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Beam search is a decoding method used when an AI model must build a sequence one element at a time. Rather than keeping only the single most likely next choice, it maintains a limited set of promising partial sequences called beams. Each step expands those candidates, scores the results, and keeps the best few until a stopping condition is reached. The method can produce stronger translations, transcriptions, or generated text than purely greedy decoding, although a wider beam increases computation and does not guarantee a better answer. Length penalties, repetition controls, and task-specific scoring are often added because the highest-probability sequence may be short, repetitive, or otherwise undesirable.

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.’