The Copenhagen ontology

Scott Aaronson has an interesting post on his personal interpretation of quantum mechanics (he's probably a 'none-of-the-above' but with a revealed preference for the MWI).

He is, however, particularly scathing about the so-called 'Copenhagen Interpretation'.

"As for Copenhagen, I’ve described it as “shut-up and calculate except without ever shutting up about it”!  I regard Bohr’s writings on the subject as barely comprehensible, and Copenhagen as less of an interpretation than a self-conscious anti-interpretation: a studied refusal to offer any account of the actual constituents of the world, and—most of all—an insistence that if you insist on such an account, then that just proves that you cling naïvely to a classical worldview, and haven’t grasped the enormity of the quantum revolution."

This seems spot on.

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'You may not be interested in ontology, but ontology is interested in you.'

What happens when a convinced adherent to the Copenhagen Interpretation is asked straight out:

"... what constitutes the "act of measurement" in a world without sentient beings? In such a world (even in a world with sentient beings) there are just physical systems with atoms and molecules all under the rule of Schrödinger's equation. So when does "collapse" occur?

When can it be decided that a measurement has been made if there are no sentient beings?

If everything is made up of particles, and the particles are under the governance of Schrödinger's equation and unitary evolution, when do "measurement" and "collapse" occur? In a world without sentient beings, what would "when the new data arrives" refer to?"

Luboš Motl answers commentator Ricky's question above (in comment 16 here):

""The conceptually right [way] to describe a world without sentient beings is that an unspecified and unknown initial wave function evolves unitarily according to Schrödinger's equation and never collapses because it's only measurements that may collapse and there are none in your theory. The complete "diffusion" of the wave function (into the linear superposition of dead and alive cats and all objects, small and big, in the most general superpositions of all conceivable states) may be said to be a problem - but another problem is that the initial state is totally unknown, too.

"It makes no sense to say that the initial wave function is a particular thing because one may only say that the wave function is a particular thing [if] something is [a] measurement - if a sentient being becomes aware of the result of some measurement. This is not happening in a universe without sentient beings. So there's no specific science to discuss in a universe without sentient beings at all. The laws may still be the same as they are in our world but they won't be applied in any particular situation because there are no particular situations or particular special wave functions in a world where no one ever measures anything.

"Einstein asked whether there is any Moon over there if no one looks. In practice, classical physics is a good enough approximation, so one may assume that the Moon is pretty much there even before observers look etc. But conceptually, if you care about similar objects for which the quantum effects are strong, the right answer is that the Moon just isn't at any particular location and has no other particular properties if no one looks. The wave function isn't a real object of any type. Its amplitudes can't be measured in a single repetition of the situation. It is only a template storing information allowing to predict probabilities of things that actually can be measured - the observables."

The arch-exponent of Copenhagenism appears to believe that the universe is really some unitary evolution in Hilbert space, presumably with space-time somehow emergent. Because ontology.

Autism & IQ, Deep Learning, Quantum Computing

Three papers (PDF) for you today.

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1. Autism As a Disorder of High Intelligence - Bernard J. Crespi

Alterations of function in the autistic brain

Crespi's paper attempts to synthesise what is know phenotypically about mental pathologies such as autism with the genetics of intelligence. His thesis is:

"... alleles for autism overlap broadly with alleles for high intelligence, which appears paradoxical given that autism is characterized, overall, by below-average IQ.

This paradox can be resolved under the hypothesis that autism etiology commonly involves enhanced, but imbalanced, components of intelligence. This hypothesis is supported by convergent evidence showing that autism and high IQ share a diverse set of convergent correlates, including large brain size, fast brain growth, increased sensory and visual-spatial abilities, enhanced synaptic functions, increased attentional focus, high socioeconomic status,more deliberative decision-making, professional and occupational interests in engineering and physical sciences, and high levels of positive assortative mating."

and

".. autism represents most broadly a disorder of high intelligence (and low imagination), and schizophrenia a disorder of high imagination (and low intelligence) .. ."

I think his argument works best at the Asperger-end of the spectrum, where brain architecture imbalances have not yet led to major social dysfunction.

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2. Deep Learning: A Critical Appraisal by Gary Marcus

Gary Marcus says "I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence."

He begins by observing starkly that "Deep learning, as it is primarily used, is essentially a statistical technique for classifying patterns, based on sample data, using neural networks with multiple layers."

Deep learning typically "knows" no more than can be inferred from regularities in the offered datasets (images, sounds, texts). With such limited data it's generally going to be impossible to infer background theories such as folk physics and folk psychology, let alone the physical and social properties of objects, people and behaviours which condition our everyday lives.

He then examines in detail just how consequentially-brittle deep learning systems are at generalising correctly outside of their training sets. This does not augur well for applications in open domains (such as self-driving cars, Internet censorship and the construction of competent social companions such as chatbots).

I completely agree with this paper, together with its conclusions that some serious new thinking is desperately required.

[Via "The Wild Week in AI"].

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3. "Quantum Computing in the NISQ era and beyond" by John Preskill

From the abstract.

"Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near
future. Quantum computers with 50-100 qubits may be able to perform tasks which
surpass the capabilities of today’s classical digital computers, but noise in quantum
gates will limit the size of quantum circuits that can be executed reliably."

This is a useful review of the field of quantum computing, aimed at (technically-minded) venture capitalists. It's accessible and describes what quantum computing is, the various ways it can be implemented and what a quantum computer can - and cannot - do.

Almost the ideal introduction.

[Via Scott Aaronson].

Being Jewish in contemporary America

Scott Aaronson is not yet 35 years old:

"I’m gratified that many people have described me as warm and friendly and helpful (“surprisingly so,” one can almost hear them add, for such a socially-inept, self-obsessed nerd!).  But there’s a reason for that.

"If I meet a new person, and they aren’t weird in the same ways I’m weird, my brain’s first questions tend to be: would this person be happy to rid the earth of me and everyone like me, regarding me as genetically defective?  Is he or she merely temporarily prevented from doing so?  In 1942, would he or she have smiled (as so much of Europe did smile) as I was loaded onto a cattle car?

"So then, if the person turns out—as most often they do—to be perfectly nice and decent, I’m so relieved and grateful that it’s like, how can I be anything but friendly and helpful in return?"

Read the whole of his extraordinary interview with Scientific American here.

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I reviewed Scott Aaronson's book, 'Quantum Computing Since Democritus' back in 2013.

Feeling black holes collide from close-up

As soon as I heard about that LIGO thing, first thing I thought of, what would it have felt like if you'd been there, maybe an AU away from those coalescing black holes?

Eventually the Internet got around to telling me.

"As I read the historic news, there’s one question that kept gnawing at me: how close would you need to have been to the merging black holes before you could, you know, feel the distortion of space?  I made a guess, [...] you’d need to be very close.

"Even if you were only as far from the black-hole cataclysm as the earth is from the sun, I get that you’d be stretched and squished by a mere ~50 nanometers (this interview with Jennifer Ouellette and Amber Stuver says 165 nanometers, but as a theoretical computer scientist, I try not to sweat factors of 3).

Even if you were 3000 miles from the black holes—New-York/LA distance—I get that the gravitational waves would only stretch and squish you by around a millimeter. Would you feel that? Not sure. At 300 miles, it would be maybe a centimeter—though presumably the linearized approximation is breaking down by that point.

[...]

"Now, the black holes themselves were orbiting about 200 miles from each other before they merged.  So, the distance at which you could safely feel their gravitational waves, isn’t too far from the distance at which they’d rip you to shreds and swallow you!

In summary, to stretch and squeeze spacetime by just a few hundred nanometers per meter, along the surface of a sphere whose radius equals our orbit around the sun, requires more watts of power than all the stars in the observable universe give off as starlight.

"People often say that the message of general relativity is that matter bends spacetime “as if it were a mattress.”  But they should add that the reason it took so long for humans to notice this, is that it’s a really friggin’ firm mattress, one that you need to bounce up and down on unbelievably hard before it quivers, and would probably never want to sleep on."

From Scott Aaronson's blog, a post appealingly titled "The universe has a high (but not infinite) Sleep Number", h/t SSC.

Victor Toth writes:

"A gravitational wave is like a passing tidal force. It squeezes you in one direction and stretches you in a perpendicular direction. If you are close enough to the source, you might feel this as a force. But the effect of gravitational waves is very weak. For your body to be stretched by one part in a thousand, you’d have to be about 15,000 kilometers from the coalescing black hole.

"At that distance, the gravitational acceleration would be more than 3.6 million g-s, which is rather unpleasant, to say the least. And even if you were in a freefalling orbit, there would be strong tidal forces, too, not enough to rip your body apart but certainly enough to make you feel very uncomfortable (about 0.25 g-forces over one meter.) So sensing a gravitational wave would be the least of your concerns.

"But then… you’d not really be sensing it anyway. You would be hearing it. Most of the gravitational wave power emitted by GW150914 was in the audio frequency range. A short chip rising in both pitch and amplitude. And the funny thing is… you would hear it, as the gravitational wave passed through your body, stretching every bit a little, including your eardrums."

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I'm reading "The Welfare Trait: How State Benefits Affect Personality" by Dr Adam Perkins of King's College, London. He talks about the employment-resistant personality and how such people feature disproportionately on welfare. There they tend to have lots of children, both for increased benefits and because they're rather feckless, (A-, C-, in the five-factor jargon).

Amazon Link

Dr Perkins is worried about dysgenic consequences - plainly the potential is there - but how big is the effect? I'm waiting to see whether Dr Perkins gets quantitative, but if he does, he'll be using the Breeder's Equation.

Time for a quick review from West Hunter - this is the Breeder's Equation:

"R = h²S.

"R is the response to selection, S is the selection differential, and h² is the narrow-sense heritability. This is the workhorse equation for quantitative genetics. The selective differential S, is the difference between the population mean and the mean of the parental population (some subset of the total population).

"For example, imagine a set of parents with IQs of 120, drawn from a population with an average IQ of 100. Suppose that the narrow-sense heritability (in that population, in that environment) is 0.5 . The average IQ of their children will be 110. That’s what is usually called regression to the mean.

"Do the same thing with a population whose average IQ is 85. We again choose parents with IQs of 120, and the narrow-sense heritability is still 0.5. The average IQ of their children will be 102.5 – they regress to a lower mean.

"You can think of it this way. In the first case, the parents have 20 extra IQ points. On average, 50% of those points are due to additive genetic factors, while the other 50% is is the product of good environmental luck. By the way, when we say ‘environmental” we mean “something other than additive genetics”. It doesn’t look as if the usual suspects – the way in which you raise your kids – contributes much to this ‘environmental’ variance, at least for adult IQ. In fact we know what it’s not, but not much about what it is, although it must include factors like test error and being hit on the head.

"The kids get the good additive genes, but have average ‘environmental’ luck – so their average IQ is 110. The luck (10 pts worth) goes away

"The 120-IQ parents drawn from the IQ-85 population have 35 extra IQ points, half of which are from good additive genes and half from good environmental luck. But in the next generation, the luck goes away… so they drop 17.5 points.

"The next point is that the luck only goes away once. If you took those kids from the first group, with average IQs of 110, and dropped them on an uninhabited but friendly island, they would presumably get around to mating eventually – and the next generation would also have an IQ of 110. With tougher selection, say by kidnapping a year’s worth of National Merit Finalists, you could create a new ethny with far higher average intelligence than any existing. Eugenics is not only possible, it’s trivial."

Dysgenics too: as personality has similar heritability to intelligence (0.5), still mulling over the application of this to the profligate underclass ...

You might also want to take a look at this.

'Quantum Computing Since Democritus' - Scott Aaronson

If you’re a computational complexity theorist, then everything looks like .. well, a problem in computational complexity. Scott Aaronson is astonishingly bright, on top of his subject and genuinely droll: this book gives you a fly-on-the-wall view of how he engaged with his students at the University of Waterloo.

We start with a tour of prerequisites. Chapter 2 covers axiomatic set theory (ZF); chapter 3 Gödel’s Completeness and Incompleteness Theorems, and Turing Machines. In chapter 4 we apply some of these ideas to artificial intelligence, discuss Turing’s Imitation Game and the state of the art in chatbots, and also Searle’s Chinese Room puzzle. Aaronson invariably provides a fresh perspective on these familiar topics although already we see the ‘lecture note’ character of this book, where details are hand-waved over (because the students already know this stuff, or they can go away and look it up).

Chapters 5 and 6 introduce us to the elementary computation complexity classes and explain the famous P not = NP conjecture. This is not a first introduction – you are assumed to already understand formal logic and concepts such as clauses, validity and unsatisfiability. Chapters 7 and 8 introduce, by way of a discussion on randomness and probabilistic computation, a slew of new complexity classes and the hypothesised relations between them, applying some of these ideas to cryptanalysis.

Chapter 9 brings us to quantum theory. Six pages in we’re talking about qubits, norms and unitary matrices so a first course on quantum mechanics under your belt would help here. The author’s computer science take on all this does bring in some refreshing new insights. We’re now equipped, in chapter 10, to talk about quantum computing. Typically this is not architecture or engineering discussion; Aaronson is a theorist, and for his community, quantum computing means a new set of complexity classes with conjectural relationships to those of classical computation.

We now go off at a tangent as the author critiques Sir Roger Penrose’s views on consciousness as a quantum gravity phenomenon. I think it’s fair to say that no-one in AI takes this idea seriously, but the author has the intellectual resources to engage Penrose on his own ground here.

In chapter 12 we crank up the technical level to talk about decoherence and hidden variable theories. This is one of the most interesting chapters but is too discursive – really important concepts are touched on and then abandoned; for example the discussion of decoherence and the 2nd Law of Thermodynamics  is set against a model of the multiverse, but it’s never quite clear whether Aaronson is assuming the reality of the Everett Interpretation or whether he has some other, more purely mathematical model in mind.

Chapter 12 reminds us that a computational complexity theorist’s idea of proof is a long way from that of a logician. We plunge into stochastic proofs, zero-knowledge proofs and probabilistically checkable proofs, all framed by a complexity analysis.

The next few chapters cover a series of topics in similar vein: quantum proofs (and their complexity classes), rebuttals of sceptical arguments against quantum computing (interesting and convincing), some technically demanding material on learning algorithms, and concepts of interactive proof.

The final few chapters are more philosophical: Aaronson applies his toolkit to topics such as the Anthropic Principle (via Bayesian reasoning); free will (he’s in favour but has a highly-idiosyncratic view of what free will is); time travel (how closed timelike curves impact on classical and quantum computation); and cosmology (black holes, the information paradox, with firewalls bringing us up-to-date).

I have to say that I did finish this book – it didn’t just sit on my coffee table, abandoned after the first few chapters, as the author rather fears in his preface. However, it has to be said that despite the author’s undeniable enthusiasm, complexity theory remains a minority taste. There are plenty of insights and novel observations even for those of us less enthralled but I hope it’s clear what kind of background the reader needs to get anything out of this volume.

To be fair, the book is already 362 pages long and to make the material less a write-up of post-graduate lecture notes and more a self-contained and smoothly-developed presentation of Aaronson’s many original insights would seem to require an inordinate amount of time and effort, without substantially increasing the likely readership. I enjoyed it, but not without a degree of frustration.