4月29日， 諾貝爾經濟學獎獲得者 Michae l Spence （邁克爾·斯賓塞 ） 出席了"2024中關村論壇——金融科技平行論壇"，并通過視頻錄像發表了主題爲"人工智能對世界經濟的變革性影響"的演講。
斯賓塞教授提出，生成式人工智能是人工智能發展的重大階段，能夠與人類進行更自然的交流，是一種超級通用技術。人類可以通過人工智能的運用來提高生産力，并提升現有勞動力的生産服務水平。他也強調了加強人工智能相關的公共政策和監管措施的制定的重要性，倡導保護版權，推進人工智能在國家内部和全球經濟中的可獲得性和擴散。
公開資料顯示，邁克爾·斯賓塞，1943年11月7日生于美國的新澤西州，1972年獲美國哈佛大學博士頭銜，現兼任美國哈佛和斯坦福（Stanford）兩所大學的教授、青島大學名譽教授，美國斯坦福大學商學院研究生院前任院長和現任名譽院長。
邁克爾·斯賓塞最重要的研究成果是市場中具有信息優勢的個體爲了避免與逆向選擇相關的一些問題發生，如何能夠将其信息"信号"可信地傳遞給在信息上具有劣勢的個體。2001年，與喬治·阿克爾洛夫、約瑟夫·斯蒂格利茨共同獲得諾貝爾經濟學獎。2017年，林重庚、邁克爾·斯賓塞等的《中國經濟中長期發展和轉型：國際視角的思考與建議》榮獲第17屆孫冶方經濟科學獎。
圖片來源：中關村互聯網金融研究院
以下爲演講翻譯，經钛媒體APP整理：
各位同事和朋友，大家好，我是邁克爾·斯賓塞，很感謝中關村論壇的組織者給我這個機會作爲今年豐富節目的一部分，與大家交談。
我今天的主題是人工智能革命的變革性經濟影響，我們正處于這一革命的早期階段。GenAI在全球範圍内産生了巨大的興趣和同樣大量的投資。我認爲可能存在過度樂觀的成分，尤其是關于大規模經濟、社會、金融體系等影響的到來時間。但我的視角稍長一些，我認爲沒有人知道這一進程将以多快的速度發生。但我想在到達那裏之前，重點讨論一下GenAI的經濟和福祉潛力。
我認爲值得注意的是，在過去的15年中，人工智能領域取得了一系列重要的突破，并非所有突破都屬于我們在早期所稱的GenAI範疇。語音識别、手寫識别、唇讀等是一系列令人矚目的成就，随後在圖像和物體識别領域又取得了一系列重大突破，并擁有衆多應用。我們現在正見證這些成果的實現。
盡管人工智能取得了進步，包括圖像識别等，但人工智能目前仍不具備像人類一樣處理複雜、快速變化的視覺環境的能力。這是關于人類與機器人以及人機協作的相關話題，将在以後的讨論中涉及。
還有其他一些突破，例如DeepMind的AlphaFold，現已成爲谷歌的一部分，能夠以相當高的準确度确定蛋白質的三維結構。該技術利用了定義蛋白質的氨基酸序列，并已成功用于預測大約2億已知蛋白質的三維結構，并作爲開源數據庫發布。因此，全球生物界可以将其作爲提高自身研究效率的工具。然後是赢得圍棋比賽，人們對其重要性的看法各不相同。但開發人工智能過程中學到的知識非常重要。在許多情況下，人工智能的表現已經超過了人類。人類表現通常是我們評估人工智能的标準。
我們稍後會回到這一點，因爲人工智能有從超人智能到次人智能的等級，大緻與人類相當，它們都有各自的用途。但爲什麽通用人工智能可能是一個非常重要的發展？從經濟和金融角度來看，有兩三個非常重要的特點。我認爲通用人工智能就像大型語言模型，具體來說，它首次擁有了你可能會稱之爲"領域切換能力"或"日常語言"的能力。它知道主題而不需要被告知。它擁有像人類一樣理解對話背景的能力，無論對話有多複雜，我認爲這是朝着通用人工智能邁出的一步，即使我們還需要一段時間才能到達那裏。所以你可以和AI談論意大利文藝複興、通貨膨脹、早期俄羅斯文學、計算機編碼，并讓它做數學題，它會很樂意地和你交流，并做出适當的回應。在某些情況下，它會給出令人驚訝的洞察力和聰明的結論。
第二點是易用性。你不需要任何技術培訓就可以使用它。這是因爲它基本上是在用我們的語言交流，就像我們用語言理解一樣。因此，ChatGPT在前兩個月就擁有了1億用戶，這是以前從未發生過的事情。
這都意味着什麽呢？ 第一個觀察是，很難找到一個經濟領域，一個知識經濟領域，其中沒有重要的、可能改變整個地球的突破性人工智能應用。我的觀點，也是普遍的觀點是，至少有潛力出現一次非常巨大的、長期的生産力提升，這将對增長、供應鏈、增長的資源約束模式産生影響。
詹姆斯·曼尼克和我在2023年底的《外交事務》上發表了一篇關于人工智能的經濟潛力的論文。但重點是，它看起來像是一種超級通用的技術，可以在知識經濟中廣泛應用，幾乎适用于所有地方。而知識經濟無處不在。我們通常将它與技術、金融、管理、計算機編程等行業聯系在一起。但實際上，知識經濟無處不在。醫院中也存在知識經濟的重要組成部分，比如醫生和護士的工作等等。在所有這些情況下，你都可以找到強大的數字助手，既可以幫助個人，也可以幫助以人工智能（GenAI）能力爲核心的系統。
爲什麽這如此重要？因爲人工智能（AI）和機器學習（ML）的進步，我們現在可以創建高度智能的數字助手，它們可以幫助我們完成各種任務，從日常工作到複雜的分析和決策。這些助手可以大大提高我們的生産力和效率，使我們能夠專注于更重要的任務。此外，它們還可以幫助我們處理大量數據，從中提取有用的信息，爲我們提供更好的決策支持。因此，擁有強大的數字助手對于在知識經濟中取得成功至關重要。
目前階段爲何如此重要？ 至少在中國，存在總需求不足的暫時性問題，而全球許多經濟體則受到供給受限的增長模式困擾。這些問題源于許多因素的共同作用，包括生産率下降、人口老齡化、勞動力減少、不斷上升的撫養比、勞動力短缺、關鍵就業部門的多元化和碎片化成本高昂，以及全球供應鏈中源自多重來源（包括氣候、疫情、金融危機和地緣政治緊張局勢）的嚴重沖擊。
最後，我們還面臨着一種我試圖識别的現象，我稱之爲"強大通縮力量的消退"。這與那個全球化高度發展、新興經濟體快速增長的時期有關。這有點像我在全球經濟中所說的"劉易斯轉折點"，當所有這些因素結合在一起，其結果是，我們面臨着幾十年來未曾有過的新的通脹壓力，實際利率上升，财政空間縮小，以及一些地方的債務困境。此外，還需要對能源轉型進行大規模投資，以實現可持續的增長模式。如果有人給我們提供了應對所有這些問題的最佳方法，比如與老齡化相關的過度負擔等問題，那會是什麽呢？
答案是，非常高且持續的生産力增長，而我們最有效的工具就在當前和未來幾代人工智能領域。問題是，我們會利用這些工具嗎？要實現這一點，需要采取一系列措施。
舉幾個例子， 斯坦福大學的埃裏克·布林森（Eric Brinelson）和他的同事們對人工智能在客戶服務中的應用進行了研究，這是全球一個非常大的就業領域。基本上，人工智能通過數千小時的客戶服務、客戶互動、音頻記錄以及績效指标的訓練，學會了如何适當地做出回應，從而創造了一個強大的數字助手，作爲客戶服務代理的助手。他們把這個工具交給了一部分客服人員試用，對其他客服人員則沒有提供。
測試結果立刻顯現出來。首先，整體上出現了顯著的生産率提升，大約爲14%。其次，它揭示了人工智能的一些特性：如果觀察不那麽有經驗的客服人員的表現變化，人工智能的影響甚至更大，大約爲35%。
事後看來，很容易猜到人工智能實際上是捕捉了與客服、客戶互動相關的學習經驗，包括哪些做法有效、哪些無效，然後以可用的形式反饋回來。因此，其總體效果是一種"升級效應"，對有經驗的客服人員産生了顯著的影響，對經驗較少的客服人員的影響則更大。但這個故事以及許多類似故事的關鍵點在于，正确的模式并非僅僅關于自動化和取代人類，而是強大的數字助手。
當機器在某些方面做得更好時，一些事情将會被自動化。就像總結他們在大量數據集（在此例中爲音頻記錄）中發現的模式一樣。但這并不意味着你将人類從劇本中抹去，而是爲（甚至是完整的系統）提供一個強大的數字助手。将數字助手與人類表現進行比較是完全自然的方式，這并沒有什麽錯，它可以幫助評估我們取得了多大的進步。但這确實會導緻一種自動化偏見。原因在于，一旦AI達到平均人類水平的表現，人們就會傾向于認爲"爲什麽不用AI取代人類呢？"需要仔細思考才能意識到，除非是在考慮某些特定的自動化方面。
AI存在于各個層面。那些超人類的AI能夠做人類無法做到的事情，比如在大量數據中識别模式及其含義，以我們無法達到的速度進行高速計算。因此，這是一個子集，它們非常重要。在這個領域，我們将看到增強，但它将是通過增強人類而不是取代人類來實現的。它隻是增加了一些東西。
還有一類事物，它們在某些方面與人類的能力相當。還有另一類子集，我想花點時間談談，雖然AI在某些方面确實無法達到我們所期望的人類水平，但它們仍然可能非常有用。這裏的總體方向是包容性增長模式。
一些研究人員發現，利用皮膚癌的圖像來訓練AI實際上可以檢測出皮膚癌。它們和您經常看的皮膚科醫生一樣好嗎？如果您容易出現這種問題，答案可能是否定的。因此，我的學生通常認爲這并不太有趣。這是個不錯的嘗試，但我們沒有成功。但問題是，這不是正确的思考方式。世界上可能有85%的人口遠離皮膚科醫生，無法獲得最佳治療。但是，如果人工智能能夠相當有效地從僅用普通手機拍攝的照片中檢測出皮膚癌，那麽隻需向人們發出足夠好的信号，就能觸發他們的反應，讓他們乘坐火車，前往80公裏外的皮膚科醫生那裏就診，而不至于太晚。
在信貸、金融、電子商務等領域，有很多應用與增長模式的包容性有關，通過人工智能驅動的算法，可以極大地擴展服務範圍。我認爲，不要讓基準成爲決定經濟應用的因素。
最後，我想簡單總結一下，與此相關的政策議程極爲複雜。其中一些與防止破壞性或有害的濫用有關，例如虛假信息、大規模的宣傳活動和欺詐等。
人們擔心就業問題。我們會有足夠的工作機會嗎？就我個人而言，這是一個長期的争論，但我并不太擔心平均就業率下降的問題。我認爲我們不會在爲人們創造工作機會方面遇到問題，但在微觀經濟層面上，這将是非常具有破壞性的。它将改變工作和技能，有些職業會衰退，而另一些職業則會增長。
這些轉變對于人們來說并不容易。政府和政策在這方面可以發揮作用，以減輕和加速這些轉變，從而避免對個人或家庭以及工人造成經濟損失或過度焦慮。
版權保護是一個重大問題。GenAI模型會吸收大量來自各個領域的文字和創意作品的數據和信息。這些作品的原創者需要在财産權方面考慮某種形式的保護。這是一個非常棘手的問題，目前仍在進行中。但在積極的一面上，這個問題不能被忽視。如果我們想要實現生産力的飛躍，就需要在國内乃至全球經濟範圍内實現技術的普及和推廣，這需要政府和公共政策的推動。我毫不懷疑我們将擁有一些非常先進的行業。
技術和金融領域可能會發展得相當迅速，管理的各個方面也将迅速發展，尤其是那些擁有資源可以探索和嘗試新技術的大公司。但問題是，我們能否将技術普及到整個經濟，并應用于那些在數字化采用方面往往落後的行業？
對于那些沒有大量資源的小型和中型企業來說呢？我們确實需要解決這個問題，不僅從包容性角度出發，因爲如果不能實現技術普及，我們就無法實現生産力飛躍。如果影響隻局限于少數可能已經準備好接受新技術的行業，那就毫無意義了。
我們需要制定旨在促進技術普及和提高可訪問性的政策。再次強調，這是政策議程的積極方面。我很擔心，至少目前在西方，政策議程的重點非常傾向于防止負面影響，而不是加速并确保我們獲得積極影響。
但是，簡而言之，我認爲科學和技術界出于某種原因爲我們提供了一套工具，這些工具在很大程度上是可以獲得的，可以廣泛應用，具有巨大的經濟、社會、醫療、教育和其他潛在價值。
目前正處于深入探索和實驗的初期，很難準确預測其長期發展情況，但看起來潛力巨大，因此，參與其中的這十年将是令人興奮的十年。
最後，請允許我指出，人工智能的進步遠未結束。我認爲我們将看到更多令人驚歎的突破，這些突破發生在一個擴大了的可能性範圍内。例如，我們似乎正處于人工智能與生物醫學科學交彙的初期。如果這是真的，它将加速正在進行的生命科學革命。所以總的來說，人們可以在這些事情上花費數小時的時間，但我希望我已經充分地證明了人工智能将成爲全球經濟，尤其是美國經濟的積極推動力。我認爲人工智能領域的另一大玩家顯然是中國。
當前世界正處于一個充滿挑戰的時期，經濟增長放緩，各種問題使得生活更加艱難，降低了經濟績效。在我看來，人工智能是推動我們朝着相反方向前進的一束亮光。
非常感謝。
以下爲演講原文：
Greetings, colleagues and friends, I'm Michael Spence. I'm grateful to the organizers of ZGC forum this year for giving me the chance to speak with you as part of this year's very rich program.
My subject today, is the transformative economic impact of the revolution in artificial intelligence, in which we find ourselves in the early stages. GenAI is generated a huge amount of interests globally, and an equally large amount of investment. I think there may be elements of excessive optimism, especially about the timing of the arrival of large impacts on economies, societies, financial systems, and so on. But I have a slightly longer time horizon. I don't think anybody knows how fast this is going to occur. But I want to focus on the economic and welfare potential of GenAI before we get there. I think it's worth noting that there were a set of important breakthroughs in the past 15 years in artificial intelligence, not all of which fit the category head under GenAI we had in the earlier days. Speech recognition, handwriting recognition, lip reading, which was extraordinary set of achievements, and then a very big and important set of breakthroughs in image and object recognition with a huge host of applications, that we are now seeing coming to fruition.
In spite of the progress of the AI, including image recognition and so on, AI don't yet have a human like ability to process very complex, rapidly evolving visual environments with no latency. That's a relevant subject for later discussion that has to do with the relationship between humans and robots and human robotic collaboration.
There were other breakthroughs, Alpha Fold, product of Deep Mind, now part of Google in determining the three dimensional structure of proteins with reasonable accuracy. Using the amino acid sequence that defines a protein that work was successful and has now been used to predict the three dimensional structure of approximately the 200 million known proteins, and then published as an open source database.So the biological community globally can use this as a productivity enhancer in their own research.
Then there was winning the games of Go, which many people have different opinions about the importance of that. But the learning that went along with developing an AI was extremely important. In many cases, now, AI exceed human performance. Human performance is usually the benchmark by which we assess AI.
We'll come back to that because there's a kind of hierarchy of AI from superhuman to subhuman with roughly at a par with human, and they all have their uses. But why is GenAI potentially such an important development? From an economic and financial point of view, there are two or three characteristics that are really important. I think of GenAI as large language models and just to be concrete for the really the first time has what you might call a domain switching capability or an ordinary language. It knows what the subject is without being told. It has a human like ability to understand the context of a conversation, no matter how complex, which I think is a step in the direction of artificial general intelligence, even if it takes us a while to get there.
So you can talk AI about the Italian renaissance and inflation, early Russian literature, computer coding, and ask it to do math problems, and it doesn't have any trouble going along with you and to respond appropriately. In some cases, with sort of surprisingly insightful and clever results. Second is accessibility. You don't really need technical training to use it. And that's because it essentially talks our language and understands in the same way we understand using language. So ChatGpt had a hundred million users in the first 2 months that just never happened before. What does it all mean?
First observation is that it's very hard to find a part of the economy, the part of the knowledge economy that where there isn't an important set of potentially transformative applications of the whole planet play of breakthroughs in AI and so my belief and one that is widely shared is that there's at least the potential for a very huge, extended productivity surge, which will have impacts on growth, supply chain, supply constraint patterns of growth. James Manic and I made this argument, and wrote a paper on foreign affairs late 2023 on the economic potential of AI. But the main point is, it looks like a super general purpose technology that can be used in the knowledge economy, pretty much everywhere. And the knowledge economy is everywhere. We associate it with sectors like technology and finance, management, computer coding, et cetera. But in fact, the knowledge economy is everywhere. There's important parts of the knowledge economy in hospitals and what doctors and nurses do and so on. And in all of these cases, you can find powerful digital assistance, both to in individuals and the systems that have as at their core, i.e. the GenAI capabilities.
Why is this so important at current stage?
There's at least a transitory problem of insufficient aggregate demand in China, whiel much of the global economy is suffering from a supply constrained patterns of growth. These are the result of a lot of things coming together, declining productivity, declining productivity, aging, declining labor forces, higher and rising dependency ratios, labor shortages, in key large employment sectors, expensive patterns of diversification and fragmentation in global supply chains that are the result of severe shocks with multiple sources including climate pandemic, financial distress, geopolitical tensions.
And finally, we have a pattern that I've tried to identify, which I call the fading of the powerful deflationary forces that were associated with that period of hyper globalization and very rapid emerging economy growth. It's like what I call a kind of Lewis Turning Point in the global economy when all of those things come together. The effect is that we have new inflationary pressures that we haven't had for three or four decades, rising real interest rates, declining fiscal space, debt distress in a number of places.
And alongside of that, the need for very large investments in the energy transition, in pursuit of sustainable growth patterns. What's the best anecdote that we could possibly have if somebody gave it to us to deal with all these things, excessive burdens on the young associated with aging and et cetera.
The answer would be, a period of very high, sustained productivity growth, and the most powerful tools we have to engineer that are very prominently in the area of the current and future generations of artificial intelligence. The question is, are we going to take advantage of this? A number of steps are required to do that? Let me mention a couple of examples.
There was a study done by Eric Brinelson and his colleagues at Stanford of the application of artificial intelligence and customer service, application or industry sector, which is a very large employment sector globally. Basically, AI was trained on literally thousands of hours of customer service, customer interactions, audio recordings, along with performance measures. And it learned how to respond appropriately, which created a sort of powerful digital assistant as an assistant to the customer service agent. They gave it to a subset of the customer service agents and knocked others and tested it.
The two conclusions emerged immediately. One, there was a very large productivity increase overall on the order of 14%. And the second one, tells you something about artificial intelligence is if you looked at the impact on performance of the less experienced customer service agents, the impact was even larger on the order of 35%.
With the benefit of hindsight, it's fairly easy to guess that what the AI is doing basically is capturing the learning that is associated with customer service, customer interactions, what works and what doesn't, and then delivering it back in a usable form.
So the net effect of that is a kind of leveling up effect, getting a noticeable impact for the experienced agents and a much bigger impact for the less experienced and impacted. But the main point of that story and many others like it is that the right model, notwithstanding a very powerful tendency to believe that this is really just all about automation and getting rid of human beings. The right model is the powerful digital assistant.
Some things will be automated when the machines are better at it. Like summarizing patterns that they finds in very large collections of data, in this case，audio recordings. But that doesn't mean you've written a human being out of the script, as opposed to giving the human being or even a full system a powerful digital assistant. Digital assistants are benchmarked against human performance is a perfectly natural way. There's nothing wrong with this to get to assess how much progress we've made. But this does lead to a kind of automation bias. And the reason is that once AI passes the average human performance. The tendency is to think why don't we get rid of the human and just use the AI. It takes a little bit of careful thought to realize that's probably not the right answer unless you're thinking about very partial aspects of automation.
AI’s come at all levels. The superhuman ones can do things that human beings just can't do in sort of recognizing patterns and their implications in vast quantities of data that are involved. High speed calculations of at a level that we just can't accomplish. So that's a subset. They're very important. And in that area, we will see augmentation, but it will be augmentation not by replacing humans. It will just adding something.
Then there's not a set of things that they do on a par with humans. There's another subset that I want to just spend a minute on which AI don't really quite measure up to the kind of human performance that we would hope for, but are still potentially very useful. The general heading here is inclusive growth patterns.
Some researchers have determined that using images of skin cancer to train AI can actually detect skin cancer. Are they as good as the dermatologists that you visit regularly? If you were prone to this kind of problem, the answer is probably no. So my students normally think that's not very interesting. It was nice try, but we didn't make it. But the problem is that it's not the right way to think about it. There probably is 85% of the world's population that doesn't live near a dermatologist and can't get the first best version of the treatment. But if AI is reasonably good at detecting skin and cancer from images that are just taken with an ordinary mobile phone, you could very easily have a significant increment to the preventive health care by giving people signals that are good enough to trigger a response and get them to get on a train and go 80 kilometers and visit a dermatologist before it's kind of too late.
There's a lot of applications in credit and finance in e-commerce and so on. That have to do with inclusiveness of growth patterns where you can extend the range of service enormously with AI-powered algorithms. And I think it's important not to let the benchmarking be the determination of what the economic application is.
Finally, I'm going to conclude by saying a few words. There's an enormously complex policy agenda associated with this. Some of it is associated with preventing destructive or damaging misuse, e.g. disinformation, campaigns, fraud at a massive scale.
There's concern about jobs. Will we have enough jobs? On my side, this is a longer argument, but I’m not too worried about the net average, a problem with job loss. I don't think we'll have a problem generating work for people, but at a more microeconomic level, it will be very disruptive. It will change work and skills. There will be some occupations that decline and others that grow.
And those are not easy transitions for people. And there is a role for government and for policy in easing and making and accelerating. Those transitions in a way that they don't produce both economic damage at the individual or household level, at the level of the worker on excessive anxiety.
There's a big issue of copyright protection. The GenAI models just vacuum up enormous and vast quantities of data and Information of writing and creative work in various areas. And the original creators of that work need to consider some kind of protection in terms of property rights. This is a very hard problem and it is a work in progress. But it can’t be simply ignored on the positive side. If we're going to get the productivity surge, we need accessibility and diffusion within countries and eventually in the global economy. And that takes government and public policy to get there. And I don't have any doubt that we'll have very advanced sectors.
The tech sectors finance will probably move fairly fast. Various parts of management will move quickly, especially among the bigger firms that have the resources to explore and experiment with this. But the question is, are we going to get it across the economy and set into sectors that tend to lag in terms of digital adoption?
In small and medium sized businesses that don't have the massive resources available? We really need to address that problem, not just from an inclusion point of view, but because we won't get the productivity surge. If the impact only comes in a few may sectors that where it's very likely that it'll get adopted anyway.
We need policies that are designed to increase to make diffusion work, to make accessibility easier and so on. And again, that's the positive side of the policy agenda.And I'm worried, at least in the west at the moment, the policy agenda is very heavily weighted toward what you might call preventing the negative side, and not so much weighted toward accelerating and ensuring we get the positive side.
But to summarize, I think we have been given for whatever reason by the scientific and technological community, a set of tools that are accessible for the most part can be made widely available that have a huge economic and social and medical and educational and other potential.
It was in the early stages of intense exploration and experimentation. It's very hard to know exactly how this will play out over time in detail. But it certainly looks like the potential is enormous, so it'll be an exciting decade to be involved in all of this.
Finally, just let me mention that the progress in AI is far from over. I think we will see more breathtaking breakthroughs that come within an expanded set of potential. For example, we seem to be in the early days of the intersection of artificial Intelligence and biomedical science. If that's true, it will turbocharge the revolution in life sciences that's already underway. So bottom line is one can spend hours on these things, but I hope I've made a pretty convincing case that this is going to be one of the positive driving forces in the global economy, certainly in the United States. And I think the other major player in artificial Intelligence is obviously China.
It's in a pretty difficult world with slowing growth and lots of things that are making life more difficult and reducing economic performance. This seems to me the bright light that's pushing us in the opposite direction.
Thanks very much.（本文首發于钛媒體APP，作者｜顔繁瑤，編輯｜劉洋雪）
更多宏觀研究幹貨，請關注钛媒體國際智庫公衆号：
