现代工程项目日益将人的判断与机器能力相结合。 工程师可以定义一个问题，然后要求人工智能工具搜索相关的研究和技术标准，生成设计选项，在模拟中测试，并比较结果。 然后，工程师可以把结果带给那些了解AI无法自行判断的部分的专家，比如设计是否能够安全地建造，在特定环境下是否合理，或者如何可能影响用户。 关于是否和如何向前迈进的最后决定仍然是人的责任。
AI可以帮助工程师进行早期的项目步骤，这些步骤曾经需要几个具有不同技术技能的人。 因此，工程师的作用正在扩大，从执行每一个技术步骤扩大到决定人、机器和专家的知识应如何在该项目上合作。
问题不是标题
工程一直被划分为学科，并有很好的理由。 电气，机械，民用，软件等工程领域需要深入的知识，需要多年才能开发。 大赦国际不取代这一需要。 它只是改变了工程师如何容易地探索一个问题的部分，而那些部分却坐落在他们自己的专业之外。
考虑一个软件工程师在医疗设备上工作。 AI可以帮助工程师审查相关的生物医学研究，理解不熟悉的术语，分析数据，并构建早期模型。
这并不能使工程师成为医生或生物医学专家。 它允许工程师在与专家合作前提出更好的问题。
在能源、运输、制造业和许多其他领域也是如此。 一个问题可能从软件问题开始，然后迅速涉及硬件、安全措施、经济学、人类行为和规章。
工程师不需要掌握每一个领域；他们需要认识到问题需要哪些专门知识，并汇集合适的人员和工具。
系统思考的重要性
因此，系统思考变得特别重要。 它需要审视整个问题：了解其各部分如何相互影响，确定制约因素和失败点，并作出权衡。
AI 新增是这一过程的新元素。 工程师必须不仅决定技术部分如何组合，而且决定给予AI什么工作，一个人必须检查什么，专家何时作出决定，何时需要重新界定问题。
工程师的价值并不在于了解一切。 它在于知道如何定义问题，如何提出正确问题，如何测试答案，以及何时另一名专家必须领导项目。
这一点至关重要，因为工程方面的技术挑战很少是静止的。 随着新的洞察力的出现，目标可能改变，无形的限制可能出现，最初的前提可能证明存在缺陷。 有效的工程师不是严格遵循起始规格，而是随着对系统的更深入了解而调整和完善范围。
因此，工程超越了掌握技术概念；它是一种适应性强、结构化的办法来应对不断变化的挑战。
新的证据可以改变目标，揭示一个隐藏的制约，或者表明最初的假说是错误的。 有效的工程师不仅仅是在最初的问题陈述上采取行动。 随着系统得到更好的理解，他们更新了它。
从这个意义上讲，工程不仅是技术知识的集合体，也是对一个项目的纪律性思考方式。
人类的判断很重要
大赦国际即使其假设薄弱，也能提供令人信服的答案。 它可以生成在一个测试中工作但另一个测试中失败的代码。 它可以总结研究，而不理解哪些证据是最强的，或者优化设计以达到错误的目标。 这种限制使人的专门知识更加重要，而不是更少。
机器在速度，尺度，搜索，图案识别，和数据生成等重要的地方是有用的。 在环境、价值观、责任和判断中，人是必不可少的。 挑战在于决定各自属于何处。
例如，AI系统可以为医疗传感器产生若干设计，并预测每个传感器将如何运行。 工程师可以测试预测，拒绝违反技术要求的设计。 临床医生可以确定传感器是否可以实际使用。 病人和从业者可能会发现工程师和开发者都没有考虑的问题。 最后的解决办法是综合各种观点。
工程师仍然是一名建筑师和专家，但也日益成为知识的协调员。
工程师的价值并不在于了解一切。 它在于知道如何定义问题，如何提出正确问题，如何测试答案，以及何时另一名专家必须领导项目。
IEEE的关键作用
广义的工程形式为IEEE创造了重要的作用。 其社会已经汇集了各种技术、工程教育、技术标准和其他领域的专门知识。 这使得IEEE很适合帮助工程师跨专业工作而不削弱专业化的价值。
IEEE可以建立更多的社区，围绕需要若干领域专门知识的问题组织起来。 这方面的例子包括可信赖的临床AI、弹性基础设施、能源可靠性、科学发现和安全自主系统。 工程师可以从家庭学科开始，然后根据项目需要与其他IEEE社区的专家合作。
IEEE还可以帮助制定人机合作的实用标准。 工程师将需要明确指导，说明何时必须检查AI生成的工作，如何记录不确定性，专家何时必须作出决定，以及系统既使用人性判断又使用机器输出时由谁负责。
这些问题正在成为日常工程的一部分。 它们不应留给每个公司或项目经理独立回答。 专业组织在工程师在工作、行业和技术领域之间流动时，可以提供跟随他们的共同期望。
更大的工程师定义
AI并没有降低工程学科的重要性；它使得它们之间的联系更容易使用。 深层的专业知识为工程师提供了判断什么是声音的基础。 系统思维有助于他们理解一个问题的不同部分是如何结合在一起的。 AI可以扩展工程师的探索，测试和跨领域的交流能力。 人的判断赋予了工作方向和责任。
工程师不只由职称或固定的任务来定义。 工程师是受过培训的人，能够了解复杂的系统，定义和重新定义问题，汇集解决这些问题所需的知识和工具，测试解决方案是否有效，并对结果承担责任。
这就是“不受约束”的工程师的含义。
专业之间的界限变得更容易跨越，机器可能承担更多的技术工作，但工程的核心仍然是人：决定什么问题值得解决，什么证据足够好，棋子如何组合，在答案不确定时该怎么办。
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苏菲安·阿尔多戈姆是IEEE成员，麻省理工学院研究附属公司，在卡利夫的Sunnyvale的Google担任高级领导。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Modern engineering projects increasingly combine human judgment with machine capability. An engineer can define a problem, then ask an artificial intelligence tool to search relevant research and technical standards, generate design options, test them in simulation, and compare the results. The engineer can then bring the results to specialists who understand the parts AI cannot judge on its own, such as whether a design can be built safely, whether it makes sense in a particular setting, or how it might affect its users. The final decision about whether and how to move forward remains a human responsibility.
AI can help engineers do early project steps that once required several people with different technical skills. The engineer’s role is therefore expanding from performing each technical step to deciding how people, machines, and specialists’ knowledge should work together on the project.
The problem, not the title
Engineering has always been divided into disciplines, and for good reasons. Electrical, mechanical, civil, software, and other engineering fields require deep knowledge that takes years to develop. AI does not replace that need. It simply changes how easily engineers can explore the parts of a problem that sit outside their own specialty.
Consider a software engineer working on a medical device. AI can help the engineer review relevant biomedical research, understand unfamiliar terms, analyze data, and build an early model.
That does not make the engineer a physician or biomedical expert. It allows the engineer to ask better questions before working with the experts.
The same is true in energy, transportation, manufacturing, and many other fields. A problem might begin as a software issue and then quickly involve hardware, safety measures, economics, human behavior, and regulations.
Engineers do not need to master every field; they need to recognize which expertise the problem requires and bring the right people and tools together.
The importance of systems thinking
Systems thinking thus becomes especially important. It requires looking at the whole problem: understanding how its parts affect one another, identifying constraints and failure points, and making trade-offs.
AI adds a new element to the process. Engineers must decide not only how the technical parts fit together but also what work to give to AI, what a person must check, when a specialist should make a decision, and when the problem needs to be redefined.
An engineer’s value is not in knowing everything. It is in knowing how to frame the problem, how to ask the right questions, how to test the answers, and when another expert must lead the project.
This aspect is crucial because technical challenges in engineering are seldom static. As new insights emerge, objectives might shift, unseen limitations can surface, and the initial premise might prove flawed. Rather than rigidly following the starting specifications, an effective engineer adapts and refines the scope as a deeper understanding of the system develops.
Thus, engineering extends beyond mastering technical concepts; it is an adaptable, structured approach to navigating evolving challenges.
New evidence can change the goal, reveal a hidden constraint, or show that the original hypothesis was wrong. Effective engineers go beyond merely acting on the initial problem statement. They update it as the system becomes better understood.
In that sense, engineering is not only a body of technical knowledge, it is also a disciplined way of thinking about a project.
Human judgment is important
AI can produce convincing answers even when its assumptions are weak. It can generate code that works in one test but fails in another. It can summarize research without understanding which evidence is strongest, or optimize a design for the wrong goal. Such limits make human expertise more important, not less.
Machines are useful where speed, scale, search, pattern recognition, and data generation matter. People are essential where context, values, responsibility, and judgment matter. The challenge is to decide where each belongs.
An AI system might generate several designs for a medical sensor, for example, and predict how each will perform. An engineer can test the predictions and reject designs that violate technical requirements. A clinician can determine whether the sensor can be realistically used. Patients and practitioners might reveal problems that neither the engineer nor the developer considered. The final solution emerges from combining the perspectives.
The engineer is still a builder and specialist but increasingly also a coordinator of knowledge.
An engineer’s value is not in knowing everything. It is in knowing how to frame the problem, how to ask the right questions, how to test the answers, and when another expert must lead the project.
IEEE’s vital role
The broader form of engineering creates an important role for IEEE. Its societies already bring together expertise in various technologies, engineering education, technical standards, and other fields. That makes IEEE well placed to help engineers work across specialties without weakening the value of specialization.
IEEE could create more communities organized around problems that require expertise from several fields. Examples include trustworthy clinical AI, resilient infrastructure, energy reliability, scientific discovery, and safe autonomous systems. An engineer could begin in a home discipline and then work with specialists from other IEEE communities as a project requires.
IEEE also can help set practical standards for human-machine collaboration. Engineers will need clear guidance on when AI-generated work must be checked, how uncertainty should be documented, when a specialist must make a decision, and who is responsible when a system uses both human judgment and machine output.
Those questions are becoming part of everyday engineering. They should not be left to each company or project manager to answer independently. A professional organization can provide common expectations that follow engineers as they move between jobs, industries, and technical fields.
A larger definition of the engineer
AI does not make engineering disciplines less important; it makes the connections between them easier to use. Deep expertise gives engineers the foundation to judge what is sound. Systems thinking helps them understand how different parts of a problem fit together. AI can extend engineers’ ability to explore, test, and communicate across fields. Human judgment gives the work direction and responsibility.
An engineer is not defined only by a job title or a fixed set of tasks. An engineer is someone trained to understand complex systems, define and redefine problems, bring together the knowledge and tools needed to solve them, test whether solutions work, and take responsibility for the outcome.
That is what it means to be an “unbound” engineer.
The boundaries between specialties become easier to cross, and machines might take on more of the technical work, but the core of engineering remains human: deciding what problem is worth solving, what evidence is good enough, how the pieces fit together, and what to do when the answer is uncertain.
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Sufian Aldogom is an IEEE member, a research affiliate at MIT, and a senior leader at Google in Sunnyvale, Calif.
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