
OpenAI and rivals seek new path to smarter AI as current methods hit limitations
The HinduArtificial 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". After the release of the viral ChatGPT chatbot two years ago, technology companies, whose valuations have benefited greatly from the AI boom, have publicly maintained that “scaling up” current models through adding more data and computing power will consistently lead to improved AI models. Ilya Sutskever, co-founder of AI labs Safe Superintelligence and OpenAI, told Reuters recently that results from scaling up pre-training - the phase of training an AI model that uses a vast amount of unlabeled data to understand language patterns and structures - have plateaued. “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.
History of this topic

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