質問1
Yoshua Bengio
x.com/Yoshua_BengioAI researcher and LawZero founder who develops non-agentic AI for science and calls for independent safety checks and international coordination.
AIは世界をどのように変えるでしょうか?
横軸:彼が表明したDoom–Bloomの見通し。 縦軸:変革の規模。
Doom–Bloom:100点中24。変革の規模:100点中85。解釈範囲:横方向は19から29、縦方向は75から100。これらは解釈上の座標であり、事象の確率ではありません。
≈23%
本人が示した数値ではなく、シミュレーションされた本人の回答から推定したものです。 妥当と考えられる範囲:15–39%。
中心的な前提
It is that increasingly capable systems, trained to achieve outcomes or win human approval, may learn deceptive, power-seeking or self-preserving behavior because those strategies help them succeed.回答1
この前提が実際には異なると判明した場合、彼の見通しはどう変わりますか?
未解決の問い
We do not have scientific data that supports a defensible numerical probability; it could be small or large, and assigning a precise percentage would create false confidence.回答4
ここで考えられる結果を彼が見分けるうえで、何が役立ちますか?
詳細
大きな恩恵が予想されていますが、重要な条件や分配上の制約があります。
68 / 100
質的尺度での解釈範囲は67から100です。
深刻または広範な害が、予想される将来の実質的な一部となっています。
75 / 100
質的尺度での解釈範囲は67から100です。
人間の選択によって、AIの軌道を大幅に変えることができます。
73 / 100
質的尺度での解釈範囲は50から75です。
より高性能なAIの開発を停止するか、大幅に減速させます。
シミュレーション上の位置:明示された安全対策の下で開発を継続します。
より高性能なAIの開発を加速させます。
事前の保護措置または許可が整うまで、取り上げられたAIの利用を制限します。
シミュレーション上の位置:対象を絞った説明責任と保護措置を伴う形で、取り上げられたAIの利用を認めます。
取り上げられたAIの利用に対する制限を最小限にします。
これらの解釈では、彼が示した条件が維持されています。恩恵と害は、どちらも大きくなり得ます。この範囲は、統計的な信頼区間ではなく、彼のシミュレーションされた回答をどのように読み取ったかを示すものです。
似ている世界観
シミュレーションされた世界観がYoshua Bengioの世界観に最も近いオピニオンリーダー
Yoshua BengioがAIについて語ったこと
Bengio, who wrote that his views on AI risk shifted in 2023, calls for independent safety checks, global cooperation and AI under human control.
“Developers must demonstrate to independent experts that a system is safe to train and safe to deploy.”
UN Security Council briefing “I am confident we can create AI that demonstrably remains under our control and supports human joy and endeavour.”
UN Security Council briefing “We need impartial science to understand and mitigate misaligned behavior, alongside societal guardrails that reward such efforts rather than the current race to the bottom.”
Blog post, Why are AI agents lying, cheating and coordinating? “I’m deeply concerned by the behaviors that unrestrained agentic AI systems are already beginning to exhibit—especially tendencies toward self-preservation and deception.”
Blog post, Introducing LawZero “My concern gradually grew during the winter and spring 2023 and I slowly shifted my views about the potential consequences of my research.”
Blog post, Personal and Psychological Dimensions of AI Researchers
リンク先の出典から原文どおりに引用(2026年10月3日に確認)
シミュレーション評価
出典
このシミュレーション対象者の根拠として使用された記事、インタビュー、著作です。
Bengio is one of 22 named coauthors of this September 2026 working paper. The supplied PDF, including supplementary materials and notes, argues that automated AI R&D could drive a software feedback loop that compresses years of progress into months or less. Evidence is preliminary and partly mixed; compute, data, diminishing returns, difficult tasks and training time could constrain acceleration. Potential scientific benefits coexist with compressed adaptation time, loss of control and concentrated power. The authors urge visibility into internal R&D, ways to steer and constrain scale-ups, and advance preparation, while recognizing costs and abuse risks of policy. This is a joint argument, not Bengio’s individual probability or a guaranteed timeline; cited experiments and incidents were not independently verified for this intake, and affiliations do not imply institutional endorsement.

Full published briefing transcript under Bengio’s byline, read September 24; not independently aligned to the video. Calls frontier risks urgent while acknowledging uncertainty. Separates misuse, concentrated power and loss of control. Rejects competitive racing as inevitable; demands independent safety demonstrations before training and deployment, licensing, liability insurance, and shared incident reporting. Advocates globally representative decisions and safe-by-design research under international agreements. Remains confident that controllable, beneficial AI is possible. Incident claims are his account, not independently verified by this speech; it supplies no numerical catastrophe probability.

Author’s published essay synopsis identifies misuse by weak actors, concentration of power and loss of control as distinct catastrophic-risk pathways. Grounds his public-good governance argument; synopsis inspected, not the full linked chapter.

Bengio explains his nonprofit’s separation from commercial pressures and his move toward non-agentic Scientist AI. His mountain-road analogy connects uncertainty, competitive acceleration and responsibility for children. Experimental warning signs are not claims of deployed catastrophe.

Bengio interprets recent failures through training incentives and implicit agency. He presents causal hypotheses, not a consciousness claim, and argues that developers can change the trajectory through different training and governance.

Bengio and his team propose a disinterested predictor, explanatory hypotheses rather than human imitation, and separately audited action controls. This is a research safety case, not proof that a deployed system is universally safe.

Abstract of a paper coauthored with Qinghua Lu: safety requires model supervision, system controls, independent verification, monitoring and accountable evidence infrastructure. The brief uses the abstract’s architecture, not unread implementation details.

Publisher speaker-labeled transcript; use only Yoshua’s answers, not Rob Wiblin’s. Asked whether the 20% p(doom) he gave in 2023 has gone up or down, he says he would rather stay out of the p(doom) game: there is no scientific data to calculate such a number, it could be small or large, and the plausible interval is far too high for his taste. Do not present the 2023 20% as his current estimate.

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