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AI researchers express doubt about the feasibility of achieving artificial general intelligence (AGI) through current scaling methods. A recent survey revealed that 76% of AI experts believe that simply expanding existing models is unlikely to lead to AGI. This skepticism marks a shift from the previous optimism surrounding generative AI advancements since 2022.
Despite the plateauing performance of transformer models, tech companies are projected to invest around $1 trillion in AI infrastructure. Researchers highlight that many AI systems still struggle with basic tasks, indicating a gap between perceived and actual capabilities. The definitions of AGI vary among companies, with some emphasizing cognitive performance while others focus on environmental interaction.
• 76% of AI researchers doubt current models will achieve AGI.
• Tech companies plan to invest $1 trillion in AI despite skepticism.
AGI refers to AI systems that can perform any intellectual task that a human can do.
Transformer models are a type of AI architecture that has improved performance through extensive data training.
Inference-time scaling involves increasing computing power for AI models to enhance query processing.
Google is involved in AI research and has defined AGI as outperforming humans on cognitive tests.
Microsoft collaborates with OpenAI and considers AGI achieved when significant profit generation is realized.
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