Insightful Investor Podcast interviewed Leonard Kleinrock: Internet Creation, AI Future on 20250708

 

Insightful Investor Podcast 專訪 Leonard Kleinrock：談網路創建與 AI 未來於 20250708

Welcome to the insightful investor podcast, a weekly series that seeks to share industry, investment and market insights. Learn more about our show at insightfulinvestor.org. Few people can say they helped invent the internet. But today's guest camp. Dr. Leonard Kleinrock is a distinguished professor of computer science at UCLA and a pioneering architect of the internet. In 1969, he supervised the sending of the very first message over what would become the internet.
歡迎收聽 Insightful Investor Podcast，這是一個每週分享產業、投資與市場洞見的系列節目。更多節目資訊請至 insightfulinvestor.org。鮮少有人能宣稱自己參與了網路的發明，但今日的嘉賓正是如此。Leonard Kleinrock 博士是加州大學洛杉磯分校的傑出計算機科學教授，同時也是網路的先驅架構師。1969 年，他監督了史上第一條透過後來成為網路的系統所發送的訊息。

Today, he joins us to share the story of the internet's origins. It's evolution across the many decades. It's been around. And the pivotal moments that shaped its development. We'll also explore his perspectives on artificial intelligence and the parallels he sees between AI's rise and the early days of the internet. Then I know you rarely do podcasts, so I thank you for joining us today. Let me start with your background. What first sparked your interest in engineering and technology? I grew up in Manhattan in the Tuff neighborhood. And as a young kid, I did all the things that young people like to do, playing in the streets of Manhattan, Obama Manhattan. Sports, puzzles, baseball, comic books, and model airplanes. And one day I was reading a Superman comic. This is about when I was in the first or second grade. And in the middle of the comic was a description of something called a crystal radio, whatever that was. What fascinated me is it claimed I could build this out of part I could find around the house. And if I did so, I'd be able to hear music through an earphone with no batteries, no power, just put it together and get music.
今天，他將與我們分享網際網路的起源故事。這項技術歷經數十年的演變，以及塑造其發展的關鍵時刻。我們也將探討他對人工智慧的看法，以及他觀察到 AI 崛起與早期網際網路發展的相似之處。我知道您很少參與播客節目，非常感謝您今天撥冗加入。首先請談談您的背景，是什麼最初點燃了您對工程與科技的興趣？我在曼哈頓的 Tuff 社區長大。小時候就像其他孩子一樣，喜歡在曼哈頓街頭玩耍——就是奧巴馬住過的那個曼哈頓。運動、拼圖、棒球、漫畫書和模型飛機都是我童年的樂趣。有天我正在看《超人》漫畫，那時大概才小學一二年級吧。漫畫中間突然介紹了某種叫「晶體收音機」的東西，當時根本不知道那是什麼。最讓我著迷的是，它說我可以用家裡隨手可得的零件組裝這玩意兒，只要成功，就能透過耳機聽到音樂——不需要電池、不用插電，組裝好就能收聽音樂。

That's pretty good. So I decided to build it. Now to do it in the Superman comic, the describe what you need it. First of all, you need it an empty toilet paper roll that was easy to find. Then I needed some wire. Well, what now the street found some wire in the car to go through. And then I needed something called a crystal. And when I pointed out, you can make a crystal out of your father's old razor blade and a piece of pencil led.
這主意挺不錯的。於是我決定動手做。在超人漫畫裡，他們會描述你需要哪些材料。首先，你需要一個空的衛生紙捲筒，這很容易找到。接著我需要一些電線。嗯，當時我在街上從一輛車裡找到了一些可用的電線。然後我還需要一種叫做晶體的東西。當我指出這點時，他們告訴你可以用你父親的舊刮鬍刀片和一小段鉛筆芯來製作晶體。

So I got that. And then I needed an earphone. Well, I didn't have an earphone. But I knew that in the candy store down the street was a telephone booth. In the telephone booth was a telephone with a hand said, and if you unscrewed the upper piece, you could pull out the earpiece. So I stole the damn thing. So far, not a penny. But then I needed something called a very book of pass. And I knew I couldn't find that in the street, so I didn't have candy store. So my mother took me down in the subway to Canal Street to the stores, which are selling surplus electronics parts from World War II. And I walked up to the first door and I banged my hand on the counter, I said, I need a very book of pass. And the guy said, what size? 

於是我搞定了那部分。接著我需要一個耳機。唉，我手邊沒有耳機。但我知道街角那家糖果店裡有個電話亭，電話亭裡有支話筒，只要把頂部旋開就能取出聽筒。所以我就把那鬼東西給偷了。到目前為止一毛錢都沒花。但後來我需要一種叫「可變電阻器」的東西。我知道這在街上可找不到，糖果店裡也不會有。所以我媽就帶我搭地鐵到運河街，那邊有些商店專門賣二戰剩餘的電子零件。我走進第一家店，用力拍了下櫃檯說：「我要買可變電阻器。」店員問：「要什麼規格的？」


And I said, I want to do that. No electricity. And it coming out of the air. Well, to be honest, I've spent the rest of my life figuring out how the damn thing works. Because action that a distance like that is magic. And it is a wonderful phenomena.
我說，我想做那個。沒有電力。它就這樣憑空出現。老實說，我花了餘生都在研究這該死的東西到底是怎麼運作的。因為那種遠距離作用就像魔法一樣。這真是個奇妙的現象。

So that got me started in electronics. And surely thereafter, I started building radios out of broken down radios. I'd cannibalized broken radios, put them together, started using vacuum tubes. And I went to my elementary school in junior high school. I built radios. But I couldn't afford to become a hand radio operator. We were poor. And I couldn't afford the rig.
這讓我開始接觸電子學。之後我當然就開始用報廢的收音機來組裝新收音機。我會拆解壞掉的收音機，把它們重新組裝，開始使用真空管。我在小學和初中時就開始動手做收音機。但我負擔不起成為業餘無線電操作員。我們家很窮。我買不起那些設備。

So by the time I got to high school, I started it some more radio electronics. In fact, you'll be interested if you allow me. RCA was publishing these books full of how all of their tubes operated. And in the front was a wonderful description of the understanding, the electronics and the technology. So I learned a lot about radio from this very old RCA radio tube manual. And perhaps some of the people watching this podcast may recognize this. And if they do, it'll give them a big thrill. At any rate, I learned on my own.
所以當我上高中時，我開始接觸更多無線電電子學。事實上，如果你允許我說，你會感興趣的。RCA 當時出版了這些書籍，詳細介紹他們所有真空管的運作原理。書的開頭部分對電子學和技術原理有精彩的解說。我就是從這本非常古老的 RCA 無線電真空管手冊中學到很多關於無線電的知識。或許正在觀看這個播客的某些人會認得這本書。如果他們認得，這會讓他們非常興奮。總之，我是自學的。

I got books out of the library. Not the mathematics side, but the engineering side, the construction side, the implementation side. And so I got very interested on electronics. And I pursued that by the time I ended college, I decided I wanted to get an electrical engineering degree. Is there anything in particular about your upbringing or your education or your environment that helps shape your independent thinking and ability to envision new possibilities? The answer is yes. For one thing, I was a bit of a loner. I like to do things on my own.
我從圖書館借閱書籍。不是數學方面的，而是工程方面的、建造方面的、實作方面的。因此我對電子學產生了濃厚興趣。到大學畢業時，我決定要取得電機工程學位。是否有任何關於你的成長背景、教育或環境特別塑造了你獨立思考的能力和展望新可能性的視野？答案是肯定的。首先，我有點獨來獨往。我喜歡自己動手做事。

I built modeling, I built radios. But I never joined with a gang of other kids. I did it on my own, figuring out on my own. And that kind of independence I feel was a real benefit. Now remember, I was growing up in the streets of Manhattan. Concrete sidewalks. And I wanted to be Tarzan. You know, I wanted to be an Indian. Out in the woods, right in the halls, it's going out. So I joined the boy scouts, so that I could go out camping out into nature. I joined the boy scouts. And I rose through the ranks and I became what's called a star scout, which is five moment pages. And at that point, my troop lead, my scout may have said, "Lan, you could become the first Eagle scout in this troop. And that was a challenge. They had been no other Eagle scouts in the troop. And to become an Eagle scout in those days was really tough. Yet I get 21 merit badges of all kinds, details.
我從小就自己動手做模型、組裝收音機。但我從不跟其他孩子成群結隊，總是獨自摸索。這種獨立性讓我受益匪淺。要知道，我可是在曼哈頓的混凝土人行道上長大的。那時我渴望成為泰山，想像自己是個印第安人，在森林裡自由奔跑。所以我加入了童子軍，這樣就能去野外露營接觸大自然。我在童子軍團裡一路晉升，最終成為「星級童軍」（需要完成五項進階考驗）。當時我的團長對我說：「藍，你有機會成為本團第一位鷹級童軍。」這可是個挑戰，因為我們團裡從未出過鷹級童軍。在那個年代，要取得鷹級童軍資格相當困難。但我最終拿到了 21 枚各式各樣的榮譽徽章，完成了所有細節要求。

And I realized this is really beyond my grasp. So let's go for it. So I tried and sure enough, I became an Eagle scout. And the reason I'm telling you that story is because I realized by achieving that very difficult goal, I realized I put confidence on myself. I realized, yes, I can achieve things if I really go after them, even though they are hard. And as a lesson to other people listening to this podcast, once you achieve your first success, be it an Eagle scout, electronics, a good piece of art, music, literature, a poem.
我意識到這真的超出了我的能力範圍。但還是決定放手一搏。經過努力後，我確實成功晉升為鷹級童軍。之所以分享這個故事，是因為在達成這個艱難目標的過程中，我建立了對自己的信心。我領悟到只要全力以赴，即使面對困難，我依然能夠達成目標。對於正在收聽這節目的聽眾，我想傳達的是：無論是成為鷹級童軍、鑽研電子學、創作藝術品、音樂、文學或詩歌，當你首次獲得成功時...

If you succeed at that, you build into your own psyche, the confidence you can do it again. And to have that sense of self capability is really important in caring for what you can do. And I find that I can carry in forward in your career. And I find that help me considerably. I consider besides the crystal radio that Eagle scout achievement, to be a critical turning point in my life. It gave me the confidence that I can achieve. Now in addition, I had wonderful parents. My father always pushed me to do more education.
...這種成功經驗會在你的心理建立起「我能再次做到」的自信。這種自我能力的認知，對於發掘自身潛能至關重要。我發現這種自信能持續推動職涯發展，對我幫助極大。除了晶體收音機之外，我認為獲得鷹級童軍資格是人生的關鍵轉折點，它賦予我「能夠成功」的信念。此外，我的父母非常優秀，父親總是不斷鼓勵我追求更多教育機會。

But my mother who had no idea what I was doing with the radios and the model airplanes, she let me do it. And I had a sense of self-confidence. And I had a sense of self-confidence. And I had a sense of self-confidence. And I had a sense of self-confidence. And I had a sense of self-confidence. And I had a sense of self-confidence. And I had a sense of self-confidence. And I had a sense of self-confidence. And I said, no way, man. I'm ready to go as a freshman to CCNY. My dad pointed out that they really needed the money in the family. My dad got nearly couldn't work anymore. And he needed me to bring money into the house. So it was decided. And I agreed, unfortunately, to go to night session, even in session, to get an electrical engineering degree.
但我母親完全不懂我在搞那些收音機和模型飛機，她還是讓我放手去做。這讓我建立起一種自信感。這種自信感不斷累積，不斷強化。當時我斬釘截鐵地說：開什麼玩笑，我可是準備好要進紐約市立學院當新鮮人的。但父親指出家裡真的很需要錢——他幾乎無法繼續工作了，需要我賺錢養家。於是事情就這麼決定了。雖然很不情願，我還是同意去上夜間部，甚至同時兼顧日間課程，就為了拿到電機工程學位。

Now who the heck goes to night session, to get a doubly degree? You know, you're talking about many extra years. Well, who does it? Well, dropouts? Crazy? Really poor dedicated kids. And the GIs coming back from World War II. And these guys understood what they wanted out of an education. They had been through some tough times. They were not there to play around.
天底下有誰會跑去夜間部拿雙學位啊？要知道這得多花好幾年時間。什麼樣的人會這麼做？輟學生？瘋子？其實是那些家境清寒卻格外認真的孩子，還有二戰歸來的大兵們。這些人都很清楚自己要從教育中獲得什麼——他們經歷過艱苦歲月，可不是來玩玩的。

They were there to get an education and get a career. So I did go to even in session, and with this interesting mix of people. At the same time, working during the daytime full time as an assistant electrical engineer, learning on the job. I started out as a technician. So I'm getting practical experience on the job. I've learned a lot of practical stuff on my way up through my hobbies and my electronics. And at night, I'm going to school to learn the theoretical side of electrical engineering.
他們來這裡是為了接受教育並開創事業。所以我也參加了課程，與這群有趣的人們一起學習。同時，我白天全職擔任助理電機工程師，在工作中學習。我最初是從技術員做起，因此在工作中獲得了實際經驗。透過我的興趣愛好和電子學，我在成長過程中學到了許多實用知識。而晚上，我則去學校學習電機工程的理論知識。

What a great combination. Now, who are my teachers at night? They were also people who were working during the day in teaching at night. So they had the practical experience working. And the theoretical understanding to be a teacher. And I remember very well, one day teacher came in and he said, "See this? It's called the transistor. And it's a better thumb to learn the language. And it's a better thumb to learn the language than it is an amplifier." Because it's very sensitive to the heat conditions.
多麼棒的組合啊。那麼，我晚上的老師是誰呢？他們也是白天工作、晚上教學的人。所以他們既有實際工作經驗，又具備擔任教師所需的理論知識。我記得很清楚，有一天老師走進教室說：「看到這個了嗎？它叫做電晶體。學習它的語言比當放大器更重要。」因為它對溫度條件非常敏感。

And he's how you adjust for that. He's how you fix it. Now, how he'd been a pure daytime professor with no experience. He would have said, "See, here's the transistor. Here's how it works." And the story. He pointed out the practicality, the theory, and how to adjust it. So I had the wonderful mix, and this is an important theme, of practical experience, which allowed me to grow my intuition, my understanding, and the theoretical background as to how things work.
而他正是教你如何調整的人。他就是解決問題的關鍵。想想看，如果他只是個毫無實務經驗的純日間部教授，他可能會說：「看，這是電晶體，這就是它的運作原理。」就這樣結束。但他指出了實用性、理論基礎以及調整方法。所以我獲得了絕妙的組合——這是非常重要的主題——實務經驗讓我培養出直覺理解力，同時也建立了事物運作原理的理論基礎。

And that combination is really very important. I mean, typically you find engineers who ask questions like, "How do things work?" The practical side. They build them, they break them, they test them, they measure them, they run them. They experiment with them. "Were mathematicians and scientists typically ask not, "How does it work?" But why does it work that way? What's the theory behind it? And either one of those is an excellent set of talents and skills, but you put them together in an unbeatable combination.
這種結合真的非常重要。我的意思是，通常你會發現工程師會問：「這東西怎麼運作？」這種實務面的問題。他們建造、破壞、測試、測量、運轉，不斷實驗。而數學家和科學家通常不問「怎麼運作」，而是問「為什麼這樣運作？背後的理論是什麼？」這兩種特質和能力都非常優秀，但當你把它們結合起來，就會形成無敵的組合。

The why and the how? Well, I was fortunate enough to have grown up in that environment, asking those questions and learning about it. When I was ready to graduate, I have to five and a half years of undergraduate even in this session. And by the way, I was top of my class, and president of my class. I heard while I was working, that there's going to be someone coming from MIT to offer wonderful graduate fellowship. And it was coming at 4 p.m. one afternoon, so I took off early and work.
為什麼會這樣？又是如何發生的？我很幸運能在這樣的環境中成長，不斷提出問題並從中學習。當我即將畢業時，即使在這段期間，我已經花了五年半的時間完成大學學業。順帶一提，我是班上的第一名，還擔任班代。在工作期間，我聽說有位來自麻省理工學院的人要來提供一個很棒的碩士獎學金。那天下午四點，他會來介紹，所以我提早下班去聽。

I want to listen to it. In this guy from MIT, Lincoln Labratory, he described his fantastic scholarship program. It was called a Stanford Associate Program. They would pay was a research assistant, as a full-time employee in the summer. They'd pay your tuition, they'd pay you some housing, and as a great master's program, a two-year program. This sounded fantastic. And he said, "If you want to get an application,
我想聽聽看。這位來自麻省理工學院林肯實驗室的傢伙，描述了他那超棒的獎學金計畫。這計畫叫做「史丹佛合作計畫」。他們會支付研究助理的薪水，夏天時是全職員工。他們會支付學費，還會提供一些住宿補助，這是一個為期兩年的優秀碩士課程。聽起來太棒了。他說：「如果你想申請的話，

see the professor in the back of the room when I'm finished lecturing." So I went to the professor at the back of the room. He was electrical engineering professor. And I said, "I'd like an application." He said, "I don't recognize you. What's your name?" I told him. He said, "I haven't seen you in a while, so you wouldn't. I go to even his session." He said, "Even his session get the hell out of here." He said, "What?"
當我講完課時，看到教室後方有位教授。」於是我走向教室後方的那位教授。他是電機工程系的教授。我對他說：「我想申請獎學金。」他回答：「我不認識你，你叫什麼名字？」我告訴他我的名字。他說：「我很久沒看到你了，所以不認得。我連他的課都會去上。」他說：「連他的課都給我滾出去。」他問：「什麼？」

He said, "You don't count." So I wrote away, "I got the application, I won the scholarship." And here I am at MIT. This was in the fall in January 1957. Come to MIT ready to do a message. With a fresh bachelor degree out of CCNY. Well, that was a bit of a shock. To walk into this venerable place called MIT, with that wonderful dome, that frightening dome, in the middle of the campus, among these kids who are best in their classroom across the country.
他說：「你不算數。」於是我寫信申請，「我拿到了申請表，贏得了獎學金。」現在我就在麻省理工學院。這是 1957 年 1 月的秋天。帶著剛從紐約市立學院獲得的學士學位來到麻省理工，準備攻讀碩士。嗯，這確實有點令人震驚。走進這所名為麻省理工的崇高學府，校園中央那座令人驚嘆又令人畏懼的圓頂建築，周圍都是來自全國各地最優秀的學生。

It was a bit of a challenge. But here I am, this kid who hardly studied at even his session. You know, I'd got a wake up at seven, get to work, come to school in the evening, take some classes, rush home, no time to do any study. The way I studied by the way, would take a night and a half piece of clothing, eight and a half by the piece of paper. And I folded it in half, fold it in half again, write all the equations down on the eight sides and study on the subway going to and from work.
這確實是個挑戰。但當時的我就是這樣一個連課堂時間都幾乎不讀書的毛頭小子。你知道嗎？我得早上七點起床工作，晚上趕來學校上幾堂課，然後匆忙回家，根本沒時間念書。順帶一提，我的讀書方式很特別——我會拿一張八開半的紙對折再對折，在八個面上寫滿方程式，然後利用通勤時在地鐵上背誦。

Why got to MIT turns out that was not sufficient. And one of the first classes I took was from the man who wrote the book, was a book called Transcience and Linear Systems. And I was told by my supervisor, who of you, to do well in this course, go see this course, they separate the men from the boys, you better do well. So I took the class, midterm comes around and I get a 50. I hadn't seen anything south of 95 since my elementary school days. And I said, what the hell is going on here? So I went to the professor and I tried to answer it was going on.
為什麼後來會去麻省理工？事實證明原先那套方法根本行不通。我選的第一門課，教材作者親自授課，那本書叫《瞬變與線性系統》。我的指導教授警告我：「這門課會區分真才實學與濫竽充數，你最好認真點。」結果期中考我只拿了 50 分——從小學畢業後我就沒拿過低於 95 分的成績。我當場傻眼：這到底是怎麼回事？於是跑去找教授理論，試圖搞清楚狀況。

And I realized that my study habits were not nearly sufficient for this new environment. Now that was quite a wake-up call. I had two choices. Choice one goes sitting in the corner and cry and give up. Choice number two say, ah, change what you're doing to correct this. I took the second of course, change my study habits and I got an A in the course. Now that wake-up call again was really important. Again, it's a lesson for people listening. This two ways to respond in the right way is to recognize what's wrong and fix.
我意識到自己的學習習慣在這個新環境中遠遠不夠。這真是當頭棒喝。當時我有兩個選擇：第一是躲在角落哭泣放棄，第二則是改變做法來修正問題。當然我選擇了後者，改變了學習習慣，最後那門課拿到了 A。這次的警醒非常重要，對聽眾也是個啟示——正確的應對方式就是找出問題並修正。

So there was an MIT. I got my MES to agree. By the way, I got married while I was an undergraduate. I'm not advising that, but I got every while I was still an undergraduate. Here I am at MIT. Getting my MES to degree ready to take a full-time job as a researcher at MIT at a very nice salary. My son is about to be born in August of 1958. And the supervisor of my MES is thesis, who is the head of the laboratory said, "Lan, you gotta get a PhD." I said, "I don't want a PhD."
當時我在麻省理工學院（MIT），取得了碩士學位。順帶一提，我大學還沒畢業就結婚了——雖然不建議大家這麼做，但我確實在大學期間完成了人生大事。在 MIT 取得碩士學位後，我本準備以研究員身份全職留校工作，薪水相當優渥。我的兒子即將在 1958 年 8 月出生。這時我的碩士論文指導教授，同時也是實驗室主任對我說：「藍，你必須攻讀博士。」我回答：「我不想讀博士。」

You know, I've got this wonderful research job. I need to earn money. My son's about to be born in a CIS. Gotta get a PhD. So we kept pushing me. And finally, I said, "Okay, I got a PhD. I've got two conditions. Condition number one. I want to work for the best professor at MIT that I know. And secondly, if I'm going to do a PhD in some years doing research, I want it to be important. I want it to have impact.
你知道嗎，我有份很棒的研究工作。我得賺錢養家，我兒子即將在 CIS 出生。必須拿到博士學位。所以大家一直催我。最後我說：「好吧，我去讀博士。但有兩個條件。第一，我要跟麻省理工學院我認識最棒的教授工作。第二，既然要花幾年時間做研究拿博士，我希望這研究很重要，能產生影響力。」

That's an epitome little problem." Well, the professor I chose, and this is getting back to one of your original questions, is, "Man named Claude Shannon. Now Claude Shannon was then and still is my idol, my mentor, my role model. He created something called Information Theory, Code in Theory, set the digital communications world into its direction now to help create everything we have today. Brilliant, man. And he was really important to me.
這就是個典型的小問題。」我選擇的教授——這又回到你最初的一個問題——是「克勞德·夏農」。克勞德·夏農當時是、現在依然是我的偶像、導師和榜樣。他創立了所謂的「資訊理論」，在理論中編碼，為數位通訊世界指明方向，幫助創造了我們今天擁有的一切。真是個天才。他對我來說非常重要。

He showed me how to do research. He showed me how to ask questions. To ask, even if you get a result, you're not done with it. Ask for example, "What is a result trying to tell you? How can you apply it somewhere else? What's the underlying mechanism?" So we really told me how to think about detailed research. And an answer to your own question, he was perhaps the most important mentor and role model that I encountered. That was great. We're going to talk about the internet and its development and its evolution.
他教會我如何做研究。他教會我如何提出問題。即使得到結果，也不能就此停止，要繼續追問：「這個結果想告訴你什麼？如何將它應用在其他地方？背後的運作機制是什麼？」這些教導讓我真正學會如何深入思考研究細節。對我個人而言，他無疑是最重要的導師與榜樣。這真是太棒了。我們接下來要談談網際網路及其發展演變。

But before we jump into that, would you provide the backdrop? I just sharing some history of how it came to be before everything that is maybe more widely understood. So the history of the internet development creation is rather interesting. What? There were actually two threads that would drive independently, which finally came together and created the internet. Let me describe them. The first I'd like to describe as the theoretical side. There were people at universities studying the way data networks, digital networks, communication networks, should function.
但在深入探討之前，能否請您先說明背景？我想先分享一些較不為人知的歷史淵源。網際網路誕生的發展歷程其實相當有趣。當時有兩條獨立發展的脈絡，最終交會並創造出網際網路。讓我分別說明：第一條脈絡是理論研究，當時大學裡有群學者專門研究數據網路、數位網路與通訊網路的運作原理。

And when I decided to pick up my research as a PhD student, I wanted to study exactly how computers could communicate with each other. What kind of a network that would need so they could talk to each other? And the reason I posed that problem was two-fold. One, as I said earlier, I didn't want to work on a problem that was small, difficult, and a little consequence. And most of my classmates were doing exactly that. There was this wonderful new field called information theory. There were many hard, open problems. Small problems that needed to be dealt with.
當我決定以博士生的身份展開研究時，我希望能深入探討電腦之間究竟該如何進行溝通。究竟需要什麼樣的網路架構，才能讓它們彼此對話？我之所以選擇這個課題，有兩個主要原因。首先，正如我之前所說，我不想研究那些範圍狹窄、難度高卻影響力有限的問題——而當時我多數同學都在做這類研究。當時有個令人振奮的新領域叫資訊理論，充斥著許多艱澀的未解難題，都是些需要處理的小型問題。

And there were busy working on really hard problems in good ones. But I realized that's not what I wanted to do. They would not have impact. Whereas at MIT Lincoln Lab, which provided the chip to me and MIT itself, I was surrounded by computers. And I knew that one day sooner or later, these computers would have to talk to each other. And there was no adequate network which would allow remote computers to interact and talk to each other. And I said, "Look, here's a problem that nobody's working on. It's an important problem.
當時同學們都忙著鑽研那些艱深但格局有限的課題。但我意識到這並非我的志向所在——那些研究不會產生重大影響。反觀在提供我研究經費的 MIT 林肯實驗室（以及麻省理工學院本身），我身邊環繞著各式電腦設備。我深知遲早有一天，這些電腦必須實現相互通訊。然而當時根本沒有合適的網路架構能讓遠端電腦互動交流。於是我告訴自己：「看吧，這是個無人涉足的重要領域。

If I can solve it, it will have impact, and I had an approach to solving it." This is exactly what I was looking for. So, for my PhD dissertation, I started looking into how computers could talk to each other. And I developed some underlying principles and approaches, recognizing that the key idea here was that the only network available at the time with the telephone level, the voice network. And then the voice network was woefully inadequate for data system to talk to each other. And the reason is, in speech, in voice, we're talking pretty continuously.
「如果我能解決這個問題，它將產生重大影響，而且我已經有了解決方案。」這正是我在尋找的。因此，在我的博士論文中，我開始研究電腦之間如何相互通信。我發展了一些基礎原則和方法，認識到當時唯一可用的網路是電話等級的語音網路。而這種語音網路對於數據系統之間的通信來說嚴重不足。原因在於，語音通話時我們幾乎是持續不斷地在說話。

When I start talking, they create a connection through the network to you. And I used that link when I talked, I used that same link coming back. And occasionally we take a cup of coffee, because we're silent about one-third of the time in voice communication. And that's acceptable. But with data communications, picture yourself at a keyboard. And you hit a character. And that character becomes a pack of a cream. It's all fun to giggle with line somewhere through the network. And by the time you hit the next character, it's an eternity before that line is used again. And those high speed lines are expensive. And data is like that. It occasionally needs a big bandwidth and then silent for a long time. So what do we do to overcome that dilemma? The idea is, don't assign a sequence of links to our conversation data or voice. But rather launch this packet into a network and let it hop through the network, find in available channels along the way without pre-assigning them. And once it's finished using a particular hop, let it go and let somebody else use that hop. Instead of keeping a sequence of links dedicated to us with this very little to sound. So the idea of dynamically sharing resources. In this case, communication links was key. And I was able to study that to analyze it and use the key idea of Q-in theory. The method matters which describe how things arrive, hang around a while, get used and leave in a stochastic random environment. And go hop hop hop through a network. Without going into the mathematical details, I developed a theory which described how to analyze these hop-by-hop networks, how to optimize them, how to understand their philosophy, understand their principles, why they work as well as they do. Our logic systems better than smaller systems, they answer, yes. Should we break these long messages into packets and hop them through the network, all these issues I was able to analyze, distribute it controlled, adaptive routing, etc. And I put together essentially a mathematical theory of data networks. And I finished that work in 1962. Now there were other people looking at similar problems. Paul Baron, a ran corporation, was working on survival networks. He was looking at the architecture such networks.
當我開始說話時，他們會透過網路與你建立連線。我說話時使用那條連結，回傳時也使用同一條連結。偶爾我們會喝杯咖啡，因為語音通訊中約有三分之一時間是靜默的。這還能接受。但數據通訊就不同了，想像你坐在鍵盤前。你敲下一個字元。那個字元變成了一小塊數據包。它會咯咯笑著在網路某處的線路上穿梭。當你敲下下一個字元時，那條線路要等好久才會再次使用。而那些高速線路非常昂貴。數據傳輸就是這樣。它偶爾需要大頻寬，然後又長時間靜默。那麼我們該如何解決這個困境？解決方案是：不要為我們的對話數據或語音預先分配一串連結。而是將這個數據包發送到網路中，讓它在網路中跳躍，沿途尋找可用通道，無需事先分配。一旦它完成某個跳躍點的使用，就釋放它讓其他人使用。而不是保留一串專屬連結給我們，卻幾乎沒在傳輸數據。 因此，動態共享資源的概念在這種情況下，通訊鏈路是關鍵。我能夠研究並分析這一點，並運用 Q 理論的核心思想。方法很重要，它描述了事物如何在隨機環境中到達、停留一段時間、被使用然後離開。並在網絡中跳躍前進。 在不深入數學細節的情況下，我發展出一套理論，描述如何分析這些逐跳網絡、如何優化它們、如何理解其運作哲學、掌握其原理，以及為何它們能如此有效運作。我們的邏輯系統確實比小型系統更好，答案是肯定的。我們是否應該將這些長訊息分割成封包並讓它們在網絡中跳躍傳輸？所有這些問題我都能分析，包括分散式控制、適應性路由等。我基本上建立了一套數據網絡的數學理論，並在 1962 年完成了這項工作。 當時也有其他人在研究類似問題。蘭德公司的保羅·巴倫正在研究生存網絡，他關注的是這類網絡的架構。

And a few years later, Donald Davies, in England at the National Physical Laboratory, was looking at how to implement some of these things. This theoretical thread of looking at how should computers talk to each other was developing, starting when I got to MIT in '57 by '59 I started working on it, etc. This thread was moving along. Meanwhile, back in 1957, there was another thread developing. In 1957, in 1958, the entire planet was dedicated to something called the International Geophysical Year. Scientists across the planet were studying the Earth, the mountains, the oceans, the atmosphere, the rivers, the continents.
幾年後，英國國家物理實驗室的唐納德·戴維斯也開始研究如何實現這些構想。這條關於「電腦該如何相互通訊」的理論脈絡持續發展——從我 1957 年進入麻省理工學院開始，到 1959 年我正式投入研究等等。這條研究主線不斷推進。與此同時，回溯到 1957 年，另一條發展線索正在成形。1957 至 1958 年間，全球科學界正全力投入一項名為「國際地球物理年」的計畫。世界各地的科學家們共同研究地球的地貌、山脈、海洋、大氣、河流與大陸板塊。

And there were study in the science across the world, and many countries involved. Well, in October of 1957, the Russians who were part of the study jumped the gun. And they launched the first artificial Earth orbiting satellite, something called Sputnik in October 1957. And that damn thing, cycled, circled around the Earth going, "Bee, beep, beep, beep." Anoint everybody, and pointing out that Russia was now ahead of everybody else, not only in space, but in science at technology. Those October '57. Well, then, President Eisenhower, the United States said, "Uh-oh, we've been caught with our pants down." We are no longer leader in science at technology, and that better not happen again. So, four months later, in February of 1958, he formed a research environment. He formed something called the Advanced Research Projects Agency, Papa, within the department of defense, to do what?
當時全球科學界都在進行相關研究，許多國家都參與其中。1957 年 10 月，參與研究的俄羅斯人搶先行動了。他們發射了第一顆人造地球軌道衛星，就是 1957 年 10 月那個叫「史普尼克」的東西。那該死的玩意兒繞著地球轉啊轉，發出「嗶、嗶、嗶、嗶」的聲音。這讓所有人都驚覺，俄羅斯不僅在太空領域，連科技方面都已經領先全球。那是 1957 年 10 月的事。於是，美國總統艾森豪說：「糟糕，我們被殺個措手不及。」我們在科技領域已不再是領頭羊，這種情況絕不能再發生。四個月後，1958 年 2 月，他成立了一個研究機構。他在國防部底下成立了「高等研究計劃署」（ARPA），目的是什麼呢？

To fund research and education in science at technology. Basically, engineering and mathematics. For the sole purpose of bringing the capability of a merit, a backup, to primarycy in those fields. So, we started funding research across the country, educational institutions, etc. And Joshua Research Labs. And the started up by funding science in the area of chemistry, aeronautics, physics, biology, space, etc. In 1962, they formed a special group to study computers, and it was called the Information Processing Techniques Office, Computers.
為了資助科學與技術領域的研究與教育，基本上就是工程和數學。唯一目的是在這些領域培養頂尖人才與後備力量。於是我們開始資助全國各地的研究機構、教育機構等，包括喬舒亞研究實驗室。最初是從化學、航空學、物理學、生物學、太空等領域的科學研究開始資助。1962 年，他們成立了一個專門研究電腦的特別小組，稱為「資訊處理技術辦公室」。

And the first set of that was the fellow named Licklider, Dr. Licklider, who was a psychologist. And he had the notion that if he put man and computers together, he got what's called a man computer symbiosis. You get the best of both, and who knows what wonderful things can happen. So we started funding computer scientists around the United States. And the way he did it was remarkable. A god bless him. He would go to some of the great scientists at various universities and research labs at the time. So he'd go to Marvin Minsky and MIT, who was great in artificial intelligence. He said, Marvin, you're a great scientist if you can bring things. He's a pile of money. Go shoot for the moon. Really go for something big. Failure is okay, but go for it. We're going to give this money for a long time. We're not going to tell you how to do it. Do what you want. And we're not going to watch you.
而第一批參與者中有一位名叫利克里德（Licklider）的博士，他是位心理學家。他提出了一個概念：如果將人類與電腦結合，就能實現所謂的「人機共生」。這樣可以結合兩者的優勢，誰知道會迸發出什麼驚人的成果呢？於是我們開始資助全美各地的電腦科學家。他的做法非常了不起——願上帝保佑他——當時他會走訪各大學和研究機構的頂尖科學家。比如他去找麻省理工學院專精人工智慧的馬文·明斯基，對他說：「馬文，你是傑出的科學家，只要你能提出構想，這裡有大筆經費。儘管放手去追求遠大目標，真的去挑戰重大突破。失敗也沒關係，但務必全力以赴。我們會長期提供這筆資金，不會干涉你的研究方法，完全尊重你的意願，更不會監管你的進度。」

It's all on your own. So what does Marvin do with this money? Well, his salary is paid by MIT. So he takes this money and he gives us to his graduate students. In the same way, you'll go to a graduate and say, "Graduates, you're doing so." So go make a seven-legged robot. Go figure it out. I'm not going to tell you how to do it. You go to your classmates.
這一切都取決於你自己。那麼馬文怎麼處理這筆錢呢？他的薪水是由麻省理工學院支付的。所以他拿了這筆錢，把它給了我們這些研究生。同樣地，你會去找一個研究生說：「研究生們，你們正在做這樣的事。」所以去做一個七條腿的機器人吧。自己去想辦法。我不會告訴你該怎麼做。你可以去找你的同學們。

Shoot for them on the right. It's really okay, but go for it. And we're going to support you for a long time. Well, this was done across the country in artificial intelligence, in graphics, in chip technology, in simulation, in all kinds of capability. So by the time 1966 came around, here's later, there were these great centers of excellent across the country. MIT with artificial intelligence, University of Utah with great graphics, University of Illinois with high performance computing, UCLA with simulation, database at Stanford Research Institute. Every time, ARPA would come to a new researcher, he's what would happen. By the way, by then, look like it stepped down. Ivan Sudlton came in 1964 by 1966, a guy named Robert Taylor was head of this group within ARPA. They go to a new researcher, and say, "We'd like to fund you for some great research." And a researcher would say, "Oh, really?" Find by me a big computer, an officer to show a bio-big computer. And then the researcher would say, "But you know, you see that graphics up in Utah? I want that here." And the high performance computer in Illinois, I want it here, et cetera, et cetera. Well, office, I've now read a map. We can't give everybody everything. But if you were in a network, you could log onto Utah and do the graphics there. Log onto Illinois, and you'd have high performance computing there. So the concept in 1960 became, let's make a data network to connect together these are. These really extensive excellence, of computer science. So the idea of needing a network to connect computers was suddenly born in 1966 at ARPA. That was the basically the government thread. Well, how to build it? I told you a moment ago, there was a theoretical thread which would have been cooking along, which we knew how to do it. So these two threads came together. In 1966, they brought in one of my MIT classmates, Larry Roberts, to head up this effort at ARPA, to create a data network which is going to be called the ARPA network. The ARPA data network. And Larry brought a bunch of us together from the theoretical side and from the government side to create a specification as to what this network should look like. Send that specification out the industry, ask industry to bid on making a machine to accomplish this technology and deploy it across the country. So those two threads, and answer the question, came together in 1966 to theoretical side, the government side, and now is a wonderful match. We understand how we have a need, we got the funding, let's make it happen. So the plans began to be going to lay out. And so how did you first conceptualize the idea of a global computer network? So the idea originally was just to connect together computers. And the original plan was to lay out a 19-note network across the United States connecting together these industrial research entities and the university research entities. So the idea was, we're going to put these devices which implement the theory. We call those a router today. We call them a package which. And the particular name associated with the package which was called an interface message processor.
往右邊射擊。真的沒問題，儘管放手去做。我們會長期支持你。當時全美各地都在人工智慧、圖形處理、晶片技術、模擬運算等各種領域進行這類研究。到了 1966 年，全美已形成多個頂尖研究中心：麻省理工專攻人工智慧、猶他大學擅長圖形處理、伊利諾大學發展高效能運算、加州大學洛杉磯分校精研模擬技術，史丹佛研究所則專注資料庫領域。每次 ARPA 接觸新研究員時都會出現這樣的場景——順帶一提，當時領導人已換成 1964 年接任的伊凡·薩瑟蘭，到了 1966 年則由羅伯特·泰勒負責該部門。他們會對研究員說：「我們想資助你進行重要研究。」研究員總會反問：「真的嗎？」然後要求配備大型電腦，官員就得展示生物級大型電腦。接著研究員會說：「但你知道猶他州的圖形技術嗎？我要在這裡也弄一套。」還有伊利諾州的高效能電腦也要，諸如此類的要求。這時官員就會表示：「我現在可看透全局了。」 我們無法給予每個人所有東西。但如果你身處網路中，就能登入猶他州使用那裡的圖形運算，登入伊利諾州則能享用高效能運算。因此在 1960 年代誕生了這樣的構想：讓我們建立數據網路來串連這些資源——這些電腦科學領域真正頂尖的卓越成果。於是，在 1966 年 ARPA（美國高等研究計劃署）突然萌生了需要建立電腦連網的概念。這基本上源自政府部門的脈絡。那麼該如何建構呢？我剛才提過，當時還有另一條理論脈絡正在醞釀，我們已掌握實作方法。這兩條脈絡最終在 1966 年交會。他們找來我在 MIT 的同窗拉里·羅伯茨主持 ARPA 這項計畫，要打造名為「ARPANET」的數據網路——ARPA 數據網路。拉里匯集了我們這群來自理論界與政府部門的人員，共同制定這套網路的技術規格。接著將規格書發給產業界，邀請業界投標製造實現這項技術的機器，並在全美部署。 因此，這兩個脈絡——理論層面與政府支持——在 1966 年完美交會。我們既理解需求所在，又獲得資金挹注，接下來就是付諸實現的時刻。規劃藍圖就此展開。 那麼，您最初是如何構思全球電腦網絡這個概念的？其實最初構想單純只是要連結電腦設備。原始計畫是在美國境內建置 19 個節點網絡，將工業研究機構與大學研究單位相互串聯。 當時的構想是：我們要部署這些能實踐理論的裝置（如今我們稱之為路由器），並採用封包交換技術（當時我們將相關設備命名為「介面訊息處理器」）。

These were standalone mini computers, which were going to be deployed across the country, connected together with high speed lines. But these computers would be running packets, which is as opposed to circuits, which are being used for the telephone network. The deployed this across the country and bring up the capability slowly. So the theory was there. We needed to implement it. So the specification went out the industry.
這些都是獨立運作的小型電腦，它們將被部署在全國各地，並透過高速線路相互連接。但這些電腦將運行封包交換技術，這與電話網路所使用的電路交換技術截然不同。我們在全國範圍內部署這套系統，並逐步提升其功能。理論已經完備，現在需要的是實際執行。於是我們向業界發出了技術規格要求。

We told the industry what we needed to please build on deploying a 19-note network. Well, it went out the industry in number of companies build. And a winner was selected around Christmas time of 1968. A company called both Veronica Newman. A research company developed a company out of Cambridge, Massachusetts. Made up of many researchers out of MIT, and the laboratory, by the way. The same place that I got my degrees from. And they presented a terrific proposal. They added a lot of capability, a lot of functionality, a lot of protocols, and good engineering to this.
我們告訴業界需要建構一個 19 個節點的網路。多家公司參與投標，最終在 1968 年聖誕節前後選出了優勝者。這家名為 Bolt Beranek and Newman（BBN）的研究公司來自麻薩諸塞州劍橋市，由許多來自麻省理工學院和實驗室的研究人員組成——順帶一提，這正是我取得學位的地方。他們提出了極具前瞻性的方案，為系統增添了許多功能、協定和優秀的工程設計。

And they were told that in eight months by September, the Labor Days of 1960, nine, you had to deliver the first one of these switches. To UCLA, which you become the first node of the open-ed. Now, why did they choose UCLA? Because, in fact, we had the technology, the understanding and theory, and the so-off to an hardware people who could build this thing. So UCLA was supposed to become and was, the network measurement center to become alive in September, Labor Day of 1969. And that was the player.
他們被告知要在八個月內，也就是 1969 年 9 月的勞動節前，必須交付第一台這樣的交換機給 UCLA，這將成為開放網路的第一個節點。那麼，為什麼選擇 UCLA 呢？因為事實上，我們擁有相關技術、理論知識，以及能夠建造這項設備的硬體團隊。因此 UCLA 預計並確實成為了網路測量中心，於 1969 年 9 月勞動節正式啟用。這就是關鍵所在。

And would you walk through your approach at that point? So the approach was, BBNN was going to deliver these things called MPs. These routers, these packet switches. But we had to find a way to interface those switches to our host computers, which were going to be running on the network. So we needed to create something called a host M interface. And each host in a network on different sides was a different host. So each one needed their own interface. So UCLA was busy creating the host M interface for our host, which was happening to be a scientific data systems, Sigma 7 computer.
你能說明當時的具體做法嗎？當時的做法是，BBNN 公司將交付這些稱為 IMP 的交換設備（這些路由器、封包交換器）。但我們必須找到方法將這些交換器與我們的主機電腦連接，這些主機將在網路上運行。因此我們需要創建一種稱為「主機 IMP 介面」的東西。網路中不同節點的主機各不相同，所以每台主機都需要自己的介面。UCLA 當時正忙著為我們的主機（恰好是 Scientific Data Systems 公司的 Sigma 7 電腦）開發主機 IMP 介面。

And that computer, I was running for the department here, as a time shared computer science research machine. And we developed this software at the host in protocol. And I put together a hardware team, a software team, a research team of graduate students, and a staff, 40 people, many from PhD students to make this all happen. And by the way, the person I put in charge of the software group was a PhD student called Steve Crocker, who created what called the request for comment series, he wrote the specification for the first host host protocol.
而那台電腦，我當時是為系上運作的，作為一台分時共享的電腦科學研究機器。我們在主機端開發了這個協定軟體。我組建了一支硬體團隊、一支軟體團隊、一支由研究生組成的研究團隊，以及工作人員共 40 人，其中許多是博士生，才讓這一切得以實現。順帶一提，我指派負責軟體組的是一位名叫史蒂夫·克羅克（Steve Crocker）的博士生，他創建了所謂的「請求評論」（RFC）系列，並撰寫了第一份主機對主機通訊協定的規格書。

And under him were people like Vince surf, well known, John pastel, Charlie Klein, a number of other people, a really super group. So it comes Labor Day Weekend of 1969. The imp is delivered. We're going to turn it on the day after Labor Day, September 2nd, basically the Tuesday after the Monday Labor Day. We turn it on and we connect the imp and the host with a 15-foot cable, and we begin to move bits back and forth from the imp. From the imp to the host. Now who is there to watch this happen?
在他麾下還有文斯·瑟夫（Vince Cerf，相當知名）、約翰·波斯特爾（John Postel）、查理·克萊恩（Charlie Klein）等一群人，真是個超級團隊。時間來到 1969 年的勞動節週末。介面訊息處理器（IMP）送達了。我們計劃在勞動節隔天——基本上是勞動節星期一之後的星期二，也就是 9 月 2 日——開機。我們啟動機器，用一條 15 英尺的纜線連接 IMP 和主機，開始讓位元在 IMP 和主機之間來回傳輸。當時有誰在場見證這一刻呢？

Everybody. Opera, BB&N, AT&T, we're going to use their long lines. GTE, we're going to use their local lines. Scientific data system we're using their host. Honeywell, who is the manufacturer of the mini-computer that made the imp. UCLA, UCLA Administration, Opera, all the researchers, and everybody was ready to point the finger to the other guy I didn't work. Well, happily the bits began to move back and forth. But one node is not a network. We didn't have a network yet.
各位。Opera、BB&N、AT&T，我們將使用他們的長途線路。GTE，我們會用他們的本地線路。科學數據系統我們採用他們的主機。Honeywell，他們是製造 IMP 迷你電腦的廠商。UCLA、UCLA 行政部門、Opera，所有研究人員，每個人都準備好把問題推給別人說不是我的錯。幸好，數據位元開始雙向傳輸了。但單一節點還不能構成網路，當時我們還沒有真正的網路。

And the plan was, September UCLA, October Stanford Research Institute, Rene 51 miles of the north would get their switch. I connected their host. November, UC Santa Barbara, and December University Utah, a four-node network initially. So in October, SRI got their imp, and Opera provided through BB&N, the first high speed piece of the backbone internet, running from UCLA to SRI, 250 miles long, running at the blazing speed of 50th. 50th, thousand bits per second.
按照計畫，九月在 UCLA，十月在史丹佛研究院（距離北方 51 英里的 Rene）會裝設他們的交換機。我連接了他們的主機。十一月是加州大學聖塔芭芭拉分校，十二月是猶他大學，最初規劃四個節點的網路。所以在十月，SRI 拿到了他們的 IMP，Opera 透過 BB&N 提供了骨幹網際網路的第一條高速線路，從 UCLA 延伸到 SRI，全長 250 英里，以驚人的 50 千位元/秒速度運作。沒錯，每秒 5 萬位元。

Are you wouldn't pay a nickel for that today? But in those days, that was high speed. So now we had the UCLA host connected to the UCLA switch, the imp. Connected through a high speed line, connected to 250 miles north to the imp at SRI, connected to their host. We now had a two-node network, and now we're ready to test the functionality of what's supposed to go on. And what is the functionality? You sit at one computer, log down to it, you log in through the network to a remote computer, log onto it, and use it services there. So we can run that test. So would you share your experience of the first message through the internet on October 29, 1969? Exactly. So we're ready to make this test the first two nodes. And we're going to send the first message over the internet, which was going to become the internet. Now, did we have a, what was that first message? Did we have a good one?
你現在連五分錢都不願意付對吧？但在那個年代，這已經是高速傳輸了。於是我們將 UCLA 的主機連接到 UCLA 的交換器（IMP），再透過高速線路向北連接 250 英里外史丹佛研究院（SRI）的 IMP，最後連接到他們的主機。這樣我們就建立了一個雙節點網路，準備測試其運作功能。什麼功能呢？就是你能坐在一台電腦前，透過網路登入遠端的另一台電腦，使用它的服務。我們可以進行這項測試了。那麼您能否分享 1969 年 10 月 29 日網際網路傳送第一條訊息的經歷？沒錯。當時我們準備在這最初的兩個節點間進行測試，即將傳送網際網路——也就是後來真正成為網際網路的那個網路——的第一條訊息。那麼，我們當時準備了什麼內容作為首條訊息呢？這個開場白夠精彩嗎？

Well, think about it. Sam, we will more. First telegraph message. He had a great message. It was what had God wrote. People would go for medical. The first telephone message came here, "What's an I need you?" How about Neil Armstrong, space, a giant leap for mankind? Those guys were smart. They understood the press, the public relations, the media.
嗯，你想想看。山姆，我們還會有更多突破。第一封電報訊息——那可是句了不起的話，是「上帝創造了何等奇蹟」。人們會為醫療技術瘋狂。首通電話問世時說的是「華生先生，快來幫我！」阿姆斯壯登月時那句「個人的一小步，人類的一大步」呢？這些前輩都很聰明，深諳媒體操作、公關技巧和大眾傳播之道。

We were just a bunch of nerds. We were sitting there one night, ready to run this little test. We had one of my program, Charlie Klein, down here with UCLA. We had another program, a build-to-vile, a pedestrian ride. And we wanted to do this login. We didn't have a message, just want to log in. Not to log in, you have to type L, O, G. And that's going to go up to the SRI host. And the SRI host is smart enough to know, "Oh, they're trying to log in. It'll type the IN for you." Now, understand, the SRI host has no idea that it's coming through a network. It thinks it's coming from a local user. A local time share user. Now, when a local time share user connects to a time sharing computer, you type a key on the keyboard. It goes to the time share computer, which echoes that character back to you and prints it on your screen. And you keep repeating that's a very short distance, very fast. We're going through in the 50 miles north. So we typed the L and just to make sure it was going on, you know, "How do we know if the message is getting up there, that character is?" We said it telephone link between Charlie and Bill. So I said it up. And Charlie typed the L instead of Bill. You get the L and Bill said "Yup." And it meant "Trint, too." Type the L, you get the L, "Yup, Type the L." Type the G, you get the G, crash. The system crashed. So, couple of comments. What was the first message ever on the internet?
我們不過是一群書呆子。那天晚上我們坐在那裡，準備進行這個小測試。我們有我的程式設計師查理·克萊恩在 UCLA 這邊，另一頭則是史丹佛研究院(SRI)的主機——就像個笨重的終端機。我們只想完成登入動作，不是要傳送訊息，單純想登入。要登入就必須輸入 L、O、G，這些指令會傳送到 SRI 主機。那台主機很聰明，它會判斷「喔，他們想登入」，然後自動幫你補上 IN。要知道，SRI 主機根本不知道訊號是透過網路傳來的，它以為是本地分時用戶在操作。當分時用戶連接到分時電腦時，你每按一個鍵，訊號就會傳到分時電腦，電腦會把字元回傳並顯示在你的螢幕上。這種短距離傳輸速度極快。但我們這次可是要往北傳 50 英里啊！於是我們輸入 L，為了確認訊號有傳出去——「我們怎麼知道那個字元有沒有成功送達呢？」——我們讓查理和比爾保持電話連線。我說「開始」，結果是查理而不是比爾輸入了 L。 你打了個 L，比爾說「沒錯。」這表示「Trint 也是。」輸入 L，得到 L，「沒錯，輸入 L。」輸入 G，得到 G，當機。系統崩潰了。所以，幾點說明。網際網路史上第一則訊息是什麼？

And the answer is "Lo." As in "Lo and behold," I added that later. Very wise. But think of it, a more prophetic, more profound, the most distinct message we couldn't have asked for. We didn't plan it, but it turned out to be beautiful. Now what crashed? Well, it wasn't our host. It wasn't our imp. It wasn't the high speed line.
答案是「Lo」。就像「Lo and behold」（看哪），這是我後來加上的。非常睿智。但想想看，還有比這更預言性、更深刻、更獨特的訊息嗎？我們根本求之不得。這並非刻意安排，結果卻美妙至極。那麼是什麼當機了？不是我們的主機。不是我們的介面處理器。也不是高速線路。

It wasn't the SRI imp. It was the SRI host. Because the SRI host, I told you, when you type the G, it got tomorrow and at the time the G, I came back to you. And it wasn't set up to handle three characters. So there was a buffer overflow. It was a piece of patchwork that we put together, which quickly failed, quickly patched it, and within the next hour we got the full message to. So, as you point out, October 29, 1969, a 10/30 night Pacific time. The first message ever on the internet was "Lo" as in "Lo" and "Behold".
不是 SRI 的介面處理器。是 SRI 的主機。因為 SRI 主機，我告訴過你，當你輸入 G 時，它會取得「tomorrow」這個字，而當時輸入 G 後，程式會回傳給你。但它沒設定好處理三個字元。所以發生了緩衝區溢位。這是我們拼湊出來的臨時方案，很快就失效了，我們迅速修補，接下來一小時內就成功傳送完整訊息。所以如你所指出的，1969 年 10 月 29 日，太平洋時間晚上 10 點 30 分。網際網路史上第一則訊息就是「Lo」，如同「看哪」的開頭。

And it went from UCLA to SRI, 3/50 miles north to the second computer on the network. And as RIS Stanford, it stands for the research institute, which was not the university, which was associated with the university. What would you say was the original spirit and culture among the early internet pioneers? So, that's a wonderful question. I'm going to hawk and back to "Licklide". "Licklide" created that culture. When he went to the research, the Minsk is a set of it. He said, "Look, we're going to give you this funding.
這段網路從加州大學洛杉磯分校（UCLA）連接到斯坦福研究院（SRI），向北延伸 3/50 英里到達網路上的第二台電腦。而 RIS 斯坦福指的是研究機構，它並非大學本身，但與大學有密切關聯。您認為早期網路先驅們最初的精神與文化是什麼？這真是個好問題。我要回溯到「利克里德」。「利克里德」塑造了這種文化。當他進入研究領域時，明斯克團隊就是這種精神的體現。他說：「聽著，我們會提供資金支持。

We're going to let you do what you want. We're not going to bother you. Failure is okay. Shoot for the moon." You know? And then he gave that to his graduate students, "What better research environment? Where you have trust in your colleagues? You are trusted. Use ethics.
我們會讓你們自由發揮。不會干涉你們。失敗是可以接受的。要敢於追求遠大目標。」明白嗎？然後他把這種理念傳遞給研究生們：「還有什麼比這更好的研究環境？在這裡你信任同事，也被同事信任。要恪守道德。

This is free. It's open. It's shared. People are behaving well. And they're all trying to achieve a common, wonderful, difficult, challenging goal. It was really one of great camaraderie. And working together. There was no sense of ownership. Of patenting, nobody patented anything.
這裡是自由的、開放的、共享的。人們都表現得很好。大家都在努力實現一個共同、美好、艱難且具挑戰性的目標。那真是一段充滿情誼的時光。大家齊心協力。沒有所有權的概念。沒有專利這回事，當時根本沒人申請專利。

No intellectual property rights. Here it is. Our gratification was. We create something somebody uses it. That's about as good as it gets. And so it was a wonderful environment. Of cooperation. Research environment couldn't be better. I told you what came out of that was, you know, graphics, networking, chip technology, artificial intelligence, on and on and on.
沒有智慧財產權。就是這樣。我們的滿足感在於。我們創造了某樣東西，有人使用它。這大概就是最棒的事了。當時的環境真是美好。充滿合作精神。研究環境再好不過了。我告訴過你，從中誕生的成果包括圖形處理、網路技術、晶片科技、人工智慧等等，不勝枚舉。

From that wonderful open environment. But it didn't last that long. Because there's another element here, which I haven't addressed. And I didn't emphasize, let me point out, opera was an agency created within the United States Department of Defense. Because that's where the funding was was. But there was no defense, no military applications intended. At least not down at the level of the researchers. You know, we were doing a theoretical engineering job. But it wasn't too many years later when things like the man's field act came in, which said, you can fund this research unless they somehow were military applications somewhere down the line. And they also said, this is an unfair way to grant. We have to have a competitive environment. What's called a broad area in that where multiple people get to bid for the project and for the funding. And to be honest with you, that sounds more fair, but to be honest with you, it slowed down the really which great work was done. It took a while to get the bidding. The contracts became smaller.
從那個美好的開放環境開始。但這種狀態並沒有持續太久。因為這裡還有另一個因素我尚未提及——容我特別指出，ARPA（高等研究計劃署）是美國國防部下設的機構，畢竟資金來源就在那裡。但這項研究從一開始就沒有國防或軍事應用的意圖，至少對我們這些基層研究者而言是如此。要知道，我們當時純粹在做理論工程研究。 不過沒過幾年，《曼斯菲爾德修正案》這類法案就出現了，規定除非研究最終能與軍事應用沾上邊，否則不得撥款。他們還主張原先的撥款方式不公平，必須建立競爭機制——也就是所謂的「廣泛領域公告」，讓多方競標研究項目與資金。說實話，這聽起來更公平，但坦白講，這嚴重拖慢了那些真正偉大的研究進度。光是招標流程就要耗費時日，合約金額也變得零碎瑣碎。

More people bidding for yet smaller work. And it somehow lost that culture to which I referred new asked about, which is a wonderful open share. Let's let's conquer the world kind of environment. It's similar to your early childhood where your mom let you do what you thought you wanted to do. And it was open. And then you fast forward to the early days of the internet. And you have the funding and they say shoot for the moon. Do whatever you want. We're not going to oversee it.
越來越多人競標更小的工作。不知怎地，它失去了我剛才提到的那種文化——那種美好的開放共享精神，那種「讓我們一起征服世界」的環境氛圍。這就像你童年時期，媽媽讓你隨心所欲地探索，那時一切都很開放。快轉到網際網路早期，資金到位時人們會說「放手去追月亮吧，想做什麼就做什麼，我們不會干涉」。

We just want you to create something great. And you go to smart people who are motivated and confident their abilities. And you give them the world to shoot for. And you start constricting it. And you can see it leads down a very different path. It does. And there's a question of equity and fairness. Certainly you have to raise those issues. But that was the time for that open free flowing flexible structure because that was a remarkable golden year of creativity. I told you all the things that emerged.
我們只希望你創造出偉大的東西。你找來那些充滿幹勁、對自己能力有信心的聰明人，給他們整片天空去追逐。但後來你開始設限，就會發現這條路走向截然不同的方向。確實如此。這裡還涉及公平與正義的問題，當然這些議題必須被提出。但那個開放、自由流動、彈性架構的時代，正是創造力迸發的黃金年代。我告訴過你所有那些誕生於此時的創新成果。

And the people you work with, you know, there was another aspect to it. Where is the faculty gave the graduate students their free reign. Those graduate students blessed them. They formed their own network. Their own network working group. Graduates students not only at the university, whether they were getting the degree, but across the United States across the world. They formed their own collaboration groups. They interacted.
與你共事的人們，你知道嗎？這其中還有另一個層面。當時的教職員給予研究生們充分的自由發揮空間。這些研究生們可謂是得天獨厚。他們自行組建了網絡。他們自己的網絡工作小組。不僅是校內的研究生，無論是否正在攻讀學位，更遍及全美乃至全球。他們自發形成協作團體，彼此互動交流。

They grew in this easy supportive open shared. Let's have a problem. Wait, you have to understand what a wonderful creative period that was. And it produced some remarkable results. Earlier you talked about taking this idea. And I know you were in papers about building the internet way before it actually occurred. But you talked about this idea of taking it to well-established large companies like AT&T as an example. What type of resistance did you face and how did you persist through that? Great question because you raised an actual port of issue. Along the way, what these ideas accepted by different constituents.
在這種輕鬆、支持性強且開放共享的環境中，他們茁壯成長。「讓我們來解決問題吧」。等等，你必須理解那是多麼美妙而充滿創造力的時期。這段時期孕育出許多非凡成果。先前你提到將這個構想——我知道你在網際網路實際建成前就發表過相關論文——但你談到將這個構想推廣給像 AT&T 這樣根基穩固的大型企業時，遇到了什麼樣的阻力？你又是如何堅持下來的？這個問題問得好，因為你觸及了實際的關鍵點。在推進過程中，這些構想如何被不同群體所接受。

And the first place where we met resistance was, as you say, I developed this mathematical theory for how to develop a data network. And so I, as well as poor barren, would go to AT&T and say AT&T build a data network. What an opportunity. We'll give you some idea how to do it. You have the way with all the do it, build it. And AT&T said it won't work. And even if it does, we want nothing to do with it. Pretty hotty attitude.
我們最初遇到阻力的地方，正如你所說，是我發展出這套關於如何建立數據網絡的數學理論。當時我和可憐的巴倫跑去 AT&T，建議他們建造數據網絡。多好的機會啊！我們可以提供一些構想，而你們擁有實現它、建造它的能力。但 AT&T 卻說這行不通，就算可行他們也不想參與。態度真是傲慢至極。

AT&T didn't even bid on the contract that BBN1. Now, I thought about that. It was a big mistake. They really lost the foresight. But you think about it. Why were there so obstinate? And the answer is, in some sense, they were right. There was no data descend. There was no need at the time for a data network. There was no business model.
AT&T 甚至沒有參與 BBN1 的合約競標。我後來思考這件事，發現這是個重大失誤，他們確實缺乏遠見。但仔細想想，為什麼他們如此頑固？某種程度上來說，他們是對的——當時根本沒有數據需求，不需要數據網絡，也沒有商業模式可言。

So as a business AT&T said no, big mistake. But you can understand that thinking at the time. So the first resistance was that instantly wanted nothing to do with it. We had a weight for the government to recognize they needed a network. And then here was it, I came to UCLA in 1963. With all the capability, but nothing to do with it. I kept doing research. Funny in 66, someone wanted to build it. And then we go. Okay. So opposites were going to build a network.
所以作為一家企業，AT&T 當時拒絕了，這真是個重大錯誤。但你能理解當時那種思維方式。第一個阻力就是他們立刻就想撇清關係。我們只能等待政府意識到他們需要一個網路。然後機會來了，我在 1963 年來到 UCLA。擁有所有技術能力，卻無用武之地。我持續進行研究。有趣的是在 1966 年，有人想建立這個網路。然後我們就開始行動。好吧。所以最終是由政府單位來建立這個網路。

And what's it going to do? It's going to take various sites that they've been supporting. And tell those sites, the MITs, the Utahs, the Illinois's, Stanford, you see, like, join a network. Take your wonderful high performance computers and put them in a network. So other people share them. And the principal investigators, those sites, What are you talking about? You're going to take my precious high performance computer and steal some of my cycles for people out in the network. No way. So our own community resisted joining a network.
這個網路要做什麼？它要把他們一直支持的各個站點串連起來。然後告訴那些站點，像是 MIT、猶他大學、伊利諾大學、史丹佛等等，加入這個網路。把你們那些性能優異的電腦放進網路裡。這樣其他人就能共享資源。而那些站點的主要負責人卻說：你們在說什麼？要把我珍貴的高性能電腦拿出來，讓網路上的人偷走一些運算週期？門都沒有。所以連我們自己的學術圈都抗拒加入這個網路。

For understandable reasons. So Larry at the time, and I went around to these sites. And we said, look, don't you want to join a network? You could be able to get access to other people's work. And so I went around and I said, look, if there was a network, how much would you use? One teletype, two teletype's work? Yeah, I wanted to tell a types. And how much would you let the outside world use your two teletype's work? So I took those numbers.
出於可以理解的原因。當時我和賴瑞走訪了這些站點，我們說：你們難道不想加入一個網路嗎？這樣就能接觸到其他人的研究成果。於是我四處詢問：如果有個網路，你們會使用多少頻寬？一台電傳打字機的流量？還是兩台？沒錯，我就是想了解具體數字。另外你們願意讓外界使用多少你們的電傳打字機頻寬？我就這樣蒐集了這些數據。

I went around to all the sites, nineteen of them, and I created a traffic matrix. And I published it. And now these guys in some sense, we'll commit it. In June of 69 before the October, before the, I opened that came up, I published that traffic matrix. But that still wasn't enough. What really clenched it was, all the way around to these sites, said, Mr. Principal Investigator, we are supporting your research. You will join a network.
我走訪了全部 19 個站點，建立了一個流量矩陣並公開發表。在某種程度上，這些單位等於做出了承諾。1969 年 6 月，在 10 月網路正式啟用前，我就公布了這個流量矩陣。但這還不夠，真正促成決定的關鍵是——當我再次走訪這些站點時，他們的主管研究員表示：「我們資助您的研究，你們必須加入這個網路。」

And of course, they joined. So that was the second level of existing. Our own community wanted nothing to do with it. So they joined. Earlier. And that's a continuation of those two threads you described earlier. The theory side and then the kind of the government support, and they kind of came together, and that helped fuel the launch exactly. But when they get on now, how much use is there? These guys have we luckily joined in?
當然，他們加入了。這就是第二層的存在。我們自己的社群原本對此毫無興趣。但他們還是加入了，而且更早。這延續了你之前提到的兩條線索：理論層面與政府支持，它們在某種程度上結合在一起，正好推動了整個計畫的啟動。但現在他們參與進來後，實際效益有多大？這些傢伙的加入對我們來說算是幸運嗎？

Was it being used? The answer is no. And the answer is no, because in fact, it was very difficult to use this network. I mean, suppose I'm sitting at UCLA, and I'd log on to Utah. What I need to log in, I need to know the command language. I need to know the applications and the services. That's a big learning process for me. So people were not willing to go through that learning process. In fact, one of the main things that were done is somebody would move, say, from Utah to UCLA, and wanted to use the machine with which they were familiar, and they'd log on across the network. So it's very sporadic use. And meanwhile, we were tested in network making a grow. However, as I mentioned before, there was something called a host host protocol, a way in which host could communicate with each other easily. And Steve Cracker wrote the first RFC describing that. And we implemented that here at UCLA, the first host host protocol, which was called the network control program, and C.P. was implemented basically in 1970. And so people began to use it a little bit. But it was decided that we ought to run a demonstration of these many sites that were coming up with their own great applications. So on October of 1972, we ran a demonstration of the open-art. In the Hilton Hotel in Washington, D.C. Bob Khan basically put it together. And he went around to all the sites and said, "Look, create a demo that you can run on this demo at the Hilton in October '72. There's going to be a major conference there. The conference will be the International Conference on Computer Communications, 1972." And so we brought together a bunch of PIs at that demo. We rented out the basement of the hotel with this conference as they can place. We brought a package which is in there, ransom-high-speed lines there, and people announced their applications. Many people bought chest-plane programs. MIT bought a bunch of robots to run around the floor. Some people bought simulation, air traffic control, and set it up to on in October '72. And we invited not only the PIs, but this was a major conference we invited the public in and showed them how to run some of the applications.
有人在使用它嗎？答案是沒有。之所以沒有，是因為實際上要使用這個網路非常困難。我的意思是，假設我坐在 UCLA，想登入猶他州的電腦。為了登入，我需要知道指令語言，還得了解應用程式和服務。這對我來說是個龐大的學習過程。所以人們不願意經歷這種學習過程。事實上，當時主要的情況是，如果有人從猶他州搬到 UCLA，想使用他們熟悉的機器，就會透過網路登入。因此使用非常零星。與此同時，我們正在測試讓網路持續擴展。不過，就像我之前提到的，有個叫做「主機間通訊協定」的東西，能讓主機彼此輕鬆溝通。史蒂夫·克羅克寫了第一份 RFC 文件來描述這個概念。我們在 UCLA 實作了這個首個主機間通訊協定，稱為「網路控制程式」（NCP），基本上是在 1970 年完成的。於是人們開始稍微使用它。但後來我們決定，應該為這些陸續上線、各自開發出優秀應用程式的眾多站點進行一次示範運作。 1972 年 10 月，我們在華盛頓特區的希爾頓酒店舉辦了一場開放式技術展示。基本上是由鮑伯·卡恩負責統籌，他走訪了所有參與單位並說：「聽著，你們要準備一個能在 1972 年 10 月希爾頓展會上運行的演示程式。屆時將有一場重要會議——1972 年國際電腦通訊會議。」於是我們在展會上聚集了一批首席研究員。我們租用了酒店地下室作為會議場地，架設了包含封包交換技術的高速傳輸線路，各單位也展示了他們的應用程式。許多人帶來了西洋棋程式，麻省理工學院則準備了一群在地板上移動的機器人。還有人展示了航空交通管制的模擬系統，這些都在 1972 年 10 月正式亮相。我們不僅邀請了首席研究員，更因這是場重要會議而開放民眾參觀，向他們展示如何操作這些應用程式。

Now, as a site, interesting story, what was the UCLA demo? The UCLA demo was that we're going to have John Pastel Sit in Washington. Log onto UCLA all the way across the country. Pull up and execute a program here at UCLA, which required a photograph, which had reached across the country back to MIT to pull up, and then that picture had to be processed back at Utah. And when the processing done, send it back to Pastel in Washington and print it at the local printer. That is a pretty good demo. Log on to UCLA, back to MIT, across the Utah, and back to Washington.
現在，作為一個網站，有趣的故事，UCLA 的演示是什麼？UCLA 的演示是這樣的：我們要讓在華盛頓的 John Pastel 登入遠在另一端的 UCLA。從 UCLA 這裡拉取並執行一個程式，這個程式需要一張照片，而這張照片必須橫跨整個國家回到 MIT 去取得，然後這張照片還得送到猶他州去處理。處理完成後，再送回華盛頓給 Pastel，並在當地的印表機列印出來。這真是個相當不錯的演示。登入 UCLA，回到 MIT，穿越猶他州，再回到華盛頓。

So John was practicing it that night. He logged on, sent the picture from Utah to his printer. But nothing printed. And it looked around the demo room, and it turns out that the robots that MIT had brought were jumping around the floor. The printer output had gone to the robots. We fixed that real fast. So you can see these demos were very, quite exciting. And it grew in a enormous amount of interest, not only on the part of the public, but we printable investigators, so what's capable now, we saw was out there. So that launched a significant increase in the use over the net. And that was a 20 point in the open adios. But again, there were locked into us there all the way, and could we demonstrate ease of use, functionality, and something useful. So would you talk through how the internet transition from a research tool to a commercial and social platform, eventually? That's a scary story. And one that's in everybody's face today. So let's talk about it.
那天晚上，約翰正在進行測試。他登入系統，從猶他州傳送圖片到他的印表機。但什麼都沒印出來。環顧展示會場後發現，原來麻省理工學院帶來的機器人正在地板上跳來跳去。印表機的輸出竟然傳到了機器人那裡。我們很快就修復了這個問題。你可以想見這些展示多麼令人興奮。這引發了極大的關注，不僅公眾感興趣，我們這些研究人員也意識到：「現在能做到這種程度了，我們見證了技術的可能性。」這促使網路使用量大幅增長。那是開放網絡發展的重要里程碑。但關鍵在於，我們始終堅持必須展示易用性、功能性與實用價值。那麼，您能否談談網際網路最終如何從研究工具轉型為商業與社交平台？這是個令人忐忑的故事，也是當今人人都切身經歷的議題。我們就來聊聊這個過程吧。

Okay. 1969, we bring up the network. We get a four node network by the end of '69 by middle of '70, where we have a 10 node network or spanning the country. And the network continued to grow, and beautifully, quickly, easily across the country with nice applications. And what was the driving force? The engineering aspect to it. Adding functionality capability, making it faster, better, easy to use. Pushing the boundaries of technology.
好的。1969 年，我們建立了網路。到了 1969 年底，我們已經擁有一個四節點的網路；到了 1970 年中，這個網路擴展到十個節點，橫跨整個國家。此後網路持續成長，在全國範圍內快速、順利地擴展，並發展出許多優秀的應用程式。那麼推動這一切的動力是什麼呢？就是工程技術層面的追求——不斷增加功能、提升效能、讓系統更快更好用，持續突破技術的邊界。

It was a research engineering project. And that was fine until 1988. In 1988, the first virus got released. The first broad-based virus got released by Robert Morris, a graduate student at Cornell. He released it, and he claimed his office made it. This virus went out, and the basically infected a large number of computers across the country. And we looked at that and we said, "Ouch, what's going on?" There was a, "Uh-oh, ah, it's just a hacker messing around."
這原本只是個研究性質的工程專案，這種狀態一直持續到 1988 年。那年，第一個廣泛傳播的病毒出現了——康乃爾大學研究生羅伯特·莫里斯釋出了這個病毒，他還聲稱是實驗室同事所為。這個病毒迅速蔓延，感染了全國大量電腦。當時我們看到這個情況都驚呼：「糟糕！發生什麼事了？」最初大家還以為「噢，不過就是駭客在惡作劇罷了」。

What a mistake. That action was a harbinger of the dark side of the network, which is about to emerge. But we ignored it. And what's interesting is, Robert Morris' father was an employee at the CIA at the time. And he announced, "At the time, it's a good thing my son will list this, because it's a warning." And we said, "What are you talking about?" He was right. So, from 1988 to 1994, the network continued to grow, and some very important things happened. Number one, Al Gore, Al Gore, recognized a report that I had shared for the National Research Council, talking about something called a National Research Network. It was of interest to him. He had me testified before one of his Senate subcommittees, which I described, what a nationwide research network would look like. He convinced the first George Bush to create what he then called the Information Superhighway, the Gigabit backbone. And that was thanks to Gore making it the high performance computing and communication act of 1991.
真是個錯誤。那個行動預示著網路黑暗面的來臨，而我們卻忽視了它。有趣的是，當時羅伯特·莫里斯的父親在中央情報局工作，他公開表示：「幸好我兒子這次會把這件事列出來，因為這是個警訊。」我們當時還反問：「你在說什麼啊？」結果他是對的。 從 1988 到 1994 年間，網路持續擴張，期間發生了幾件重要大事。首先，高爾參議員注意到我為國家研究委員會撰寫的報告，內容提到所謂的「國家研究網路」。這引起他的興趣，還邀請我在參議院小組委員會作證，讓我詳細說明全國性研究網路的樣貌。後來他說服老布希總統建立了「資訊高速公路」計畫，也就是千兆位元骨幹網路。這要歸功於高爾推動的《1991 年高性能計算與通信法案》。

So, the first event that occurred in this period between '88 and '94 was, we now have a backbone, a gigabit backbone with capability. Second thing, in early 1980s, NSF began to deploy supercomputers around the country. And by the tail end of 1988, 1980s, they wanted to connect the supercomputers together. And what better network than the orphaned at the connecting together, but the orphaned wasn't fast enough. So NSF increased the speed first to one and a half megabits per second, and then to 45 megabits per second, and created something called the NSF net. Now, when NSF came into the picture, the constituency of the players in the orphaned, and changed, instead of just computer scientists, we now have scientists, chemists, physicists, biologists, oceanographers, psychologists, a much broader community. Now, where does research chemists work? As an example, either at a university or a chemical research laboratory in a large chemical company. So picture that, you got this research laboratory in a large chemical company. On the opinette, and these guys are doing what? What are those chemists doing? They're using email. This email was the most seductive application at the time. Now, this email is being used inside this boundary of the research group in the company, but the rest of the chemical company comes in, "Oh, that email looks interesting. The staff, the management, the owners, they see this and they want it."
因此，在 1988 至 1994 年間發生的第一件大事是：我們現在擁有了一個具備傳輸能力的骨幹網路——千兆位元骨幹網。第二件事是，1980 年代初期，美國國家科學基金會(NSF)開始在全國各地部署超級電腦。到了 1980 年代末期的 1988 年，他們希望將這些超級電腦相互連接。而當時有什麼網路比這個被遺棄的網路更適合串聯呢？但問題是這個被遺棄的網路速度不夠快。於是 NSF 先將速度提升至每秒 1.5 兆位元，接著又提高到每秒 45 兆位元，並建立了所謂的「NSFNET」。 當 NSF 介入後，這個被遺棄網路的使用者組成也隨之改變——不再只有電腦科學家，現在還包括科學家、化學家、物理學家、生物學家、海洋學家、心理學家等更廣泛的群體。那麼研究化學家都在哪裡工作呢？舉例來說，他們可能在大學或大型化工企業的研究實驗室工作。想像一下：某家大型化工企業的研究實驗室接上了這個網路，這些化學家都在做什麼？他們在使用電子郵件。在當時，電子郵件是最具吸引力的應用程式。 現在，這封電子郵件僅在公司研究小組的範圍內使用，但化學公司的其他人看到了，他們說：「喔，那封郵件看起來很有趣。」員工、管理層、老闆們看到後都想要。

So suddenly, in the late '80s and early '90s, a demand for dot coms began to emerge. Now, we have a demand, we have a capability, we got a backbone network which can support this, but what's missing? What's missing is a simple user interface. Well, in the early '90s, what happens? The worldwide web appears. A simple to use graphical user interface, and suddenly all this combination comes in, and now it began to reach out to the consumer world, to the general world.
於是突然間，在 1980 年代末到 1990 年代初，對.com 網域的需求開始浮現。現在我們有需求、有技術能力，也有了能支援的骨幹網路，但還缺少什麼？缺少的是簡單的使用者介面。那麼，1990 年代初發生了什麼？全球資訊網出現了。一個簡單易用的圖形使用者介面突然問世，這一切組合起來，開始觸及消費者和普羅大眾的世界。

And now, so suddenly consumers are on it, companies are on it. And around this time, on April 12th of 1994, another critical event occurred. The first broad-based spam message was launched. It reached most of the people on the network. It was launched by two lawyers on April 12th, 1994. And what it was was a message going out, and by the time I got a copy of that message by the email message. And it said, "Was reaching out and said, "Look, there's a green card lottery coming up.
現在，消費者突然開始使用，企業也紛紛加入。大約在這個時期，1994 年 4 月 12 日，另一個關鍵事件發生了。第一封廣泛傳播的垃圾郵件被發送出去，幾乎觸及網路上的所有人。這封郵件是由兩位律師在 1994 年 4 月 12 日發出的。當我透過電子郵件收到這封訊息時，內容是：「我們想通知您，即將舉辦綠卡抽籤活動。」

We will help you get in the lottery, come to us, hire us, pay us, or let you get in." Those lawyers were advertising on our research network. And that's not allowed. We were a guest, we said, "Auggest." And it's time we said, "Uh-oh." So we sent the email back to those lawyers. We said, "You can't do this. How dare you shame on your season thisist." We sent so much email back to their server that we took down their server.
「我們會幫你抽中樂透，來找我們、雇用我們、付錢給我們，否則就讓你中獎。」那些律師在我們的研究網路上打廣告。這是不被允許的。我們是客座單位，我們說：「建議。」現在是時候該說：「糟糕。」所以我們回信給那些律師。我們說：「你們不能這樣做。你們竟敢在這種季節做這種可恥的事。」我們回傳了太多郵件到他們的伺服器，結果把他們的伺服器搞當機了。

So an unintended consequence of the first broad-based spam message was the first denial of service attack. But it was too late. It was too late. The commercial world saw that he was a way to reach the consumer public. They realized, this is not a research engineering network. This is a shopping mall. This is a social network. This is an entertainment channel. What wonderful capability to reach out and make this into a commercial success. So an answer to the question was around that time that the focus and the energy going into the development of the now called the internet, shifted from research engineering to commercialization. The network took a significant shift to the left. And now the energy was how to seduce the consumer to spend their money. And we've seen that development continuously now. And of course, you know, that drove the directions and the energy in a wrong direction. At the same time, since we enabled so many people to come on, we brought in the power of the internet, the power is that anybody with the computer. And aligned to the internet.
因此，第一封廣泛發送的垃圾郵件帶來了一個意想不到的後果——首次阻斷服務攻擊。但為時已晚。商業世界發現這是接觸消費大眾的途徑。他們意識到，這不再只是研究工程網路，而是購物中心、社交平台、娛樂頻道。多麼驚人的潛力，能將此轉化為商業成功。 大約在那個時期，發展重心明顯轉移。如今所謂的網際網路，其研發能量從工程研究轉向商業化應用。整個網路生態大幅向左傾斜，現在大家關注的是如何吸引消費者掏錢。這種趨勢至今仍持續發展。當然，這也導致發展方向與能量被誤導至錯誤的軌道。 與此同時，由於我們讓如此多人能輕易接入網路，我們釋放了網際網路的力量——這力量在於任何擁有電腦的人。只要連上網路。

No matter how poor or dirty or banana peels on the floor in an adurde environment, can reach out at almost no cost instantly to millions of people. And influence them, connect with them, etc. Now that's the power of the network and it's also a perfect formula for the dark side of the network. And so it began to emerge. We began to see these terrible things come onto the internet, you know, fraud, denial of service, fake information, etc. And around that time is it began to emerge? I said, "Oh my goodness, the internet is going towards juvenile teenage years. And it'll mature." Didn't happen.
無論環境多麼簡陋、骯髒或地板上有香蕉皮，幾乎都能零成本瞬間觸及數百萬人。影響他們、與他們建立連結等等。這就是網路的力量，同時也是網路陰暗面的完美配方。於是這些現象開始浮現。我們開始看到這些糟糕的事物出現在網路上，像是詐騙、阻斷服務攻擊、假訊息等等。大約就是在那個時期開始出現的？我當時說：「天啊，網路正進入青少年叛逆期。它會成熟的。」結果並沒有。

It didn't happen. The internet is now in some sense in a worse situation, because the social networks we began to dominate and became influencing what was going on in the information, the misinformation, the motors, the directions. And it's risen in some very nasty ways. And now it's even more serious. Now it's not only nuisance hackers and a nuisance. We've got nation states who put in boundaries around their internet. And when they do that, you lose the free access across the world. We've got organized crime.
事實並非如此。從某種意義上說，網路現在的處境反而更糟，因為社群媒體開始主導並影響資訊流向、錯誤訊息傳播、動機與方向。這些現象以某些非常惡劣的方式加劇。如今情況甚至更加嚴峻。現在不僅是那些令人困擾的駭客在搗亂。我們還面臨國家主權在網路邊界設限的問題。當他們這麼做時，你就失去了全球自由存取的能力。更有組織犯罪集團的威脅。

We've got people who are committing fraud, serious fraud, across the network and organized ways. And the social networks and the bubbles that are being created in the internet, as you know, are devastating. And it's very hard to control these things. And we don't have the mechanism in the internet at this point to try to prevent that. So an answer to the story is, we've taken a ride from the time that spam message came out in a direction which is really a really unsettling situation when I was the internet. What would you say are the most surprising or significant changes as the internet has evolved? We've been able to predict very effectively the infrastructure of the internet.
我們發現有人在網路上進行詐騙，嚴重的詐騙行為，而且是有組織性的。正如你所知，社交網絡和在網際網路上形成的同溫層效應正在造成毀滅性影響。這些現象非常難以控制。目前網際網路還沒有機制能夠預防這種情況。所以問題的答案是，從垃圾郵件出現的那一刻起，我們就走上了一條令人不安的道路，這在我參與網際網路發展時就已經是相當令人不安的狀況。你認為網際網路發展至今，最令人驚訝或最重要的變化是什麼？我們在預測網際網路的基礎設施方面做得相當成功。

Ice speed networks basically capability in the walls, smart spaces, wireless networks, devices, and high speed networks and connectivity which works. What we've not been able to predict well are the applications and the services. We didn't predict email coming. It came on a Sunday with a month dominated the traffic of the internet. We didn't see PDP networks. We didn't see user generated content like YouTube. We didn't see a blockchain coming in. We didn't see search engines.
基本上，我們成功預測了高速網絡的能力，包括智能空間、無線網絡、設備以及運作良好的高速網絡連接。但我們沒能準確預測的是應用程式和服務的發展。我們沒預料到電子郵件的出現——它在某個週日突然問世，並在一個月內就主導了網際網路的流量。我們沒預見到 PDP 網絡的興起，沒預見到像 YouTube 這樣的用戶生成內容平台，沒預見到區塊鏈技術的出現，也沒預見到搜索引擎的發展。

We didn't see shopping malls. We didn't see all of these things that are come about and the social networks. You know, to go back to social networks. I'm going to take us back a bit. I'm going to answer a question you didn't ask. And that is when was the concept, the vision of what we now have as the internet, first articulated. When did someone see this? And I can ask people listening to this to wonder, is anyone they can think of or any person?
我們當時沒預見到購物中心。我們沒預見到所有這些後來出現的事物，包括社交網絡。說到社交網絡，讓我帶大家回顧一下。我要回答一個你沒問的問題。那就是：我們現在所知的網際網路概念和願景，最初是在什麼時候被提出的？什麼時候有人預見了這一切？我可以請聽眾們思考一下，他們能想到任何人或特定人物嗎？

Well, I'm going to quote somebody and I'm going to ask you to think about when and who this could have been. The quote said essentially, it will be possible for business man in New York to reach out across the ocean using a device no larger than a watch. Instantly, I don't must know course to his colleague in London or elsewhere. And send easily any picture, drawing, text, speech immediately. Now, first of all, whoever that was, they're talking about what we now call the internet. So, who do you think that was?
我要引用某人的話，並請你思考這可能是什麼時候、由誰說的。這段話大意是：未來將有可能讓紐約的商人透過一個不超過手錶大小的裝置，瞬間跨越大洋聯繫他在倫敦或其他地方的同事，輕鬆即時傳送任何圖片、圖表、文字或語音。首先，無論說這話的是誰，他描述的就是我們現在所稱的網際網路。那麼，你認為這會是誰呢？

And when do you think it was said? Well, I know the answer because I've studied, that's why I don't want to spoil the surprise. Well, it is a surprise. It was Nicola Tesla and he said that 1908 more than a century ago. And he was talking about the telegraph network. He didn't talk about video because there was no video. But he had the concept. Along the way people like HG Wells, Vannevabush, Liclider, even myself articulated visions, much of which is come true, some of which hasn't. But my point is, the vision was there, but it had a weight for the technology to catch up before it could be implemented. And that happened in 1969 when communication technology was sufficiently enough and chip technology was fast enough to allow these rapid switching to take place. And as little anecdote, months before the improv did UCLA, UCLA put out a press release. And that press release was an interview with me, which I'm quoted as saying were to the effect that these computer networks once they're deployed will be always on, always available, everywhere, and anybody with any device can get on at any time.
那你覺得這段話是什麼時候說的？其實我知道答案，因為我做過研究，所以不想破壞驚喜。沒錯，這確實是個驚喜。說這話的是尼古拉·特斯拉，時間是 1908 年——超過一個世紀以前。當時他談論的是電報網絡，沒提到影像（因為那時還沒有影像技術），但他已經掌握了這個概念。後來像 H.G.威爾斯、萬尼瓦爾·布希、利克萊德，甚至我自己都提出過各種願景，其中大部分已實現，有些則尚未成真。但我想說的是：願景一直存在，只是需要等待技術趕上才能實現。這個轉折點出現在 1969 年——當時通訊技術已足夠成熟，晶片運算速度也快到能實現快速交換。順帶分享個小故事：在 UCLA 進行 IMP 實驗前幾個月，校方發布了篇新聞稿。那篇報導是對我的專訪，我當時說的話大意是：「這些電腦網絡一旦部署完成，將會隨時在線、隨處可用，任何人用任何裝置都能隨時接入。」

And it will be simple to use as electricity, and it will be as invisible as electricity. So in some sense, I was predicting web-based IP services, I was predicting simple access. I was predicting great technology, lots of views, but the one thing that I totally missed, totally missed was social networks. I was talking about computers talking to each other or people talking to the computers, but not people that people. That was something we totally missed.
它會像電力一樣簡單易用，也會像電力一樣無形存在。從某種意義上說，我當時預測到了基於網絡的 IP 服務，預測到了簡便的接入方式。我預見了偉大的技術和豐富的視野，但唯一完全沒料到的就是社交網絡。我談論的是電腦之間的對話，或是人與電腦的交流，卻完全忽略了人與人之間的互動。這點我們徹底失算了。

And one thing I predicted would just still not happen is the invisibility. You know, electricity is a fantastic service. It's two plugs in the wall. You plug in, you get electricity. You don't need a password, you don't need a sign on, you don't need all these complications. Plug in, you get it. The internet is not that easy. The password, you need keyboards, you need small things, you need permissions, you need Wi-Fi, etc.
而我預測至今仍未實現的一點就是這種無形性。要知道，電力服務堪稱完美——牆上兩個插座，插上就能用。不需要密碼、不用登錄、沒有任何繁瑣程序。插上即用。但網路還沒這麼簡單，需要密碼、鍵盤、各種小裝置，需要權限設定、Wi-Fi 連接等等。

Well, it's slowly becoming invisible as we have deployed technology in our walls, integrated circuit, etc. But it's happening, but my point is there will are a number of visions way back when and now we finally articulated it. What's fascinating about the history, they just shared, took about 60 years since Tesla made that description until the internet was effectively born. And here we are almost 60 years later from that point and it's still not invisible. So parts of the technology move fast and parts of a moving, incredibly slow.
隨著我們將技術融入牆壁、積體電路等，它正逐漸變得無形。但重點在於，過去有許多構想，如今我們終於將其具體實現。特斯拉提出那個描述後，歷經約 60 年網際網路才真正誕生，這段歷史令人著迷。而從那時算起又過了近 60 年，它仍未完全隱形。可見科技某些部分發展飛快，某些部分卻進展得異常緩慢。

Yes, and you as an interesting point. The internet before it took this bad turn had more than 20 years, 25 years to be curated, to be fined, to be improved, to be structured. Some of these new technologies come out are coming out and hitting us very quickly without a proper curation time. I'll give you one example, blockchain. Blockchain just came out and it came out with a dollar sign in its mouth, which basically has corrupted the idea of a distributed ledger and it's really broad applications. And there were other technology which are coming out very quickly without proper curation time. So if you had the power to rewrite history and you could go back and change one thing about how the internet developed, what would it be?
是的，你提出了一個有趣的觀點。在網路走向負面發展之前，它其實經歷了超過 20 年、甚至 25 年的精心培育、修正、改進與結構化過程。但現在有些新科技問世速度太快，缺乏適當的培育期就衝擊我們。舉個例子——區塊鏈技術。區塊鏈一出現就帶著金錢符號，這從根本上扭曲了分散式帳本技術的理念及其廣泛應用的可能性。還有其他技術也是未經適當培育期就快速湧現。那麼，如果你有能力改寫歷史，回到過去改變網路發展過程中的一件事，你會選擇改變什麼？

I would recognize the potential for abuse of the internet. And I would have put in early on some proper protection, some control. And two things I surely would have done and it was partially done but not followed up was first of all to have strong user authentication. To have some way to prove that the person that's communicating with you is who he or she claims to be. One way to prove that right now the man in the middle kind of thing is a serious problem. You can be talking to someone that could be impersonating some third party. So strong user authentication. And the second thing would have been strong file authentication. That the file I just delivered to you is the one I sent you and has not been corrupted. Some way to prove that.
我當時就該意識到網際網路可能被濫用的風險。我會在早期就建立適當的防護機制與管控措施。其中有兩件事我肯定會做——雖然後來部分實現了但沒有持續完善——首先是建立嚴密的用戶身份驗證機制。要有辦法證明正在與你通訊的人確實是他所聲稱的那位。目前中間人攻擊就是個嚴重問題，你可能正在與假冒第三方的對象對話。所以必須有強固的用戶驗證。第二件事則是建立嚴密的檔案驗證機制，確保我傳送給你的檔案就是原始檔案且未被篡改。必須有方法能證明這點。

Now we had some simple ways to do that early on. But we never followed up in some serious ways. Why not? Because the community we were dealing with was trustworthy. We were honest. We had a common goal in mind. We were sending false information or impersonating people. So had we installed those early protections the first thing we should have done was turn them off. And then as the need arose crank them up slowly. But that still would not have protected us against all of the abuses we have today.
早期我們確實有一些簡單的方法來實現這個目標。但我們從未認真地持續跟進。為什麼不呢？因為我們當時面對的社群是值得信賴的。我們都很誠實。我們懷抱著共同的目標。沒有人會發送虛假資訊或冒充他人。所以如果當初我們安裝了那些早期的防護措施，第一件該做的事其實是把它們關掉。然後隨著需求出現再慢慢加強。但即便如此，這些措施仍無法保護我們免於如今所見的所有濫用行為。

The really bad act is it's hard to control them. And I'm not sure what we could have done back then. To prevent evil. File minded people from abusing what we have today because criminals have always been with us in time and memorial. And it's the few bad act is that can cause the broad spread and very effective impact on the good systems of the world. So you just highlighted some of the downsides of the internet. But obviously it's had profound impact over the last several decades. Would you say overall you're satisfied with where the internet stands today or not? That is fired is a powerful word. I'm happy with the way it's evolved because it's given some enormous good.
真正的難題在於這些惡意行為難以控制。我不確定在當時我們能採取什麼措施來預防邪惡。那些心懷不軌的人總會濫用現有的技術，畢竟犯罪行為自古以來就與人類共存。正是少數的惡意行為，能對世界上良善的系統造成廣泛且極具破壞性的影響。您剛才提到了網際網路的一些負面影響。但顯然過去幾十年來它帶來了深遠的變革。總體而言，您對當今網際網路的現狀感到滿意嗎？「滿意」是個強烈的詞。我對它的演進感到欣慰，因為它帶來了巨大的益處。

I mean anybody can gain access to the world's education. We're giving people for free 4,000 years of knowledge easily accessible of great value to them at no cost. And we made a lot of people to interact in ways they never could before. Can reach out across the world from your basement. And reach out to the great minds and great colleagues and great companions. On the other hand, the dark side is there, including some of this stuff. So I'm really unhappy about that, but I'm thrilled at the wonders that the internet has provided. And it's changed society in many ways for the better in some ways for the worse.
我的意思是，任何人都能獲取全球的教育資源。我們免費提供人們四千年來極具價值的知識，讓他們輕鬆取得且無需付費。我們讓許多人能以從未有過的方式互動，從自家地下室就能與全世界連結，接觸到偉大的思想家、優秀的同僚與夥伴。但另一方面，黑暗面也確實存在，包括某些不良內容。這點讓我相當不悅，但網路帶來的奇蹟仍令我振奮。它在許多方面改變了社會，有些變得更好，有些則變得更糟。

And we are facing a serious challenge now, is how to correct and adjust some of the serious problems we basically opened up. Are there any lessons in particular that you feel future innovators should draw from the internet's history? Yes, to look forward to the potential dangers of the technologies we're unleashing. And lots of technology being unleashed right now. Look, there's quantum, this fusion, there's blockchain, this AI, and all these sort of new wonderful technologies coming out. And I'm not sure how much fourth thought it is, into where those could lead down bad pads that need to be corrected and anticipated now.
我們現在正面臨一個嚴峻的挑戰，就是如何修正和調整我們當初基本上放任發展所造成的一些嚴重問題。您認為未來的創新者特別應該從網際網路的歷史中汲取哪些教訓？是的，要預見我們正在釋放的技術可能帶來的危險。現在有大量技術正在被釋放。你看，有量子技術、核融合、區塊鏈、人工智慧，各種奇妙的新技術不斷湧現。我不確定人們究竟花了多少心思去思考這些技術可能導致的負面後果，而這些問題現在就需要被修正和預先防範。

Because many of these new technologies are not going through the 25 years of curation. They've exploded out there, people as an old, let's launch it, let's use it, oh how wonderful. But not how terrible it can become. What would you see as the future of the internet and where do we go from here? So the future of the internet is interesting because new magnificent technologies are now joining into it. And the most prominent is AI. AI is emerging very quickly. It started out back in the 1950s, like 50s, with the Marvin Minsk's and the John McCarthy's doing some ballak AI.
因為許多這些新技術並未經歷 25 年的淬煉。它們突然爆發，人們就像老樣子，推出它、使用它，噢，多麼美妙。但卻沒考慮到它可能變得多麼糟糕。您如何看待網際網路的未來？我們又該何去何從？網際網路的未來很有趣，因為現在有許多卓越的新技術正加入其中。最突出的就是人工智慧。AI 正迅速崛起，它始於 1950 年代，像是馬文·閔斯基和約翰·麥卡錫等人當時就在研究一些基礎 AI。

And now it's all about large language, models, and neural networks of great capability with great concerns because you don't know how they work. That's a whole other story. But I think that AI is going through it's early, very rapid growth stages. And people are concerned about the ethics and the dangers and the unleashing of what could be uncontrollable functionality. So people are thinking about it. But I also fear that it's being launched, far more quickly, by powerful forces of industry, that are not as concerned with the dangers of a more with the how to exploit it. Now in my, or you know, altruistic academic mind,
而現在一切都圍繞著大型語言模型和具備強大能力的神經網絡，這也引發了極大擔憂，因為你根本不知道它們是如何運作的。這完全是另一個故事了。但我認為人工智慧正處於早期、非常快速的成長階段。人們擔心其中的倫理問題、潛在危險，以及可能釋放出無法控制的功能。所以大家正在思考這些問題。但我也擔心，它正被產業界的強大力量以更快的速度推動發展，這些力量對潛在危險的關注程度，遠不及對如何利用它的興趣。以我這種，你知道的，充滿理想主義的學術思維來看，

I like to think that AI itself may be the solution to some of the problems that AI itself is creating. That's some of the dangers that's creating, that some of the dangers that the internet itself has created, like to watch over to curate what's going on there in the way the AI systems are being deployed and what they're given access to. AI is very powerful and it may be powerful enough to control itself. Now that's a wild dream, I don't have the solution, but I think it has the potential to be the answer to the problems that we as humans are having great difficulty with beat on the internet, beat AI, be some of these other applications. And that's just a pipe dream, but it's optimistic fuel. I wanted to ask you a few questions about AI, but first, if we, we kind of close the chapter on the internet, looking back. Do you feel that the discovery and development of the internet stands among the most transformational inventions in human history? I think it does. I think it was inevitable. It's certainly changed every aspect of humanity being social, technology, entertainment, education, just industry, commerce. It's enabling great strides and great capabilities and great efficiencies and great dangers. And the fact when you get something so powerful is exactly when you have to worry about the way it can be abused and misused. And therefore we need the great minds of today to add some sanity to the way in which these things are being deployed and to be honest with you. The tech industry is not the best example of people who are monitoring in the right way.
我傾向認為人工智慧本身或許能解決一些由 AI 所引發的問題。這些問題正是 AI 所帶來的風險，就像網路本身也曾製造出某些危險一樣，需要監管機制來審視 AI 系統的部署方式及其存取權限。AI 具有極強大的能力，或許強大到足以自我約束。當然這只是個天馬行空的想法，我並沒有具體解決方案，但我認為 AI 確實有潛力成為那些讓人類在網路上、在 AI 應用領域中備感棘手的難題之解方。雖然這目前只是個美好願景，但至少提供了樂觀的動力。 在我們深入探討 AI 之前，我想先請您回顧網路發展史。您是否認為網路的發明與發展堪稱人類史上最具變革性的創舉之一？我確實這麼認為。這項發明是歷史必然，它徹底改變了人類社會的每個面向——從社交模式、科技發展、娛樂型態、教育方式到產業結構與商業活動，無一不受其影響。 它帶來了巨大的進步、強大的能力、卓越的效率，同時也伴隨著巨大的危險。當你擁有如此強大的東西時，正是我們必須擔心它可能被濫用和誤用的時候。因此，我們需要當今最優秀的人才來為這些技術的部署方式增添一些理性。老實說，科技產業在正確監管方面並不是最好的榜樣。

They have a profit motive, they have to make money, they have to make the shareholders happy. And I worry, I'm feeling worried that the driving forces are not there to curate in the proper way. So let me ask you a few questions about the parallels and lessons from the internet as it relates to AI. How do you think the current wave of AI innovation compares to the internet's early days in terms of vision, risk taking and also collaboration? So in terms of genealogy, both have had a very long history. But the way AI has evolved, it's gone from a particular approach called symbolic AI where we were understanding how things work and building functionality that we could observe, control and predict. Once we got to the neural networks, networks which are great at doing facial recognition, image recognition and many other things, we don't understand how they perform. We're trying to provide some explainability for these networks, but it's not yet here. So look at the situation, we have these neural networks which are extremely capable. And they're optimized to do certain things, which brings me to the related point, let's talk about optimize systems. A which knew in that book so an example. Whenever you have an optimize system, you've got systems that perform very well in the domain for which they were designed. And I'm going to give you an example. Something we all understand, AIM radio versus FM radio. AIM radio is terrible all the time. And as you move from the base station, the further you move, the lauzeer gets. FM is terrific.
他們有營利動機，必須賺錢，必須讓股東開心。而我擔心的是，推動力量並未以正確方式進行把關。讓我問你幾個關於網路與 AI 之間相似之處與教訓的問題。你認為當前這波 AI 創新浪潮，在願景、冒險精神及合作方面，與網路早期發展相比如何？就發展脈絡而言，兩者都有很長的歷史。但 AI 的演進方式是從所謂的符號 AI 開始，那時我們試圖理解事物運作原理，建立我們能觀察、控制與預測的功能。當我們進入神經網路階段後，這些擅長人臉辨識、圖像識別等任務的網路，其運作原理我們卻無法理解。我們正試圖為這些網路提供可解釋性，但目前尚未實現。看看現狀：我們擁有這些能力極強的神經網路，它們被優化來執行特定任務——這讓我聯想到另一個相關重點，讓我們來談談優化系統。 一本書中提到的例子。每當你有一個優化系統時，就會有在設計領域表現非常出色的系統。我要舉個例子。我們都明白的東西，AM 廣播與 FM 廣播。AM 廣播一直都很糟糕。當你遠離基站時，距離越遠，聲音就越模糊。FM 廣播則非常出色。

And it starts out with wonderful reception. One, until you reach the boundary for which it was designed and then it collapses badly. Now, if you know where the boundary is, you'll know where it's good. But if we have a system which is to perform well and do important things. And you don't know what the boundary of its functionality is. You're in serious danger. And I'll give you a particular example from the past in AI. Many years ago, there was a program written in AI program written to play checkers with computer play checkers with you.
它一開始接收效果很好。直到你到達設計的邊界，然後就會急劇惡化。現在，如果你知道邊界在哪裡，你就知道它在哪裡表現良好。但如果我們有一個系統要表現良好並做重要的事情。而你不知道它的功能邊界在哪裡。你就處於嚴重的危險之中。我會給你一個過去在人工智慧領域的具體例子。許多年前，有一個人工智慧程式被寫來玩跳棋，讓電腦和你玩跳棋。

And it was using road learning and the developer of that program would play games with it. And one day the developer changed the algebraic sign of the object function from plus to minus by accident. The machine was now programmed to lose. Not to win. And when he started playing with it, the scary part is he couldn't tell it was trying to lose. Because in order to lose well, you've got to get control of the board and men make the big sacrifice. And he didn't know it. Okay, now let's raise forward to today.
它當時正在使用路徑學習，而該程式的開發者會與它玩遊戲。有一天開發者不小心把目標函數的代數符號從正號改成了負號。這台機器現在被設定成要輸，而不是贏。當他開始與它對弈時，可怕的是他無法分辨它正在試圖輸掉比賽。因為要輸得漂亮，你必須掌控棋盤，並做出重大犧牲。而他並不知道這一點。好，現在讓我們快轉到今天。

Let's take a known network program whose job it is to increase and improve the economy of the United States. And it's optimized to do that. And it's happily doing it. If it reaches the boundary of which you could do it well, it's going to collapse. Or if it suddenly decides or someone makes a mistake and says, "Now you want to ruin the economy now, it's dates." You won't know it until perhaps it collapses. So optimize systems, which we don't understand how they work. And we may not know what the boundaries of their limits are, are extremely dangerous. And my sense that's what new networks are today. They're very capable. They have a range of a witch and a domain of which they work well. And we don't know how they work. And we're trying to fix that and try to understand them. And by the way, they're great for many things. They can detect the way the ocean waves of flight of inexplainable is going on in the ocean surface in a way that humans never could. But there's a fear there. And until we get to understand these things a little better, I worry about deploying them willy-nilly in many domains of importance. One of the most fascinating aspects of where you described in terms of the early days of the internet is the environment that was created where you had governments come in with funding and giving you the re-wrained to shoot for the moon and be innovative. Do you feel like AI research today at the environment for it? Fossures the same kind of bold foundational work that enabled the internet to thrive? It's enabling open-ended research and serious and powerful funding. Guidance, I'm not sure how much guidance there is, but the thing that worked for the internet was no control. Let them find every use of great interest and value. Whether the AI research community will continue to find valuable, powerful and beneficial functionality is an open question.
讓我們以一個已知的網路程式為例，它的任務是提升並改善美國經濟。這個程式被優化來達成此目標，而且運作得很順利。如果它達到能良好運作的邊界，就會崩潰。或者如果它突然決定——又或者有人犯錯並下指令說：「現在你要摧毀經濟，這是日期。」你可能要等到它崩潰才會發現。所以優化那些我們不了解運作原理的系統，加上我們可能不知道它們的極限邊界在哪，是極度危險的。我認為這就是當今新型網路的現狀。它們能力很強，在特定範圍和領域內表現優異，但我們卻不清楚它們如何運作。我們正試圖解決這個問題並理解它們。順帶一提，它們在許多方面都很出色，例如能以人類永遠無法做到的方式，偵測海洋表面那些難以解釋的波浪飛行現象。但這其中存在隱憂。在我們對這些事物有更深入理解之前，我擔心貿然將它們部署在許多重要領域會帶來風險。 你在描述網際網路早期時最引人入勝的部分，就是當時創造出的環境——政府投入資金並給予你們放手創新的自由，讓你們能大膽追夢。你覺得現今 AI 研究的環境是否也具備同樣條件？是否促成了像當年推動網際網路蓬勃發展的那種大膽基礎研究？它確實支持開放式研究並提供可觀資金。至於指導方針...我不確定現有多少指導原則，但網際網路成功的關鍵正是「不加控制」。讓人們自行發掘最具價值與意義的應用方式。而 AI 研究社群能否持續找出有價值、強大且有益的功能，仍是未定之天。

But the capability is a lot of money going into it, a lot of independent research is going in different directions. So it's got the way with all and similar in some sense to what the internet did. But the guidance was not there for the internet and I don't think it's there for AI. Are there lessons from the internet's commercialization phase that are most relevant to AI's current trajectory? Yes, the lesson that we learned badly in the internet was we didn't anticipate the dark side. Now I think it was smart enough to anticipate the dark side of AI and try to protect against it. So the similarity is there and I think we have the advantage of knowing where we're heading. But it's an uncertain road.
但這項技術需要投入大量資金，許多獨立研究正朝不同方向發展。在某種程度上，這與當年網際網路的發展軌跡相似。然而網際網路當初缺乏明確指引，我認為人工智慧目前也面臨相同困境。從網際網路商業化階段中，有哪些經驗教訓最適用於人工智慧當前的發展路徑？有的，我們從網際網路學到的慘痛教訓就是未能預見其陰暗面。如今我認為我們已足夠明智，能夠預見人工智慧的潛在風險並嘗試防範。兩者確有相似之處，但我們現在的優勢在於更清楚發展方向。不過這仍是條充滿不確定性的道路。

I mean, this is a frontier. And do we have only the neveline players? No. We have again rogue organizations and countries that have mischief in mind. And it is danger. I mean, it's not unlikely to proliferation. How do you control that kind of a thing? And the consequences of not controlling a severe. And we've been very bad at both. How can the AI community avoid repeating some of this similar mistakes such as short-term thinking or loss of public trust? Well,
我的意思是，這是一片未知疆域。參與者只有正派企業嗎？不。我們還得面對心懷不軌的流氓組織與國家。這確實存在危險性，技術擴散的可能性很高。該如何管控這種情況？失控的後果將十分嚴重。而我們過去在這兩方面的表現都很糟糕。人工智慧社群該如何避免重蹈覆轍，例如短視近利或失去公眾信任？這個嘛，

I think we need to get the various stakeholders to engage in proper discussions. And in terms of the evolving internet, I can think of at least four stakeholders. The first set of stakeholders are the scientists who create the technology. Second set of stakeholders are the commercial folks who implement it in Deployed. The third stakeholders are the government. And the four set of stakeholders are you and me, the user community. Now the scientists are trying not only to develop new technology, but trying to find protective mechanisms against the dark side. It's up to us to try to find, you know, home, morphic encryption, secrecy, etc. Which will protect us against impaired things and we're moving that direction. But the technology world, these commercial world, they've got to cooperate and they've got to provide the ability for people to express their concerns about what's going on. Now I'll jump ahead. What's the role of the government? The role of the government mainly is to provide a forum whereby the stakeholders can discuss these issues. And maybe provide some oversight, some large level oversight that industry cannot do on its own. But the group that's doing the least are the user community. The user community is not complaining.
我認為我們需要讓各方利害關係人進行適當的討論。就以不斷演進的網際網路來說，我至少能想到四個利害關係群體。第一群是創造技術的科學家，第二群是實際部署應用的商業人士，第三是政府單位，第四群則是你我這樣的終端使用者群體。 目前科學家們不僅致力開發新技術，更試圖建立防範黑暗面的保護機制。我們必須共同探索同態加密、隱私保護等解決方案，這些技術能抵禦有害事物，而我們正朝此方向前進。至於科技產業與商業領域，他們必須配合並提供管道，讓人們能對現況表達憂慮。 現在讓我超前談談政府的角色。政府主要職責在於提供平台讓利害關係人討論這些議題，或許還需進行某種產業自身無法達成的高層級監管。 但表現最消極的群體是用戶社群。用戶社群並沒有提出抗議。

What's speaking up about the abuses to which they're subject. You know, your privacy is being invaded. Your assets are being stolen. You're being asked to sign legal documents you don't understand. And only lately, we begin to see industry say, here's, here's the privacy policy. I'm going to apply to you. But they describe it in a way you can understand. There should be a way to describe the privacy policy that you'll be in subject to. In some graphical way, this is what it looks like.
他們對於自身遭受的侵害保持沉默。要知道，你的隱私正被侵犯、資產遭竊取，還被迫簽署根本看不懂的法律文件。直到最近，我們才開始看到業界表示：「這是我們將對你實施的隱私政策。」但他們至少用你能理解的方式說明。應該要有某種方式能讓你明白自己將受到怎樣的隱私政策約束，比如用圖像化呈現政策樣貌。

You got so much of this, little bit of this, less of this. And you should have a picture of what you were willing to do. And if what they're offering doesn't match what you want, you say, "By buy, or you say modify, or negotiate." That kind of interaction among the stakeholders has to take place. And I don't see yet a rich environment whereby that discussion is taking place among all these various stakeholders. So in terms of that kind of advice taking place not onto the internet, but for the people moving into these new technologies like AI, like blockchain, like fusion, like quantum, these to take place across the board.
你已經掌握了這麼多，還有一點這個，少一點那個。你應該對自己願意做的事情有個概念。如果他們提供的條件不符合你的期望，你可以說「再見」，或者要求調整，或者進行談判。這種利益相關者之間的互動必須發生。但我還沒看到一個豐富的環境能讓所有這些不同的利益相關者進行這樣的討論。所以，就這種建議的採納而言，不僅僅是在網路上，對於那些進入人工智慧、區塊鏈、核融合、量子計算等新技術領域的人來說，這些互動需要全面展開。

We have to bring in all the players and have all of them express what they're thinking of, what they're afraid of, what solutions they can offer. Now that's, you know, pie in the sky, but I think that's the mindset we need going forward instead of individual thrusts in individual directions for individual gains. That's going to lead us down a very dangerous path. You've described your vision for the internet as invisible to users like electricity. An example, did you brought up to you see AI becoming as seamlessly embedded in daily life? Yes, I do, and it's part of the internet being invisible. I like to imagine, as I did in that vision I had back in July of 1969.
我們必須讓所有參與者加入，讓他們表達自己的想法、擔憂以及能提供的解決方案。這聽起來或許是天方夜譚，但我認為這才是我們未來應有的思維模式，而非各自為政、只追求個人利益。那種做法只會將我們引向危險的境地。 您曾將對網際網路的願景描述為「像電力般對使用者隱形」。舉例來說，您是否認為人工智慧也將如此無縫融入日常生活？是的，我確實這麼認為，這正是網際網路隱形化的一部分。就像我在 1969 年 7 月提出的那個願景一樣，我喜歡這樣想像。

I should be able to walk into a room. And the room should know I've walked into it. And I should be able to interact with the room the way you and I are talking now, namely with speech, with gestures, with facial expressions, with haptics, with the way you and I interact without having a keyboard or a technology or one of these damn things, trying to keep words. And it should know what my privileges, my profiles, my preferences are. So what can enable can provide them to me if I walked up to a physical device, it should be enabled with those capabilities that I want. And it should anticipate what I want, be able to be an agent for me,
我應該能夠走進一個房間。而這個房間應該知道我已經走進去了。我應該能夠像現在你我交談一樣與房間互動，也就是透過語音、手勢、面部表情、觸覺，就像你我互動時不需要鍵盤或科技產品那樣，不用拿著這些該死的東西試圖輸入文字。它應該知道我的權限、個人資料和偏好設定。所以當我走近一個實體設備時，它應該能根據我的需求提供相應功能。它還應該能預測我的需求，成為我的代理人，

make suggestions, and interact with where humans do. Using all of the internet capability, but the AI capabilities as well. I want an agent with whom I can interact, which is very much like interacting with you. And maybe it is you with capability and enhancing you in your interaction with me. I'd like to say, what we're moving to is a global interface intelligent interface surrounding us. But it's in the ether, it's in the environment, it's in our tables, our cars, our walls, our fingertips, our bodies, and the environment in which we engage.
提出建議，並在人類活動的場所與人互動。運用所有網路功能，同時結合人工智慧技術。我想要一個能與我互動的代理人，就像和你互動一樣自然。或許它就是具備能力的你，在與我的互動中強化你的角色。我想說，我們正在邁向一個環繞著我們的全球智慧介面。但它存在於以太中，存在於環境裡，存在於我們的桌子、汽車、牆壁、指尖、身體，以及我們所處的環境之中。

And sometimes technology can advance faster than society can accept that advancement, so you kind of need both to line up. You do and typically they don't. To me it feels like technology that the pace of advancement is so fast. I just generally feel like it's probably ahead of where society on average is comfortable. Do you agree with that? I do. And you can create a great new widget and deploy it and then forget what it can do and be surprised what it can do with that anticipating those uses. And some of the uses is not what you want. But you right, technology is moving very quickly.
有時候科技的進步速度會快過社會能夠接受的程度，所以兩者需要相互配合。雖然理論上應該如此，但實際上往往並非如此。對我來說，科技發展的步調實在太快了。我總覺得科技可能已經超前於社會大眾普遍感到舒適的程度。你同意這個觀點嗎？我同意。你可以創造出一個很棒的新產品並部署它，然後忘記它能做什麼，最後對它的功能感到驚訝——因為你根本沒預料到那些用途。而有些用途可能並不是你想要的。但你說得對，科技確實發展得非常快速。

But that's been the nature of a human civilization from time and memorial. You discover I and things change. You discover the wheel, things change. You discover the internet, things change. Are there unique challenges or opportunities that AI presents compared to the internet? Yes, as I said, one of the scary things about AI is we don't know how the latest versions work. And you know, will they escape toward the main that we can't even control? Will they start running things? Will be on their ability to control them.
但這正是人類文明自古以來的本質。你發現火，世界就改變了；你發明輪子，世界就改變了；你創造網際網路，世界又改變了。相較於網際網路，人工智慧是否帶來獨特的挑戰或機會？是的，就像我說的，人工智慧令人擔憂的一點是，我們根本不知道最新版本是如何運作的。你知道嗎？它們會不會脫離掌控，甚至開始主導一切？這取決於我們控制它們的能力。

The internet typically did not do that. It was people interacting with the internet which made its functionality and its applications. AI can go beyond that. The idea of robot is also in the picture there. How do they behave? What kind of morality and ethics do you instill in them? Who decides what they are? It's a little worry service that how we're going to manage to. I hate to use the word control. But help direct the way those things move into the future.
網際網路通常不會那樣做。是人們與網際網路的互動造就了它的功能和應用。人工智慧可以超越這一點。機器人的概念也在其中扮演角色。它們會如何表現？我們該賦予它們什麼樣的道德與倫理？由誰來決定它們的本質？這確實是個令人憂心的問題——我們該如何管理（我不喜歡用「控制」這個詞），但確實需要引導這些技術未來的發展方向。

It's a challenging time. For sure. What do you see the greatest opportunities for AI to compliment or even extend the internet's legacy? The ability to understand information and events to discover new mathematics. Discover new principles that may be we as humans have been having trouble with or unable to. To put together ideas. We need to get the information to create new recognition is one of the models of human civilization. AI is ability to do that quicker the weekend more effective than we can. But hopefully in ways that are benevolent. But the ability for a new network or a large language model to have all the knowledge as out of the world.
這確實是個充滿挑戰的時代。人工智慧在哪些方面最能延續甚至擴展網際網路的遺產？我認為是理解資訊與事件的能力，進而發現新的數學原理，或是破解人類長期無法解決的難題。整合各種概念、獲取資訊以產生新的認知，這本就是人類文明的運作模式。人工智慧能以更快速、更有效率的方式完成這些工作——但希望是以良性的方式。想想看，一個新型態的網路或大型語言模型能掌握全世界所有的知識...

All the knowledge is out there and understandable and accessible and composable. Something that we as humans don't have. And by the way that brings me to another point which so much goes backwards in time is the following. I think that computers are the worst enemy of critical thinking. And I say that because we delegate too much to the computer we don't put into our head things that we should know. For example how many people really know what Archimedes principle is or Maxwell's equations or understand physics or understand how things work. And they just relegated even do arithmetic. They relegated to a computer. And if it's not up here that means you can't think with it. You can ask a computer to think with it.
所有的知識都在那裡，易於理解、取得且可組合。這是我們人類所不具備的。順帶一提，這讓我想到了另一點，也就是以下這個不斷回溯的現象。我認為電腦是批判性思考的最大敵人。我這麼說是因為我們過度依賴電腦，沒有把應該知道的東西記在腦海裡。舉例來說，有多少人真正知道阿基米德原理是什麼，或是馬克士威方程組，或是理解物理學，或是明白事物運作的原理。他們甚至把算術都交給電腦處理。如果知識不在你的腦海裡，就意味著你無法用它來思考。你可以要求電腦幫你思考，但你自己卻做不到。

But when you take a shower or drive a car or fall asleep. You want ideas percolating your head to generate new ideas and if it's not there, if you relegated to the computer. You've lost. So the computer is great for remembering and handling things that it does well. But you can't take it all out of your head. And I find that's the case very often with someone my graduate student. So I'd now. I'll give them a mathematical model to work out. They'll come back a few weeks later and show me why versus X. They've simulated the performance of some system I've asked them to.
但當你洗澡、開車或入睡時，你會希望腦中醞釀的想法能激發新點子。如果把這些都交給電腦處理，你就輸了。電腦擅長記憶和處理它拿手的事，但你不能把所有思考都外包出去。我發現這種情況在我的研究生身上很常見。現在我會給他們一個數學模型去解，幾週後他們回來展示 Y 與 X 的關係圖，模擬了我要求的某個系統表現。

And I'll look at the performance I'll say, oh that looks interesting. That looks like it's straight line. What does this slope with that line mean? It terms you model. No idea. Why is the asymptote that value? Don't know. It terms with the physical model. And then I'll ask them another question. I say, what if I doubled some parameter?
我會看著數據說：「這結果挺有意思，看起來是條直線。這個斜率在模型裡代表什麼？」他們答不上來。「為什麼漸近線是這個數值？」也不知道。這與實體模型有何關聯？接著我會再問：「如果我把某個參數加倍會怎樣？」

What will that curve look like? And their answer is, I'll simulate it again and figure it out. I say, no, no, you need a model to be able to explain to me what it will be. And once they come up with the result, say they do get a great result. They don't ask questions like, what is that result telling me? Is there a principle that's trying to expose to me? Can I use it somewhere else? It's the, it's the why that I asked before that the physical is asked. What is there to learn out of it?
那條曲線會是什麼樣子？他們的回答是，我再模擬一次就能知道了。我說，不不，你需要一個模型來向我解釋它會是什麼。一旦他們得出結果，假設真的得到一個很棒的結果。他們不會問這樣的問題：這個結果告訴我什麼？是否有某個原理試圖向我揭示？我能在其他地方運用它嗎？這就是我之前問的「為什麼」，物理學家會問的問題。從中能學到什麼？

They don't want to ask that question. It's move on. And so the down level of understanding largely because they've got the computer around to address some of these issues. You can rely on that crunch and overly rely on it. Yes. Yes. And then we don't make great minds. That's right. And so is your sense that we're entering a new golden age of innovation today? Yes, and no. If the innovation comes out of these, these computers and these AI systems, where's the human?
他們不想問這個問題。就這樣繼續下去。理解層次的下降，很大程度上是因為他們有電腦可以處理這些問題。你可以依賴這種運算，但過度依賴它。沒錯。沒錯。然後我們就培養不出偉大的思想家。正是如此。那麼你的感覺是，我們今天正進入一個創新的新黃金時代嗎？是，也不是。如果創新來自這些電腦和人工智慧系統，那人類在哪裡？

If the human goes along and collaborates in this process, then we have a winning situation, I think. But if you relegate and just let it run on its own, you'll give it up the human element and you'll give it up a lot of what could be great, great innovation. And moving to a world where you don't know what the heck is going on. And that's a scary scenario. I think of it as humans are good at certain things. Computers are good at certain things. And two together could be very powerful.
如果人類能夠參與並協作這個過程，我認為我們就能創造雙贏的局面。但如果你完全放手讓它自行運作，就會喪失人性元素，也會錯失許多可能產生的偉大創新。最終進入一個你根本搞不清楚狀況的世界，這是很可怕的場景。我認為人類擅長某些事，電腦擅長某些事，兩者結合將能發揮強大力量。

But if you as a human just rely on the computer for all the things that it does and you give up the things that you're good at, you can easily be replaced by computer. You're exactly right. In fact, go back to look light at the man computer symbiosis. Anglebot said the same thing. He had this augmentation system put humans and computer systems together. And they augment each other. And that augmentation system is extremely powerful. And we don't want to give that up. And this is a danger that we do if we just relegate it all to the artificial world.
但如果人類只是完全依賴電腦處理所有事務，放棄自己擅長的領域，就很容易被電腦取代。你說得完全正確。事實上，回顧「人機共生」概念時，恩格爾巴特也提出相同觀點。他設計的增強系統將人類與電腦系統結合，彼此互相強化。這種增強系統極具威力，我們不該放棄它。如果我們把所有事務都交給人工智慧處理，就會面臨這種危險。

Well said. Well, Len, you've spent a lot of time with us. You've shared about a century of internet vision and history, development, evolution and and provided some glimpse into the future with AI and future technologies. I appreciate all the insights you shared with us. It was fascinating for me and and I hope for our listeners as well. I'm sorry to thank you. Thanks for listening. We hope you enjoyed this episode. Please visit our website at insightfulinvestor.org to access past shows and learn more about our podcast.
說得好。Len，你花了這麼多時間與我們分享，談論了將近一個世紀的網路願景、歷史、發展與演進，還讓我們一窺 AI 與未來科技的樣貌。非常感謝你帶來的所有洞見，對我來說真是精彩絕倫，相信聽眾們也同樣收穫良多。真的很感謝你。謝謝大家的收聽，希望你們喜歡這一集節目。歡迎造訪我們的網站 insightfulinvestor.org，收聽過往節目並了解更多關於我們播客的資訊。