News
Quote:
This method allows models to dedicate more processing power to challenging tasks like math or coding problems or complex operations that demand human-like reasoning and decision-making.
“It turned out that having a bot think for just 20 seconds in a hand of poker got the same boosting performance as scaling up the model by 100,000x and training it for 100,000 times longer,” said Noam Brown, a researcher at OpenAI who worked on o1, at TED AI conference in San Francisco last month.
And second quote:
The implications could alter the competitive landscape for AI hardware, thus far dominated by insatiable demand for Nvidia’s AI chips. Prominent venture capital investors, from Sequoia to Andreessen Horowitz, who have poured billions to fund expensive development of AI models at multiple AI labs including OpenAI and xAI, are taking notice of the transition and weighing the impact on their expensive bets.
“This shift will move us from a world of massive pre-training clusters toward inference clouds, which are distributed, cloud-based servers for inference,” Sonya Huang, a partner at Sequoia Capital, told Reuters.
This is interesting.