This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-ninth section in the reading guide: Crunch time. This corresponds to the last chapter in the book, and the last discussion here (even though the reading guide shows a mysterious 30th section). 

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: Chapter 15

Summary

1. As we have seen, the future of AI is complicated and uncertain. So, what should we do? (p255)
2. Intellectual discoveries can be thought of as moving the arrival of information earlier. For many questions in math and philosophy, getting answers earlier does not matter much. Also people or machines will likely be better equipped to answer these questions in the future. For other questions, e.g. about AI safety, getting the answers earlier matters a lot. This suggests working on the time-sensitive problems instead of the timeless problems. (p255-6)
3. We should work on projects that are robustly positive value (good in many scenarios, and on many moral views)
4. We should work on projects that are elastic to our efforts (i.e. cost-effective; high output per input)
5. Two objectives that seem good on these grounds: strategic analysis and capacity building (p257)
6. An important form of strategic analysis is the search for crucial considerations. (p257)
7. Crucial consideration: idea with the potential to change our views substantially, e.g. reversing the sign of the desirability of important interventions. (p257)
8. An important way of building capacity is assembling a capable support base who take the future seriously. These people can then respond to new information as it arises. One key instantiation of this might be an informed and discerning donor network. (p258)
9. It is valuable to shape the culture of the field of AI risk as it grows. (p258)
10. It is valuable to shape the social epistemology of the AI field. For instance, can people respond to new crucial considerations? Is information spread and aggregated effectively? (p258)
11. Other interventions that might be cost-effective: (p258-9)
    1. Technical work on machine intelligence safety
    2. Promoting 'best practices' among AI researchers
    3. Miscellaneous opportunities that arise, not necessarily closely connected with AI, e.g. promoting cognitive enhancement
12. We are like a large group of children holding triggers to a powerful bomb: the situation is very troubling, but calls for bitter determination to be as competent as we can, on what is the most important task facing our times. (p259-60)

Another view

Alexis Madrigal talks to Andrew Ng, chief scientist at Baidu Research, who does not think it is crunch time:

Andrew Ng builds artificial intelligence systems for a living. He taught AI at Stanford, built AI at Google, and then moved to the Chinese search engine giant, Baidu, to continue his work at the forefront of applying artificial intelligence to real-world problems.

So when he hears people like Elon Musk or Stephen Hawking—people who are not intimately familiar with today’s technologies—talking about the wild potential for artificial intelligence to, say, wipe out the human race, you can practically hear him facepalming.

“For those of us shipping AI technology, working to build these technologies now,” he told me, wearily, yesterday, “I don’t see any realistic path from the stuff we work on today—which is amazing and creating tons of value—but I don’t see any path for the software we write to turn evil.”

But isn’t there the potential for these technologies to begin to create mischief in society, if not, say, extinction?

“Computers are becoming more intelligent and that’s useful as in self-driving cars or speech recognition systems or search engines. That’s intelligence,” he said. “But sentience and consciousness is not something that most of the people I talk to think we’re on the path to.”

Not all AI practitioners are as sanguine about the possibilities of robots. Demis Hassabis, the founder of the AI startup DeepMind, which was acquired by Google, made the creation of an AI ethics board a requirement of its acquisition. “I think AI could be world changing, it’s an amazing technology,” he told journalist Steven Levy. “All technologies are inherently neutral but they can be used for good or bad so we have to make sure that it’s used responsibly. I and my cofounders have felt this for a long time.”

So, I said, simply project forward progress in AI and the continued advance of Moore’s Law and associated increases in computers speed, memory size, etc. What about in 40 years, does he foresee sentient AI?

“I think to get human-level AI, we need significantly different algorithms and ideas than we have now,” he said. English-to-Chinese machine translation systems, he noted, had “read” pretty much all of the parallel English-Chinese texts in the world, “way more language than any human could possibly read in their lifetime.” And yet they are far worse translators than humans who’ve seen a fraction of that data. “So that says the human’s learning algorithm is very different.”

Notice that he didn’t actually answer the question. But he did say why he personally is not working on mitigating the risks some other people foresee in superintelligent machines.

“I don’t work on preventing AI from turning evil for the same reason that I don’t work on combating overpopulation on the planet Mars,” he said. “Hundreds of years from now when hopefully we’ve colonized Mars, overpopulation might be a serious problem and we’ll have to deal with it. It’ll be a pressing issue. There’s tons of pollution and people are dying and so you might say, ‘How can you not care about all these people dying of pollution on Mars?’ Well, it’s just not productive to work on that right now.”

Current AI systems, Ng contends, are basic relative to human intelligence, even if there are things they can do that exceed the capabilities of any human. “Maybe hundreds of years from now, maybe thousands of years from now—I don’t know—maybe there will be some AI that turn evil,” he said, “but that’s just so far away that I don’t know how to productively work on that.”

The bigger worry, he noted, was the effect that increasingly smart machines might have on the job market, displacing workers in all kinds of fields much faster than even industrialization displaced agricultural workers or automation displaced factory workers.

Surely, creative industry people like myself would be immune from the effects of this kind of artificial intelligence, though, right?

“I feel like there is more mysticism around the notion of creativity than is really necessary,” Ng said. “Speaking as an educator, I’ve seen people learn to be more creative. And I think that some day, and this might be hundreds of years from now, I don’t think that the idea of creativity is something that will always be beyond the realm of computers.”

And the less we understand what a computer is doing, the more creative and intelligent it will seem. “When machines have so much muscle behind them that we no longer understand how they came up with a novel move or conclusion,” he concluded, “we will see more and more what look like sparks of brilliance emanating from machines.”

Andrew Ng commented:

Enough thoughtful AI researchers (including Yoshua Bengio​, Yann LeCun) have criticized the hype about evil killer robots or "superintelligence," that I hope we can finally lay that argument to rest. This article summarizes why I don't currently spend my time working on preventing AI from turning evil. 

Notes

1. Replaceability

'Replaceability' is the general issue of the work that you do producing some complicated counterfactual rearrangement of different people working on different things at different times. For instance, if you solve a math question, this means it gets solved somewhat earlier and also someone else in the future does something else instead, which someone else might have done, etc. For a much more extensive explanation of how to think about replaceability, see 80,000 Hours. They also link to some of the other discussion of the issue within Effective Altruism (a movement interested in efficiently improving the world, thus naturally interested in AI risk and the nuances of evaluating impact).

2. When should different AI safety work be done?

For more discussion of timing of work on AI risks, see Ord 2014. I've also written a bit about what should be prioritized early.

3. Review

If you'd like to quickly review the entire book at this point, Amanda House has a summary here, including this handy diagram among others: 

4. What to do?

If you are convinced that AI risk is an important priority, and want some more concrete ways to be involved, here are some people working on it: FHI, FLI, CSER, GCRI, MIRI, AI Impacts (note: I'm involved with the last two). You can also do independent research from many academic fields, some of which I have pointed out in earlier weeks. Here is my list of projects and of other lists of projects. You could also develop expertise in AI or AI safety (MIRI has a guide to aspects related to their research here; all of the aforementioned organizations have writings). You could also work on improving humanity's capacity to deal with such problems. Cognitive enhancement is one example. Among people I know, improving individual rationality and improving the effectiveness of the philanthropic sector are also popular. I think there are many other plausible directions. This has not been a comprehensive list of things you could do, and thinking more about what to do on your own is also probably a good option.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. What should be done about AI risk? Are there important things that none of the current organizations are working on?
2. What work is important to do now, and what work should be deferred?
3. What forms of capability improvement are most useful for navigating AI risk?

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

This is the last reading group, so how to proceed is up to you, even more than usually. Thanks for joining us! 

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-eighth section in the reading guide: Collaboration.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Collaboration” from Chapter 14

Summary

1. The degree of collaboration among those building AI might affect the outcome a lot. (p246)
2. If multiple projects are close to developing AI, and the first will reap substantial benefits, there might be a 'race dynamic' where safety is sacrificed on all sides for a greater chance of winning. (247-8)
3. Averting such a race  dynamic with collaboration should have these benefits:
   1. More safety
   2. Slower AI progress (allowing more considered responses)
   3. Less other damage from conflict over the race
   4. More sharing of ideas for safety
   5. More equitable outcomes (for a variety of reasons)
4. Equitable outcomes are good for various moral and prudential reasons. They may also be easier to compromise over than expected, because humans have diminishing returns to resources. However in the future, their returns may be less diminishing (e.g. if resources can buy more time instead of entertainments one has no time for).
5. Collaboration before a transition to an AI economy might affect how much collaboration there is afterwards. This might not be straightforward. For instance, if a singleton is the default outcome, then low collaboration before a transition might lead to a singleton (i.e. high collaboration) afterwards, and vice versa. (p252)
6. An international collaborative AI project might deserve nearly infeasible levels of security, such as being almost completely isolated from the world. (p253)
7. It is good to start collaboration early, to benefit from being ignorant about who will benefit more from it, but hard because the project is not yet recognized as important. Perhaps the appropriate collaboration at this point is to propound something like 'the common good principle'. (p253) 
8. 'The common good principle': Superintelligence should be developed only for the benefit of all of humanity and in the service of widely shared ethical ideals. (p254)

Another view

Miles Brundage on the Collaboration section:

This is an important topic, and Bostrom says many things I agree with. A few places where I think the issues are less clear:

• Many of Bostrom’s proposals depend on AI recalcitrance being low. For instance, a highly secretive international effort makes less sense if building AI is a long and incremental slog. Recalcitrance may well be low, but this isn’t obvious, and it is good to recognize this dependency and consider what proposals would be appropriate for other recalcitrance levels. 
• Arms races are ubiquitous in our global capitalist economy, and AI is already in one. Arms races can stem from market competition by firms or state-driven national security-oriented R+D efforts as well as complex combinations of these, suggesting the need for further research on the relationship between AI development, national security, and global capitalist market dynamics. It's unclear how well the simple arms race model here matches the reality of the current AI arms race or future variations of it. The model's main value is probably in probing assumptions and inspiring the development of richer models, as it's probably too simple in to fit reality well as-is. For instance, it is unclear that safety and capability are close to orthogonal in practice today. If many AI people genuinely care about safety (which the quantity and quality of signatories to the FLI open letter suggests is plausible), or work on economically relevant near-term safety issues at each point is important, or consumers reward ethical companies with their purchases, then better AI firms might invest a lot in safety for self-interested as well as altruistic reasons. Also, if the AI field shifts to focus more on human-complementary intelligence that requires and benefits from long-term, high-frequency interaction with humans, then safety and capability may be synergistic rather than trading off against each other. Incentives related to research priorities should also be considered in a strategic analysis of AI governance (e.g. are AI researchers currently incentivized only to demonstrate capability advances in the papers they write, and could incentives be changed or the aims and scope of the field redefined so that more progress is made on safety issues?).
• ‘AI’ is too course grained a unit for a strategic analysis of collaboration. The nature and urgency of collaboration depends on the details of what is being developed. An enormous variety of artificial intelligence research is possible and the goals of the field are underconstrained by nature (e.g. we can model systems based on approximations of rationality, or on humans, or animals, or something else entirely, based on curiosity, social impact, and other considerations that could be more explicitly evaluated), and are thus open to change in the future. We need to think more about differential technology development within the domain of AI. This too will affect the urgency and nature of cooperation.

Notes

1. In Bostrom's description of his model, it is a bit unclear how safety precautions affect performance. He says 'one can model each team's performance as a function of its capability (measuring its raw ability and luck) and a penalty term corresponding to the cost of its safety precautions' (p247), which sounds like they are purely a negative. However this wouldn't make sense: if safety precautions were just a cost, then regardless of competition, nobody would invest in safety. In reality, whoever wins control over the world benefits a lot from whatever safety precautions have been taken. If the world is destroyed in the process of an AI transition, they have lost everything! I think this is the model Bostrom means to refer to. While he says it may lead to minimum precautions, note that in many models it would merely lead to less safety than one would want. If you are spending nothing on safety, and thus going to take over a world that is worth nothing, you would often prefer to move to a lower probability of winning a more valuable world. Armstrong, Bostrom and Shulman discuss this kind of model in more depth.

2. If you are interested in the game theory of conflicts like this, The Strategy of Conflict is a great book. 

3. Given the gains to competitors cooperating to not destroy the world that they are trying to take over, research on how to arrange cooperation seems helpful for all sides. The situation is much like a tragedy of the commons, except for the winner-takes-all aspect: each person gains from neglecting safety, while exerting a small cost on everyone. Academia seems to be pretty interested in resolving tragedies of the commons, so perhaps that literature is worth trying to apply here.

4. The most famous arms race is arguably the nuclear one. I wonder to what extent this was a major arms race because nuclear weapons were destined to be an unusually massive jump in progress. If this was important, it leads to the question of whether we have reason to expect anything similar in AI.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. Explore other models of competitive AI development.
2. What policy interventions help in promoting collaboration?
3. What kinds of situations produce arms races?
4. Examine international collaboration on major innovative technology. How often does it happen? What blocks it from happening more? What are the necessary conditions? Examples: Concord jet, LHC, international space station, etc.
5. Conduct a broad survey of past and current civilizational competence. In what ways, and under what conditions, do human civilizations show competence vs. incompetence? Which kinds of problems do they handle well or poorly? Similar in scope and ambition to, say, Perrow’s Normal Accidents and Sagan’s The Limits of Safety. The aim is to get some insight into the likelihood of our civilization handling various aspects of the superintelligence challenge well or poorly. Some initial steps were taken here and here.
6. What happens when governments ban or restrict certain kinds of technological development? What happens when a certain kind of technological development is banned or restricted in one country but not in other countries where technological development sees heavy investment?
7. What kinds of innovative technology projects do governments monitor, shut down, or nationalize? How likely are major governments to monitor, shut down, or nationalize serious AGI projects?
8. How likely is it that AGI will be a surprise to most policy-makers and industry leaders? How much advance warning are they likely to have? Some notes on this here.

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about what to do in this 'crunch time'. To prepare, read Chapter 15. The discussion will go live at 6pm Pacific time next Monday 30 March. Sign up to be notified here.

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-seventh section in the reading guide: Pathways and enablers.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Pathways and enablers” from Chapter 14

Summary

1. Is hardware progress good?
   1. Hardware progress means machine intelligence will arrive sooner, which is probably bad.
   2. More hardware at a given point means less understanding is likely to be needed to build machine intelligence, and brute-force techniques are more likely to be used. These probably increase danger.
   3. More hardware progress suggests there will be more hardware overhang when machine intelligence is developed, and thus a faster intelligence explosion. This seems good inasmuch as it brings a higher chance of a singleton, but bad in other ways:
      1. Less opportunity to respond during the transition
      2. Less possibility of constraining how much hardware an AI can reach
      3. Flattens the playing field, allowing small projects a better chance. These are less likely to be safety-conscious.
   4. Hardware has other indirect effects, e.g. it allowed the internet, which contributes substantially to work like this. But perhaps we have enough hardware now for such things.
   5. On balance, more hardware seems bad, on the impersonal perspective.
2. Would brain emulation be a good thing to happen?
   1. Brain emulation is coupled with 'neuromorphic' AI: if we try to build the former, we may get the latter. This is probably bad.
   2. If we achieved brain emulations, would this be safer than AI? Three putative benefits:
      1. "The performance of brain emulations is better understood"
         1. However we have less idea how modified emulations would behave
         2. Also, AI can be carefully designed to be understood
      2. "Emulations would inherit human values"
         1. This might require higher fidelity than making an economically functional agent
         2. Humans are not that nice, often. It's not clear that human nature is a desirable template.
      3. "Emulations might produce a slower take-off"
         1. It isn't clear why it would be slower. Perhaps emulations would be less efficient, and so there would be less hardware overhang. Or perhaps because emulations would not be qualitatively much better than humans, just faster and more populous of them
         2. A slower takeoff may lead to better control
         3. However it also means more chance of a multipolar outcome, and that seems bad.
   3. If brain emulations are developed before AI, there may be a second transition to AI later.
      1. A second transition should be less explosive, because emulations are already many and fast relative to the new AI. 
      2. The control problem is probably easier if the cognitive differences are smaller between the controlling entities and the AI.
      3. If emulations are smarter than humans, this would have some of the same benefits as cognitive enhancement, in the second transition.
      4. Emulations would extend the lead of the frontrunner in developing emulation technology, potentially allowing that group to develop AI with little disturbance from others.
      5. On balance, brain emulation probably reduces the risk from the first transition, but added to a second  transition this is unclear.
   4. Promoting brain emulation is better if:
      1. You are pessimistic about human resolution of control problem
      2. You are less concerned about neuromorphic AI, a second transition, and multipolar outcomes
      3. You expect the timing of brain emulations and AI development to be close
      4. You prefer superintelligence to arrive neither very early nor very late
3. The person affecting perspective favors speed: present people are at risk of dying in the next century, and may be saved by advanced technology

Another view

I talked to Kenzi Amodei about her thoughts on this section. Here is a summary of her disagreements:

Bostrom argues that we probably shouldn't celebrate advances in computer hardware. This seems probably right, but here are counter-considerations to a couple of his arguments.

The great filter

A big reason Bostrom finds fast hardware progress to be broadly undesirable is that he judges the state risks from sitting around in our pre-AI situation to be low, relative to the step risk from AI. But the so called 'Great Filter' gives us reason to question this assessment.

The argument goes like this. Observe that there are a lot of stars (we can detect about ~10^22 of them). Next, note that we have never seen any alien civilizations, or distant suggestions of them. There might be aliens out there somewhere, but they certainly haven't gone out and colonized the universe enough that we would notice them (see 'The Eerie Silence' for further discussion of how we might observe aliens). 

This implies that somewhere on the path between a star existing, and it being home to a civilization that ventures out and colonizes much of space, there is a 'Great Filter': at least one step that is hard to get past. 1/10^22 hard to get past. We know of somewhat hard steps at the start: a star might not have planets, or the planets may not be suitable for life. We don't know how hard it is for life to start: this step could be most of the filter for all we know.

If the filter is a step we have passed, there is nothing to worry about. But if it is a step in our future, then probably we will fail at it, like everyone else. And things that stop us from visibly colonizing the stars are may well be existential risks.

At least one way of understanding anthropic reasoning suggests the filter is much more likely to be at a step in our future. Put simply, one is much more likely to find oneself in our current situation if being killed off on the way here is unlikely.

So what could this filter be? One thing we know is that it probably isn't AI risk, at least of the powerful, tile-the-universe-with-optimal-computations, sort that Bostrom describes. A rogue singleton colonizing the universe would be just as visible as its alien forebears colonizing the universe. From the perspective of the Great Filter, either one would be a 'success'. But there are no successes that we can see.

What's more, if we expect to be fairly safe once we have a successful superintelligent singleton, then this points at risks arising before AI.

So overall this argument suggests that AI is less concerning than we think and that other risks (especially early ones) are more concerning than we think. It also suggests that AI is harder than we think.

Which means that if we buy this argument, we should put a lot more weight on the category of 'everything else', and especially the bits of it that come before AI. To the extent that known risks like biotechnology and ecological destruction don't seem plausible, we should more fear unknown unknowns that we aren't even preparing for.

How much progress is enough?

Bostrom points to positive changes hardware has made to society so far. For instance, hardware allowed personal computers, bringing the internet, and with it the accretion of an AI risk community, producing the ideas in Superintelligence. But then he says probably we have enough: "hardware is already good enough for a great many applications that could facilitate human communication and deliberation, and it is not clear that the pace of progress in these areas is strongly bottlenecked by the rate of hardware improvement."

This seems intuitively plausible. However one could probably have erroneously made such assessments in all kinds of progress, all over history. Accepting them all would lead to madness, and we have no obvious way of telling them apart.

In the 1800s it probably seemed like we had enough machines to be getting on with, perhaps too many. In the 1800s people probably felt overwhelmingly rich. If the sixties too, it probably seemed like we had plenty of computation, and that hardware wasn't a great bottleneck to social progress.

If a trend has brought progress so far, and the progress would have been hard to predict in advance, then it seems hard to conclude from one's present vantage point that progress is basically done.

Notes

1. How is hardware progressing?

I've been looking into this lately, at AI Impacts. Here's a figure of MIPS/$ growing, from Muehlhauser and Rieber.

(Note: I edited the vertical axis, to remove a typo)

2. Hardware-software indifference curves

It was brought up in this chapter that hardware and software can substitute for each other: if there is endless hardware, you can run worse algorithms, and vice versa. I find it useful to picture this as indifference curves, something like this: 

(Image: Hypothetical curves of hardware-software combinations producing the same performance at Go (source).)

I wrote about predicting AI given this kind of model here.

3. The potential for discontinuous AI progress

While we are on the topic of relevant stuff at AI Impacts, I've been investigating and quantifying the claim that AI might suddenly undergo huge amounts of abrupt progress (unlike brain emulations, according to Bostrom). As a step, we are finding other things that have undergone huge amounts of progress, such as nuclear weapons and high temperature superconductors:

(Figure originally from here)

4. The person-affecting perspective favors speed less as other prospects improve

I agree with Bostrom that the person-affecting perspective probably favors speeding many technologies, in the status quo. However I think it's worth noting that people with the person-affecting view should be scared of existential risk again as soon as society has achieved some modest chance of greatly extending life via specific technologies. So if you take the person-affecting view, and think there's a reasonable chance of very long life extension within the lifetimes of many existing humans, you should be careful about trading off speed and risk of catastrophe.

5. It seems unclear that an emulation transition would be slower than an AI transition. 

One reason to expect an emulation transition to proceed faster is that there is an unusual reason to expect abrupt progress there.

6. Beware of brittle arguments

This chapter presented a large number of detailed lines of reasoning for evaluating hardware and brain emulations. This kind of concern might apply.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. Investigate in more depth how hardware progress affects factors of interest
2. Assess in more depth the likely implications of whole brain emulation 
3. Measure better the hardware and software progress that we see (e.g. some efforts at AI Impacts, MIRI, MIRI and MIRI)
4. Investigate the extent to which hardware and software can substitute (I describe more projects here)
5. Investigate the likely timing of whole brain emulation (the Whole Brain Emulation Roadmap is the main work on this)

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about how collaboration and competition affect the strategic picture. To prepare, read “Collaboration” from Chapter 14 The discussion will go live at 6pm Pacific time next Monday 23 March. Sign up to be notified here.

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-sixth section in the reading guide: Science and technology strategy. Sorry for posting late—my car broke.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Science and technology strategy” from Chapter 14

Summary

1. This section will introduce concepts that are useful for thinking about long term issues in science and technology (p228)
2. Person affecting perspective: one should act in the best interests of everyone who already exists, or who will exist independent of one's choices (p228) 
3. Impersonal perspective: one should act in the best interests of everyone, including those who may be brought into existence by one's choices. (p228)
4. Technological completion conjecture: "If scientific and technological development efforts do not cease, then all important basic capabilities that could be obtained through some possible technology will be obtained." (p229)
   1. This does not imply that it is futile to try to steer technology. Efforts may cease. It might also matter exactly when things are developed, who develops them, and in what context.
5. Principle of differential technological development: one should slow the development of dangerous and harmful technologies relative to beneficial technologies (p230)
6. We have a preferred order for some technologies, e.g. it is better to have superintelligence later relative to social progress, but earlier relative to other existential risks. (p230-233)
7. If a macrostructural development accelerator is a magic lever which slows the large scale features of history (e.g. technological change, geopolitical dynamics) while leaving the small scale features the same, then we can ask whether pulling the lever would be a good idea (p233). The main way Bostrom concludes that it matters is by affecting how well prepared humanity is for future transitions.
8. State risk: a risk that persists while you are in a certain situation, such that the amount of risk is a function of the time spent there. e.g. risk from asteroids, while we don't have technology to redirect them. (p233-4)
9. Step risk: a risk arising from a transition. Here the amount of risk is mostly not a function of how long the transition takes. e.g. traversing a minefield: this is not especially safer if you run faster. (p234)
10. Technology coupling: a predictable timing relationship between two technologies, such that hastening of the first technology will hasten the second, either because the second is a precursor or because it is a natural consequence. (p236-8) e.g. brain emulation is plausibly coupled to 'neuromorphic' AI, because the understanding required to emulate a brain might allow one to more quickly create an AI on similar principles.
11. Second guessing: acting as if "by treating others as irrational and playing to their biases and misconceptions it is possible to elicit a response from them that is more competent than if a case had been presented honestly and forthrightly to their rational faculties" (p238-40)

Another view

There is a common view which says we should not act on detailed abstract arguments about the far future like those of this section. Here Holden Karnofsky exemplifies it:

I have often been challenged to explain how one could possibly reconcile (a) caring a great deal about the far future with (b) donating to one of GiveWell’s top charities. My general response is that in the face of sufficient uncertainty about one’s options, and lack of conviction that there are good (in the sense of high expected value) opportunities to make an enormous difference, it is rational to try to make a smaller but robustly positivedifference, whether or not one can trace a specific causal pathway from doing this small amount of good to making a large impact on the far future. A few brief arguments in support of this position:

• I believe that the track record of “taking robustly strong opportunities to do ‘something good'” is far better than the track record of “taking actions whose value is contingent on high-uncertainty arguments about where the highest utility lies, and/or arguments about what is likely to happen in the far future.” This is true even when one evaluates track record only in terms of seeming impact on the far future. The developments that seem most positive in retrospect – from large ones like the development of the steam engine to small ones like the many economic contributions that facilitated strong overall growth – seem to have been driven by the former approach, and I’m not aware of many examples in which the latter approach has yielded great benefits.
• I see some sense in which the world’s overall civilizational ecosystem seems to have done a better job optimizing for the far future than any of the world’s individual minds. It’s often the case that people acting on relatively short-term, tangible considerations (especially when they did so with creativity, integrity, transparency, consensuality, and pursuit of gain via value creation rather than value transfer) have done good in ways they themselves wouldn’t have been able to foresee. If this is correct, it seems to imply that one should be focused on “playing one’s role as well as possible” – on finding opportunities to “beat the broad market” (to do more good than people with similar goals would be able to) rather than pouring one’s resources into the areas that non-robust estimates have indicated as most important to the far future.
• The process of trying to accomplish tangible good can lead to a great deal of learning and unexpected positive developments, more so (in my view) than the process of putting resources into a low-feedback endeavor based on one’s current best-guess theory. In my conversation with Luke and Eliezer, the two of them hypothesized that the greatest positive benefit of supporting GiveWell’s top charities may have been to raise the profile, influence, and learning abilities of GiveWell. If this were true, I don’t believe it would be an inexplicable stroke of luck for donors to top charities; rather, it would be the sort of development (facilitating feedback loops that lead to learning, organizational development, growing influence, etc.) that is often associated with “doing something well” as opposed to “doing the most worthwhile thing poorly.”
• I see multiple reasons to believe that contributing to general human empowerment mitigates global catastrophic risks. I laid some of these out in a blog post and discussed them further in my conversation with Luke and Eliezer.

Notes

1. Technological completion timelines game
The technological completion conjecture says that all the basic technological capabilities will eventually be developed. But when is 'eventually', usually? Do things get developed basically as soon as developing them is not prohibitively expensive, or is thinking of the thing often a bottleneck? This is relevant to how much we can hope to influence the timing of technological developments.

Here is a fun game: How many things can you find that could have been profitably developed much earlier than they were?

Some starting suggestions, which I haven't looked into:

Wheeled luggage: invented in the 1970s, though humanity had had both wheels and luggage for a while.

Hot air balloons: flying paper lanterns using the same principle were apparently used before 200AD, while a manned balloon wasn't used until 1783.

Penicillin: mould was apparently traditionally used for antibacterial properties in several cultures, but lots of things are traditionally used for lots of things. By the 1870s many scientists had noted that specific moulds inhibited bacterial growth.

Wheels: Early toys from the Americas appear to have had wheels (here and pictured is one from 1-900AD; Wikipedia claims such toys were around as early as 1500BC). However wheels were apparently not used for more substantial transport in the Americas until much later.

Image: "Remojadas Wheeled Figurine"

There are also cases where humanity has forgotten important insights, and then rediscovered them again much later, which suggests strongly that they could have been developed earlier.

2. How does economic growth affect AI risk?

Eliezer Yudkowsky argues that economic growth increases risk. I argue that he has the sign wrong. Others argue that probably lots of other factors matter more anyway. Luke Muehlhauser expects that cognitive enhancement is bad, largely based on Eliezer's aforementioned claim. He also points out that smarter people are different from more rational people. Paul Christiano outlines his own evaluation of economic growth in general, on humanity's long run welfare. He also discusses the value of continued technological, economic and social progress more comprehensibly here. 

3. The person affecting perspective

Some interesting critiques: the non-identity problem, taking additional people to be neutral makes other good or bad things neutral too, if you try to be consistent in natural ways.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. Is macro-structural acceleration good or bad on net for AI safety? 
2. Choose a particular anticipated technology. Is it's development good or bad for AI safety on net?
3. What is the overall current level of “state risk” from existential threats? 
4. What are the major existential-threat “step risks” ahead of us, besides those from superintelligence? 
5. What are some additional “technology couplings,” in addition to those named in Superintelligence, ch. 14?
6. What are further preferred orderings for technologies not mentioned in this section?

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about the desirability of hardware progress, and progress toward brain emulation. To prepare, read “Pathways and enablers” from Chapter 14. The discussion will go live at 6pm Pacific time next Monday 16th March. Sign up to be notified here.

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-fifth section in the reading guide: Components list for acquiring values.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Component list” and “Getting close enough” from Chapter 13

Summary

1. Potentially important choices to make before building an AI (p222)
   • What goals does it have?
   • What decision theory does it use?
   • How do its beliefs evolve? In particular, what priors and anthropic principles does it use? (epistemology)
   • Will its plans be subject to human review? (ratification)
2. Incentive wrapping: beyond the main pro-social goals given to an AI, add some extra value for those who helped bring about the AI, as an incentive (p222-3)
3. Perhaps we should indirectly specify decision theory and epistemology, like we have suggested doing with goals, rather than trying to resolve these issues now. (p224-5)
4. An AI with a poor epistemology may still be very instrumentally smart, but for instance be incapable of believing the universe could be infinite (p225)
5. We should probably attend to avoiding catastrophe rather than maximizing value (p227) [i.e. this use of our attention is value maximizing..]
6. If an AI has roughly the right values, decision theory, and epistemology maybe it will correct itself anyway and do what we want in the long run (p227)

Another view

Paul Christiano argues (today) that decision theory doesn't need to be sorted out before creating human-level AI. Here's a key bit, but you might need to look at the rest of the post to understand his idea well:

Really, I’d like to leave these questions up to an AI. That is, whatever work Iwould do in order to answer these questions, an AI should be able to do just as well or better. And it should behave sensibly in the interim, just like I would.

To this end, consider the definition of a map U' : [Possible actions] → ℝ:

U'(a) = “How good I would judge the action a to be, after an idealized process of reflection.”

Now we’d just like to build an “agent” that takes the action a maximizing 𝔼[U'(a)]. Rather than defining our decision theory or our beliefs, we will have to come up with some answer during the “idealized process of reflection.” And as long as an AI is uncertain about what we’d come up with, it will behave sensibly in light of its uncertainty.

This feels like a cheat. But I think the feeling is an illusion. 

(more)

Notes

1. MIRI's Research, and decision theory

MIRI focuses on technical problems that they believe can't be delegated well to an AI. Thus MIRI's technical research agenda describes many such problems and questions. In it, Nate Soares and Benja Fallenstein also discuss the question of why these can't be delegated:

Why can’t these tasks, too, be delegated? Why not, e.g., design a system that makes “good enough” decisions, constrain it to domains where its decisions are trusted, and then let it develop a better decision theory, perhaps using an indirect normativity approach (chap. 13) to figure out how humans would have wanted it to make decisions?

We cannot delegate these tasks because modern knowledge is not sufficient even for an indirect approach. Even if fully satisfactory theories of logical uncertainty and decision theory cannot be obtained, it is still necessary to have a sufficient theoretical grasp on the obstacles in order to justify high confidence in the system’s ability to correctly perform indirect normativity.

Furthermore, it would be risky to delegate a crucial task before attaining a solid theoretical understanding of exactly what task is being delegated. It is possible to create an intelligent system tasked with developing better and better approximations of Bayesian updating, but it would be difficult to delegate the abstract task of “find good ways to update probabilities” to an intelligent system before gaining an understanding of Bayesian reasoning. The theoretical understanding is necessary to ensure that the right questions are being asked.

If you want to learn more about the subjects of MIRI's research (which overlap substantially with the topics of the 'components list'), Nate Soares recently published a research guide. For instance here's some of it on the (pertinent this week) topic of decision theory:

Existing methods of counterfactual reasoning turn out to be unsatisfactory both in the short term (in the sense that they systematically achieve poor outcomes on some problems where good outcomes are possible) and in the long term (in the sense that self-modifying agents reasoning using bad counterfactuals would, according to those broken counterfactuals, decide that they should not fix all of their flaws). My talk “Why ain’t you rich?” briefly touches upon both these points. To learn more, I suggest the following resources:

1. Soares & Fallenstein’s “Toward idealized decision theory” serves as a general overview, and further motivates problems of decision theory as relevant to MIRI’s research program. The paper discusses the shortcomings of two modern decision theories, and discusses a few new insights in decision theory that point toward new methods for performing counterfactual reasoning.

If “Toward idealized decision theory” moves too quickly, this series of blog posts may be a better place to start:

1. Yudkowsky’s “The true Prisoner’s Dilemma” explains why cooperation isn’t automatically the ‘right’ or ‘good’ option.

2. Soares’ “Causal decision theory is unsatisfactory” uses the Prisoner’s Dilemma to illustrate the importance of non-causal connections between decision algorithms.

3. Yudkowsky’s “Newcomb’s problem and regret of rationality” argues for focusing on decision theories that ‘win,’ not just on ones that seem intuitively reasonable. Soares’ “Introduction to Newcomblike problems” covers similar ground.

4. Soares’ “Newcomblike problems are the norm” notes that human agents probabilistically model one another’s decision criteria on a routine basis.

MIRI’s research has led to the development of “Updateless Decision Theory” (UDT), a new decision theory which addresses many of the shortcomings discussed above.

1. Hintze’s “Problem class dominance in predictive dilemmas” summarizes UDT’s dominance over other known decision theories, including Timeless Decision Theory (TDT), another theory that dominates CDT and EDT.

2. Fallenstein’s “A model of UDT with a concrete prior over logical statements” provides a probabilistic formalization.

However, UDT is by no means a solution, and has a number of shortcomings of its own, discussed in the following places:

1. Slepnev’s “An example of self-fulfilling spurious proofs in UDT” explains how UDT can achieve sub-optimal results due to spurious proofs.

2. Benson-Tilsen’s “UDT with known search order” is a somewhat unsatisfactory solution. It contains a formalization of UDT with known proof-search order and demonstrates the necessity of using a technique known as “playing chicken with the universe” in order to avoid spurious proofs.

For more on decision theory, here is Luke Muehlhauser and Crazy88's FAQ.

2. Can stable self-improvement be delegated to an AI?

Paul Christiano also argues for 'yes' here:

“Stable self-improvement” seems to be a primary focus of MIRI’s work. As I understand it, the problem is “How do we build an agent which rationally pursues some goal, is willing to modify itself, and with very high probability continues to pursue the same goal after modification?”

The key difficulty is that it is impossible for an agent to formally “trust” its own reasoning, i.e. to believe that “anything that I believe is true.” Indeed, even the natural concept of “truth” is logically problematic. But without such a notion of trust, why should an agent even believe that its own continued existence is valuable?

I agree that there are open philosophical questions concerning reasoning under logical uncertainty, and that reflective reasoning highlights some of the difficulties. But I am not yet convinced that stable self-improvement is an especially important problem for AI safety; I think it would be handled correctly by a human-level reasoner as a special case of decision-making under logical uncertainty. This suggests that (1) it will probably be resolved en route to human-level AI, (2) it can probably be “safely” delegated to a human-level AI. I would prefer use energy investigating other aspects of the AI safety problem... (more)

3. On the virtues of human review

Bostrom mentions the possibility of having an 'oracle' or some such non-interfering AI tell you what your 'sovereign' will do. He suggests some benefits and costs of this—namely, it might prevent existential catastrophe, and it might reveal facts about the intended future that would make sponsors less happy to defer to the AI's mandate (coherent extrapolated volition or some such thing). Four quick thoughts:

1) The costs and benefits here seem wildly out of line with each other. In a situation where you think there's a substantial chance your superintelligent AI will destroy the world, you are not going to set aside what you think is an effective way of checking, because it might cause the people sponsoring the project to realize that it isn't exactly what they want, and demand some more pie for themselves. Deceiving sponsors into doing what you want instead of what they would want if they knew more seems much, much, much much less important than avoiding existential catastrophe.

2) If you were concerned about revealing information about the plan because it would lift a veil of ignorance, you might artificially replace some of the veil with intentional randomness.

3) It seems to me that a bigger concern with humans reviewing AI decisions is that it will be infeasible. At least if the risk from an AI is that it doesn't correctly manifest the values we want. Bostrom describes an oracle with many tools for helping to explain, so it seems plausible such an AI could give you a good taste of things to come. However if the problem is that your values are so nuanced that you haven't managed to impart them adequately to an AI, then it seems unlikely that an AI can highlight for you the bits of the future that you are likely to disapprove of. Or at least you have to be in a fairly narrow part of the space of AI capability, where the AI doesn't know some details of your values, but for all the important details it is missing, can point to relevant parts of the world where the mismatch will manifest.

4) Human oversight only seems feasible in a world where there is much human labor available per AI. In a world where a single AI is briefly overseen by a programming team before taking over the world, human oversight might be a reasonable tool for that brief time. Substantial human oversight does not seem helpful in a world where trillions of AI agents are each smarter and faster than a human, and need some kind of ongoing control.

4. Avoiding catastrophe as the top priority

In case you haven't read it, Bostrom's Astronomical Waste is a seminal discussion of the topic.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. See MIRI's research agenda
2. For any plausible entry on the list of things that can't be well delegated to AI, think more about whether it belongs there, or how to delegate it.

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about strategy in directing science and technology. To prepare, read “Science and technology strategy” from Chapter 14. The discussion will go live at 6pm Pacific time next Monday 9 March. Sign up to be notified here.

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-fourth section in the reading guide: Morality models and "Do what I mean".

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Morality models” and “Do what I mean” from Chapter 13.

Summary

1. Moral rightness (MR) AI: AI which seeks to do what is morally right
   1. Another form of 'indirect normativity'
   2. Requires moral realism to be true to do anything, but we could ask the AI to evaluate that and do something else if moral realism is false
   3. Avoids some complications of CEV
   4. If moral realism is true, is better than CEV (though may be terrible for us)
2. We often want to say 'do what I mean' with respect to goals we try to specify. This is doing a lot of the work sometimes, so if we could specify that well perhaps it could also just stand alone: do what I want. This is much like CEV again.

Another view

Olle Häggström again, on Bostrom's 'Milky Way Preserve':

The idea [of a Moral Rightness AI] is that a superintelligence might be successful at the task (where we humans have so far failed) of figuring out what is objectively morally right. It should then take objective morality to heart as its own values.^1,2

Bostrom sees a number of pros and cons of this idea. A major concern is that objective morality may not be in humanity's best interest. Suppose for instance (not entirely implausibly) that objective morality is a kind of hedonistic utilitarianism, where "an action is morally right (and morally permissible) if and only if, among all feasible actions, no other action would produce a greater balance of pleasure over suffering" (p 219). Some years ago I offered a thought experiment to demonstrate that such a morality is not necessarily in humanity's best interest. Bostrom reaches the same conclusion via a different thought experiment, which I'll stick with here in order to follow his line of reasoning.³ Here is his scenario:

The AI [...] might maximize the surfeit of pleasure by converting the accessible universe into hedonium, a process that may involve building computronium and using it to perform computations that instantiate pleasurable experiences. Since simulating any existing human brain is not the most efficient way of producing pleasure, a likely consequence is that we all die.

Bostrom is reluctant to accept such a sacrifice for "a greater good", and goes on to suggest a compromise:

The sacrifice looks even less appealing when we reflect that the superintelligence could realize a nearly-as-great good (in fractional terms) while sacrificing much less of our own potential well-being. Suppose that we agreed to allow almost the entire accessible universe to be converted into hedonium - everything except a small preserve, say the Milky Way, which would be set aside to accommodate our own needs. Then there would still be a hundred billion galaxies devoted to the maximization of pleasure. But we would have one galaxy within which to create wonderful civilizations that could last for billions of years and in which humans and nonhuman animals could survive and thrive, and have the opportunity to develop into beatific posthuman spirits.

If one prefers this latter option (as I would be inclined to do) it implies that one does not have an unconditional lexically dominant preference for acting morally permissibly. But it is consistent with placing great weight on morality. (p 219-220)

What? Is it? Is it "consistent with placing great weight on morality"? Imagine Bostrom in a situation where he does the final bit of programming of the coming superintelligence, to decide between these two worlds, i.e., the all-hedonium one versus the all-hedonium-except-in-the-Milky-Way-preserve.⁴ And imagine that he goes for the latter option. The only difference it makes to the world is to what happens in the Milky Way, so what happens elsewhere is irrelevant to the moral evaluation of his decision.⁵ This may mean that Bostrom opts for a scenario where, say, 10²⁴ sentient beings will thrive in the Milky Way in a way that is sustainable for trillions of years, rather than a scenarion where, say, 10⁴⁵ sentient beings will be even happier for a comparable amount of time. Wouldn't that be an act of immorality that dwarfs all other immoral acts carried out on our planet, by many many orders of magnitude? How could that be "consistent with placing great weight on morality"?⁶

Notes

1. Do What I Mean is originally a concept from computer systems, where the (more modest) idea is to have a system correct small input errors.

2. To the extent that people care about objective morality, it seems coherent extrapolated volition (CEV) or Christiano's proposal would lead the AI to care about objective morality, and thus look into what it is. Thus I doubt it is worth considering our commitments to morality first (as Bostrom does in this chapter, and as one might do before choosing whether to use a MR AI), if general methods for implementing our desires are on the table. This is close to what Bostrom is saying when he suggests we outsource the decision about which form of indirect normativity to use, and eventually winds up back at CEV. But it seems good to be explicit.

3. I'm not optimistic that behind every vague and ambiguous command, there is something specific that a person 'really means'. It seems more likely there is something they would in fact try to mean, if they thought about it a bunch more, but this is mostly defined by further facts about their brains, rather than the sentence and what they thought or felt as they said it. It seems at least misleading to call this 'what they meant'. Thus even when '—and do what I mean' is appended to other kinds of goals than generic CEV-style ones, I would expect the execution to look much like a generic investigation of human values, such as that implicit in CEV.

4. Alexander Kruel criticizes 'Do What I Mean' being important, because every part of what an AI does is designed to be what humans really want it to be, so it seems unlikely to him that AI would do exactly what humans want with respect to instrumental behaviors (e.g. be able to understand language, and use the internet and carry out sophisticated plans), but fail on humans' ultimate goals:

Outsmarting humanity is a very small target to hit, requiring a very small margin of error. In order to succeed at making an AI that can outsmart humans, humans have to succeed at making the AI behave intelligently and rationally. Which in turn requires humans to succeed at making the AI behave as intended along a vast number of dimensions. Thus, failing to predict the AI’s behavior does in almost all cases result in the AI failing to outsmart humans.

As an example, consider an AI that was designed to fly planes. It is exceedingly unlikely for humans to succeed at designing an AI that flies planes, without crashing, but which consistently chooses destinations that it was not meant to choose. Since all of the capabilities that are necessary to fly without crashing fall into the category “Do What Humans Mean”, and choosing the correct destination is just one such capability.

I disagree that it would be surprising for an AI to be very good at flying planes in general, but very bad at going to the right places in them. However it seems instructive to think about why this is.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. Are there other general forms of indirect normativity that might outsource the problem of deciding what indirect normativity to use?
2. On common views of moral realism, is morality likely to be amenable to (efficient) algorithmic discovery?
3. If you knew how to build an AI with a good understanding of natural language (e.g. it knows what the word 'good' means as well as your most intelligent friend), how could you use this to make a safe AI?

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about other abstract features of an AI's reasoning that we might want to get right ahead of time, instead of leaving to the AI to fix. We will also discuss how well an AI would need to fulfill these criteria to be 'close enough'. To prepare, read “Component list” and “Getting close enough” from Chapter 13. The discussion will go live at 6pm Pacific time next Monday 2 March. Sign up to be notified here.

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-third section in the reading guide: Coherent extrapolated volition.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “The need for...” and “Coherent extrapolated volition” from Chapter 13

Summary

1. Problem: we are morally and epistemologically flawed, and we would like to make an AI without locking in our own flaws forever. How can we do this?
2. Indirect normativity: offload cognitive work to the superintelligence, by specifying our values indirectly and having it transform them into a more usable form.
3. Principle of epistemic deference: a superintelligence is more likely to be correct than we are on most topics, most of the time. Therefore, we should defer to the superintelligence where feasible.
4. Coherent extrapolated volition (CEV): a goal of fulfilling what humanity would agree that they want, if given much longer to think about it, in more ideal circumstances. CEV is popular proposal for what we should design an AI to do. 
5. Virtues of CEV:
   1. It avoids the perils of specification: it is very hard to specify explicitly what we want, without causing unintended and undesirable consequences. CEV specifies the source of our values, instead of what we think they are, which appears to be easier.
   2. It encapsulates moral growth: there are reasons to believe that our current moral beliefs are not the best (by our own lights) and we would revise some of them, if we thought about it. Specifying our values now risks locking in wrong values, whereas CEV effectively gives us longer to think about our values.
   3. It avoids 'hijacking the destiny of humankind': it allows the responsibility for the future of mankind to remain with mankind, instead of perhaps a small group of programmers.
   4. It avoids creating a motive for modern-day humans to fight over the initial dynamic: a commitment to CEV would mean the creators of AI would not have much more influence over the future of the universe than others, reducing the incentive to race or fight. This is even more so because a person who believes that their views are correct should be confident that CEV will come to reflect their views, so they do not even need to split the influence with others.
   5. It keeps humankind 'ultimately in charge of its own destiny': it allows for a wide variety of arrangements in the long run, rather than necessitating paternalistic AI oversight of everything.
6. CEV as described here is merely a schematic. For instance, it does not specify which people are included in 'humanity'.

Another view

Part of Olle Häggström's extended review of Superintelligence expresses a common concern—that human values can't be faithfully turned into anything coherent:

Human values exhibit, at least on the surface, plenty of incoherence. That much is hardly controversial. But what if the incoherence goes deeper, and is fundamental in such a way that any attempt to untangle it is bound to fail? Perhaps any search for our CEV is bound to lead to more and more glaring contradictions? Of course any value system can be modified into something coherent, but perhaps not all value systems cannot be so modified without sacrificing some of its most central tenets? And perhaps human values have that property? 

Let me offer a candidate for what such a fundamental contradiction might consist in. Imagine a future where all humans are permanently hooked up to life-support machines, lying still in beds with no communication with each other, but with electrodes connected to the pleasure centra of our brains in such a way as to constantly give us the most pleasurable experiences possible (given our brain architectures). I think nearly everyone would attach a low value to such a future, deeming it absurd and unacceptable (thus agreeing with Robert Nozick). The reason we find it unacceptable is that in such a scenario we no longer have anything to strive for, and therefore no meaning in our lives. So we want instead a future where we have something to strive for. Imagine such a future F₁. In F₁ we have something to strive for, so there must be something missing in our lives. Now let F₂ be similar to F₁, the only difference being that that something is no longer missing in F₂, so almost by definition F₂ is better than F₁ (because otherwise that something wouldn't be worth striving for). And as long as there is still something worth striving for in F₂, there's an even better future F₃ that we should prefer. And so on. What if any such procedure quickly takes us to an absurd and meaningless scenario with life-suport machines and electrodes, or something along those lines. Then no future will be good enough for our preferences, so not even a superintelligence will have anything to offer us that aligns acceptably with our values. 

Now, I don't know how serious this particular problem is. Perhaps there is some way to gently circumvent its contradictions. But even then, there might be some other fundamental inconsistency in our values - one that cannot be circumvented. If that is the case, it will throw a spanner in the works of CEV. And perhaps not only for CEV, but for any serious attempt to set up a long-term future for humanity that aligns with our values, with or without a superintelligence.

Notes

1. While we are on the topic of critiques, here is a better list:

1. Human values may not be coherent (Olle Häggström above, Marcello; Eliezer responds in section 6. question 9)
2. The values of a collection of humans in combination may be even less coherent. Arrow's impossibility theorem suggests reasonable aggregation is hard, but this only applies if values are ordinal, which is not obvious.
3. Even if human values are complex, this doesn't mean complex outcomes are required—maybe with some thought we could specify the right outcomes, and don't need an indirect means like CEV (Wei Dai)
4. The moral 'progress' we see might actually just be moral drift that we should try to avoid. CEV is designed to allow this change, which might be bad. Ideally, the CEV circumstances would be optimized for deliberation and not for other forces that might change values, but perhaps deliberation itself can't proceed without our values being changed (Cousin_it)
5. Individuals will probably not be a stable unit in the future, so it is unclear how to weight different people's inputs to CEV. Or to be concrete, what if Dr Evil can create trillions of emulated copies of himself to go into the CEV population. (Wei Dai)
6. It is not clear that extrapolating everyone's volition is better than extrapolating a single person's volition, which may be easier. If you want to take into account others' preferences, then your own volition is fine (it will do that), and if you don't, then why would you be using CEV?
7. A purported advantage of CEV is that it makes conflict less likely. But if a group is disposed to honor everyone else's wishes, they will not conflict anyway, and if they aren't disposed to honor everyone's wishes, why would they favor CEV? CEV doesn't provide any additional means to commit to cooperative behavior. (Cousin_it) 
8. More in Coherent Extrapolated Volition section 6. question 9

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. Specify a method for instantiating CEV, given some assumptions about available technology.
2. In practice, to what degree do human values and preferences converge upon learning new facts? To what degree has this happened in history? (Nobody values the will of Zeus anymore, presumably because we all learned the truth of Zeus’ non-existence. But perhaps such examples don’t tell us much.) See also philosophical analyses of the issue, e.g. Sobel (1999).
3. Are changes in specific human preferences (over a lifetime or many lifetimes) better understood as changes in underlying values, or changes in instrumental ways to achieve those values? (driven by belief change, or additional deliberation)
4. How might democratic systems deal with new agents being readily created?

If you are interested in anything like this, you might want to mention it in the comments, and see whether other people have useful thoughts.

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about more ideas for giving an AI desirable values. To prepare, read “Morality models” and “Do what I mean” from Chapter 13. The discussion will go live at 6pm Pacific time next Monday 23 February. Sign up to be notified here.

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-second section in the reading guide: Emulation modulation and institutional design.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Emulation modulation” through “Synopsis” from Chapter 12.

Summary

1. Emulation modulation: starting with brain emulations with approximately normal human motivations (the 'augmentation' method of motivation selection discussed on p142), and potentially modifying their motivations using drugs or digital drug analogs.
   1. Modifying minds would be much easier with digital minds than biological ones
   2. Such modification might involve new ethical complications
2. Institution design (as a value-loading method): design the interaction protocols of a large number of agents such that the resulting behavior is intelligent and aligned with our values.
   1. Groups of agents can pursue goals that are not held by any of their constituents, because of how they are organized. Thus organizations might be intentionally designed to pursue desirable goals in spite of the motives of their members.
   2. Example: a ladder of increasingly intelligent brain emulations, who police those directly above them, with equipment to advantage the less intelligent policing ems in these interactions.

Another view

Robin Hanson also favors institution design as a method of making the future nice, though as an alternative to worrying about values:

On Tuesday I asked my law & econ undergrads what sort of future robots (AIs computers etc.) they would want, if they could have any sort they wanted.  Most seemed to want weak vulnerable robots that would stay lower in status, e.g., short, stupid, short-lived, easily killed, and without independent values. When I asked “what if I chose to become a robot?”, they said I should lose all human privileges, and be treated like the other robots.  I winced; seems anti-robot feelings are even stronger than anti-immigrant feelings, which bodes for a stormy robot transition.

At a workshop following last weekend’s Singularity Summit two dozen thoughtful experts mostly agreed that it is very important that future robots have the right values.  It was heartening that most were willing accept high status robots, with vast impressive capabilities, but even so I thought they missed the big picture.  Let me explain.

Imagine that you were forced to leave your current nation, and had to choose another place to live.  Would you seek a nation where the people there were short, stupid, sickly, etc.?  Would you select a nation based on what the World Values Survey says about typical survey question responses there?

I doubt it.  Besides wanting a place with people you already know and like, you’d want a place where you could “prosper”, i.e., where they valued the skills you had to offer, had many nice products and services you valued for cheap, and where predation was kept in check, so that you didn’t much have to fear theft of your life, limb, or livelihood.  If you similarly had to choose a place to retire, you might pay less attention to whether they valued your skills, but you would still look for people you knew and liked, low prices on stuff you liked, and predation kept in check.

Similar criteria should apply when choosing the people you want to let into your nation.  You should want smart capable law-abiding folks, with whom you and other natives can form mutually advantageous relationships.  Preferring short, dumb, and sickly immigrants so you can be above them in status would be misguided; that would just lower your nation’s overall status.  If you live in a democracy, and if lots of immigration were at issue, you might worry they could vote to overturn the law under which you prosper.  And if they might be very unhappy, you might worry that they could revolt.

But you shouldn’t otherwise care that much about their values.  Oh there would be some weak effects.  You might have meddling preferences and care directly about some values.  You should dislike folks who like the congestible goods you like and you’d like folks who like your goods that are dominated by scale economics.  For example, you might dislike folks who crowd your hiking trails, and like folks who share your tastes in food, thereby inducing more of it to be available locally.  But these effects would usually be dominated by peace and productivity issues; you’d mainly want immigrants able to be productive partners, and law-abiding enough to keep the peace.

Similar reasoning applies to the sort of animals or children you want.  We try to coordinate to make sure kids are raised to be law-abiding, but wild animals aren’t law abiding, don’t keep the peace, and are hard to form productive relations with.  So while we give lip service to them, we actually don’t like wild animals much.

A similar reasoning should apply what future robots you want.  In the early to intermediate era when robots are not vastly more capable than humans, you’d want peaceful law-abiding robots as capable as possible, so as to make productive partners.  You might prefer they dislike your congestible goods, like your scale-economy goods, and vote like most voters, if they can vote.  But most important would be that you and they have a mutually-acceptable law as a good enough way to settle disputes, so that they do not resort to predation or revolution.  If their main way to get what they want is to trade for it via mutually agreeable exchanges, then you shouldn’t much care what exactly they want.

The later era when robots are vastly more capable than people should be much like the case of choosing a nation in which to retire.  In this case we don’t expect to have much in the way of skills to offer, so we mostly care that they are law-abiding enough to respect our property rights.  If they use the same law to keep the peace among themselves as they use to keep the peace with us, we could have a long and prosperous future in whatever weird world they conjure.  In such a vast rich universe our “retirement income” should buy a comfortable if not central place for humans to watch it all in wonder.

In the long run, what matters most is that we all share a mutually acceptable law to keep the peace among us, and allow mutually advantageous relations, not that we agree on the “right” values.  Tolerate a wide range of values from capable law-abiding robots.  It is a good law we should most strive to create and preserve.  Law really matters.

Hanson engages in more debate with David Chalmers' paper on related matters.

Notes

1. Relatively much has been said on how the organization and values of brain emulations might evolve naturally, as we saw earlier. This should remind us that the task of designing values and institutions is complicated by selection effects.

2. It seems strange to me to talk about the 'emulation modulation' method of value loading alongside the earlier less messy methods, because they seem to be aiming at radically different levels of precision (unless I misunderstand how well something like drugs can manipulate motivations). For the synthetic AI methods, it seems we were concerned about subtle differences in values that would lead to the AI behaving badly in unusual scenarios, or seeking out perverse instantiations. Are we to expect there to be a virtual drug that changes a human-like creature from desiring some manifestation of 'human happiness' which is not really what we would want to optimize on reflection, to a truer version of what humans want? It seems to me that if the answer is yes, at the point when human-level AI is developed, then it is very likely that we have a great understanding of specifying values in general, and this whole issue is not much of a problem.

3. Brian Tomasik discusses the impending problem of programs experiencing morally relevant suffering in an interview with Dylan Matthews of Vox. (p202)

4. If you are hanging out for a shorter (though still not actually short) and amusing summary of some of the basics in Superintelligence, Tim Urban of WaitButWhy just wrote a two part series on it. 

5. At the end of this chapter about giving AI the right values, it is worth noting that it is mildly controversial whether humans constructing precise and explicitly understood AI values is the key issue for the future turning out well. A few alternative possibilities:

• A few parts of values matter a lot more than the rest —e.g. whether the AI is committed to certain constraints (e.g. law, property rights) such that it doesn't accrue all the resources matters much more than what it would do with its resources (see Robin above).
• Selection pressures determine long run values anyway, regardless of what AI values are like in the short run. (See Carl Shulman opposing this view).
• AI might learn to do what a human would want without goals being explicitly encoded (see Paul Christiano).

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. What other forms of institution design might be worth investigating as means to influence the outcomes of future AI?
2. How feasible might emulation modulation solutions be, given what is currently known about cognitive neuroscience?
3. What are the likely ethical implications of experimenting on brain emulations?
4. How much should we expect emulations to change in the period after they are first developed? Consider the possibility of selection, the power of ethical and legal constraints, and the nature of our likely understanding of emulated minds.

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will start talking about how to choose what values to give an AI, beginning with 'coherent extrapolated volition'. To prepare, read “The need for...” and “Coherent extrapolated volition” from Chapter 13. The discussion will go live at 6pm Pacific time next Monday 16 February. Sign up to be notified here.

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, and an index of posts so far see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome. This week we discuss the twenty-first section in the reading guide: Value learning.

This post summarizes the section, and offers a few relevant notes, and ideas for further investigation. Some of my own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post, or to look at everything. Feel free to jump straight to the discussion. Where applicable and I remember, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: “Value learning” from Chapter 12

Summary

1. One way an AI could come to have human values without humans having to formally specify what their values are is for the AI to learn about the desired values from experience.
2. To implement this 'value learning' we would need to at least implicitly define a criterion for what is valuable, which we could cause the AI to care about. Some examples of criteria:
   1.  'F' where 'F' is a thing people talk about, and their words are considered to be about the concept of interest (Yudkowsky's proposal) (p197-8, box 11)
   2. Whatever is valued by another AI elsewhere in the universe values (Bostrom's 'Hail Mary' proposal) (p198-9, box 12)
   3. What a specific virtual human would report to be his value function, given a large amount of computing power and the ability to create virtual copies of himself. The virtual human can be specified mathematically as the simplest system that would match some high resolution data collected about a real human (Christiano's proposal). (p200-1)
3. The AI would try to maximize these implicit goals given its best understanding, while at the same time being motivated to learn more about its own values.
4. A value learning agent might have a prior probability distribution over possible worlds, and also over correct sets of values conditional on possible worlds. Then it could choose its actions to maximize their expected value, given these probabilities.

Another view

Paul Christiano describes an alternative to loading values into an AI at all:

Most thinking about “AI safety” has focused on the possibility of goal-directed machines, and asked how we might ensure that their goals are agreeable to humans. But there are other possibilities.

In this post I will flesh out one alternative to goal-directed behavior. I think this idea is particularly important from the perspective of AI safety.

Approval-directed agents

Consider a human Hugh, and an agent Arthur who uses the following procedure to choose each action:

Estimate the expected rating Hugh would give each action if he considered it at length. Take the action with the highest expected rating.

I’ll call this “approval-directed” behavior throughout this post, in contrast with goal-directed behavior. In this context I’ll call Hugh an “overseer.”

Arthur’s actions are rated more highly than those produced by any alternative procedure. That’s comforting, but it doesn’t mean that Arthur is optimal. An optimal agent may make decisions that have consequences Hugh would approve of, even if Hugh can’t anticipate those consequences himself. For example, if Arthur is playing chess he should make moves that are actually good—not moves that Hugh thinks are good.

...[However, there are many reasons Hugh would want to use the proposal]...

In most situations, I would expect approval-directed behavior to capture the benefits of goal-directed behavior, while being easier to define and more robust to errors.

If this interests you, I recommend the much longer post, in which Christiano describes and analyzes the proposal in much more depth.

Notes

1. An analogy
An AI doing value learning is in a similar situation to me if I want to help my friend but don't know what she needs. Even though I don't know explicitly what I want to do, it is defined indirectly, so I can learn more about it. I would presumably follow my best guesses, while trying to learn more about my friend's actual situation and preferences. This is also what we hope the value learning AI will do.

2. Learning what to value
If you are interested in value learning, Dewey's paper is the main thing written on it in the field of AI safety.

3. Related topics
I mentioned inverse reinforcement learning and goal inference last time, but should probably have kept them for this week, to which they are more relevant. Preference learning is another related subfield of machine learning, and learning by demonstration is generally related. Here is a quadcopter using inverse reinforcement learning to infer what its teacher wants it to do. Here is a robot using goal inference to help someone build a toy.

4. Value porosity
Bostrom has lately written about a new variation on the Hail Mary approach, in which the AI at home is motivated to trade with foreign AIs (via everyone imagining each other's responses), and has preferences that are very cheap for foreign AIs to guess at and fulfil.

5. What's the difference between value learning and reinforcement learning?
We heard about reinforcement learning last week, and Bostrom found it dangerous. Since it also relies on teaching the AI values by giving it feedback, you might wonder how exactly the proposals relate to each other.

Suppose the owner of an AI repeatedly comments that various actions are 'friendly'. A reinforcement learner would perhaps care about hearing the word 'friendly' as much as possible. A value learning AI on the other hand would take use of the word 'friendly' as a clue about a hidden thing that it cares about. This means if the value learning AI could trick the person into saying 'friendly' more, this would be no help to it—the trick would just make the person's words a less good clue. The reinforcement learner on the other hand would love to get the person to say 'friendly' whenever possible. This difference also means the value learning AI might end up doing things which it does not expect its owner to say 'friendly' about, if it thinks those actions are supported by the values that it learned from hearing 'friendly'.

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions, some inspired by Luke Muehlhauser's list, which contains many suggestions related to parts of Superintelligence. These projects could be attempted at various levels of depth.

1. Expand upon the value learning proposal. What kind of prior over what kind of value functions should a value learning AI be given? As an input to this, what evidence should be informative about the AI's values?
2. Analyze the feasibility of Christiano’s proposal for addressing the value-loading problem. 
3. Analyze the feasibility of Bostrom’s “Hail Mary” approach to the value-loading problem.
4. Analyze the feasibility of Christiano's newer proposal to avoid learning values.
5. Investigate the applicability of the related fields mentioned above to producing beneficial AI.

How to proceed

This has been a collection of notes on the chapter.  The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about the two other ways to direct the values of AI. To prepare, read “Emulation modulation” through “Synopsis” from Chapter 12. The discussion will go live at 6pm Pacific time next Monday 9 February. Sign up to be notified here.

I've been working on a thing with Paul Christiano that might interest some of you: the AI Impacts project.

The basic idea is to apply the evidence and arguments that are kicking around in the world and various disconnected discussions respectively to the big questions regarding a future with AI. For instance, these questions: 

• What should we believe about timelines for AI development?
• How rapid is the development of AI likely to be near human-level? 
• How much advance notice should we expect to have of disruptive change?
• What are the likely economic impacts of human-level AI?
• Which paths to AI should be considered plausible or likely?
• Will human-level AI tend to pursue particular goals, and if so what kinds of goals?
• Can we say anything meaningful about the impact of contemporary choices on long-term outcomes?

Today, public discussion on these issues appears to be highly fragmented and of limited credibility. More credible and clearly communicated views on these issues might help improve estimates of the social returns to AI investment, identify neglected research areas, improve policy, or productively channel public interest in AI.

The goal of the project is to clearly present and organize the considerations which inform contemporary views on these and related issues, to identify and explore disagreements, and to assemble whatever empirical evidence is relevant.

The project is provisionally organized as a collection of posts concerning particular issues or bodies of evidence, describing what is known and attempting to synthesize a reasonable view in light of available evidence. These posts are intended to be continuously revised in light of outstanding disagreements and to make explicit reference to those disagreements.

In the medium run we'd like to provide a good reference on issues relating to the consequences of AI, as well as to improve the state of understanding of these topics. At present, the site addresses only a small fraction of questions one might be interested in, so only suitable for particularly risk-tolerant or topic-neutral reference consumers. However if you are interested in hearing about (and discussing) such research as it unfolds, you may enjoy our blog.

If you take a look and have thoughts, we would love to hear them, either in the comments here or in our feedback form. 

Crossposted from my blog.