Book Reviews

Book review: The Vital Question: Energy, Evolution, and the Origins of Complex Life, by Nick Lane.

This book describes a partial theory of how life initially evolved, followed by a more detailed theory of how eukaryotes evolved.

Lane claims the hardest step in evolving complex life was the development of complex eukaryotic cells. Many traits such as eyes and wings evolved multiple times. Yet eukaryotes have many traits which evolved exactly once (including mitochondria, sex, and nuclear membranes).

Eukaryotes apparently originated in a single act of an archaeon engulfing a bacterium. The result wasn’t very stable, and needed to quickly evolve (i.e. probably within a few million years) a sophisticated nucleus, plus sexual reproduction.

Only organisms that go through these steps will be able to evolve a more complex genome than bacteria do. This suggests that complex life is rare outside of earth, although simple life may be common.

The book talks a lot about mitochondrial DNA, and make some related claims about aging.

Cells have a threshold for apoptosis which responds to the effects of poor mitochondrial DNA, killing weak embryos before they can take up much parental resources. Lane sees evolution making important tradeoffs, with species that have intense energy demands (such as most birds) setting their thresholds high, and more ordinary species (e.g. rats) setting the threshold lower. This tradeoff causes less age-related damage in birds, at the cost of lower fertility.

Lane claims that the DNA needs to be close to the mitochondria in order to make quick decisions. I found this confusing until I checked Wikipedia and figured out it probably refers to the CoRR hypothesis. I’m still confused, but at least now I can attribute the confusion to the topic being hard. Aubrey de Grey’s criticism of CoRR suggests there’s a consensus that CoRR has problems, and the main confusion revolves around the credibility of competing hypotheses.

Lane is quite pessimistic about attempts to cure aging. Only a small part of that disagreement with Aubrey can be explained by the modest differences in their scientific hypotheses. Much of the difference seems to come from Lane’s focus on doing science, versus Aubrey’s focus on engineering. Lane keeps pointing out (correctly) that cells are really complex and finely tuned. Yet Lane is well aware that evolution makes many changes that affect aging in spite of the complexity. I suspect he’s too focused on the inadequacy of typical bioengineering to imagine really good engineering.

Some less relevant tidbits include:

  • why vibrant plumage in male birds may be due to females being heterogametic
  • why male mammals age faster than females

Many of Lane’s ideas are controversial, and only weakly supported by the evidence. But given the difficulty of getting good evidence on these topics, that still represents progress.

The book is pretty dense, and requires some knowledge of biochemistry. It has many ideas and evidence that were developed since I last looked into this subject. I expect to forget many of those ideas fairly quickly. The book is worth reading if you have enough free time, but understanding these topics does not feel vital.

Book review: Notes on a New Philosophy of Empirical Science (Draft Version), by Daniel Burfoot.

Standard views of science focus on comparing theories by finding examples where they make differing predictions, and rejecting the theory that made worse predictions.

Burfoot describes a better view of science, called the Compression Rate Method (CRM), which replaces the “make prediction” step with “make a compression program”, and compares theories by how much they compress a standard (large) database.

These views of science produce mostly equivalent results(!), but CRM provides a better perspective.

Machine Learning (ML) is potentially science, and this book focuses on how ML will be improved by viewing its problems through the lens of CRM. Burfoot complains about the toolkit mentality of traditional ML research, arguing that the CRM approach will turn ML into an empirical science.

This should generate a Kuhnian paradigm shift in ML, with more objective measures of the research quality than any branch of science has achieved so far.

Burfoot focuses on compression as encoding empirical knowledge of specific databases / domains. He rejects the standard goal of a general-purpose compression tool. Instead, he proposes creating compression algorithms that are specialized for each type of database, to reflect what we know about topics (such as images of cars) that are important to us.
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Book review: The Moral Economy: Why Good Incentives Are No Substitute for Good Citizens, by Samuel Bowles.

This book has a strange mixture of realism and idealism.

It focuses on two competing models: the standard economics model in which people act in purely self-interested ways, and a more complex model in which people are influenced by context to act either altruistically or selfishly.

The stereotypical example comes from the semi-famous Haifa daycare experiment, where daycare centers started fining parents for being late to pick up children, and the parents responded by being later.

The first half of the book is a somewhat tedious description of ideas that seem almost obvious enough to be classified as common sense. He points out that the economist’s model is a simplification that is useful for some purposes, yet it’s not too hard to find cases where it makes the wrong prediction about how people will respond to incentives.

That happens because society provides weak pressures that produce cooperation under some conditions, and because financial incentives send messages that influence whether people want to cooperate. I.e. the parents appear to have previously felt obligated to be somewhat punctual, but then inferred from the fines that it was ok to be late as long as they paid the price.[*].

The book advocates more realism on this specific issue. But it’s pretty jarring to compare that to the idealistic view the author takes on similar topics, such as acquiring evidence of how people react, or modeling politicians. He treats the Legislator (capitalized like that) as a very objective, well informed, and altruistic philosopher. That model may sometimes be useful, but I’ll bet that, on average, it produces worse predictions about legislators’ behavior than does the economist’s model of a self-interested legislator.

The book becomes more interesting around chapter V, when it analyzes the somewhat paradoxical conclusion that markets sometimes make people more selfish, yet cultures that have more experience with markets tend to cooperate more.

He isn’t able to fully explain that, but he makes some interesting progress. One factor that’s important to focus on is the difference between complete and incomplete contracts. Complete contracts describe everything a buyer might need to know about a product or service. An example of an incomplete contract would be an agreement to hire a lawyer to defend me – I don’t expect the lawyer to specify how good a defense to expect.

Complete contracts enable people to trade without needing to trust the seller, which can lead to highly selfish attitudes. Incomplete contracts lead to the creation of trust between participants, because having frequent transactions depends on some implicit cooperation.

The book ends by promoting the “new” idea that policy ought to aim for making people be good. But it’s unclear who disagrees with that idea. Economists sometimes sound like they disagree, because they often say that policy shouldn’t impose one group’s preferences on another group. But economists are quite willing to observe that people generally prefer cooperation over conflict, and that most people prefer institutions that facilitate cooperation. That’s what the book mostly urges.

The book occasionally hints at wanting governments to legislate preferences in ways that go beyond facilitating cooperation, but doesn’t have much of an argument for doing so.

[*] – The book implies that the increased lateness was an obviously bad result. This seems like a plausible guess. But I find it easy to imagine conditions where the reported results were good (i.e. the parents might benefit from being late more than it costs the teachers to accommodate them).

However, that scenario depends on the fines being high enough for the teachers to prefer the money over punctuality. They appear not to have been consulted, so success at that would have depended on luck. It’s unclear whether the teachers were getting overtime pay when parents were late, or whether the fines benefited only the daycare owner.

Book review: Are We Smart Enough to Know How Smart Animals Are?, by Frans de Waal.

This book is primarily about discrediting false claims of human uniqueness, and showing how easy it is to screw up evaluations of a species’ cognitive abilities. It is best summarized by the cognitive ripple rule:

Every cognitive capacity that we discover is going to be older and more widespread than initially thought.

De Waal provides many anecdotes of carefully designed experiments detecting abilities that previously appeared to be absent. E.g. asian elephants failed mirror tests with small, distant mirrors. When experimenters dared to put large mirrors close enough for the elephants to touch, some of them passed the test.

Likewise, initial observations of behaviorist humans suggested they were rigidly fixated on explaining all behavior via operant conditioning. Yet one experimenter managed to trick a behaviorist into demonstrating more creativity, by harnessing the one motive that behaviorists prefer over their habit of advocating operant conditioning: their desire to accuse people of recklessly inferring complex cognition.

De Waal seems moderately biased toward overstating cognitive abilities of most species (with humans being one clear exception to that pattern).

At one point he gave me the impression that he was claiming elephants could predict where a thunderstorm would hit days in advance. I checked the reference, and what the elephants actually did was predict the arrival of the wet season, and respond with changes such as longer steps (but probably not with indications that they knew where thunderstorms would hit). After rereading de Waal’s wording, I decided it was ambiguous. But his claim that elephants “hear thunder and rainfall hundreds of miles away” exaggerates the original paper’s “detected … at distances greater than 100 km … perhaps as much as 300 km”.

But in the context of language, de Waal switches to downplaying reports of impressive abilities. I wonder how much of that is due to his desire to downplay claims that human minds are better, and how much of that is because his research isn’t well suited to studying language.

I agree with the book’s general claims. The book provides evidence that human brains embody only small, somewhat specialized improvements on the cognitive abilities of other species. But I found the book less convincing on that subject than some other books I’ve read recently. I suspect that’s mainly due to de Waal’s focus on anecdotes that emphasize what’s special about each species or individual. Whereas The Human Advantage rigorously quantifies important ways in which human brains are just a bigger primate brain (but primate brains are special!). Or The Secret of our Success (which doesn’t use particularly rigorous methods) provides a better perspective, by describing a model in which ape minds evolve to human minds via ordinary, gradual adaptations to mildly new environments.

In sum, this book is good at explaining the problems associated with research into animal cognition. It is merely ok at providing insights about how smart various species are.

Book review: Made-Up Minds: A Constructivist Approach to Artificial Intelligence, by Gary L. Drescher.

It’s odd to call a book boring when it uses the pun “ontology recapitulates phylogeny”[1]. to describe a surprising feature of its model. About 80% of the book is dull enough that I barely forced myself to read it, yet the occasional good idea persuaded me not to give up.

Drescher gives a detailed model of how Piaget-style learning in infants could enable them to learn complex concepts starting with minimal innate knowledge.
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Book review: The Age of Em: Work, Love and Life when Robots Rule the Earth, by Robin Hanson.

This book analyzes a possible future era when software emulations of humans (ems) dominate the world economy. It is too conservative to tackle longer-term prospects for eras when more unusual intelligent beings may dominate the world.

Hanson repeatedly tackles questions that scare away mainstream academics, and gives relatively ordinary answers (guided as much as possible by relatively standard, but often obscure, parts of the academic literature).

Assumptions

Hanson’s scenario relies on a few moderately controversial assumptions. The assumptions which I find most uncertain are related to human-level intelligence being hard to understand (because it requires complex systems), enough so that ems will experience many subjective centuries before artificial intelligence is built from scratch. For similar reasons, ems are opaque enough that it will be quite a while before they can be re-engineered to be dramatically different.

Hanson is willing to allow that ems can be tweaked somewhat quickly to produce moderate enhancements (at most doubling IQ) before reaching diminishing returns. He gives somewhat plausible reasons for believing this will only have small effects on his analysis. But few skeptics will be convinced.

Some will focus on potential trillions of dollars worth of benefits that higher IQs might produce, but that wealth would not much change Hanson’s analysis.

Others will prefer an inside view analysis which focuses on the chance that higher IQs will better enable us to handle risks of superintelligent software. Hanson’s analysis implies we should treat that as an unlikely scenario, but doesn’t say what we should do about modest probabilities of huge risks.

Another way that Hanson’s assumptions could be partly wrong is if tweaking the intelligence of emulated Bonobos produces super-human entities. That seems to only require small changes to his assumptions about how tweakable human-like brains are. But such a scenario is likely harder to analyze than Hanson’s scenario, and it probably makes more sense to understand Hanson’s scenario first.

Wealth

Wages in this scenario are somewhat close to subsistence levels. Ems have some ability to restrain wage competition, but less than they want. Does that mean wages are 50% above subsistence levels, or 1%? Hanson hints at the former. The difference feels important to me. I’m concerned that sound-bite versions of book will obscure the difference.

Hanson claims that “wealth per em will fall greatly”. It would be possible to construct a measure by which ems are less wealthy than humans are today. But I expect it will be at least as plausible to use a measure under which ems are rich compared to humans of today, but have high living expenses. I don’t believe there’s any objective unit of value that will falsify one of those perspectives [1].

Style / Organization

The style is more like a reference book than a story or an attempt to persuade us of one big conclusion. Most chapters (except for a few at the start and end) can be read in any order. If the section on physics causes you to doubt whether the book matters, skip to chapter 12 (labor), and return to the physics section later.

The style is very concise. Hanson rarely repeats a point, so understanding him requires more careful attention than with most authors.

It’s odd that the future of democracy gets less than twice as much space as the future of swearing. I’d have preferred that Hanson cut out a few of his less important predictions, to make room for occasional restatements of important ideas.

Many little-known results that are mentioned in the book are relevant to the present, such as: how the pitch of our voice affects how people perceive us, how vacations affect productivity, and how bacteria can affect fluid viscosity.

I was often tempted to say that Hanson sounds overconfident, but he is clearly better than most authors at admitting appropriate degrees of uncertainty. If he devoted much more space to caveats, I’d probably get annoyed at the repetition. So it’s hard to say whether he could have done any better.

Conclusion

Even if we should expect a much less than 50% chance of Hanson’s scenario becoming real, it seems quite valuable to think about how comfortable we should be with it and how we could improve on it.

Footnote

[1] – The difference matters only in one paragraph, where Hanson discusses whether ems deserve charity more than do humans living today. Hanson sounds like he’s claiming ems deserve our charity because they’re poor. Most ems in this scenario are comfortable enough for this to seem wrong.

Hanson might also be hinting that our charity would be effective at increasing the number of happy ems, and that basic utilitarianism says that’s preferable to what we can do by donating to today’s poor. That argument deserves more respect and more detailed analysis.

Book review: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World, by Leslie Valiant.

This book provides some nonstandard perspectives on machine learning and evolution, but doesn’t convince me there’s much advantage to using those perspectives. I’m unsure how much of that is due to his mediocre writing style. He often seems close to saying something important, but never gets there.

He provides a rigorous meaning for the concept of learnability. I suppose that’s important for something, but I can’t recall what.

He does an ok job of explaining how evolution is a form of learning, but Eric Baum’s book What is Thought? explains that idea much better.

The last few chapters, where he drifts farther from his areas of expertise, are worse. Much of what he says there only seems half-right at best.

One example is his suggestion that AI researchers ought to put a lot of thought into how teaching materials are presented (similar to how schools are careful to order a curriculum, from simple to complex concepts). I doubt that that reflects a reasonable model of human learning: children develop an important fraction of their intelligence before school age, with little guidance for the order in which they should learn concepts (cf. Piaget’s theory of cognitive development); and unschooled children seem to choose their own curriculum.

My impression of recent AI progress suggests that a better organized “curriculum” is even farther from being cost-effective there – progress seems to be coming more from better ways of incorporating unsupervised learning.

I’m left wondering why anyone thinks the book is worth reading.

Book review: The Midas Paradox: Financial Markets, Government Policy Shocks, and the Great Depression, by Scott B Sumner.

This is mostly a history of the two depressions that hit the U.S. in the 1930s: one international depression lasting from late 1929 to early 1933, due almost entirely to problems with an unstable gold exchange standard; quickly followed by a more U.S.-centered depression that was mainly caused by bad labor market policies.

It also contains some valuable history of macroeconomic thought, doing a fairly good job of explaining the popularity of theories that are designed for special cases (such as monetarism and Keynes’ “general” theory).

I was surprised at how much Sumner makes the other books on this subject that I’ve read seem inadequate.
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Book review: The Human Advantage: A New Understanding of How Our Brain Became Remarkable, by Suzana Herculano-Houzel.

I used to be uneasy about claims that the human brain was special because it is large for our body size: relative size just didn’t seem like it could be the best measure of whatever enabled intelligence.

At last, Herculano-Houzel has invented a replacement for that measure. Her impressive technique for measuring the number of neurons in a brain has revolutionized this area of science.

We can now see an important connection between the number of cortical neurons and cognitive ability. I’m glad that the book reports on research that compares the cognitive abilities of enough species to enable moderately objective tests of the relevant hypotheses (although the research still has much room for improvement).

We can also see that the primate brain is special, in a way that enables large primates to be smarter than similarly sized nonprimates. And that humans are not very special for a primate of our size, although energy constraints make it tricky for primates to reach our size.

I was able to read the book quite quickly. Much of it is arranged in an occasionally suspenseful story about how the research was done. It doesn’t have lots of information, but the information it does have seems very new (except for the last two chapters, where Herculano-Houzel gets farther from her area of expertise).

Added 2016-08-25:
Wikipedia has a List of animals by number of neurons which lists the long-finned pilot whale as having 37.2 billion cortical neurons, versus 21 billion for humans.

The paper reporting that result disagrees somewhat with Herculano-Houzel:

Our results underscore that correlations between cognitive performance and absolute neocortical neuron numbers across animal orders or classes are of limited value, and attempts to quantify the mental capacity of a dolphin for cross-species comparisons are bound to be controversial.

But I don’t see much of an argument against the correlation between intelligence and cortical neuron numbers. The lack of good evidence about long-finned pilot whale intelligence mainly implies we ought to be uncertain.

Book review: The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter, by Joseph Henrich.

This book provides a clear explanation of how an ability to learn cultural knowledge made humans evolve into something unique over the past few million years. It’s by far the best book I’ve read on human evolution.

Before reading this book, I thought human uniqueness depended on something somewhat arbitrary and mysterious which made sexual selection important for human evolution, and wondered whether human language abilities depended on some lucky mutation. Now I believe that the causes of human uniqueness were firmly in place 2-3 million years ago, and the remaining arbitrary events seem much farther back on the causal pathway (e.g. what was unique about apes? why did our ancestors descend from trees 4.4 million years ago? why did the climate become less stable 3 million years ago?)

Human language now seems like a natural byproduct of previous changes, and probably started sooner (and developed more gradually) than many researchers think.

I used to doubt that anyone could find good evidence of cultures that existed millions of years ago. But Henrich provides clear explanations of how features such as right-handedness and endurance running demonstrate important milestones in human abilities to generate culture.

Henrich’s most surprising claim is that there’s an important sense in which individual humans are no smarter than other apes. Our intellectual advantage over apes is mostly due to a somewhat special-purpose ability to combine our individual brains into a collective intelligence. His evidence on this point is weak, but it’s plausible enough to be interesting.

Henrich occasionally exaggerates a bit. The only place where that bothered me was where he claimed that heart attack patients who carefully adhered to taking placebos were half as likely to die as patients who failed to reliably take placebos. The author wants to believe that demonstrates the power of placebos. I say the patient failure to take placebos was just a symptom of an underlying health problem (dementia?).

I’m a bit surprised at how little Robin Hanson says about the Henrich’s main points. Henrich suggests that there’s cultural pressure to respect high-status people, for reasons that are somewhat at odds with Robin’s ally/coalition based reasons. Henrich argues that knowledge coming from high-status people, at least in hunter-gatherer societies, tended to be safer than knowledge from more directly measurable evidence. The cultural knowledge that accumulates over many generations aggregates information that could not be empirically acquired in a short time.

So Henrich implies it’s reasonable for people to be confused about whether evidence based medicine embodies more wisdom than eminence based medicine. Traditional culture has become less valuable recently due to the rapid changes in our environment (particularly the technology component of our environment), but cultures that abandoned traditions too readily were often hurt by consequences which take decades to observe.

I got more out of this book than a short review can describe (such as “How Altruism is like a Chili Pepper”). Here’s a good closing quote:

we are smart, but not because we stand on the shoulders of giants or are giants ourselves. We stand on the shoulders of a very large pyramid of hobbits.