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AI Experts Say We’re on the Wrong Path to Achieving Human-Like AI

Mar 30, 2025 - gizmodo.com
A panel of AI researchers at the AAAI’s 2025 Presidential Panel on the Future of AI Research has concluded that the current pursuit of artificial general intelligence (AGI) is misguided. The report, compiled by 24 experts, highlights a significant mismatch between public perception and the reality of AI capabilities, with 79% of respondents noting this discrepancy hinders research. A majority believe that scaling current AI approaches won't achieve AGI, advocating instead for a cautious, collaborative, and responsible development approach. Despite the hype, AI has made significant progress, moving from limited applications to more general use cases like chatbots, though challenges in factuality and trustworthiness remain.

The report emphasizes that AI's development is not fading but progressing towards a "plateau of productivity" as per the Gartner Hype Cycle. Researchers are critically evaluating AI's current state, focusing on safety, ethical governance, and innovation. The report suggests that future improvements may come from new training methods and organizing AI systems into cooperating teams of agents to enhance trustworthiness. As AI continues to evolve, the focus remains on forward-moving innovation and improvement in both AI system construction and deployment.

Key takeaways:

  • The field of artificial intelligence is pursuing artificial general intelligence (AGI) in a way that many researchers believe is incorrect, with 76% of respondents stating that scaling up current approaches will not be sufficient to achieve AGI.
  • There is a significant mismatch between public perception and the reality of AI capabilities, with 79% of respondents noting this discrepancy and 90% saying it hinders AI research.
  • AI has made significant progress in recent years, moving from limited applications to more general use cases, but issues like AI factuality and trustworthiness remain unsolved.
  • AI researchers emphasize a cautious, collaborative, and responsible approach to AI development, prioritizing safety, ethical governance, and gradual innovation over a race toward AGI.
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