Ex-OpenAI researcher bets $100 billion will flow into training data because scaling alone won't cut it

Former OpenAI researcher Andrew Ho and Cambridge researcher Adam Hunt argue that large language models are becoming more specialized rather than versatile, with progress stagnating in areas outside coding and math. Ho is leaving OpenAI to start a company focused on specialized training data and predicts AI labs will need to spend over $100 billion on targeted data collection, as scaling alone is insufficient.

Former OpenAI employee Andrew Ho and Cambridge researcher Adam Hunt see a growing problem with large language models. Instead of becoming more versatile, the models are becoming more specialized, excelling at coding and math while stagnating or even regressing in other areas. Ho is leaving OpenAI to start a company focused on specialized training data and predicts that AI labs will need to spend more than $100 billion on targeted data collection. The article Ex-OpenAI researcher bets $100 billion will flow into training data because scaling alone won't cut it https://the-decoder.com/ex-openai-researcher-bets-100-billion-will-flow-into-training-data-because-scaling-alone-wont-cut-it/ appeared first on The Decoder https://the-decoder.com .