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Google DeepMind Explores the Path to ASI
Google DeepMind researchers are formalizing the transition from human-level AGI to artificial superintelligence (ASI), proposing four distinct development pathways.
What: The report outlines scaling AGI, paradigm shifts, recursive improvement, and multi-agent collectives as the primary vectors for reaching ASI, cautioning that progress may occur as a series of transformative events rather than a single sudden step change.
Why it matters: This signals that major labs are shifting their long-term strategic planning to address systemic societal impacts beyond initial AGI deployment.
Decoder
- AGI (Artificial General Intelligence): A hypothetical AI system that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.
- ASI (Artificial Superintelligence): A hypothetical AI system that surpasses the combined cognitive capabilities of the smartest human beings across all fields.
Original article
Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations. Achieving this goal would have profound and far-reaching impacts on human society, which raises many complex questions for the decade ahead. This report investigates how AI itself might continue to develop in a post-AGI world along the continuum of machine intelligence. The endpoint of this continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the main focus of this report: the transition from human-level AGI to artificial general superintelligence, which, intuitively, can be understood as a system that is more intelligent and cognitively capable than large organisations of humans. After characterizing ASI, the report discusses four potential pathways from AGI to ASI: scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi-agent collectives. The report then discusses possible frictions and bottlenecks along these pathways. Determining whether the impact of these frictions will be negligible or substantial raises a number of concrete open research questions. Due to large uncertainties for predicting ASI progress, it cannot be ruled out that AI progress might continue to accelerate over the next years. This could imply that the image of a single transformative step change, caused by the introduction of human-level AGI into our society, could be inaccurate. More apt might be the prospect of a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology. Preparing for this prospect requires a massively interdisciplinary endeavour of global scope and interest.
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