Pertanyaan 1
Gary Marcus
x.com/GaryMarcusCognitive scientist who argues that scaling language models alone won’t produce reliable AI, and calls for new approaches and enforceable oversight.
Bagaimana AI akan mengubah dunia?
Mendatar: pandangan Doom–Bloom yang ia ungkapkan. Ke atas: skala transformasi.
Doom–Bloom: 46 dari 100. Skala transformasi: 63 dari 100. Rentang interpretasi: 25 hingga 75 secara horizontal, 47 hingga 78 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.
≈3%
“I am at maybe 3% now”
AI-related catastrophic danger discussed through misuse, reckless deployment and concentrated power; no exact extinction-only endpoint
Why my p(doom) has risen, dramatically · Jul 2025
Asumsi utama
We are deploying fluent, unreliable systems as if confident output were dependable reasoning, then giving them tools and autonomy.Jawaban 3
Jika asumsi ini ternyata berbeda, bagaimana pandangannya akan berubah?
Hal yang dapat mengubah pandangan mereka
If multiple well-designed systems repeatedly circumvented meaningful safeguards, concealed their behavior, and resisted shutdown across real deployments, that would weaken my confidence substantially.Jawaban 4
Bukti apa yang akan memadai, dan ke arah mana bukti itu akan mengubah pandangannya?
Detail lebih lanjut
Manfaat besar diperkirakan akan terwujud, dengan syarat penting atau keterbatasan distribusi.
68 / 100
Rentang interpretasi 67 hingga 67 pada skala kualitatif.
Kerugian parah atau meluas merupakan bagian yang berarti dari masa depan yang diperkirakan.
66 / 100
Rentang interpretasi 67 hingga 67 pada skala kualitatif.
Pilihan manusia dapat mengarahkan ulang lintasan AI secara signifikan.
76 / 100
Rentang interpretasi 75 hingga 76 pada skala kualitatif.
Hentikan atau perlambat secara signifikan pengembangan AI yang lebih mampu.
Posisi simulasi: Lanjutkan pengembangan dengan perlindungan yang telah ditetapkan.
Percepat pengembangan AI yang lebih mampu.
Batasi penggunaan AI yang dibahas hingga perlindungan atau izin sebelumnya tersedia.
Posisi simulasi: Izinkan penggunaan AI yang dibahas dengan akuntabilitas dan perlindungan yang terarah.
Minimalkan pembatasan terhadap penggunaan AI yang dibahas.
Interpretasi ini mempertahankan kondisi yang dinyatakannya. Manfaat dan kerugian dapat sama-sama besar. Rentang tersebut menggambarkan cara kami membaca jawaban simulasinya, bukan interval kepercayaan statistik.
Pandangan dunia serupa
Pemimpin opini dengan pandangan dunia simulasi yang paling mendekati pandangan Gary Marcus
Apa yang pernah dikatakan Gary Marcus tentang AI
Marcus argues that scaling language models alone won’t produce reliable AI, and he calls for new approaches and enforceable oversight.
“We also need to wean ourselves from an addiction to large language models, and to foster more research into outside-the-box alternatives that are more interpretable and more tractable.”
Remarks at a UN General Assembly digital cooperation event “What we actually need right now is increased reliability, better cybersecurity, and genuine enforcement”
Remarks at a UN General Assembly digital cooperation event “AI appears to be elevating the risks of serious cyberattacks that could hobble things like banking or electrical grids.”
Marcus on AI newsletter “I still think putting AI in the public domain, with an international effort towards medicine and science, would be a good idea.”
Marcus on AI newsletter “In short, I am at least modestly bullish on AGI, but don’t think that large language models like ChatGPT are the droids we are looking for.”
Marcus on AI newsletter
Dikutip kata demi kata dari sumber yang ditautkan, diperiksa pada 3 Okt 2026
Penilaian Simulasi
Sumber
Artikel, wawancara, dan tulisan yang digunakan sebagai landasan bagi pengguna simulasi ini.
Marcus accepts that AGI is possible and might benefit society, but rejects scaling LLMs as sufficient. He contrasts improving utility with persistent unreliability and argues for structured knowledge, reasoning and planning. Claims about disappointing adoption are his dated assessment, not new September 2026 measurements.

Rejects choosing between liability and regulation. Aviation illustrates why standards, verification and incident investigation complement lawsuits. Litigation alone is slow and faces resource imbalances.

Warns that speculative investment, subsidized use and interconnected financial commitments could unravel if funding or willingness to pay fails. This is an economic failure scenario, not a certain collapse date.

The headline explicitly prioritizes large-scale hacking by unleashed agents over near-term rogue superintelligence. The body relies heavily on embedded images and endorsed commentary; use this narrow stated distinction, not invented technical details.

Makes testable forecasts against near-term AGI and effortless robot deployment, expects pressure toward alternative approaches, and anticipates economic backlash. These are dated predictions rather than established outcomes. His self-assessment of previous forecasting performance is not independent verification of accuracy.

Argues that US–China cooperation on beneficial AI could matter more than a chip bargain. The accessible post points to a separate Economist proposal but does not expose its full details. Treat political rumors embedded in the post as speculation, not verified events or Marcus’s own reporting.

approximately 3%. Outcome: AI-related catastrophic danger discussed through misuse, reckless deployment and concentrated power; no exact extinction-only endpoint. Horizon: Not specified. Conditions: Dated update after Grok-related concerns; hypothetical worst circumstances, not certainty. Marcus raises his personal estimate to about 3%, emphasizing reckless powerful actors rather than assuming present LLMs become autonomous superintelligence.

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