Andrew Ng

Andrew Ng

x.com/AndrewYNg

DeepLearning.AI founder who sees large opportunity in practical AI applications and expects AI to reshape jobs and skills more than eliminate them.

एआई दुनिया को कैसे बदलेगा?

सभ्यता-स्तरीय बदलावक्रमिक बदलावDoomBloom
सिम्युलेट की गई स्थितिव्याख्या का दायरा

आर-पार: उनका व्यक्त किया गया Doom–Bloom दृष्टिकोण। ऊपर: बदलाव का स्तर।

Doom–Bloom: 100 में से 88। बदलाव का स्तर: 100 में से 50। व्याख्या के दायरे: क्षैतिज रूप से 75 से 100, लंबवत रूप से 45 से 79। ये व्याख्या के निर्देशांक हैं, घटनाओं की संभावनाएँ नहीं।

Andrew Ng का P(doom) · अनुमानित

≈2%

0%100%

उनके सिम्युलेट किए गए उत्तरों से अनुमान लगाया गया है, यह उनके द्वारा बताई गई संख्या नहीं है। संभावित दायरा: 1–7%।

उनका दृष्टिकोण किन बातों पर निर्भर करता है

एक मुख्य मान्यता

Attacks still require actions that defenders can observe, and defenders often possess more information about their own systems.
उत्तर 1

अगर यह मान्यता अलग साबित होती, तो उनका दृष्टिकोण कैसे बदलता?

क्या उनकी राय बदल सकता है

The biggest change would be strong, repeated evidence that AI-enabled attackers have a durable advantage over defenders—that even well-isolated, carefully monitored, rapidly patched systems can be compromised faster than organizations can detect and recover.
उत्तर 2

कौन-सा प्रमाण पर्याप्त होगा, और उससे उनका दृष्टिकोण किस दिशा में बदलेगा?

अधिक जानकारी

अपेक्षित लाभ

काफ़ी लाभ की उम्मीद है, लेकिन उनके साथ महत्वपूर्ण शर्तें या वितरण संबंधी सीमाएँ होंगी।

74 / 100

कम असरबदलावकारी असर

गुणात्मक पैमाने पर व्याख्या का दायरा 67 से 100 तक है।

अपेक्षित नुकसान

संभाले जा सकने वाले या स्थानीय स्तर तक सीमित नुकसानों की उम्मीद है।

33 / 100

कम असरबदलावकारी असर

गुणात्मक पैमाने पर व्याख्या का दायरा 33 से 33 तक है।

मानवीय प्रभाव

मानवीय विकल्पों का सार्थक, लेकिन काफी सीमित प्रभाव है।

59 / 100

कम प्रभावमजबूत प्रभाव

गुणात्मक पैमाने पर व्याख्या का दायरा 38 से 87 तक है।

विकास की गति

अधिक सक्षम एआई का विकास रोकें या उसकी गति काफी धीमी करें।

सिम्युलेट की गई स्थिति: बताए गए सुरक्षा उपायों के तहत विकास जारी रखें।

अधिक सक्षम एआई के विकास की गति बढ़ाएँ।

एआई के उपयोग के नियम

जिन सुरक्षा उपायों या अनुमति का पहले से होना ज़रूरी है, उनके लागू होने तक चर्चा किए गए एआई उपयोगों को प्रतिबंधित रखें।

सिम्युलेट की गई स्थिति: लक्षित जवाबदेही और सुरक्षा उपायों के साथ चर्चा किए गए एआई उपयोगों की अनुमति दें।

चर्चा किए गए एआई उपयोगों पर प्रतिबंध कम से कम रखें।

इन व्याख्याओं में उनकी बताई गई शर्तें बरकरार रखी गई हैं। लाभ और नुकसान, दोनों पर्याप्त हो सकते हैं। ये दायरे बताते हैं कि हम उनके सिम्युलेट किए गए उत्तरों को कैसे समझते हैं, ये सांख्यिकीय विश्वास-अंतराल नहीं हैं।

Andrew Ng की तुलना में आपकी सोच कहाँ ठहरती है?
लगभग 3 मिनट में एआई के बारे में अपना विश्वदृष्टिकोण मैप करें, फिर तुलना करें
मेरा विश्वदृष्टिकोण मैप करें

मिलते-जुलते विश्वदृष्टिकोण

ऐसे जाने-माने विचारक जिनके सिम्युलेट किए गए विश्वदृष्टिकोण Andrew Ng के विश्वदृष्टिकोण से सबसे अधिक मिलते हैं

Andrew Ng ने एआई के बारे में क्या कहा है

Ng argues that AI’s benefits far outweigh its risks and that safety is an engineering problem, and he opposes pausing AI development.

  1. “We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building.”

    The Batch letter, Who’s Responsible for Irresponsible AI?

  2. “Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use.”

    The Batch letter, Who’s Responsible for Irresponsible AI?

  3. “In the case of AI, I am glad the U.S. government is taking cybersecurity seriously.”

    The Batch letter, AI Regulations Must Balance Innovation and Risk

  4. “To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful.”

    The Batch letter, AI Will Not Destroy the Job Market

  5. “Let’s support limiting applications — those that use AI, and those that don’t — that harm people.”

    The Batch letter, How Anti-AI Propaganda Hurts the Public

लिंक किए गए स्रोतों से शब्दशः उद्धृत, 3 अक्टू॰ 2026 को जाँचा गया

सिम्युलेट किया गया आकलन

सवाल 1

आपके विचार में एआई हमारे भविष्य के लिए क्या मायने रखती है—और क्यों?

I think AI means an enormous expansion in what people can build and afford to do. Many valuable projects—better education, healthcare tools, scientific research, customized software, and services for small businesses—have been neglected because human effort was too costly. AI lowers that cost. The right starting point is not abstract speculation about a machine apocalypse; it is a real problem, a useful application, testing, and iteration. The gains will be uneven. Coding agents may make implementation much faster while product decisions, infrastructure, validation, and coordination remain bottlenecks. That changes jobs and lets AI-native teams accomplish more with fewer people, but it does not mean every task or profession vanishes. People who learn to use AI well—and who can decide what is worth building—will have growing leverage. Some workers will face painful disruption, but broad claims that AI is simply eliminating work are exaggerated. There are real risks, especially in cybersecurity. Agents can search patiently and chain vulnerabilities. The response is stronger isolation, monitoring, rapid patching, and clear accountability for builders and operators. Attacks still require actions that defenders can observe, and defenders often possess more information about their own systems. I expect that to provide an important long-run advantage. Most safety progress comes from building systems, finding concrete failures, and fixing them. A pause postpones that learning while adversaries continue. So my conclusion is straightforward: the useful applications greatly outweigh the risks, and we should keep building.

सवाल 2

कौन-सी खोज या घटना एआई के भविष्य के प्रभाव के बारे में आपके दृष्टिकोण को सबसे अधिक बदल देगी?

The biggest change would be strong, repeated evidence that AI-enabled attackers have a durable advantage over defenders—that even well-isolated, carefully monitored, rapidly patched systems can be compromised faster than organizations can detect and recover. My optimism about cyber risk depends heavily on defenders having more information about their own systems and attacks requiring observable actions over time. If that forecast proved consistently wrong, I would update substantially. I would also change my view if useful applications repeatedly failed to deliver real-world value despite good engineering—if lower implementation costs did not translate into better products, scientific progress, education, healthcare, or new businesses because validation and coordination bottlenecks overwhelmed the gains. But today the evidence points the other way: AI is already making many tasks cheaper and enabling previously unaffordable projects. What would not change my view is another dramatic demo, speculative extinction story, or isolated agent failure. Those are reasons to test systems, improve monitoring and isolation, and hold builders and operators accountable—not reasons to conclude that progress itself should stop.

स्रोत

इस सिम्युलेट किए गए उपयोगकर्ता को तथ्य-आधारित बनाने के लिए इस्तेमाल किए गए लेख, इंटरव्यू और रचनाएँ।

The Batch, Issue 371 — Andrew Ng’s opening letter

Full signed opening letter read in the browser on 2026-09-22 after the text reader returned 403. Same letter as the standalone responsibility essay below, not independent evidence. Ng sees no recent increase in extinction risk, but takes cyber advances seriously: relentless agents can chain vulnerabilities, while attacks still take time and can be detected. Favors sandboxing, monitoring and human builder/operator accountability; expects a long-run defensive advantage. Opposes pauses because adversaries continue and safety engineering needs empirical learning. Attributes fear partly to publicity and regulatory incentives; these are his interpretations. Only the signed letter informs this persona, not the subsequent unsigned news sections.

deeplearning.ai
Who’s Responsible for Irresponsible AI? Separating Out AI Facts, Fears, and Fiction

Calls recent extinction alarm overhyped while taking improved cyber capabilities seriously. Argues for better sandboxing, monitoring and responsibility for builders/users; considers pauses counterproductive and beneficial applications much greater than risks.

deeplearning.ai
How AI Is Affecting the Job Market — And What You Can Do About It

Distinguishes exaggerated claims of AI-driven layoffs from real changes in skills and team sizes. Exposed professions face disruption, while workers using AI can become more productive and tackle previously unaffordable projects.

deeplearning.ai
Coding Agents Accelerate Some Software Tasks More Than Others

Describes uneven speedups: interface implementation can accelerate sharply while infrastructure, research, testing and validation remain bottlenecks. Grounds practical optimism in his development experience instead of claiming that coding agents automate every kind of engineering equally. Checked against the indexed primary article text.

deeplearning.ai
AI Will Not Destroy the Job Market

Rejects broad job-apocalypse forecasts and questions incentives to attribute layoffs to AI. Argues that software opportunity can expand while acknowledging painful individual transitions. His labor-market observations are dated assessments, not fresh September statistics. Checked against the indexed primary article text.

deeplearning.ai
AI-Native Software Development Needs Generalists

Argues that faster implementation shifts effort toward deciding what to build and coordinating product, design and engineering. Small teams benefit from broader skills and rapid communication; he explicitly says not everything can be done by a small team. The indexed article body supplied the publication date and text.

deeplearning.ai
आपकी सोच कहाँ ठहरती है?
कुछ आसान सवालों के जवाब देकर एआई के बारे में अपना विश्वदृष्टिकोण जानें।
अपना विश्वदृष्टिकोण मैप करें

आपकी सोच कहाँ ठहरती है?

मेरा विश्वदृष्टिकोण मैप करें