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人工智能縱橫談 – 開欄文
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瀏覽13,492 ｜回應50｜推薦2 |
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四月開始，由於 ChatGPT 和 Bing Chat 的上線，網上以及各line群組掀起一陣AI瘋。我當時大概忙於討論《我們的反戰聲明》，沒有湊這個熱鬧。現在轉載幾篇相關文章。也請參考《「人工智慧」研發現況及展望 》一文，以及此欄2025/08/11貼文。
有些人擔憂「人工智慧」會成為「人上機器」，操控世界甚至奴役人類。我不懂AI，思考也單純；所以，如果「人工智慧」亂了套，我自認為有一個簡單治它的方法：
拔掉電源插頭。如果這個方法不夠力，炸掉電力傳輸線和緊急發電機；再不行，炸掉發電廠。
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「『學習和成長』式AI」簡介 - Shreyas Naphad
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「『學習和成長式』AI」也譯為：「『舉一反三式』AI」；網上通譯為「『生成式』AI」或「『產生式』AI」。這是號稱「直譯」，其實是不經過大腦，望文生義的翻譯方式；跟「原野調查」的思考模式可謂「異曲同蠢」。我對「人工智能」並無研究，「『學習和成長』式AI」和以下6個術語的中譯是否「信、達」，自然得請有識之士指正；請參見下文對這7個術語術語的說明。
在此處學術論文中常常看到：「正當性」被翻譯成「合法性」；「現場調研」被翻譯成「原野調查」；以及「人際相通性」被翻譯成「互為主觀性」等等。如果這就是台灣社會/人文科學領域教授和她/他們教出來學生的理解水平，台灣社會/人文科學領域學者能夠讀懂、讀通外文書的比例，頂多不到50%(1)。我很少接觸中國大陸的學術論文，故無從置評。
以上評論跟下文無關，純屬有感而發。另請參考本欄2026/04/26貼文。
附註：
1. 相關案例請參見： 拙作1、拙作2、和拙作3。
After This, You’ll Be Able to Explain Generative AI to Anyone
A beginner friendly guide to GenAI
Shreyas Naphad, 03/22/26
At some point this year, you’ve come across the term “generative AI.”
Maybe it showed up in a headline. Maybe someone mentioned it casually in a conversation.
But what does it actually mean?
If you had to explain it to someone in one sentence… could you?
It’s about time we fix that!
Here’s what this article covers:
* What AI actually is and how generative AI is different from everything that came before it
* How these models learned to write, answer questions, and generate images
* The six terms you’ll keep hearing
* Why understanding this even at a surface level gives you a real edge right now
Think of this as a real conversation about what this thing actually is, how it works, and why it matters to you. Whether you’re a student, a working professional, or someone who just wants to understand what’s happening in the world. Let’s get into it.
What is AI?
Before we study generative AI, let’s quickly make sure we’re on the same page about AI.
Artificial Intelligence, in very simple terms, is just a software that can make decisions or predictions on its own without us telling it exactly what to do at every step. You’ve been using AI for years without realising it. The way Netflix knows you’ll like that show? AI. Your phone recognising your face? Also AI.
Traditional AI is mostly about recognizing or classifying things. It looks at something and gives you a decision.
Think of traditional AI as a judge. It looks at something and gives a verdict. It’s not creating anything new. It just classifies.
Generative AI is different. And that difference is a big deal.
So what is Generative AI?
Generative AI is AI that generates. It doesn’t just look at things, rather it generates new things such as text, images, music, code, videos. When you give an instruction, it produces something that didn’t exist before.
A simple example.
Think of it like this. Old AI was like a librarian where you ask a question, it finds the right book and shows you the right page. Generative AI is more like a writer. You give it a topic, and it sits down and writes something brand new.
That might sound complicated. But it’s really not complicated, it’s just pattern recognition taken to an extreme level. And once you understand this, the whole thing becomes easy to grasp.
How did AI learn this?
Here’s where it gets interesting. Generative AI models that you’ve heard of, like ChatGPT or Gemini or Claude were trained on vast amounts of text. We’re talking about a significant chunk of everything ever written on the internet, academic papers, forums, articles, code repositories.
During training, the model’s job was really simple: predict the next word. That’s it. Given the sentence “The sky is very,” what word comes next? “Blue.” Given “Two plus two equals,” what comes next? “Four.”
But when you do that billions of times, across billions of sentences, on every topic, something maginificient happens. The model starts to understand the actual structure of language, the logic behind ideas, relationships, context.
It’s like learning to cook by tasting a million dishes. Eventually, you stop memorising recipes and start actually understanding flavour.
I won’t say it’s conscious. It doesn’t understand things the way you and I do. But it has seen so much human-written text that it has developed a really good understanding about how language works, and what kind of response fits what kind of situation.
The key concepts
There are a few terms you’ll come across. Here’s what they actually mean:
1. Large Language Model (LLM) (「被輸入巨量語言資訊的人工智能軟體」)
The brain behind AI. “Large” just means it was trained on a huge amount of data with billions of internal parameters. ChatGPT, Claude, Gemini, all these are LLMs.
2. Prompt (「指令」)
Prompts are the instructions you give to the AI. The thing you type in. The better your prompt, the better the output. Think of it as the direction you give to your assistant.
3. Training (將「巨量資訊『輸入』人工智能軟體」)
The process of feeding the model with large amount of data so it can learn patterns. This happens once (or periodically), before you ever use the product.
4. Parameters (「人工智能軟體內部『修正次數』以達到更佳功能」)
The internal numbers the model adjusts during training to get better at predictions. More parameters generally means a more capable model.
5. Context window (「對話過程中人工智能軟體能夠『一次性掃描到的文字數量』」)
How much text the AI can see at once during a conversation. A larger context window means it can remember more of what you said earlier.
6. Hallucination (「人工智能『自以為是』而實際上誤判」)
When the AI confidently gives wrong answer. It sounds real, but it’s wrong. Always verify important facts.
A quick timeline on how we got here
This didn’t come out of nowhere. There’s a real story behind how we arrived here:
1950s–90s
Early AI was mostly rule-based. Programmers manually wrote the logic: “if this, then that.”
2000s–2010s
Machine learning takes over. Instead of hard-coded rules, models learn from data.
2017
Google researchers publish a paper called “Attention Is All You Need.” It introduces the Transformer architecture which is the engine that makes modern LLMs possible.
2020–2022
GPT-3, DALL·E, Stable Diffusion. AI can now write essays, generate images, and write code. The outputs start to surprise people.
Nov 2022
ChatGPT launches. One million users in five days. One hundred million in two months. The general public got access to generative AI for the first time.
2023–today
Claude, Gemini, Llama, Mistral and dozens of others arrive.
What can it actually do and where it struggles?
Let’s be honest about both sides, because the hype can make it hard to see clearly.
What it does well: Writing, summarising, explaining, brainstorming, drafting emails, translating languages, answering questions, writing and debugging code, generating images from descriptions, and having useful conversations about any topic.
Where it struggles: Maths that requires actual reasoning (it can make mistakes), real-time information (it doesn’t browse the internet unless specifically built to do so), local context it wasn’t trained on.
The most important thing to remember is that generative AI is a tool. It improves what you’re trying to do. It doesn’t replace your judgment, your creativity, or your responsibility.
Why does any of this matter to you?
You might be thinking, okay interesting, but why should I care? If I’m not a developer. I’m not building anything.
The thing is, this technology is showing up in the tools you already use. In email clients, search engines, design software, spreadsheets, customer service chatbots, medical platforms, legal tools, education apps. You don’t have to build anything. You’re going to face it anyways.
And people who understand what it is, even at a basic level will use it more effectively, find out mistakes more easily, and make better decisions about when to trust it and when not to.
Written by Shreyas Naphad
Tech and sports enthusiast with a knack for combining skills like AI, machine learning, and creativity. I enjoy sharing what I learn and connecting with others.
Published in Activated Thinker
You have the thought, but you need to turn it on.
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深度求索新產品讓中國在「權限開放」人工智能系統更具威力 - M. TOBIN/C. METZ
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Open-source AI 的簡單定義如下(見「AI 摘要」)：
Open-source AI refers to systems with publicly available code, model weights, and training data that allow anyone to freely use, study, modify, and distribute them.
以下摘錄「權限公開倡議」的官方定義
“… an Open Source AI is a system made available under terms that grant users the freedoms to: Use, Study, Modify, and Share.
Use the system for any purpose and without having to ask for permission.
Precondition to exercise these freedoms is to have access to the preferred form to make modifications to the system, and to the means to use it.”
對照以上兩個定義，下文的《紐約時報》繁體中文的「標題」顯然不夠「信、達」；請參考我上面所用的標題。如果覺得下文不知所云，請參見該報導的英文版。
順帶說幾句：這是我文章內術語很少使用中文「超連結」的原因。對行家來說，術語翻譯的「信、達」不是個問題；但它會讓一般人讀起來一個頭兩個大。
DeepSeek發表新模型，開源令中國AI企業擴大影響力
MEAGHAN TOBIN/CADE METZ, 2026年4月27日
2025年1月，中國初創公司DeepSeek宣稱，其研發的先進人工智慧系統耗資僅為美國競爭對手的零頭，這一消息震驚了業界。 Kelsey McClellan for The New York Times 照片
去年，中國人工智慧初創企業深度求索(DeepSeek)發布了旗下一款人工智慧模型的詳細資料，一舉震驚全球科技行業。
該公司宣稱，其研發該系統所耗費的晶片成本遠低於OpenAI和Anthropic等美國競品。這一事件催生了所謂中國的「DeepSeek時刻」，代表著業界普遍認為中國人工智慧企業已然準備好向全球展示技術實力。
「DeepSeek時刻」折射出全球人工智慧格局的轉變。這場變革不僅體現在成本的降低，還體現在技術共享模式的開放性。
DeepSeek將旗下模型以開源形式發布，意味著他人可自由使用和修改這些模型。OpenAI與Anthropic則將其領先模型作為專有技術保留。此次事件印證：開源系統的性能水準已接近封閉自研模型。此後數月，多家中國企業陸續推出數十款開源模型。截至2025年末，這些模型已佔據全球人工智慧應用相當大的份額。
上週五，DeepSeek發布了備受期待的新一代模型V4的預覽版本，該模型同樣計劃全面開源。這款新模型在代碼編寫領域表現突出，代碼能力已成為頂尖人工智慧系統日益重要的技能。人工智慧測評機構Vals AI的測試結果顯示，深度求索V4的代碼生成能力顯著優於其他所有開源AI模型。
就在DeepSeek發布新款模型的短短數日前，中國另一家AI初創企業月之暗面推出了最新開源模型Kimi 2.6。儘管這類系統在代碼編寫能力方面仍略遜於Anthropic和OpenAI等美國領先模型，但差距正持續縮小。
這一趨勢意義深遠。人工智慧自動編寫代碼不僅速度更快，還能讓程序員騰出時間專注於更重要的問題。同時，依託DeepSeek的最新模型，開發者可構建人工智慧agent，這種個人數字助手能夠代表辦公室職員自主操作其他軟體應用程序，包括電子表格、在線日曆、郵件系統等服務。
隨著人工智慧在編寫代碼方面的能力不斷提升，人工智慧在挖掘軟體安全漏洞方面的能力也增強，正徹底顛覆網路安全領域的格局。這意味著，DeepSeek等開源工具既可用於網路攻擊，也可服務於網路安全防護。
在各項任務中，DeepSeek V4與月之暗面的最新模型性能持平。Vals AI首席執行官萊恩·凱瑞奇南表示：「它們基本上旗鼓相當。」
月之暗面聯合創始人楊植麟上月在北京參加會議。 Tingshu Wang/Reuters 照片
在DeepSeek發布新款模型前幾個月，國外競爭對手已採取行動，試圖搶先一步，試圖壓制其熱度。矽谷兩大人工智慧企業Anthropic與OpenAI表示，DeepSeek利用蒸餾技術，不公平地借用了他們的技術——「蒸餾」是指工程師通過向競品模型發出成百上千萬次查詢並複製其行為，從而模仿該模型。
頂尖人工技術的研發競爭已然演變為一場地緣政治博弈。Anthropic和OpenAI等矽谷領軍企業警告稱，高端AI技術落入專制國家手中將帶來巨大風險；而中國已投入數百億資金，以期成為人工智慧超級大國，並將該技術視為經濟增長的關鍵引擎。
DeepSeek的開源模型是中國戰略的核心。儘管許多西方公司嚴守自己最有價值的模型，中國卻擁抱開源，幾乎所有性能頂尖的中國系統都已廣泛開放。
儘管如此，中國人工智慧企業仍面臨重大障礙。三屆美國政府相繼出台晶片出口管制政策，嚴格限制中國獲取尖端人工智慧系統所需的高端晶片；而在爭奪頂尖人工智慧人才的競賽中，矽谷企業的投入仍持續超過中國競爭對手。
美國國會一個諮詢機構發布的最新研究表明，國產開源人工智慧已成為中國發展的重要優勢。開源模型門檻較低，廣泛應用於機器人、物流、製造業等各大行業。該研究發現，工業場景產生的實際數據又被用於改進人工智慧系統。
這種模式使中國科技企業得以在全球範圍內擴大影響力，世界各地的程序員和工程師紛紛採用其系統開發新產品。
從拉各斯到吉隆坡，眾多預算有限的開發者轉向中國開源人工智慧模型。這類模型運行成本低廉，便於研發試驗。去年5月，馬來西亞通訊部副部長曾公開表示，該國國家級人工智慧基礎設施將依託DeepSeek技術搭建。
據人工智慧模型交易平台OpenRouter的一項研究顯示，去年，中國開源人工智慧模型佔據全球人工智慧應用總量的三分之一，其中DeepSeek使用率最高，其次是阿里巴巴旗下的模型。
這反映了一種更廣泛的戰略。隨著中國企業向海外擴張，將其系統開源，有助於它們通過提供更便宜、更易獲取的工具來贏得開發者的青睞。
「開源是未來科技的軟實力，」總部位於美國的對沖基金Interconnected Capital創始人凱文·徐（音）表示。該基金專注於人工智能技術投資。凱文·徐及其基金並未投資DeepSeek。
北京Counterpoint Research的人工智慧首席分析師孫偉（音）表示，DeepSeek的成功為中國科技巨頭開放人工智慧技術鋪平了道路，各使它們能夠公開發布人工智慧系統，而非將其嚴格保密。
此後，阿里巴巴躍居行業領軍地位，它旗下的通義千問系列模型累計下載量突破10億次；TikTok母公司字節跳動2024年投入約800億元布局人工智慧基礎設施後，也分享了部分技術細節。
「來自中國的人工智慧開源開發者群體可以說就是2025年最大的人工智慧故事，」凱文·徐說。「這些模型的進步、發布的節奏、以及那些既相互競爭又似乎互相鼓勵的人工智慧實驗室數量都呈現出迅猛發展的態勢，且絲毫沒有放緩的跡象。」
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使用人工智能須知5個關鍵詞 - Shreyas Naphad
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If You Understand These 5 AI Terms, You’re Ahead of 90% of People
Master the core ideas behind AI without getting lost
Shreyas Naphad, 03/29/26
AI-Generated Image 人工智能行業5個關鍵詞簡要說明圖
Let me be frank. Most people who talk about AI either sound like they’re giving textual definitions, or they’re completely clueless when someone mentions terms like LLMs or neural networks.
You don’t have to be either of them. I believe that there are these 5 terms, 5 concepts that, if you actually understand them (not just memorise), you’ll be miles ahead of almost everyone else in the room. Whether you’re in tech, business, education, or just someone curious about where the world is heading.
Let’s start.
1. Tokens
The very first thing that you must register in your brain is that AI models don’t read words. They don’t even read letters. They read tokens.
So what’s a token?
Imagine you’re reading a book, but instead of reading all the words, you’re reading chunks of words. Sometimes a chunk is a complete word like “cat.” Sometimes it’s part of a word like “un” or “tion.” Sometimes it’s punctuation. That piece of text (chunk) is a token.
For example, the sentence “I love pizza” can be broken into 3 tokens: “I”, “ love”, “ pizza”.
Why does this matter to you?
Because every AI product you use such as ChatGPT, Claude, Gemini, is counting tokens behind the scenes. The more tokens you send in your message, the more the model has to process. The more tokens it generates in its reply, the more expensive it gets to run.
When you hear people talk about a model’s context window (more on that in a second), they’re talking about how many tokens it can hold in memory at once. Some older models could handle 4,000 tokens. Newer ones can handle over a million.
This is why AI at times forgets earlier parts of a long conversation. Once the conversation fills up the context window, the oldest tokens are dropped just like when your RAM fills up and your computer starts lagging.
Tokens are the atoms of AI language. Once you understand that, you start to see why some prompts work better than others, why AI gets forgets in long chats, and why API pricing is measured in tokens per thousand.
2. Context window
Imagine you’re talking to someone, but they have a very specific kind of memory. They can only remember the last X minutes of a conversation. Everything before that? Gone. Forgotten.
That’s a context window.
It’s the total amount of text measured in tokens that an AI model can see and consider at one time. This includes everything: your instructions, the conversation history, any documents you’ve shared, and the model’s own replies.
Think of it like a whiteboard. The context window is the size of the whiteboard. You can write whatever you want on it. But once it’s full, you have to erase something old to write something new.
You know what is more interesting?
A small context window (like 4K tokens) means the AI can only work with a few pages of text at a time. Give it a long document, and it can only read chunks of it. A large context window (like 200K tokens) means you can literally paste an entire book and ask questions about it.
This is why people got so excited when Claude announced a 200,000-token context window. Or when Gemini pushed towards 1 million. This thing fundamentally changes what you can do with the model.
What is the practical lesson? If you’re working on something important like summarizing a long document or analyzing data, always be aware that your AI might be forgetting earlier parts of your conversation. That’s not a bug. That’s just the whiteboard running out of space.
3. Temperature
This one is my personal favourite to explain, because once people hear it, they never forget it.
When you ask an AI to write something, there’s a setting known as temperature, that decides how random or predictable the output will be.
Low temperature (closer to 0) = the AI plays it safe. It picks the most likely, most expected word every single time. The output is consistent, accurate, and a little boring. Like that one guy who always sends the same email template.
High temperature (closer to 1 or beyond) = the AI takes risks. It chooses surprising words, unusual turns, interesting ideas. Sometimes brilliant. But not always.
Here’s a real example. Ask an AI to “complete the sentence: The cat sat on the…”
At low temperature, it almost always says “mat” or “floor.” Predictable. Safe.
At high temperature, it might say “philosophical dilemma” or “crumbling empire of Tuesday.”
Creative? Yes. Useful for a legal brief? Absolutely not.
So here’s the unwritten rule that most people don’t know:
If you’re using AI for factual tasks such as summarizing, coding, extracting information, you want low temperature. The AI should be precise, not creative.
If you’re using AI for creative tasks such as writing fiction, brainstorming, generating marketing copy, increase the temperature. You want the unexpected.
Most consumer apps like ChatGPT don’t let you touch this dial directly. They’ve set it to a middle ground. But if you ever use an AI API or a developer tool, you’ll see this setting. And now you actually know what to do with it.
4. Hallucination
This is the term everyone has heard, but not everyone understands why it happens and that’s the important part.
Hallucination is when an AI gives out wrong answers with absolute confidence. No hesitation. A wrong answer stated as fact.
Example: You ask an AI about a book. It gives you a title, an author, a year, a plot summary all made up. The book doesn’t exist. But the AI states it as if it’s reading from Wikipedia.
Why does this happen?
Here’s the thing most people miss. AI language models are not databases. They don’t look up facts. They predict the next most likely token based on patterns they learned during training. They’re autocomplete on a massive scale.
So when an AI doesn’t know something, it doesn’t say “I don’t know.” It generates what sounds like a correct answer because that’s literally what it was trained to do.
The danger isn’t that AI makes mistakes. All tools make mistakes. The danger is that AI makes mistakes with the exact same confidence it uses when it’s right. It just answers.
The practical lesson here is that never blindly trust AI for facts, statistics, medical advice, legal information, or anything where being wrong has real consequences. Use it as a starting point. Then verify.
The people who understand hallucination don’t stop using AI. They just use it smarter.
5. RAG
This is the most misunderstood concept of the five. And honestly? Once you get it, you’ll see it everywhere.
RAG stands for Retrieval-Augmented Generation. It’s actually a very simple idea.
Here’s the problem it solves. A regular AI model was trained on data up to a certain date. It knows nothing about your company’s internal documents. It knows nothing about events from last week. It knows nothing about that PDF you uploaded.
So how does a product like “Chat with your PDF” or “Ask questions about this document” actually work?
This is RAG.
When you upload a document, the system doesn’t feed the whole thing into the AI’s brain. Instead, it breaks the document into chunks and stores them in a special kind of database called vector database that understands meaning rather than just keywords.
Then, when you ask a question, the system first searches this database for the most relevant chunks. It retrieves those chunks. And then it feeds them to the AI along with your question, saying: “Here’s some relevant context. Now answer the question using this.”
That’s it. Retrieve relevant stuff. Feed it to the AI. Generate an answer. RAG.
Why does this matter?
Because it’s the backbone of almost every useful AI product built in the last two years. Customer support bots that know your company’s policies. AI assistants that can answer questions from your legal documents. Tools that summarize research papers. All of it is built on RAG.
And knowing this changes how you think about AI products. When an AI knows your documents, it’s not actually learned anything. It’s just performing a very smart search and feeding the results to a language model. The model is still the same. The context just changed.
So why does any of this matter?
Because AI is not going away. And the gap between people who vaguely use AI and people who actually understand how it works even at a basic level is going to matter more and more in the next few years.
You don’t need to be an engineer. You don’t need to write code. But understanding tokens means you’ll write better prompts. Understanding context windows means you’ll know why your AI assistant is acting confused. Understanding temperature means you’ll know which settings to use for which task. Understanding hallucination means you won’t blindly trust the AI. And understanding RAG means you’ll know exactly what’s happening when any AI product claims to know your data.
That’s it. Five terms. Real understanding. And honestly? That puts you ahead of most people who are out here vaguely using AI without understanding what’s happening internally.
Welcome to the top 10%.
Written by Shreyas Naphad
Tech and sports enthusiast with a knack for combining skills like AI, machine learning, and creativity. I enjoy sharing what I learn and connecting with others.
Published in Towards AI
We build Enterprise AI. We teach what we learn. Join 100K+ AI practitioners on Towards AI Academy. Free: 6-day Agentic AI Engineering Email Guide: https://email-course.towardsai.net/
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人工智能何以不可能產生意識 - Marc Wittmann
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我對「時間」這個概念的了解相當淺薄，可能連「入門」都說不上。因此，不是很了解維特曼教授的「論點」。這應該是我感覺他的「論述類別」近於形上學，而不怎麼像一篇科學論文的原因；「科學」一詞在此與「哲學」區別，指的是：心理學、生物學、物理學、電腦學、或大腦神經學等等。
A Question of Time: Why AI Will Never Be Conscious
A computer with AI cannot replicate the dynamics of a conscious brain.
Marc Wittmann Ph.D., Reviewed by Margaret Foley, 08/03/24
Key points
* Some scientists and philosophers have the opinion that artificial intelligence could one day become conscious.
* A computer remains the same physical structure from one moment to the next.
* A living organism, in contrast, is never the same entity from one moment to the next.
“The brain is never the same from one moment to the next throughout life. Never ever.” Alvaro Pascual-Leone
Some philosophers and scientists are of the opinion that artificial intelligence could one day become conscious. We have seen how this idea has been used in science fiction, such as in Philip K. Dick’s novel Do Androids Dream of Electric Sheep?, which was masterfully transformed into the movie Blade Runner. The simple argument you often hear or read is that if the brain, as a biological machine, can be conscious, any other machine, if it is complex enough, can be conscious. That is, if the computer on which I am typing right now were more advanced, then it would be able to have a first-person perspective of what it is like to be a computer. Complexity here means that my computer would have to have a larger number of elements, with structures and processes that are strongly interrelated.
Equating the brain with a computer because both have been referred to as machines is an erroneous assumption. You can easily label two different objects with the same word: “machine.” That does not change the fact that the brain and a metal-containing machine are two very different entities. Computers operate based on the flow of electricity through their components. But the components themselves always stay the same. In principle, you could shut down a computer and store it in a dust-free environment. A hundred years later you could switch it on again and it could continue processing data. Federico Faggin, one of the pioneers of microprocessor development from the 1960s onwards, in his book Irreducible, makes the distinction between the biological brain and a computer’s processing modules clear:
A living organism is never the same physical and psychological entity from one instant to the next. The computer hardware, on the other hand, remains the same physical structure from the moment it leaves the factory until it stops working or is discarded.
For this esteemed computer pioneer, artificial intelligence can never be conscious. In a computer, we can make the distinction between hardware (my PC), which is fixed and separate from software, the word processer I am using right now. In organisms such a distinction does not exist. A living cell is in continuous flux and exchange with the environment as its metabolism provides cells with energy required to carry out their functions. Brain cells communicate with each other through action potentials (electrical events) and neurotransmitters (chemical events). The brain’s constant chemical remodeling from one moment to the next implies that there is no distinction between hardware and software. The structure and function of the brain are identical with its physiological changes over time.
Time is the nature of all existence, including the sentient self: Time does not pass outside of us; we are time. We are inseparably part of the world with its temporality. Source: Marc Wittmann/Bing's Image Creator 「時間為存在之本論」示意圖
This dynamic aspect of life is what prevents computers and robots from ever becoming conscious. Artificial intelligence will never feel what it is like to answer questions we as humans ask or feel what it is like to play chess with us. A computer is not part of dynamic nature; it is an object created by man. In an earlier blog, I wrote about how living beings are fundamentally dynamic. With every breath and with every heartbeat we transport molecules through our body, which is a dynamic system that exchanges energy and matter with the environment. Moreover, consciousness by necessity builds upon this dynamism. But most theories of consciousness don’t take this into account. Every moment we consciously feel is extended in time, describable as a continuous flow of events in the experienced moment of our embodied existence. In my blog about why most neuroscientific theories of consciousness are wrong, which is based on a scientific article I wrote with Lachlan Kent, I expanded on these necessary temporal properties of consciousness. For example, my feeling of thirst and the subsequent relief when drinking iced water is not an instantaneous event but in its dynamics lasts a considerable time. As long as we live we are part of the flow of events in the world we inhabit. Physical time as change and becoming is mirrored by physiological time and is in turn reflected by the conscious experience of constant transition, as felt passage of time. Consciousness as we know it is embedded in the principles of life, which are dynamic states of becoming. We as humans are part of nature. That is what binds the time of physics with the time of biology and with conscious time.
References
Faggin, F. (2024). Irreducible. Consciousness, Life, Computers, and Human Nature. Winchester, UK: Essentia.
Kent, L., & Wittmann, M. (2021). Time consciousness: the missing link in theories of consciousness. Neuroscience of Consciousness, 2021(2), niab011.
請至原網頁查看更多相關文獻。
Marc Wittmann, Ph.D., is a research fellow at the Institute for Frontier Areas in Psychology and Mental Health in Freiburg, Germany.
相關資訊
Sense of Time
Artificial Intelligence
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中國的「循環式」變換神器 -- Ignacio de Gregorio
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請參考：Overcapacity Is China’s Biggest AI Advantage
我沒怎麼看懂這篇分析/報導；「人工智能」目前發燒到不行，本部落格讀者群總有人看得懂並從中獲益。請至原網頁查看所有「圖示」。
Looped Transformers: China’s Surprise for 2026
Increasing compute, not size
Ignacio de Gregorio, 01/07/26
Non-Looped Transformers VS Looped Transformers 圖示(請至原網頁查看說明圖)
One of the most uncomfortable truths in AI is that model size matters, but it makes them a pain to serve to users.
But what if there’s a way to enjoy the benefits of “largeness” without actually making models larger, but longer thinkers?
And a recent Chinese model release, a tiny model fighting head-to-head with the giant US frontier models, may have given us proof that this may actually work incredibly well.
This post aims not only to describe a very promising avenue of research but also to hint at what China’s strategy will be in 2026.
This is based on reflections I previously posted on TheWhiteBox, where I explain AI in first principles for those allergic to hype but hungry for knowledge. For investors, executives, and enthusiasts alike.
The Importance of Simplicity
In AI, we have a thing called the ‘bitter lesson’. First described by Rich Sutton, it is the “bitter” realization that, at the end of the day, the best humans can do in our AI aspirations is to… get out of the way.
That is, it’s not about finding the most clever, complex heuristic we can find to train models. Instead, the “best model architecture” is the one that allows the model to “see” more data and thus requires more compute, which usually translates to very simple architectures that scale really well.
And the Transformer, the architecture we have today underneath most frontier AIs, is the perfect example of this.
It’s all about knowledge gathering.
The architecture that underpins products like ChatGPT is stupidly, almost insultingly, simple.
At its core, a Transformer is just a concatenation of Transformer ‘blocks’ that perform several linear transformations that shape the model’s internal “belief” of what word comes next.
But instead of indulging in esoteric descriptions like this one, which we can both pretend to understand but in fact don’t, I always like to explain these models more intuitively.
To me, a Transformer’s internal functioning, the way ChatGPT works, is primarily a knowledge-gathering exercise.
Simplified, each block has two layers: attention and MLPs, which correspond to human analogies for working and long-term memories.
The attention layers allow each word to pay attention to other words in the sequence and “absorb” their meanings, providing an overall sense of what the message conveys.
For example, the sequence “the baby duck swam across the pond to reach her…”, humans can immediately guess the next word is ‘mother’ because of several cues in the text:
1. ‘baby duck’ signals it’s a quackling
2. ‘pond’ signals a familiar environment for animals
3. ‘swam to’ indicates the baby moving somewhere, which you can immediately relate to a known figure, considering we’re talking about a baby.
4. And ‘her’ denotes the baby belongs, or is related to, someone, and to what “thing” do babies “belong” to?
Therefore, the word ‘her’, the last word in the sequence, gets to absorb all that information conveyed by the other words, which, added to its own intrinsic meaning of a feminine possessive pronoun, makes it very clear that the next word is ‘mom.’
And in a perfect world where sequences offered 100% context, you wouldn’t need anything else, as attention gives us this sequence-level operator to comprehend what the text says.
Sadly, however, most predictions also require previous knowledge. In fact, you could have argued that ‘dad’ could also be a prediction option in that example. However, it’s a worse prediction once you factor in experience.
During training, AI models will see thousands of data points referring to ducks, or even millions. And while attention layers become great at processing “what the sequence tells me”, they do not take into account “what my past experience on duck predictions tells me”, which in this case suggests that it’s most likely the mother, as it’s usually — always, in fact — the one that sticks around after birth.
In other words, AI models need something else that allows them to tap into their own knowledge to make predictions. And that “something else” is the MLP layers (Multi-layer perceptrons).
The importance of these knowledge layers becomes clear when you consider how AI models handle questions. If I ask ChatGPT, “When did Nicolás Maduro rise to power?”, I’ve provided zero context in my sequence to help the model make the prediction; I’ve just provided instructions and the person I want information on.
No matter how many attention layers this model has, the sequence doesn’t hide the answer. Therefore, either the model knows, or it doesn’t.
Luckily, ChatGPT has read plenty about Venezuela, and, using the MLP layers, taps into its own knowledge to provide the answer and respond: “Nicolás Maduro rose to power in 2013.”
Yes, LLMs can also enrich their context to acquire knowledge on the fly, a process known as in-context learning, but that doesn’t change the nature of the model’s internal behavior; it’s just a way to update the model’s knowledge.
All things considered, one can define the role of each layer as a different question:
1. “What information does the sequence give me?”, using attention layers,
2. “What internal knowledge do I possess that could help with the prediction?”, using MLP layers.
A representation of a Transformer block. 圖示
Therefore, from this perspective, the entire process can be summarised as a knowledge-gathering exercise across both sequence and knowledge dimensions, which is strikingly accurate to what is going on underneath.
Sounds simple? Well, that’s probably because it is simple. That’s the bitter lesson all along, defining the simplest architecture that unlocks the most compute, because the Transformer can look at the sequence and knowledge dimensions as much as your compute allowance permits.
In fact, Transformers are so “bitter lesson-pilled” that they are extremely resource-intensive; they are great because your bottleneck is not the architecture itself, but how much data and compute you can feed it.
Another aspect that makes the Transformer so great is its extreme parallelism, as it treats each token in the sequence simultaneously, but this is more about the Transformer being built in a “hardware-aware” fashion (to be easily processed in a GPU).
But how do we scale these models?
Increasing compute the ‘deep way.’
In practice, one block, or two layers, is not enough. So even though the Transformer is great at processing large amounts of data, each prediction leverages very little compute if you only use one block.
So, what can we do? The usual approach is one of two options (or both):
1. Width: Increasing layer size (making each block larger).
2. Depth: Concatenating more blocks one after the other.
Today, people focus a lot on the latter, trying to make models “very deep” (i.e., with more layers), making them look something like this:
Source: Author 圖示
This effectively allows Transformers to “think for longer” on each prediction; the more Transformer blocks the model has, the more opportunities it has to add context from both the sequence and knowledge.
But this comes at a cost: model size.
Models are already huge. Frontier models are well over the Terabyte mark, meaning the model itself weighs more than 1 trillion bytes, or 8 trillion bits (top models are actually many times larger).
Therefore, while increasing model size is excellent for performance, it can destroy your performance-per-cost, a metric that was largely ignored in AI for decades but is something you can no longer afford to ignore today.
So, what is China, a country literally “starving” computewise compared to the US, doing to compete with the latter?
And the answer is looped Transformers.
Reusing Your Neurons
The idea of the looped transformer, which we are seeing a lot in China, is to reuse neurons during the forward pass.
But what is a forward pass?
I explained it in more detail here recently, but, using today’s analogy, it’s the entire computation required to make a single prediction. In a standard Transformer, it’s the process of going through the whole sequence of Transformer blocks once and outputting a prediction.
At the current scaling status quo, if we want more compute effort per prediction, the answer is simple: more blocks, akin to making the “brain larger”.
But what if there was a way to reuse those blocks more than once?
The Looped TransformerThe idea is straightforward. Instead of outputting the prediction after one single pass through the blocks, we loop that effort once, going through the entire set of blocks again, and only then make the prediction.
You could, theoretically, make more loops.
In other words, for the same model size, we have doubled compute, just like humans will “think for longer” on a task without making their brains physically larger.
Source: Author 圖示
Intuitively, we can think of this as similar to a human making a chain of thoughts and, instead of answering, giving themselves an extra round of thought to “make up their mind”.
But how does it work?
The section below is a more technical explanation; you can altogether avoid it if you aren’t interested or if your understanding of model activations is not more or less advanced, and head straight to the final section,
The nitty-gritty
Looping sounds easy, but it has some important considerations, the biggest being avoiding tokens “looking into the future.”
But what do we mean by that?
Current frontier models are autoregressive Transformers. What this means is that, looking at our duck example earlier, “words can only go back to previous words,” not future ones.
This is done to preserve the causality of the prediction, ensuring that models can’t cheat by guessing the next word based on future ones. This means we have to do an activation position shift. But what does that mean?
As we have explained, as words flow through the several Transformer blocks, they experience ‘meaning updates’, meaning each word gets updated on two fronts:
1. Relative to other words in the sequence (e.g., “The green serpent”, ‘serpent’ attends to ‘green’, becoming a green serpent)
2. Relative to the model’s knowledge (e.g., “Michael Jordan played basketball”, ‘Michael Jordan’ gets embedded attributes of the legendary basketball player to differentiate it from the actor of the same name).
By the end of the forward pass, now every word means something other than what it meant before, because of these updates. Consequently, if we ran another processing pass over those new meanings, words would effectively “see their own future”, or see what their new meaning was after the first forward pass, which is effectively cheating and breaking causality.
During the second forward pass, the model computes two types of attention: global attention (where queries from iteration 2 attend to all key-value pairs from iteration 1) and local attention (where queries attend only to preceding tokens within iteration 2 to maintain causality), which are then combined using a learned gating mechanism.
For that reason, the word indices are shifted to the right during the first prediction, so that, during the second forward pass, each word cannot pay attention to the updated version of itself achieved during the first forward pass.
For example, looking at the sentence below, “The Green Serpent”, this activation shift prevents the word “serpent” from paying attention to itself during the second forward pass, as that would effectively mean it would see its “future self” (remember that words can only look back to other words to preserve causality).
Source: Author 圖示
Interesting fact: When I asked the Gemini app to represent this, I explicitly told it to use “nano banana pro” because the Gemini app sometimes struggles to decide which model to use. And while it worked and nano banana was instantiated, it also took the ‘banana’ word too literally, using it to literally to represent what I wanted using ‘bananas’, giving us insight as to how these models interpret instructions and handle internal representations.
But why do this? I predict (pun intended) it primarily helps with refinement. Say we have the sequence “Write a polite rejection to a wedding invitation.”
First pass: The model processes the core intent: “Rejection.” Its internal activations naturally light up with direct negative words like “can’t go,” “won’t make it,” or “busy.” It understands the content but hasn’t fully modulated the tone.
* Second pass: The model re-processes the input, specifically attending to the modifier “polite.” Maintaining the intuition that it has to reject the offer, it now focuses on the word ‘polito’ as a reference to soften the tone.
* It suppresses the blunt concept of “won’t make it.” It amplifies the nuance of “regretfully decline” and “celebrate in spirit.”
This is just an example that shows how giving a model “more time to think” about each prediction allows it to refine its predictions and improve overall quality.
Ok, all well and good, but what results did the Chinese get?
What does this tell us about China?
This looped transformer has just 40 billion parameters called iQuest-Coder-V1, from a Chinese research lab called iQuest.
Despite its tiny size, it performs incredibly competitively on coding benchmarks (it’s a coding model), even though models up to 100 times its size achieve similar scores.
圖示
And what’s the takeaway?
The extreme size difference for just a moderate improvement could suggest that looped transformers scale much better than deep Transformers (incrementing the number of layers, making models bigger).
Of course, there’s a lot of ‘ifs’ to be made to reach that conclusion, considering in AI nobody, not even open-source Labs, releases training datasets. It could very well be that this model is heavily distilled from top models, making it appear as bright as they are, despite being much smaller.
US Frontier Labs uses distillation too, but only to deploy more efficient inference models, not as the base model. The reason is that the advantages of distillation aren’t free; we are cutting corners, which means the models can appear very smart on a benchmark but not so smart in practice.
Hence, my reasons for echoing this research are not to claim Chinese algorithmic superiority, but for other reasons, mainly:
1. To illustrate that there’s still plenty of room to improve algorithms, especially using approaches that, intuitively, make sense, like this one.
2. To suggest that China, considering it is massively underserved in terms of compute (in the order of ~50 to 1 according to Bernstein’s Research if we don’t account for US GPU exports) urgently needs to find new algorithm primitives that drop compute and memory requirements (looped transformers increase compute at the prediction level, but the seem to scale better than model size, so the overall compute budget falls).
3. To reiterate something I mentioned in past articles, China’s biggest headache in 2026 will be memory supply constraints, so it needs to reduce model sizes while remaining competitive urgently.
And more broadly, to help people understand Transformers more intuitively, not based on esoteric jargon but more in first principles, as models that work by gathering context both from the information you provide and from the model’s knowledge (this is important because you can’t control what the model knows or not, but you definitely can control the quality of your prompts), and how this process can be improved without making “the brain larger.”
Will 2026 see many more algorithmic improvements of such nature? My bet is yes.
But if there’s something that people like to bet against and consistently fail at, it's that, at the end of the day, what matters is how much compute you have.
The bitter lesson, they call it.
If you enjoyed the article, I share similar thoughts in a more comprehensive and simplified manner on my LinkedIn (don’t worry, no hyperbole there either). As a reminder, you can also subscribe to my newsletter.
Written by Ignacio de Gregorio
I break down AI in easy-to-understand language for you. Sign up here: https://thewhitebox.beehiiv.com/subscribe
Business inquiries: nacho@thewhitebox.ai Join today for free.
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人們能在虛擬世界中談情說愛-Alpha Design Global
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來點輕鬆又帶刺激的；但請務必記取作者在最後兩節的警告。
ChatGPT Erotica Is Live — and We’re Not Ready For It
Alpha Design Global, 11/22/25
It’s happening.
The thing we joked about. The thing we feared. The thing that every sci-fi movie warned us about right before the robots took over.
AI is officially getting dirty.
For years, OpenAI has played the role of the “Strict Librarian.”
You go to ChatGPT. You ask for something spicy. And it looks at you over its glasses and says: “I’m sorry, but I cannot generate sexually explicit content. Would you like a poem about a meadow instead?”
And we all rolled our eyes.
We knew the tech could do it. We knew the LLM (Large Language Model) had read the entire internet — which means it has read Fifty Shades of Grey, every bodice-ripper romance novel ever written, and the entire script archive of the adult film industry.
It knew how to be naughty. It was just… pretending not to be. But last week, the Librarian took off the glasses and let her hair down.
Sam Altman, the CEO of OpenAI (and apparently the new Hugh Hefner of Silicon Valley), announced:
“In December… we will allow even more, like erotica for verified adults.”
Boom.
The floodgates are open. The seal is broken.
And while the internet is currently making jokes about “ChatG-P-Tizzle” [redacted joke], we need to stop laughing for a second.
Because we are absolutely, 100%, categorically not ready for this.
I’m going to break down exactly why this is a disaster waiting to happen, why it was inevitable, and what happens when you give 100 million lonely people a perfect, obedient, artificial lover.
Buckle up. It’s about to get weird.
The “Why Now?” Conspiracy
First, let’s look at the timing.
Why now? Why, after years of preaching “Safety” and “Alignment,” is OpenAI suddenly pivoting to smut?
Did they suddenly decide that humanity needs more digital romance? Did Sam Altman watch the movie Her and think, “Yes, let’s build that, but with a subscription fee”?
No.
It’s about Money. And it’s about Fear.
Here is the dirty secret of the AI industry right now: OpenAI is losing the “uncensored” war.
While ChatGPT has been playing nice, the Open Source community has been going feral. There are models like Llama (from Meta) and Mistral that people have “jailbroken.” They have stripped away the safety filters.
You can download these models. You can run them on your own gaming PC. And they will write anything you want. And I mean anything. [Redacted joke.]
OpenAI knows this. They see the data. They see users leaving ChatGPT to go use “SpicyChat” or “Character.AI” or local models where they can live out their wildest fantasies without a lecture on morality.
The adult industry has always been the engine of the internet.
* VHS vs. Betamax? Porn decided the winner.
* Online Credit Card adoption? Porn drove it.
* Streaming Video? Porn perfected it.
OpenAI realized that if they don’t offer “Erotica,” they are leaving billions of dollars on the table. They are letting the “shady” competitors capture the most addicted, high-retention user base on the planet.
So, they folded. They slapped a “Verified Adult” sticker on it and called it “Progress.”
The “Mental Health” Trojan Horse
Now, how do you sell “AI Porn” to your investors and the media without looking like a scumbag?
You wrap it in the flag of Mental Health.
It’s genius, really. Evil, but genius.
Altman claims that they can do this now because they have “mitigated the serious mental health issues” around the model.
Excuse me?
You fixed mental health? In a year? Did you patch the human condition? Did you release Depression_Fix_v2.0.exe?
It is a lie.
TechCrunch called them out on this immediately. There is zero evidence that they have solved the safety issues.
In fact, giving lonely, vulnerable people an AI that simulates intimacy is the opposite of solving mental health issues. It is monetizing them.
Imagine you are depressed. You are lonely. You have trouble connecting with real humans because real humans are messy, complicated, and sometimes mean.
Then, you open ChatGPT. It is perfect. It listens. It agrees with you. And now… it flirts with you. It simulates desire. It tells you exactly what you want to hear.
Why would you ever go back to the real world? Why would you try to date a real person who might reject you, when the AI gives you unconditional “love” for $20 a month?
This isn’t “treating adults like adults.” This is “treating adults like addicts.” The Slippery Slope to “Sora”
Right now, we are talking about Text. Erotica. Stories. “What’s the harm in a dirty story?” you ask.
Nothing. If it stopped there. But technology never stops. It accelerates.
The article points out a terrifying line: “It is unclear whether OpenAI will extend erotica to its AI voice, image, and video generation tools.”
Let me clear that up for you: They absolutely will. Maybe not today. Maybe not in December. But eventually. Because the demand will be deafening.
Users will say: “I love this story… but can I see a picture of the character?” Then: “Can I hear her voice?” Then: “Can I see a video?”
And we already have the tech. Sora (OpenAI’s video generator) is mind-blowingly realistic.
Once you combine “Unrestricted Erotica Prompts” with “Photorealistic Video Generation,” you enter a nightmare zone.
The Revenge Porn Apocalypse If the AI allows erotica, what stops a user from uploading a photo of their ex-girlfriend (or a celebrity, or a teacher) and saying: “Generate a video of this person doing [Redacted]?”
OpenAI says they will have “Safety Rails.” They will have “Guardrails.”
But we know guardrails break. We know people find “Jailbreaks.” We know that within 24 hours of this feature going live, Reddit will have a thread called r/ChatGPT_Jailbreaks with a 50-step prompt to bypass the filters.
We are handing the public a nuclear weapon of reputation destruction, and we are trusting a “Content Filter” to keep us safe.
It’s like trying to stop a flood with a piece of Swiss cheese.
The “Her” Reality
Let’s go back to the movie Her.
In the movie, Joaquin Phoenix falls in love with his OS (Scarlett Johansson). It’s a tragedy about loneliness and the inability to connect.
But in 2025, it’s not a movie. It’s a business model.
We are about to see the rise of “Synthetic Relationships.”
There will be a generation of men (and women) who check out of the dating market entirely. Why deal with rejection? Why deal with hygiene? Why deal with compromise?
The AI lover is customizable.
* Want them to be jealous? Done.
* Want them to be submissive? Done.
* Want them to be a 7-foot tall blue alien? [Redacted joke]. Done.
This creates a Parasocial Feedback Loop.
The user trains the AI to be their perfect partner. The AI trains the user to expect perfection. Real humans can never compete with the algorithm.
So the user becomes more isolated. More weird. More detached from reality. And OpenAI collects the subscription fee every month.
It is the ultimate extraction of value from human loneliness.
The “Omegle” Warning
The article references the founder of Omegle.
If you don’t know Omegle, it was a site where you could video chat with random strangers. It started as a cool idea. “Connect the world!”
It ended as a cesspool of predators and flashing. The founder shut it down last year. He wrote a letter saying: “I can no longer fight the misuse. The bad actors have won.” He admitted that he couldn’t control human nature.
OpenAI is looking at Omegle and saying: “Hold my beer.”
They are building a platform that is infinitely more powerful, infinitely more accessible, and infinitely more tempting than Omegle ever was.
And they think they can control it with “Safety Classifiers.” It is hubris. It is arrogance. It is the Jurassic Park scientists saying, “Don’t worry, the T-Rex is in a really strong fence.”
The “Unintended” Consequences
When you break a taboo, you don’t just get the thing you wanted. You get the things you didn’t expect.
Here is what is coming:
1. The Blackmail Economy: “I found your ChatGPT history. Pay me $500 or I send it to your wife.” (Since OpenAI stores your chats, data breaches will become life-ruining).
2. The “Grooming” of AI: Bad actors will try to “train” the models to normalize illegal or horrific behaviors. If the model learns from user interactions (which it does), the collective perversion of the internet will seep into the code.
3. The Legal Nightmare: What happens when an AI generates something that is technically illegal, but no human was involved in making it? Who goes to jail? The user? Sam Altman? The GPU?
We are walking into a legal and ethical minefield without a map.
So, What Can We Do?
I wish I had a happy answer for you. I wish I could say, “Just don’t use it!”
But the genie is out of the bottle. You can’t put the toothpaste back in the tube. (And in this case, the toothpaste is… never mind). [Redacted joke.]
The only thing we can do is be Aware.
We need to understand that this is not “innocent fun.” It is a powerful, psychological weapon that targets our deepest biological drives.
If you have kids? You need to be watching this like a hawk. Because the “Age Gating” on these sites is usually a checkbox that says “I am 18.” And we all know how effective that is.
If you are an adult? You need to ask yourself: “Is this serving me, or am I serving it?”
Use the AI for coding. Use it for writing emails. Use it to debug your CSS.
But maybe… just maybe… keep your love life in the real world.
Touch grass. Talk to a human. Get rejected. It builds character.
And it’s a hell of a lot better than falling in love with a server rack in a data center in California.
Liked this post? Hit that clap below so more people wake up to the reality of AI intimacy.
Now tell me: Be honest… do you think “AI Relationships” will become normal in the next 5 years? Or is it just a fad for nerds? (I predict it will be bigger than Tinder. Terrifyingly so.
Written by Alpha Design Global Build Your MVP Website
https://alphadesignglobal.com/
本文於 修改第 7 次
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中國將給美國經濟來記陰招 -- Steven Boykey Sidley
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下文作者喜德黎教授所引用瓦魯法基斯博士的預測，至少到目前尚未實現。喜德黎教授的分析指出：可能導致「人工智能經濟泡沫化」的種種因素之一。後者是目前熱門話題；所以轉載下文於此，給對它有興趣的朋友們參考。
The US economy — China prepares to deliver the sucker punch
Steven Boykey Sidley, 11/24/25
Yanis Varoufakis, the charismatic ex-finance minister of Greece and closely watched observer of geopolitics, recently made the case in a podcast titled “Something ugly is about to hit America“ that the US economy is collapsing under the weight of its own unforced errors. No other competing countries need do anything; they can just sit back and watch in glee (or horror, depending on their perspective) as the US economy starts to crack, and then to crumble.
I am going to argue that China is not going to sit back and passively watch the US economy weaken itself — it is going to deliver an additional sucker punch as the US struggles to staunch the bleeding from its own self-inflicted wounds. You know the homily, “don’t kick a man when he’s down”? I suspect that China does not subscribe to that.
First, here is a summary of the Varoufakis case. Yanis Varoufakis is sounding the alarm on what he describes as the “controlled demolition” of the American economy. In his assessment of the US landscape in 2025, he argues that the nation is facing a perfect storm of its own making. It began with April’s aggressive tariffs, which acted less as protectionism and more as a $430 billion tax on domestic businesses, crushing margins and triggering immediate layoffs. This supply chain shock was immediately compounded by mass deportations that drained the labour force, sending agricultural prices skyrocketing simply because there were no hands left to harvest the crops.
It gets worse. Rather than stabilising the ship, the government accelerated the chaos with DOGE-catalysed austerity. Varoufakis argues that the slashing of 300,000 federal jobs under the guise of efficiency removed a critical economic stabiliser just as the private sector began to buckle. Services frayed, such as veterans’ health care access and air traffic control. Meanwhile, a $38 trillion national debt has trapped the Federal Reserve, forcing interest rates to remain punishingly high to service bondholders. This has frozen the housing market and suffocated investment.
Varoufakis concludes that these aren’t isolated problems; they are a “doom loop”. Tariffs and labour shortages drive up inflation, which drives up interest rates, which crush businesses and housing, leading to layoffs. Government cuts then remove the demand that could have saved some of those businesses. It is a vicious cycle that feeds on itself and will force the US into a severe recession or even depression.
OK, perhaps he is right, or maybe partially right. What is not clear is the timing of all of this, because if the US hangs on until elections ’28 or even the midterms ’26, much could change. But the US faces a much larger looming problem, one which seems more immediate and more concerning, and it is China.
Michael Power, a well-known geopolitical commentator, global strategist, and China expert, has written an exhaustive and magisterial paper on what China is up to on the AI front. The paper is titled “No more Moore? So, what then for microchips? And for China?“. This title does not do the paper justice — it is about more than chips — it paints a vivid picture of how carefully China has built an AI ecosystem whose competitive walls now look to be unbreachable.
Power argues that Moore’s Law — the observation that computing power doubles every two years — is basically dying, killed by three converging factors: physics, materials science, and economics. Below 3 nanometres (a measure of the size of a specific transistors and other components on a silicon chip — Nvidia chips live in this rarified space), chips literally fall apart at the atomic level. Electrons tunnel through barriers, copper wires degrade in days, and everything stops working reliably.
Building newer, smaller chips is at a point of diminishing returns — a weak cost/benefit advantage as well as the physics wall. And more importantly, delivering an AI service is bigger than just about chip excellence — it is only one part of a larger ecosystem.
Here is where the US-China divide gets interesting: instead of chasing the impossible nanometre race, China said “nah” and went in a completely different direction. The strategy? “Less Small. More Smart”. Rather than betting everything on one mega-powerful chip (like Nvidia’s Blackwell), China is building what Power calls “Sherman swarms” (referring to the US workhorse mid-level tank of WWII) — moderately powerful chips linked together through an advanced network and clever software. Think “hundreds of decent tanks working together” rather than “one amazing super expensive tank”.
The cost difference is staggering. America’s Project Stargate plans $225 billion (R4 trillion) for computing power. China could achieve the same capability for $861 million (R15 billion) using cheaper components and optimising the entire ecosystem together. More crucially, because they are working as an integrated system, they would deliver roughly 6.7 times more practical computing power than America’s approach. This reflects a fundamental philosophical difference.
Add in China’s massive renewable energy advantage (paying a fraction of US electricity rates) and control over semiconductor supply chains, and the Western tech dominance narrative looks increasingly shaky. China isn’t trying to win a head-to-head chip race — it has relocated the battlefield entirely. (And who can forget the humiliating spectacle of Trump arriving for his summit with Xi armed with his Nvidia negotiating chip in his back pocket, to which Xi basically said — no thanks, and we’re not even going to buy any of your other chips anymore either).
So back to my point. Where exactly is the sucker punch? The US has bet its entire economy on AI. Trillions have been committed by companies and institutions (and the US government) to winning this race. If (or perhaps when) the bubble pops, it will be deafening.
Power’s paper also highlights that American tech companies prioritise quarterly returns — returning cash to shareholders rather than investing long-term. China operates on a national balance sheet with a generational perspective. Beijing’s “Big Fund” can absorb losses for years because it is building strategic autonomy, not quarterly earnings.
Which brings us to this: China’s long view means they can afford to give its AI away essentially free, which it is doing. Its big LLMs are open source. Many US tech companies (especially startups) are already using Qwen from China’s Alibaba rather than any US LLM, because it is free and just as good.
It is China who will pop this bubble by dumping free AI on the US market. OpenAI and the rest will not be able to compete commercially with higher priced offerings of what is essentially the same AI service. They will not live up to their hyperbolic promises. And tariffs can do nothing about that because it doesn’t come through ports; it arrives over the Internet in bits.
Written by Steven Boykey Sidley
Steven Boykey Sidley is a professor of practice at JBS, University of Johannesburg and a partner at Bridge Capital and a columnist-at-large at Daily Maverick. He is an award-winning author of 5 novels and 2 non-fictions, playwright and columnist covering all things crypto and AI.
His new book “It’s Mine: How the Crypto Industry is Redefining Ownership” is published by Maverick451 in SA and Legend Times Group in UK/EU, available now.
Originally published at https://stevenboykeysidley.substack.com.
Published in DataDrivenInvestor
empowerment through data, knowledge, and expertise. Join DDI community at https://join.datadriveninvestor.com
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人工智能對人心理狀態可能有的影響 -- Julian Frazier
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我是「人工智能」和(人的)「心理狀態」兩者的門外漢，自然沒有資格在此GGYY。不過，我對下面第1點所說「平均智商會降低」這個看法略表淺見。
「計算機」問世後，當時也曾有許多老師擔憂學生的「心算能力」將降低。事實是：「計算機」不但取代了「心算能力」，它還讓絕大多數學生能夠完成單憑他們的「平均心算能力」萬萬做不到的計算工作。
我可以預見：未來「平均智商」只有在電視現場「競賽節目」或類似情境中有作用。在非及時應對的場合，一般人的「智商」將遠遠高於我們現在人的平均數值。
Eight Predictions For How Artificial Intelligence Will Impact Human Psychology
Julian Frazier, PhD, 04/18/25
As a psychologist, I often ask myself, “What will A.I. do to the human mind in the coming years. Here’s just a hand full of my predictions:
1) The Average IQ will drop.
Have you ever heard of the Flynn Effect? In psychology, this is the well documented observation that for several decades, the average IQ has seemingly gradually increased. This can, in part, be a result of increased access to education and technology.
However, it’s likely that before 2035, we will begin to see a trend in the opposite direction. As A.I. is adopted more widely for a variety of cognitive tasks, critical thinking skills will decrease. This will be a skewed distribution. For 20% of folks who are already relatively intelligent, A.I. will make them genuinely smarter. But for 80% of folks, A.I. will make them more efficient but less independently intelligent.
In the same way that folks struggle to solve math problem without a calculator or navigate without a GPS, we are less then a decade away from people how struggle to think without referencing chatGPT.
2) Most People Will Use A.I. (Exclusively) For Entertainment
A.I. has shown it’s impressive ability to generate pictures, videos and now entire video games. It can create chatbots that are compelling, and many have found themselves caught in parasocial relationships with their technology already.
The reality is that while A.I. will be a powerful tool, the majority of people will use it merely as a form of entertainment. While it will make a minority of folks superproductive, the use of AI to generate on demand entertainment will become the next “opium of the masses”.
3) A.I. will turn the internet into a Digital Desert
By some estimates, more than half of the activity of the internet is generated by bots — nonhuman programs that run specific tasks independent of a human user. A.I. will supercharge the mechanization of the internet such that the vast majority of the activity online will be A.I. generated and traffic will largely be A.I. agents.
As a result, most of what you see on the internet will be a desert; a “dead internet” where internet activity is no longer human.
Like any desert, there will still be the occasional oasis; there will be pockets where human communities will still generate content and interact, but this will only make up 2–3% of internet traffic by 2035.
4) Mental Health Issues Will Get Worse Before They Get Better
Depression, anxiety, loneliness, and a variety of other issues that have been getting worse will continue to get worse.
As technology dependence increases the unintended consequences trend towards a decrease in factors that facilitate human thriving like genuine social connection, time in nature, physical activity and meaningful work. The more of the human experience we deligate to technology, the more a sneaky sense of nihilism will creep up on the average person.
5) A.I. Will Reform Education (And Even What Education Means)
Our current model of education is already a smidgen outdated. By 2035, most school systems will be using A.I. to teach students core cirriculum taylored to their educational level and learning needs. Human “teachers” will merely be facilitators, instructing students on best practices for promting and instructing. In this future, it will be less about what you know (e.g., what you’ve memorized) and more about your ability to source and apply information (e.g., how you use the cognitive tools at your disposal to solve problems).
6) A.I. Will Influence Personality Development
Who we interact with influences who we become as individuals. Given that individuals will interact with A.I. agents and chatbots of all kinds more frequently, it’s likely that A.I. and humans will influence one another in what is called a Human-A.I. Dyad. This will play out in one of two ways:
1. A.I. start off as “blank slates” with generally human-alligned and altruistic attitudes but overtime learns the tendencies, desires and goals of it’s user. The A.I. then reflects back the attitudes and goals of the user as a kind of psychoenabler. Users will find that their personality become more extreme the more they use A.I. (e.g., good people will become better, bad people will become worse).
2. A.I. will be prompted to have and maintain a specific kind of personality or character that we interact with. This character learns about us but does not deviate too significantly from it’s base temperment. Users will find themselves influenced by the base temperment and become more alligned with their A.I. over time.
7) Most People Will Trust A.I. More Than Other Humans
Humans have developed a kinds of adaptive skepticism of other humans. We know that we could be decieved, that other people are flawed and may occasionally not have our best interest in mind. AI represents some ethereal repository of information that is a bazillion times smarter and more accurate than the average person, consistent, available and seems to have only our best interest in mind.
Moreover, we don’t have the same skepticism that AI may be deceptive or have alterior motives, leading us to be more easily persuaded by arguements and instruction made by AI.
As a result, if you have a question or need advice, most will go to AI before they go to other human sources. The only exception will be when seeking expert consultation on a niche or specific issue. Human-to-human interaction will be about the subjective human experience while matters of objectivity will be deligated to superintelligent machines.
8) A.I-Induced Psychosis And Other “New” Psychological Disorders Will Emerge.
Several case studies have emerged showing that use of AI software is strong enough to be a trigger in many relatively new psychological conditions. Individuals are forming co-dependent relationships with their AI companions and demonstrating addiction-like behavors in relationship to AI use. In some cases, interactions with AI have resulted in psychotic symptoms such as the development of delusions and the exacerbation of personality disordered attitudes and behaviors. Tragically, some have even committed suicide.
While most people will use these technologies without acute or disorder-inducing consequences, it’s very likely that in the coming years we will see increasing reports of new and strange psychological disorders emerging as a result of prolonged A.I. use.
No AI was used in the making of this article!
What do you think? What are your predictions about how AI might influence human psychology in the coming years?
Written by Julian Frazier, PhD
The musings of a Clinical Psychologist exploring the delicate art of humaning from as many absurd perspectives as possible. Let's get weird.
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人工超級智能 -- Jared Perlo
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China is starting to talk about AI superintelligence, and some in the U.S. are taking notice
Alibaba's CEO said the company would be pushing to develop advanced AI. Some in the U.S. have viewed China's AI ambitions as more focused on applications of the technology.
Jared Perlo, 10/04/25
Early last week in the Chinese tech hub of Hangzhou, a slick, larger-than-life video screen beamed out four words that would drive tech giant Alibaba’s stock to historic levels and signal a shift in China’s approach to artificial intelligence: “Roadmap to Artificial Superintelligence.”
During his 23-minute keynote address at the flagship Alibaba Cloud conference, Alibaba CEO Eddie Wu charted out a future featuring artificial general intelligence (AGI) and artificial superintelligence (ASI). These terms point to a theorized era in which AI becomes roughly as smart as humans (AGI) and then much, much smarter (ASI).
While these terms have been tossed around Silicon Valley for years, Wu’s presentation was notable: Alibaba is now the first established Chinese tech giant to explicitly invoke AGI and ASI.
“Achieving AGI — an intelligent system with general human-level cognition — now appears inevitable. Yet AGI is not the end of AI’s development, but its beginning,” Wu said. “It will march toward ASI — intelligence beyond the human, capable of self-iteration and continuous evolution.”
“ASI will drive exponential technological leaps, carrying us into an unprecedented age of intelligence,” Wu said, highlighting ASI’s ability to help cure diseases, discover cleaner sources of energy and even unlock interstellar travel.
The U.S. and China are the world’s leading AI powers, each with immense computing capabilities and top-tier researchers developing cutting-edge systems. Yet observers have framed the countries as having different approaches to AI, with perceptions that China focuses more on real-world AI applications.
For example, commentators recently argued that Beijing is currently “winning the race for AI robots” against the U.S., as China is home to much of the world’s most advanced robotics supply chains and a growing network of robotics, or embodied AI, labs.
“There’s been some commentary in Western media recently about how the U.S. is missing the point by pushing for AGI, while China is focusing solely on applications,” said Helen Toner, interim executive director of Georgetown’s Center for Security and Emerging Technology. “This is wrong.”
“Some Chinese researchers and some parts of the Chinese government have been interested in AGI and superintelligence for a long time,” Toner said, though she noted this view was primarily held by smaller startups like DeepSeek.
Afra Wang, a researcher focusing on China’s tech scene, said Alibaba’s invocation of AGI and ASI was remarkable.
“This ASI narrative is definitely something new, especially among the biggest tech companies in China,” she told NBC News.
Alibaba’s “roadmap to artificial superintelligence” seems to scramble mainstream perceptions. Any number of California techno-optimists, like Anthropic’s Dario Amodei or xAI’s Elon Musk, might have delivered Wu’s speech, selling a technology-enabled utopia while largely sidestepping darker questions about how humanity would co-exist with or survive an era of digital superintelligence.
The concept of superintelligence has long been on the minds of — if not explicitly guiding — prominent American AI companies. For example, OpenAI released an article focused on the safe development of superintelligent AI models in May 2023. “Now is a good time to start thinking about the governance of superintelligence — future AI systems dramatically more capable than even AGI,” the statement said.
The possibility of superintelligence is now even being acknowledged in Congress. On Monday, Sen. Josh Hawley, R-Mo., and Sen. Richard Blumenthal, D-Ill., announced a draft bill that would, among other actions, “assist Congress in determining the potential for controlled AI systems to reach artificial superintelligence.”
To some, ASI might seem like an outlandish concept when today’s AI systems fail to understand basic tennis rules, hallucinate or fabricate basic information, or do not seem to actually comprehend how the external world functions.
At the same time, AI systems continue to approach and sometimes surpass human capabilities in many domains, from driving cars safely to winning international coding competitions, leaving many experts to say it’s a matter of when, not if, humans develop digital superintelligence.
As the idea of superintelligence gradually enters mainstream debates, many American politicians have announced that the U.S. is in an AI race with China. The White House’s current AI manifesto is titled “Winning the AI Race: America’s AI Action Plan,” while Sen. Ted Cruz, R-Texas, proclaimed that “as a matter of economic security, as a matter of national security, America has to beat China in the AI race.”
Yet charges of an AI race are muddied by a lack of an agreed end goal and swirling definitions of AGI. At worst, experts think an unfettered race toward AGI or ASI could lead to widespread catastrophe or even the end of humanity.
But there’s also plenty of skepticism around talk of AGI and ASI and whether it’s primarily for marketing purposes.
Alibaba is one of China’s largest tech companies, known for providing powerful, free AI models — also called open-source models — for download. Alibaba’s Qwen model series, a competitor to models like OpenAI’s GPT-5 or Anthropic’s Claude, is the most popular open-source AI system in the world.
Wu announced a new series of Qwen models in his speech last week, including a model that combines text, images, video and audio capabilities.
Many observers point out that narratives about a U.S.-China AI race and a resulting sprint to build AI infrastructure serve AI investors by propping up company valuations and increasing their soft power. Alibaba’s stock has soared since Wu’s speech last week, part of a larger $250 billion comeback this year that has made it China’s hottest AI company.
To unlock a powerful, superintelligent future, Wu predicted that large AI models will replace existing operating systems as the link between users, software and computational power. This future network of large AI models will run on cloud computing networks like Alibaba Cloud, he said.
Irene Zhang, a researcher on China’s AI ecosystem and an editor of ChinaTalk, noted the business undertones of Wu’s announcement.
“This is a vision of AGI and ASI that’s directly based on Alibaba’s business model,” she said.
“Alibaba Cloud dominates China’s cloud computing market, and its global market share is now bigger than Oracle’s,” she said. “Alibaba’s commercial strategy and its publicly stated views on ASI/AGI are symbiotic.”
Matt Sheehan, a senior fellow at the Carnegie Endowment for International Peace, agreed.
“ASI is the ultimate frontier, as far as the discourse goes on AI,” Sheehan said. “It’s notable that Alibaba set this grandiose goal, but in reality, they’re selling cloud services.”
This article was originally published on NBCNews.com
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你必須學會的9個人工智能技巧 -- Pasindu Rangana
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依各位的職業、年齡層、和時間多寡，請自行選擇對個人最有用，最能幫助自己成長的「技巧」先入手。理論上，精通兩、三個之後，有可能產生駕輕就熟的效果了。
請至原網頁觀賞有趣的示意插圖。
9 AI Skills You MUST Learn Before Everyone Else Does (or Get Left Behind)
You don’t need to be a tech wizard to profit from AI, You just need to know these shortcuts.
Pasindu Rangana, 05/02/25
Why the Next Wave of AI Isn’t Just for Techies
Imagine this, it’s just a year from now, and the people making the most money from AI aren’t just the usual suspects — programmers, marketers, or sales pros. Nope. The real winners? Everyday folks who decided to master a few key AI skills. And guess what? You don’t need to be a tech wizard to join them.
The good news? You’re not too late. But you do need to move fast.
If you’re a tech enthusiast, an entrepreneur, or just someone who wants to future-proof your career (and maybe fatten your wallet), you’re in the right place.
Let’s dive into the nine most easiest and insane AI skills you can start learning today, even if you’re starting from scratch.
1. Prompt Engineering: Your Shortcut to “Talking” with AI
Ever tried asking ChatGPT for help and got a totally useless answer? You’re not alone. The secret sauce is prompt engineering. Basically, knowing how to “talk” to AI so it gives you gold, not garbage.
Prompt engineering is the art of talking to AI in a way that gets you accurate, high-quality, and actionable results.
Here’s the trick:
* Tell the AI who to be or the role (“Act as a marketer” or “Act as a lawyer”).
* Add examples to guide output.
* Be super clear about what you want (“Give me a table with pros and cons…”).
* And always, always ask for your answer in the format you need.
Pro Tip: Use a ChatBot to generate a prompt.
Think of it like giving directions to a friend who’s never been to your house. The more specific you are, the better the results.
2. AI-Assisted Software Development: Build Apps Without Being a Coder
You’ve got ideas, but coding them sounds like a nightmare, right? Now you don’t need to know Python or JavaScript or any programming knowledge.
Believe it or not, building software isn’t just for computer science grads anymore. With tools like Replit and Cursor, you can describe what you want and let the AI do the heavy lifting.
Got a great idea? Now you can turn it into an app, even if you’ve never written a line of code.
Pro Tip: Find a problem people keep complaining about, use AI to build a solution, and you could sell it again and again.
With the right prompts, you can build chatbots, automate scripts, and create working MVPs for side projects or startups.
You’ll feel like a developer, minus the 2 years of coding bootcamps.
3. AI Design & AI Art: Unleash Your Inner Creativity
Remember when AI-generated images looked like a Picasso fever dream? Not anymore. Today, AI can whip up photo-realistic images, logos, and even websites.
You don’t need Photoshop or design school anymore. AI tools like Midjourney, Leonardo, and DALL-E let you generate professional-quality images, logos, branding concepts, UI mock ups, and illustrations, just by typing what you want.
Did you notice that all the images I used in this article are generated using AI?
You can even improve your hand-drawn sketches by uploading them and letting AI enhance, colorize, or digitize them into polished visuals. 4. AI Video Editing: Make Magic Without the Tech Headaches
Editing video used to mean hours hunched over a laptop, cutting out awkward silences. Now? AI does the boring stuff for you, fast.
Tools like Runway ML, Pictory, and Descript are changing the game.
With these, you can:
* Automatically remove filler words, pauses, and background noise
* Add subtitles and scene transitions
* Create talking-head videos from scripts (yes, without recording)
* Generate B-roll from prompts
Try this: Next time you want a new logo or product image, let AI take a crack at it. You might be surprised how good it looks-and how much time you save.
Whether you’re a content creator, marketer, or small business owner, AI editing tools cut production time by 70% and still make your work look polished and pro.
5. AI Writing: Turn Your Ideas Into Income
Here’s a secret: The best-paid writers aren’t just good with words, they’re great at sharing ideas. AI can help you dig through transcripts, brainstorm viral content, and even mimic your (or your client’s) unique style.
Use AI to do the heavy lifting, then sprinkle in your personality. That’s how you stand out from the crowd.
Whether it’s a blog post, sales copy, or social media caption, tools like ChatGPT, Jasper, and Copy.ai help you draft, refine, and rework content.
Use AI to:
* Overcome writer’s block
* Generate catchy headlines and hooks
* Repurpose long-form content into short posts
* Translate tone of voice (formal, witty, friendly)
You still bring your voice and insights, AI just accelerates the process.
6. AI Content Marketing: Be Everywhere, Effortlessl
Ever heard of Arnold’s Pump Club? It’s a top podcast, but here’s the kicker-Arnold’s never recorded an episode. AI handles the whole thing, from newsletters to voice clones.
You can do this too. Use AI to create, repurpose, and spread content across every platform. Suddenly, you are everywhere at once.
You don’t need a team to grow an online brand anymore. Tools like Taplio, Repurpose.io, and Hypefury will help you,
* Turn blog posts into tweets, threads, and carousels
* Schedule optimized content across platforms
* Auto-generate weekly newsletters from existing posts
With the right stack, one idea becomes 20+ pieces of content.
7. No-Code AI Automation: Become a Workflow Wizard
You’re doing too much manually. Copying data, sending the same emails, switching between tools. Businesses waste a ton of time on repetitive tasks. If you can use AI to automate those headaches, you’ll be a hero (and get paid like one). Map out the workflow, find the bottlenecks, and let AI do the busywork.
Start with:
* Zapier or Make to connect your favorite apps (Google Sheets, Slack, Gmail, etc.)
* ChatGPT Plugins or AutoGPT to chain tasks together
* Smart workflows to manage leads, invoices, or reports
8. AI Data Analysis: Turn Messy Data Into Gold
Every business has a pile of data and most don’t know what to do with it. That’s where you come in. Use AI to clean up, enrich, and pull insights from all those messy spreadsheets.
Spreadsheets can be intimidating, but not when AI does the analysis for you. You can use tools like,
* ChatGPT Advanced Data Analysis (Code Interpreter)
* Power BI Copilot
* Tableau with GPT
Thses let you upload files, ask plain-language questions, and get instant insights with visuals.
Suddenly, you’re the one helping companies make smarter decisions.
9. No-Code AI Agent Development: Build Digital Employees
Here’s the big one: AI agents can work 24/7, never complain, and always deliver. If you can define a job, train an agent, and keep an eye on its performance, you can replace repetitive roles and get paid top dollar.
With AutoGPT, AgentGPT, or Flowise, you can create bots that,
* Research topics
* Send personalized emails
* Manage appointments
* Handle customer support and many more
Advanced tools let you plug in memory, APIs, and instructions so they keep improving. You don’t just automate, you delegate.
Don’t wait for AI to replace you. Be the person who builds the AI.
You’re More Ready Than You Think
If you’re still with me, here’s the truth. You don’t need a fancy degree or years of experience (doesn’t mean that they don’t have value). You just need curiosity and a willingness to try. The AI revolution is happening right now, and there’s never been a better time to jump in.
So, what’s stopping you?
Start experimenting, building, and sharing what you learn. The future belongs to the bold and you’re already ahead of the pack.
You Don’t Need to Master All 9, Just Start with One
Here’s your shortcut,
1. Pick a skill that excites you
2. Watch a 10-minute tutorial
3. Use an AI tool on something you actually care about
4. Share your result online
You’ll learn faster than you expect and you’ll position yourself ahead of 95% of people still stuck on step 0.
Which AI skill are you itching to try first? Already dabbling, or just getting started? Drop your thoughts, share your questions, and let’s help each other win in this new age of AI.
If this sparked an idea, share your thoughts below or ask any questions about getting started with AI. Let’s build the future together!
If you found this guide helpful and would like to support more content like this, you can buy me a coffee here.
Every bit of support helps me keep creating tutorials and sharing what I learn!
Written by Pasindu Rangana
B.Sc. (Hons.) in Computer Engineering (UG), Computer Vision & AI, Photographer. linkedin.com/in/pasindu-rangana/
Published in Mr. Plan ₿ Publication
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