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Data Poisoning - Page 6

Data poisoning is an attack that changes or inserts examples in a model’s training pipeline to corrupt what the system learns. An attacker may aim to reduce overall accuracy, create a hidden backdoor triggered by a specific pattern, bias decisions against a target, or influence a generative model’s responses. Poisoning can enter through public data collection, compromised suppliers, user feedback, labels, retrieval indexes, or repeated fine-tuning. Defenses include provenance tracking, access control, anomaly detection, robust training, duplicate analysis, trusted validation sets, and review of unexpected behavior. The attack is difficult to diagnose because harmful examples may appear ordinary and their effect may emerge only after deployment.

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer
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AI & Machine Learning, Immersive Reality (AR, VR, MR, and XR), News, Startups & Investment

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer

By • 3 mins read

Andrew Tulloch, co-founder of the $12 billion AI startup Thinking Machines Lab, has joined Meta after previously rejecting what reports described as a $1.5 billion offer — a figure Meta has since called ‘inaccurate and ridiculous.’