1. Biology and health 1. 生物与健康
Biology is probably the area where scientific progress has the greatest potential to directly and unambiguously improve the quality of human life. In the last century some of the most ancient human afflictions (such as smallpox) have finally been vanquished, but many more still remain, and defeating them would be an enormous humanitarian accomplishment. Beyond even curing disease, biological science can in principle improve the baseline quality of human health, by extending the healthy human lifespan, increasing control and freedom over our own biological processes, and addressing everyday problems that we currently think of as immutable parts of the human condition.
生物学可能是科学进步最有可能直接、毫不含糊地改善人类生活质量的领域。上个世纪，一些最古老的人类疾病（如天花）终于被消灭，但还有更多疾病仍然存在，战胜它们将是一项巨大的人道主义成就。除了治愈疾病之外，生物科学原则上还可以通过延长人类健康寿命、增强对自身生物过程的控制和自由以及解决我们目前认为是人类状况不可改变的日常问题来改善人类健康的基本质量。
In the “limiting factors” language of the previous section, the main challenges with directly applying intelligence to biology are data, the speed of the physical world, and intrinsic complexity (in fact, all three are related to each other). Human constraints also play a role at a later stage, when clinical trials are involved. Let’s take these one by one.
上一节中提到了“限制因素”，将智能直接应用于生物学的主要挑战是数据、物理世界的速度和内在复杂性（事实上，这三者是相互关联的）。在后期临床试验中，人为因素也会发挥作用。让我们一一介绍这些因素。
Experiments on cells, animals, and even chemical processes are limited by the speed of the physical world: many biological protocols involve culturing bacteria or other cells, or simply waiting for chemical reactions to occur, and this can sometimes take days or even weeks, with no obvious way to speed it up. Animal experiments can take months (or more) and human experiments often take years (or even decades for long-term outcome studies). Somewhat related to this, data is often lacking—not so much in quantity, but quality: there is always a dearth of clear, unambiguous data that isolates a biological effect of interest from the other 10,000 confounding things that are going on, or that intervenes causally in a given process, or that directly measures some effect (as opposed to inferring its consequences in some indirect or noisy way). Even massive, quantitative molecular data, like the proteomics data that I collected while working on mass spectrometry techniques, is noisy and misses a lot (which types of cells were these proteins in? Which part of the cell? At what phase in the cell cycle?).
细胞、动物甚至化学过程的实验都受到物理世界速度的限制：许多生物学方案涉及培养细菌或其他细胞，或者只是等待化学反应发生，而这有时可能需要几天甚至几周的时间，而且没有明显的方法来加快速度。动物实验可能需要数月（或更长时间），而人体实验通常需要数年（甚至数十年才能进行长期结果研究）。与此相关的是，数据通常缺乏——不是数量，而是质量：总是缺乏清晰、明确的数据，这些数据可以将感兴趣的生物学效应从正在发生的其他 10,000 个混杂因素中分离出来，或者对给定过程进行因果干预，或者直接测量某种影响（而不是以某种间接或嘈杂的方式推断其后果）。即使是大量的定量分子数据，比如我在研究质谱技术时收集的蛋白质组学数据，也是嘈杂的，并且会遗漏很多信息（这些蛋白质存在于哪些类型的细胞中？细胞的哪个部分？处于细胞周期的哪个阶段？）。
In part responsible for these problems with data is intrinsic complexity: if you’ve ever seen a diagram showing the biochemistry of human metabolism, you’ll know that it’s very hard to isolate the effect of any part of this complex system, and even harder to intervene on the system in a precise or predictable way. And finally, beyond just the intrinsic time that it takes to run an experiment on humans, actual clinical trials involve a lot of bureaucracy and regulatory requirements that (in the opinion of many people, including me) add unnecessary additional time and delay progress.
造成这些问题的部分原因是数据本身的复杂性：如果你曾经见过显示人类新陈代谢生物化学的图表 ，你就会知道它非常 很难隔离这个复杂系统的任何部分的影响，甚至更难干预 系统以精确或可预测的方式运行。最后，不仅仅是运行所需的固有时间 作为一项人体实验，实际的临床试验涉及许多官僚机构和监管要求 这（包括我在内很多人的看法）会增加不必要的额外时间并延缓进度。
Given all this, many biologists have long been skeptical of the value of AI and “big data” more generally in biology. Historically, mathematicians, computer scientists, and physicists who have applied their skills to biology over the last 30 years have been quite successful, but have not had the truly transformative impact initially hoped for. Some of the skepticism has been reduced by major and revolutionary breakthroughs like AlphaFold (which has just deservedly won its creators the Nobel Prize in Chemistry) and AlphaProteo11, but there’s still a perception that AI is (and will continue to be) useful in only a limited set of circumstances. A common formulation is “AI can do a better job analyzing your data, but it can’t produce more data or improve the quality of the data. Garbage in, garbage out”.
鉴于这一切，许多生物学家长期以来一直对人工智能和“大数据”在生物学中的价值持怀疑态度 。从历史上看，过去 30 年来，数学家、计算机科学家和物理学家将自己的技能应用于生物学，取得了相当大的成功，但并没有产生最初希望的真正变革性影响。一些怀疑论已经因 AlphaFold （其创造者刚刚当之无愧地获得了诺贝尔化学奖 ）和 AlphaProteo 11 等重大革命性突破而有所减弱，但人们仍然认为人工智能是（并将继续是） 仅在有限的情况下有用。一种常见的表述是“人工智能可以更好地分析 但它无法产生更多数据，也无法提高数据质量。“垃圾进，垃圾出”。
But I think that pessimistic perspective is thinking about AI in the wrong way. If our core hypothesis about AI progress is correct, then the right way to think of AI is not as a method of data analysis, but as a virtual biologist who performs all the tasks biologists do, including designing and running experiments in the real world (by controlling lab robots or simply telling humans which experiments to run – as a Principal Investigator would to their graduate students), inventing new biological methods or measurement techniques, and so on. It is by speeding up the whole research process that AI can truly accelerate biology. I want to repeat this because it’s the most common misconception that comes up when I talk about AI’s ability to transform biology: I am not talking about AI as merely a tool to analyze data. In line with the definition of powerful AI at the beginning of this essay, I’m talking about using AI to perform, direct, and improve upon nearly everything biologists do.
但我认为，悲观的观点是对人工智能的误解。如果我们关于人工智能发展的核心假设是正确的，那么正确的思考方式就不是将人工智能视为一种数据分析方法，而是将其视为一个虚拟生物学家，执行生物学家所做的所有任务，包括在现实世界中设计和运行实验（通过控制实验室机器人或简单地告诉人类要进行哪些实验——就像首席研究员告诉研究生那样），发明新的生物学方法或测量技术等等。人工智能真正加速生物学发展，就是通过加快整个研究过程 。 我想重复这一点，因为这是我在谈论人工智能改变生物学的能力时最常见的误解：我说的人工智能不仅仅是一种分析数据的工具。根据本文开头对强大人工智能的定义，我说的是使用人工智能来执行、指导和改进生物学家所做的几乎所有事情。
To get more specific on where I think acceleration is likely to come from, a surprisingly large fraction of the progress in biology has come from a truly tiny number of discoveries, often related to broad measurement tools or techniques12 that allow precise but generalized or programmable intervention in biological systems. There’s perhaps ~1 of these major discoveries per year and collectively they arguably drive >50% of progress in biology. These discoveries are so powerful precisely because they cut through intrinsic complexity and data limitations, directly increasing our understanding and control over biological processes. A few discoveries per decade have enabled both the bulk of our basic scientific understanding of biology, and have driven many of the most powerful medical treatments.
为了更具体地说明我认为加速可能来自何处，生物学进步中令人惊讶的很大一部分来自极少数的发现，这些发现通常与广泛的测量工具或技术有关 。 允许对生物系统进行精确但普遍或可编程的干预。也许 每年约有 1 项重大发现，它们共同推动了生物学 50% 以上的进步。 这些发现之所以如此强大，正是因为它们突破了内在的复杂性和数据 限制，直接增加了我们对生物过程的理解和控制。一些 每十年的发现不仅使我们对生物学有了大部分基本的科学理解，而且 推动了许多最有效的医疗治疗方法。
Some examples include: 一些示例包括：
CRISPR: a technique that allows live editing of any gene in living organisms (replacement of any arbitrary gene sequence with any other arbitrary sequence). Since the original technique was developed, there have been constant improvements to target specific cell types, increasing accuracy, and reducing edits of the wrong gene—all of which are needed for safe use in humans.
CRISPR ：一种允许实时编辑生物体内任何基因的技术（用任意序列替换任意基因序列）。自原始技术开发以来，一直在不断改进， 以针对特定细胞类型，提高准确性，减少对错误基因的编辑——所有这些都是确保人类安全使用所必需的。
Various kinds of microscopy for watching what is going on at a precise level: advanced light microscopes (with various kinds of fluorescent techniques, special optics, etc), electron microscopes, atomic force microscopes, etc.
用于精确观察正在发生的事情的各种显微镜：先进的光学显微镜（采用各种荧光技术、特殊光学等）、电子显微镜、原子力显微镜等。
Genome sequencing and synthesis, which has dropped in cost by several orders of magnitude in the last couple decades.
基因组测序和合成的成本在过去的几十年里已经下降了几个数量级。
Optogenetic techniques that allow you to get a neuron to fire by shining a light on it.
光遗传学技术可以让你通过照射光来激发神经元。
mRNA vaccines that, in principle, allow us to design a vaccine against anything and then quickly adapt it (mRNA vaccines of course became famous during COVID).
mRNA 疫苗 ，原则上，可以让我们设计一种针对任何疾病的疫苗，然后快速适应它（mRNA 疫苗当然在 COVID 期间变得有名）。
Cell therapies such as CAR-T that allow immune cells to be taken out of the body and “reprogrammed” to attack, in principle, anything.
CAR-T 等细胞疗法 原则上，它可以将免疫细胞从体内取出并“重新编程”以进行攻击， 任何事物。
Conceptual insights like the germ theory of disease or the realization of a link between the immune system and cancer13.
概念性见解，例如疾病的细菌理论或免疫系统与癌症之间的联系的认识 13 。
I’m going to the trouble of listing all these technologies because I want to make a crucial claim about them: I think their rate of discovery could be increased by 10x or more if there were a lot more talented, creative researchers. Or, put another way, I think the returns to intelligence are high for these discoveries, and that everything else in biology and medicine mostly follows from them.
我之所以要费尽心思列出所有这些技术，是因为我想对它们做出一个至关重要的声明： 我认为如果有更多才华横溢、富有创造力的研究人员，它们的发现率可能会提高 10 倍甚至更多 。 或者换句话说， 我认为这些发现的智力回报很高 ，生物学和医学中的其他一切大多都源于这些发现。
Why do I think this? Because of the answers to some questions that we should get in the habit of asking when we’re trying to determine “returns to intelligence”. First, these discoveries are generally made by a tiny number of researchers, often the same people repeatedly, suggesting skill and not random search (the latter might suggest lengthy experiments are the limiting factor). Second, they often “could have been made” years earlier than they were: for example, CRISPR was a naturally occurring component of the immune system in bacteria that’s been known since the 80’s, but it took another 25 years for people to realize it could be repurposed for general gene editing. They also are often delayed many years by lack of support from the scientific community for promising directions (see this profile on the inventor of mRNA vaccines; similar stories abound). Third, successful projects are often scrappy or were afterthoughts that people didn’t initially think were promising, rather than massively funded efforts. This suggests that it’s not just massive resource concentration that drives discoveries, but ingenuity.
我为什么会这样想？因为当我们试图确定“智力回报”时，我们应该养成问一些问题的习惯。首先，这些发现通常是由极少数研究人员做出的，通常是同一个人反复进行研究，这表明需要技巧而不是随机搜索（后者可能表明长时间的实验是限制因素）。其次，它们通常“可以”比现在早几年被创造出来：例如，CRISPR 是细菌免疫系统中自然产生的组成部分， 自 80 年代以来就为人所知 ，但人们又花了 25 年时间才意识到它可以重新用于一般的基因编辑。它们也常常因为缺乏科学界对有希望的方向的支持而被推迟了很多年（参见 mRNA 疫苗发明者的简介 ；类似的故事比比皆是）。第三，成功的项目往往是零碎的，或者是事后才想到的，人们最初认为它们没有前景，而不是得到了大量资助的努力。这表明，推动发现的不仅是大规模的资源集中，还有独创性。
Finally, although some of these discoveries have “serial dependence” (you need to make discovery A first in order to have the tools or knowledge to make discovery B)—which again might create experimental delays—many, perhaps most, are independent, meaning many at once can be worked on in parallel. Both these facts, and my general experience as a biologist, strongly suggest to me that there are hundreds of these discoveries waiting to be made if scientists were smarter and better at making connections between the vast amount of biological knowledge humanity possesses (again consider the CRISPR example). The success of AlphaFold/AlphaProteo at solving important problems much more effectively than humans, despite decades of carefully designed physics modeling, provides a proof of principle (albeit with a narrow tool in a narrow domain) that should point the way forward.
最后，尽管其中一些发现具有“连续依赖性”（你需要先做出发现 A，才能拥有做出发现 B 的工具或知识）——这又可能导致实验延迟——但许多发现，或许是大多数发现，都是独立的，这意味着可以同时并行进行多项工作。这两个事实，以及我作为一名生物学家的一般经验，都强烈地告诉我，如果科学家更聪明，更善于将人类拥有的大量生物学知识联系起来（再次考虑 CRISPR 的例子），那么还有数百个这样的发现等待着我们去实现。尽管经过了数十年精心设计的物理建模， AlphaFold / AlphaProteo 仍然能够比人类更有效地解决重要问题，这提供了一个原理证明（尽管是在狭窄的领域中使用了狭窄的工具），应该可以指明前进的方向。
Thus, it’s my guess that powerful AI could at least 10x the rate of these discoveries, giving us the next 50-100 years of biological progress in 5-10 years.14 Why not 100x? Perhaps it is possible, but here both serial dependence and experiment times become important: getting 100 years of progress in 1 year requires a lot of things to go right the first time, including animal experiments and things like designing microscopes or expensive lab facilities. I’m actually open to the (perhaps absurd-sounding) idea that we could get 1000 years of progress in 5-10 years, but very skeptical that we can get 100 years in 1 year. Another way to put it is I think there’s an unavoidable constant delay: experiments and hardware design have a certain “latency” and need to be iterated upon a certain “irreducible” number of times in order to learn things that can’t be deduced logically. But massive parallelism may be possible on top of that15.
因此，我猜测强大的人工智能至少可以将这些发现的速度提高 10 倍，让我们在 5-10 年内实现未来 50-100 年的生物学进步 。14 为什么不提高 100 倍？也许有可能，但这里既有序列依赖性 实验时间变得很重要：在一年内取得 100 年的进步需要很多东西 第一次就做对，包括动物实验和设计显微镜或 昂贵的实验室设施。我实际上愿意接受这个（也许听起来很荒谬）的想法，即我们可以 5-10 年内实现 1000 年的进步，但 1 年内实现 100 年进步的可能性非常低。换句话说，我认为存在不可避免的持续延迟：实验和硬件设计具有一定的“延迟”，需要进行一定次数的“不可减少”迭代，才能学习无法通过逻辑推理得出的东西。但在此基础上实现大规模并行性可能是可能的 15 。
What about clinical trials? Although there is a lot of bureaucracy and slowdown associated with them, the truth is that a lot (though by no means all!) of their slowness ultimately derives from the need to rigorously evaluate drugs that barely work or ambiguously work. This is sadly true of most therapies today: the average cancer drug increases survival by a few months while having significant side effects that need to be carefully measured (there’s a similar story for Alzheimer’s drugs). This leads to huge studies (in order to achieve statistical power) and difficult tradeoffs which regulatory agencies generally aren’t great at making, again because of bureaucracy and the complexity of competing interests.
那临床试验呢？尽管临床试验中存在很多官僚主义和拖延，但事实是，临床试验的拖延很大一部分（但绝不是全部！）最终源于需要严格评估那些效果不佳或效果不明确的药物。可悲的是，当今大多数疗法都是如此：普通的抗癌药物可以延长患者几个月的生存期，但副作用很大，需要仔细测量（阿尔茨海默病药物也有类似情况）。这导致需要进行大量研究（以获得统计能力）和艰难的权衡，而监管机构通常不擅长做出权衡，这同样是因为官僚主义和利益竞争的复杂性。
When something works really well, it goes much faster: there’s an accelerated approval track and the ease of approval is much greater when effect sizes are larger. mRNA vaccines for COVID were approved in 9 months—much faster than the usual pace. That said, even under these conditions clinical trials are still too slow—mRNA vaccines arguably should have been approved in ~2 months. But these kinds of delays (~1 year end-to-end for a drug) combined with massive parallelization and the need for some but not too much iteration (“a few tries”) are very compatible with radical transformation in 5-10 years. Even more optimistically, it is possible that AI-enabled biological science will reduce the need for iteration in clinical trials by developing better animal and cell experimental models (or even simulations) that are more accurate in predicting what will happen in humans. This will be particularly important in developing drugs against the aging process, which plays out over decades and where we need a faster iteration loop.
当某种药物确实效果很好时，它的进展会快得多：有一个加速审批的轨道，当效果规模较大时，审批的难度就会大得多。新冠的 mRNA 疫苗在 9 个月内就获得了批准——比通常的速度快得多。话虽如此，即使在这些条件下，临床试验的速度仍然太慢——mRNA 疫苗应该在约 2 个月内获得批准 。但这种延迟（一种药物从头到尾大约需要 1 年）加上大规模并行化以及需要进行一些但不是太多的迭代（“几次尝试”），与 5-10 年内的彻底转变非常兼容。更乐观的是， 人工智能支持的生物科学可能会减少临床试验中对迭代的需求 通过开发更好的动物和细胞实验模型（甚至模拟）来进行试验，这些模型更 准确预测人类会发生什么。这对于发展 抗衰老药物会持续几十年，因此我们需要更快的迭代循环。
Finally, on the topic of clinical trials and societal barriers, it is worth pointing out explicitly that in some ways biomedical innovations have an unusually strong track record of being successfully deployed, in contrast to some other technologies16. As mentioned in the introduction, many technologies are hampered by societal factors despite working well technically. This might suggest a pessimistic perspective on what AI can accomplish. But biomedicine is unique in that although the process of developing drugs is overly cumbersome, once developed they generally are successfully deployed and used.
最后，关于临床试验和社会障碍的话题，值得明确指出的是，与其他一些技术相比，生物医学创新在某些方面有着异常强劲的成功部署记录 16 。正如引言中提到的，许多技术尽管在技术上运行良好，但受到社会因素的阻碍。这可能表明人们对人工智能能取得的成就持悲观态度 。 但生物医学的独特之处在于，尽管开发药物的过程过于繁琐，但一旦开发出来，它们通常就能成功部署和使用。
To summarize the above, my basic prediction is that AI-enabled biology and medicine will allow us to compress the progress that human biologists would have achieved over the next 50-100 years into 5-10 years. I’ll refer to this as the “compressed 21st century”: the idea that after powerful AI is developed, we will in a few years make all the progress in biology and medicine that we would have made in the whole 21st century.
综上所述，我的基本预测是，人工智能赋能的生物学和医学将使我们能够将人类生物学家在未来 50-100 年内取得的进展压缩到 5-10 年内。我将其称为“压缩的 21 世纪”：即在开发出强大的人工智能后，我们将在几年内取得整个 21 世纪在生物学和医学方面取得的所有进展。
Although predicting what powerful AI can do in a few years remains inherently difficult and speculative, there is some concreteness to asking “what could humans do unaided in the next 100 years?”. Simply looking at what we’ve accomplished in the 20th century, or extrapolating from the first 2 decades of the 21st, or asking what “10 CRISPR’s and 50 CAR-T’s” would get us, all offer practical, grounded ways to estimate the general level of progress we might expect from powerful AI.
尽管预测强大的人工智能在未来几年内能做什么仍然具有固有的困难和推测性，但问“未来 100 年，人类可以不借助外力做什么？”还是有一定的现实意义的。只要看看我们在 20 世纪取得的成就，或者从 21 世纪的前 20 年进行推断，或者问“10 个 CRISPR 和 50 个 CAR-T”能给我们带来什么，所有这些都提供了实用、有根据的方法来估计我们可能期望强大的人工智能取得的总体进步水平。
Below I try to make a list of what we might expect. This is not based on any rigorous methodology, and will almost certainly prove wrong in the details, but it’s trying to get across the general level of radicalism we should expect:
下面我试着列出我们可能期望的内容。这并非基于任何严格的方法， 和 几乎肯定会在细节上被证明是错误的，但它试图传达一般的 等级 我们应该期待激进主义：
Reliable prevention and treatment of nearly all17 natural infectious disease. Given the enormous advances against infectious disease in the 20th century, it is not radical to imagine that we could more or less “finish the job” in a compressed 21st. mRNA vaccines and similar technology already point the way towards “vaccines for anything”. Whether infectious disease is fully eradicated from the world (as opposed to just in some places) depends on questions about poverty and inequality, which are discussed in Section 3.
可靠地预防和治疗几乎所有 17 种自然传染病。 鉴于 20 世纪对抗传染病的巨大进步， 想象一下，我们或多或少可以在压缩的 21 世纪“完成这项工作”。mRNA 疫苗和 相似的 技术已经指明了“ 万能疫苗 ”的方向。传染病能否从世界彻底根除 （而不是只在某些地方根除）取决于贫困和不平等问题，这些问题将在第 3 部分进行讨论。
Elimination of most cancer. Death rates from cancer have been dropping ~2% per year for the last few decades; thus we are on track to eliminate most cancer in the 21st century at the current pace of human science. Some subtypes have already been largely cured (for example some types of leukemia with CAR-T therapy), and I’m perhaps even more excited for very selective drugs that target cancer in its infancy and prevent it from ever growing. AI will also make possible treatment regimens very finely adapted to the individualized genome of the cancer—these are possible today, but hugely expensive in time and human expertise, which AI should allow us to scale. Reductions of 95% or more in both mortality and incidence seem possible. That said, cancer is extremely varied and adaptive, and is likely the hardest of these diseases to fully destroy. It would not be surprising if an assortment of rare, difficult malignancies persists.
消除大多数癌症 。过去几十年来，癌症死亡率每年下降约 2% ；因此，以目前人类科学的发展速度，我们有望在 21 世纪消除大多数癌症。一些亚型癌症已经基本治愈（例如，使用 CAR-T 疗法治疗某些类型的白血病），我可能更期待能够研发出针对早期癌症并预防癌症的选择性药物 不断增长。人工智能还将使治疗方案能够非常精细地适应癌症的个性化基因组——这些方案目前是可能的，但在时间和人类专业知识方面非常昂贵，人工智能应该可以让我们扩大规模。死亡率和发病率降低 95% 或更多似乎是可能的。话虽如此，癌症的种类繁多，适应性强，可能是这些疾病中最难完全摧毁的。如果各种罕见、难治的恶性肿瘤持续存在，也就不足为奇了。
Very effective prevention and effective cures for genetic disease. Greatly improved embryo screening will likely make it possible to prevent most genetic disease, and some safer, more reliable descendant of CRISPR may cure most genetic disease in existing people. Whole-body afflictions that affect a large fraction of cells may be the last holdouts, however.
非常有效的预防和有效的治疗遗传病 。大大 改进 胚胎筛选 有可能预防大多数遗传疾病，并且一些更安全、更可靠 CRISPR 的后代可能治愈现存人类的大多数遗传疾病。全身疾病 那 然而，影响大部分细胞的可能是最后的坚持者。
Prevention of Alzheimer’s. We’ve had a very hard time figuring out what causes Alzheimer’s (it is somehow related to beta-amyloid protein, but the actual details seem to be very complex). It seems like exactly the type of problem that can be solved with better measurement tools that isolate biological effects; thus I am bullish about AI’s ability to solve it. There is a good chance it can eventually be prevented with relatively simple interventions, once we actually understand what is going on. That said, damage from already-existing Alzheimer’s may be very difficult to reverse.
预防阿尔茨海默氏症 。我们一直很难弄清楚阿尔茨海默氏症的病因（它与β-淀粉样蛋白有某种关联，但实际细节似乎非常复杂 ）。 它 似乎正是可以通过更好的测量工具来解决的问题 隔离 生物效应；因此我对人工智能解决这个问题的能力充满信心。它很有可能 能 一旦我们真正理解了什么，最终可以通过相对简单的干预措施来预防 是 正在发生。话虽如此，已经存在的阿尔茨海默病造成的损害可能很难逆转。
Improved treatment of most other ailments. This is a catch-all category for other ailments including diabetes, obesity, heart disease, autoimmune diseases, and more. Most of these seem “easier” to solve than cancer and Alzheimer’s and in many cases are already in steep decline. For example, deaths from heart disease have already declined over 50%, and simple interventions like GLP-1 agonists have already made huge progress against obesity and diabetes.
改善大多数其他疾病的治疗 。这是一个包罗万象的类别， 其他 糖尿病、肥胖症、心脏病、自身免疫性疾病等。大多数 这些 似乎比癌症和阿尔茨海默病更容易解决，而且在许多情况下已经陷入困境 衰退。 例如，心脏病造成的死亡人数已经下降了 50% 以上，简单的干预措施 喜欢 GLP-1 激动剂已经在治疗肥胖症方面取得了巨大进展，并且 糖尿病。
Biological freedom. The last 70 years featured advances in birth control, fertility, management of weight, and much more. But I suspect AI-accelerated biology will greatly expand what is possible: weight, physical appearance, reproduction, and other biological processes will be fully under people’s control. We’ll refer to these under the heading of biological freedom: the idea that everyone should be empowered to choose what they want to become and live their lives in the way that most appeals to them. There will of course be important questions about global equality of access; see Section 3 for these.
生物自由 。过去 70 年，节育、生育、 体重管理等方面取得了进展。但我怀疑人工智能加速的 生物学 将极大地扩展可能性：体重、外貌、生殖等 生物过程将完全由人类控制。我们将在 标题 生物自由： 每个人都应该有权选择自己想要的东西 到 成为他们自己最喜欢的人，并以他们最喜欢的方式生活。当然会有 重要的 关于全球访问平等的问题；请参阅第 3 节。
Doubling of the human lifespan18. This might seem radical, but life expectancy increased almost 2x in the 20th century (from ~40 years to ~75), so it’s “on trend” that the “compressed 21st” would double it again to 150. Obviously the interventions involved in slowing the actual aging process will be different from those that were needed in the last century to prevent (mostly childhood) premature deaths from disease, but the magnitude of change is not unprecedented19. Concretely, there already exist drugs that increase maximum lifespan in rats by 25-50% with limited ill-effects. And some animals (e.g. some types of turtle) already live 200 years, so humans are manifestly not at some theoretical upper limit. At a guess, the most important thing that is needed might be reliable, non-Goodhart-able biomarkers of human aging, as that will allow fast iteration on experiments and clinical trials. Once human lifespan is 150, we may be able to reach “escape velocity”, buying enough time that most of those currently alive today will be able to live as long as they want, although there’s certainly no guarantee this is biologically possible.
人类寿命翻倍 18 。 这听起来似乎有些激进，但 20 世纪的预期寿命几乎增加了 2 倍 （从约 40 岁增加到约 75 岁），因此“趋势”是“压缩的 21 世纪”将再次翻倍至 150 岁。显然，减缓实际衰老过程所涉及的干预措施与上个世纪预防（主要是儿童）因疾病而过早死亡所需的干预措施不同，但变化的幅度并非史无前例 19 。具体来说，已经存在可以将大鼠的最大寿命延长 25-50% 且副作用有限的药物 。并且 一些 动物（例如某些类型的乌龟）已经活了 200 年，因此人类显然还没有达到某种程度 理论上的上限。猜测一下，最重要的可能是可靠的， 非古德哈特 人类衰老的生物标志物，因为这将允许实验和临床试验的快速迭代。 一旦人类寿命达到 150 岁，我们或许能够达到“逃逸速度”，从而赢得足够的时间 最多 的当今人类将能够活到他们想要的年纪，尽管 当然 不能保证这在生物学上是可能的。
It is worth looking at this list and reflecting on how different the world will be if all of it is achieved 7-12 years from now (which would be in line with an aggressive AI timeline). It goes without saying that it would be an unimaginable humanitarian triumph, the elimination all at once of most of the scourges that have haunted humanity for millennia. Many of my friends and colleagues are raising children, and when those children grow up, I hope that any mention of disease will sound to them the way scurvy, smallpox, or bubonic plague sounds to us. That generation will also benefit from increased biological freedom and self-expression, and with luck may also be able to live as long as they want.
值得一看这份清单，并思考如果从现在起 7-12 年后实现所有这些目标（这将符合积极的 AI 时间表），世界将会有多么不同。毋庸置疑，这将是一场难以想象的人道主义胜利，一下子消除了困扰人类数千年的大多数祸害。我的许多朋友和同事都在抚养孩子，当这些孩子长大后，我希望他们听到的任何疾病听起来都像我们听到坏血病、 天花或鼠疫一样。这一代人还将受益于生物自由和自我表达的增加，如果幸运的话，他们也许能够活到他们想要的年龄。
It’s hard to overestimate how surprising these changes will be to everyone except the small community of people who expected powerful AI. For example, thousands of economists and policy experts in the US currently debate how to keep Social Security and Medicare solvent, and more broadly how to keep down the cost of healthcare (which is mostly consumed by those over 70 and especially those with terminal illnesses such as cancer). The situation for these programs is likely to be radically improved if all this comes to pass20, as the ratio of working age to retired population will change drastically. No doubt these challenges will be replaced with others, such as how to ensure widespread access to the new technologies, but it is worth reflecting on how much the world will change even if biology is the only area to be successfully accelerated by AI.
除了一小部分期待强大 AI 的人之外，这些变化会让所有人大吃一惊。例如，美国数千名经济学家和政策专家目前正在讨论如何保持社会保障和医疗保险的偿付能力，更广泛地说，如何降低医疗成本（医疗费用主要来自 70 岁以上的人，尤其是那些患有癌症等绝症的人）。如果这一切在 20 年内实现，这些项目的状况可能会得到根本改善，因为工作年龄人口与退休人口的比例将发生巨大变化。毫无疑问，这些挑战将被其他挑战所取代，比如如何确保广泛使用新技术，但值得思考的是，即使生物学是唯一一个被 AI 成功加速的领域，世界将发生多大的变化。
参考
https://darioamodei.com/machines-of-loving-grace
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