Science and Technology

I use Beeminder occasionally. The site’s emails normally suffice to bug me into accomplishing whatever I’ve committed to doing. But I only use it for a few tasks for which my motivation is marginal. Most of the times that I consider using Beeminder, I either figure out how to motivate myself properly, or (more often) decide that my goal isn’t important.

The real value of Beeminder is that if I want to compel future-me to do something, I can’t give up by using the excuse that future-me is lazy or unreliable. Instead, I find myself wondering why I’m unwilling to risk $X to make myself likely to complete the task. That typically causes me to notice legitimate doubts about how highly I value the result.

Book review: The Charisma Myth: How Anyone Can Master the Art and Science of Personal Magnetism, by Olivia Fox Cabane.

This book provides clear and well-organized instructions on how to become more charismatic.

It does not make the process sound easy. My experience with some of her suggestions (gratitude journalling and meditation) seems typical of her ideas – they took a good deal of attention, and probably caused gradual improvements in my life, but the effects were subtle enough to leave lots of uncertainty about how effective they were.

Many parts of the book talk as if more charisma is clearly better, but occasionally she talks about downsides such as being convincing even when you’re wrong. The chapter that distinguishes four types of charisma (focus, kindness, visionary, and authority) helped me clarify what I want and don’t want from charisma. Yet I still feel a good deal of conflict about how much charisma I want, due to doubts about whether I can separate the good from the bad. I’ve had some bad experiences in with feeling and sounding confident about investments in specific stocks has caused me to lose money by holding those stocks too long. I don’t think I can increase my visionary or authority charisma without repeating that kind of mistake unless I can somehow avoid talking about investments when I turn on those types of charisma.

I’ve been trying the exercises that are designed to boost self-compassion, but my doubts about the effort required for good charisma and about the desirability of being charismatic have limited the energy I’m willing to put into it.

Book review: Value-Focused Thinking: A Path to Creative Decisionmaking, by Ralph L. Keeney.

This book argues for focusing on values (goals/objectives) when making decisions, as opposed to the more usual alternative-focused decisionmaking.

The basic idea seems good. Alternative-focused thinking draws our attention away from our values and discourages us from creatively generating new possibilities to choose from. It tends to have us frame decisions as responses to problems, which leads us to associate decisions with undesirable emotions, when we could view decisions as opportunities.

A good deal of the book describes examples of good decisionmaking, but those rarely provide insight into how to avoid common mistakes or to do unusually well.

Occasionally the book switches to some dull math, without clear explanations of what benefit the rigor provides.

The book also includes good descriptions of how to measure the things that matter, but How to Measure Anything by Douglas Hubbard does that much better.

I recently got Bose QuietComfort 15 Acoustic Noise Cancelling Headphones.

I had previously tried passive earplugs and headphones that claimed 30 dB noise reduction, and got little value out of them.

The noise cancelling headphones suppress a good deal more train (BART) noise, enough that I’m now able to read nonfiction while on the train.

It won’t help with the situations where noise bothers me most (multiple conversations nearby) because it mainly eliminates predictable noises. It makes speech sound more distant without affecting the speech volume a lot. But reducing the cost of train and plane travel is valuable enough that I feel foolish about not having tried them earlier.

Book review: The Depths: The Evolutionary Origins of the Depression Epidemic, by Johnathan Rottenberg.

This book presents a clear explanation of why the basic outlines of depression look like an evolutionary adaptation to problems such as famine or humiliation. But he ignores many features that still puzzle me. Evolution seems unlikely to select for suicide. Why does loss of a child cause depression rather than some higher-energy negative emotion? What influences the breadth of learned helplessness?

He claims depression has been increasing over the last generation or so, but the evidence he presents can easily be explained by increased willingness to admit to and diagnose depression. He has at least one idea why it’s increasing (increased pressure to be happy), but I can come up with ideas that have the opposite effect (e.g. increased ease of finding a group where one can fit in).

Much of the book has little to do with the origins of depression, and is dominated by descriptions of and anecdotes about how depression works.

He spends a fair amount of time talking about the frequently overlooked late stages of depression recovery, where antidepressants aren’t much use and people can easily fall back into depression.

The book includes a bit of self-help advice to use positive psychology, and to not rely on drugs for much more than an initial nudge in the right direction.

Book review: Superintelligence: Paths, Dangers, Strategies, by Nick Bostrom.

This book is substantially more thoughtful than previous books on AGI risk, and substantially better organized than the previous thoughtful writings on the subject.

Bostrom’s discussion of AGI takeoff speed is disappointingly philosophical. Many sources (most recently CFAR) have told me to rely on the outside view to forecast how long something will take. We’ve got lots of weak evidence about the nature of intelligence, how it evolved, and about how various kinds of software improve, providing data for an outside view. Bostrom assigns a vague but implausibly high probability to AI going from human-equivalent to more powerful than humanity as a whole in days, with little thought of this kind of empirical check.

I’ll discuss this more in a separate post which is more about the general AI foom debate than about this book.

Bostrom’s discussion of how takeoff speed influences the chance of a winner-take-all scenario makes it clear that disagreements over takeoff speed are pretty much the only cause of my disagreement with him over the likelihood of a winner-take-all outcome. Other writers aren’t this clear about this. I suspect those who assign substantial probability to a winner-take-all outcome if takeoff is slow will wish he’d analyzed this in more detail.

I’m less optimistic than Bostrom about monitoring AGI progress. He says “it would not be too difficult to identify most capable individuals with a long-standing interest in [AGI] research”. AGI might require enough expertise for that to be true, but if AGI surprises me by only needing modest new insights, I’m concerned by the precedent of Tim Berners-Lee creating a global hypertext system while barely being noticed by the “leading” researchers in that field. Also, the large number of people who mistakenly think they’ve been making progress on AGI may obscure the competent ones.

He seems confused about the long-term trends in AI researcher beliefs about the risks: “The pioneers of artificial intelligence … mostly did not contemplate the possibility of greater-than-human AI” seems implausible; it’s much more likely they expected it but were either overconfident about it producing good results or fatalistic about preventing bad results (“If we’re lucky, they might decide to keep us as pets” – Marvin Minsky, LIFE Nov 20, 1970).

The best parts of the book clarify many issues related to ensuring that an AGI does what we want.

He catalogs more approaches to controlling AGI than I had previously considered, including tripwires, oracles, and genies, and clearly explains many limits to what they can accomplish.

He briefly mentions the risk that the operator of an oracle AI would misuse it for her personal advantage. Why should we have less concern about the designers of other types of AGI giving them goals that favor the designers?

If an oracle AI can’t produce a result that humans can analyze well enough to decide (without trusting the AI) that it’s safe, why would we expect other approaches (e.g. humans writing the equivalent seed AI directly) to be more feasible?

He covers a wide range of ways we can imagine handling AI goals, including strange ideas such as telling an AGI to use the motivations of superintelligences created by other civilizations

He does a very good job of discussing what values we should and shouldn’t install in an AGI: the best decision theory plus a “do what I mean” dynamic, but not a complete morality.

I’m somewhat concerned by his use of “final goal” without careful explanation. People who anthropomorphise goals are likely to confuse at least the first few references to “final goal” as if it worked like a human goal, i.e. something that the AI might want to modify if it conflicted with other goals.

It’s not clear how much of these chapters depend on a winner-take-all scenario. I get the impression that Bostrom doubts we can do much about the risks associated with scenarios where multiple AGIs become superhuman. This seems strange to me. I want people who write about AGI risks to devote more attention to whether we can influence whether multiple AGIs become a singleton, and how they treat lesser intelligences. Designing AGI to reflect values we want seems almost as desirable in scenarios with multiple AGIs as in the winner-take-all scenario (I’m unsure what Bostrom thinks about that). In a world with many AGIs with unfriendly values, what can humans do to bargain for a habitable niche?

He has a chapter on worlds dominated by whole brain emulations (WBE), probably inspired by Robin Hanson’s writings but with more focus on evaluating risks than on predicting the most probable outcomes. Since it looks like we should still expect an em-dominated world to be replaced at some point by AGI(s) that are designed more cleanly and able to self-improve faster, this isn’t really an alternative to the scenarios discussed in the rest of the book.

He treats starting with “familiar and human-like motivations” (in an augmentation route) as an advantage. Judging from our experience with humans who take over large countries, a human-derived intelligence that conquered the world wouldn’t be safe or friendly, although it would be closer to my goals than a smiley-face maximizer. The main advantage I see in a human-derived superintelligence would be a lower risk of it self-improving fast enough for the frontrunner advantage to be large. But that also means it’s more likely to be eclipsed by a design more amenable to self-improvement.

I’m suspicious of the implication (figure 13) that the risks of WBE will be comparable to AGI risks.

  • Is that mainly due to “neuromorphic AI” risks? Bostrom’s description of neuromorphic AI is vague, but my intuition is that human intelligence isn’t flexible enough to easily get the intelligence part of WBE without getting something moderately close to human behavior.
  • Is the risk of uploaded chimp(s) important? I have some concerns there, but Bostrom doesn’t mention it.
  • How about the risks of competitive pressures driving out human traits (discussed more fully/verbosely at Slate Star Codex)? If WBE and AGI happen close enough together in time that we can plausibly influence which comes first, I don’t expect the time between the two to be long enough for that competition to have large effects.
  • The risk that many humans won’t have enough resources to survive? That’s scary, but wouldn’t cause the astronomical waste of extinction.

Also, I don’t accept his assertion that AGI before WBE eliminates the risks of WBE. Some scenarios with multiple independently designed AGIs forming a weakly coordinated singleton (which I consider more likely than Bostrom does) appear to leave the last two risks in that list unresolved.

This books represents progress toward clear thinking about AGI risks, but much more work still needs to be done.

Book review: Our Mathematical Universe: My Quest for the Ultimate Nature of Reality, by Max Tegmark.

His most important claim is the radical Platonist view that all well-defined mathematical structures exist, therefore most physics is the study of which of those we inhabit. His arguments are more tempting than any others I’ve seen for this view, but I’m left with plenty of doubt.

He points to ways that we can imagine this hypothesis being testable, such as via the fine-tuning of fundamental constants. But he doesn’t provide a good reason to think that those tests will distinguish his hypothesis from other popular approaches, as it’s easy to imagine that we’ll never find situations where they make different predictions.

The most valuable parts of the book involve the claim that the multiverse is spatially infinite. He mostly talks as if that’s likely to be true, but his explanations caused me to lower my probability estimate for that claim.

He gets that infinity by claiming that inflation continues in places for infinite time, and then claiming there are reference frames for which that infinite time is located in a spatial rather than a time direction. I have a vague intuition why that second step might be right (but I’m fairly sure he left something important out of the explanation).

For the infinite time part, I’m stuck with relying on argument from authority, without much evidence that the relevant authorities have much confidence in the claim.

Toward the end of the book he mentions reasons to doubt infinities in physics theories – it’s easy to find examples where we model substances such as air as infinitely divisible, when we know that at some levels of detail atomic theory is more accurate. The eternal inflation theory depends on an infinitely expandable space which we can easily imagine is only an approximation. Plus, when physicists explicitly ask whether the universe will last forever, they don’t seem very confident. I’m also tempted to say that the measure problem (i.e. the absence of a way to say some events are more likely than others if they all happen an infinite number of times) is a reason to doubt infinities, but I don’t have much confidence that reality obeys my desire for it to be comprehensible.

I’m disappointed by his claim that we can get good evidence that we’re not Boltzmann brains. He wants us to test our memories, because if I am a Boltzmann brain I’ll probably have a bunch of absurd memories. But suppose I remember having done that test in the past few minutes. The Boltzmann brain hypothesis suggests it’s much more likely for me to have randomly acquired the memory of having passed the test than for me to actually be have done the test. Maybe there’s a way to turn Tegmark’s argument into something rigorous, but it isn’t obvious.

He gives a surprising argument that the differences between the Everett and Copenhagen interpretations of quantum mechanics don’t matter much, because unrelated reasons involving multiverses lead us to expect results comparable to the Everett interpretation even if the Copenhagen interpretation is correct.

It’s a bit hard to figure out what the book’s target audience is – he hides the few equations he uses in footnotes to make it look easy for laymen to follow, but he also discusses hard concepts such as universes with more than one time dimension with little attempt to prepare laymen for them.

The first few chapters are intended for readers with little knowledge of physics. One theme is a historical trend which he mostly describes as expanding our estimate of how big reality is. But the evidence he provides only tells us that the lower bounds that people give keep increasing. Looking at the upper bound (typically infinity) makes that trend look less interesting.

The book has many interesting digressions such as a description of how to build Douglas Adams’ infinite improbability drive.

Book review: Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat.

This book describes the risks that artificial general intelligence will cause human extinction, presenting the ideas propounded by Eliezer Yudkowsky in a slightly more organized but less rigorous style than Eliezer has.

Barrat is insufficiently curious about why many people who claim to be AI experts disagree, so he’ll do little to change the minds of people who already have opinions on the subject.

He dismisses critics as unable or unwilling to think clearly about the arguments. My experience suggests that while it’s normally the case that there’s an argument that any one critic hasn’t paid much attention to, that’s often because they’ve rejected with some thought some other step in Eliezer’s reasoning and concluded that the step they’re ignoring wouldn’t influence their conclusions.

The weakest claim in the book is that an AGI might become superintelligent in hours. A large fraction of people who have worked on AGI (e.g. Eric Baum’s What is Thought?) dismiss this as too improbable to be worth much attention, and Barrat doesn’t offer them any reason to reconsider. The rapid takeoff scenarios influence how plausible it is that the first AGI will take over the world. Barrat seems only interested in talking to readers who can be convinced we’re almost certainly doomed if we don’t build the first AGI right. Why not also pay some attention to the more complex situation where an AGI takes years to become superhuman? Should people who think there’s a 1% chance of the first AGI conquering the world worry about that risk?

Some people don’t approve of trying to build an immutable utility function into an AGI, often pointing to changes in human goals without clearly analyzing whether those are subgoals that are being altered to achieve a stable supergoal/utility function. Barrat mentions one such person, but does little to analyze this disagreement.

Would an AGI that has been designed without careful attention to safety blindly follow a narrow interpretation of its programmed goal(s), or would it (after achieving superintelligence) figure out and follow the intentions of its authors? People seem to jump to whatever conclusion supports their attitude toward AGI risk without much analysis of why others disagree, and Barrat follows that pattern.

I can imagine either possibility. If the easiest way to encode a goal system in an AGI is something like “output chess moves which according to the rules of chess will result in checkmate” (turning the planet into computronium might help satisfy that goal).

An apparently harder approach would have the AGI consult a human arbiter to figure out whether it wins the chess game – “human arbiter” isn’t easy to encode in typical software. But AGI wouldn’t be typical software. It’s not obviously wrong to believe that software smart enough to take over the world would be smart enough to handle hard concepts like that. I’d like to see someone pin down people who think this is the obvious result and get them to explain how they imagine the AGI handling the goal before it reaches human-level intelligence.

He mentions some past events that might provide analogies for how AGI will interact with us, but I’m disappointed by how little thought he puts into this.

His examples of contact between technologically advanced beings and less advanced ones all refer to Europeans contacting Native Americans. I’d like to have seen a wider variety of analogies, e.g.:

  • Japan’s contact with the west after centuries of isolation
  • the interaction between neanderthals and humans
  • the contact that resulted in mitochondria becoming part of our cells

He quotes Vinge saying an AGI ‘would not be humankind’s “tool” – any more than humans are the tools of rabbits or robins or chimpanzees.’ I’d say that humans are sometimes the tools of human DNA, which raises more complex questions of how well the DNA’s interests are served.

The book contains many questionable digressions which seem to be designed to entertain.

He claims Google must have an AGI project in spite of denials by Google’s Peter Norvig (this was before it bought DeepMind). But the evidence he uses to back up this claim is that Google thinks something like AGI would be desirable. The obvious conclusion would be that Google did not then think it had the skill to usefully work on AGI, which would be a sensible position given the history of AGI.

He thinks there’s something paradoxical about Eliezer Yudkowsky wanting to keep some information about himself private while putting lots of personal information on the web. The specific examples Barrat gives strongly suggests that Eliezer doesn’t value the standard notion of privacy, but wants to limit peoples’ ability to distract him. Barrat also says Eliezer “gave up reading for fun several years ago”, which will surprise those who see him frequently mention works of fiction in his Author’s Notes.

All this makes me wonder who the book’s target audience is. It seems to be someone less sophisticated than a person who could write an AGI.

A somewhat new hypothesis:

The Intense World Theory states that autism is the consequence of a supercharged brain that makes the world painfully intense and that the symptoms are largely because autistics are forced to develop strategies to actively avoid the intensity and pain.

Here’s a more extensive explanation.

This hypothesis connects many of the sensory peculiarities of autism with the attentional and social ones. Those had seemed like puzzling correlations to me until now.

However, it still leaves me wondering why the variations is sensory sensitivities seem much larger with autism. The researchers suggest an explanation involving increased plasticity, but I don’t see a strong connection between the Intense World hypothesis and that.

One implication (from this page):

According to the intense world perspective, however, warmth isn’t incompatible with autism. What looks like antisocial behavior results from being too affected by others’ emotions—the opposite of indifference.

Indeed, research on typical children and adults finds that too much distress can dampen ordinary empathy as well. When someone else’s pain becomes too unbearable to witness, even typical people withdraw and try to soothe themselves first rather than helping—exactly like autistic people. It’s just that autistic people become distressed more easily, and so their reactions appear atypical.