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A recent survey of 475 AI researchers reveals that 76% believe current large language models (LLMs) are unlikely to achieve artificial general intelligence (AGI). This skepticism challenges the tech industry's narrative that more data and resources will lead to breakthroughs in AI capabilities. Experts suggest that the generative AI industry has hit a dead end, with diminishing returns on investment.
The survey highlights fundamental limitations in the architecture of existing AI models, particularly their reliance on large feedforward circuits. As companies like OpenAI face stagnation in model performance, alternatives such as reasoning models and probabilistic programming are emerging as potential paths forward. The success of companies like DeepSeek indicates that innovation in AI design is still possible despite current challenges.
• 76% of AI researchers doubt current models can achieve AGI.
• DeepSeek demonstrates performance at lower costs than Silicon Valley models.
AGI refers to a type of AI that can learn and perform tasks as well as or better than humans.
LLMs are AI models designed to understand and generate human language, but their scaling may not lead to AGI.
This deep learning architecture, developed by Google, enables models to learn from vast amounts of data.
OpenAI is known for developing advanced AI models like GPT-4, but faces challenges in achieving further advancements.
DeepSeek has matched the performance of high-cost AI models at a fraction of the cost, showcasing innovative potential.
Google's transformer architecture has significantly influenced the development of modern AI models.
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