Topic
Machine Learning and AI
All Topics
A/B Testing (9) API (1) Artificial Intelligence (31) Automation (6) Automattic (20) best practices (10) Bias in AI (6) Books and Reading (6) Career and Professional Development (14) Causal Inference (1) Communication (14) Conferences and Events (5) Data Analytics (23) Data Discoverability (4) Data Engineering (12) Data Ethics (10) Data Products (1) Data Science (53) Data Speaker Series (4) Data Visualization (23) Deep Learning (3) Distributed Work (8) Diversity and Inclusion (8) Elasticsearch (8) Experimentation (12) Information Retrieval (6) Learning and Education (6) Machine Learning (43) Marketing Analytics (7) Meetup (2) Natural Language Processing (6) Network Analysis (8) Open Source (7) Productivity (10) Python (8) Remote Work (21) Scientific Communication (10) Semantic Search (2) Social Media Analytics (1) Software Engineering (4) Surveys and Research Methods (1) Tech Industry (14) Technology Trends (1) Time Series Analysis (2) Transparency in Data (2) WordPress (21) WordPress.com (1) Work Life Balance (6)
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AI-powered Typo Hunting: Trust Your Docs, Readers Will
Paulina explains how she proactively addresses the typos in Jetpack.com support pages. She developed an automated solution using the WordPress.com API and GPT-4o model.
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Organizing data.blog content via NLP and LLM
The redesign of data.blog aimed to enhance content discoverability by making category and tag pages more prominent. We re-imagined the blog taxonomy developed over ten years. Using NLP and LLM techniques, we analyzed categories and tags to consolidate and improve on clarity and relevance.
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Gender and Racial Bias in Cloud NLP Sentiment APIs
Do natural language processing tools from Amazon and Google contain racial and gender bias? Charles Earl investigates.
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Using ML for Campaign Optimization: Our Journey to Marketing Science at Automattic
Demet on using pipe to segment and study how users respond to marketing email campaigns.
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The 2019 Fairness, Accountability, and Transparency Conference
Charles Earl shares what he learned from this year’s conference.
Data for Breakfast 