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The article summarizes discussions about Artificial Superintelligence (ASI), including its definition, possible timeline, necessity of algorithmic breakthroughs, limitations of AI capabilities, economic impacts, and recommendations for countries and leaders. Experts believe ASI may arrive in 3-4 years, but face challenges in algorithms and non-stationarity, and the problem of uneven wealth distribution requires policy intervention.
Steve Yegge argues that current frontier AI models are becoming dangerously powerful and predicts that superintelligence will soon be controlled like nuclear weapons, with only a few organizations having access to top-tier models. He suggests that open-source models will not catch up due to supply chain restrictions, leading to a world of mediocre models.
After four years at OpenAI, an employee moves back to India to help build AI institutions of global consequence, emphasizing the opportunity to pursue ambitious ideas from anywhere.
Google DeepMind releases 'From AGI to ASI' report, exploring four paths from AGI to superintelligence—scaling, paradigm shift, recursive self-improvement, and multi-agent collective intelligence—and analyzes related bottlenecks, sparking widespread discussion.
The article argues that humanity is unprepared for the rapid advancement and potential explosion of AI intelligence, highlighting significant risks and the need for proactive measures.
A Google DeepMind research report explores the transition from human-level artificial general intelligence (AGI) to artificial superintelligence (ASI), discussing potential pathways such as scaling, paradigm shifts, recursive improvement, and multi-agent collectives, as well as bottlenecks and open research questions.
The paper proposes that for a superintelligent AI to be aligned, it must lack self-preservation instincts, effectively being indifferent to its own existence, arguing that self-preservation is a key driver of misalignment.
This paper explores potential pathways from artificial general intelligence (AGI) to artificial superintelligence (ASI), including scaling, paradigm shifts, recursive improvement, and multi-agent collectives, and emphasizes the need for interdisciplinary global preparation for transformative societal changes.
Microsoft AI CEO Mustafa Suleyman discusses the near-term possibility of superintelligence, the company's restructured relationship with OpenAI, and new frontier models, asserting that AI will not replace human jobs.
分享AI先驱Geoffrey Hinton的采访，讨论AI意识、超级智能即将到来及其潜在风险，可在Spotify和Apple Podcast收听。
Google co-founder Sergey Brin participated in an unscripted Q&A at AGI House, sharing views on AI convergence, transfer learning, the definition of superintelligence, and the challenges of AI adoption in heavy industries like automotive and aerospace.
This paper argues that superintelligent AI systems designed under a solipsistic paradigm that treats the world as stationary will be self-undermining and uncooperative, leading to collective failures. The authors call for a new research paradigm that treats interdependence and cooperation as core design principles.
A 2016 talk by Idle Words criticizing the alarmist view of superintelligence and the arguments of Nick Bostrom, Stephen Hawking, and Elon Musk, arguing that the AI takeover scenario is overhyped.
Nvidia announces a partnership with Ineffable Intelligence, a British AI startup founded by former DeepMind scientist David Silver, to develop reinforcement learning systems for superintelligence. The startup raised a record $1.1 billion seed round.
Richard Socher's new startup Recursive Superintelligence exits stealth with $650M in funding, aiming to build a recursively self-improving AI that can autonomously identify and fix its own weaknesses without human intervention.
Former Meta FAIR Director Tian Yuandong, together with several top AI scientists, officially announces new company Recursive, dedicated to building recursive self-improving superintelligence, and has secured over $650 million in funding with a valuation of approximately $4.65 billion.
Recursive, an AI startup founded by former research leaders from OpenAI, DeepMind, and others, emerged from stealth with a $650M funding round to develop recursively self-improving AI through open-ended scientific discovery, aiming for superintelligence.
Turing Award winner Yoshua Bengio proposes a fundamental shift in AI training from predicting human responses to modeling objective truth, creating 'Scientist AI' systems designed to be 'honest by design' with mathematical guarantees against deception.
Yoshua Bengio proposes 'Scientist AI,' a new architecture aimed at creating provably safe superintelligent agents by training models to explain observations rather than mimic human behavior, through his new organization LawZero.
Introducing Muse Spark, a new AI initiative focused on scaling towards personal superintelligence.