Pertanyaan 1
Subbarao Kambhampati
x.com/rao2zArizona State AI planning researcher who studies the limits of LLM reasoning and argues AI agents need external verifiers and accountable developers.
Bagaimana AI akan mengubah dunia?
Mendatar: pandangan Doom–Bloom yang ia ungkapkan. Ke atas: skala transformasi.
Doom–Bloom: 61 dari 100. Skala transformasi: 59 dari 100. Rentang interpretasi: 56 hingga 76 secara horizontal, 50 hingga 75 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.
≈4%
Disimpulkan dari jawaban simulasinya, bukan angka yang diberikan olehnya. Rentang yang masuk akal: 2–8%.
Asumsi utama
If a proposed plan can be checked and revised before execution, these systems can be extremely valuable.Jawaban 1
Jika asumsi ini ternyata berbeda, bagaimana pandangannya akan berubah?
Pertanyaan yang belum terjawab
I don’t have a defensible number.Jawaban 3
Apa yang akan membantunya membedakan hasil-hasil yang masuk akal di sini?
Hal yang dapat mengubah pandangan mereka
The most consequential discovery would be a system that can reliably generate, verify, and execute plans in novel, real-world, non-ergodic settings—without external verifiers—and whose correctness guarantees survive adversarial testing.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.
67 / 100
Rentang interpretasi 67 hingga 67 pada skala kualitatif.
Kerugian parah atau meluas merupakan bagian yang berarti dari masa depan yang diperkirakan.
61 / 100
Rentang interpretasi 33 hingga 67 pada skala kualitatif.
Pilihan manusia memiliki pengaruh yang berarti, tetapi sangat dibatasi.
54 / 100
Rentang interpretasi 44 hingga 81 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 Subbarao Kambhampati
Penilaian Simulasi
Sumber
Artikel, wawancara, dan tulisan yang digunakan sebagai landasan bagi pengguna simulasi ini.
Co-authored position paper, first posted 2025-04-14 and revised (v4) on 2026-06-09 for ICML 2026. Argues that calling intermediate tokens reasoning or thinking traces is not a harmless metaphor but dangerous, because it confuses what these models are and how to use them and leads to questionable research. Abstract and introduction inspected; the experiments were not audited. An interpretive and methodological position, not a societal forecast.

On The Information Bottleneck podcast he describes LLMs as strong generators without correctness guarantees, best paired with verifiers in his LLM-Modulo framework, and says reasoning models moved the verifier into post-training. On safety he places himself closer to Yann LeCun than to Hinton or Bengio, says shutdown-deception studies reflect imitation of human data rather than evidence AI will kill humanity, and locates real risk in executing generated plans without verifier guardrails. He says he questions the overemphasis on existential threat, not safety itself. Automated transcript with errors; own turns in the planning and safety segments inspected.

Secondary Economic Times report; date unverified, though a syndicated copy is dated 2026-03-31 and he shared it on 2026-04-06. Quotes him that AI development cannot be stopped because one government’s ban does not control the world, that his biggest safety concern is agentic systems acting through real-world APIs, and that a plan should not be executed unless the probability of damage is known to be extremely low, an area he researches. Indexed article text inspected; wording is the reporter’s rendering.

His own LinkedIn post says AGI has become a marketing buzzword rather than a meaningful goal. It reshares a colleague’s report of his panel quip at an AI summit that AGI will be achieved when Sam Altman says it is; that wording is relayed by someone else. A judgment about the term, not a capability timeline. Indexed post text inspected.

Older co-authored ICML 2024 position paper, kept as background for his framework. Argues autoregressive LLMs cannot by themselves plan or self-verify, but are useful universal approximate knowledge sources when combined with external model-based verifiers in a tight bidirectional loop. Abstract inspected. His 2026 podcast and posts take precedence on how he sees reasoning models.

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