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Data Matters: Our thoughts and views on the state of digital data
Quick Concepts: Active Learning
Understand how active learning works and how it can optimize the ML model training process.
Eliminating Bias from Hiring Using Artificial Intelligence
When used ethically, Artificial Intelligence can eliminate unconscious bias in the hiring process, allowing everyone an equal opportunity.
Quick Concepts: Data-Centric AI
Understand the basics of data-centric AI and what it means for AI implementation.
New Report: Data Challenges in AI
New research breaks down insights from industry professionals on top data needs, practices, and challenges, along with best practices to optimize AI.
The Ethics of Content Moderation: Who Protects the Protectors?
Harmful content exacts a harsh toll on content moderators’ mental health. Strategies for protecting those who shield us from toxic content.
An Introduction to 3D Lidar
Lidar is an established ranging technology that is beginning to find a foothold in industry and mainstream life. How lidar works and what to expect as it evolves.
Keeping ML Models on Target by Managing Drift
Machine learning models experience drift, or degradation, over time due to changes in data and externalities. Here are some ways to detect, monitor, and mitigate drift in ML models.
5 Questions to Ask Before Getting Started with Synthetic Data Generation
Learn how to accelerate and improve model training with clean, anonymized synthetic data manufactured to mirror situations in the real world.
Best Approaches to Mitigate Bias in AI Models
Investigate types of bias and examine how the implementation of preventative techniques can help reduce bias in AI systems | ML Training Data
On-Device Artificial Intelligence: A Game Changer
With next-gen AI chips, artificial intelligence is moving off the cloud and onto personal devices. This opens new edge computing opportunities for individuals, businesses, and communities.