Different pieces of software have different quality. Websites are usually on the crappy end of the scale. Central parts of operating systems are towards the opposite end. Also, many commercial products are developed with little testing. But there are methodologies for better testing, even mathematical proofs of correctness. Those are usually not used in commercial development, because they require some time and qualification, and companies prefer to hire cheap coders and have the product soon, even if it is full of bugs. And generally, because software companies are usually managed Dilbert-style.
However, it is possible to have mathematical proofs about algorithm correctness (any decent university teaches these methods as parts of informatics), so in these debates it is usually assumed that people who would develop an AI would use these methods.
To a person who knows this, your comment sounds a bit like: "my childhood toy broke easily, therefore it is impossible to ever build a railway that would not fall apart below the weight of a train".
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
The chapter synopsis includes a good summary of all of the value-loading techniques, which I'll remind you of here instead of re-summarizing too much:
Another view
Robin Hanson also favors institution design as a method of making the future nice, though as an alternative to worrying about values:
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:
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.
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.