Not Every AI Problem is a Data Problem: What Can Data Shape Predict About Fit for Data-Driven Scaling?
Abstract
This article is targeting an external publication like CACM and is meant to be an opinion piece.
Abstract:
Large Language Models (LLMs) have revolutionized the AI landscape, demonstrating remarkable capabilities across a wide range of tasks. Each new model seemingly reinforces the notion that modern transformer-based AI can conquer any challenge if armed with sufficient compute and data. However, the scaling-driven paradigm is far from a universal solution to AI’s diverse challenges. For example, while scaling has accelerated certain applications, such as robotics, it has yet to show significant impact in others, such as identifying misinformation. Currently, there is no clear framework for distinguishing which use cases thrive from scaling with more data and which demand alternative approaches.
We are beginning to observe that the shape of data itself may hold valuable clues that could inform the success of data-driven scaling. For instance, insights from topological data analysis suggest that examining structural patterns and stability of data across multiple scales can help determine when scaling will be advantageous.
Moreover, the practicalities of data acquisition impose additional constraints that we must factor into the scaling equation upfront. Factors such as availability of high quality data, with its highly nuanced definition, complexity and resource intensity of data collection, and availability of proper evaluation benchmarks determine not just the effectiveness but also viability of scaling.
We have translated these emerging insights about data shape and nature of data acquisition into a practical framework of questions that evaluate predictiveness of historical data, stability of data patterns, clarity of data requirements, feasibility of high-quality data collection, and ease of assessing data quality. Together, these answers can help practitioners make more informed decisions about when scaling is more likely to yield successful outcomes. We have applied the framework to several AI use cases as an example. These early observations highlight a critical need for continued research in this domain.
Full draft link: https://docs.google.com/document/d/1f-HQ69KA4Ec7lNeWI-lTHUurhC0WMO4lNUiDUAEPKes/edit?usp=sharing
Abstract:
Large Language Models (LLMs) have revolutionized the AI landscape, demonstrating remarkable capabilities across a wide range of tasks. Each new model seemingly reinforces the notion that modern transformer-based AI can conquer any challenge if armed with sufficient compute and data. However, the scaling-driven paradigm is far from a universal solution to AI’s diverse challenges. For example, while scaling has accelerated certain applications, such as robotics, it has yet to show significant impact in others, such as identifying misinformation. Currently, there is no clear framework for distinguishing which use cases thrive from scaling with more data and which demand alternative approaches.
We are beginning to observe that the shape of data itself may hold valuable clues that could inform the success of data-driven scaling. For instance, insights from topological data analysis suggest that examining structural patterns and stability of data across multiple scales can help determine when scaling will be advantageous.
Moreover, the practicalities of data acquisition impose additional constraints that we must factor into the scaling equation upfront. Factors such as availability of high quality data, with its highly nuanced definition, complexity and resource intensity of data collection, and availability of proper evaluation benchmarks determine not just the effectiveness but also viability of scaling.
We have translated these emerging insights about data shape and nature of data acquisition into a practical framework of questions that evaluate predictiveness of historical data, stability of data patterns, clarity of data requirements, feasibility of high-quality data collection, and ease of assessing data quality. Together, these answers can help practitioners make more informed decisions about when scaling is more likely to yield successful outcomes. We have applied the framework to several AI use cases as an example. These early observations highlight a critical need for continued research in this domain.
Full draft link: https://docs.google.com/document/d/1f-HQ69KA4Ec7lNeWI-lTHUurhC0WMO4lNUiDUAEPKes/edit?usp=sharing
