# Data labeling Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/data-labeling
> Markdown URL: https://aitinkerers.org/technologies/data-labeling.md
> Technology record last updated: 2026-05-01T03:23:51Z
> Generated: 2026-09-20T19:46:34Z

Data labeling transforms raw, unstructured data (images, text, video) into structured, annotated training sets, providing the 'ground truth' for supervised machine learning models.

This technology is the critical preprocessing step for successful AI development: it assigns meaningful tags to raw data, enabling algorithms to learn patterns and make accurate predictions. The process involves human-in-the-loop (HITL) annotators or semi-automated tools to perform tasks like bounding box creation for computer vision or named entity recognition for natural language processing (NLP). High-quality, accurately labeled datasets are non-negotiable; they directly mitigate 'garbage in, garbage out' scenarios, ensuring models—like those used in autonomous vehicles or healthcare diagnostics—achieve the required 95%+ performance metrics.

- Official technology site: https://www.ibm.com/topics/data-labeling
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [LABEL: LLM Assistance for Better Evaluation Labels](https://seattle.aitinkerers.org/talks/rsvp_kadYvSmadkQ)

A demo workflow and UX for labeling data, using it to evaluate LLM-evaluators, and then aligning the LLM-evaluator to human judgments (and perhaps optimizing the evaluator!) P.S., Kyle Corbitt of OpenPipe will be demoing something similar, and I hope to have a faceoff with them by going before them so the audience can decide the pros and cons of each.

- Event context: AI Tinkerers - Seattle - October 2024 Meetup — 2024-10-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_kadYvSmadkQ

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- [LLM Evaluators](https://aitinkerers.org/technologies/llm-evaluators) ([Markdown](https://aitinkerers.org/technologies/llm-evaluators.md)) — 1 public demo
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