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长期滋生不健康杂念的调整方法
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长期滋生不健康杂念的调整方法
杂念本身人人都会有，区分关键：只是脑子里闪过、不影响行为，还是控制不住反复回想、内心煎熬、甚至催生冲动。核心思路是：不对抗念头、切断强化、疏导内心、规范认知、调整生活。
一、当下念头冒出来时：不要硬压，越压制越严重
心理学里有“白熊效应”：越告诉自己不要想，念头反而更清晰。
1. 客观旁观，不自我批判
出现不好的想法时，不要立刻自责、骂自己肮脏、阴暗。告诉自己：念头只是大脑随机产生的思绪，不等于我本人，更不代表我一定会去做。产生想法不代表人品差，不必为此极度愧疚内耗。
2. 快速抽离，转移感官注意力
- 动身体：起身走动、洗手、喝水、拉伸、出门走两分钟；
- 感官落地：专注听周围声音、摸桌面、感受呼吸，把注意力从脑海拉回现实；
- 做即时小事：做家务、刷题、工作、刷简单短视频，占用大脑短时记忆。
3. 简单标记法
心里轻轻一句：“又出现负面杂念了”，然后不再跟进思考，任由它自己淡化，不顺着念头往下脑补细节。
二、根源减少杂念：调整认知与心态
很多不健康杂念来自压抑、焦虑、空虚、压力、欲望无处安放。
1. 分清“想法”和“行动”的边界
人会有各种本能、阴暗、冲动类念头，是正常心理现象。真正决定善恶的是：你是否反复回味、是否打算付诸行动。只要不跟随、不实施，就无需自我否定。
2. 梳理内心积压的情绪
杂念频繁往往是情绪出口缺失：孤独、压抑、愤怒、自卑、压力大都会催生混乱思绪。
可以写日记，把所有压抑的感受全部写下来，不用修饰，写完相当于情绪释放，大脑就不会反复制造杂念。
3. 修正扭曲的思维习惯
若杂念多是色情、怨恨、报复、伤害他人类：
- 少刺激源：减少刷低俗短视频、暴力猎奇内容、容易勾起不良想象的图文；
- 建立正向价值观：多看温和、正向、有同理心的内容，潜移默化重塑思维偏好。
三、长期习惯改造，从根源降低杂念频率
1. 作息与身体（最容易被忽略）
熬夜、久坐、缺乏运动、饮食油腻，会让大脑自控力大幅下降，杂念、欲望、负面思绪会明显变多。
- 固定作息，不熬夜；
- 每天30分钟运动，跑步、散步、打球都可以，运动能分泌稳定情绪的激素；
- 睡前少玩手机，黑暗环境下大脑更容易胡思乱想。
2. 填满空闲时间，避免大脑放空
人在无事可做、独处发呆、睡前躺床上时，最容易陷入杂念循环。
培养长期爱好：读书、手工、乐器、散步、养花、学习技能，让大脑长期有正向事物填充，没有空间滋生负面想象。
3. 减少独处沉溺，适度社交
长期封闭独处，缺少与人正常交流，思维容易走向偏激、阴暗。多和家人、朋友简单聊天，参与集体活动，建立正常、健康的人际互动。
四、分情况的进阶处理
1. 只是偶尔乱想，能自主控制
坚持上面的转移注意力、规律生活即可，不用过度焦虑。
2. 反复强迫性杂念，挥之不去，内心极度痛苦
明明不想想，大脑却不受控反复冒出伤害、低俗、恐怖想法，伴随心慌、自责、失眠，这大概率是强迫思维，属于心理困扰，建议寻求心理咨询师疏导。
3. 杂念伴随强烈冲动，忍不住想付诸伤害自己/他人的行为
这已经超出普通杂念范畴，一定要及时和信任的家人倾诉，尽快联系心理医生或精神科门诊，专业干预才能保障自己和他人安全。
五、重要心态提醒
不要追求“完全没有杂念”，普通人做不到。
真正的内心清净，不是脑子里一点坏想法都不出现，而是念头来了不纠结、不回味、不纵容，迅速拉回现实，长久坚持后，杂念出现的频率会自然越来越低。
如果哪天我喜欢上别人，你会怎么办
如果哪天我喜欢上别人，你会怎么办呢？
你大大方方告诉我就行。
我能为你做的最后一件事，就是送你离开。
可是爱真的说放下就能放下吗？
心若定，眼前人即是心上人。
心若不定，永远会有更好的人。
If one day I fall in love with someone else,
what will you do?
Just tell me openly.
The last thing I can do for you
is to send you away.
But can love really be let go?
If your heart is set, the one in front of you is your love.
If the heart is unstable,
there will always be better people.
AI时代真正分水岭可能不在实验室
供订户阅读
https://www.zaobao.com.sg/forum/views/story20260622-9245589?utm_source=android-share&utm_medium=app
2026-06-22
联合早报
作者: 廖永健 (Laurence Liew) 是新加坡全国人工智能核心 (AI Singapore) 人工智能产业创新总监
作者: 石建政 (Willie Shi Jianzheng) 是新加坡社会科学大学客座讲师、新加坡国立大学李光耀公共政策学院访问学者
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新加坡的处境也许比表面上更有利。它从来不是全球最大的科技市场，也无意在基础模型的研发上与中美正面竞争。但这不意味着它在AI时代缺乏优势——恰恰相反，它拥有一些常被低估的结构性条件。
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过去三年，全球人工智能（AI）竞争几乎被简化为一场“大模型竞赛”。美国有OpenAI、Anthropic和谷歌，中国有深度求索（DeepSeek）、阿里巴巴和字节跳动，欧洲则忙于搭建监管框架。无论是新闻、论坛还是资本市场，人们反复追问的都是同一个问题：谁能训练出更强大的模型？于是，算力、晶片、参数与训练成本，成了衡量一个国家AI实力的标准答案。但如果把视线从实验室转向真实世界，会看到另一种正在浮现的景象。一些坐拥先进模型的机构，并没有换来预期中的生产力提升；一些并不研发基础模型的经济体，却在快速地把AI转化为实际能力，推动产业升级与公共服务改善。这提醒我们：AI时代真正的较量，也许并不只发生在实验室里。
决定未来国家竞争力的，未必是谁拥有最强的模型，而是谁能最快、最稳地把AI嵌入社会与经济的真实运行之中。下一轮竞争比拼的，可能不是技术能力，而是制度能力。这一判断并非凭空而来。先进模型正变得越来越容易获得：今天无论大企业还是中小企业，都能通过云端调用世界领先的大语言模型。模型之间仍有差距，但获取技术的门槛在迅速下降。真正拉开距离的，已不再是“是否拥有AI”，而是“能否让AI真正运转起来”。很多部署之所以失败，并不是因为模型不够聪明，而是因为组织还没准备好——流程没有重新设计，权责边界含糊，数据难以流通，员工也不清楚该如何与机器协作。结果，先进技术长期停在试点阶段，进不了核心业务。换句话说，AI带来的最大挑战，往往不是技术创新，而是制度与组织的创新。
我们不妨把支撑这种创新的能力，称为一个国家的“AI操作系统”。它不是某一套软件，而是一整套让AI能够被广泛、可靠地应用的制度基础设施：数码基础设施、人才培养体系、监管框架、行业标准、组织治理能力，以及社会信任机制。过去衡量科技实力，人们计算的是论文、专利与算力；到了应用阶段，更该问的是：一个社会能多快地把新技术接进自己的运行系统。未来国与国之间的差距，很可能不取决于谁拥有最多的图形处理器（GPU），而取决于谁拥有最成熟的AI操作系统。
从这个角度看，新加坡的处境也许比表面上更有利。它从来不是全球最大的科技市场，也无意在基础模型的研发上与中美正面竞争。但这不意味着它在AI时代缺乏优势——恰恰相反，它拥有一些常被低估的结构性条件。
第一，新加坡是一个高度制度化的社会。无论金融、医疗还是公共服务，多数机构都有清晰的流程、成熟的合规文化与较强的执行力。这种制度化有时被视为保守，但对AI落地而言却是难得的优势。AI并不擅长在混乱中工作，它更适合规则明确、责任清晰、流程稳定的环境。
第二，新加坡的经济早已高度数码化。数码身份、电子支付与数据基础设施多年前就已铺设到位，这降低AI落地最常见的障碍——系统无法与底层业务数据打通。
第三，人口老龄化与劳动力的有限增长，使采用AI不只是一种战略选择，更是一种现实需要。历史经验表明，“必要”往往比“热情”更能推动技术的真正普及。
第四，新加坡的体量使跨部门协调成为可能。在许多大型经济体中，政府部门、高校、行业组织与培训机构总是各自为政，在新加坡则更容易朝同一方向行动。一项新的国家战略发布后，往往能较快形成从政策到培训、从产业到教育的联动。这种协调能力过去并不显眼，但在技术扩散速度决定胜负的AI时代，它可能成为关键变量。
这些条件并非纸上谈兵。自2017年以来，新加坡全国人工智能核心（AI Singapore）已在本地推动超过300个应用型AI项目，落地率约六成，而业界普遍引用、源自麻省理工的基准大约只有5%。两者的差距并不在模型质量，而在“次序”——真正成功的项目，都是在写下第一行代码之前，就先完成流程梳理、组织准备与责任界定。以AI Singapore与本地电力企业杨忠礼西拉雅能源（YTL PowerSeraya）的合作为例，团队先把系统嵌入既有工作流程，再谈技术实现，最终换来更快的决策与更高的操作准确度。
这60%与5%的落差，指向一个常被忽视的事实：决定AI成败的，往往不是模型本身，而是一套可复制的落地方法。先界定问题与责任，再嵌入流程，最后才谈技术实现——这种“集成先行”的次序，正是制度能力的具体体现。它无法靠购买更强的模型获得，只能靠一个社会长期积累的组织经验来沉淀。这种吸收能力也体现在人才培养上。在新加坡全国人工智能核心的学徒计划中，约八成学员并非计算机科学出身，而是被“嫁接”上AI能力的各行业从业者。这反映的并非培养少数尖端研究者，而是借助全国性的技能再培训，扩大整个社会消化AI的容量。
与此同时，新加坡持续投入建设的，是另一种容易被忽略的基础设施——信任。从代理式AI监管模式框架（Model AI Governance Framework for Agentic AI）、AI治理测试框架和工具箱（AI Verify），到与ISO/IEC 42001等国际标准接轨，这些举措常被误读为额外的监管负担。但它们真正的作用，是让金融、医疗等高度受监管的行业“敢于”把AI用在重要决策上。
前面几项条件关乎一个社会“能不能”吸收AI，而治理与信任关乎它“愿不愿意”。两者缺一不可。当然，这一切都不构成成功的保证。同样的制度化，既可能为AI提供结构，也可能在监管者把每一次部署都视为风险而非能力时，变成阻碍。优势能否兑现，取决于新加坡能否认清：自己在AI时代的比较优势，不在于“造模型”，而在于“用好模型”。
这套逻辑的意义，还不止于新加坡自身。在整个东南亚，多数经济体都不会成为基础模型的研发中心，却同样面临如何把AI转化为生产力的难题。如果新加坡能率先把“用好AI”的制度能力做成可借鉴的范式，它在区域中的价值，将不再取决于模型有多大，而在于能否输出一套让AI可靠落地的治理与组织经验。
很多人仍习惯用专利数量、研发投入和模型规模来衡量AI实力。这些指标当然重要，但它们衡量的主要是创新的供给侧。AI时代真正的难题，在于供给与吸收之间的距离：技术出现之后，能否被社会广泛采用、被组织有效利用、被转化为生产力提升与公共价值？这些问题最终考验的不是实验室，而是制度。
真正的分水岭也由此清晰起来：它不在于哪些国家拥有GPU、哪些没有，而在于哪些组织与国家已经具备让AI真正运转的制度能力，哪些还停留在一轮又一轮的试点之中。试点可以无限进行，价值却始终无法兑现，而把AI嵌入核心流程的能力，才是把技术变成生产力的那道关口。实验室决定一项技术的上限，组织决定它能否照进现实，而制度则决定一个国家最终能把技术带到多远。过去人们以为，AI的竞争是模型与模型之间的竞争。未来我们或许会发现，真正决定成败的，是一个国家如何组织自己。
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作者: 廖永健 (Laurence Liew) 是新加坡全国人工智能核心 (AI Singapore) 人工智能产业创新总监
作者: 石建政 (Willie Shi Jianzheng) 是新加坡社会科学大学客座讲师、新加坡国立大学李光耀公共政策学院访问学者
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The Real Watershed of the AI Era May Not Be in the Laboratory
For subscribers only
Translated by ChatGPT
https://www.zaobao.com.sg/forum/views/story20260622-9245589?utm_source=android-share&utm_medium=app
2026-06-22
Lianhe Zaobao
Author: Laurence Liew is Director of AI Innovation at AI Singapore.
Author: Willie Shi Jianzheng is a Visiting Lecturer at the Singapore University of Social Sciences and a Visiting Scholar at the Lee Kuan Yew School of Public Policy, National University of Singapore.
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"Singapore’s position may be more advantageous than it appears on the surface. It has never been the world’s largest technology market, nor does it intend to compete head-on with China and the United States in developing foundation models. But that does not mean it lacks advantages in the AI era—on the contrary, it possesses several structural strengths that are often underestimated."
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Over the past three years, global competition in artificial intelligence (AI) has largely been reduced to a “large-model race.” The United States has OpenAI, Anthropic, and Google; China has DeepSeek, Alibaba, and ByteDance; while Europe has been busy constructing regulatory frameworks. Whether in the news, forums, or capital markets, people keep asking the same question: who can train the most powerful model? As a result, computing power, chips, parameters, and training costs have become the standard metrics for measuring a nation’s AI strength.
But if we shift our attention from laboratories to the real world, a different picture begins to emerge. Some organizations with access to advanced models have not achieved the productivity gains they expected. Meanwhile, some economies that do not develop foundation models are rapidly converting AI into practical capabilities, driving industrial upgrading and improving public services.
This reminds us that the real contest in the AI era may not take place solely in laboratories.
What determines a nation’s future competitiveness may not be who possesses the strongest model, but who can integrate AI into the actual functioning of society and the economy most quickly and reliably. The next phase of competition may not be about technological capability, but institutional capability.
This conclusion is not without basis. Advanced models are becoming increasingly accessible. Today, both large corporations and small and medium-sized enterprises can access world-leading large language models through the cloud. Differences among models remain, but the barriers to acquiring the technology are rapidly falling.
What truly creates separation is no longer whether one has AI, but whether one can make AI work in practice.
Many deployments fail not because the models are insufficiently intelligent, but because organizations are unprepared. Processes have not been redesigned, responsibilities remain unclear, data cannot flow effectively, and employees do not know how to collaborate with machines. As a result, advanced technologies remain stuck in pilot projects and never enter core business operations.
In other words, the greatest challenge brought by AI is often not technological innovation, but institutional and organizational innovation.
We might call the capability that supports such innovation a nation’s “AI operating system.” It is not a specific software package, but a comprehensive set of institutional infrastructure that enables AI to be applied broadly and reliably: digital infrastructure, talent development systems, regulatory frameworks, industry standards, organizational governance capabilities, and mechanisms of social trust.
In the past, technological strength was measured by papers, patents, and computing power. At the application stage, however, a more important question is: how quickly can a society connect new technologies to its existing operating systems?
Future differences between countries may depend less on who possesses the most graphics processing units (GPUs) and more on who has developed the most mature AI operating system.
Viewed from this perspective, Singapore’s position may be more favorable than it appears.
It has never been the world’s largest technology market, nor does it intend to compete directly with China and the United States in foundation-model development. Yet this does not mean it lacks advantages in the AI era. On the contrary, it possesses several structural strengths that are often underestimated.
First, Singapore is a highly institutionalized society. Whether in finance, healthcare, or public services, most organizations have clearly defined processes, mature compliance cultures, and strong execution capabilities. This institutionalization is sometimes seen as conservative, but for AI deployment it is a valuable advantage. AI does not function well in chaos; it is better suited to environments with clear rules, well-defined responsibilities, and stable processes.
Second, Singapore’s economy is already highly digitalized. Digital identities, electronic payments, and data infrastructure were established years ago. This reduces one of the most common barriers to AI deployment: the inability of systems to connect with underlying business data.
Third, population aging and limited labor-force growth make AI adoption not merely a strategic choice but a practical necessity. Historical experience shows that necessity often drives genuine technological adoption more effectively than enthusiasm.
Fourth, Singapore’s size makes cross-sector coordination possible. In many large economies, government agencies, universities, industry associations, and training institutions often operate independently. In Singapore, it is easier for them to move in the same direction. Once a new national strategy is introduced, alignment from policy to training and from industry to education can often be achieved relatively quickly.
This coordinating capability may not have been particularly visible in the past, but in an AI era where the speed of technology diffusion determines success, it could become a crucial variable.
These conditions are not merely theoretical.
Since 2017, AI Singapore has driven more than 300 applied AI projects locally, achieving an implementation rate of about 60 percent. By comparison, an industry benchmark commonly cited from MIT is roughly 5 percent. The difference does not lie in model quality but in sequencing.
Projects that succeed are those that complete process mapping, organizational preparation, and responsibility definition before writing the first line of code.
For example, in AI Singapore’s collaboration with local power company YTL PowerSeraya, the team first embedded the system into existing workflows before addressing technical implementation. The result was faster decision-making and higher operational accuracy.
The gap between 60 percent and 5 percent points to an often-overlooked fact: what determines AI success is usually not the model itself, but a repeatable deployment methodology.
First define the problem and responsibilities, then embed the solution into workflows, and only afterward address technical implementation. This sequence of “integration first” is a concrete expression of institutional capability.
It cannot be obtained simply by purchasing a more powerful model. It can only emerge from an accumulation of organizational experience over time.
This absorptive capacity is also reflected in talent development.
Within AI Singapore’s apprenticeship program, about 80 percent of participants do not come from computer science backgrounds. Instead, they are professionals from various industries who have AI capabilities grafted onto their existing expertise.
The goal is not merely to cultivate a small number of elite researchers, but to expand society’s overall capacity to absorb AI through nationwide reskilling efforts.
At the same time, Singapore continues to invest in another form of infrastructure that is often overlooked: trust.
From the Model AI Governance Framework for Agentic AI and the AI Verify governance testing framework and toolkit, to alignment with international standards such as ISO/IEC 42001, these initiatives are often misunderstood as additional regulatory burdens.
Their true purpose, however, is to give highly regulated sectors such as finance and healthcare the confidence to use AI in important decision-making processes.
The earlier conditions concern whether a society can absorb AI; governance and trust concern whether it wants to.
Both are indispensable.
Of course, none of this guarantees success.
The same institutionalization that provides structure for AI can also become an obstacle if regulators treat every deployment as a risk rather than a capability.
Whether these advantages can be realized depends on whether Singapore recognizes that its comparative advantage in the AI era lies not in building models, but in using models effectively.
The significance of this logic extends beyond Singapore itself.
Across Southeast Asia, most economies will not become centers for foundation-model development, yet they face the same challenge of transforming AI into productivity gains.
If Singapore can become the first to develop a replicable model of institutional capability for “using AI well,” its regional value will no longer depend on the size of its models, but on its ability to export governance and organizational expertise that enables AI to be deployed reliably.
Many people still measure AI strength using patent counts, R&D spending, and model scale.
These indicators are certainly important, but they primarily measure the supply side of innovation.
The real challenge of the AI era lies in the distance between supply and absorption: after technology emerges, can it be widely adopted by society, effectively utilized by organizations, and converted into productivity gains and public value?
Ultimately, these questions test not laboratories, but institutions.
This is where the real watershed becomes clear.
It is not about which countries possess GPUs and which do not. It is about which organizations and nations have developed the institutional capability to make AI genuinely operational, and which remain trapped in one pilot project after another.
Pilot projects can continue indefinitely, but value may never be realized. The ability to embed AI into core processes is the gateway that transforms technology into productivity.
Laboratories determine the upper limits of a technology. Organizations determine whether it can enter reality. Institutions determine how far a nation can ultimately take it.
In the past, people assumed that AI competition was a contest between models.
In the future, we may discover that what truly determines success or failure is how a nation organizes itself.
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Author: Laurence Liew is Director of AI Innovation at AI Singapore.
Author: Willie Shi Jianzheng is a Visiting Lecturer at the Singapore University of Social Sciences and a Visiting Scholar at the Lee Kuan Yew School of Public Policy, National University of Singapore.
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