2025

A Birthday Reflection on Leadership and Growth

This week, I turn 35 on July 31st. As I pause to reflect, I feel both grateful and determined. Grateful for the people, challenges, and opportunities that have shaped me. Determined to become a better version of myself in the year ahead. My birthday goal is simple yet profound: to continuously sharpen my leadership skills.

Leadership, to me, is no longer just about managing tasks or making decisions. It’s about inspiring others, adapting with purpose, and leading with empathy. In a world that shifts rapidly, where change is the only constant, it’s not enough to rely on old ways of thinking. The real challenge today is to lead in the unknown, to guide people through complexity with clarity and heart. That takes more than strategy. It takes character, courage, and connection.

I’ve come to realize that leadership isn’t defined by a title. You don’t need to be a CEO or a senior executive to lead. True leadership shows up in everyday moments, supporting a teammate, resolving conflict with grace, encouraging someone to take their next step. Whether you're managing a small project or raising a family, the way you show up can shift the entire energy of a room. That is leadership.

One of the most powerful lessons I’ve learned is this: people don’t want to be managed. They want to be seen, heard, and valued. Influence doesn’t come from authority. It comes from trust. And trust is built by showing up consistently, listening deeply, and acting with integrity. In the words of Dale Carnegie, “You can make more friends in two months by becoming genuinely interested in other people than you can in two years by trying to get people interested in you.” Leadership begins with that kind of genuine interest, in people, in their hopes, in their potential.

Arguments, ego, and control often block the path to effective leadership. Real influence comes from humility and emotional intelligence. It’s not about proving others wrong, but about guiding them toward what’s right, together. When we create space for others to shine, we all rise. When we speak with respect, offer appreciation, and give people a good reputation to live up to, we unlock extraordinary results.

As I step into this new chapter of my life, I choose to lead not by force, but by example. I choose to speak less and listen more. To mentor when I can, to learn always, and to remind people of their own greatness. I know that leadership isn’t about reaching the top, it’s about lifting others up along the way.

The world needs more leaders who lead with heart, who believe in others, and who stay grounded in their values. This is the kind of leader I aspire to be. So here's to 35, a new year, a new commitment, and a renewed purpose. Let’s lead with purpose. Let’s lead with love. Let’s lead, together.

領導與成長的生日反思

這週，7月31日，我即將迎來35歲生日。當我停下腳步反思時，心中充滿了感激與決心。感激那些塑造了我的人、挑戰與機會；同時，也決心在新的一年成為更好的自己。我今年的生日目標很簡單，卻意義深遠：持續磨練我的領導力。

對我而言，領導不再只是管理任務或做決策，而是激勵他人、有意義地調整自己，並以同理心帶領團隊。在這個瞬息萬變、唯有改變是不變的世界中，單靠舊有的思維模式已不敷使用。真正的挑戰，是在未知中帶領人們，在複雜中指引方向，並以清晰與真誠同行。這需要的不只是策略，還需要品格、勇氣與連結。

我逐漸意識到，領導力並不取決於頭銜。你不需要是CEO或高階主管才能領導。真正的領導體現在日常生活中：在同事低潮時伸出援手，在衝突中以優雅解決，或在關鍵時刻鼓勵他人踏出下一步。不論是領導一個小專案，還是經營一個家庭，你的態度都能改變整個氛圍。這就是領導。

我學到的一個重要教訓是：人們不想被管理，他們渴望被看見、被傾聽、被重視。影響力並非來自權威，而是來自信任。而信任來自一致的行動、深度的傾聽與誠實的表現。正如戴爾・卡內基所說：「你可以在兩個月內交到比花兩年還多的朋友，只要你真心對他人感興趣，而不是讓他人對你感興趣。」領導力始於那份真誠的關心——對人、對夢想、對潛力的關心。

爭論、自我與控制常常是有效領導的絆腳石。真正的影響力來自謙遜與情緒智慧。不是為了證明別人錯，而是要一起走向正確的方向。當我們給予他人發光發熱的空間，我們全體都能提升。當我們以尊重發言、給予肯定，並幫助他人建立值得效法的聲譽時，我們將解鎖非凡的成果。

走進人生新篇章，我選擇以身作則，而非強迫命令；選擇少說多聽；能指導時就給予幫助，並時時學習，提醒每個人他們本身的價值與潛能。我深知，領導的真諦不在於登上巔峰，而是在於扶持他人一同攀登。

這個世界需要更多有愛、有信念、並堅守價值觀的領導者。這就是我想成為的那種領袖。所以，為了35歲的新旅程，一份新的承諾，一個再度覺醒的使命——讓我們懷著目的去領導，懷著愛去領導，一起踏上這條領導之路。

How I Learned to Lead, Influence, and Drive Change in a World That Isn't Always Fair

Last Sunday, I stood on the stage of the National University of Singapore, receiving my Master of Technology in Digital Leadership from NUS-ISS. It was a powerful moment of reflection, not just on academic achievement, but on a deeper journey I’ve taken as a leader, an architect, and an individual trying to make meaningful change in a complex world.

In my role as a Solution Architect in the core banking sector, I help banks transition from legacy systems to modern, cloud-native platforms. This is a form of transformation that goes far beyond technology. It touches people, culture, and power structures that have existed for decades. As I became more involved in these large-scale transformations, one truth became increasingly clear: those with power are not always the most capable. And often, those who are capable are not given power.

I saw it firsthand. In global firms, leadership often mirrors legacy power structures. In one UK-based company, most leadership roles were held by white men. As an Asian professional, I quickly learned that traditional career ladders were not designed for people like me. I had to find new ways to lead, without formal authority, without a title, and often without a seat at the table. I had to learn how to speak up, how to influence, and how to drive change from the margins.

That’s where my journey at NUS came in. This program didn’t just teach me frameworks and strategies, it taught me how leadership really works. And as uncomfortable as the truth may be, the world is not a meritocracy. Power has not disappeared, it has not become more evenly distributed, and the rules that govern it remain deeply entrenched. If we want to do good in the world, more good people must gain power. And to do that, we must stop avoiding the topic and start mastering it.

Leadership skill is the ability to effectively understand others at work and use that knowledge to influence others to enhance personal and organizational objectives. Research shows it is the single strongest predictor of career success. It is what allows people to build influence, improve relationships, manage impressions, and create change, regardless of their title.

I used to think influence was about charisma or being extroverted. But I’ve learned that leadership skill is not manipulation. It is a conscious and strategic effort to build trust, gain allies, and drive outcomes that matter. It allows capable professionals to change the game instead of playing by one that was never designed for them.

This insight changed how I saw leadership. It is not about control. It is about creating environments where others can thrive. It is about putting yourself and your allies in positions to influence. It is about understanding that if change could happen without power, it would have happened already. The first step to real transformation is understanding how leadership works and being willing to use it for the benefit of others.

That is what my graduation truly represents. Not just the end of a degree, but the beginning of a new commitment. To lead boldly. To speak up when it is easier to stay silent. To build networks that lift people up. To challenge outdated norms and glass ceilings. And to ensure that leadership is not a privilege for the few, but a responsibility for the many.

To my professors, classmates, family, and friends, thank you. Your support helped me grow not just as a student, but as a leader. I carry this degree not just with pride, but with purpose.

Let us be the kind of leaders who do not wait for permission. Let us understand power, use it wisely, and change lives, change organizations, and change the world. One conversation, one decision, and one act of courage at a time.

如何在這個不總是公平的世界中學會領導、影響他人與推動改變

上週日，我站在新加坡國立大學的舞台上，從NUS-ISS領取了我的科技碩士（數字領導）。那一刻不僅象徵著學術上的成就，更是我作為一位領導者、系統架構師，以及在這個複雜世界中努力帶來實質改變的個人，所經歷的一段深刻旅程的見證。

身為核心銀行領域的客戶架構師，我協助銀行從傳統的舊有系統轉型為現代化的雲原生平台。這種轉型遠不只是技術升級，它更關乎人、文化與延續數十年的權力結構。當我越來越深入參與這些大規模的轉型計劃時，有一個真相變得越來越明顯：掌握權力的人，往往不是最有能力的人，而真正有能力的人，反而常常無法獲得應有的權力。

我親眼見證這一切。在許多跨國企業中，領導階層往往反映出舊有的權力結構。在某間設於英國的公司，大多數領導職位皆由白人男性擔任。作為一名亞洲人，我很快就領悟到，傳統的職涯階梯並不是為我們這樣的人設計的。我必須尋找新的方式來發揮影響力，不依靠職稱、不依靠正式授權，甚至往往沒有發言的位置。我學會了如何開口說話、如何建立影響力、如何從邊緣推動變革。

這也是我選擇進入NUS的原因。這個課程不僅教會我理論架構與策略，更讓我真正理解領導力的運作方式。儘管這個真相令人不安，但世界並不是一個憑實力說話的地方。權力從未消失，也沒有變得更公平分配，那些支配權力運作的規則依然根深蒂固。如果我們想為這個世界帶來善意的改變，就必須讓更多有善念的人掌握權力。而為了達成這點，我們必須停止逃避對權力的討論，開始認真掌握它。

所謂的領導技能，是能夠有效理解職場上他人行為，並運用這些知識去影響他人，以達成個人與組織目標的能力。研究顯示，這是職涯成功最強而有力的指標。它能幫助人們建立影響力、改善人際關係、塑造正面印象，並帶來實質的改變，無論你是否擁有職銜。

我曾以為影響力等同於魅力或外向性格，但後來我明白，領導技能並不是操控別人。它是一種有意識且具策略性的努力，是建立信任、凝聚盟友、推動具意義成果的過程。它讓有能力的專業人士得以改變遊戲規則，而不是被動遵循一個從未為他們量身打造的制度。

這個洞察徹底改變我對領導的看法。領導不是掌控，而是創造讓他人得以茁壯成長的環境。是將自己與夥伴放在具影響力的位置，是認知到如果改變不需要權力，那改變早就發生了。真正的轉型，第一步是理解領導力的運作方式，並願意將其用在對他人有益的方向上。

這也正是我這次畢業所真正代表的意義。不只是完成學業，更是新承諾的開始。勇敢地領導，在他人選擇沉默時挺身而出，建立能提升彼此的網絡，挑戰過時的規範與看不見的天花板。並確保領導不再只是少數人的特權，而是多數人的責任。

感謝我的教授、同學、家人與朋友，是你們的支持讓我不只成為一位畢業生，更成為一位有責任感的領導者。我帶著的不僅是榮譽，更是使命。

讓我們成為那種不等待許可的領導者。理解權力，善用權力，改變人生、改變組織、改變世界。從一場對話開始，從一個決定開始，從一份勇氣開始。

Embracing Failure Leads to Success and Personal Growth

In a world obsessed with success, we rarely pause to honor failure. Yet failure is not our enemy, it’s our greatest teacher. When society doesn't provide second chances, it doesn’t just deny people the opportunity to recover, it breeds fear. Fear of making the wrong move. Fear of not being good enough. Fear of falling behind.

This fear is paralyzing. In many parts of the world today, the path to success has become increasingly narrow. From a young age, we’re taught to follow a rigid formula: get good grades, attend a prestigious school, land the right job, and settle down by a certain age. If you deviate from the plan, you risk being labeled a failure. The safety nets that once caught us when we fell, like time to explore interests, jobs that welcomed potential over pedigree, or relationships that weren’t defined by timelines, have grown thinner. Now, one misstep can feel like the end of the road.

But here’s the truth. Every successful person has failed. Often. Repeatedly. What sets them apart isn’t perfection, it’s the courage to keep going. Decision paralysis doesn’t come from a lack of ability, it comes from a fear of getting it wrong. Ironically, it’s often those who have never failed who fear failure the most. Because they’ve never built the muscle of resilience, they’ve never discovered how capable they truly are.

We need a mindset shift. As psychologist Carol Dweck teaches, those with a growth mindset believe that abilities can be developed. They embrace challenges, persist through obstacles, and see effort as the path to mastery. They understand that failure isn’t the opposite of success, it’s part of the journey. On the other hand, a fixed mindset traps people in the need to constantly prove themselves, avoiding anything that risks failure. And so, their world stays small.

The good news is that mindset can change. It starts with how we respond to failure, not with shame, but with encouragement. When someone stumbles, lift them up. When they make progress, no matter how small, celebrate it. Growth doesn't happen in silence, it thrives in supportive environments.

And if you’ve ever struggled with indecision or self-doubt, know this. You’re not alone. You’re not broken. You’re human. The goal isn’t to be fearless, it’s to fear less. To take that next step even when the path ahead is unclear. To try again, even when you’ve failed before.

Creativity, innovation, and reinvention don’t come from comfort zones. They are born from disruption, from stepping outside the familiar. Neuroscience shows that movement, rest, and curiosity can unlock new ideas. Einstein found inspiration while riding his bicycle. You can find yours in a walk, a good night’s sleep, or a conversation that challenges your thinking. The world is full of sparks, you just have to be open to catching them.

Most importantly, remember that your worth is not defined by your achievements. It’s defined by your willingness to grow. Resilience is not about never falling. It’s about how you rise. It’s about facing the unknown with a brave heart and a clear purpose.

So if you’re standing at a crossroads, afraid to choose, afraid to fail, take the leap anyway. The people who change the world aren’t the ones who never fail. They are the ones who fall, get up, and keep moving forward.

Because in the end, failure is not the opposite of success. It is the foundation of it.

當我們擁抱失敗，才能走向成功與個人成長

在這個對成功近乎痴迷的世界裡，我們鮮少停下腳步去看見、甚至尊重失敗。然而，失敗並不是敵人，它是最偉大的老師。如果社會不提供重新出發的機會，不只是讓人失去復原的機會，更會在心中種下恐懼——害怕做錯決定、害怕自己不夠好、害怕被遠遠拋在後頭。

這種恐懼令人無法動彈。如今，世界上許多地方對於「成功」的定義變得愈加狹隘。從小我們就被教導要遵循某種既定公式：考取高分、進入名校、找到好工作，然後在適當年齡結婚成家。只要偏離這條路，就有可能被視為失敗者。那些曾經支持我們的安全網——探索興趣的時間、看重潛力而非學歷的工作機會、不受時間綁架的人際關係——正日益式微。如今，只要犯一個錯，人生就可能陷入絕境。

但事實是，世上每一位成功人士都曾失敗，而且不是一次，而是無數次。他們與眾不同的地方，不在於完美，而是在於跌倒後依然選擇站起來、繼續走下去。所謂「選擇困難」往往並非來自能力的不足，而是來自對犯錯的恐懼。諷刺的是，那些從未經歷過失敗的人，反而最害怕失敗。因為他們從未鍛鍊過「復原」這項能力，也從未真正看見自己的潛力。

我們需要一場心態的轉變。正如心理學家卡蘿·杜維克所說，擁有「成長型心態」的人相信能力是可以培養的。他們勇於面對挑戰、堅持克服障礙，並視努力為達成精熟的道路。他們明白，失敗不是成功的反面，而是通往成功的一部分。反之，「定型心態」的人會不斷要求自己表現完美，害怕冒險，於是只選擇安全、熟悉的事情做，人生的空間也就越來越狹窄。

好消息是，心態是可以改變的。改變，從我們如何看待失敗開始。不是用羞愧，而是用鼓勵。當有人跌倒時，伸出手扶他一把；當他有一點點進步時，與他一起慶祝。成長不會發生在沉默之中，而是在支持與理解的環境中發芽。

如果你曾經在決定面前猶豫不決，曾經懷疑過自己，請記住——你並不孤單。你沒有壞掉，你只是人類。目標不是「沒有恐懼」，而是「減少恐懼」。即使前方充滿未知，你也可以邁出那一步；即使過去曾經跌倒，也還是值得再次嘗試。

創意、創新與重新出發，從不誕生於舒適圈，它們誕生於改變，來自踏出熟悉的那一刻。神經科學告訴我們，運動、休息與好奇心可以激發全新的想法。愛因斯坦在騎腳踏車時得到靈感，你也可以在散步中、在一場好眠之後、或是一場激盪思想的對話中找到屬於你的火花。這個世界充滿了靈感的火花，只要你願意敞開心扉，就有機會接住它們。

最重要的是，永遠不要讓成就定義你的價值。真正定義你的是，你是否願意成長。所謂的「韌性」，不是從不跌倒，而是每次跌倒後都能重新站起來。是面對未知時，仍然懷著勇敢的心與清晰的方向。

所以，如果你正站在人生的十字路口，害怕選擇，害怕失敗，請仍然勇敢地踏出那一步。改變世界的人，從來不是那些從不失敗的人，而是那些即使跌倒，也會再次站起來，繼續前行的人。

因為，失敗從不是成功的反面，它是成功的根基。

Struggle Is the Teacher for Continuous Improvement

We all dream of a life that flows smoothly, where goals are met on time, habits stick effortlessly, and everything unfolds exactly as we imagined. But life doesn’t work that way. And perhaps that’s its greatest gift.

There’s something deeply human about falling short. You set out to read a book a week. You promise yourself to exercise daily. The intention is pure, the motivation is real. But days pass. Work piles up. Distractions win. You skip a workout. You miss a chapter. And suddenly, you're left wondering, "Why can’t I stick to what I planned?"

It’s easy to feel defeated. But here’s the truth: those moments of falling short aren’t failures. They are invitations. Each missed goal and undone task is a chance to pause, reflect, and understand yourself better. It's in those very moments that growth begins.

I’ve made countless promises to myself. Read more. Move more. Learn more. Sometimes I keep them. Often I don’t. But over time, I’ve learned not to judge myself for it. Instead, I ask deeper questions. What’s getting in the way? What patterns keep showing up? What can I change tomorrow? These questions have taught me more than any perfectly executed plan ever could.

This journey of trying, stumbling, learning, and trying again is what makes life so incredibly rich. It's not about perfection. It's about progress. Behavioral science reminds us that our habits and actions are shaped by environments, emotions, and countless unseen factors. Understanding this gives us power. It shifts the narrative from "I failed" to "I’m figuring it out."

And isn’t that the whole point? We’re all works in progress. Every time you show up, even imperfectly, you are growing. Every time you reflect instead of retreat, you build resilience. Every time you restart, you prove to yourself that you haven’t given up.

So if you’ve fallen behind on your goals, whether it’s reading more books, exercising daily, starting a new project, or simply getting through the week, don’t beat yourself up. Celebrate the fact that you care enough to try. That is courage. That is strength.

Life isn't beautiful because it’s predictable. It’s beautiful because it challenges us, reveals us, and gives us endless opportunities to rise. Let your unmet goals be the beginning, not the end. Let your setbacks fuel your next step. And above all, trust that every imperfect effort is still moving you forward.

If you take just one thing from this post, let it be this. You don’t have to be perfect to be proud of yourself. Keep showing up. Keep learning. Keep becoming the person you’re meant to be.

Thank you for being part of this journey. We are all in it together.

奮鬥是持續成長最好的老師

我們都嚮往一個順利無阻的生活，目標準時達成、習慣輕鬆養成，一切如想像般完美展開。但人生並不是這樣運作的，而這或許正是它最美好的地方。

未達預期，其實是一種極為真實的人類經驗。你立志每週讀一本書，承諾自己每天運動。初衷真誠，動機充足。但日子一天天過去，工作堆積如山，注意力被拉走，你錯過了一次運動，也跳過了一章讀書內容，然後開始自問：「為什麼我總是做不到我所計劃的？」

這時我們很容易感到挫敗。但事實是：那些未竟的時刻並非失敗，而是邀請。每一個沒完成的目標和被擱置的任務，都是一次暫停、反思和深入了解自己的契機。而也正是在這些時刻，成長悄悄地開始了。

我曾對自己許下無數承諾。多閱讀、多運動、多學習。有時候我做到了，但更多時候沒有。然而，隨著時間過去，我學會了不再責備自己，而是改問更深入的問題：是什麼卡住了我？有哪些模式一再出現？明天我能改變些什麼？這些提問教會我的，比任何完美執行的計畫還要多。

這段不斷嘗試、跌倒、學習、再出發的旅程，正是讓人生如此豐富的原因。它不是關於完美，而是關於進步。行為科學提醒我們，習慣和行動受環境、情緒及無數潛在因素所影響。了解這一點，就等於掌握了改變的力量。這會讓我們從「我失敗了」的自責，轉向「我正在搞懂自己」的自我成長。

這不就是人生的意義嗎？我們都是尚未完成的作品。每一次即使不完美地出現，你都在進步；每一次選擇反思而非退縮，你都在建立韌性；每一次重啟，你都向自己證明你沒有放棄。

所以如果你落後了，不論是讀書目標、每日運動、新計劃的開展，或只是撐過這一週，請不要責怪自己。慶幸你願意嘗試，這就是勇氣，這就是力量。

人生之所以美，不在於它的可預測性，而是它挑戰我們、揭示我們，也不斷給予我們站起來的機會。讓那些未完成的目標成為新的起點，不是終點。讓你的挫敗點燃下一步的動力。最重要的是，相信每一份不完美的努力，都在推動你向前。

如果你能從這篇文章中帶走一個訊息，那就是：你不需要完美，也可以為自己感到驕傲。繼續出現，繼續學習，繼續成為那個你注定要成為的人。

謝謝你成為這段旅程的一部分。我們一起努力。

Essential Concepts in Generative AI

As a solution architect exploring the fascinating world of Generative AI and Large Language Models (LLMs), I've come across a range of technical terms that can feel overwhelming at first. This blog post breaks them down into simple, digestible explanations for non-technical readers who are curious about how modern AI works.

Instead of splitting text into full words, subword tokenization breaks words into smaller units. This helps AI models understand rare or new words by combining familiar smaller parts. Unicode normalization ensures consistent formatting by resolving multiple representations of the same character, like accented letters, so that the model doesn't get confused by different encodings. Chunking breaks large documents into smaller, manageable pieces to make it easier for AI systems to retrieve and process relevant parts.

Since transformer models process words in parallel, positional encoding is used to tell the model the order of words in a sentence. Cosine similarity helps measure how similar two pieces of text are by comparing the direction of their meaning vectors, which is useful when checking if content is contextually related.

An encoder model processes input (like a sentence) into a form that AI can understand and work with. Multi-head attention allows the model to look at different parts of the sentence simultaneously, capturing relationships between words no matter their position. This is facilitated through Q (Query), K (Key), and V (Value) matrices, which help the model match what's being asked with relevant information and content.

Layer normalization keeps the training process stable by making sure the values going through each layer of the network remain balanced. Activation functions like ReLU and Sigmoid determine how a model processes informationReLU is efficient and suitable for deep networks, while sigmoid is often used when outputs need to represent probabilities. However, deep networks can suffer from the vanishing gradient problem, where early layers stop learning due to increasingly small gradient values. Quantization helps improve efficiency by shrinking model size using smaller numerical formats, making AI models faster and more memory-efficient. Similarly, memory pooling techniques allow the system to reuse memory space efficiently during training and inference.

To improve learning efficiency, few-shot learning enables models to generalize from just a few examples instead of needing thousands. Transfer learning helps models get a head start by reusing knowledge from a previously trained model, saving time and resources. Chain-of-thought prompting improves reasoning by encouraging the model to walk through its logic step-by-step, much like how humans solve problems.

Evaluation is vital to ensure models perform well. Stratified k-fold cross-validation ensures each test set has a fair representation of different categories. A paired t-test statistically compares two model versions to see if there's a meaningful performance difference. BLEU scores evaluate how close a machine translation is to a human translation by comparing overlapping sentence fragments, while ROUGE scores are used to assess the quality of text summaries by measuring overlap with reference summaries.

To improve performance and efficiency, dynamic batching enables systems to group and process tasks more flexibly, adjusting batch sizes based on current demand. GPU-accelerated columnar data processing with zero-copy memory access leverages powerful graphics hardware to process large datasets rapidly, avoiding the overhead of unnecessary memory transfers.

Diffusion models rely on two processes: forward diffusion adds noise to data over time, while reverse diffusion removes that noise to generate realistic outputs like images or text. Retrieval-Augmented Generation (RAG) enhances AI responses by searching a knowledge base for relevant information before generating an answer, improving factual accuracy.

Lastly, for computer vision applications, image transformation techniques such as flipping, rotation, and zooming help AI models learn to recognize objects from various angles and contexts, boosting generalization performance.

Understanding these concepts is a big step toward grasping how today's AI systems work. As we continue exploring the world of Generative AI, these building blocks help us create smarter, faster, and more reliable applications.

生成式人工智慧的基本概念

作為一位解決方案架構師，我正在探索生成式人工智慧（Generative AI）與大型語言模型（LLMs）這個迷人的領域，期間接觸了許多一開始讓人難以理解的技術術語。這篇部落格將這些概念拆解為簡單易懂的解釋，幫助對現代 AI 感興趣的非技術讀者理解它是如何運作的。

傳統上，文字會被分割成整個詞彙，但「子詞分詞」（Subword Tokenization）則是將單詞拆解成更小的單位，幫助 AI 模型理解罕見或新出現的單字，因為這些字可以由熟悉的部分組合而成。「Unicode 正規化」則是將文字轉換成一致的編碼格式，解決例如重音符號的不同表示方式，避免模型因編碼不一致而混淆。透過「分塊處理」（Chunking），大型文件可以被切割為較小的段落，方便 AI 系統快速檢索並處理相關內容。

由於 Transformer 模型是以平行方式處理文字，因此必須透過「位置編碼」（Positional Encoding）告訴模型各個詞語在句子中的順序。「餘弦相似度」（Cosine Similarity）則是用來衡量兩段文字的語義相似程度，透過比較其向量方向來評估內容是否相關。

「編碼器模型」（Encoder Model）會將輸入（如一段句子）轉換為模型可以理解和處理的形式。「多頭注意力機制」（Multi-Head Attention）讓模型可以同時關注句子的不同部分，捕捉詞語之間的位置無關關係。這過程依賴 Q（Query）、K（Key）與 V（Value）矩陣，協助模型將問題與相關資訊對應起來並擷取合適的內容。

「層正規化」（Layer Normalization）則是確保每一層神經網路在訓練時的數值穩定，避免失控。「啟動函數」（Activation Functions）如 ReLU 與 Sigmoid 用於控制訊號的流動，ReLU 高效且適合深層網路，而 Sigmoid 常用於需要輸出機率的情境。然而，深層網路可能會遭遇「梯度消失問題」（Vanishing Gradient Problem），即早期層學不到東西，因為梯度變得非常微小。「量化」（Quantization）透過使用較小的數字表示方式來縮減模型大小，提高效率並節省記憶體空間。「記憶池化技術」（Memory Pooling）則能在訓練與推理過程中重複利用記憶體空間，進一步提升效能。

為了提高學習效率，「少量學習」（Few-Shot Learning）讓模型只需少量示例即可泛化，而無需大量資料。「遷移學習」（Transfer Learning）則是讓模型利用已學會的知識快速切入新任務，節省時間與資源。「思路鏈引導」（Chain-of-Thought Prompting）則鼓勵模型像人類一樣一步步思考與推理，提升解題能力。

為了驗證模型表現，「分層 K 摺交叉驗證」（Stratified k-Fold Cross-Validation）確保測試資料中各類別比例公平。「成對 t 檢定」（Paired t-Test）則能用統計方法比較兩個模型的表現是否具有顯著差異。「BLEU 分數」被用於機器翻譯任務中，評估 AI 產出的翻譯與人類翻譯有多接近；而「ROUGE 分數」則用於摘要任務，透過比較與原文的重疊率來評估品質。

為了提升執行效率，「動態批次處理」（Dynamic Batching）允許系統依據當下負載靈活地調整一次處理的任務數量。「GPU 加速的欄式資料處理與零拷貝記憶體存取」讓系統能快速處理大型資料集，並避免不必要的記憶體轉移開銷。

「擴散模型」（Diffusion Models）透過兩個步驟生成內容：前向擴散逐步加入雜訊；反向擴散則將這些雜訊一點一滴去除，產生出逼真的圖像或文字。「檢索增強生成」（Retrieval-Augmented Generation, RAG）則是在 AI 回答前先檢索知識庫中的相關資訊，讓回答更加正確與可靠。

最後，在電腦視覺應用中，「影像轉換技術」（如翻轉、旋轉與縮放）能幫助模型學會從不同角度辨識物體，提升其泛化能力。

理解這些概念是掌握現代 AI 系統運作方式的重要一步。隨著我們持續探索生成式 AI 的世界，這些基礎知識將幫助我們打造更聰明、更快速、更可靠的應用。