Luke Muehlhauser: InNerode（2007），您讲述了混合系统控制的起源故事。金宝博官方1990年在Pacifica举行的DARPA会议似乎特别开创性。如您所描述的那样：

会议的目的是探索如何清理主要的瓶颈，这是对战斗空间中航空海洋部队等大型军事系统的控制。金宝博官方

Can you describe in more detail what DARPA’s long-term objectives for that meeting seemed to be? Presumably they hoped the meeting would spur new lines of research that would allow them to solve particular control problems in the next 5-20 years?

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Luke Muehlhauser: InCarbin等。（2013），您和您的合着者在场，这是一种新的编程语言，“使开发人员能够提出……[程序]在不可靠的硬件执行时产生正确结果的概率。”与早期方法的依赖是如何实现可靠的近似计算？

迈克尔·卡宾： 这是一个很好的问题。与不可靠的组件合作的构建应用已成为分布式系统社区和其他研究过如何构建耐断层的系统的社区的长期目标。金宝博官方容错系统的一个关键目标是即使在系统组成部分中存在错误的情况下，金宝博官方也能提供正确的结果。

This goal stands in contrast to the goal of the unreliable hardware that we have targeted in my work. Specifically, hardware designers are considering new designs that will — purposely — expose components that may silently produce incorrect results with some non-negligible probability. These hardware designers are working in a subfield that is broadly called approximate computing.

近似计算社区的关键思想是，许多大规模计算（例如机器学习，大数据分析，财务分析和媒体处理）在其结果质量与所需时间和资源之间都具有自然权衡产生结果。利用这一事实，研究人员设计了许多采用现有应用金宝博娱乐程序的技术，并将其修改以将其结果质量交换以增加性能或减少功耗。

我的小组已经进行的一个例子是，仅跳过我们已经通过测试证明的计算的一部分，而无需显着影响应用程序结果的整体质量。另一种方法是执行应用程序的部分，这些应用程序自然耐受这些新不可靠的硬件系统的错误。金宝博官方

一个自然的后续问题是，开发人员以前如何处理近似？

这些大规模应用自然是近似的，因为精确的解决方案通常是棘手的，甚至可能不存在（例如，机器学习）。因此，这些应用程序的开发人员通常从如何计算准确结果的精确模型开始，然后将该模型用作设计可拖动算法的指南和相应的实现，该实现返回更近似的解决方案。因此，这些开发人员一直在手动将近似值应用于其算法（及其实现）以及对其算法准确性的推理一段时间。一个主要的例子是数值分析领域及其对科学计算的贡献。

The emerging approximate computing community represents the realization that programming languages, runtime systems, operating systems, and hardware architectures can not only help developers navigate the approximations they need to make when building these applications, but also that these systems can incorporate approximations themselves. So for example, the hardware architecture may itself export unreliable hardware components that an application’s developers can then use as one of their many tools for performing approximation.
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Luke Muehlhauser: You were a participant in the 2007 workshop that led to FHI’sWhole Brain Emulation: A Roadmapreport. The report summarizes the participants’ views on several issues. Would you mind sharing your自己的estimates on some of the key questions from the report? In particular, at what level of detail do you think we’ll need to emulate a human brain to achieve WBE? (molecules, proteome, metabolome, electrophysiology, spiking neural network, etc.)

(By “WBE” I mean what the report calls success criterion 6a (“social role-fit emulation”), so as to set aside questions of consciousness and personal identity.)

Randal Koene：将您的问题主要基于2007年的报告将是有问题的。所有涉及的人几乎都达成共识，该报告并不构成“路线图”，因为它实际上并没有制定一个具体 /设计精良的理论计划，通过该计划，整个大脑仿真既可能又可行。2007年白皮书几乎完全关注结构数据获取，并且没有明确解决未知（“黑匣子”）系统中系统识别的问题。金宝博官方这个问题是有关“细节水平”等问题的基础。它立即迫使您考虑约束：什么是成功/令人满意的大脑仿真？

System identification (in small) is demonstrated by the neuroprosthetic work of Ted Berger. Taking that example and proof-of-principle, and applying it to the whole brain leads to a plan for decomposition into feasible parts. That’s what the actual roadmap is about.

I don’t know if you’ve encountered these two papers, but you might want to read and contrast with the 2007 report:

• Koene，R。2012。Experimental Research In Whole Brain Emulation: The Need For Innovative In-Vivo Measurement Techniques。
• Koene，R。2012。Fundamentals Of Whole Brain Emulation: State, Transition And Update Representations。

我认为WBE将涉及一系列不同级别的细节。例如，泰德·伯杰（Ted Berger）的工作在假体海马上已经显示出来，通常足以在神经尖峰的尖峰时序和模式下模仿。从功能上的角度来看，在该级别上的仿真很有可能捕获我们可感知的东西。考虑一下，突触前和突触后尖峰时间的差异是突触加强的基础（峰值依赖性增强），即长期记忆的编码。尖峰火车用于传达感觉输入（视觉，听觉等）。尖峰模式用于驱动肌肉组（运动，语音等）。

That said, a good emulation will probably require a deeper level of data acquisition for parameter estimation and possible also a deeper level of emulation in some cases, for example if we try to distinguish different types of synaptic receptors, and therefore how particular neurons can communicate with each other. I’m sure there are many other examples.
因此，我的直觉（严格来说是一个直觉！）是整个大脑仿真最终将涉及一系列工具，这些工具可以在一个级别上进行大多数数据获取，但是在某些地方或有时会更深入地潜入以挑选本地动态。

I think it is likely that we will need to acquire structure data at least at the level of current connectomics that enables identification of small axons/dendrites and synapses. I also think it is likely that we will need to carry out much electrophysiology, amounting to what is now called the Brain Activity Map (BAM).
我认为我们不太可能需要在整个大脑中绘制所有蛋白质或分子 - 尽管我们很可能会在大脑的代表性成分中彻底研究每个蛋白质或分子，以便学习如何最好地关联可测量的数量带有参数和动力学在仿真中表示。

（请不要将我的答案解释为“峰值神经网络”，因为这不是指数据采集级别，而是针对人工神经网络的某种类型的网络抽象。
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Luke Muehlhauser： 您的book opens with a concise argument against theabsurdity heuristic— the rule of thumb which says “If a theory sounds absurd to my human psychology, it’s probably false.” You write:

只有那些进化赋予我们的直觉aspects of physics that had survival value for our distant ancestors, such as the parabolic orbits of flying rocks (explaining our penchant for baseball). A cavewoman thinking too hard about what matter is ultimately made of might fail to notice the tiger sneaking up behind and get cleaned right out of the gene pool. Darwin’s theory thus makes the testable prediction that whenever we use technology to glimpse reality beyond the human scale, our evolved intuition should break down. We’ve repeatedly tested this prediction, and the results overwhelmingly support Darwin. At high speeds, Einstein realized that time slows down, and curmudgeons on the Swedish Nobel committee found this so weird that they refused to give him the Nobel Prize for his relativity theory. At low temperatures, liquid helium can flow upward. At high temperatures, colliding particles change identity; to me, an electron colliding with a positron and turning into a Z-boson feels about as intuitive as two colliding cars turning into a cruise ship. On microscopic scales, particles schizophrenically appear in two places at once, leading to the quantum conundrums mentioned above. On astronomically large scales… weirdness strikes again: if you intuitively understand all aspects of black holes [then you] should immediately put down this book and publish your findings before someone scoops you on the Nobel Prize for quantum gravity… [also,] the leading theory for what happened [in the early universe] suggests that space isn’t merely really really big, but actually infinite, containing infinitely many exact copies of you, and even more near-copies living out every possible variant of your life in two different types of parallel universes.

Like much of modern physics, the假设motivatingMIRI’s work can easily run afoul of a reader’s own absurdity heuristic. What are your best tips for getting someone to give up the absurdity heuristic, and try to judge hypotheses via argument and evidence instead?

Max Tegmark：这是一个非常重要的问题：我认为荒谬的启发式方法是一种认知偏见，这不仅对任何希望做出基本发现的科学家造成了毁灭性的危害，而且对于希望避免灭绝的任何有性物种而言，也是危险的。尽管让大多数人完全放弃这种偏见似乎令人生畏，但我认为，如果我们专注于一个特定的示例，这会更容易。例如，尽管我们对蛇的本能恐惧是天生的和发展的，但我们对枪支的本能恐惧（缺少印加人缺乏）。Just as people learned to fear nuclear weapons through blockbuster horror movies such as “The Day After”, rational fear of unfriendly AI could undoubtedly be learned through a future horror movie that’s less unrealistic than Terminator III, backed up by a steady barrage of rational arguments from organizations such as MIRI.

在the mean time, I think a good strategy is to confront people with some incontrovertible fact that violates their absurdity heuristic and the whole notion that we’re devoting adequate resources and attention to existential risks. For example, I like to ask why more people have heard of Justin Bieber than of Vasili Arkhipov, even though it wasn’t Justin who singlehandedly prevented a Soviet nuclear attack during the Cuban Missile Crisis.

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Luke Muehlhauser: InRossi等。（2011），您和您的合着者迅速调查了计算社会选择中的各种方法，包括偏好聚合的方法，例如投票规则。在Narodytska等。（2012），您和您的合着者研究了将投票规则结合起来的问题，以在每个投票规则的不同获奖者之间进行径流。您认为这项工作的某些合理的实际应用是什么？

托比·沃尔什（Toby Walsh）: As humans, we’re all used to voting: voting for our politicians, or voting for where to go out. In the near future, we’ll hand over some of that responsibility to computational agents that will help organize our lives. Think Siri on steroids. In such situations, we often have many choices as there can be a combinatorial number of options. This means we need to consider computational questions: How do we get computer(s) to work with such rich decision spaces? How do we efficiently collect and represent users’ preferences?

I should note that computer systems are already voting. The粪便system for controlling traffic lights has the controllers of different intersections vote for what should be the common cycle time for the lights. Similarly, the航天飞机有5台控制计算机对其遵循的行动进行了投票。

Computational social choice is, however, more than just voting. It covers many other uses of preferences. Preferences are used to allocate scarce resources. I prefer, for example, a viewing slot on this expensive telescope when the moon is high from the horizon. Preferences are also used to allocate people to positions. I prefer, for example, to be matched to a hospital with a good pediatrics depts.劳埃德·沙普利（Lloyd Shapley）won the Nobel Prize in Economics recently for looking at such allocation problems. There are many appealing applications in areas like kidney transplant, and school choice.

我们从机器学习中学到的一件有趣的事情是，您经常make better decisions when you combine the opinions of several methods。It’s therefore likely that we’ll get better results by combining together voting methods. For this reason, we’ve been looking at how voting rules combine together.

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Luke Muehlhauser： 您的生物反应报告卡评估美国的生物反应能力。在我们探索您的发现之前，您能说一下报告的生产方式，以及是什么促使其创建的？

兰德尔·拉尔森（Randall Larsen）：9/11委员会建议一个单独的委员会检查大规模杀伤性武器（WMD）恐​​怖主义威胁。参议院和众议院的两党领导层要求美国前参议员鲍勃·格雷厄姆（Bob Graham）（D-FL）和吉姆人才（R-MO）领导国会预防大规模杀伤性武器扩散和恐怖主义武器（WMD委员会）。WMD委员会于2008年12月完成了其工作，并发布了一份报告，世界有风险。2009年3月，国会两党领导人要求参议员格雷厄姆和人才重新建立委员会继续其工作，并提供有关进度的成绩单。这是第一个延长第二年的国会委员会。

I became the Executive Director for the WMD Commission’s second year, and in January 2010, the Commission released aWMD Report Cardassessing 37 aspects of the WMD threat. The grades ranged from A’s to F’s. The failing grade that received the most attention, both on Capitol Hill and in the press, was the F grade for “preparedness to respond to a biological attack.”

At the commissioners’ final meeting in December 2009, they encouraged Senators Graham and Talent to continue their work with a focus on the biological threat. To do so, a not-for-profit organization (501c3) was created in March 2010, The Bipartisan WMD Terrorism Research Center (WMD Center). Senators Graham and Talented agreed to serve on the board of advisors, I became the CEO, and recruited Lynne Kidder to serve as the President.

启动该项目是一个挑战，因为许多支持此类工作的传统国家安全组织仅着眼于核威胁，这是国会观点的反映。创建WMD委员会的立法并未包含生物恐怖或生物学一词，因为世界有风险清楚地将生物恐怖主义确定为最有可能的WMD威胁。

我们从2011年1月开始招募一支世界一流的高级顾问团队，开始在生物响应报告卡上工作。他们包括一名前食品药品监督管理局的副局长，前生物福利总裁的前特别助理，美国医学协会的灾难响应主任，副总裁兼兰德·卫生总监，萨宾疫苗研究所的创始主席，以及公共卫生，急诊医学和环境修复领域的专家。

顾问委员会帮助对该项目的方法进行了告知，帮助定义了生物反应的类别，然后以问题的形式提出了指标，以评估每个类别中的能力。

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