's self-debugging and iterative refinement process shows how AI can evolve and improve its outputs, ensuring higher accuracy in code generation.

• This innovative technique, where the end product dictates the training data, enhances the model's learning, making it adaptable to various inputs and scenarios.

• The augmentation of diverse data (like hand-drawn sketches) highlights the importance of data diversity in training robust AI models.

⚒️ Actionable Steps

• Scrape Websites for Data: Collect website data and their corresponding code, using tools like Selenium for screenshots. This creates an initial dataset of images paired with code.

• Model Training and Debugging: Next, train the AI model  to generate code from screenshots, then execute and debug the code in a browser, refining it through multiple iterations.

• Apply Hindsight Relabeling: Use the “hindsight relabeling” approach to adjust the training dataset based on the actual output, thus improving the model's accuracy.

• Implement Data Augmentation: The final step involves aggressively modifying the data, and changing visual elements to enhance the model's ability to generalize from diverse and complex inputs.

That’s all for today’s deep dive!

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