Pergunta 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.
Como a IA mudará o mundo?
Na horizontal: a perspectiva Doom–Bloom expressa por ele. Para cima: escala da transformação.
Doom–Bloom: 24 de 100. Escala da transformação: 85 de 100. Intervalos de interpretação: 19 a 29 na horizontal, 75 a 100 na vertical. Estas são coordenadas de interpretação, não probabilidades de eventos.
≈23%
Inferido a partir das respostas simuladas dele, não de um número que essa pessoa forneceu. Intervalo plausível: 15–39%.
Uma premissa central
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.Resposta 1
Se essa premissa se revelasse diferente, como a perspectiva dele mudaria?
Uma questão não resolvida
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.Resposta 4
O que ajudaria essa pessoa a distinguir os resultados plausíveis aqui?
Mais detalhes
Esperam-se benefícios substanciais, com condições importantes ou limites de distribuição.
68 / 100
Intervalo de interpretação de 67 a 100 na escala qualitativa.
Danos graves ou generalizados são uma parte relevante do futuro esperado.
75 / 100
Intervalo de interpretação de 67 a 100 na escala qualitativa.
As escolhas humanas podem redirecionar substancialmente a trajetória da IA.
73 / 100
Intervalo de interpretação de 50 a 75 na escala qualitativa.
Interromper ou desacelerar substancialmente o desenvolvimento de uma IA mais capaz.
Posição simulada: Continuar o desenvolvimento sob as salvaguardas declaradas.
Acelerar o desenvolvimento de uma IA mais capaz.
Restringir os usos da IA discutidos até que proteções prévias ou uma autorização estejam em vigor.
Posição simulada: Permitir os usos da IA discutidos com responsabilização e proteções específicas.
Minimizar as restrições aos usos da IA discutidos.
Estas interpretações mantêm as condições que ele declarou. Tanto os benefícios quanto os danos podem ser substanciais. Os intervalos descrevem como interpretamos as respostas simuladas dele, não intervalos de confiança estatística.
Visões de mundo semelhantes
Líderes de opinião cujas visões de mundo simuladas são mais próximas da visão de Yoshua Bengio
O que Yoshua Bengio já disse sobre a IA
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
Citações literais das fontes indicadas, verificadas em 3 de out. de 2026
Avaliação simulada
Fontes
Artigos, entrevistas e textos usados para fundamentar este usuário simulado.
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.

Onde você se situa?
Mapear minha visão de mundo