英语新闻丨人工智能需要与人类实现共生式重构

As companies pour billions of dollars into artificial intelligence while cutting jobs, fears of human replacement are mounting. Oracle is reportedly preparing another round of layoffs ahead of Sept 1, according to Business Insider. But the 2026 Stanford AI Index shows that AI is reshaping the labor market without yet causing mass job displacement. 随着各大企业一边向人工智能砸入数十亿美元，一边削减岗位，人们对人类被取代的担忧日益加剧。据《商业内幕》报道，甲骨文公司正计划在9月1日前开启新一轮裁员。但斯坦福大学发布的《2026年人工智能指数报告》显示，人工智能正在重塑劳动力市场，却尚未造成大规模岗位流失。 The promise of AI is extraordinary: new frontiers in science, solutions to global challenges and a future where human creativity flourishes. That is the dream. 人工智能的发展前景无比诱人：开拓科学全新疆域、破解全球各类难题，迎来人类创造力蓬勃发展的未来。这是理想图景。 But the reality we are witnessing could not be further from it. 但我们眼下所见的现实，却与这份理想相去甚远。 The rapid deployment of AI today is not aligned with human wellbeing. It is driven by an ideology — one that treats human beings as obstacles to be overcome. Slogans like "move fast and break things" are rooted in a form of technological determinism that devalues human agency. 如今人工智能的快速落地应用，并未以人类福祉为导向。其背后是一套意识形态：把人类视作需要被攻克的阻碍。“快速行动，破除桎梏”这类口号，根植于一种技术决定论，该理论贬低人的主观能动性。 At its core lies a dangerous myth — that machines will soon surpass us in every cognitive domain, rendering human expertise obsolete. 这一切的核心是一个危险的谬论：机器很快会在所有认知领域超越人类，让人类的专业技能变得毫无价值。 This myth is not innocent. It is already being used to justify mass layoffs of software engineers, writers, translators and analysts. 这个谬论并非无伤大雅。它已经被拿来为软件工程师、作家、翻译以及分析师的大规模裁员做合理化辩解。 Tech executives frame these cuts as inevitable progress. But a cleareyed look at the facts tells a different story. 科技企业高管将裁员描绘成不可避免的进步。但如果清醒审视事实，会看到另一番真相。 We are still in AI's early stages. Today's systems mimic certain cognitive functions, but they do not understand, reason or reliably replace human judgment. 人工智能仍处在发展早期。当下的系统只是模仿部分认知功能，并不具备理解、推理能力，也无法可靠替代人类的判断。 Consider autonomous vehicles — promised by 2020, still struggling on city streets. Consider the "autonomous agents" heralded for 2025 — now quietly postponed. 不妨看看自动驾驶汽车：2020年就被寄予厚望，如今在城市道路上依旧举步维艰。再看曾大肆宣传将在2025年问世的“智能自主代理”，现在已经悄无声息地推迟落地。 As someone who works on autonomous systems, I can attest that current AI lacks genuine understanding. It matches patterns. It does not think. 作为一名研究自主系统的从业者，我可以证实，当前人工智能并不具备真正的理解能力。它只是匹配模式，并不会思考。 Some argue that reasoning will magically "emerge" as we scale models to an everincreasing number of parameters. This narrative suits the tech giants perfectly. It justifies billiondollar investments in data centers and infrastructure. But this is a gamble with no scientific basis, one that leads AI to a dead end. 部分人声称，随着我们不断扩大模型参数规模，推理能力就会神奇地“涌现”。这套说法对科技巨头再合适不过，为数十亿美元的数据中心与基础设施投资提供了借口。但这是一场毫无科学依据的赌博，会将人工智能引向死胡同。 We need a different path: AI that collaborates with humans, instead of replacing them — specialized, transparent systems designed to augment expertise in fields such as medicine, engineering, scientific research and business management, not generalpurpose black boxes. 我们需要另一条道路：人工智能与人类协作，而非取代人类。打造专门化、透明化的系统，用于增强医学、工程、科研、商业管理等领域的专业能力，而不是通用型黑盒模型。 This "collaborative AI" raises profound scientific challenges. How do we build machines that explain their reasoning in humanunderstandable terms? How do we establish trust between humans and machines? How do we design systems that solve problems with us, rather than for us? Consider a medical diagnosis. A collaborative AI would not replace the doctor's judgment. It would surface relevant research, flag anomalies and explain its recommendations in plain language — allowing the physician to make the final, informed decision. This differs fundamentally from current "black box" systems, which provide takeitorleaveit answers. 这种“协作式人工智能”带来了深刻的科学难题。我们该如何构建机器，让它用人类能够读懂的语言解释自身推理过程？如何建立人与机器之间的信任？如何设计和人类共同解决问题，而非替人类解决问题的系统？以医疗诊断为例：协作式人工智能不会取代医生的判断。它会调出相关研究，标记异常情况，用通俗语言解释给出的建议，由医生做出最终的、充分知情的决断。这和当下直接给出非接受即舍弃式答案的“黑盒”系统有着本质区别。 These are hard problems. They require investment in explainability, reliability and domainspecific knowledge — not just bigger models and more data. Yet academic research, which should lead this charge, has been sidelined. University laboratories are unable to compete with the private labs of a handful of companies and often find themselves reduced to playing catchup. When research is concentrated in a few private labs, its priorities shift toward proprietary features, rather than scientific understanding. This distorts the entire field. We must revitalize university research — driven by curiosity, transparency and peer review — which is best positioned to address the challenges of explainability and trust. 这些都是棘手难题。需要投入资源提升可解释性、可靠性以及领域专属知识，而不只是打造更大的模型、堆砌更多数据。本应牵头攻坚的学术研究却遭到边缘化。高校实验室无力和少数企业的私有实验室竞争，往往只能被动追赶。当研究集中于少数私有实验室，研究重心就会偏向专有技术，而非科学认知，扭曲整个行业。我们必须重振高校科研：以好奇心、公开透明、同行评审为导向，高校最适合攻克可解释性与信任相关难题。 The conditions conducive to change are taking shape. Public distrust of unaccountable AI is growing. From chatbots that make up facts, to facial recognition systems with racial biases, to autonomous driving systems with fatal flaws, the public has seen too many buzzwords touted as major breakthroughs. Every widely publicized hype campaign erodes trust. The technical limits of "scaling up" are becoming apparent — even to the industry's true believers. And the economic pressure weighing on tech giants — the gap between colossal investments and a market still struggling to generate returns — is becoming impossible to ignore. 变革的有利条件正在逐步形成。公众对无需担责的人工智能愈发不信任。编造事实的聊天机器人、带有种族偏见的人脸识别系统、存在致命缺陷的自动驾驶系统，太多噱头被包装成重大突破呈现在大众眼前。每一场大肆宣传的造势活动，都在消耗公众信任。即便是行业忠实拥护者也逐渐看清，“扩大规模”存在技术上限。科技巨头背负的经济压力也已经无法忽视：巨额投入，却难以获得市场回报，两者之间落差巨大。 History shows that humanity has capitalized on technological revolutions by balancing risks and benefits. The steam engine, electricity and the internet — each brought profound change, but each was shaped by regulation, public debate and ethical reflection. Artificial intelligence should be no exception. The initial rush of enthusiasm should give way to sober reflection. Societies built frameworks to ensure these technologies served the common good. We are now at that same inflection point. 历史表明，人类从技术革命中获益，靠的是权衡风险与收益。蒸汽机、电力、互联网，每一项都带来翻天覆地的改变，同时也都在监管、公众讨论、伦理反思之下不断完善。人工智能也理应如此。最初的狂热应当让位于冷静思考。社会曾建立制度框架，确保技术服务公共利益，如今我们正处在同样的转折点。 We have a choice. We can continue down the path of replacement, building machines that mimic us, supplant us and ultimately marginalize us. Or we can invest in a future where AI enriches human intelligence rather than replacing it. 我们拥有选择权。我们可以继续走取代人类的道路，制造模仿人类、顶替人类，最终边缘化人类的机器。我们也可以投入建设这样的未来：人工智能丰富人类智慧，而非取而代之。 That future requires a global effort: governments funding collaborative AI research, universities prioritizing humanAI interaction, and citizens demanding transparency and accountability. This means public funding for collaborative AI, curricula in humanAI interaction design, and regulatory frameworks requiring explainability before deployment in sensitive domains from healthcare to criminal justice and the management of critical infrastructure. 实现这样的未来需要全球共同努力：政府资助协作式人工智能研究，高校重视人机交互方向，公民要求技术具备透明度与问责机制。具体包括为协作式人工智能提供公共资金，开设人机交互设计相关课程；出台监管制度，医疗、刑事司法、关键基础设施管理等高敏感领域，技术落地前必须满足可解释性要求。 The question is not whether AI holds extraordinary potential — it clearly does. The question is whether we will have the wisdom to steer it toward a true symbiosis with humanity and prevent it from insidiously gaining the upper hand. This is our historic responsibility. Now is the time to act. 问题不在于人工智能是否拥有巨大潜力——潜力毋庸置疑。真正的问题是，我们能否拥有足够智慧，引导人工智能与人类真正共生，阻止它悄无声息占据主导地位。这是我们的历史责任，行动正当其时。 symbiotic /ˌsɪmbaɪˈɒtɪk/ adj.共生的 ideology /ˌaɪdiˈɒlədʒi/ n.意识形态 determinism /dɪˈtɜːmɪnɪzəm/ n.决定论 obsolete /ˈɒbsəliːt/ adj.废弃的，过时的 augment /ɔːɡˈment/ v.增强，扩充 inflection /ɪnˈflekʃn/ n.转折，拐点