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Ablation is an experimental method used to determine which parts of an AI system actually contribute to its results. Researchers remove a feature, module, data source, training objective, or architectural component while keeping the rest of the setup as consistent as possible. A meaningful drop in performance suggests that the removed element was useful; little change may indicate redundancy or an overstated contribution. Ablation studies are common in machine learning papers and product experiments because headline accuracy alone cannot show why a system works. Reliable conclusions require repeated runs, comparable compute budgets, suitable metrics, and awareness that components may interact rather than contribute independently.

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.’