There’s No Fire Alarm for Artificial General Intelligence

||Analysis

What is the function of a fire alarm?

One might think that the function of a fire alarm is to provide you with important evidence about a fire existing, allowing you to change your policy accordingly and exit the building.

In the classic experiment by Latane and Darley in 1968, eight groups of three students each were asked to fill out a questionnaire in a room that shortly after began filling up with smoke. Five out of the eight groups didn’t react or report the smoke, even as it became dense enough to make them start coughing. Subsequent manipulations showed that a lone student will respond 75% of the time; while a student accompanied by two actors told to feign apathy will respond only 10% of the time. This and other experiments seemed to pin down that what’s happening is pluralistic ignorance. We don’t want to look panicky by being afraid of what isn’t an emergency, so we try to look calm while glancing out of the corners of our eyes to see how others are reacting, but of course they are also trying to look calm.

(I’ve read a number of replications and variations on this research, and the effect size is blatant. I would not expect this to be one of the results that dies to the replication crisis, and I haven’t yet heard about the replication crisis touching it. But we have to put a maybe-not marker on everything now.)

A fire alarm creates common knowledge, in the you-know-I-know sense, that there is a fire; after which it is socially safe to react. When the fire alarm goes off, you know that everyone else knows there is a fire, you know you won’t lose face if you proceed to exit the building.

The fire alarm doesn’t tell us with certainty that a fire is there. In fact, I can’t recall one time in my life when, exiting a building on a fire alarm, there was an actual fire. Really, a fire alarm isweaker火灾的证据比从门下传来的烟雾的证据。

但是火警警报告诉我们，对火的反应在社会上可以。它可以肯定地向我们保证，如果我们现在以有序的方式退出，我们不会感到尴尬。

在我看来，这是人们错误地对自己的信念的信念，例如当有人大声认可其城市的球队赢得大型比赛时，就会要求下注。他们没有自觉地区分大喊球队将获胜的令人振奋的令人振奋，这是预期球队将获胜的感觉。

When people look at the smoke coming from under the door, I think they think their uncertain wobbling feeling comes from not assigning the fire a high-enough probability of really being there, and that they’re reluctant to act for fear of wasting effort and time. If so, I think they’re interpreting their own feelings mistakenly. If that was so, they’d get the same wobbly feeling on hearing the fire alarm, or even more so, because fire alarms correlate to fire less than does smoke coming from under a door. The uncertain wobbling feeling comes from the worry that others believe differently, not the worry that the fire isn’t there. The reluctance to act is the reluctance to be seen looking foolish, not the reluctance to waste effort. That’s why the student alone in the room does something about the fire 75% of the time, and why people have no trouble reacting to the much weaker evidence presented by fire alarms.

It’s now and then proposed that we ought to start reacting later to the issues of Artificial General Intelligence (background here），因为据说，我们离它很远，以至于今天不可能做生产力的工作。

（有关今天有事物可行的直接论证，请参阅：Soares and Fallenstein（2014/2017）；Amodei, Olah, Steinhardt, Christiano, Schulman, and Mané (2016）；或泰勒，尤德科夫斯基，拉维克托尔和克里奇（2016）。

(If none of those papers existed or if you were an AI researcher who’d read them but thought they were all garbage, and you wished you could work on alignment but knew of nothing you could do, the wise next step would be to sit down and spend two hours by the clock sincerely trying to think of possible approaches. Preferably without self-sabotage that makes sure you don’t come up with anything plausible; as might happen if, hypothetically speaking, you would actually find it much more comfortable to believe there was nothing you ought to be working on today, because e.g. then you could work on other things that interested you more.)

（但是不要紧。）

So if AGI seems far-ish away, and you think the conclusion licensed by this is that you can’t do any productive work on AGI alignment yet, then the implicit alternative strategy on offer is: Wait for some unspecified future event that tells us AGI is coming near; andthen我们都知道，可以开始进行AGI对齐是可以的。

This seems to me to be wrong on a number of grounds. Here are some of them.

一：正如斯图尔特·罗素（Stuart Russell）所观察到的那样，如果您从太空获得无线电信号并与望远镜一起发现飞船，并且您知道外星人在三十年来降落，那么今天您仍然开始考虑这一点。

You’re not like, “Meh, that’s thirty years off, whatever.” You certainly don’t casually say “Well, there’s nothing we can do until they’re closer.” Not without spending two hours, or at leastfive minutes按照时钟的态度，关于您现在是否应该开始的任何东西进行头脑风暴。

如果您说外星人在三十年后来了，因此您今天什么都不做……好吧，如果这些是更有效的时间s, somebody would ask for a schedule of what you thought ought to be done, starting when, how long before the aliens arrive. If you didn’t have that schedule ready, they’d know that you weren’t operating according to a worked table of timed responses, but just procrastinating and doing nothing; and they’d correctly infer that you probably hadn’t searched very hard for things that could be done today.

In Bryan Caplan’s terms, anyone who seems quite casual about the fact that “nothing can be done now to prepare” about the aliens ismissing a mood; they should be much more alarmed at not being able to think of any way to prepare. And maybe ask if somebody else has come up with any ideas? But never mind.

二：历史表明，对于公众，甚至对于科学家而言，甚至不在关键的内心圈子中，甚至对于科学家来说inthat key circle, it is very often the case that key technological developments still seem decades away, five years before they show up.

In 1901, two years before helping build the first heavier-than-air flyer, Wilbur Wright told his brother that powered flight wasfifty years away。

1939年，他亲自监督了一堆铀砖的第一个关键链反应前三年，恩里科·费米（Enrico Fermi）发声90% confidencethat it was不可能的to use uranium to sustain a fission chain reaction. I believe Fermi also said a year after that, aka two years before the denouement, thatif裂变的净功率甚至可能是可能的（随后他授予了一些更大的合理性），然后休假五十年。但是为此，我忽略了引用。

当然，如果您不是赖特兄弟（Wright Brothers）或Enrico Fermi，那么您会感到惊讶。世界上大多数人都知道，当原子武器醒来是广岛的头条新闻时，它们是一件事情。有尊敬的知识分子说四年after赖特传单that heavier-than-air flight was impossible, because knowledge propagated more slowly back then.

Were there events that, in事后看，今天，我们可以看出，迹象表明比空中飞行或核能更重？Sure, but if you go back and read the actual newspapers from that time and see what people actually said about it then, you’ll see that they did not know that these were signs, or that they were very uncertain that these might be signs. Some playing the part of Excited Futurists proclaimed that big changes were imminent, I expect, and others playing the part of Sober Scientists tried to pour cold water on all that childish enthusiasm; I expect that part was more or less exactly the same decades earlier. If somewhere in that din was a superforecaster who said “decades” when it was decades and “5 years” when it was five, good luck noticing them amid all the noise. More likely, the superforecasters were the ones who said “Could be tomorrow, could be decades” both when the big development was a day away and when it was decades away.

One of the major modes by which hindsight bias makes us feel that the past was more predictable than anyone was actually able to predict at the time, is that in hindsight we know what we ought to notice, and we fixate on only one thought as to what each piece of evidence indicates. If you look at what people actually say at the time, historically, they’ve usually got no clue what’s about to happen three months before it happens, because they don’t know which signs are which.

我是说你could说“ Agi已有50年了”，并让这些词恰好是真实的。人们还说，实际上数十年来数十年来，动力飞行距离几十年了，这些人恰好是正确的。问题在于，无论哪种方式，一切都看起来都一样，如果您实际上是在生活历史，而不是以后阅读它。

It’s not that whenever somebody says “fifty years” the thing always happens in two years. It’s that this confident prediction of things being far away corresponds to an epistemic state about the technology that feels the same way internally until you are very very close to the big development. It’s the epistemic state of “Well, I don’t see how to do the thing” and sometimes you say that fifty years off from the big development, and sometimes you say it two years away, and sometimes you say it while the Wright Flyer is flying somewhere out of your sight.

Three:Progress is driven by peak knowledge, not average knowledge.

如果费米（Fermi）和赖特（Wrights）看不到三年的时间，请想象其他任何人都必须看到它有多难。

If you’re not at the global peak of knowledge of how to do the thing, and looped in on all the progress being made at what will turn out to be the leading project, you aren’t going to be able to see of your own knowledge根本迫在眉睫的大发展。Unless you are very good at perspective-taking in a way that wasn’t necessary in a hunter-gatherer tribe, and very good at realizing that other people may know techniques and ideas of which you have no inkling even that you do not know them. If you don’t consciously compensate for the lessons of history in this regard; then you will promptly say the decades-off thing. Fermi wasn’t still thinking that net nuclear energy was impossible or decades away by the time he got to 3 months before he built the first pile, because at that point Fermi was looped in on everything and saw how to do it. But anyone not looped in probably still felt like it was fifty years away while the actual pile was fizzing away in a squash court at the University of Chicago.

人们似乎并没有自动弥补这一事实，即大型发展的时机是该领域的峰值知识的函数，这是最了解并拥有最好的想法的人们所感动的门槛。尽管他们自己有平均知识；因此，他们自己所知道的并不是关于何时发生重大发展的有力证据。我认为他们根本没有考虑这一点，而只是利用自己的困难感使它注视着它。如果他们认为比这更故意和反思的事情，并将真实的工作纳入纠正可能偏向镜片的因素，那么他们就不会在我可以阅读的任何地方写下自己的推理。

要知道AGI已经几十年了，我们需要对Agi足够了解，以了解缺少哪些难题，以及这些作品的难度。在难题完成之前，这种见解不太可能可用。这也可以说，对于前缘以外的任何人来说，这个难题看起来比在边缘看起来更不完整。该项目可能会在证明它们之前发布其理论，尽管我希望不要。但是现在也有未经证实的理论。

And again, that’s not to say that people saying “fifty years” is a certain sign that something is happening in a squash court; they were saying “fifty years” sixty years ago too. It’s saying that anyone who thinks technological时间线are actually forecastable, in advance, by people who are not looped in to the leading project’s progress reports and who don’t share all the best ideas about exactly how to do the thing and how much effort is required for that, is learning the wrong lesson from history. In particular, from reading history books that neatly lay out lines of progress and their visible signs that we all knownow是important and evidential. It’s sometimes possible to say useful conditional things about the consequences of the big development whenever it happens, but it’s rarely possible to make confident predictions about the定时的发展,除了一个一到两年或者izon. And if you are one of the rare people who can call the timing, if people like that even exist, nobody else knows to pay attention to you and not to the Excited Futurists or Sober Skeptics.

四：The future uses different tools, and can therefore easily do things that are very hard now, or do with difficulty things that are impossible now.

Why do we know that AGI is decades away? In popular articles penned by heads of AI research labs and the like, there are typically three prominent reasons given:

(A) The author does not know how to build AGI using present technology. The author does not know where to start.

(B) The author thinks it is really very hard to do the impressive things that modern AI technology does, they have to slave long hours over a hot GPU farm tweaking hyperparameters to get it done. They think that the public does not appreciate how hard it is to get anything done right now, and is panicking prematurely because the public thinks anyone can just fire up Tensorflow and build a robotic car.

(C) The author spends a lot of time interacting with AI systems and therefore is able to personally appreciate all the ways in which they are still stupid and lack common sense.

We’ve now considered some aspects of argument A. Let’s consider argument B for a moment.

Suppose I say: “It is now possible for one comp-sci grad to do in a week anything that N+ years ago the research community could do with neural networks根本。”n有多大？

在Twitter上我得到了一些答案whose credentials I don’t know, but the most common answer was five, which sounds about right to me based on my own acquaintance with machine learning. (Though obviously not as a literal universal, because reality is never that neat.) If you could do something in 2012 period, you can probably do it fairly straightforwardly with modern GPUs, Tensorflow, Xavier initialization, batch normalization, ReLUs, and Adam or RMSprop or just stochastic gradient descent with momentum. The modern techniques are just that much better. To be sure, there are things we can’t do now with just those simple methods, things that require tons more work, but those things were not possible at all in 2012.

In machine learning, when you can do something at all, you are probably at most a few years away from being able to do it easily using the future’s much superior tools. From this standpoint, argument B, “You don’t understand how hard it is to do what we do,” is something of a non-sequitur when it comes to timing.

声明B对我来说像卢瑟福所表达的同样情绪in 1933when he called net energy from atomic fission “moonshine”. If you were a nuclear physicist in 1933 then you had to split all your atoms by hand, by bombarding them with other particles, and it was a laborious business. If somebody talked about getting net energy from atoms, maybe it made you feel that you were unappreciated, that people thought your job was easy.

But of course this will always be the lived experience for AI engineers on serious frontier projects. You don’t get paid big bucks to do what a grad student can do in a week (unless you’re working for a bureaucracy with no clue about AI; but that’s not Google or FB). Your personal experience will总是那就是您要花几个月的时间付钱。因此，这种个人经历的改变不是您可以用作火灾警报的东西。

Those playing the part of wiser sober skeptical scientists would obviously agree in the abstract that our tools will improve; but in the popular articles they pen, they just talk about the painstaking difficulty of this year’s tools. I think that when they’re in that mode they are not even trying to forecast what the tools will be like in 5 years; they haven’t written down any such arguments as part of the articles I’ve read. I think that when they tell you that AGI is decades off, they are literally giving an estimate of对他们有多长时间就像使用当前的工具和知识建立AGI一样。这就是为什么他们强调搅动线性代数堆的困难，直到它吐出好的答案。我认为他们根本没有想象这种体验可能会在少于五十年的时间内变化。如果他们明确考虑了基于当前的主观难度估算未来技术时间表的偏见，并试图弥补这种偏见，那么他们尚未写下我阅读的任何地方的推理。我也从未听说过这种预测方法，从历史上看得很好。

五：Okay, let’s be blunt here. I don’t think most of the discourse about AGI being far away (或者that it’s near) is being generated by models of future progress in machine learning. I don’t think we’re looking at wrong models; I think we’re looking at no models.

I was once at a conference where there was a panel full of famous AI luminaries, and most of the luminaries were nodding and agreeing with each other that of course AGI was very far off, except for two famous AI luminaries who stayed quiet and let others take the microphone.

I got up in Q&A and said, “Okay, you’ve all told us that progress won’t be all that fast. But let’s be more concrete and specific. I’d like to know what’s theleast您非常自信的令人印象深刻的成就cannot在未来两年内完成。”

There was a silence.

Eventually, two people on the panel ventured replies, spoken in a rather more tentative tone than they’d been using to pronounce that AGI was decades out. They named “A robot puts away the dishes from a dishwasher without breaking them”, andWinograd schemas。具体而言，“我非常有信心的是，Winograd模式（最近我们的结果在50％，范围为60％），在未来两年中，无论人们使用什么技术。”

该小组后的几个月后，Winograd模式出人意料地突破了。突破并没有破解80％，因此有三个加油助威的差距，但我希望预测因子现在还剩一年的时间可能会感到稍微紧张。（我认为这不是我记得读过的突破，但罗布出现了this paperas an example of one that could have been submitted at most 44 days after the above conference and gets up to 70%.)

但这不是重点。关键是在我的问题之后倒下的沉默，最终我只得到了两个回复，以暂定的音调说。When I asked for concrete feats that were impossible in the next two years, I think that that’s when the luminaries on that panel switched to trying to build a mental model of future progress in machine learning, asking themselves what they could or couldn’t predict, what they knew or didn’t know. And to their credit, most of them did know their profession well enough to realize that forecasting future boundaries around a rapidly moving field is actuallyreally hard，没有人知道下个月的Arxiv会出现什么，并且他们需要将广泛的可信度间隔放置在二十四个月后的Arxiv论文中可能会发生多少进展。

(Also, Demis Hassabis was present, so they all knew that if they named something insufficiently impossible, Demis would have DeepMind go and do it.)

我提出的问题是与小组讨论完全不同的类型，需要心理上下文转换：组装的灯具实际上必须尝试咨询他们在机器学习中的粗糙，稀缺的直觉模型，并找出未来的经验，并找出未来的经验，如果有的话，他们的领域模型肯定会在两年的时间内禁止。与其很好地散发出具有社会性的言语行为，旨在杀死对AGI的炒作，并受到观众的可预测掌声。

I’ll be blunt: I don’t think the confident long-termism has been thought out at all. If your model has the extraordinary power to say what will be impossible in ten years after another one hundred and twenty months of arXiv papers, then you ought to be able to say much weaker things that are impossible in two years, and you should have those predictions queued up and ready to go rather than falling into nervous silence after being asked.

In reality, the two-year problem is hard and the ten-year problem is laughably hard. The future is hard to predict in general, our predictive grasp on a rapidly changing and advancing field of science and engineering is very weak indeed, and it doesn’t permit narrow credible intervals on what can’t be done.

Grace et al. (2017) surveyed the predictions of 352 presenters at ICML and NIPS 2015. Respondents’ aggregate forecast was that the proposition “all occupations are fully automatable” (in the sense that “for any occupation, machines could be built to carry out the task better and more cheaply than human workers”) will not reach 50% probability until 121 years hence. Except that a randomized subset of respondents were instead asked the slightly different question of “when unaided machines can accomplish every task better and more cheaply than human workers”, and in this case held that this was 50% likely to occurwithin 44 years。

That’s what happens when you ask people to produce an estimate they can’t estimate, and there’s a social sense of what the desirable verbal behavior is supposed to be.

When I observe that there’s no fire alarm for AGI, I’m not saying that there’s no possible equivalent of smoke appearing from under a door.

What I’m saying rather is that the smoke under the door is always going to be arguable; it is not going to be a clear and undeniable and absolute sign of fire; and so there is never going to be a fire alarm producing common knowledge that action is now due and socially acceptable.

There’s an old trope saying that as soon as something is actually done, it ceases to be called AI. People who work in AI and are in a broad sense pro-accelerationist and techno-enthusiast, what you might call the Kurzweilian camp (of which I am not a member), will sometimes rail against this as unfairness in judgment, as moving goalposts.

This overlooks a real and important phenomenon of adverse selection against AI accomplishments: If you can do something impressive-sounding with AI in 1974, then that is because that thing turned out to be doable in some cheap cheaty way, not because 1974 was so amazingly great at AI. We are uncertain about how much cognitive effort it takes to perform tasks, and how easy it is to cheat at them, and the first “impressive” tasks to be accomplished will be those where we were most wrong about how much effort was required. There was a time when some people thought that a computer winning the world chess championship would require progress in the direction of AGI, and that this would count as a sign that AGI was getting closer. When Deep Blue beat Kasparov in 1997, in a Bayesian sense we did learn something about progress in AI, but we also learned something about chess being easy. Considering the techniques used to construct Deep Blue, most of what we learned was “It is surprisingly possible to play chess without easy-to-generalize techniques” and not much “A surprising amount of progress has been made toward AGI.”

门下的alphago烟雾是否在10年或更短的时间内具有AGI的迹象？人们以前曾经给出过您在结束前看到的例子。

Looking over the paper describing AlphaGo’s architecture, it seemed to me that we是mostly learning that available AI techniques were likely to go further towards generality than expected, rather than about Go being surprisingly easy to achieve with fairly narrow and ad-hoc approaches. Not that the method scales to AGI, obviously; but AlphaGo did look like a product of相对地general insights and techniques being turned on the special case of Go, in a way that Deep Blue wasn’t. I also updated significantly on “The general learning capabilities of the human cortical algorithm are less impressive, less difficult to capture with a ton of gradient descent and a zillion GPUs, than I thought,” because if there were anywhere we expected an impressive hard-to-match highly-natural-selected but-still-general cortical algorithm to come into play, it would be in humans playing Go.

Maybe if we’d seen a thousand Earths undergoing similar events, we’d gather the statistics and find that a computer winning the planetary Go championship is a reliable ten-year-harbinger of AGI. But I don’t actually know that. Neither do you. Certainly, anyone can publicly argue that we just learned Go was easier to achieve with strictly narrow techniques than expected, as was true many times in the past. There’s no possible sign short of actual AGI, no case of smoke from under the door, for which we know that this is definitely serious fire and now AGI is 10, 5, or 2 years away. Let alone a sign where we know everyone else will believe it.

无论如何，机器学习中的多位主要科学家已经发表了文章，告诉我们他们的火灾警报标准。他们会相信人工通用情报是迫在眉睫的：

（a）当他们亲自看到如何使用当前工具构建AGI时。他们一直在说的是目前并不是正确的，以激发人们认为Agi可能临近的人的愚蠢。

（b）当他们的个人工作没有使他们对一切都很困难的感觉时。他们要说的是，这是一个无知的外行人所没有拥有的关键知识，他们认为Agi可能会靠近，他们只相信他们从未熬到凌晨2点，直到凌晨2点试图获得生成性的对抗网络来稳定。

(C) When they are very impressed by how smart their AI is relative to a human being in respects that still feel magical to them; as opposed to the parts they do know how to engineer, which no longer seem magical to them; aka the AI seeming pretty smart in interaction and conversation; aka the AI actually being an AGI already.

So there isn’t going to be a fire alarm. Period.

There is never going to be a time before the end when you can look around nervously, and see that it is now clearly common knowledge that you can talk about AGI being imminent, and take action and exit the building in an orderly fashion, without fear of looking stupid or frightened.

So far as I can presently estimate, now that we’ve had AlphaGo and a couple of other maybe/maybe-not shots across the bow, and seen a huge explosion of effort invested into machine learning and an enormous flood of papers, we are probably going to occupy our present epistemic state until very near the end.

By saying we’re probably going to be in roughly this epistemic state until almost the end, Idon’t意思是说我们知道AGI迫在眉睫，或者在此期间的AI中不会有重要的新突破。我的意思是，很难猜测AGI需要多少进一步的见解，或者需要多长时间才能获得这些见解。在下一次突破之后，我们仍然不知道需要多少突破，这使我们处于与以前几乎相同的认知状态。无论接下来是什么发现和里程碑，都可能仍然很难猜测需要多少进一步的见解，并且时间表将继续变得模糊。也许研究人员金宝博娱乐的热情和资金将进一步上升，我们可以说时间表正在缩短；或者，也许我们会再次参加AI冬季，我们会知道这是一个迹象，表明事情将花费比以其他方式更长的时间。但是我们仍然不知道how long.

在某种程度上,我们可能会看到突然arXiv的洪水papers in which really interesting and fundamental and scary cognitive challenges seem to be getting done at an increasing pace. Whereupon, as this flood accelerates, even some who imagine themselves sober and skeptical will be unnerved to the point that they venture that perhaps AGI is only 15 years away now, maybe, possibly. The signs might become so blatant, very soon before the end, that people start thinking it is socially acceptable to say that maybe AGI is 10 years off. Though the signs would have to be pretty darned blatant, if they’re to overcome the social barrier posed by luminaries who are estimating arrival times to AGI using their personal knowledge and personal difficulties, as well as all the historical bad feelings about AI winters caused by hype.

But even if it becomes socially acceptable to say that AGI is 15 years out, in those last couple of years or months, I would still expect there to be disagreement. There will still be others protesting that, as much as associative memory and human-equivalent cerebellar coordination (or whatever) are now solved problems, they still don’t know how to construct AGI. They will note that there are no AIs writing computer science papers, or holding a truly sensible conversation with a human, and castigate the senseless alarmism of those who talk as if we already knew how to do that. They will explain that foolish laypeople don’t realize how much pain and tweaking it takes to get the current systems to work. (Although those modern methods can easily do almost anything that was possible in 2017, and any grad student knows how to roll a stable GAN on the first try using the tf.unsupervised module in Tensorflow 5.3.1.)

When all the pieces are ready and in place, lacking only the last piece to be assembled by the very peak of knowledge and creativity across the whole world, it will still seem to the average ML person that AGI is an enormous challenge looming in the distance, because they still won’t personally know how to construct an AGI system. Prestigious heads of major AI research groups will still be writingarticlesdecrying the folly of fretting about the total destruction of all Earthly life and all future value it could have achieved, and saying that we should not let this distract us fromreal, respectable concerns像贷款批准系统一样，意外吸收了人类偏见金宝博官方。

当然，未来很难详细预测。很难以至于我不仅承认自己的无能为力，而且还提出了更强烈的积极陈述，没有其他人也能做到。“开创性的Arxiv论文泛滥”场景可能是一种可能发生的方式，但这是我为具体性而弥补的令人难以置信的特定情况。当然，这不是我观看其他类似地球的文明发展的丰富经验。我确实在“曼哈顿项目之外没有太多的标志在广岛”上放置了很大一部分概率，因为这种情况很简单。任何更复杂的事情都只是一个充满的故事burdensome detailsthat aren’t likely to all be true.

But no matter how the details play out, I do predict in a very general sense that there will be no fire alarm that is not an actual running AGI—no unmistakable sign before then that everyone knows and agrees on, that lets people act without feeling nervous about whether they’re worrying too early. That’s just not how the history of technology has usually played out in much simpler cases like flight and nuclear engineering, let alone a case like this one where all the signs and models are disputed. We already know enough about the uncertainty and low quality of discussion surrounding this topic to be able to say with confidence that there will be no unarguable socially accepted sign of AGI arriving 10 years, 5 years, or 2 years beforehand. If there’s any general social panic it will be by coincidence, based on terrible reasoning, uncorrelated with real timelines except by total coincidence, set off by a Hollywood movie, and focused on relatively trivial dangers.

没有人对这种火灾警报的任何实际说明并不巧合，并令人信服地争论了我们剩下的时间，以及我们应该开始哪些项目。如果有人编写了该提议，那么下一个写一个人会说完全不同的话。And probably neither of them will succeed at convincing me that they know anything prophetic about timelines, or that they’ve identified any sensible angle of attack that is (a) worth pursuing at all and (b) not worth starting to work on right now.

在我看来，将所有动作推迟到完全未指定的未来警报响起，这意味着鲁ck的命令足够好，以至于持续失败的定律开始发挥作用。

持续失败的定律是规则，说明您的国家在所有银行帐户和信贷申请上都无法使用明文9个数字的密码，那么您的国家在下一次灾难之后不足以纠正课程其中揭示了一亿个密码。一个文明足以纠正该产品的课程，以您希望他们的反应方式对其做出反应，这是足够有能力的，不仅可以首先犯错。当一个系统大规金宝博官方模而显然，而不是巧妙的和在能力的边缘上发生故障时，下一个产品不会导致系统突然突然智能地进行事情。

The law of continued failure is especially important to keep in mind when you are dealing with big powerful systems or high-status people that you might feel nervous about derogating, because you may be tempted to say, “Well, it’s flawed now, but as soon as a future prod comes along, everything will snap into place and everything will be all right.” The systems about which this fond hope is actually warranted look like they are mostly doing all the important things right already, and only failing in one or two steps of cognition. The fond hope is almost never warranted when a person or organization or government or social subsystem is currently falling massively short.

忽略三十年来外星人登陆前景所需的愚蠢已经足够好，以至于辩论的其他有缺陷的因素不足为奇。

And with all of that going wrong simultaneously today, we should predict that the same system and incentives won’t produce correct outputs after receiving an uncertain sign that maybe the aliens are landing in five years instead. The law of continued failure suggests that if existing authorities failed in enough different ways at once to think that it makes sense to try to derail a conversation about existential risk by saying the real problem is the security on self-driving cars, the default expectation is that they will still be saying silly things later.

犯大量同时错误的人通常并没有将所有不正确的想法下意识地标记为“不正确”。即使有动力，他们也无法突然翻转以巧妙地执行所有校正的推理步骤。Yes, we have various experiments showing that monetary incentives can reduce overconfidence and political bias, but (a) that’s reduction rather than elimination, (b) it’s with extremely clear short-term direct incentives, not the nebulous and politicizable incentive of “a lot being at stake”, and (c) that doesn’t mean a switch is flipping all the way to “carry out complicated correct reasoning”. If someone’s brain contains a switch that can flip to enable complicated correct reasoning at all, it’s got enough internal precision and skill to think mostly-correct thoughts now instead of later—at least to the degree that some conservatism and double-checking gets built into examining the conclusions that people know will get them killed if they’re wrong about them.

没有符号和前瞻性，没有阈值交叉, that suddenly causes people to wake up and start doing things systematically correctly. People who can react that competently to any sign at all, let alone a less-than-perfectly-certain not-totally-agreed item of evidence that islikelya wakeup call, have probably already done the timebinding thing. They’ve already imagined the future sign coming, and gone ahead and thought sensible thoughts earlier, like Stuart Russell saying, “If you know the aliens are landing in thirty years, it’s still a big deal now.”

回到funding-starved初期是什么now MIRI, I learned that people who donated last year were likely to donate this year, and people who last year were planning to donate “next year” would quite often this year be planning to donate “next year”. Of course there were genuine transitions from zero to one; everything that happens needs to happen for a first time. There were college students who said “later” and gave nothing for a long time in a genuinely strategically wise way, and went on to get nice jobs and start donating. But I also learned well that, like many cheap and easy solaces, saying the word “later” is addictive; and that this luxury is available to the rich as well as the poor.

I don’t expect it to be any different with AGI alignment work. People who are trying to get what grasp they can on the alignment problem will, in the next year, be doing a little (or a lot) better with whatever they grasped in the previous year (plus, yes, any general-field advances that have taken place in the meantime). People who want to defer that until after there’s a better understanding of AI and AGI will, after the next year’s worth of advancements in AI and AGI, want to defer work until a better future understanding of AI and AGI.

确实有些人want对齐,get done因此now试图使他们的大脑关于如何获得强化学习者之类的东西可靠地确定因果环境模型中的特定元素而不是感官奖励术语或者defeat the seeming tautologicalness of updated (non-)deference。其他人则宁愿从事其他事情，因此会宣布今天没有工作可以做不是spending two hours quietly thinking about it first before making that declaration. And this will not change tomorrow, unless perhaps tomorrow is when we wake up to some interesting newspaper headlines, and probably not even then. The luxury of saying “later” is not available only to the truly poor-in-available-options.

过了一会儿,我开始告诉altruis有效ts in college: “If you’re planning to earn-to-give later, then for now, give around $5 every three months. And never give exactly the same amount twice in a row, or give to the same organization twice in a row, so that you practice the mental habit of re-evaluating causes and re-evaluating your donation amounts on a regular basis.Don’t学习总是说“以后”的精神习惯。

同样，如果有人actuallygoing to work on AGI alignment “later”, I’d tell them to, every six months, spend a couple of hours coming up with the best current scheme they can devise for aligning AGI and doing useful work on that scheme. Assuming, if they must, that AGI were somehow done with technology resembling current technology. And publishing their best-current-scheme-that-isn’t-good-enough, at least in the sense of posting it to Facebook; so that they will have a sense of embarrassment about naming a scheme that does not look like somebody actually spent two hours trying to think of the best bad approach.

我们将来会更好地了解AI，我们会学到的东西可能会使我们有更多的信心，即特定的研究方法将与AGI相关。金宝博娱乐类似于尼克·博斯特罗姆（Nick Bostrom Publishing）的未来社会学发展可能会有更多超级智能, Elon Musk tweeting about it and thereby heaving a rock through the Overton Window, or more respectable luminaries like Stuart Russell openly coming on board. The future will hold more AlphaGo-like events to publicly and privately highlight new ground-level advances in ML technique; and it may somehow be that this does不是让我们处于与已经见过Alphago和Gans等的同一认知状态。它可能发生！我看不到确切的方式，但是未来确实有能力在这方面引起惊喜。

But before waiting on that surprise, you should ask whether your uncertainty about AGI timelines is really uncertainty at all. If it feels to you that guessing AGI might have a 50% probability in N years is not enough knowledge to act upon, if that feels scarily uncertain and you want to wait for more evidence before making any decisions… then ask yourself how you’d feel if you believed the probability was 50% in N years, and everyone else on Earth also believed it was 50% in N years, and everyone believed it was right and proper to carry out policy P when AGI has a 50% probability of arriving in N years. If that visualization feels very different, then any nervous “uncertainty” you feel about doing P is not really about whether AGI takes much longer than N years to arrive.

And you are almost surely going to be stuck with that feeling of “uncertainty” no matter how close AGI gets; because no matter how close AGI gets, whatever signs appear will almost surely not produce common, shared, agreed-on public knowledge that AGI has a 50% chance of arriving in N years, nor any agreement that it is therefore right and proper to react by doing P.

And if all that did become common knowledge, then P is unlikely to still be a neglected intervention, or AI alignment a neglected issue; so you will have waited until sadly late to help.

But far more likely is that the common knowledge just isn’t going to be there, and so it will always feel nervously “uncertain” to consider acting.

你可以尽管如此,行动或者not act. Not act until it’s too late to help much, in the best case; not act at all until after it’s essentially over, in the average case.

I don’t think it’s wise to wait on an unspecified epistemic miracle to change how we feel. In all probability, you’re going to be in this mental state for a while—including any nervous-feeling “uncertainty”. If you handle this mental state by saying “later”, that general policy is not likely to have good results for Earth.

Further resources: