Thinknow Synthetic

ThinkNow Synthetic is an innovative synthetic sample solution that addresses the biases commonly found in AI-generated data.

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Key Features

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Hybrid Data Collection

Panel Data: Utilize our extensive panel database from DigaYGane.com, which includes millions of data points from Hispanic, Black, AANHPI, and LGBTQIA+ consumers collected over the past decade.
Synthetic Data: Generate synthetic survey responses using LLMs trained on the General Social Survey (GSS) and our multicultural datasets, ensuring diverse and representative data.

High Accuracy and Speed

Missing Data Imputation: Fill in gaps in survey data where certain questions were not asked during specific periods.
Retrodiction: Predict responses for past periods to understand historical opinion trends.
Zero-Shot Prediction: Predict responses to new questions that have not been previously asked, expanding the scope of opinion prediction.

Bias Mitigation

By training LLMs on both the GSS and our multicultural data, ThinkNow Synthetic minimizes the risk of bias, ensuring that synthetic data is representative of diverse communities.

Benefits:

  • Cost-Effectiveness: Reduce costs associated with conducting large-scale surveys by supplementing panel data with synthetic data.
  • Data Completeness: Create comprehensive datasets by filling in missing responses and expanding the range of questions.
  • Cultural Relevance: Enhance the cultural relevance and accuracy of your insights with data that authentically represents diverse communities.
  • Speed: Our synthetic responses are generated on-demand as opposed to traditional responses which require time to field.

Technical Overview:

Scholarly Foundations:

  1. AI-Augmented Surveys: Utilizes advanced AI models to enhance survey methodologies by predicting opinions, filling data gaps, and predicting historical trends with high accuracy.
  2. Simulation of Human Samples: Generates synthetic survey responses that mimic real-world distributions, enabling efficient study of opinion trends and hypothesis testing.

Custom AI Algorithm:

The ThinkNow Synthetic solution employs a custom AI algorithm that integrates insights from both scholarly works. Our algorithm follows these key steps:

  1. Data Integration:
    • Training Data: Combines the General Social Survey (GSS) data with our proprietary multicultural datasets from DigaYGane.com, ensuring a diverse and representative training set.
    • Neural Embeddings: Utilizes neural embeddings of survey questions, individual beliefs, and temporal contexts to personalize and enhance the prediction capabilities of the LLMs.
  2. Model Training:
    • Fine-Tuning: The LLMs are fine-tuned on this integrated dataset, leveraging techniques such as transfer learning to improve accuracy and reduce biases.
    • Bias Mitigation: Special focus is given to minimizing biases by ensuring the training data is diverse and representative of various demographic groups.
    • Specialization: Clients can submit past survey results to train the model for specific purposes and types of research.
  3. Synthetic Data Generation:
    • Missing Data Imputation: The model fills in missing survey responses to create complete datasets.
    • Retrodiction: Generates historical data points to understand opinion trends over time.
    • Zero-Shot Prediction: Predicts responses to new, previously unasked questions, expanding the range of insights.

Applications:

  • Market Research: Obtain accurate and comprehensive data to understand market trends and consumer preferences across diverse communities.
  • Social Science Research: Study societal trends and behaviors with historical and current data that is representative of diverse populations.
  • Policy Making: Gauge public opinion on emerging issues to make informed decisions with data that reflects the views of all communities.

How it Works

Client Engagement

A client approaches ThinkNow with a quantitative study requiring 1,000 completes

Data Integration

ThinkNow Synthetic combines 500 completes from our proprietary panel with 500 completes generated by our synthetic sample solution

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Delivery

The final dataset is faster to acquire, more accurate, and cost-effective, providing clients with reliable insights to inform their campaigns and strategies

FAQs

ThinkNow Synthetic employs advanced AI algorithms, including Large Language Models (LLMs) fine-tuned on diverse datasets such as the General Social Survey (GSS) and ThinkNow's proprietary multicultural data. These algorithms are optimized for tasks like missing data imputation, retrodiction, and zero-shot prediction, ensuring high accuracy and reliability in survey responses.

ThinkNow Synthetic ensures diversity representation through a dual approach:

  • Multicultural Data Integration: We integrate extensive datasets from DigaYGane.com, capturing insights from Hispanic, Black, AANHPI, and LGBTQIA+ communities over a decade.
  • Algorithmic Bias Mitigation: Our AI models are trained on diverse datasets like the GSS, minimizing biases and ensuring that synthetic responses accurately reflect the demographics and cultural nuances of the U.S. population.

The delivery timeline for ThinkNow Synthetic varies based on project specifics. Generally, we can provide initial results within 7 days of project initiation, with complete datasets typically delivered within 2 weeks. For precise timelines, please contact our team to discuss your specific research needs.

ThinkNow Synthetic offers several advantages over traditional methods:

  • Cost-Effectiveness: Integrates synthetic data with panel data, reducing costs associated with large-scale surveys.
  • Speed: Generates on-demand synthetic responses, accelerating research timelines compared to traditional fielding.
  • Data Completeness: Fills missing data gaps and expands the scope of questions, creating more comprehensive datasets for analysis.

Yes, ThinkNow Synthetic can be tailored to meet specific research objectives. Clients can customize the AI models by submitting past survey results, refining the synthetic data generation process for targeted insights and applications.