【國際關係深度評：經典復刻】 第四次工業革命：人工智能要多久，才會顛覆全球?
Added 2025-02-17 07:01:01 +0000 UTC當人工智能發展一日千里，全球就業市場亦備受挑戰，媒體不定期列出在未來十年可能因人工智能而消失的工種；這些新聞、清單，並非危言聳聽。正如筆者不斷強調，與過往幾次工業轉型不同，這次受人工智能影響的不只是低技術工種，一般白領階層、專業人士，很可能才是重災區。但究竟具體影響會於何時出現？
人工智能與自動化的應用，先受到影響的，大概是在坐在流水線上的工人。現在發達國家的工廠除了極精密程序外，基本上已由機械人操作；不少連鎖快餐店也已經做到從點餐到付款，都不需職員處理。過去一年，在中美兩國成為熱話的無人商店，其實早在我們小時候看的《日本風情畫》已經出現，只是現時的商店增加了電腦分析商品銷路、顧客購買習慣的功能，「人」的價值，就進一步降低。
在以上例子，受影響的是藍領工人，但隨着人工智能發展，白領、行政人員、專業人士的工作，也可能消失。例如美聯社已經使用電腦軟件，編寫財經、體育新聞；即使是律師，《紐約時報》也曾專題報道過一些如搜集相類官司的工作，已經由電腦負責；剛有報道更說頂級律師要92分鐘完成的協議，人工智能只需9秒。
正因為人工智能、自動化對不同階層都影響深遠，不少民調機構、智庫、金融機構都希望研判具體影響的時間表和路線圖。據美國皮尤研究中心（Pew Research Center）調查發現，近2/3美國人相信，機械人或電腦將在未來50年，可做到人類的大部份工作。花旗集團與牛津大學學者組成的研究團隊指出，在英國，將有35％工作受自動化影響，美國則是47％。
值得注意的是，這個數字在發展中國家更高，例如中國是77％，印度是69％。國際勞工組織（International Labour Organization）的研究也得出類似結果，在柬埔寨、印尼、菲律賓等國，將會共有1.37億人的工作，將因機械人的發展而受到威脅。總之，即使不少人憧憬新科技帶來新的就業機會，但這些職位空缺，恐怕比他們「毀滅」的少得多。
假如這些推論正確，人工智能、自動化的發展，並不容易帶來美好新世界，甚至可能製造更嚴重的不平等。據經濟歷史學者艾倫（Robert Allen）研究，工業革命開始時，技術的改變雖然使國家的利潤率與資本增加，但工人的平均薪酬並未隨之提升，直至19世紀中葉出現連番抗爭，才有所改善。
「第四波工業化」的情況，只會更甚，科技公司如美國的微軟、Google與Facebook，中國的BAT(百度、阿里巴巴與騰訊)等，將成為這一波浪潮的最大受益人，除了得到更多投資，政府亦會推出有利他們的政策優惠，以求通過高科技穩定管治效益。
資訊科技界的員工自然得到更多工作機會，他們近年薪酬不斷上升，已可見市場對這些職位的需求。但除了他們，感到興奮的大概就只有「網絡情緒輔導員」一類工種，其他所有人，都會面對嚴峻挑戰。
（待續）
*改變自沈旭暉《信報財經新聞》文章
▶️ 蕭少滔：Elon Musk 掌管「政府效率部」：區塊鏈投票、全民基本收入、天翻地覆的改變，瞬間到來
https://www.patreon.com/posts/115976258
Comments
只想得到資本回報…
Carl Yang
2025-02-22 14:49:26 +0000 UTC最上游的會壟斷，其他的韭菜，未來世界的 polarization 可能更誇張
堅離地書院 College
2025-02-21 00:42:07 +0000 UTCYes unfortunately polarization is the trend. A few extremely well-off elites, Vs "the others", in the coming new world.
堅離地書院 College
2025-02-19 18:40:06 +0000 UTC佢係刻意想傳遞呢類訊息
堅離地書院 College
2025-02-19 18:39:19 +0000 UTCAI做唔做到1件事, 行業有無引入另一回事, 華人社會係無創新風氣, 又睇重監管, 監管方無進步, 整體都無人會做
Good Year
2025-02-18 01:50:56 +0000 UTC資訊科技界的員工都唔一定有前途, 因為AI 搞掂哂, coding 第1個受影響最慘, 因為AI 集translation ( 人係唔洗同AI講專業用語 佢可以自己理解, 配對) 寫code, debug 於一身, 仲更快理解D客要咩只會更死。AI暫時都無創意, 人類就只有創意呢個最後一關。
Good Year
2025-02-18 01:43:07 +0000 UTC「要多久，才會顛覆全球」 — we can only talk about the things we know: 1/ most guesses about technology diffusion are wrong, and sometimes wrong by orders of magnitude 2/ constraints of getting there — e.g. how many mega data centres do we need, and where are they? How much electric power do we need, and how many nuclear power plants can we build in the US? How long would it take to rebuild a half century old electricity grid? How many chips do we need, and where do we make them? 3/ Algorithms, narrow AI, AGI, agents on one hand; automation, robots, humanoids on the other, all have different constraints. Who makes them and where, and how many? 4/ Learning from human experience from the internet is one thing, how do they create new knowledge, collect data, research and experiment in the real world? 5/ There will be economic cycles. How much capital is required and who pays for all this? What are people willing to pay for — just to do something, do it cheaper or do it better? 6/ Humans are general purpose. Job specifications generally has more than one item. A lawyer offers legal advice, is also a sales person and negotiator. When will we reach AGI? 7/ Who will help deploy them in specific industries and globally? Suppose a humanoid does surgery in an emergency room, who trains it? who wants to be operated on by a humanoid? who is responsible when it goes wrong? is it more likely a human doctor will be assisted by a machine or replaced by a machine? 8/ Who will be managing events and projects? Who will deploy the vaccines for the next Covid, who will put out the next fire in LA, who will build the next building, who will experiment, test, build and fix the next plane? 9/ Institutional, physical infrastructures are built around humans. Humans will resist some of these changes. Who will be organising and managing humans, and how long would it take for these infrastructures to evolve? 10/ Who is going to serve the Digital Overlord, supposing there is one? I know if I try to answer these questions I will be wrong. Coming back to the timeline question, we do know: 1/ software advances has been accelerating exponentially enabled by breakthrough in compute (GPUs), but there will be constraints building them 2/ some technology adoption like smartphone happens very fast, while energy transitions (charcoal to coal to fossil fuel) can take decades or a century. AI is much broader in scope than either of them 3/ the more dependencies there are, the longer it takes. There are fewer dependencies in things like a general chatbot, more in others applications 4/ At this point, it looks like it will be more of an assist than a replacement for most scenarios, which isn’t offering enough value to justify the current cost for wider adoption. Despite the exponential technical advancements, the deployment so far looks narrow and seems more like an evolution more than a revolution. But that could change any time. From the above, it seems like if you are good at what you are doing, no matter what field you are in, we and our kids have a good chance of surviving, insofar as this continues to look like an evolution and a human assist in the foreseeable future. The divergence between who is good at something and who is not will increase. Life long learning is the key. Technologists have unlimited imagination and tend to get lost in talking about what AI can potentially do. Playing Go on a chessboard is neat, the rules are so well defined, but the real world is messy. Elon prophesied about FSD in 2 years in 2015, which is narrow AI, we are still nowhere close at all. The real world has constraints, just tabling a few of them here.
lyk
2025-02-18 00:30:56 +0000 UTC人要點生存
zzz1eep
2025-02-17 15:27:41 +0000 UTCElon Musk個DOGE都係搵後生仔用AI查政府條數，會計佬都係咁先啦 。
Sunny Wong
2025-02-17 10:43:43 +0000 UTC
