Bayesian Investor Blog

Book review: Global Catastrophic Risks by Nick Bostrom, and Milan Cirkovic.
This is a relatively comprehensive collection of thoughtful essays about the risks of a major catastrophe (mainly those that would kill a billion or more people).
Probably the most important chapter is the one on risks associated with AI, since few people attempting to create an AI seem to understand the possibilities it describes. It makes some implausible claims about the speed with which an AI could take over the world, but the argument they are used to support only requires that a first-mover advantage be important, and that is only weakly dependent on assumptions about that speed with which AI will improve.
The risks of a large fraction of humanity being killed by a super-volcano is apparently higher than the risk from asteroids, but volcanoes have more of a limit on their maximum size, so they appear to pose less risk of human extinction.
The risks of asteroids and comets can’t be handled as well as I thought by early detection, because some dark comets can’t be detected with current technology until it’s way too late. It seems we ought to start thinking about better detection systems, which would probably require large improvements in the cost-effectiveness of space-based telescopes or other sensors.
Many of the volcano and asteroid deaths would be due to crop failures from cold weather. Since mid-ocean temperatures are more stable that land temperatures, ocean based aquaculture would help mitigate this risk.
The climate change chapter seems much more objective and credible than what I’ve previously read on the subject, but is technical enough that it won’t be widely read, and it won’t satisfy anyone who is looking for arguments to justify their favorite policy. The best part is a list of possible instabilities which appear unlikely but which aren’t understood well enough to evaluate with any confidence.
The chapter on plagues mentions one surprising risk - better sanitation made polio more dangerous by altering the age at which it infected people. If I’d written the chapter, I’d have mentioned Ewald’s analysis of how human behavior influences the evolution of strains which are more or less virulent.
There’s good news about nuclear proliferation which has been under-reported - a fair number of countries have abandoned nuclear weapons programs, and a few have given up nuclear weapons. So if there’s any trend, it’s toward fewer countries trying to build them, and a stable number of countries possessing them. The bad news is we don’t know whether nanotechnology will change that by drastically reducing the effort needed to build them.
The chapter on totalitarianism discusses some uncomfortable tradeoffs between the benefits of some sort of world government and the harm that such government might cause. One interesting claim:

totalitarian regimes are less likely to foresee disasters, but are in some ways better-equipped to deal with disasters that they take seriously.

Steve Omohundro has recently written a paper and given a talk (a video should become available soon) on AI ethics with arguments whose most important concerns resemble Eliezer Yudkowsky’s. I find Steve’s style more organized and more likely to convince mainstream researchers than Eliezer’s best attempt so far.
Steve avoids Eliezer’s suspicious claims about how fast AI will take off, and phrases his arguments in ways that are largely independent of the takeoff speed. But a sentence or two in the conclusion of his paper suggests that he is leaning toward solutions which assume multiple AIs will be able to safeguard against a single AI imposing its goals on the world. He doesn’t appear to have a good reason to consider this assumption reliable, but at least he doesn’t show the kind of disturbing certainty that Eliezer has about the first self-improving AI becoming powerful enough to take over the world.
Possibly the most important news in Steve’s talk was his statement that he had largely stopped working to create intelligent software due to his concerns about safely specifying goals for an AI. He indicated that one important insight that contributed to this change of mind came when Carl Shulman pointed out a flaw in Steve’s proposal for a utility function which included a goal of the AI shutting itself off after a specified time (the flaw involves a small chance of physics being different from apparent physics and how the AI will evaluate expected utilities resulting from that improbable physics).

Tim Freeman has a paper which clarifies many of the issues that need to be solved for humans to coexist with a superhuman AI. It comes close to what we would need if we had unlimited computing power. I will try amplify on some of the criticisms of it from the sl4 mailing list.
It errs on the side of our current intuitions about what I consider to be subgoals, rather than trusting the AI’s reasoning to find good subgoals to meet primary human goal(s). Another way to phrase that would be that it fiddles with parameters to get special-case results that fit our intuitions rather than focusing on general purpose solutions that would be more likely to produce good results in conditions that we haven’t yet imagined.
For example, concern about whether the AI pays the grocer seems misplaced. If our current intuitions about property rights continue to be good guidelines for maximizing human utility in a world with a powerful AI, why would that AI not reach that conclusion by inferring human utility functions from observed behavior and modeling the effects of property rights on human utility? If not, then why shouldn’t we accept that the AI has decided on something better than property rights (assuming our other methods of verifying that the AI is optimizing human utility show no flaws)?
Is it because we lack decent methods of verifying the AI’s effects on phenomena such as happiness that are more directly related to our utility functions? If so, it would seem to imply that we have an inadequate understanding of what we mean by maximizing utility. I didn’t see a clear explanation of how the AI would infer utility functions from observing human behavior (maybe the source code, which I haven’t read, clarifies it), but that appears to be roughly how humans at their best make the equivalent moral judgments.
I see similar problems with designing the AI to produce the “correct” result with Pascal’s Wager. Tim says “If Heaven and Hell enter into a decision about buying apples, the outcome seems difficult to predict”. Since humans have a poor track record at thinking rationally about very small probabilities and phenomena such as Heaven that are hard to observe, I wouldn’t expect AI unpredictability in this area to be evidence of a problem. It seems more likely that humans are evaluating Pascal’s Wager incorrectly than that a rational AI which can infer most aspects of human utility functions from human behavior will evaluate it incorrectly.

An amusing parody: A Thinking Apes Critique of Trans-Simianism (HT Mark Atwood).

Book review: Beyond AI: Creating the Conscience of the Machine by J. Storrs Hall
The first two thirds of this book survey current knowledge of AI and make some guesses about when and how it will take off. This part is more eloquent than most books on similar subjects, and its somewhat different from normal perspective makes it worth reading if you are reading several books on the subject. But ease of reading is the only criterion by which this section stands out as better than competing books.
The last five chapters that are surprisingly good, and should shame most professional philosophers whose writings by comparison are a waste of time.
His chapter on consciousness, qualia, and related issues is more concise and persuasive than anything else I’ve read on these subjects. It’s unlikely to change the opinions of people who have already thought about these subjects, but it’s an excellent place for people who are unfamiliar with them to start.
His discussions of ethics using game theory and evolutionary pressures is an excellent way to frame ethical discussions.
My biggest disappointment was that he starts to recognize a possibly important risk of AI when he says “disparities among the abilities of AIs … could negate the evolutionary pressure to reciprocal altruism”, but then seems to dismiss that thoughtlessly (”The notion of one single AI taking off and obtaining hegemony over the whole world by its own efforts is ludicrous”).
He probably has semi-plausible grounds for dismissing some of the scenarios of this nature that have been proposed (e.g. the speed at which some people imagine an AI would take off is improbable). But if AIs with sufficiently general purpose intelligence enhance their intelligence at disparate rates for long enough, the results would render most of the book’s discussion of ethics irrelevant. The time it took humans to accumulate knowledge didn’t give Neanderthals much opportunity to adapt. Would the result have been different if Neanderthals had learned to trade with humans? The answer is not obvious, and probably depends on Neanderthal learning abilities in ways that I don’t know how to analyze.
Also, his arguments for optimism aren’t quite as strong as he thinks. His point that career criminals are generally of low intelligence is reassuring if the number of criminals is all that matters. But when the harm done by one relatively smart criminal can be very large (e.g. Mao), it’s hard to say that the number of criminals is all that matters.
Here’s a nice quote from Mencken which this book quotes part of:

Moral certainty is always a sign of cultural inferiority. The more uncivilized the man, the surer he is that he knows precisely what is right and what is wrong. All human progress, even in morals, has been the work of men who have doubted the current moral values, not of men who have whooped them up and tried to enforce them. The truly civilized man is always skeptical and tolerant, in this field as in all others. His culture is based on ‘I am not too sure.’

Another interesting tidbit is the anecdote that H.G. Wells predicted in 1907 that flying machines would be built. In spite of knowing a lot about attempts to build them, he wasn’t aware that the Wright brothers had succeeded in 1903.
If an AI started running in 2003 that has accumulated the knowledge of a 4-year old human and has the ability to continue learning at human or faster speeds, would we have noticed? Or would the reports we see about it sound too much like the reports of failed AIs for us to pay attention?

Book review: How to Survive a Robot Uprising: Tips on Defending Yourself Against the Coming Rebellion by Daniel H. Wilson
This book combines good analyses of recent robotics research with an understanding of movie scenarios about robot intentions (”how could millions of dollars of special effects lead us astray?”) to produce advice of unknown value about how humans might deal with any malicious robots of the next decade or two.
It focuses mainly on what an ordinary individual or small groups can do to save themselves or postpone their demise, and says little about whether a major uprising can be prevented.
The book’s style is somewhat like the Daily Show’s style, mixing a good deal of accurate reporting with occasional bits of obvious satire (”Robots have no emotions. Sensing your fear could make a robot jealous”), but it doesn’t quite attain the Daily Show’s entertainment value.
Its analyses of the weaknesses of current robot sensors and intelligence should make it required reading for any science fiction author or movie producer who wants to appear realistic (I haven’t been paying enough attention to those fields recently to know whether such people still exist). But it needs a bit of common sense to be used properly. It’s all too easy to imagine a gullible movie producer following its advice to have humans build a time machine and escape to the Cretaceous without pondering whether the robots will use similar time machines to follow them.

Nick Bostrom has a good paper on Astronomical Waste: The Opportunity Cost of Delayed Technological Development, which argues that under most reasonable ethical systems that aren’t completely selfish or very parochial, our philanthropic activities ought to be devoted primarily toward preventing disasters that would cause the extinction of intelligent life.
Some people who haven’t thought about the Fermi Paradox carefully may overestimate the probability that most of the universe is already occupied by intelligent life. Very high estimates for that probability would invalidate Bostrom’s conclusion, but I haven’t found any plausible arguments that would justify that high a probability.
I don’t want to completely dismiss Malthusian objections that life in the distant future will be barely worth living, but the risk of a Malthusian future would need to be well above 50 percent to substantially alter the optimal focus of philanthropy, and the strongest Malthusian arguments that I can imagine leave much more uncertainty than that. (If I thought I could alter the probability of a Malthusian future, maybe I should devote effort to that. But I don’t currently know where to start).
Thus the conclusion seems like it ought to be too obvious to need repeating, but it’s far enough from our normal experiences that most of us tend to pay inadequate attention to it. So I’m mentioning it in order to remind people (including myself) of the need to devote more of our time to thinking about risks such as those associated with AI or asteroid impacts.

At the recent AGI workshop, Michael Anissimov concisely summarized one of the reasons to worry about AI: the greatest risk is that there won’t be small risks leading up to it.

I was somewhat disappointed by the latest Accelerating Change Conference, which might have been great for people who have never been to that kind of conference before, but didn’t manage enough novelty to be terribly valuable to those who attended the first one. Here are a few disorganized tidbits I got from it.
Bruno Olshausen described our understanding of the neuron as pre-newtonian, and said a neuron might be as complex as a pentium.
Joichi Ito convinced me that Wikipedia has a wider range of uses than my stereotype of it as a dictionary/encyclopedia suggested. For example, its entry on Katrina seems to be a better summary of the news than what I can get via the traditional news media.
Cory Ondrejka pointed out the negative correlation between the availability of violent video games and some broad measure of U.S. crime. He hinted this might say something about causation, but reminded people of the appropriate skepticism by noting the correlation between the decline in pirates and global warming.
Someone reported that Second Life is growing at an impressive pace. I’ve tried it a little over a somewhat flaky wireless connection and wasn’t too excited; I’ll try to get my iBook connected to my dsl line and see if a more reliable connection makes it nicer.
Tom Malone talked about how declining communications costs first enabled the creation of large companies with centralized hierarchies and are now decentralizing companies. His view of Ebay was interesting - he pointed out that it could be considered a retailer with one of the largest number of employees, except that it has outsourced most of its employees (i.e. the people who make a living selling through Ebay). He also mentioned that Intel has some internal markets for resources such as manufacturing capacity.
Daniel Amen criticized modern psychiatry for failing to look at the brain for signs of physical damage. He provided strong anecdotal evidence that the brain imaging services he sell can sometimes tell people how to fix mental problems that standard psychiatry can’t diagnose, but left plenty of doubt as to whether his successes are frequent enough to justify his fees.
T. Colin Campbell described some evidence that eating animal protein is unhealthy. He didn’t convince me that he was a very reliable source of information, but his evidence against casein (a milk protein) sounded fairly strong.
One odd comment from Robin Raskin (amidst an annoying amount of thoughtless sensationalism) was that kids don’t use email anymore. They send about two emails per day [i.e. they’ve switch to IM]. The idea that sending two emails per day amounts to abandoning email makes me wonder to what extent I’m out of touch with modern communication habits.
An amusing joke, attributed to Eric Drexler:
Q: Why did Douglas Hofstadter cross the road?
A: To make this joke possible.

Book Review: The Singularity Is Near : When Humans Transcend Biology by Ray Kurzweil
Kurzweil does a good job of arguing that extrapolating trends such as Moore’s Law works better than most alternative forecasting methods, and he does a good job of describing the implications of those trends. But he is a bit long-winded, and tries to hedge his methodology by pointing to specific research results which he seems to think buttress his conclusions. He neither convinces me that he is good at distinguishing hype from value when analyzing current projects, nor that doing so would help with the longer-term forecasting that constitutes the important aspect of the book.
Given the title, I was slightly surprised that he predicts that AIs will become powerful slightly more gradually than I recall him suggesting previously (which is a good deal more gradual than most Singulitarians). He offsets this by predicting more dramatic changes in the 22nd century than I imagined could be extrapolated from existing trends.
His discussion of the practical importance of reversible computing is clearer than anything else I’ve read on this subject.
When he gets specific, large parts of what he says seem almost right, but there are quite a few details that are misleading enough that I want to quibble with them.
For instance (on page 244, talking about the world circa 2030): “The bulk of the additional energy needed is likely to come from new nanoscale solar, wind, and geothermal technologies.” Yet he says little to justify this, and most of what I know suggests that wind and geothermal have little hope of satisfying more than 1 or 2 percent of new energy demand.
His reference on page 55 to “the devastating effect that illegal file sharing has had on the music-recording industry” seems to say something undesirable about his perspective.
His comments on economists thoughts about deflation are confused and irrelevant.
On page 92 he says “Is the problem that we are not running the evolutionary algorithms long enough? … This won’t work, however, because conventional genetic algorithms reach an asymptote in their level of performance, so running them for a longer period of time won’t help.” If “conventional” excludes genetic programming, then maybe his claim is plausible. But genetic programming originator John Koza claims his results keep improving when he uses more computing power.
His description of nanotech progress seems naive. (page 228) “Drexler’s dissertation … laid out the foundation and provided the road map still being followed today.” (page 234): “each aspect of Drexler’s conceptual designs has been validated”. I’ve been following this area pretty carefully, and I’m aware of some computer simulations which do a tiny fraction of what is needed, but if any lab research is being done that could be considered to follow Drexler’s road map, it’s a well kept secret. Kurzweil then offsets his lack of documentation for those claims by going overboard about documenting his accurate claim that “no serious flaw in Drexler’s nanoassembler concept has been described”.
Kurzweil argues that self-replicating nanobots will sometimes be desirable. I find this poorly thought out. His reasons for wanting them could be satisfied by nanobots that replicate under the control of a responsible AI.
I’m bothered by his complacent attitude toward the risks of AI. He sometimes hints that he is concerned, but his suggestions for dealing with the risks don’t indicate that he has given much thought to the subject. He has a footnote that mentions Yudkowsky’s Guidelines on Friendly AI. The context could lead readers to think they are comparable to the Foresight Guidelines on Molecular Nanotechnology. Alas, Yudkowsky’s guidelines depend on concepts which are hard enough to understand that few researchers are likely to comprehend them, and the few who have tried disagree about their importance.
Kurzweil’s thoughts on the risks that the simulation we may live in will be turned off are somewhat interesting, but less thoughtful than Robin Hanson’s essay on How To Live In A Simulation.
A couple of nice quotes from the book:
(page 210): “It’s mostly in your genes” is only true if you take the usual passive attitude toward health and aging.
(page 301): Sex has largely been separated from its biological function. … So why don’t we provide the same for … another activity that also provides both social intimacy and sensual pleasure - namely, eating?