brain

All posts tagged brain

Book review: Surfing Uncertainty: Prediction, Action, and the Embodied Mind, by Andy Clark.

Surfing Uncertainty describes minds as hierarchies of prediction engines. Most behavior involves interactions between a stream of information that uses low-level sensory data to adjust higher level predictive models of the world, and another stream of data coming from high-level models that guides low-level sensory processes to better guess the most likely interpretations of ambiguous sensory evidence.

Clark calls this a predictive processing (PP) model; others refer to is as predictive coding.

The book is full of good ideas, presented in a style that sapped my curiosity.

Jeff Hawkins has a more eloquent book about PP (On Intelligence), which focuses on how PP might be used to create artificial intelligence. The underwhelming progress of the company Hawkins started to capitalize on these ideas suggests it wasn’t the breakthrough that AI researchers were groping for. In contrast, Clark focuses on how PP helps us understand existing minds.

The PP model clearly has some value. The book was a bit more thorough than I wanted at demonstrating that. Since I didn’t find that particularly new or surprising, I’ll focus most of this review on a few loose threads that the book left dangling. So don’t treat this as a summary of the book (see Slate Star Codex if you want that, or if my review is too cryptic to understand), but rather as an exploration of the questions that the book provoked me to think about.

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[Warning: long post, of uncertain value, with annoyingly uncertain conclusions.]

This post will focus on how hardware (cpu power) will affect AGI timelines. I will undoubtedly overlook some important considerations; this is just a model of some important effects that I understand how to analyze.

I’ll make some effort to approach this as if I were thinking about AGI timelines for the first time, and focusing on strategies that I use in other domains.

I’m something like 60% confident that the most important factor in the speed of AI takeoff will be the availability of computing power.

I’ll focus here on the time to human-level AGI, but I suspect this reasoning implies getting from there to superintelligence at speeds that Bostrom would classify as slow or moderate.
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Book review: The End of Alzheimer’s, by Dale E. Bredesen.

Alzheimer’s can be at least postponed for years in most people, and maybe fully cured.

The main catches: It only works if started early enough (and Bredesen only has crude guesses about what’s early enough), the evidence is less rigorous than I’d like, and it’s not a medical treatment, it’s a quantified self approach on steroids ketones.

My guess is that the book is roughly 70% correct. If so, that’s an enormous advance.
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Book review: Into the Gray Zone: A Neuroscientist Explores the Border Between Life and Death, by Adrian Owen.

Too many books and talks have gratuitous displays of fMRIs and neuroscience. At last, here’s a book where fMRIs are used with fairly good reason, and neuroscience is explained only when that’s appropriate.

Owen provides evidence of near-normal brain activity in a modest fraction of people who had been classified as being in a persistent vegetative state. They are capable of answering yes or no to most questions, and show signs of understanding the plots of movies.

Owen believes this evidence is enough to say they’re conscious. I suspect he’s mostly right about that, and that they do experience much of the brain function that is typically associated with consciousness. Owen doesn’t have any special insights into what we mean by the word consciousness. He mostly just investigates how to distinguish between near-normal mental activity and seriously impaired mental activity.

So what were neurologists previously using to classify people as vegetative? As far as I can tell, they were diagnosing based on a lack of motor responses, even though they were aware of an alternate diagnosis, total locked-in syndrome, with identical symptoms. Locked-in syndrome and persistent vegetative state were both coined (in part) by the same person (but I’m unclear who coined the term total locked-in syndrome).

My guess is that the diagnoses have been influenced by a need for certainty. (whose need? family members? doctors? It’s not obvious).

The book has a bunch of mostly unremarkable comments about ethics. But I was impressed by Owen’s observation that people misjudge whether they’d want to die if they end up in a locked-in state. So how likely is it they’ll mispredict what they’d want in other similar conditions? I should have deduced this from the book stumbling on happiness, but I failed to think about it.

I’m a bit disturbed by Owen’s claim that late-stage Alzheimer’s patients have no sense of self. He doesn’t cite evidence for this conclusion, and his research should hint to him that it would be quite hard to get good evidence on this subject.

Most books written by scientists who made interesting discoveries attribute the author’s success to their competence. This book provides clear evidence for the accidental nature of at least some science. Owen could easily have gotten no signs of consciousness from the first few patients he scanned. Given the effort needed for the scans, I can imagine that that would have resulted in a mistaken consensus of experts that vegetative states were being diagnosed correctly.

Book review: Darwin’s Unfinished Symphony: How Culture Made the Human Mind, by Kevin N. Laland.

This book is a mostly good complement to Henrich’s The Secret of our Success. The two books provide different, but strongly overlapping, perspectives on how cultural transmission of information played a key role in the evolution of human intelligence.

The first half of the book describes the importance of copying behavior in many animals.

I was a bit surprised that animals as simple as fruit flies are able to copy some behaviors of other fruit flies. Laland provides good evidence that a wide variety of species have evolved some ability to copy behavior, and that ability is strongly connected to the benefits of acquiring knowledge from others and the costs of alternative ways of acquiring that knowledge.

Yet I was also surprised that the value of copying is strongly limited by the low reliability with which behavior is copied, except with humans. Laland makes plausible claims that the need for high-fidelity copying of behavior was an important driving force behind the evolution of bigger and more sophisticated brains.

Laland claims that humans have a unique ability to teach, and that teaching is an important adaptation. He means teaching in a much broader sense than we see in schooling – he includes basic stuff that could have preceded language, such as a parent directing a child’s attention to things that the child ought to learn. This seems like a good extension to Henrich’s ideas.

The most interesting chapter theorizes about the origin of human language. Laland’s theory that language evolved for teaching provides maybe a bit stronger selection pressure than other theories, but he doesn’t provide much reason to reject competing theories.

Laland presents seven criteria for a good explanation of the evolution of language. But these criteria look somewhat biased toward his theory.

Laland’s first two criteria are that language should have been initially honest and cooperative. He implies that it must have been more honest and cooperative than modern language use is, but he isn’t as clear about that as I would like. Those two criteria seem designed as arguments against the theory that language evolved to impress potential mates. The mate-selection theory involves plenty of competition, and presumably a fair amount of deception. But better communicators do convey important evidence about the quality of their genes, even if they’re engaging in some deception. That seems sufficient to drive the evolution of language via mate-selection pressures.

Laland’s theory seems to provide a somewhat better explanation of when language evolved than most other theories do, so I’m inclined to treat it as one of the top theories. But I don’t expect any consensus on this topic anytime soon.

The book’s final four chapters seemed much less interesting. I recommend skipping them.

Henrich’s book emphasized evidence that humans are pretty similar to other apes. Laland emphasizes ways in which humans are unique (language and teaching ability). I didn’t notice any cases where they directly contradicted each other, but it’s a bit disturbing that they left quite different impressions while saying mostly appropriate things.

Henrich claimed that increasing climate variability created increased rewards for the fast adaptation that culture enabled. Laland disagrees, saying that cultural change itself is a more plausible explanation for the kind of environmental change that incentivized faster adaptation. My intuition says that Laland’s conclusion is correct, but he seems a bit overconfident about it.

Overall, Laland’s book is less comprehensive and less impressive than Henrich’s book, but is still good enough to be in my top ten list of books on the evolution of intelligence.

Update on 2017-08-18: I just read another theory about the evolution of language which directly contradicts Laland’s claim that early language needed to be honest and cooperative. Wild Voices: Mimicry, Reversal, Metaphor, and the Emergence of Language claims that an important role of initial human vocal flexibility was to deceive other species.

Book review: The Hungry Brain: Outsmarting the Instincts That Make Us Overeat, by Stephan Guyenet.

Researchers who studied obesity in rats used to have trouble coaxing their rats to overeat. The obvious approaches (a high fat diet, or a high sugar diet) were annoyingly slow. Then they stumbled on the approach of feeding human junk food to the rats, and made much faster progress.

What makes something “junk food”? The best parts of this book help to answer this, although some ambiguity remains. It mostly boils down to palatability (is it yummier than what our ancestors evolved to expect? If so, it’s somewhat addictive) and caloric density.

Presumably designers of popular snack foods have more sophisticated explanations of what makes people obese, since that’s apparently identical to what they’re paid to optimize (with maybe a few exceptions, such as snacks that are marketed as healthy or ethical). Yet researchers who officially study obesity seem reluctant to learn from snack food experts. (Because they’re the enemy? Because they’re low status? Because they work for evil corporations? Your guess is likely as good as mine.)

Guyenet provides fairly convincing evidence that it’s simple to achieve a healthy weight while feeling full. (E.g. the 20 potatoes a day diet). To the extent that we need willpower, it’s to avoid buying convenient/addictive food, and to avoid restaurants.

My experience is that I need a moderate amount of willpower to follow Guyenet’s diet ideas, and that it would require large amount of willpower if I attended many social events involving food. But for full control over my weight, it seemed like I needed to supplement a decent diet with some form of intermittent fasting (e.g. alternate day calorie restriction); Guyenet says little about that.

Guyenet’s practical advice boils down to a few simple rules: eat whole foods that resemble what our ancestors ate; don’t have other “food” anywhere that you can quickly grab it; sleep well; exercise; avoid stress. That’s sufficiently similar to advice I’ve heard before that I’m confident The Hungry Brain won’t revolutionize many people’s understanding of obesity. But it’s got a pretty good ratio of wisdom to questionable advice, and I’m unaware of reasons to expect much more than that.

Guyenet talks a lot about neuroscience. That would make sense if readers wanted to learn how to fix obesity via brain surgery. The book suggests that, in the absence of ethical constraints, it might be relatively easy to cure obesity by brain surgery. Yet I doubt such a solution would become popular, even given optimistic assumptions about safety.

An alternate explanation is that Guyenet is showing off his knowledge of brains, in order to show that he’s smart enough to have trustworthy beliefs about diets. But that effect is likely small, due to competition among diet-mongers for comparable displays of smartness.

Or maybe he’s trying to combat dualism, in order to ridicule the “just use willpower” approach to diet? Whatever the reason is, the focus on neuroscience implies something unimpressive about the target audience.

You should read this book if you eat a fairly healthy diet but are still overweight. Otherwise, read Guyenet’s blog instead, for a wider variety of health advice.

Book review: Other Minds: The Octopus, the Sea, and the Deep Origins of Consciousness, by Peter Godfrey-Smith.

This book describes some interesting mysteries, but provides little help at solving them.

It provides some pieces of a long-term perspective on the evolution of intelligence.

Cephalopods’ most recent common ancestor with vertebrates lived way back before the Cambrian explosion. Nervous systems back then were primitive enough that minds didn’t need to react to other minds, and predation was a rare accident, not something animals prepared carefully to cause and avoid.

So cephalopod intelligence evolved rather independently from most of the minds we observe. We could learn something about alien minds by understanding them.

Intelligence may even have evolved more than once in cephalopods – nobody seems to know whether octopuses evolved intelligence separately from squids/cuttlefish.

An octopus has a much less centralized mind than vertebrates do. Does an octopus have a concept of self? The book presents evidence that octopuses sometimes seem to think of their arms as parts of their self, yet hints that their concept of self is a good deal weaker than in humans, and maybe the octopus treats its arms as semi-autonomous entities.

2.

Does an octopus have color vision? Not via its photoreceptors the way many vertebrates do. Simple tests of octopuses’ ability to discriminate color also say no.

Yet octopuses clearly change color to camouflage themselves. They also change color in ways that suggest they’re communicating via a visual language. But to whom?

One speculative guess is that the color-producing parts act as color filters, with monochrome photoreceptors in the skin evaluating the color of the incoming light by how much the light is attenuated by the filters. So they “see” color with their skin, but not their eyes.

That would still leave plenty of mystery about what they’re communicating.

3.

The author’s understanding of aging implies that few organisms die of aging in the wild. He sees evidence in Octopuses that conflicts with this prediction, yet that doesn’t alert him to the growing evidence of problems with the standard theories of aging.

He says octopuses are subject to much predation. Why doesn’t this cause them to be scared of humans? He has surprising anecdotes of octopuses treating humans as friends, e.g. grabbing one and leading him on a ten-minute “tour”.

He mentions possible REM sleep in cuttlefish. That would almost certainly have evolved independently from vertebrate REM sleep, which must indicate something important.

I found the book moderately entertaining, but I was underwhelmed by the author’s expertise. The subtitle’s reference to “the Deep Origins of Consciousness” led me to expect more than I got.

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.

Connectomes are not sufficient by themselves to model brain behavior. Brain modeling has been limited more by the need for good information about the dynamic behavior of individual neurons.

The paper Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans looks like an important step toward overcoming this limitation. The authors observed the behavior of many individual neurons in a moving nematode.

They still can’t reliably map the neurons they observed to standard C. elegans neuron names:

The neural position validation experiments presented here, however, have led us to conclude that worm-to-worm variability in neuronal position in the head is large enough to pose a formidable challenge for neuron identification.

But there are enough hints about which neurons do what that I’m confident this problem can be solved if enough effort is devoted to it.

My biggest uncertainty concerns applying this approach to mammalian brains. Mammalian brains aren’t transparent enough to be imaged this way. Are C. elegans neurons similar enough that we can just apply the same models to both? I suspect not.

I’d like to see more discussion of uploaded ape risks.

There is substantial disagreement over how fast an uploaded mind (em) would improve its abilities or the abilities of its progeny. I’d like to start by analyzing a scenario where it takes between one and ten years for an uploaded bonobo to achieve human-level cognitive abilities. This scenario seems plausible, although I’ve selected it more to illustrate a risk that can be mitigated than because of arguments about how likely it is.

I claim we should anticipate at least a 20% chance a human-level bonobo-derived em would improve at least as quickly as a human that uploaded later.

Considerations that weigh in favor of this are: that bonobo minds seem to be about as general-purpose as humans, including near-human language ability; and the likely ease of ems interfacing with other software will enable them to learn new skills faster than biological minds will.

The most concrete evidence that weighs against this is the modest correlation between IQ and brain size. It’s somewhat plausible that it’s hard to usefully add many neurons to an existing mind, and that bonobo brain size represents an important cognitive constraint.

I’m not happy about analyzing what happens when another species develops more powerful cognitive abilities than humans, so I’d prefer to have some humans upload before the bonobos become superhuman.

A few people worry that uploading a mouse brain will generate enough understanding of intelligence to quickly produce human-level AGI. I doubt that biological intelligence is simple / intelligible enough for that to work. So I focus more on small tweaks: the kind of social pressures which caused the Flynn Effect in humans, selective breeding (in the sense of making many copies of the smartest ems, with small changes to some copies), and faster software/hardware.

The risks seem dependent on the environment in which the ems live and on the incentives that might drive their owners to improve em abilities. The most obvious motives for uploading bonobos (research into problems affecting humans, and into human uploading) create only weak incentives to improve the ems. But there are many other possibilities: military use, interesting NPCs, or financial companies looking for interesting patterns in large databases. No single one of those looks especially likely, but with many ways for things to go wrong, the risks add up.

What could cause a long window between bonobo uploading and human uploading? Ethical and legal barriers to human uploading, motivated by risks to the humans being uploaded and by concerns about human ems driving human wages down.

What could we do about this risk?

Political activism may mitigate the risks of hostility to human uploading, but if done carelessly it could create a backlash which worsens the problem.

Conceivably safety regulations could restrict em ownership/use to people with little incentive to improve the ems, but rules that looked promising would still leave me worried about risks such as irresponsible people hacking into computers that run ems and stealing copies.

A more sophisticated approach is to improve the incentives to upload humans. I expect the timing of the first human uploads to be fairly sensitive to whether we have legal rules which enable us to predict who will own em labor. But just writing clear rules isn’t enough – how can we ensure political support for them at a time when we should expect disputes over whether they’re people?

We could also find ways to delay ape uploading. But most ways of doing that would also delay human uploading, which creates tradeoffs that I’m not too happy with (partly due to my desire to upload before aging damages me too much).

If a delay between bonobo and human uploading is dangerous, then we should also ask about dangers from other uploaded species. My intuition says the risks are much lower, since it seems like there are few technical obstacles to uploading a bonobo brain shortly after uploading mice or other small vertebrates.

But I get the impression that many people associated with MIRI worry about risks of uploaded mice, and I don’t have strong evidence that I’m wiser than they are. I encourage people to develop better analyses of this issue.