SAN FRANCISCO — Artificial intelligence companies like OpenAI are seeking to overcome unexpected delays and challenges in the pursuit of ever-bigger large language models by developing training techniques that use more human-like ways for algorithms to 'think'.
AI scientists, researchers and investors believed these techniques could reshape the AI arms race.SAN FRANCISCO — Artificial intelligence companies like OpenAI are seeking to overcome unexpected delays and challenges in the pursuit of ever-bigger large language models by developing training techniques that use more human-like ways for algorithms to "think".
Dr Sutskever is widely credited as an early advocate of achieving massive leaps in generative AI advancement through the use of more data and computing power in pre-training, which eventually created ChatGPT."The 2010s were the age of scaling, now we're back in the age of wonder and discovery once again. Everyone is looking for the next thing," Dr Sutskever said. "Scaling the right thing matters more now than ever.
They are more likely to have hardware-induced failure given how complicated the system is; researchers may not know the eventual performance of the models until the end of the run, which can take months. For example, instead of immediately choosing a single answer, a model could generate and evaluate multiple possibilities in real-time, ultimately choosing the best path forward.
The secret sauce of the o1 series is another set of training carried out on top of 'base' models like GPT-4, and the company says it plans to apply this technique with more and bigger base models.
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