Pertanyaan 1
Yann LeCun
x.com/ylecunAI researcher and AMI Labs founder who is optimistic about intelligent machines and argues they need world models, not just bigger language models.
Bagaimana AI akan mengubah dunia?
Mendatar: pandangan Doom–Bloom yang ia ungkapkan. Ke atas: skala transformasi.
Doom–Bloom: 89 dari 100. Skala transformasi: 73 dari 100. Rentang interpretasi: 75 hingga 100 secara horizontal, 50 hingga 78 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.
≈0%
“p(doom) is smaller than the probability of an extinction-level asteroid hitting the earth”
Undefined “p(doom)”; he benchmarks it against an extinction-level asteroid impact and says it is far less likely than a nuclear holocaust
I didn't say p(doom) was zero · Apr 2026
Asumsi utama
An intelligent system must solve new problems it was not explicitly trained on, anticipate the consequences of actions, and plan in unfamiliar situations.Jawaban 1
Jika asumsi ini ternyata berbeda, bagaimana pandangannya akan berubah?
Hal yang dapat mengubah pandangan mereka
The biggest change would be evidence that the world-model program is fundamentally insufficient—that systems cannot learn useful abstractions, plan reliably, and generalize to unfamiliar situations without impractical amounts of supervision or data.Jawaban 5
Bukti apa yang akan memadai, dan ke arah mana bukti itu akan mengubah pandangannya?
Detail lebih lanjut
Manfaat transformatif yang bernilai luas diperkirakan akan terwujud.
97 / 100
Rentang interpretasi 100 hingga 100 pada skala kualitatif.
Kerugian yang dapat dikelola atau bersifat lokal diperkirakan akan terjadi.
31 / 100
Rentang interpretasi 33 hingga 33 pada skala kualitatif.
Pilihan manusia dapat mengarahkan ulang lintasan AI secara signifikan.
75 / 100
Rentang interpretasi 50 hingga 100 pada skala kualitatif.
AI diperkirakan akan tetap menjadi alat dengan kemampuan terbatas.
AI diperkirakan akan menyamai manusia dalam sebagian besar pekerjaan kognitif.
Posisi simulasi: AI diperkirakan akan jauh melampaui manusia dalam berbagai pekerjaan kognitif.
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 Yann LeCun
Apa yang pernah dikatakan Yann LeCun tentang AI
LeCun argues that AI can be designed to stay safe and useful, and that systems trained only on text won’t reach human-level intelligence.
“Ultimately, the goal is to amplify human intelligence and bring those benefits to humanity, which I think is intrinsically good.”
Nebius Science interview “We’re never going to get to human-level AI by just training on text. It’s just not going to happen.”
Mixture of Experts interview “It makes little sense to attribute a probability to an event on which we have agency.”
Post on X “The nice thing about an AI system is that you can design it in such a way that it cannot escape its guardrails.”
Newsweek interview “The desire to dominate is not correlated with intelligence at all.”
TIME interview
Dikutip kata demi kata dari sumber yang ditautkan, diperiksa pada 2 Okt 2026
Penilaian Simulasi
Sumber
Artikel, wawancara, dan tulisan yang digunakan sebagai landasan bagi pengguna simulasi ini.
Distinguishes useful LLM symbol manipulation from learning the physical world and planning in unfamiliar situations. Advocates predictive representations and world models; discusses difficult, unfinished research rather than a solved replacement.

Expects applications in physical systems and control, with much longer timelines for versatile household robots. Scientific applications have great potential. Supports open foundational research, and expects human judgment and education to remain important. The interviewer is affiliated with a compute supplier to his lab.

Older grounding for his rejection of intelligence automatically implying a desire for dominance, preference for controllable objectives and open AI, and optimism about widely available intelligent assistance. Treat these as conceptual positions, not fresh 2026 capability measurements.

Coauthored research reports a compact world model learning from pixels and planning across selected control tasks. The abstract supports a concrete alternative to language-only learning and a focus on useful physical representations. It does not demonstrate general human-level intelligence; the source was reviewed at abstract level.

Coauthored theoretical work links representation learning to recovery of latent world structure and planning under specified distributional assumptions. Adds technical content to the world-model program while making clear that a conditional mathematical result is not a universal guarantee. Summary is limited to the authors’ abstract.

In Axios’s interview, LeCun criticizes hype-driven career advice, defends the value of education and predicts that capable tools expand people’s ability to direct work. Adds his confident social optimism to the architectural critique; these are his forecasts rather than settled labor-market findings.

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