Luke Muehlhauser: In Nerode (2007), you tell the origin story of hybrid systems control. A 1990 DARPA meeting in Pacifica seems to have been particularly seminal. As you describe it:

the purpose of the meeting was to explore how to clear a major bottleneck, the control of large military systems such as air-land-sea forces in battle space.

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: In Carbin et al. (2013), you and your co-authors present Rely, a new programming language that “enables developers to reason about the… probability that [a program] produces the correct result when executed on unreliable hardware.” How is Rely different from earlier methods for achieving reliable approximate computing?

Michael Carbin: This is a great question. Building applications that work with unreliable components has been a long-standing goal of the distributed systems community and other communities that have investigated how to build systems that are fault-tolerant. A key goal of a fault tolerant system is to deliver a correct result even in the presence of errors in the system’s constituent components.

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.

The key idea of the approximate computing community is that many large-scale computations (e.g., machine learning, big data analytics, financial analysis, and media processing) have a natural trade-off between the quality of their results and the time and resources required to produce a result. Exploiting this fact, researchers have devised a number of techniques that take an existing application and modify it to trade the quality of its results for increased performance or decreased power consumption.

One example that my group has worked on is simply skipping parts of a computation that we have demonstrated — through testing — can be elided without substantially affecting the overall quality of the application’s result. Another approach is executing portions of an application that are naturally tolerant of errors on these new unreliable hardware systems.

A natural follow-on question to this is, how have developers previously dealt with approximation?

These large-scale applications are naturally approximate because exact solutions are often intractable or perhaps do not even exist (e.g., machine learning). The developers of these applications therefore often start from an exact model of how to compute an accurate result and then use that model as a guide to design a tractable algorithm and a corresponding implementation that returns a more approximate solution. These developers have therefore been manually applying approximations to their algorithms (and their implementations) and reasoning about the accuracy of their algorithms for some time. A prime example of this is the field of numerical analysis and its contributions to scientific computing.

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’s Whole Brain Emulation: A Roadmap report. The report summarizes the participants’ views on several issues. Would you mind sharing your own 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: It would be problematic to base your questions largely on the 2007 report. All of those involved are pretty much in agreement that said report did not constitute a “roadmap”, because it did not actually lay out a concrete / well devised theoretical plan by which whole brain emulation is both possible and feasible. The 2007 white paper focuses almost exclusively on structural data acquisition and does not explicitly address the problem of system identification in an unknown (“black box”) system. That problem is fundamental to questions about “levels of detail” and more. It immediately forces you to think about constraints: What is successful/satisfactory brain emulation?

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.

I think that a range of different levels of detail will be involved in WBE. For example, as work by Ted Berger on a prosthetic hippocampus has already shown, it may often be adequate to emulate at the level of spike timing and patterns of neural spikes. It is quite possible that, from a functional perspective, emulation at that level can capture that which is perceptible to us. Consider, differences of pre- and post-synaptic spike times are the basis for synaptic strengthening (spike-timing dependent potentiation), i.e. encoding of long term memory. Trains of spikes are used to communicate sensory input (visual, auditory, etc). Patterns of spikes are used to drive groups of muscles (locomotion, speech, etc).

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.
So, my hunch (strictly a hunch!) is that whole brain emulation will ultimately involve a combination of tools that carry out most data acquisition at one level, but which in some places or at some times dive deeper to pick up local dynamics.

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).
I think is is less likely that we will need to map all proteins or molecules throughout an entire brain – though it is very likely that we will be studying each of those thoroughly in representative components of brains in order to learn how best to relate measurable quantities with parameters and dynamics to be represented in emulation.

(Please don’t interpret my answer as “spiking neural networks”, because that does not refer to a data acquisition level, but a certain type of network abstraction for artificial neural networks.)
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Luke Muehlhauser: Your book opens with a concise argument against the absurdity heuristic — the rule of thumb which says “If a theory sounds absurd to my human psychology, it’s probably false.” You write:

Evolution endowed us with intuition only for those 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 hypotheses motivating MIRI’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: That’s a very important question: I think of the absurdity heuristic as a cognitive bias that’s not only devastating for any scientist hoping to make fundamental discoveries, but also dangerous for any sentient species hoping to avoid extinction. Although it appears daunting to get most people to drop this bias altogether, I think it’s easier if we focus on a specific example. For instance, whereas our instinctive fear of snakes is innate and evolved, our instinctive fear of guns (which the Incas lacked) is learned. 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.

In 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: In Rossi et al. (2011), you and your co-authors quickly survey a variety of methods in computational social choice, including methods for preference aggregation, e.g. voting rules. In Narodytska et al. (2012), you and your co-authors examine the issue of combining voting rules to perform a run-off between the different winners of each voting rule. What do you think are some plausible practical applications of this work — either soon or after further theoretical development?

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 SCATS 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 Space Shuttle had 5 control computers which voted on whose actions to follow.

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. 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.

One interesting thing we’ve learnt from machine learning is that you often 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: Your Bio-Response Report Card assesses the USA’s bio-response capabilities. Before we explore your findings, could you say a bit about how the report was produced, and what motivated its creation?

Randall Larsen: The 9/11 Commission recommended that a separate commission examine the terrorism threat from weapons of mass destruction (WMD). The bipartisan leadership in the Senate and House asked former US Senators Bob Graham (D-FL) and Jim Talent (R-MO) to head the Congressional Commission on the Prevention of Weapons of Mass Destruction Proliferation and Terrorism (WMD Commission). The WMD Commission completed its work in December 2008 and published a report, World at Risk. In March 2009, the bipartisan leadership of Congress asked Senators Graham and Talent to re-establish the Commission to continue its work and provide a report card on progress. This was the first Congressional Commission to be extended for a second year.

I became the Executive Director for the WMD Commission’s second year, and in January 2010, the Commission released a WMD Report Card assessing 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.

Launching the project was a bit of a challenge, since many of the traditional national security organizations that support such work were solely focused on the nuclear threat—a reflection of the Congressional perspective. The legislation that created the WMD Commission had not contained the words bioterrorism or biology—ironic since World at Risk clearly identified bioterrorism as the most likely WMD threat.

We began work on the Bio-Response Report Card in January 2011 by recruiting a world-class team of senior advisors. They included a former Deputy Administrator of the Food and Drug Administration, a former Special Assistant to the President for Biodefense, the Director of Disaster Response at the American Medical Association, the VP and Director of RAND Health, the Founding President of the Sabin Vaccine Institute, and experts in the fields of public health, emergency medicine, and environmental remediation.

The Board of Advisors helped inform methodology of the project, helped define the categories of bio-response, and then proposed metrics in the form of questions, by which to assess capabilities in each category.

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