Categories: FAANG

Designing Data: Proactive Data Collection and Iteration for Machine Learning

Lack of diversity in data collection has caused significant failures in machine learning (ML) applications. While ML developers perform post-collection interventions, these are time intensive and rarely comprehensive. Thus, new methods to track and manage data collection, iteration, and model training are necessary for evaluating whether datasets reflect real world variability. We present designing data, an iterative, bias mitigating approach to data collection connecting HCI concepts with ML techniques. Our process includes (1) Pre-Collection Planning, to reflexively prompt and document…
AI Generated Robotic Content

Recent Posts

10 Ways to Use Embeddings for Tabular ML Tasks

Embeddings — vector-based numerical representations of typically unstructured data like text — have been primarily…

16 hours ago

Over-Searching in Search-Augmented Large Language Models

Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they…

16 hours ago

How Omada Health scaled patient care by fine-tuning Llama models on Amazon SageMaker AI

This post is co-written with Sunaina Kavi, AI/ML Product Manager at Omada Health. Omada Health,…

16 hours ago

Anthropic launches Cowork, a Claude Desktop agent that works in your files — no coding required

Anthropic released Cowork on Monday, a new AI agent capability that extends the power of…

17 hours ago

New Proposed Legislation Would Let Self-Driving Cars Operate in New York State

New York governor Kathy Hochul says she will propose a new law allowing limited autonomous…

17 hours ago

From brain scans to alloys: Teaching AI to make sense of complex research data

Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements,…

17 hours ago