Here is one of the most relevant read I had this past year, about synthetic data. Check my summary here. And find the full post at the end of this post.
Main topic: Synthetic Data / AI Training Data / Data Quality Challenges
In this previous post, I came across what is synthetic data.
In a Nutshell: The article explores the growing trend of using synthetic data (AI-generated data) for training AI models, examining both its potential benefits and significant risks, particularly as traditional data sources become more restricted.
The landscape of AI training is undergoing a significant transformation as major tech companies like Anthropic, Meta, and OpenAI increasingly turn to synthetic data for model development.
This shift is driven by several pressing challenges in the traditional data ecosystem:
approximately 35% of leading websites now actively block AI scrapers, data licensing and human annotation costs have become prohibitively expensive, and experts project a critical data shortage between 2026 and 2032.
The market for synthetic data is expected to capitalize on these challenges, with projections showing growth to $2.34 billion by 2030.
However, this transition brings its own set of complex challenges, including the risk of compounding hallucinations where AI-generated errors multiply through subsequent generations, model collapse leading to decreased creativity and increased bias, and persistent issues with sampling bias and quality degradation over time.
Despite these concerns, several successful applications demonstrate the potential of synthetic data: Writer's Palmyra X 004 model achieved significant cost savings at $700,000 compared to traditional development costs of $4.6 million, while industry giants like Meta, OpenAI, and Amazon have successfully implemented synthetic data in various applications, from Movie Gen captions to GPT-4o's Canvas feature and Alexa's training.
This complex interplay of opportunities and challenges suggests that while synthetic data presents a promising solution to data scarcity, its implementation requires careful consideration and balanced approaches to ensure quality and reliability.
Why should we care?
This development represents a critical juncture in AI development as the industry grapples with data scarcity and quality issues. The success or failure of synthetic data could significantly impact the future of AI development, costs, and accessibility, while raising important questions about data quality and model reliability.
What marketers can do with it?
- Monitor synthetic data developments for potential cost reductions in AI implementation
- Consider implications for data collection and privacy strategies
- Prepare for potential changes in AI model training and deployment costs
- Evaluate the trade-offs between synthetic and real data in marketing applications
- Stay informed about quality indicators for AI models using synthetic data
- Consider hybrid approaches using both synthetic and real data
- Develop strategies to verify AI output quality and reliability
- Plan for potential changes in data acquisition and management practices
- Monitor developments in data generation and annotation technologies
See full post here: The promise and perils of synthetic data | TechCrunch
"- Consider hybrid approaches using both synthetic and real data." This one really speaks to me. As a simple example, I think back to a research project I did back in very early 2023, and I used ChatGPT to come up with information and data on a topic I knew nothing about (this was before tools like Perplexity started providing references behind the answers they provided) I didn't know what I didn't know, so it was hard to tell if what I was reading was correct or not (I had to assume it was). Only as I progressed in my research, looked at online information, spoke to humans, did I then realize that certain nuggets in the initial research were not correct (and also updated my notes and report with the correct information). AI was able to help reduce massively the collection of information and market analysis (reducing the time from something that would take weeks and months of reading and discussions to just a few days/weeks). The challenge is not knowing what one doesn't know. In order to be able to evaluate the quality of synthetic data and to determine how to fit it with real data, one has to be really careful and knowledgeable (or surround oneself with knowledgeable people). And that can be challenging when it is something technical or deeply nested (such as IT data, code, AI models themselves, etc.) that is hard to grasp, even for the most knowledgeable folks, due to inherent complexity in the data.