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A recent survey of 475 AI researchers reveals that 76% believe scaling large language models will not lead to artificial general intelligence (AGI). This contradicts tech industry claims that more data and resources could enable AI to surpass human intelligence. Experts suggest that the current approaches have hit a dead end, with diminishing returns on investment and performance.
The survey highlights fundamental limitations in existing AI architectures, particularly in their ability to represent complex concepts. Companies like DeepSeek have demonstrated that high performance can be achieved at lower costs, challenging the notion that only large-scale models can succeed. Despite these challenges, there is optimism for future AI development through innovative reasoning models and probabilistic programming.
• 76% of AI researchers doubt scaling models will achieve AGI.
• DeepSeek challenges the cost and performance of Silicon Valley AI models.
AGI refers to AI systems that can learn and perform tasks as effectively as humans.
LLMs are AI models designed to understand and generate human-like text based on training data.
This deep learning architecture enables models to learn from vast amounts of data, improving their responses.
Google developed the transformer architecture, which underpins many modern AI models.
OpenAI is known for its development of advanced AI models, including the GPT series.
DeepSeek has achieved competitive AI performance at lower costs, challenging established models.
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