Innovations in Synthetic Data for AI and Market Research

Last week, I posted about the critical role of foundational data capabilities in successfully implementing #GenerativeAI and its related use cases. Key challenges are related to data quality, data infrastructure, and data privacy & security. Let’s look at the last one today. When it comes to training or operating Gen AI models, there’s often a need for personal and potentially sensitive data from individuals or companies. This data can be crucial for the AI to learn and generate accurate, relevant outputs. However, individuals and organizations might be hesitant to share their data due to privacy concerns and the fear of misuse. The reluctance is understandable, as such data can reveal a lot about a person or an organization’s private details. To address these privacy challenges, there are at least three effective approaches: establishing proactive privacy policies and controls, relying on third-party data, and using synthetic data. Being proactive about #privacy is key. If sensitive data is needed, it’s essential to be transparent and clear about why it’s being collected and how it will benefit the data provider. A straightforward and easy-to-understand privacy policy, rather than a lengthy, legalese document, builds trust. And then you need to ensure that foundational capabilities and processes are in place to uphold these policies, of course. A single privacy incident can significantly damage a reputation that was built up over years. In some cases, depending on the #GenAI application, using third-party data can be a viable alternative to using clients’ data. For example, a Gen AI model developed for market analysis might use publicly available consumer behavior data instead of directly gathering data from specific customers. This approach reduces the burden of convincing customers to share their data and lessens the obligation to protect it, as less of it is in your hands. Another innovative solution is the use of synthetic data. Synthetic data is artificially generated #data that mimics real data characteristics without containing any actual personal information. It has its drawbacks, and it doesn’t work in every scenario, but it can be a powerful tool, especially in scenarios where privacy concerns are paramount. In a project I was involved in, we developed a Gen AI solution to create executive summaries highlighting key insights and trends from survey data. Instead of using actual client data, which would have been risky and biased, we used Gen AI to generate thousands of realistic survey responses, complete with the kind of grammar mistakes and inconsistencies found in real responses. This synthetic data then served as the training material for a different, independent #management information Gen AI application, effectively avoiding the pitfalls of using sensitive, real data. For more ➡️ https://lnkd.in/er-bAqrd

NVIDIA AI Introduces Nemotron-4 340B: A Family of Open Models that Developers can Use to Generate Synthetic Data for Training Large Language Models (LLMs) NVIDIA has recently unveiled the Nemotron-4 340B, a groundbreaking family of models designed to generate synthetic data for training large language models (LLMs) across various commercial applications. This release marks a significant advancement in generative AI, offering a comprehensive suite of tools optimized for NVIDIA NeMo and NVIDIA TensorRT-LLM and includes cutting-edge instruct and reward models. This initiative aims to provide developers with a cost-effective and scalable means to access high-quality training data, which is crucial for enhancing the performance and accuracy of custom LLMs. The Nemotron-4 340B includes three variants: Instruct, Reward, and Base models, each tailored to specific functions in the data generation and refinement process. ✅ The Nemotron-4 340B Instruct model is designed to create diverse synthetic data that mimics the characteristics of real-world data, enhancing the performance and robustness of custom LLMs across various domains. This model is essential for generating initial data outputs, which can be refined and improved upon. ✅ The Nemotron-4 340B Reward model is crucial in filtering and enhancing the quality of AI-generated data. It evaluates responses based on helpfulness, correctness, coherence, complexity, and verbosity. This model ensures that the synthetic data is high quality and relevant to the application’s needs. ✅ The Nemotron-4 340B Base model serves as the foundational framework for customization. Trained on 9 trillion tokens, this model can be fine-tuned using proprietary data and various datasets to adapt to specific use cases. It supports extensive customization through the NeMo framework, allowing for supervised fine-tuning and parameter-efficient methods like low-rank adaptation (LoRA). Full read: https://lnkd.in/g2JNGpW5 Technical report: https://lnkd.in/gXSBQnA6 Models: https://lnkd.in/gyBQh-wZ NVIDIA NVIDIA AI

Recently I posted about why I am paying attention to synthetic data as a CMO, and I wanted to hone in on one of the reasons why I think it holds so much value for marketers -- the ability to address bias in data. As marketers, we might not be using AI technologies to save lives. As such, one could argue that a bias in our data has minimal real-world repercussions. But that hardly means that we don't have the ability to improve lives as marketers. We're in the unique position of connecting brands and purpose with people and their purpose. I lead a team of marketers at a company that values and creates technology to help organizations make better decisions, and at the forefront of that we consider ethics and trustworthiness of our tech. This company value also happens to be my own value. To be ethical marketers (and, honestly, humans), my team and I need to do our part in reducing bias, independent of perceived real-world impact. Again, enter synthetic data. Synthetic data generation can help by creating more representative datasets. If certain groups are underrepresented in our experiential data used for a marketing campaign, it will lead to an output of biased model predictions. By leveraging synthetic data, we can create supplementary data for underrepresented groups, ensuring a fair distribution for our campaign. We can also design synthetic data sets specifically to exclude biases that are present in our available experiential data. Consider a marketing team at a bank. With synthetic data, that team can create data for demographics that have been historically underserved, offering them a financial future may not have previously dreamed possible. Owning a home or starting a small business because a bank loan helped bring a person's aspirations to fruition -- an opportunity like this one to use synthetic data can actually improve lives and make an impact in the bank's community. There isn't a downside to mitigating bias. This matters to me, to my marketers, and to furthering a mission of promoting ethical and trustworthy AI practices across the board. #SyntheticData #marketing

Ever wondered if synthetic data is just another way of weighting data? 🤔 In a rapidly evolving field like market research, it's crucial to stay ahead of the curve. My latest article dives deep into the world of synthetic data, exploring how techniques like SMOTE can revolutionize consumer insights. From overcoming the challenges of studying niche segments to ensuring robust predictive models, this comprehensive guide sheds light on both the opportunities and limitations of synthetic data. If you're skeptical about the practicality and accuracy of synthetic data, this article is for you. Learn how to responsibly adopt synthetic data, strike the right balance between innovation and precision, and unlock new dimensions in your market research. #MarketResearch #SyntheticData #DataScience #SMOTE #ConsumerInsights #Innovation

In the realm of building machine learning models, there are typically two primary data sources: organic data, stemming directly from customer activities, and synthetic data, generated artificially through a deliberate process. Each holds its unique value and serves a distinct purpose. This blog post, written by the Data Scientists at Expedia Group, shares how their team leveraged synthetic search data to enable flight price forecasting.   -- [Business need] The primary objective is to develop a price forecasting model that can offer future flight pricing predictions to customers. For instance, it aims to inform customers whether flight prices are likely to rise or fall in the next 7 days, aiding them in making informed purchasing decisions.    -- [Challenges] However, organic customer search data falls short due to its sparsity, even for the most popular routes. For instance, it's rare to see daily searches for two-way flights from SFO to LAX for every conceivable combination of departure and arrival dates in the upcoming three months. The limitations of this organic data are evident, making it challenging to construct a robust forecasting model.   -- [Solution] This is where synthetic search data comes into play. By systematically simulating search activities on the same route and under identical configurations, such as travel dates, on a regular basis, it provides a more comprehensive and reliable source of information. Leveraging synthetic data is a potent tool for systematic exploration, but it requires a well-balanced approach to ensure that the benefits outweigh the associated costs. Striking this balance is essential for unlocking the full potential of synthetic data in data science models. – – –  To better illustrate concepts in this and future tech blogs, I created one podcast "Snacks Weekly on Data Science" (https://lnkd.in/gKgaMvbh) to make them more accessible. It's now available on Spotify and Apple podcasts. Please check it out, and I appreciate your support! #machinelearning #datascience #search #synthetic #data #forecasting https://lnkd.in/gRjR5tTQ