Q&A: Pangram CEO
Summary
Pangram, an AI detection application co-founded in 2023 by former Google engineer Max Spero, addresses the challenge of distinguishing human-written content from AI-generated text. Spero highlights the societal importance of human authorship, citing risks like widespread "slop" and large-scale deceptive operations. Early AI detectors, relying on "perplexity" and "burstiness," achieved 95-99% accuracy but produced too many errors, particularly with formulaic texts or non-native English. Pangram employs a deep-learning classifier, continuously fine-tuned with extensive datasets and "hard negative mining" to achieve a 1 in 10,000 false positive rate while maximizing recall. The system also differentiates between full AI generation and AI edits. Pangram is currently utilized by universities, academic conferences like NeurIPS, and some publishers to ensure content integrity, though the publishing sector's adoption has been slow.
Key takeaway
For publishers, academic institutions, or data curators concerned about content authenticity, you should evaluate advanced AI detection technologies like Pangram. Relying on outdated perplexity metrics is insufficient; instead, consider deep-learning classifiers calibrated for low false positives (e.g., 1 in 10,000) and the ability to distinguish AI edits. Implementing such tools can safeguard human authorship, prevent large-scale deceptive content, and maintain the integrity of your published works or datasets.
Key insights
AI detection is evolving beyond simple metrics to deep learning, crucial for preserving human authorship and combating large-scale deception.
Principles
- Human writing embodies a social contract.
- AI models' inherent preferences are detectable.
- False positives are more damaging than false negatives.
Method
Pangram trains a deep-learning classifier using labeled human and AI texts, fine-tuning with hard negative mining to reduce false positives, and continuously updating with new datasets and AI edit detection.
In practice
- Use AI detectors for academic integrity.
- Screen datasets for AI-generated "slop."
- Implement AI policies in publishing.
Topics
- AI Detection
- Deep Learning Classifiers
- Content Authenticity
- Academic Integrity
- Publishing Ethics
- Large Language Models
- Perplexity & Burstiness
Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Ethicist, Director of AI/ML
Related on AIssential
Editorial summary, takeaway, and curation by AIssential. Original article published by AI Policy Perspectives.