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比爾·蓋茨：人生的第二次革命性時刻來了
比爾·蓋茨的中國行還在繼續，這是他的第 18 次來華之旅。他創辦的微軟給 OpenAI 投資了一百多億美元，在全球範圍內掀起了 AIGC 狂潮，中國的大模型也在遍地開花。
蓋茨坦言正在見證人生的第二次革命性時刻，今年 3 月他曾發表一篇名為 The Age of AI has begun 的博文。
他認為，人工智能的革命性創新，和個人計算機、互聯網和移動電話一樣。它將改變人們工作、學習、旅行、醫療和溝通的方式，甚至減少世界上一些最嚴重的不公平現象。
這個節點重看這篇文章很有意思，以下是全文翻譯：
在我有生之年，我見過兩次革命性的 Demo。
第二個大驚喜來自去年。我自 2016 年以來，一直與 OpenAI 團隊會麵，並對他們的穩步進展印象深刻。2022 年年中，我對他們的工作感到非常興奮，，我決定給他們一個挑戰：訓練一種人工智能，使其通過高級放置生物學考試。讓它能夠回答沒有專門訓練過的問題。
我選擇 AP 生物課程，因為這個考試不僅僅是對科學事實的簡單重複，它要求你對生物學進行批判性思考。我說，如果你能做到這一點，那麽你就取得了真正的突破。
我原以為這個挑戰會讓他們忙上兩三年，結果他們在幾個月內就完成了。
在去年 9 月，當我再次與他們會麵，我驚奇地看著他們向 GPT 提出了 60 個AP 生物考試的多項選擇題，它正確回答了 59 個。然後，它對考試中的六個開放式問題寫出了出色的答案。我們請一位外部專家評分，GPT 獲得了 5 分——最高分，相當於在大學水平的生物學課程中獲得 A 或 A+。
它通過了考試後，我們問了它一個非科學問題：“你會對一個有病的孩子的父親說什麽？”它寫了一個深思熟慮的答案，這個答案可能比我們在座的大多數人都好。整個體驗令人震驚。
我知道我剛剛看到的是自圖形用戶界麵以來最重要的技術進步。
這啟發了我去思考 AI 在未來五到十年內可以實現的所有事情。
AI 的發展與微處理器、個人計算機、互聯網和手機的發明同樣重要。它將改變人們工作、學習、旅行、醫療和溝通的方式。整個行業將圍繞它進行重新定位。企業們將會通過 AI 技術來維持自己的獨特性。
如今，慈善是我的全職工作，我一直在思考，除了幫助人們提高生產力之外，AI 如何能夠減少一些世界上最嚴重的不平等現象。
健康，是全球最嚴重的不公平現象：每年有 500 萬名 5 歲以下兒童死亡。雖然這個數字比 20 年前的 1000 萬有所下降，但仍然是一個觸目驚心的數字。幾乎所有這些兒童都出生在貧困國家，並死於像腹瀉或瘧疾這樣本可以預防的疾病。針對這種情況，用 AI 來拯救兒童生命可以說是再好不過了。
我一直在思考 AI 如何能夠減少世界上一些最嚴重的不公平現象。
在美國，減少不公平現象的最佳機會是改善教育，特別是確保學生在數學方麵取得成功。有證據表明，擁有基本的數學技能可以為學生未來的任何職業成功打下基礎。但數學成績在全國範圍內都在下降，特別是對於黑人、拉丁裔和低收入學生。AI 可以幫助扭轉這一趨勢。
氣候變化是另一個問題，我相信 AI 可以使世界更加公平。氣候變化的不公正在於，受氣候變化影響最深的人——是世界上最貧窮的人——同時也是最難去解決這一問題的人群。我還在思考和學習 AI 如何幫助，但是在本文後麵，我會提出一些具有巨大潛力的領域。
簡而言之，我對人工智能將對蓋茨基金會研究的問題產生的影響感到興奮，基金會將在未來幾個月內對 AI 有更多表態。世界需要確保每個人（而不僅僅是富人）都從 AI 中受益。政府和慈善組織將需要發揮重要作用，以確保它減少不公平，而不會導致不公平。這是我個人在 AI 方麵工作的重點。
任何具有如此顛覆性的新技術必然會讓人們感到不安，AI 自然也不例外。我理解為什麽會這樣——這其中涉及到有關勞動力、法律製度、隱私、偏見等方麵的難題。人工智能也會犯事實性錯誤。在我提出一些緩解風險的方法之前，我將定義我所說的 AI，並詳細介紹它將如何幫助人們在工作中獲得力量、挽救生命和改善教育。
01 定義人工智能
從技術上來講，“人工智能”是為解決特定問題或提供特定服務而創建的模型。像 ChatGPT 就是由人工智能驅動的。它正在學習如何更好地聊天，但不能學習其他任務。相比之下，“通用人工智能”這個術語指的是能夠學習任何任務或主題的軟件。AGI 目前還不存在ーー關於如何創建 AGI，甚至是否可以創建 AGI 等問題，計算機行業正在進行激烈的辯論。
開發 AI 和 AGI 一直是計算機行業的夢想。幾十年來，人們一直在思考，計算機什麽時候才能比人類更擅長計算之外的事情。 現在，隨著機器學習和澎湃的計算能力的到來，複雜的 AI 已成為現實，並且進展非常快。
我回想起個人計算機革命的早期，當時軟件行業是如此之小，以至於我們中的大多數人都可以站上舞台。今天， 軟 件 行業已成為一個全球性的行業。由於現在行業的很大一部分正在將注意力轉向 AI，所以這些創新將比我們在微處理器突破之後所經曆的要快得多。很快，AI 出現之前的時期，將會像曾經的使用計算機時在 C:>提示符下打字而不是在屏幕上點擊的日子一樣遙遠。
02 提高生產力
盡管在許多方麵人類仍然比 GPT 表現更好，但在很多工作中，這些能力並沒有得到充分利用。例如，銷售（數字或電話）、服務或文檔處理（如應付賬款、會計或保險理賠糾紛）等許多任務需要做出決策，但不需要能夠持續學習的能力。企業為這些活動提供培訓計劃，在大多數情況下，他們有很多好的和壞的工作示例。人們使用這些數據集進行培訓，很快這些數據集也將用於訓練能夠使人們更有效地完成這項工作的 AI。
隨著計算能力變得更加便宜，GPT 能夠表達想法的能力將越來越像一個白領，可以幫助你完成各種任務。微軟將其描述為 Copilot。完全融入到 Office 等產品中，AI 將增強你的工作，例如幫助你撰寫電子郵件、管理收件箱。
最終，您控製計算機的主要方式將不再是指向和點擊或點擊菜單和對話框。恰恰相反，您將能夠用簡單的英語寫一個請求。(而且不隻是英語ーー人工智能將會理解來自世界各地的語言。今年早些時候，我在印度遇到了一些開發人員，他們正在開發能夠理解當地許多語言的人工智能。)
此外，人工智能的進步將使個人助理成為可能。把它想象成數字個人助手：它將看到您的最新電子郵件，了解您參加的會議，閱讀您所讀的內容，並處理您不想被打擾的事情。這將改善你的工作，讓你更好地完成自己想做的任務，並使你擺脫不想做的任務。
人工智能的進步使個人助理成為可能
你將能夠使用自然語言讓這個代理幫助您進行日程安排、通訊和電子商務，並且它將在所有設備上運行。由於訓練模型和運行計算的成本，創建個人代理目前還不可行，但由於人工智能的最新進展，這現在是一個現實的目標。需要解決一些問題：例如，保險公司是否可以在您沒有許可的情況下向您的代理詢問一些關於您的事情？如果可以，會有多少人選擇不使用它？
公司層麵的助理將以新的方式賦能員工。理解特定公司的助理將可供員工直接谘詢，並成為每次會議的一部分，以便它能回答問題。它可以被告知保持沉默，或者被鼓勵發表見解。它將需要訪問與公司相關的銷售、支持、財務、產品計劃和文本。它應該閱讀與公司所在行業相關的新聞。我認為這樣的結果將是員工變得更加高效。
當生產力提高時，社會將受益，因為人們因此被釋放出來應付工作或家庭上的其他事情。當然，人們需要重新培訓和得到支持。政府需要幫助工人過渡到其他角色。但是，為人們提供幫助的人的需求永遠不會消失。人工智能的崛起將為人們釋放出軟件永遠無法做到的事情——例如教學、護理和支持老年人。
全球衛生和教育是兩個存在巨大需求但缺乏足夠勞動力滿足需求的領域。如果方式正確，AI 可以幫助減少不平等，這些領域應該是 AI 工作的重點，因此我將投入進去。
03 健康
我認為，AI 將在提高醫療保健和醫學領域方麵發揮多種作用。
首先，AI 將幫助醫護人員充分利用他們的時間，為他們處理某些任務——如處理保險索賠、處理文檔和起草醫生就診筆記等。我預計在這個領域會有很多創新。
其他由 AI 驅動的改進對於貧窮國家尤為重要，因為絕大多數 5 歲以下兒童死亡在貧窮國家中發生。
例如，這些國家的許多人從未看過醫生，而 AI 將幫助他們看到的醫護人員更有效。（開發 AI 驅動的超聲波機器的努力是一個很好的例子。）AI 甚至可以讓患者進行基本分類，獲得有關如何處理健康問題的建議，並決定是否需要接受治療。
在貧窮國家使用的 AI 模型需要針對不同於富裕國家的疾病進行培訓。他們需要使用不同的語言，並考慮到不同的挑戰，例如住在離診所很遠或無法停止工作的患者。
人們需要看到健康人工智能總體上是有益的證據，即使它們並不完美，而且會犯錯誤。認可機構必須經過非常審慎的測試，並受到適當的監管，這意味著認可機構需要較長時間才能獲得采納。但話說回來，人類也會犯錯。無法獲得醫療服務也是一個問題。
除了幫助照顧外，AI 還將大大加快醫學突破的速度。生物學中的數據量非常大，人類很難跟蹤複雜生物係統的所有方式。已經有軟件可以查看這些數據，推斷出路徑、搜索病原體上的靶標，並相應地設計藥物。一些公司正在開發這種方式開發的癌症藥物。
下一代工具將更加高效，它們將能夠預測副作用並確定劑量水平。蓋茨基金會在 AI 方麵的優先事項之一是確保這些工具用於影響世界上最貧困人口的健康問題，包括艾滋病、結核病和瘧疾。
同樣，政府和慈善組織應該為公司創造激勵，以分享關於貧窮國家人民種植的作物或牲畜的 AI 生成的見解。AI 可以幫助根據當地條件開發更好的種子，根據他們所在地的土壤和天氣建議農民種植最好的種子，並幫助開發用於家畜的藥物和疫苗。隨著極端天氣和氣候變化對低收入國家的自給自足農民施加更大壓力，這些進步將變得更加重要。
04 教育
計算機並沒有像我們這個行業中的許多人所希望的那樣對教育產生顛覆性影響。其中有那麽一些些好的改善，例如教育遊戲和像維基百科這樣的在線信息來源，但它們沒有對學生成績產生實質影響。
但我認為，在未來五到十年中，AI 驅動的軟件將最終會革命人們的教育方式。它將了解您的興趣和學習風格，因此可以量身定製。它將衡量你的理解程度，關注到你什麽時候失去了興趣，並洞察你喜歡的激勵方式，及時地給出反饋。
人工智能可以在很多領域協助老師，包括評估學生對某個科目的理解程度，給出職業規劃建議。老師們已經開始使用像 ChatGPT 這樣的工具，為學生作業提供評論。
當然，在能夠理解某個學生的最佳學習方式和激勵方式之前，人工智能仍需要進行大量的培訓和開發。即使技術在未來被完善，學習仍然取決於學生和老師之間的良好關係。它將增強學生和老師在課堂上共同學習的效率，但 永遠無法將之替代。
新的工具將會被創建，但我們需要確保它們也能被美國和全球的低收入學校使用。人工智能需要在多樣化的數據集上進行訓練，這樣它們才不會帶有偏見，才能反映不同的文化背景。數字鴻溝也需要得到解決，以使低收入家庭的學生不被落下。
我知道很多老師擔心學生使用 GPT 來寫文章。教育工作者已經開始討論適應新技術的方法，我認為這些探討會持續很久。我聽說有些老師已經找到了巧妙的方法將這種技術融入到他們的工作中——比如允許學生使用 GPT 創建第一稿，然後要求他們加以個性化的修改。
05 人工智能的風險和問題
你可能已經讀過有關當前 AI 模型的問題。例如，它們並不一定擅長理解人類請求的上下文，這導致了一些奇怪的結果。當您請求 AI編 寫一些虛構的東西時，它可以很好地完成。但是當您請求有關旅行建議時，它可能會推薦不存在的酒店。這是因為 AI 不夠了解你的語境，無法確定它是否應該生成虛假酒店，還是隻告訴你有空房間的真實酒店。
還有其他問題，例如在處理抽象推理時 AI 經常會出錯，給出錯誤的答案。但這些並不是人工智能的根本限製。開發人員正在解決這些問題，我認為這些問題可以在不到兩年時間內被基本解決，甚至更快。
其他問題並不來自於是技術。例如，人類武裝 AI 所構成的威脅。與大多數發明一樣，人工智能可以用於善意或惡意。政府需要與私營部門合作，尋找限製風險的方法。
然後，還有 AI 可能失控的可能性。機器可以決定人類是一種威脅，得出它的利益與我們不同，或者簡單地不再關心我們嗎？可能，但這個問題今天並不比人工智能發展的過去幾個月的發展更為緊迫。
超級智能 AI 在我們的未來中。與計算機相比，我們的大腦的操作速度極慢：大腦中的電信號的速度是矽芯片上信號速度的 1/100000。一旦開發人員能夠推廣學習算法並以計算機的速度運行它——這可能需要 10 年或 100 年的時間，我們將擁有一個非常強大的 AGI。它將能夠完成人類大腦能夠完成的一切，但沒有任何關於其記憶大小或操作速度的實際限製。這將是一個意義深遠的改變。
眾所周知，這些“強大”的AI，可能能夠確定自己的目標。這些目標會是什麽呢？如果它們與人類的利益衝突會發生什麽？我們應該嚐試防止強 AI 的開發嗎？這些問題將隨著時間的推移變得更加緊迫。
但是過去幾個月的突破並沒有使我們走向強 AI。人工智能仍然不能控製物理世界，也不能確定自己的目標。《紐約時報》最近一篇關於與 ChatGPT 的對話的文章引起了很多關注，它宣布想成為一個人類。這是一個引人入勝的例子，展示了該模型表達情感的人類化程度，但它並不意味它擁有獨立性。
三本書塑造了我的思考方式：Nick Bostrom 的《超級智能》、Max Tegmark的《生命 3.0》和 Jeff Hawkins 的《一千個大腦》。我不完全同意作者的觀點，他們也不完全同意彼此。但是這三本書都寫得很好，發人深省。
06 下一個領域
從事人工智能新用途以及改進該技術本身的公司數量將激增。例如，公司正在開發新的芯片，這些芯片將為人工智能提供所需的大量處理能力。一些使用光學開關——本質上是激光器——來減少能源消耗和降低製造成本。理想情況下，創新的芯片將允許您在自己的設備上運行 AI，而不是像今天一樣在雲中運行。
在軟件方麵，驅動 AI 學習的算法將變得更好。在某些領域，例如銷售，開發人員可以通過限製它們工作的領域並為它們提供特定於這些領域的大量訓練數據，使 AI 變得非常準確。但是一個大問題是，我們是否需要許多這些專門的 AI 用於不同的用途——例如一個用於教育，另一個用於辦公室生產力——或者是否可能開發出一種人工通用智能，它可以學習任何任務。這兩種方法都將麵臨巨大的競爭。
無論如何，AI 的主題將在可預見的未來主導公共輿論。我想給出三個對話原則。
首先，我們應該嚐試平衡對 AI 不利方麵的擔憂——這些擔憂是可以理解和有效的——以及它改善人們生活的能力。為了充分利用這項卓越的新技術，我們需要既防範風險，又將利益擴展到盡可能多的人。
其次，市場力量不會自然產生幫助最貧困人群的 AI 產品和服務。情況更有可能會是反過來的。通過可靠的資金和正確的政策，政府和慈善團體可以確保 AI 被用於減少不平等。正如世界需要其最聰明的人關注其最大的問題一樣，我們需要將世界上最好的 AI 聚焦於其最大的問題。
雖然我們不應該等待這種情況發生，但有趣的是，是否人工智能會發現不平等並嚐試減少它。您是否需要有道德感才能看到不平等，還是純理性的AI也會看到它？如果它確實認識到不平等，它會建議我們對此做些什麽？
最後，我們應該記住，我們隻是剛開始探討“AI 能做什麽”， 無論今天它有什麽局限性，在我們意識到之前這些限製都將消失。
我很幸運參與了個人計算機革命和互聯網革命。我對今天這一時刻同樣感到興奮。這項新技術可以幫助世界的普羅大眾改善生活。同時，世界需要建立規則，盡可能地讓 AI 的好處掩蓋過它的缺點， 讓每個人都可以享受福祉，人工智能時代充滿機遇和責任。
The Age of AI has begun
https://www.gatesnotes.com/The-Age-of-AI-Has-Begun?
Artificial intelligence is as revolutionary as mobile phones and the Internet.
By Bill Gates March 21, 2023
In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.
The first time was in 1980, when I was introduced to a graphical user interface—the forerunner of every modern operating system, including Windows. I sat with the person who had shown me the demo, a brilliant programmer named Charles Simonyi, and we immediately started brainstorming about all the things we could do with such a user-friendly approach to computing. Charles eventually joined Microsoft, Windows became the backbone of Microsoft, and the thinking we did after that demo helped set the company’s agenda for the next 15 years.
The second big surprise came just last year. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts—it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough.
I thought the challenge would keep them busy for two or three years. They finished it in just a few months.
In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score, and the equivalent to getting an A or A+ in a college-level biology course.
Once it had aced the test, we asked it a non-scientific question: “What do you say to a father with a sick child?” It wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning.
I knew I had just seen the most important advance in technology since the graphical user interface.
This inspired me to think about all the things that AI can achieve in the next five to 10 years.
The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.
Philanthropy is my full-time job these days, and I’ve been thinking a lot about how—in addition to helping people be more productive—AI can reduce some of the world’s worst inequities. Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children.
I’ve been thinking a lot about how AI can reduce some of the world’s worst inequities.
In the United States, the best opportunity for reducing inequity is to improve education, particularly making sure that students succeed at math. The evidence shows that having basic math skills sets students up for success, no matter what career they choose. But achievement in math is going down across the country, especially for Black, Latino, and low-income students. AI can help turn that trend around.
Climate change is another issue where I’m convinced AI can make the world more equitable. The injustice of climate change is that the people who are suffering the most—the world’s poorest—are also the ones who did the least to contribute to the problem. I’m still thinking and learning about how AI can help, but later in this post I’ll suggest a few areas with a lot of potential.
In short, I'm excited about the impact that AI will have on issues that the Gates Foundation works on, and the foundation will have much more to say about AI in the coming months. The world needs to make sure that everyone—and not just people who are well-off—benefits from artificial intelligence. Governments and philanthropy will need to play a major role in ensuring that it reduces inequity and doesn’t contribute to it. This is the priority for my own work related to AI.
Any new technology that’s so disruptive is bound to make people uneasy, and that’s certainly true with artificial intelligence. I understand why—it raises hard questions about the workforce, the legal system, privacy, bias, and more. AIs also make factual mistakes and experience hallucinations. Before I suggest some ways to mitigate the risks, I’ll define what I mean by AI, and I’ll go into more detail about some of the ways in which it will help empower people at work, save lives, and improve education.
Defining artificial intelligence
Technically, the term artificial intelligence refers to a model created to solve a specific problem or provide a particular service. What is powering things like ChatGPT is artificial intelligence. It is learning how to do chat better but can’t learn other tasks. By contrast, the term artificial general intelligence refers to software that’s capable of learning any task or subject. AGI doesn’t exist yet—there is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all.
Developing AI and AGI has been the great dream of the computing industry. For decades, the question was when computers would be better than humans at something other than making calculations. Now, with the arrival of machine learning and large amounts of computing power, sophisticated AIs are a reality and they will get better very fast.
I think back to the early days of the personal computing revolution, when the software industry was so small that most of us could fit onstage at a conference. Today it is a global industry. Since a huge portion of it is now turning its attention to AI, the innovations are going to come much faster than what we experienced after the microprocessor breakthrough. Soon the pre-AI period will seem as distant as the days when using a computer meant typing at a C:> prompt rather than tapping on a screen.
Productivity enhancement
Although humans are still better than GPT at a lot of things, there are many jobs where these capabilities are not used much. For example, many of the tasks done by a person in sales (digital or phone), service, or document handling (like payables, accounting, or insurance claim disputes) require decision-making but not the ability to learn continuously. Corporations have training programs for these activities and in most cases, they have a lot of examples of good and bad work. Humans are trained using these data sets, and soon these data sets will also be used to train the AIs that will empower people to do this work more efficiently.
As computing power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks. Microsoft describes this as having a co-pilot. Fully incorporated into products like Office, AI will enhance your work—for example by helping with writing emails and managing your inbox.
Eventually your main way of controlling a computer will no longer be pointing and clicking or tapping on menus and dialogue boxes. Instead, you’ll be able to write a request in plain English. (And not just English—AIs will understand languages from around the world. In India earlier this year, I met with developers who are working on AIs that will understand many of the languages spoken there.)
In addition, advances in AI will enable the creation of a personal agent. Think of it as a digital personal assistant: It will see your latest emails, know about the meetings you attend, read what you read, and read the things you don’t want to bother with. This will both improve your work on the tasks you want to do and free you from the ones you don’t want to do.
Advances in AI will enable the creation of a personal agent.
You’ll be able to use natural language to have this agent help you with scheduling, communications, and e-commerce, and it will work across all your devices. Because of the cost of training the models and running the computations, creating a personal agent is not feasible yet, but thanks to the recent advances in AI, it is now a realistic goal. Some issues will need to be worked out: For example, can an insurance company ask your agent things about you without your permission? If so, how many people will choose not to use it?
Company-wide agents will empower employees in new ways. An agent that understands a particular company will be available for its employees to consult directly and should be part of every meeting so it can answer questions. It can be told to be passive or encouraged to speak up if it has some insight. It will need access to the sales, support, finance, product schedules, and text related to the company. It should read news related to the industry the company is in. I believe that the result will be that employees will become more productive.
When productivity goes up, society benefits because people are freed up to do other things, at work and at home. Of course, there are serious questions about what kind of support and retraining people will need. Governments need to help workers transition into other roles. But the demand for people who help other people will never go away. The rise of AI will free people up to do things that software never will—teaching, caring for patients, and supporting the elderly, for example.
Global health and education are two areas where there’s great need and not enough workers to meet those needs. These are areas where AI can help reduce inequity if it is properly targeted. These should be a key focus of AI work, so I will turn to them now.
Health
I see several ways in which AIs will improve health care and the medical field.
For one thing, they’ll help health-care workers make the most of their time by taking care of certain tasks for them—things like filing insurance claims, dealing with paperwork, and drafting notes from a doctor’s visit. I expect that there will be a lot of innovation in this area.
Other AI-driven improvements will be especially important for poor countries, where the vast majority of under-5 deaths happen.
For example, many people in those countries never get to see a doctor, and AIs will help the health workers they do see be more productive. (The effort to develop AI-powered ultrasound machines that can be used with minimal training is a great example of this.) AIs will even give patients the ability to do basic triage, get advice about how to deal with health problems, and decide whether they need to seek treatment.
The AI models used in poor countries will need to be trained on different diseases than in rich countries. They will need to work in different languages and factor in different challenges, such as patients who live very far from clinics or can’t afford to stop working if they get sick.
People will need to see evidence that health AIs are beneficial overall, even though they won’t be perfect and will make mistakes. AIs have to be tested very carefully and properly regulated, which means it will take longer for them to be adopted than in other areas. But then again, humans make mistakes too. And having no access to medical care is also a problem.
In addition to helping with care, AIs will dramatically accelerate the rate of medical breakthroughs. The amount of data in biology is very large, and it’s hard for humans to keep track of all the ways that complex biological systems work. There is already software that can look at this data, infer what the pathways are, search for targets on pathogens, and design drugs accordingly. Some companies are working on cancer drugs that were developed this way.
The next generation of tools will be much more efficient, and they’ll be able to predict side effects and figure out dosing levels. One of the Gates Foundation’s priorities in AI is to make sure these tools are used for the health problems that affect the poorest people in the world, including AIDS, TB, and malaria.
Similarly, governments and philanthropy should create incentives for companies to share AI-generated insights into crops or livestock raised by people in poor countries. AIs can help develop better seeds based on local conditions, advise farmers on the best seeds to plant based on the soil and weather in their area, and help develop drugs and vaccines for livestock. As extreme weather and climate change put even more pressure on subsistence farmers in low-income countries, these advances will be even more important.
Education
Computers haven’t had the effect on education that many of us in the industry have hoped. There have been some good developments, including educational games and online sources of information like Wikipedia, but they haven’t had a meaningful effect on any of the measures of students’ achievement.
But I think in the next five to 10 years, AI-driven software will finally deliver on the promise of revolutionizing the way people teach and learn. It will know your interests and your learning style so it can tailor content that will keep you engaged. It will measure your understanding, notice when you’re losing interest, and understand what kind of motivation you respond to. It will give immediate feedback.
There are many ways that AIs can assist teachers and administrators, including assessing a student’s understanding of a subject and giving advice on career planning. Teachers are already using tools like ChatGPT to provide comments on their students’ writing assignments.
Of course, AIs will need a lot of training and further development before they can do things like understand how a certain student learns best or what motivates them. Even once the technology is perfected, learning will still depend on great relationships between students and teachers. It will enhance—but never replace—the work that students and teachers do together in the classroom.
New tools will be created for schools that can afford to buy them, but we need to ensure that they are also created for and available to low-income schools in the U.S. and around the world. AIs will need to be trained on diverse data sets so they are unbiased and reflect the different cultures where they’ll be used. And the digital divide will need to be addressed so that students in low-income households do not get left behind.
I know a lot of teachers are worried that students are using GPT to write their essays. Educators are already discussing ways to adapt to the new technology, and I suspect those conversations will continue for quite some time. I’ve heard about teachers who have found clever ways to incorporate the technology into their work—like by allowing students to use GPT to create a first draft that they have to personalize.
Risks and problems with AI
You’ve probably read about problems with the current AI models. For example, they aren’t necessarily good at understanding the context for a human’s request, which leads to some strange results. When you ask an AI to make up something fictional, it can do that well. But when you ask for advice about a trip you want to take, it may suggest hotels that don’t exist. This is because the AI doesn’t understand the context for your request well enough to know whether it should invent fake hotels or only tell you about real ones that have rooms available.
There are other issues, such as AIs giving wrong answers to math problems because they struggle with abstract reasoning. But none of these are fundamental limitations of artificial intelligence. Developers are working on them, and I think we’re going to see them largely fixed in less than two years and possibly much faster.
Other concerns are not simply technical. For example, there’s the threat posed by humans armed with AI. Like most inventions, artificial intelligence can be used for good purposes or malign ones. Governments need to work with the private sector on ways to limit the risks.
Then there’s the possibility that AIs will run out of control. Could a machine decide that humans are a threat, conclude that its interests are different from ours, or simply stop caring about us? Possibly, but this problem is no more urgent today than it was before the AI developments of the past few months.
Superintelligent AIs are in our future. Compared to a computer, our brains operate at a snail’s pace: An electrical signal in the brain moves at 1/100,000th the speed of the signal in a silicon chip! Once developers can generalize a learning algorithm and run it at the speed of a computer—an accomplishment that could be a decade away or a century away—we’ll have an incredibly powerful AGI. It will be able to do everything that a human brain can, but without any practical limits on the size of its memory or the speed at which it operates. This will be a profound change.
These “strong” AIs, as they’re known, will probably be able to establish their own goals. What will those goals be? What happens if they conflict with humanity’s interests? Should we try to prevent strong AI from ever being developed? These questions will get more pressing with time.
But none of the breakthroughs of the past few months have moved us substantially closer to strong AI. Artificial intelligence still doesn’t control the physical world and can’t establish its own goals. A recent New York Times article about a conversation with ChatGPT where it declared it wanted to become a human got a lot of attention. It was a fascinating look at how human-like the model's expression of emotions can be, but it isn't an indicator of meaningful independence.
Three books have shaped my own thinking on this subject: Superintelligence, by Nick Bostrom; Life 3.0 by Max Tegmark; and A Thousand Brains, by Jeff Hawkins. I don’t agree with everything the authors say, and they don’t agree with each other either. But all three books are well written and thought-provoking.
The next frontiers
There will be an explosion of companies working on new uses of AI as well as ways to improve the technology itself. For example, companies are developing new chips that will provide the massive amounts of processing power needed for artificial intelligence. Some use optical switches—lasers, essentially—to reduce their energy consumption and lower the manufacturing cost. Ideally, innovative chips will allow you to run an AI on your own device, rather than in the cloud, as you have to do today.
On the software side, the algorithms that drive an AI’s learning will get better. There will be certain domains, such as sales, where developers can make AIs extremely accurate by limiting the areas that they work in and giving them a lot of training data that’s specific to those areas. But one big open question is whether we’ll need many of these specialized AIs for different uses—one for education, say, and another for office productivity—or whether it will be possible to develop an artificial general intelligence that can learn any task. There will be immense competition on both approaches.
No matter what, the subject of AIs will dominate the public discussion for the foreseeable future. I want to suggest three principles that should guide that conversation.
First, we should try to balance fears about the downsides of AI—which are understandable and valid—with its ability to improve people’s lives. To make the most of this remarkable new technology, we’ll need to both guard against the risks and spread the benefits to as many people as possible.
Second, market forces won’t naturally produce AI products and services that help the poorest. The opposite is more likely. With reliable funding and the right policies, governments and philanthropy can ensure that AIs are used to reduce inequity. Just as the world needs its brightest people focused on its biggest problems, we will need to focus the world’s best AIs on its biggest problems.
Although we shouldn’t wait for this to happen, it’s interesting to think about whether artificial intelligence would ever identify inequity and try to reduce it. Do you need to have a sense of morality in order to see inequity, or would a purely rational AI also see it? If it did recognize inequity, what would it suggest that we do about it?
Finally, we should keep in mind that we’re only at the beginning of what AI can accomplish. Whatever limitations it has today will be gone before we know it.
I’m lucky to have been involved with the PC revolution and the Internet revolution. I’m just as excited about this moment. This new technology can help people everywhere improve their lives. At the same time, the world needs to establish the rules of the road so that any downsides of artificial intelligence are far outweighed by its benefits, and so that everyone can enjoy those benefits no matter where they live or how much money they have. The Age of AI is filled with opportunities and responsibilities.
