張亞勤｜未來(lái)，每個(gè)人、每個(gè)設(shè)備都將擁有智能體

2025年11月18-20日，以“青年之智，解碼未來(lái)”為主題的2025全球青年領(lǐng)袖年度對(duì)話(huà)會(huì)在北京成功舉辦。本屆對(duì)話(huà)會(huì)由全球化智庫(kù)（CCG）主辦、“國(guó)際青年領(lǐng)袖對(duì)話(huà)（GYLD）”項(xiàng)目秘書(shū)處協(xié)辦，并得到北京市海淀區(qū)人才工作局和海淀區(qū)人民政府外事辦公室支持。清華大學(xué)智能科學(xué)講席教授、智能產(chǎn)業(yè)研究院院長(zhǎng)張亞勤出席11月19日的開(kāi)幕式，并發(fā)表題為《擁抱人工智能新浪潮：人工智能向善》 的主旨演講。

Themed "Decoding the Future with Young Minds," the Global Young Leaders Dialogue Annual Forum 2025 was successfully held in Beijing from November 18 to 20. The event was hosted by the Center for China and Globalization (CCG), co-organized by the Secretariat of the Global Young Leaders Dialogue (GYLD) program, and supported by the Haidian District Human Resources Bureau and the Haidian District Foreign Affairs Office.At the Opening Ceremony on November 19, Dr. Ya-Qin Zhang, Chair Professor of AI Science and Dean of the Institute for AI Industry Research (AIR) at Tsinghua University, delivered a keynote speech titled “Embracing the New Wave of AI: AI for Better.”

以下附張亞勤博士演講重點(diǎn)摘要。英文轉(zhuǎn)錄和中文譯文均僅供參考，具體內(nèi)容以視頻演講為準(zhǔn)。

Below are selected highlights from Dr. Ya-Qin Zhang’s speech. The English transcription and Chinese translation are for reference only; please refer to the video speech for the accurate content.

Many thanks, Huiyao and Miao Lu for the very kind invitation. It's always exciting to interact with young leaders.

非常感謝輝耀和苗綠的熱情邀請(qǐng)，與青年領(lǐng)袖交流總令人振奮。

I want to talk about some new exciting developments on AI, a subject I've been working on for over 30 years, from Microsoft to Baidu, and now at Tsinghua University. The theme is AI for better. I want to talk about some megatrends in technology, some state of the art in AI, comparisons between China, the US, and the rest of the world, and finally, future trends in the AI industry.

我想談?wù)勅斯ぶ悄茴I(lǐng)域一些激動(dòng)人心的新進(jìn)展，這已是我深耕三十余年的課題——從微軟到百度，再到如今的清華大學(xué)。我的主題是“人工智能向善”。我將談及技術(shù)領(lǐng)域的宏觀趨勢(shì)、人工智能的前沿案例、中美及全球AI發(fā)展比較，并最后展望人工智能產(chǎn)業(yè)的未來(lái)走向。

Indeed, AI is the technology engine behind the Fourth Industrial Revolution. This new wave of AI is a convergence of digital AI, physical AI, and biological AI. This is the backdrop for why I started AIR, the Institute for AI Industry Research at Tsinghua University, about five years ago. It's very lucky that we are able to recruit some of the best minds in the world. Now we have over 20 professors and 400 top-notch PhD students, postdocs, and research scientists.

的確，人工智能是第四次工業(yè)革命背后的技術(shù)引擎。當(dāng)前這一輪人工智能浪潮，本質(zhì)上是數(shù)字AI、物理AI與生物AI的深度融合。正是在這樣的時(shí)代背景下，我在大約五年前創(chuàng)立了清華大學(xué)智能產(chǎn)業(yè)研究院（AIR）。我們非常幸運(yùn)能夠招募全球最優(yōu)秀的研究者。目前，研究院擁有20多位教授，以及400多名頂尖博士生、博士后和研究科學(xué)家。

I will use a few examples to illustrate some of the state of the art in digital AI, physical AI and biological AI.

接下來(lái)，我將通過(guò)幾個(gè)具體案例，介紹數(shù)字AI、物理AI和生物AI的一些前沿案例。

First, our work on large language models. We work closely with leading companies like ByteDance, Alibaba, and DeepSeek. A major advance in large language models recently is reinforcement learning and reasoning models. Our professors and researchers developed DAPO with ByteDance, which improved reinforcement learning performance and efficiency by several times. With DeepSeek, we work on defining a better reward model for GRM. With Alibaba's Qwen, we work on agentic AI. One thing about our work is that we publish everything—our research, algorithms, code, and data.

首先是我們?cè)诖笳Z(yǔ)言模型方面的工作。我們與字節(jié)跳動(dòng)、阿里巴巴、深度求索等領(lǐng)先企業(yè)保持著緊密合作。近來(lái)，大語(yǔ)言模型的一個(gè)重要突破在于強(qiáng)化學(xué)習(xí)和推理模型方面。我們的教授和研究人員與字節(jié)跳動(dòng)聯(lián)合研發(fā)了DAPO算法，使強(qiáng)化學(xué)習(xí)的性能和效率提升了數(shù)倍。我們與深度求索合作，優(yōu)化GRM通用獎(jiǎng)勵(lì)模型；并與阿里巴巴的千問(wèn)團(tuán)隊(duì)共同推進(jìn)AI智能體研究。值得一提的是，我們的研究成果、算法、代碼和數(shù)據(jù)都全面公開(kāi)發(fā)布。

The second example on physical AI is autonomous driving—driverless cars. When I was President of Baidu, I started a program called the Apollo. An example of this work is Apollo Go. Right now, we have the largest self-driving fleet in the world, particularly in Wuhan, with over 1,500 cars covering 17 million people across 3,000 square kilometers. This is the most challenging part of physical AI or embodied AI. A self-driving car is a special robot that accumulates all the critical pieces of AI. A critical advancement is the large language models in the past few years. Now we're able to generate data, cover some of the long-tail cases, do a lot of simulation, and conduct road tests. Our data, after 200 million kilometers [of driving], shows [autonomous driving] is 17 times safer than people. We haven't had a single casualty or major accident. This will be the foundation for embodied AI, and we will use the same foundation model for industrial, social, or home robots.

第二個(gè)例子來(lái)自物理AI領(lǐng)域的自動(dòng)駕駛，也就是無(wú)人駕駛汽車(chē)。我在擔(dān)任百度總裁期間，啟動(dòng)了“阿波羅”計(jì)劃，其代表性應(yīng)用便是“蘿卜快跑”。目前，我們已經(jīng)擁有全球規(guī)模最大的自動(dòng)駕駛車(chē)隊(duì)，尤其是在武漢，超過(guò)1500輛無(wú)人車(chē)覆蓋約3000平方公里、服務(wù)1700萬(wàn)人口。自動(dòng)駕駛是物理AI或具身智能中最具挑戰(zhàn)性的方向之一。一輛無(wú)人車(chē)，本質(zhì)上是一種集成了幾乎所有關(guān)鍵AI能力的特殊機(jī)器人。近年來(lái)，大語(yǔ)言模型的發(fā)展成為關(guān)鍵突破，使我們能夠生成更多數(shù)據(jù)、覆蓋長(zhǎng)尾場(chǎng)景、開(kāi)展大規(guī)模仿真和道路測(cè)試。基于累計(jì)2億公里的行駛數(shù)據(jù)分析，無(wú)人駕駛的安全性已達(dá)到人類(lèi)駕駛的17倍。我們目前為止沒(méi)有發(fā)生一起傷亡或一起重大事故。這將成為具身智能的基礎(chǔ)，未來(lái)我們也會(huì)將這一基礎(chǔ)模型用于工業(yè)機(jī)器人、社會(huì)服務(wù)機(jī)器人以及家庭機(jī)器人。

Third, biological AI. Professor Liu Yang developed the world's first AI Agent Hospital. It simulates a real hospital in Beijing, and everything is virtual: doctors, patients, nurses, and pharmacists are all agents. We emulate 21 different departments. The agents are evolving in this virtual world at a much faster speed than the real world. The result is that in two days, we're able to cover the work of a large 3A hospital in two or three years, with much higher accuracy—achieving an accuracy of 92% on the USMLE (US Medical Licensing Examination) benchmark. For an average doctor who has a license in the US, the average is 65%. [AI doctors] are not trying to replace real doctors, but to be agents and assistants to doctors for faster and more accurate diagnosis—also useful for rural areas without good medical access.

第三個(gè)例子是生物AI。劉洋教授團(tuán)隊(duì)開(kāi)發(fā)了全球首個(gè)“AI智能體醫(yī)院”。它完整模擬了一家北京的真實(shí)醫(yī)院，醫(yī)生、患者、護(hù)士和藥劑師全部由智能體構(gòu)成，共覆蓋21個(gè)科室。在這個(gè)虛擬世界中，智能體的學(xué)習(xí)和進(jìn)化速度遠(yuǎn)快于現(xiàn)實(shí)世界。結(jié)果是，僅用兩天時(shí)間，就能完成一家大型三甲醫(yī)院兩到三年的工作量，并以美國(guó)執(zhí)業(yè)醫(yī)師考試（USMLE）為基準(zhǔn)取得了92%的準(zhǔn)確率，而美國(guó)持證醫(yī)生的平均水平約為65%。需要強(qiáng)調(diào)的是，這些AI醫(yī)生并非試圖取代真實(shí)的醫(yī)生，而是作為智能體助手，幫助醫(yī)生更快、更準(zhǔn)確地作出診斷，尤其對(duì)醫(yī)療資源不足的農(nóng)村地區(qū)來(lái)說(shuō)幫助性很大。

We also have about 1/3 of our professors working on AI for new drug development. I agree with Nobel laureate Demis Hassabis that AI is going to accelerate drug development dramatically. We are changing how drug is developed and how trials are conducted. Obviously, AI is becoming super powerful but also risky. I personally work with a few leading AI scientists and a few Nobel laureates to form the International Dialogue on AI Safety. The risk for AI is real, especially when expanding from digital to physical and biological domains, and we need specific and very prompt measures to take care of this.

此外，我們約有三分之一的教授正從事AI驅(qū)動(dòng)的新藥研發(fā)工作。我認(rèn)同諾貝爾獎(jiǎng)得主德米斯·哈薩比斯的判斷：人工智能將極大加速藥物研發(fā)進(jìn)程。我們正在改變藥物研發(fā)和臨床試驗(yàn)的方式。當(dāng)然，AI在變得越來(lái)越強(qiáng)大的同時(shí)，也伴隨著風(fēng)險(xiǎn)。我本人也與多位頂尖AI科學(xué)家和諾貝爾獎(jiǎng)得主共同召開(kāi)人工智能安全國(guó)際對(duì)話(huà)。隨著AI從數(shù)字領(lǐng)域拓展到物理和生物領(lǐng)域，風(fēng)險(xiǎn)將更加復(fù)雜，我們必須采取具體、及時(shí)的治理措施。

Let me also talk about AI in China versus the US. I get asked all the time: what’s the difference? Which one is ahead? In the US, OpenAI, Microsoft, Google Deep Mind, Anthropic, Grok, and Meta are hyperscalers. In China, models like DeepSeek, Qwen from Alibaba, and Doubao from ByteDance stand out. In terms of chips and infrastructure, the US is ahead, with the exception of electricity. China is actually way ahead in terms of electricity grid. In terms of models and software, China is more open and has smarter models with more efficient architectures. The US has more frontier, bigger models, and they are more closed. In terms of applications, China is probably ahead. My view is that it's not about who is going to win. I think both China and the US are going to win, and Europe and Africa are going to win. Everybody can be a winner. We are at the very beginning of a new revolution and everybody will benefit from this revolution. Healthy competition is wonderful. This is not a zero-sum game.

關(guān)于中美人工智能的比較，我經(jīng)常被問(wèn)到：區(qū)別是什么？誰(shuí)更領(lǐng)先？在美國(guó)，有OpenAI、微軟、谷歌DeepMind、Anthropic、Grok和Meta等超大規(guī)模機(jī)構(gòu)；在中國(guó)，則有深度求索、阿里巴巴的通義千問(wèn)、字節(jié)跳動(dòng)的豆包等優(yōu)秀模型。在芯片和基礎(chǔ)設(shè)施方面，美國(guó)整體領(lǐng)先，但電力供應(yīng)是一個(gè)例外。事實(shí)上，中國(guó)在電力網(wǎng)絡(luò)方面明顯領(lǐng)先。在模型和軟件層面，中國(guó)更為開(kāi)源，模型架構(gòu)也更高效；美國(guó)則擁有更多前沿、規(guī)模更大的模型，但整體更為閉源。在應(yīng)用軟件方面，中國(guó)可能走在前列。但在我看來(lái)，這并不是一場(chǎng)你輸我贏的競(jìng)爭(zhēng)。中美會(huì)贏，歐洲和非洲同樣會(huì)贏，所有人都將是贏家。我們正站在一場(chǎng)全新革命的起點(diǎn)，所有人都將從中受益。健康的競(jìng)爭(zhēng)是好事，這不是零和博弈。

Finally, what is the future of AI? Just a disclaimer: I don’t represent anybody. My personal view is that, first, we are moving from generative AI to agentic AI, and we are actually moving from agentic AI to agentic network. We're moving from internet of PCs, internet of mobiles, internet of things, to the internet of agents. In the future, every person or every device will have agents. They will all interact, work together, evolve and come back with answer to serve the people. Secondly, both open and closed models will move forward. I see 80% of models will be open and 20% will be closed. We are also going to see a lot of foundation models, but the biggest opportunities will reside in vertical models—for robots, biology, and prediction. The opportunity there is at least 100 times bigger than that of foundation models. You can also have a lot of edge models, meaning models on your phones, your PCs, and your glasses, and that’s going to be a huge market as well. Third, a critical piece of generative AI is the scaling law. Scaling in pre-training is already slowing down, because data is almost depleted. You can still see the growth, but it has flattened out. The key intelligence lies in post-training, in agents, inference, reinforcement learning, and post-training scaling.

最后談?wù)勎覀€(gè)人對(duì)AI未來(lái)的看法。需要說(shuō)明的是，我的觀點(diǎn)不代表其他任何人。第一，我們正在從生成式AI走向AI智能體，并進(jìn)一步走向智能體網(wǎng)絡(luò)。從PC互聯(lián)網(wǎng)、移動(dòng)互聯(lián)網(wǎng)、物聯(lián)網(wǎng)，逐步邁向智能體互聯(lián)網(wǎng)。未來(lái)，每個(gè)人、每個(gè)設(shè)備都將擁有智能體，它們相互協(xié)作、持續(xù)進(jìn)化，最終帶著答案更好地服務(wù)人類(lèi)。第二，開(kāi)源與閉源模型將并行發(fā)展。我預(yù)計(jì)大約80%的模型會(huì)是開(kāi)源的，20%是閉源的。基礎(chǔ)模型會(huì)很多，但最大的機(jī)會(huì)將來(lái)自垂直領(lǐng)域模型——例如機(jī)器人、生物和預(yù)測(cè)等領(lǐng)域。這些垂類(lèi)模型所蘊(yùn)含的機(jī)會(huì)，至少是基礎(chǔ)模型的百倍。此外，還會(huì)出現(xiàn)大量端側(cè)模型，也就是部署在手機(jī)、個(gè)人電腦、智能眼鏡等設(shè)備上的模型，這同樣將形成一個(gè)巨大的市場(chǎng)。第三，生成式人工智能的一個(gè)關(guān)鍵機(jī)制是“規(guī)模定律（或尺度定律）”。目前，預(yù)訓(xùn)練階段的規(guī)模擴(kuò)展已經(jīng)明顯放緩，主要原因在于可用數(shù)據(jù)正在接近枯竭。雖然能力仍在增長(zhǎng)，但增速已趨于平緩。未來(lái)真正的智能突破，將更多來(lái)自后訓(xùn)練階段，包括智能體、推理、強(qiáng)化學(xué)習(xí)以及后訓(xùn)練的擴(kuò)展。

People always ask me when we are going to get AGI, Artificial General Intelligence. But there are hurdles to get there. We need new architectures, a better understanding of the physical world, a world model, and we need to do better job with memory, which is a key part of human intelligence. My personal take is that we can achieve AGI in digital or informational AI in probably less than five years. In physical AI, driverless cars will be the first to pass a Turing test, probably in three to five years. For humanoids, probably ten years. For biological AI—like a neural link of different sensors connecting your brain with AI—that will probably take another 15 to 20 years.

很多人問(wèn)我，通用人工智能（AGI）什么時(shí)候會(huì)到來(lái)？實(shí)現(xiàn)AGI仍面臨諸多挑戰(zhàn)：我們需要新的模型架構(gòu)，對(duì)物理世界更深入的理解，需要真正的世界模型，還需要在“記憶”這一人類(lèi)智力的關(guān)鍵要素上取得突破。我的個(gè)人判斷是，在數(shù)字或信息AI領(lǐng)域，AGI可能在五年內(nèi)實(shí)現(xiàn)；在物理AI中，無(wú)人駕駛可能率先通過(guò)圖靈測(cè)試，大約需要三到五年；人形機(jī)器人可能需要十年；而生物AI，例如通過(guò)多種傳感器將人腦與人工智能連接的腦機(jī)接口技術(shù)，或許還需要十五到二十年。

Overall, I'm very optimistic about what AI can do for the world. It can do tremendously good, and it can also hurt people if we don't take the right direction. The future relies on young talents, on all of you.

總體而言，我對(duì)人工智能為世界帶來(lái)的可能性保持高度樂(lè)觀。若能把握正確方向，它將帶來(lái)巨大的福祉；反之也可能給人類(lèi)帶來(lái)傷害。未來(lái)最終掌握在年輕一代手中，也正是在座各位的手中。

Thank you.

謝謝大家。

CCG 圖書(shū)

● 出版 | 中國(guó)科學(xué)技術(shù)出版社

● 作者 | 苗綠

圖書(shū)介紹

《在全球化的世界中行走》講述了苗綠博士作為全球化智庫(kù)聯(lián)合創(chuàng)始人，在個(gè)人成長(zhǎng)、海內(nèi)外求學(xué)、創(chuàng)辦智庫(kù)、國(guó)際交流、民間外交、為國(guó)家建言獻(xiàn)策等過(guò)程中的諸多故事與心路歷程。作為慕尼黑安全會(huì)議青年領(lǐng)袖代表，苗綠博士曾對(duì)話(huà)聯(lián)合國(guó)秘書(shū)長(zhǎng)古特雷斯，開(kāi)啟 2021 慕安會(huì)第一問(wèn)；她是比利時(shí)國(guó)王會(huì)見(jiàn)的七位全球青年領(lǐng)袖之一；她發(fā)起的“國(guó)際青年領(lǐng)袖對(duì)話(huà)”項(xiàng)目，推動(dòng)了國(guó)際間青年的交流互鑒，得到中國(guó)國(guó)家領(lǐng)導(dǎo)人的回信；她經(jīng)常受邀參加國(guó)際高端論壇，在巴黎和平論壇、多哈論壇等重要國(guó)際場(chǎng)合，參與設(shè)置議程，打造國(guó)際交流新敘事，以全球視野講述時(shí)代中國(guó)，展現(xiàn)了新時(shí)代中國(guó)智庫(kù)人的風(fēng)采。

● 出版 | 中信出版集團(tuán)

● 作者 | 王輝耀，苗綠

圖書(shū)介紹

《21世紀(jì)的中國(guó)與全球化》首先梳理了全球化的變遷與理論發(fā)展，從技術(shù)與人本等新的視角觀察全球化，并做出全球化的界定，總結(jié)了后疫情時(shí)代新型全球化具備的特征，然后對(duì)中國(guó)融入全球化的歷史與現(xiàn)實(shí)進(jìn)行了全面總結(jié)，用數(shù)據(jù)與事實(shí)說(shuō)明，中國(guó)正在從全球化的受益者發(fā)展為反哺者，正在通過(guò)自身發(fā)展推動(dòng)全球化進(jìn)程，并嘗試承擔(dān)起更多國(guó)際責(zé)任，為全球治理創(chuàng)新貢獻(xiàn)方案。作者對(duì)全球化發(fā)展的理論和文獻(xiàn)做了梳理，回顧了全球化在世界和中國(guó)的發(fā)展歷程，指出全球化走到了一個(gè)十字路口。本書(shū)從第四章開(kāi)始，兩位作者對(duì)中國(guó)推動(dòng)全球化實(shí)現(xiàn)包容性和公平性發(fā)展的路徑進(jìn)行了探索，通過(guò)發(fā)揮中國(guó)的優(yōu)勢(shì)和特點(diǎn)，讓中國(guó)為全球化發(fā)展注入新動(dòng)力。作者基于長(zhǎng)期的研究以及與國(guó)內(nèi)國(guó)際、官產(chǎn)學(xué)各界有影響力重要人士的對(duì)話(huà)交流等，對(duì)中國(guó)的全球化發(fā)展路徑及全球治理創(chuàng)新等形成了新的思考，提出中國(guó)推動(dòng)全球化發(fā)展的三大支柱與七大路徑。

● 出版 | 中國(guó)科學(xué)技術(shù)出版社

● 作者 | 王輝耀

圖書(shū)介紹

本書(shū)深度剖析了中國(guó)在全球化浪潮中的角色演變與抉擇，及其對(duì)全球未來(lái)的影響。全書(shū)分為三部分，第一部分回顧了中國(guó)融入全球化的歷程，展示了中國(guó)從一個(gè)封閉的農(nóng)業(yè)國(guó)家逐步轉(zhuǎn)型為全球第二大經(jīng)濟(jì)體的過(guò)程。書(shū)中詳細(xì)探討了中國(guó)在貿(mào)易、投資、跨國(guó)企業(yè)崛起等方面的角色變遷，以及教育、人才和文化紐帶在這一進(jìn)程中的重要作用。第二部分探討了中國(guó)在國(guó)際舞臺(tái)上的崛起及其對(duì)全球治理的影響。作者分析了中國(guó)在多極化世界中的地位變化，風(fēng)云激蕩中的中國(guó)外交，中美關(guān)系的復(fù)雜性，以及中國(guó)在崛起的、更加一體化的亞洲中的角色。同時(shí)，還討論了中歐關(guān)系的發(fā)展與挑戰(zhàn)。第三部分審視了多邊主義面臨的挑戰(zhàn)和改革。書(shū)中探討了如何共同應(yīng)對(duì)全球性挑戰(zhàn)，尋找自由貿(mào)易的發(fā)展方向，以及“一帶一路”倡議的發(fā)展。通過(guò)這些討論，展示出中國(guó)在全球治理中的積極參與和貢獻(xiàn)。

● 出版 | 中國(guó)科學(xué)技術(shù)出版社

● 編著 | 王輝耀、苗綠

圖書(shū)介紹

《對(duì)話(huà)世界：理解新時(shí)代的全球化》全書(shū)分為三部分：第一部分“全球化發(fā)展史”回顧了全球化的歷程，從古代貿(mào)易到現(xiàn)代經(jīng)濟(jì)轉(zhuǎn)型，探討了全球化的起源與演變。通過(guò)與耶魯大學(xué)教授瓦萊麗·韓森、《金融時(shí)報(bào)》首席經(jīng)濟(jì)評(píng)論員馬丁·沃爾夫和《世界是平的》作者托馬斯·弗里德曼的對(duì)話(huà)，揭示了全球化的多層次發(fā)展。第二部分“彌合全球不平等與赤字”探討了全球化帶來(lái)的不平等和治理赤字問(wèn)題。諾貝爾經(jīng)濟(jì)學(xué)獎(jiǎng)得主安格斯·迪頓、巴黎和平論壇主席帕斯卡爾·拉米、亞洲協(xié)會(huì)副所長(zhǎng)溫迪·卡特勒等嘉賓，分享了他們對(duì)全球經(jīng)濟(jì)不平等、貿(mào)易體系和制度改革的看法。第三部分《權(quán)力轉(zhuǎn)移與大國(guó)關(guān)系》分析了21世紀(jì)的權(quán)力轉(zhuǎn)移和大國(guó)關(guān)系，特別是中美關(guān)系的復(fù)雜性。通過(guò)與哈佛大學(xué)教授格雷厄姆·艾利森、“軟實(shí)力之父”約瑟夫·奈、布魯金斯學(xué)會(huì)主席約翰·桑頓等專(zhuān)家的對(duì)話(huà)，討論了大國(guó)競(jìng)爭(zhēng)、合作以及全球治理的未來(lái)。

● 出版 | 中信出版集團(tuán)

● 編著 | 王輝耀

圖書(shū)介紹

作為海內(nèi)外決策層和廣大公眾理解中美關(guān)系時(shí)廣泛引用的框架，“修昔底德陷阱”將成為未來(lái)幾十年對(duì)全球秩序有決定性影響的問(wèn)題。在與全球化智庫(kù)(CCG)理事長(zhǎng)王輝耀的對(duì)話(huà)中，格雷厄姆·艾利森就中美關(guān)系和中美地緣政治競(jìng)爭(zhēng)、中國(guó)崛起、美國(guó)外交政策、美蘇關(guān)系、全球地緣政治、核武器、朝鮮問(wèn)題、新冠疫情及影響等議題進(jìn)行了深入闡述；全面、系統(tǒng)性地展示了艾利森對(duì)“修昔底德陷阱”和中美經(jīng)濟(jì)、金融、科技、軍事、外交等多個(gè)方面存在的結(jié)構(gòu)性矛盾和競(jìng)爭(zhēng)的看法；深入而透徹地分析了中美雙方實(shí)力的變化，以及發(fā)生戰(zhàn)爭(zhēng)的風(fēng)險(xiǎn)；坦誠(chéng)而直率地提出了跨越“修昔底德陷阱”的方法和建議。

● 出版 | 中信出版集團(tuán)

● 作者 | 王輝耀，苗綠

圖書(shū)介紹

《我向世界說(shuō)中國(guó)》是由全球化智庫(kù)（CCG）主任王輝耀和秘書(shū)長(zhǎng)苗綠基于“世界新格局下的中國(guó)對(duì)外敘事及話(huà)語(yǔ)權(quán)重塑”問(wèn)題研究的重要成果，由中信出版集團(tuán)出版。據(jù)悉，該書(shū)講述了全球化智庫(kù)近年來(lái)立足芒克辯論會(huì)、慕尼黑安全會(huì)議、巴黎和平論壇、達(dá)沃斯論壇等知名國(guó)際舞臺(tái)，與各國(guó)政商學(xué)界知名人士暢談國(guó)際時(shí)局與未來(lái)趨勢(shì)，回應(yīng)各方對(duì)于中國(guó)的關(guān)切和質(zhì)疑，詮釋中國(guó)的發(fā)展模式，降低外界對(duì)中國(guó)的誤解，通過(guò)多層次、多主體、多元化、多渠道國(guó)際交流及傳播，以全球視野講述時(shí)代中國(guó)，積極塑造可信可愛(ài)可敬的中國(guó)形象的生動(dòng)故事。同時(shí)，本書(shū)立足國(guó)際形勢(shì)變化和全球傳播新格局，針對(duì)中國(guó)應(yīng)當(dāng)如何開(kāi)展對(duì)外交流和傳播工作、如何創(chuàng)新外宣方式講好中國(guó)故事等問(wèn)題進(jìn)行了深入淺出的剖析。