风萧萧_Frank
以文会友
比尔·盖茨：人生的第二次革命性时刻来了
比尔·盖茨的中国行还在继续，这是他的第 18 次来华之旅。他创办的微软给 OpenAI 投资了一百多亿美元，在全球范围内掀起了 AIGC 狂潮，中国的大模型也在遍地开花。
盖茨坦言正在见证人生的第二次革命性时刻，今年 3 月他曾发表一篇名为 The Age of AI has begun 的博文。
他认为，人工智能的革命性创新，和个人计算机、互联网和移动电话一样。它将改变人们工作、学习、旅行、医疗和沟通的方式，甚至减少世界上一些最严重的不公平现象。
这个节点重看这篇文章很有意思，以下是全文翻译：
在我有生之年，我见过两次革命性的 Demo。
第二个大惊喜来自去年。我自 2016 年以来，一直与 OpenAI 团队会面，并对他们的稳步进展印象深刻。2022 年年中，我对他们的工作感到非常兴奋，，我决定给他们一个挑战：训练一种人工智能，使其通过高级放置生物学考试。让它能够回答没有专门训练过的问题。
我选择 AP 生物课程，因为这个考试不仅仅是对科学事实的简单重复，它要求你对生物学进行批判性思考。我说，如果你能做到这一点，那么你就取得了真正的突破。
我原以为这个挑战会让他们忙上两三年，结果他们在几个月内就完成了。
在去年 9 月，当我再次与他们会面，我惊奇地看着他们向 GPT 提出了 60 个AP 生物考试的多项选择题，它正确回答了 59 个。然后，它对考试中的六个开放式问题写出了出色的答案。我们请一位外部专家评分，GPT 获得了 5 分——最高分，相当于在大学水平的生物学课程中获得 A 或 A+。
它通过了考试后，我们问了它一个非科学问题：“你会对一个有病的孩子的父亲说什么？”它写了一个深思熟虑的答案，这个答案可能比我们在座的大多数人都好。整个体验令人震惊。
我知道我刚刚看到的是自图形用户界面以来最重要的技术进步。
这启发了我去思考 AI 在未来五到十年内可以实现的所有事情。
AI 的发展与微处理器、个人计算机、互联网和手机的发明同样重要。它将改变人们工作、学习、旅行、医疗和沟通的方式。整个行业将围绕它进行重新定位。企业们将会通过 AI 技术来维持自己的独特性。
如今，慈善是我的全职工作，我一直在思考，除了帮助人们提高生产力之外，AI 如何能够减少一些世界上最严重的不平等现象。
健康，是全球最严重的不公平现象：每年有 500 万名 5 岁以下儿童死亡。虽然这个数字比 20 年前的 1000 万有所下降，但仍然是一个触目惊心的数字。几乎所有这些儿童都出生在贫困国家，并死于像腹泻或疟疾这样本可以预防的疾病。针对这种情况，用 AI 来拯救儿童生命可以说是再好不过了。
我一直在思考 AI 如何能够减少世界上一些最严重的不公平现象。
在美国，减少不公平现象的最佳机会是改善教育，特别是确保学生在数学方面取得成功。有证据表明，拥有基本的数学技能可以为学生未来的任何职业成功打下基础。但数学成绩在全国范围内都在下降，特别是对于黑人、拉丁裔和低收入学生。AI 可以帮助扭转这一趋势。
气候变化是另一个问题，我相信 AI 可以使世界更加公平。气候变化的不公正在于，受气候变化影响最深的人——是世界上最贫穷的人——同时也是最难去解决这一问题的人群。我还在思考和学习 AI 如何帮助，但是在本文后面，我会提出一些具有巨大潜力的领域。
简而言之，我对人工智能将对盖茨基金会研究的问题产生的影响感到兴奋，基金会将在未来几个月内对 AI 有更多表态。世界需要确保每个人（而不仅仅是富人）都从 AI 中受益。政府和慈善组织将需要发挥重要作用，以确保它减少不公平，而不会导致不公平。这是我个人在 AI 方面工作的重点。
任何具有如此颠覆性的新技术必然会让人们感到不安，AI 自然也不例外。我理解为什么会这样——这其中涉及到有关劳动力、法律制度、隐私、偏见等方面的难题。人工智能也会犯事实性错误。在我提出一些缓解风险的方法之前，我将定义我所说的 AI，并详细介绍它将如何帮助人们在工作中获得力量、挽救生命和改善教育。
01 定义人工智能
从技术上来讲，“人工智能”是为解决特定问题或提供特定服务而创建的模型。像 ChatGPT 就是由人工智能驱动的。它正在学习如何更好地聊天，但不能学习其他任务。相比之下，“通用人工智能”这个术语指的是能够学习任何任务或主题的软件。AGI 目前还不存在ーー关于如何创建 AGI，甚至是否可以创建 AGI 等问题，计算机行业正在进行激烈的辩论。
开发 AI 和 AGI 一直是计算机行业的梦想。几十年来，人们一直在思考，计算机什么时候才能比人类更擅长计算之外的事情。 现在，随着机器学习和澎湃的计算能力的到来，复杂的 AI 已成为现实，并且进展非常快。
我回想起个人计算机革命的早期，当时软件行业是如此之小，以至于我们中的大多数人都可以站上舞台。今天， 软 件 行业已成为一个全球性的行业。由于现在行业的很大一部分正在将注意力转向 AI，所以这些创新将比我们在微处理器突破之后所经历的要快得多。很快，AI 出现之前的时期，将会像曾经的使用计算机时在 C:>提示符下打字而不是在屏幕上点击的日子一样遥远。
02 提高生产力
尽管在许多方面人类仍然比 GPT 表现更好，但在很多工作中，这些能力并没有得到充分利用。例如，销售（数字或电话）、服务或文档处理（如应付账款、会计或保险理赔纠纷）等许多任务需要做出决策，但不需要能够持续学习的能力。企业为这些活动提供培训计划，在大多数情况下，他们有很多好的和坏的工作示例。人们使用这些数据集进行培训，很快这些数据集也将用于训练能够使人们更有效地完成这项工作的 AI。
随着计算能力变得更加便宜，GPT 能够表达想法的能力将越来越像一个白领，可以帮助你完成各种任务。微软将其描述为 Copilot。完全融入到 Office 等产品中，AI 将增强你的工作，例如帮助你撰写电子邮件、管理收件箱。
最终，您控制计算机的主要方式将不再是指向和点击或点击菜单和对话框。恰恰相反，您将能够用简单的英语写一个请求。(而且不只是英语ーー人工智能将会理解来自世界各地的语言。今年早些时候，我在印度遇到了一些开发人员，他们正在开发能够理解当地许多语言的人工智能。)
此外，人工智能的进步将使个人助理成为可能。把它想象成数字个人助手：它将看到您的最新电子邮件，了解您参加的会议，阅读您所读的内容，并处理您不想被打扰的事情。这将改善你的工作，让你更好地完成自己想做的任务，并使你摆脱不想做的任务。
人工智能的进步使个人助理成为可能
你将能够使用自然语言让这个代理帮助您进行日程安排、通讯和电子商务，并且它将在所有设备上运行。由于训练模型和运行计算的成本，创建个人代理目前还不可行，但由于人工智能的最新进展，这现在是一个现实的目标。需要解决一些问题：例如，保险公司是否可以在您没有许可的情况下向您的代理询问一些关于您的事情？如果可以，会有多少人选择不使用它？
公司层面的助理将以新的方式赋能员工。理解特定公司的助理将可供员工直接咨询，并成为每次会议的一部分，以便它能回答问题。它可以被告知保持沉默，或者被鼓励发表见解。它将需要访问与公司相关的销售、支持、财务、产品计划和文本。它应该阅读与公司所在行业相关的新闻。我认为这样的结果将是员工变得更加高效。
当生产力提高时，社会将受益，因为人们因此被释放出来应付工作或家庭上的其他事情。当然，人们需要重新培训和得到支持。政府需要帮助工人过渡到其他角色。但是，为人们提供帮助的人的需求永远不会消失。人工智能的崛起将为人们释放出软件永远无法做到的事情——例如教学、护理和支持老年人。
全球卫生和教育是两个存在巨大需求但缺乏足够劳动力满足需求的领域。如果方式正确，AI 可以帮助减少不平等，这些领域应该是 AI 工作的重点，因此我将投入进去。
03 健康
我认为，AI 将在提高医疗保健和医学领域方面发挥多种作用。
首先，AI 将帮助医护人员充分利用他们的时间，为他们处理某些任务——如处理保险索赔、处理文档和起草医生就诊笔记等。我预计在这个领域会有很多创新。
其他由 AI 驱动的改进对于贫穷国家尤为重要，因为绝大多数 5 岁以下儿童死亡在贫穷国家中发生。
例如，这些国家的许多人从未看过医生，而 AI 将帮助他们看到的医护人员更有效。（开发 AI 驱动的超声波机器的努力是一个很好的例子。）AI 甚至可以让患者进行基本分类，获得有关如何处理健康问题的建议，并决定是否需要接受治疗。
在贫穷国家使用的 AI 模型需要针对不同于富裕国家的疾病进行培训。他们需要使用不同的语言，并考虑到不同的挑战，例如住在离诊所很远或无法停止工作的患者。
人们需要看到健康人工智能总体上是有益的证据，即使它们并不完美，而且会犯错误。认可机构必须经过非常审慎的测试，并受到适当的监管，这意味着认可机构需要较长时间才能获得采纳。但话说回来，人类也会犯错。无法获得医疗服务也是一个问题。
除了帮助照顾外，AI 还将大大加快医学突破的速度。生物学中的数据量非常大，人类很难跟踪复杂生物系统的所有方式。已经有软件可以查看这些数据，推断出路径、搜索病原体上的靶标，并相应地设计药物。一些公司正在开发这种方式开发的癌症药物。
下一代工具将更加高效，它们将能够预测副作用并确定剂量水平。盖茨基金会在 AI 方面的优先事项之一是确保这些工具用于影响世界上最贫困人口的健康问题，包括艾滋病、结核病和疟疾。
同样，政府和慈善组织应该为公司创造激励，以分享关于贫穷国家人民种植的作物或牲畜的 AI 生成的见解。AI 可以帮助根据当地条件开发更好的种子，根据他们所在地的土壤和天气建议农民种植最好的种子，并帮助开发用于家畜的药物和疫苗。随着极端天气和气候变化对低收入国家的自给自足农民施加更大压力，这些进步将变得更加重要。
04 教育
计算机并没有像我们这个行业中的许多人所希望的那样对教育产生颠覆性影响。其中有那么一些些好的改善，例如教育游戏和像维基百科这样的在线信息来源，但它们没有对学生成绩产生实质影响。
但我认为，在未来五到十年中，AI 驱动的软件将最终会革命人们的教育方式。它将了解您的兴趣和学习风格，因此可以量身定制。它将衡量你的理解程度，关注到你什么时候失去了兴趣，并洞察你喜欢的激励方式，及时地给出反馈。
人工智能可以在很多领域协助老师，包括评估学生对某个科目的理解程度，给出职业规划建议。老师们已经开始使用像 ChatGPT 这样的工具，为学生作业提供评论。
当然，在能够理解某个学生的最佳学习方式和激励方式之前，人工智能仍需要进行大量的培训和开发。即使技术在未来被完善，学习仍然取决于学生和老师之间的良好关系。它将增强学生和老师在课堂上共同学习的效率，但 永远无法将之替代。
新的工具将会被创建，但我们需要确保它们也能被美国和全球的低收入学校使用。人工智能需要在多样化的数据集上进行训练，这样它们才不会带有偏见，才能反映不同的文化背景。数字鸿沟也需要得到解决，以使低收入家庭的学生不被落下。
我知道很多老师担心学生使用 GPT 来写文章。教育工作者已经开始讨论适应新技术的方法，我认为这些探讨会持续很久。我听说有些老师已经找到了巧妙的方法将这种技术融入到他们的工作中——比如允许学生使用 GPT 创建第一稿，然后要求他们加以个性化的修改。
05 人工智能的风险和问题
你可能已经读过有关当前 AI 模型的问题。例如，它们并不一定擅长理解人类请求的上下文，这导致了一些奇怪的结果。当您请求 AI编 写一些虚构的东西时，它可以很好地完成。但是当您请求有关旅行建议时，它可能会推荐不存在的酒店。这是因为 AI 不够了解你的语境，无法确定它是否应该生成虚假酒店，还是只告诉你有空房间的真实酒店。
还有其他问题，例如在处理抽象推理时 AI 经常会出错，给出错误的答案。但这些并不是人工智能的根本限制。开发人员正在解决这些问题，我认为这些问题可以在不到两年时间内被基本解决，甚至更快。
其他问题并不来自于是技术。例如，人类武装 AI 所构成的威胁。与大多数发明一样，人工智能可以用于善意或恶意。政府需要与私营部门合作，寻找限制风险的方法。
然后，还有 AI 可能失控的可能性。机器可以决定人类是一种威胁，得出它的利益与我们不同，或者简单地不再关心我们吗？可能，但这个问题今天并不比人工智能发展的过去几个月的发展更为紧迫。
超级智能 AI 在我们的未来中。与计算机相比，我们的大脑的操作速度极慢：大脑中的电信号的速度是硅芯片上信号速度的 1/100000。一旦开发人员能够推广学习算法并以计算机的速度运行它——这可能需要 10 年或 100 年的时间，我们将拥有一个非常强大的 AGI。它将能够完成人类大脑能够完成的一切，但没有任何关于其记忆大小或操作速度的实际限制。这将是一个意义深远的改变。
众所周知，这些“强大”的AI，可能能够确定自己的目标。这些目标会是什么呢？如果它们与人类的利益冲突会发生什么？我们应该尝试防止强 AI 的开发吗？这些问题将随着时间的推移变得更加紧迫。
但是过去几个月的突破并没有使我们走向强 AI。人工智能仍然不能控制物理世界，也不能确定自己的目标。《纽约时报》最近一篇关于与 ChatGPT 的对话的文章引起了很多关注，它宣布想成为一个人类。这是一个引人入胜的例子，展示了该模型表达情感的人类化程度，但它并不意味它拥有独立性。
三本书塑造了我的思考方式：Nick Bostrom 的《超级智能》、Max Tegmark的《生命 3.0》和 Jeff Hawkins 的《一千个大脑》。我不完全同意作者的观点，他们也不完全同意彼此。但是这三本书都写得很好，发人深省。
06 下一个领域
从事人工智能新用途以及改进该技术本身的公司数量将激增。例如，公司正在开发新的芯片，这些芯片将为人工智能提供所需的大量处理能力。一些使用光学开关——本质上是激光器——来减少能源消耗和降低制造成本。理想情况下，创新的芯片将允许您在自己的设备上运行 AI，而不是像今天一样在云中运行。
在软件方面，驱动 AI 学习的算法将变得更好。在某些领域，例如销售，开发人员可以通过限制它们工作的领域并为它们提供特定于这些领域的大量训练数据，使 AI 变得非常准确。但是一个大问题是，我们是否需要许多这些专门的 AI 用于不同的用途——例如一个用于教育，另一个用于办公室生产力——或者是否可能开发出一种人工通用智能，它可以学习任何任务。这两种方法都将面临巨大的竞争。
无论如何，AI 的主题将在可预见的未来主导公共舆论。我想给出三个对话原则。
首先，我们应该尝试平衡对 AI 不利方面的担忧——这些担忧是可以理解和有效的——以及它改善人们生活的能力。为了充分利用这项卓越的新技术，我们需要既防范风险，又将利益扩展到尽可能多的人。
其次，市场力量不会自然产生帮助最贫困人群的 AI 产品和服务。情况更有可能会是反过来的。通过可靠的资金和正确的政策，政府和慈善团体可以确保 AI 被用于减少不平等。正如世界需要其最聪明的人关注其最大的问题一样，我们需要将世界上最好的 AI 聚焦于其最大的问题。
虽然我们不应该等待这种情况发生，但有趣的是，是否人工智能会发现不平等并尝试减少它。您是否需要有道德感才能看到不平等，还是纯理性的AI也会看到它？如果它确实认识到不平等，它会建议我们对此做些什么？
最后，我们应该记住，我们只是刚开始探讨“AI 能做什么”， 无论今天它有什么局限性，在我们意识到之前这些限制都将消失。
我很幸运参与了个人计算机革命和互联网革命。我对今天这一时刻同样感到兴奋。这项新技术可以帮助世界的普罗大众改善生活。同时，世界需要建立规则，尽可能地让 AI 的好处掩盖过它的缺点， 让每个人都可以享受福祉，人工智能时代充满机遇和责任。
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.
