A growing belief in Silicon Valley suggests that the advancements in large AI models may be slowing, potentially impacting the anticipated arrival of human-level AI. Initially, it was assumed that with enough data and computing power, artificial general intelligence (AGI) would naturally emerge. This belief drove tech giants to invest heavily, as seen with OpenAI raising $6. 6 billion and Elon Musk's xAI raising funds for advanced Nvidia chips. However, insiders now observe that these large language models (LLMs) aren't improving as rapidly as expected, even with substantial investment. Critics like Gary Marcus argue LLMs reaching AGI was always a myth. One challenge is the limited language-based data available for AI training, creating a potential wall against further progress.
Some experts, like Sasha Luccioni, believe the industry's focus on model size over purpose stalled progress. Despite these concerns, leaders in the AI industry maintain optimism about reaching AGI eventually. OpenAI's strategy shift, due to slower-than-expected improvements, involves enhancing current model capabilities rather than relying solely on increased data and computing power. Their new o1 model focuses on improving reasoning for more accurate answers. OpenAI and others suggest it's time to refine existing AI capabilities for specific tasks, much like using fire effectively rather than just adding more fuel. This reflects the idea of moving from making frequent errors to thoughtful problem-solving, akin to how students transition during their education journey.
Silicon Valley Rethinks AI Approach Amid Slowing Progress
Hitachi, Ltd.
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