Surprise: Free Will Needs Quantum Physics to Fail, Physicists Show

Some physicists believe that human consciousness is somehow linked to the indeterministic element of quantum physics. But according to a surprising new argument that just appeared on the arXiv, a world where everything is ruled by quantum physics is incompatible with the idea of free will. Let’s take a look.

Quantum Computers Could Test Free Will, Researchers Claim

In quantum physics, the idea that observed correlations between particles come from the way that the measurement device affects the particles is called a violation of “Measurement Independence” or “Superdeterminism.” The authors of a new paper claim that the more complicated one makes an experiment, the larger the violation of measurement independence must be to explain observations. They conclude that quantum computers can test free will. Wait what? I’ve had a look at the paper.

Does AI Already Have Free Will?

AI is becoming an increasingly important decision maker in our society whether you like it or not. But can an AI have free will? And should it be made responsible for its decisions? Philosophers are arguing both sides of the issue – let’s take a look.

Has Physics Ruled out Free Will?

Do humans have free will or to the the laws of physics imply that such a concept is not much more than a fairy tale? Do we make decisions? Did the big bang start a chain reaction of cause and effects leading to the creation of this video? That's what we'll talk about today.



Transcript, links to references, and discussion on Patreon.

I’ve said it all before but here we go again

[I didn't write the title and byline
and indeed didn't see it until it
appeared online.]

For reasons I don’t fully understand, particle physicists have recently started picking on me again for allegedly being hostile, and have been coming at me with their usual ad homimen attacks.

What’s going on? I spent years trying to understand why their field isn’t making progress, analyzing the problem, and putting forward a solution. It’s not that I hate particle physics, it’s rather to the contrary, I think it’s too important to let it die. But they don’t like to hear that their field urgently needs to change direction, so they attack me as the bearer of bad news. 

But trying to get rid of me isn’t going to solve their problem. For one thing, it's not working. More importantly, everyone can see that nothing useful is coming out of particle physics, it’s just a sink of money. Lots of money. And soon enough governments are going to realize that particle physics is a good place to save money that they need for more urgent things. It would be in particle physicists’ own interest to listen to what I have to say.

And I have said this all many times before but I hate long twitter threads, so let me just summarize it in one blogpost:

a) Predictions for fundamentally new phenomena made from new theories in particle physics have all been wrong ever since the completion of the standard model in the 1970s. You have witnessed this ongoing failure in the popular science media. All their ideas were either falsified or they have been turned into eternally amendable and fapp unfalsifiable models, like supersymmetry.

b) Saying that “it’s difficult” explains why they haven’t managed to find new phenomena, but it doesn’t explain why their predictions are constantly wrong. 

c) Scientists should learn from failure. If particle physicists’ method of theory-development isn’t working, they should analyze why, and change their methods. But this isn’t happening.

My answer to why their current method isn’t working is that their new theories (often in the form of new particles) do not solve any problems in the existing theories. They just add unnecessary clutter. When theoretical predictions were correct in the past, they solved problems of consistency (example: the Higgs, anti-particles, neutrinos, general relativity, etc).

Two common misunderstandings: Note that I do NOT say theorists in the past used this argument to make their predictions. I am merely noting in hindsight that’s what they did. It’s what the successful predictions have in common, and we should learn from history. Neither do I say that theoretical predictions were the ONLY way that progress happened. Of course not. Progress can also happen by experimental discoveries. But the more expensive new experiments become, the more careful we have to be about deciding which experiments to make, so we need solid theoretical predictions.

In many cases, particle physicists have made up pseudo-problems that they claim their new particles solve. Pseudo-problems are metaphysical misgivings, often a perceived lack of beauty. A typical example is the alleged problem with the Higgs mass being too small (that was behind the idea that the LHC should see supersymmetry). It’s a pseudo-problem because there is obviously nothing wrong with the Higgs-mass being what it is, seeing that they can very well make predictions with the standard model and its Higgs as it is. 

(I sometimes see particle physicists claiming that supersymmetry “explains” the Higgs-mass. This is bluntly wrong. You cannot calculate the Higgs-mass from supersymmetric models, it remains a free parameter.)

Other pseudo-problems are the baryon asymmetry or the smallness of the cosmological constant etc. I have a list that distinguishes problems from pseudo-problems here.

So my recommendation is that theory development should focus on resolving inconsistencies, and stop wasting time on pseudo-problems. Real problems are eg the lacking quantization of gravity, dark matter, the measurement problem in quantum mechanics, as well as several rather technical issues with quantum mechanics (see the above mentioned list).

When I say “dark matter” I refer to the inconsistency between observation and theory. Note that to solve this problem one does NOT need details of the particles. That’s another point which particle physicists like to misunderstand. You fit the observations with an energy density and that’s pretty much it. You don’t need to fumble together entire “hidden sectors” with “portals” and other nonsense. Come on, people, wake up! This isn’t proper science!

There are several reasons why particle physicists can’t and don’t want to make this change. The most important one is that it would dramatically impede their capability to produce papers. And papers are what keeps grant cycles churning. This is a systemic problem. Next problem is that they can’t believe that what I say can possibly be correct because they have grown up in a community that has taught them their current methods are good. That’s group think in action.

There are solutions to both of these problems, but they require changes from within the community.

Particle physicists, rather unsurprisingly, don’t like the idea that they have to change. Their responses are boringly predictable.

They almost all attack me rather than my argument. Typically they will make claims like I’m just “trying to sell books” or that I “want attention” or that I “like to be contrarian” or that, in one way or another, I don’t know what I am talking about. I yet have to find a particle physicists who actually engaged with the argument I made. Indeed most of them never bother finding out what I said in the first place.

A novel accusation that I recently heard for the first time is that I allegedly refuse to argue with them. A particle physicist claimed on twitter that I had been repeatedly invited to give a seminar at CERN but declined, something she had been told by someone else. This is untrue. I have to my best knowledge never declined an opportunity to talk to particle physicists, even though I have been yelled at repeatedly. I was never invited to give a seminar at CERN. 

The particle physicist who made this claim actually went and asked the main seminar organizers at CERN and they confirmed that I was never invited. She apologized. So it’s all good, except that it documents they have been circulating lies about me in the attempt to question my expertise. (Another symptom of social reinforcement.)

There have also been several instances in the past where particle physicists called senior people at my workplace to complain about me, probably in the hope to intimidate me or to get me fired. It speaks much for my institution that the people in charge exerted no pressure on me. (In other words, don't bother calling them, it’s not going to help.)

The only “arguments” I hear from particle physicists are misunderstandings that I have cleared up thousands of times in the past. Like the dumb claim that inventing particles worked for Dirac. Or that I’m “anti-science” because I think building a bigger collider isn’t a good investment right now.

You would think that scientists should be interested in finding out how their field can make progress, but particle physicists just desperately try to make me go away, as if I was the problem. 

But hey, here’s a pro-tip: If you want to sell books, I recommend you don’t write them about theoretical high energy physics. It’s not a topic that has a huge market. Also, I have way more attention than I need or want. I don’t want attention, I want to see progress. And I don’t like being contrarian, I am just not afraid of being contrarian when it’s necessary.

As a consequence of these recent insults targeted at me, I wrote an opinion piece for the Guardian that appeared on Monday. Please note the causal order: I wrote the piece because particle physicists picked on me in a renewed attempt to justify continuing with their failed methods, not the other way round. 

It's not that I think they will finally see the light. But yeah I’m having fun for sure.

The New Meta-Materials for Superlenses and Invisibility Cloaks

[This is a transcript of the video embedded below. Some of the explanations may not make sense without the animations in the video.]


Meta is the Greek prefix for “after” and Aristotle used the phrase “metaphysics” for the stuff in his writing that came literally “after” he was done with the physics. Metaphysics is concerned with some of the most important questions we face at this critical moment in human history. Questions like whether the holes in cheese exist, whether cheese exists, or whether only the atoms that make up the cheese exist.

But this is not what we’ll talk about today. This video is about metamaterials which, I assure you, have nothing to do with cheese. Though, maybe, a little bit. Metamaterials are the next technological stage “after” materials. It’s a research area that has progressed incredibly quickly in the past decade, and that includes superlenses, invisibility cloaks, earthquake protection, and also chocolate. What are metamaterials, and what are they good for? That’s what we’ll talk about today.

First things first, what are metamaterials? A linguistic approach might lead you to think a metamaterial is what comes after the material, so I guess, that’d be the bill. But that’s not quite right. A metamaterial has custom-designed micro-structures which give a material new properties. These micro-structures are typically arrays that resonate at specific frequencies, and that interact either with acoustic waves or with electromagnetic waves. This way, metamaterials can be used to control sound, heat, light, and even earthquakes.

This sounds pretty abstract, so let us start with a concrete example, the superlens.

When you take an image of an object, with your eyes or with a camera, you collect light that reflects off the surface of an object with a lens. Lenses work by “refraction” which means they change the angle at which the light travels. If an object is too close to the lens, the refraction can no longer converge the light. For this reason, you can’t take images of things that are too close to the lens.

But not all the light that reflects from an object gets away. The part that gets away is called the far field, but there is another part of the light called the near field, which stays near the surface of the object. The electromagnetic waves in the near field are oscillating like usual, but they don’t travel into the distance, they decay exponentially. It’s also called an “evanescent wave”.

This figure shows how waves enter a medium at a surface, which is the red line. The top image is a normal, refracted wave, which continues traveling through space but the angle changes when it enters the medium. The bottom image shows an evanescent wave, which decays with distance from the surface. The evanescent waves contain tiny details of the structure of the object, but since they don’t reach the camera, those details are lost. And you can’t get the camera arbitrarily close to the object, because then you couldn’t refocus the light. And that’s a shame because you might not be able to count the hairs in my eyebrows after all.

But in 2000, the British physicist Sir John Pendry of Imperial College in London found a way to use the information in the near field. He said, it’s easy enough, you just use a material that has a negative refractive index.

What does it mean for a material to have a negative refractive index? Normal materials don’t have this, but metamaterials can. When a ray of light enters a medium, then the refractive indexes of the two media relate the angles. This is called Snell’s law. If the refractive index of the medium is negative, then this means the continuation of the ray in the medium is also reflected from the normal to the surface. So, it goes back into the direction it comes from. How would that look like?

Well, as I said, stuff that we normally encounter in daily life doesn’t have a negative refractive index, so I can’t show you a photo. But we can illustrate what it would look like. You probably remember the “broken pencil” illusion. If you put a pencil half into a glass of water, then the part in the water appears shifted to the side. It’s because the light is refracted in the water but the brain interprets the visual input as if the light travels in straight lines. If the water had a negative refraction index, then the lower part of the pencil wouldn’t just seem shifted, it’d also be reflected to the other side.

Aaron Danner had the great idea to use a raytracer to create a 3-d image of a pool filled with water that has a negative refraction index. Here is the image of the pool with normal water. And here is the image with the negative refractive index. The thing to pay attention to are those three black lines, which indicate the corner of the pool. You’d normally expect this to be out of sight, but since this strange water mixes refraction with reflection, you can now see it. If there were fish in the pool they’d appear to be floating on top of the water. Which, I don't know if you know this, but it’s not what a fish is supposed to do.

What’s this got to do with lenses? Well remember that you need lenses to collect rays of light. But if you put a sheet of a medium with negative refractive index between two with normal refractive index, that’ll basically turn the light rays around and effectively focus them. It acts like a lens. And, here comes the important bit, this also works for evanescent waves which usually get lost. They get focused too, and are prevented from decaying. This is why metamaterials with a negative refraction image can reach a resolution that’s impossible to reach with normal lenses.

A superlens was built for the first time in 2005 by researchers at UC Berkeley. Their lens was made of a silver sheet that was merely 35 nanometers thick. In this case, the structure of the material comes from oscillations in the electron density in the silver which amplifies the evanescent waves coming from the object. You have to put the object directly into contact with the silver surface for that to work.

This image (A) is a lithograph taken with a focused ion beam, so this is the control image. This image (C) is the optical control without superlens. And this one (B) is the superlens image. You can clearly see that the superlens image has a higher resolution. This graph D shows the difference in accuracy between imaging with the superlens, that’s the blue curve, compared to imaging without the superlens, that’s the red curve.

Though this jump in resolution might sound good, these lenses are rather impractical. You have to put the metamaterial directly into contact with whatever you want to image and then your camera on top. So it does away with selfie sticks, but unfortunately also ruins your makeup. This is why, last year, a group of researchers from Iran and Switzerland published a paper in Scientific Reports, in which they propose to use a metamaterial to turn the near field into a far field, so you can put your camera elsewhere.

They call this device a “hyperlens” which to me sounds like it’s a superlens that’s had too much coffee, but they mean a grid of aluminum nanorods that resonate at wavelengths in the visible part of the spectrum. For now, this is just a computer simulation, but the idea is that the resonance converts the evanescent modes into propagating modes, so then you can capture them elsewhere. The researchers claim that at least in their numerical simulations this structure can image biological tissues with a resolution of a tenth of the wave-length of the light. The resolution limit of conventional lenses is about a quarter of a wave-length.


Let’s then talk about what’s the probably best known application of metamaterials, the invisibility cloak. You may have read the headlines a few years ago about this. Metamaterials make invisibility cloaks possible because with a negative refraction index you can bend light in the opposite direction to what normal materials do. This means that, at least in theory, with the right combination of materials and metamaterials, you can bend light around an object. This appears to us as if the object isn’t there, again because the brains assume that light travels in straight lines.

This sounds pretty cool, and indeed scientists have some things to show, or maybe in this case it’s better to say *not show. Early experiments in the mid-2000’s mainly used microwaves. But in 2015, a team of researchers from China made an invisibility cloak that works in the infrared. In this Figure (Figure 1f) you see how the light is redirected. They used several triangles of germanium and put them in a very precise geometric configuration so that it creates a hidden area inside. You might say that this isn’t much of a metamaterial, but it’s the same idea: you custom-design structures to redirect waves as you want. Into this hidden region they put a mouse. (Figure 2b). Then they took an image with and without the cloak (Figure 4a and 4b). Half of the mouse is gone!

Invisibility cloaks in the visible part of the spectrum haven’t yet been made, but some semi-invisibility shields exist, for example this one from a company named Hyperstealth Corp. These don’t work by bending the light around objects, but by spreading the light in the horizontal plane. If you have a narrow object, then its image will be overpowered by the light coming from the sides of the object which blurs out what is behind. This works particularly well when the background is uniform. However, it’s not really an invisibility shield. Easiest way to build an invisibility shield is put a camera behind you and project that on a screen in front of you.

You can also use metamaterials to manipulate electromagnetic fields that are not in the optical range. For example, as I explained in this earlier video, the main problem with wireless power transfer is that power decreases with very rapidly with distance from the sender. A “magnetic superlens”, however, could extend this reach.

That this works was shown in a paper by a group of American researchers in 2014. This figure shows the difference between wireless power transfer using a magnetic superlens compared to wireless power transfer through free space. On the y-axis, we have wireless power transfer efficiency, and on the x-axis, we have distance in meters. The solid black line represents wireless power transfer through free space, which drops quickly to near-zero values as distance increases.

The colored lines represent wireless power transfer with the use of a magnetic superlens made up of metamaterials. You see that at best you can extend the reach by a few centimeters. And notice that the efficiency is in all cases in the single digits. So, nice idea, but in practice it doesn’t make much of a difference.

Another type of wave you can manipulate are acoustic waves. Acoustic metamaterials aren’t really a new thing. Sound absorption foam like this one uses basically the same idea. It has a lot of tiny holes. So you see, it’s kind of like cheese. The holes make it very difficult for sound waves in certain frequencies to bounce back which basically kills echo. If I wrap this around my head, you’ll hear the difference. Wrapping your head into one of those will generally improve your experience of the world, highly recommended.

Metamaterials are more sophisticated versions of this. You can for example design them so that they only absorb particular frequencies, this is called a sonic or phononic crystal. Another thing you can do is to reflect the signal back without spreading it out. This was done by a team of researchers from China and the USA in 2018. The material they used was just a plastic dish with a spiral structure that effectively changes the refractive index. They say an application could be to make vehicles easier to detect. Though I suspect that their metamaterial would sell better if it made a car less easy to detect.

You can also use acoustic metamaterials to build an acoustic type of superlens, which has been done for ultrasound, but it’s the kind of solution still looking for a problem. And, as you can guess, they are trying to build acoustic invisibility shields. This has been done for example underwater with ultrasound which is great if you want to hide from dolphins. And in 2014, a group from Duke University used a pyramid with a special surface structure that makes it reflect sound as if it was an empty plane. Here is how this pyramid would look looks like if you could see sound. The pyramid is hollow, so you can hide stuff inside. Maybe they’ve finally figured out what the Egyptians were up to?

Another application of metamaterials is earthquake protection. Like you can use structures in materials to change how light and sound propagates, you can change the properties of the ground to change how seismic waves propagate. For this you embed structures around or under buildings so that seismic waves are diverted around the building. You basically make the building invisible to earthquakes.

For example, a group at MIT’s Lincoln lab use arrays of boreholes that are either filled or empty to redirect seismic waves. They haven’t actually build a real world example, but they have made measurements on downscaled physical models and they have done computer simulations.

This image is an illustration for how seismic barriers could work in theory. The green squiggly lines are the surface waves, the blue squiggly lines are P-waves, and the black arrows are the S-waves. All these waves get partly redirected and diffused.

At least in a computer simulation, the cloaking effect is quite impressive as you can see in this image from a 2017 paper. For this, they used data from a real earthquake, the Hector Mine earthquake that happened in Southern California in 1999. It had a magnitude of 7.1. The metamaterial barriers effectively reduced it to an earthquake of magnitude 4.5. And just a few months ago, a group from China proposed another metamaterial to dampen seismic waves. They want to use steel embedded with cylinders of foam.

Image A of this figure shows an aerial view of a seismic wave moving through unprotected soil – without protection, the wave moves without losing energy, exposing any infrastructure atop the soil to the full power of the seismic wave. In Image B, the metamaterial array effectively neutralizes the wave. Here you see the effectiveness of the metamaterial array from a side view – in Image A, the seismic wave travels across the surface uninterrupted, while in Image B, the metamaterial array dissipates the wave at Line C. The authors claim that their system can dampen seismic surface waves in the range of 0 point 1 to 20 Hertz with up to 85 percent efficiency.

And as promised, a tasty example to finish. A team of researchers from the Netherlands have created an edible metamaterial. It’s made of chocolate in multiple s-shaped pieces that makes the chocolate more or less crunchy, depending on the direction you chew it. And if you think about it YouTubers do this too when they cut breaths out of their videos and zoom back and forth in every other sentence. This structural changes affects how you travel through a video. So we’re really doing meta-videos.

Metamaterials have opened a whole new dimension to material design, and as you can see, they are well on the way to application already. We will certainly come back to this topic in the future, so if you want to stay up to date, don’t forget to subscribe.

If You Need a Break, Try Some Physics. (By which I really mean, please buy my new book.)

If I could, I would lock myself up in a cabin in the woods and not read any news for two weeks. But I find cabins in the woods creepy, and I’d miss the bunny pics on twitter. And in any case, I have something better to offer. 

If you want to take a step back from current affairs, why not fill your mind with some of the big mysteries of our existence? It works like a charm for my mental health. Why do we only get older and not younger? Are there copies of us in other universes? Can particles think? Has physics ruled out free will? Will we ever have a theory of everything? Does science have limits? Can information be destroyed? Will we ever know how the universe began? Is human behavior predictable? Ponder these mysteries for an hour a day and it’ll clear your head beautifully. I speak from experience.

I discuss this all these questions and many more in my new book “Existential Physics: A Scientist’s Guide to Life’s Biggest Questions” which will be on sale in the USA and Canada beginning next week, on August 9. I hope this book will help you separate what physicists know about those big questions from what they just speculate about. 

You can buy a signed copy from Midtown Scholar here (but note that they ship only in the USA and Canada). The UK Edition will be published on August 18. The publication date for the German translation has tentatively been set to March 28, 2023. There’ll be a couple of more translations following next year. Some more info about the book (reviews etc) here.

Is the brain a computer?

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[What follows is a transcript of the video embedded below. Some of the explanations may not make sense without the animations in the video.]

My grandmother was a computer, and I don’t mean there was a keypad on her chest. My grandmother calculated orbits of stars, with logarithmic tables and a slide ruler. But in which sense are brains similar to the devices we currently call computers, and in which sense not? What’s the difference between what they can do? And is Roger Penrose right in saying that Gödel’s theorem tells us human thought can’t just be computation? That’s what we’ll talk about today.

If you have five apples and I give you two, how many apples do you have in total? Seven. That’s right. You just did a computation. Does that mean your brain is a computer? Well, that depends on what you mean by “computer” but it does mean that I have two fewer apples than I did before. Which I am starting to regret. Because I could really go for an apple right now. Could you give me one of them back?

So whether your brain is a computer depends on what you mean by “computer”. A first attempt at answering the question may be to say a computer is something that does a computation, and a computation, according to Google is “the action of mathematical calculation”. So in that sense the human brain is a computer.

But if you ask Google what a computer is, it says it’s “an electronic device for storing and processing data, typically in binary form, according to instructions given to it in a variable program”. The definition on Wikipedia is pretty much the same and I think this indeed captures what most of us mean by “computer”. It’s those things we carry around to brush up selfies, but that can also be used for, well, calculations.

Let’s look at this definition again in more detail. It’s an electronic device. It stores and processes data. The data are typically in binary form. And you can give it instructions in a variable program. Now the second and last points, storing and processing data, and that you can give it instructions, also apply to the human brain. This leaves the two properties: it’s an electronic device and it typically uses binary data, which makes a computer different to the human brain. So let’s look at these two.

That an electronic computer is “digital” just means that it works with discrete data, so data whose values are separated by steps, commonly in a binary basis. The neurons in the brain, on the contrary, behave very differently. Here’s a picture of a nerve ending. In orange and blue you see the parts of the synapse that release molecules called “neurotransmitters”. Neurotransmitters encode different signals, and neurons respond to those signals gradually and in many different ways. So a neuron is not like a binary switch that’s either on or off.

But maybe this isn’t a very important difference. For one thing, you can simulate a gradual response to input on a binary computer just by giving weights to variables. Indeed, there’s an entire branch of mathematics for reasoning with such inputs. It’s called fuzzy logic and it’s the best logic to pet of all the logic. Trust me, I’m a physicist.

Neural networks which are used for artificial intelligence use a similar idea by giving weights to nodes and sometimes also the links of the network. Of course these algorithms still use a physical basis that is ultimately discrete and digital in binary. It’s just that on that binary basis you can mimic the gradual behavior of neurons very well. This already shows that saying that a computer is digital whereas neurons aren’t may not be all that relevant.

Another reason this isn’t a particularly strong distinction is that digital computers aren’t the only computers that exist. Besides digital computers there are analog computers which work with continuous data, often in electric, mechanical, or even hydraulic form. An example is the slide ruler that my grandma used. But you can also use currents, voltages and resistors to multiply numbers using Ohm’s law.

Analog computers are currently having somewhat of a comeback, and it’s not because millennials want to take selfies with their record players. It’s because you can use analog computers for matrix multiplications in neural networks. In an entirely digital neural network, a lot of energy is wasted in storing and accessing memory, and that can be bypassed by coding the multiplication directly into an analog element. But analog computers are only used for rather special cases exactly because you need to find a physical system that does the computation for you.

Is the brain analog or digital? That’s a difficult question. On the one hand you could say that the brain works with continuous currents in a continuous space, so that’s analog. On the other hand thresholds effects can turn on and off suddenly and basically make continuous input discrete. And the currents in the brain are ultimately subject of quantum mechanics, so maybe they’re partly discrete.   

But your brain is not a good place for serious quantum computing. For one thing, that’s because it’s too busy trying to remember how many seasons of Doctor Who there are just in case anyone stops you on the street and asks. But more importantly it’s because quantum effects get destroyed too easily. They don’t survive in warm and wiggly environments. It is possible that some neurological processes require quantum effects, but just how much is currently unclear, I’ll come back to this later.

Personally I would say that the distinction that the brain isn’t digital whereas typical computers that we currently use are, isn’t particularly meaningful. The reason we currently mostly use digital computers is because the discrete data prevent errors and the working of the machines is highly reproducible.

Saying that a computer is an electronic device whereas the brain isn’t, seems to me likewise a distinction that we make in every-day language, alright, but that isn’t operationally relevant. For one thing, the brain also uses electric signals, but more importantly, I think when we wonder what’s the difference between a brain and a computer we really wonder about what they can do and how they do it, not about what they’re made of or how they are made.

So let us therefore look a little closer at what brains and computers do and how they do it, starting with the latter: What’s the difference between how computers and brains do their thing?

Computers outperform humans in many tasks, for example just in doing calculations. This is why my grandmother used those tables and slide-rulers. We can do calculations if we have to, but it takes a long time and it’s tedious and it’s pretty clear that human brains aren’t all that great at multiplying 20 digit numbers.  

But hey, we did manage to build machines that can do these calculations for us! And along the way we discovered electricity and semi-conductors and programming and so on. So in some sense, you could say, we actually did learn to do those calculations. Just not with our own brains, because those are tired from memorizing facts about Doctor Who. But in case you are good at multiplying 20 digit numbers, you should totally bring that up at dinner parties. That way, you’ll finally will have something to talk about.

This example captures the key difference between computers and human brains. The human brain took a long time to evolve. Natural selection has given us a multi-tasking machine for solving problems, a machine that’s really good in adapting to new situations with new problems. Present-day computers, on the contrary, are built for very specific purposes and that’s what they’re good at. Even neural nets haven’t changed all that much about this specialization.

Don’t get me wrong, I think artificial intelligence is really interesting. There’s a lot we can do with it, and we’ve only just scratched the surface. Maybe one day it’ll actually be intelligent. But it doesn’t work like the human brain.

This is for several reasons. One reason is what we already mentioned above, that in the human brain the neural structure is physical whereas in a neural net it’s software coded on another physical basis.

But this might change soon. There are some companies which are producing computer chips similar to neurons. The devices made of them are called “neuromorphic computers”. These chips have “neurons” that fire independently, so they are not synchronized by a clock, like in normal processors. An example of this technology is Intel’s Loihi 2 which has one million “neurons” interconnected via 120 million synapses. So maybe soon we’ll have computers with a physical basis similar to brains. Maybe I’ll finally be able to switch mine for one that hasn’t forgotten why it went to the kitchen by the time it gets there.

Another difference which may soon fade away is memory storage. At present, memory storage works very differently for computers and brains. In computers, memories are stored in specific places, for example your hard drive, where electronic voltages change the magnetization of small units called memory cells between two different states. You can then read it out again or override it, if you get tired of Kate Bush.

But in the brain, memories aren’t stored in just one place, and maybe not in places at all. Just exactly how we remember things is still subject of much research. But we know for example that motor memories like riding a bike uses brain regions called the basal ganglia and cerebellum. Short-term working memory, on the other hand, heavily uses the prefrontal cortex. Then again, autobiographical memories from specific events in our lives, use the hippocampus and can, over the course of time, be transferred to the neocortex.

As you see memory storage in the brain is extremely complex and differentiated, which is probably why mine sometimes misplace the information about why I went into the kitchen. And not only are there many different types of memory, it’s also that neurons both process and store information, whereas computers use different hardware for both.

However, on this account too, researchers are trying to make computers more similar to brains. For example, researchers from the University of California in San Diego are working in something called memcomputers, which combines data processing and memory storage in the same chip.

Maybe more importantly, the human brain has much more structure than the computers we currently use. It has areas which specialize in specific functions. For example, the so called Broca's area in the frontal lobe specializes in language processing and speech production; the hypothalamus controls, among other things, body temperature, hunger and the circadian rhythm. We are also born with certain types of knowledge already, for example a fear of dangerous animals like spiders, snakes, or circus clowns. We also have brain circuits for stereo vision. If your eyes work correctly, your brain should be able to produce 3-d information automatically, it’s not like you have to first calculate it and then program your brain.

Another example of pre-coded knowledge is a basic understanding of natural laws. Even infants understand, for example, that objects don’t normally just disappear. We could maybe say it’s a notion of basic locality. We’re born with it. And we also intuitively understand that things which move will take some time to come to a halt. The heavier they are, the longer it will take. So, basically Newton’s laws. They’re hardwired. The reason for this is probably that it benefits survival if infants don’t have to learn literally everything from scratch. I was upset to learn, though, that infants aren’t born knowing Gödel’s theorem. I want to talk to them about it, and I think nature needs to work on this.

That some of our knowledge is pre-coded into structure is probably also partly the reason why brains are vastly more energy efficient than today’s supercomputers. The human brain consumes on the average 20 Watts whereas a supercomputer typically consumes a million times as much, sometimes more.

For example, Frontier, hosted at the Oak Ridge Leadership Computing Facility and currently the fastest supercomputer in the world consumes 21MWatt on average and 29MW at peak performance. To run the thing, they had to build a new power line and a cooling system that pumps around 6000 gallons of water. For those of you who don’t know what a gallon is, that’s a lot of water. The US department of energy is currently building a new supercomputer, Aurora, which is expected to become the world’s fastest computer by the end of the year. It will need about 60MW.

Again the reason that the human brain is so much more efficient is almost certainly natural selection, because saving energy benefits survival. Which is also what I tell my kids when they forget to turn the lights off when leaving a room.

Another item we can add to the list of differences is that the brain adapts and repairs itself, at least to some extent. This is why, if you think about it, brains are much more durable than computers. Brains work reasonably well for 80 years on average, sometimes as long as 120 years. No existing computer would last remotely as long. One particularly mind blowing case (no pun intended) is that of Carlos Rodriguez, who had a bad car accident when he was 14. He had stolen the car, was on drugs, and crashed head first. Here he is in his own words.  

Not only did he survive, he is in reasonably good health. Your computer is less likely to survive a crash than you, even if it remembered to wear its seatbelt. Sometimes it just takes a single circuit to fail and it’ll become useless. Supercomputing clusters need to be constantly repaired and maintained. A typical supercomputer cluster has more than a hundred maintenance stops a year and requires a staff of several hundred people. Just to keep working.  

To name a final difference between the ways that brains and computers currently work: brains are still much better at parallel processing. The brain has about 80 billion neurons, and each of them can process more than one thing at a time. Even for so-called massively parallel supercomputers these numbers are still science fiction. The current record for parallel processing is the Chinese supercomputer Sunway TaihuLight. It has 40,960 processing modules, each with 260 processor cores, which means a total of 10,649,600 processor cores! That’s of course very impressive, but still many orders of magnitude from the 80 billion that your brain has. And maybe it would have 90 billion if you stopped wasting all your time watching Doctor Who.

So those are some key differences between how brains and computers do things, now let us talk about the remaining point, what they can do.

Current computers, as we’ve seen, represent everything in bits, but not everything we know can be represented this way. It’s impossible, for example, to write down the number pi or any other irrational number in a sequence of bits. This means that not even the best supercomputer in the world can compute the area of a circle of radius 1, exactly, it can only approximate it. If we wanted to get pi exactly, it would take an infinite amount of time, like me trying to properly speak English. Fun fact: The current record for calculating digits of pi is 62.8 trillion digits.

But even though we can’t write down all the digits of pi, we can work with pi. We do this all the time, though, just among us, it isn’t all that uncommon for theoretical physicists to set pi equal to 1.

In any case, we can deal with pi as an abstract transcendental number, whereas computers are constrained to finitely many digits. So this looks like the human brain can do something that computers can’t.

However, this would be jumping to conclusions. The human brain can’t hold all the digits of pi any more than a computer. We just deal with pi as a mathematical definition with certain properties. And computers can do the same. With suitable software they are capable of abstract reasoning just like we are. If you ask your computer software if pi is a rational number it’ll hopefully say no. Unless it’s kidding in which case maybe you can think of more interesting conversation to have with it.

This brings us to an argument that Penrose has made, that human thought can’t be described by any computer algorithm. Penrose’s argument is basically this. Gödel showed that any sufficiently complex set of mathematical axioms can be used to construct statements which are true, but their truth is unprovable within that system of axioms. The fact that we can see the truth of any Gödel sentence, by virtue of Gödel’s theorem, tells us that no algorithm can beat human thought.

Now, if you look at all that we know about classical mechanics, then you can capture this very well in an algorithm. Therefore, Penrose says, quantum mechanics is the key ingredient for human consciousness. It’s not that he says consciousness affects quantum processes. It’s rather the other way round, quantum processes create consciousness. According to Penrose, at least.

But does this argument about Gödel’s theorem actually work? Think back to what I said earlier, computers are perfectly capable of abstract reasoning if programmed suitably. Indeed, Gödel’s theorem itself has been proved algorithmically by a computer. So I think it’s fair to say that computers understand Gödel’s theorem as much or as little as we do. You can start worrying if they understand it better.

This leaves open the question of course whether a computer would ever have been able to come up with Gödel’s proof to begin with. The computer that proved Gödel’s theorem was basically told what to do. Gödel wasn’t. Tim Palmer has argued that indeed this is where quantum mechanics becomes relevant.

By the way, I explain Penrose’s argument about Gödel’s theorem and consciousness in more detail in my new book Existential Physics. The book also has interviews with Roger Penrose and Tim Palmer.

So let’s wrap up. Current computers still differ from brains in a number of ways. Notably it’s that the brain is a highly efficient multi-purpose apparatus whereas, in comparison, computers are special purpose machines. The hardware of computers is currently very different from neurons in the brain, memory storage works differently, and the brain is still much better at parallel processing, but current technological developments will soon allow building computers that are more similar to brains in these regards.

When it comes to the question if there’s anything that brains can do which computers will not one day also be able to do, the answer is that we don’t know. And the reason is, once again, that we don’t really understand quantum mechanics.

Quantum Games -- Really!

[This is a transcript of the video embedded below. Some of the explanations may not make sense without the animations in the video.]

It’s difficult to explain quantum mechanics with words. We just talked about this the other day. The issue is, we simply don’t have the words to describe something that we don’t experience. But what if you could experience quantum effects. Not in the real world, but at least in a virtual world, in a computer game? Wait, there are games for quantum mechanics? Yes, there are, and better still, they are free. Where do you get these quantum games and how do they work? That’s what we’ll talk about today.

We’ll start with a game that’s called “Escape Quantum” which you can play in your browser.

“You find yourself in a place of chaos. Whether it’s a dream or an illusion escapes you as the shiny glint of a key catches your eye. A goal, a prize, an escape, whatever it means for you takes hold in your mind as your body pushes you forward, into the unknown.”

Alright. Let’s see.

Escape Quantum is an adventure puzzle game where you walk around and have to find keys and cards to unlock doors. The navigation works by keyboard and is pretty simple and straight forward. The main feature of the game is to introduce you to the properties of a measurement in quantum mechanics, that if you don’t watch an object, its wave-function can spread out and next time you look, it may be at a different place.

So sometimes you have to look away from something to make it change place. And if there’s something you don’t want to change place, you have to keep looking at it. At times this game can be a bit frustrating because much of it is dictated by random chance, but then that’s how it goes in quantum mechanics. Once you learn the principles the game can be completed quickly. Escape Quantum isn’t particularly difficult, but it’s both interesting and fun.

Another little game we tried is called quantum playground which also runs in your browser.

Yes, hello. What you do here is that you click on some of those shiny spheres to initialize the position of a quantum particle. You can initialize several of them together. Then you click the button down here which will solve the Schrödinger equation, with those boundary conditions, and you can see what happens to the initial distribution. You can then click somewhere to make a measurement, which will suddenly collapse the wave-function and the particle will be back in one place.

There isn’t much gameplay in this one, but it’s a nice and simple visualization of the spread of the wave-function and the measurement process. Didn’t really understand what this little bird thing is.

Somewhat more gameplay is going on in the next one which is called “Particle in a Box”.  This too runs in your browser but this time you control a character, that’s this little guy here, who can move side to side or jump up and down.

The game starts with a brief lesson about potential and kinetic energy in the classical world. You collect energy in terms of a lightning bolt and give it to a particle that’s rolling in a pit. This increases the energy of the particle and it escapes the pit. Then you can move on to the quantum world.

First you get a quick introduction. The quantum particle is trapped in a box, as the title of the game says. So it doesn’t have a definite position, but instead has a probability distribution that describes where it’s most likely to be if a measurement is made. Measurements happen spontaneously and if a measurement happens then one of these circles appears in a particular place.

You can then move on to the actual game which introduces you to the notion of energy levels. The particle starts at the lowest energy level. You have to collect photons, that’s those colorful things, with the right energy to move the particle from one energy level to the next. If you happen to run into a particle at a place where it’s being measured, that’s bad luck, and you have to start over. You can see here that when the particle changes to a higher energy level, then its probability distribution also changes. So you collect the photons until the particle’s in the highest energy level and then you can exit and go to the next room.

The controls of this one are little fiddly but they work reasonably well. This game isn’t going to test your puzzle-solving skills or reflexes, but does a good job in illustrating some key concepts in quantum mechanics: probability distributions, measurements, and energy levels.

The next one is called “Psi and Delta”. It was developed by the same team as “Particle in a Box” and works similarly, but this time you control a little robot that looks somewhat like BB8 from Star Wars. There’s no classical physics introduction in this one, you go straight to the quantum mechanics. Like the previous game, this one is based on two key features of quantum mechanics: that particles don’t have a definite position but a probability distribution, and that a measurement will “collapse” the wave-function and then the particle is in a particular place.

But in this game you have to do a little more. There’s an enemy robot, that’s this guy, which will try to get you, but to do so it will have to cross a series of platforms. If you press this lever, you make a measurement and the particle is suddenly in one place. If it’s in the same place as the enemy robot, the robot will take damage. If you damage it enough, it’ll explode and you get to the next level.

The levels increase in complexity, with platforms of different lengths and complicated probability distributions. Later in the game, you have to use lamps of specific frequencies to change the probability distribution into different shapes. Again, the controls can be a little fiddly, but this game has some charm. It requires a bit of good timing and puzzle solving skills too.

Next game we look at is called “Hello Quantum” and it’s a touchscreen game that you can play on your phone or tablet. You first have to download and install it, there’s no browser version for this one, but there’s one for android and one for ios. The idea here is that you have to control qubit states by applying quantum gates. The qubits are either on or off or something you don’t know. Quantum gates are the operations that a quantum computer computes with. They basically move around entanglement. In this game, you get an initial state, and a target state that you have to reach by applying the gates.

The game tells you the minimal number of moves by which you can solve the puzzle, and encourages you to try to find this optimal solution. You’re basically learning how to engineer a particular quantum state and how a quantum computer actually computes.

The app is professionally designed and works extremely well. The game comes with detailed descriptions of the gates and the physical processes behind them, but you can play it without any knowledge of qubits, or any understanding of what the game is trying to represent, just by taking note of the patterns and how the different gates move the black and white circles around. So this works well as a puzzle game whether or not you want to dig deep into the physics.

This brings us to the last game in our little review which is called the Quantum FlyTrap. This is again a game that you can play in your browser and it’s essentially a quantum optics simulator. This red triangle is your laser source, and the green venus flytraps are the detectors. You’re supposed to get the photons from the laser to the detectors, with certain additional requirements, for example you have to get a certain fraction of the photons to each detector.

You do this by dragging different items around and rotating them, like the mirrors and beam splitters and non-linear crystals and so on. In later levels you have to arrange mirrors to get the photons through a maze without triggering any bombs or mines.

A downside of this game is that the instructions aren’t particularly good. It isn’t always clear what the goal is in each level, until you fail and you get some information about what you were supposed to do in the first place. That said, the levels are fun puzzles with a unique visual style. I’ve found this to be a quite remarkable simulator. You can even use it to click together your own experiment.  

Are we too many people or too few?

[This is a transcript of the video embedded below. Some of the explanations may not make sense without the animations in the video.]

There’s too many men, too many people, making too many problems. That’s how Genesis put it. Elon Musk, on the other hand, thinks there are too few people on the planet. “A lot of people think there’s too many people on the planet, but I think there’s, in fact, too few.” Okay, so who is right? Too many people or too few? That’s what we’ll talk about today.

This graph shows the increase of world population in the past twelve-thousand years. Leaving aside this dip in the 14th century when the plague wiped out big parts of the population in Europe and Asia, it looks pretty much like exponential growth.

If we extrapolate this curve, then in a thousand years there’ll be a few trillions of us! But this isn’t how population growth works. Sooner or later all species run into resource limits of some kind. So when will we hit ours?

When it comes to the question how close humans are to reaching this planet’s resource limits the two extremes are doomsters and boomsters. Yes, doomsters and boomsters sound like rival gangs from a rock musical that are about to break out in song, but reality is a bit less dire. We’ll look at what both sides have to say and then we look at what science says.

The doomsters have a long tradition, going back at least to Thomas Malthus in the 18^th century. Malthus said, in a nutshell, the population is growing faster than food production and it’ll become increasingly more difficult to feed everyone. If that ever does happen, it’d be a huge bummer because, I don’t know about you guys, but I’d really like to keep eating food. Especially cheese. I’d really like to keep eating cheese.

Malthus’ problem was popularized in a 1968 book by Paul Ehrlich called The Population Bomb, title says it all. Ehrlich predicted that by the 1980s famines would be commonplace and global death rates would rise. As you may have noticed, this didn’t happen. In reality, death rates have dropped, continue to drop, and on the average calorie consumption has globally increased. Still Ehrlich claims that he was in principle right, it’ll just take somewhat longer than he anticipated.

Indeed, the Club of Rome report of 1972 predicted that we would reach the “limits to growth” in the mid 21^st century, and population would steeply decrease after that basically because we weren’t careful enough handling the limited resources we have.

Several analyses in the early 21^st century found that so far the business as usual predictions from the Club of Rome aren’t far off reality.

The Earth Overshoot Day is an intuitive way to quantify just how bad we are at using our resources. The idea was put forward by Andrew Simms from the University of Sussex and it’s to calculate by which date in each calendar year we’ve used up the resources that Earth regenerates in that year. If that date is before the end of the year, this means that each year we shrink the remaining resources which ultimately isn’t sustainable.  

In this figure you see the Earth Overshoot Days since 1970. As you can see, in the past ten years or so we used up all renewable resources in early August. In 2020, the COVID pandemic pushed that date temporarily back by a couple of days but now we’re back on track to reach Overshoot Day sooner and sooner. It’s like groundhog day meets honey, I shrunk the resources, clearly not something anyone wants.

So the doomster’s fears aren’t entirely unjustified. We’ve arguably not been dealing with our resources responsibly.  Overpopulation isn’t pretty and it’s very real already in some places. For example, the population density in Los Angeles is about 3000 people per square kilometer but that of Manila in the Philippines is more than ten times higher, a stunning 43 thousand people per square kilometer. There’s so little space, some families have settled in the cemetery. As a general rule, and I hope you’ll all agree, I think people should not have to sleep near dead bodies when possible.

Such extreme overpopulation benefits the spread of diseases and makes it very difficult to enforce laws meant to keep the environment clean, which is a health risk. You may argue the actual problem here isn’t overpopulation but poverty, but really it’s neither in isolation, it’s the relation between them. The number of people grows faster than the resources they’d need to keep the living standard at least stable. 

On the global level, the doomsters argue, the root problem of climate change and the loss of biodiversity that accompanies it is that there’s too many people on the planet.

You may have seen the headlines some years ago. “Want to fight climate change? Have fewer children!” “Scientists Say Having Fewer Kids Is Our Best Bet To Reduce Climate Change” “Science proves kids are bad for earth”. These headlines summarized a 2017 article that appeared in the magazine Environmental Research Letters. Its authors had looked at 39 peer-reviewed papers and government reports. They wanted to find out what lifestyle choices have the biggest impact on our personal share of emissions.

Turns out that recycling doesn’t make much of a difference, neither makes changing your car or avoiding transatlantic flight, which is unfortunate for those of you who are scared of flying, as not flying to protect the environment is no longer a good excuse. The one thing that really made a difference was not having children. Indeed, it was 25 times more important than the next one which was “live car free”. The key reason they arrived at this conclusion is that they assumed you inherit half the carbon emissions of your children and then a quarter of your grandchildren, etc.

Fast forward to the headlines of 2022 and we read that men are getting vasectomies so they don’t have to feel guilty if they keep driving a car. Elon Musk has meanwhile fathered eight children, though maybe by the time I’ve finished this sentence he has a few more. So let’s then look at the other side of the argument, the boomsters.

The boomsters’ fire is fueled by just how wrong both Malthus and Ehrlich were. They were both wrong because they dramatically underestimated how much technological progress would improve agricultural yield and how that in return would improve health and education and lead to more technological progress. Boomsters extrapolate this past success and argue that human ingenuity will always save the day.

To illustrate this point, the economist Julian Simon has developed what’s called the Simon Abundance Index. You may think it tells you if there is an abundance of Simons, but no, it tells you instead the abundance of 50 basic commodities and their relation to population growth. His list of basic commodities contains every-day needs such as uranium, platinum, and tobacco, but doesn’t contain cheese. Seems that Mr Simons and I don’t quite have the same idea of basic commodities.

The index is calculated as the ratio of the price of the commodity and the average hourly wage, so basically it’s a measure of how much of the stuff you’d be able to buy.

The index is normalized to 1980 which marks one hundred percent. In 2020, the index reached 708 point 4 percent. And hey, the curve goes mostly up, so certainly that’s a good thing. Boomsters like to quote this index to prove something.

Now, this seems a little overly simplistic and you may wonder what the amount of tobacco you can buy with your earnings has to do with natural resources. Indeed, if you look for this index in the scientific literature you won’t find it – it isn’t generally accepted as a good measure of resource abundance. What it captures is the tendency of technology to increase efficiency, which leads to dropping prices so long as resources are available. Tells you nothing about how long the resources will last.

However, the boomsters do have a point in that pessimistic predictions from the past didn’t come true and that underpopulation is also a problem. Indeed, countries like Canada, Norway, and Sweden, have an underpopulation problem in their northern territories. It’s just hard to keep up living standards if there aren’t enough people to maintain them, that’s true for infrastructure but also education and health services. A civilization as complex as the one we currently have would be impossible to maintain with merely some million people. There’d just not be enough of us to learn and carry out all the necessary tasks, like making youtube videos!

Another problem is the age distribution. For most of history, it’s had a pyramid shape, with more young people than old ones. This example shows the population pyramid for Japan and how it changed in the past century. When people have fewer children this changes to an “inverted pyramid”, with more old people than young ones, which makes it difficult to take proper care of the elderly.

The transition is already happening in countries such as Japan and South Korea and will soon happen in most of the developed world. But the inverted pyramid comes from decrease in population, not from underpopulation, so it’s a temporary problem that should resolve once a population stabilizes.

Okay, so we’ve seen what the doomsters and boomsters say, now let’s look at what science says.

A useful term to talk about overpopulation is the “carrying capacity” of an ecosystem, that is the maximum population of a given organism that the ecosystem can sustain indefinitely. So what we want to know is the carrying capacity of Earth for humans.

Scientists disagree about the best and most accurate way of determining that number and estimates vary dramatically. Most estimates lie  in the range between four and 16 billion people, but some pessimists say the carrying capacity is more like 2 billion so we’ve long exceeded it and some optimists think we can squeeze more than 100 billion people on the planet.

These estimates vary so much because they depend on factors that are extremely hard to predict. For example, how many people we can feed depends on what their typical diet is. Earth can sustain more vegans than it can sustain Jordan Petersons who eat nothing but meat, though some of you may think even one Jordan Peterson is too much. And of course the estimates depend on how quickly you think technology improves together with population increase which is basically guesswork.

The bottom line is that the conservative estimate for the carrying capacity of earth is roughly the current population, but if we’re very optimistic we might make it to a hundred billion. Another thing we can do is try to infer trends from population data.  

The graph I showed you in the beginning may look like an exponential increase, but this isn’t quite right. If you look at the past 50 years in more detail you can see that the rate of growth has been steady at about one billion people every 12 years. That’s not exponential. What’s going on becomes clearer if we look at the fertility rate in different regions of the planet.

The fertility rate is what demographers call the average number of children a woman gives birth to. If the number falls below approximately 2 point 1, then the size of the population starts to fall. The 2.1 is called replacement level fertility. It’s worth mentioning that the 2.1 is the replacement fertility in developed countries with a low child mortality rate. If child mortality is high, the replacement fertility level is higher.  

Current fertility rates differ widely between different nations. In the richest nations, fertility rates have long dropped below the replacement level for example, the current fertility rate in the USA is 1.81 and in Japan 1.33. But in the developing world fertility rates are still high for example in Afghanistan 6.01; and in /niːˈʒeə/ 7.08. How is this situation going to develop?

We don’t know, of course, but we can extrapolate the trends. In October 2020, The Lancet published the results of a massive study in which they did just that. A team of researchers from the University of Washington made forecasts for population trends in 185 countries from the present to the year 2100. They used several models to forecast the evolution of migration, educational attainment, use of contraceptives, and so on, and calculated the effects on life expectancy and birth rate.

According to their forecast, global population will peak in the year 2064 at 9.73 billion and gradually decline to 8.79 billion by 2100. By then, the fertility rate will have dropped to only 1.66 globally (95% 1.33-2.08).

This is remarkably consistent with the Club of Rome report. They also looked at individual countries. For example, by 2100 China is forecasted to decrease its population by 48 percent to the small, measly number of 732 million people. No wonder Xi Jinping is asking Chinese people to have more babies.

Both the US and the UK are expected to keep roughly the same population thanks mostly to immigration. Japan is expected to stay at its current low fertility rate and consequently its population will decrease from the current 128.4 million to only 59.7 million.

Just a few weeks ago Musk commented on this, claiming that Japan could “cease to exist”. Well, we have seen that Japan will indeed likely halve its population by the end of the century and if you extrapolate this trend indefinitely then, yeah, it’ll cease to exist. But let’s put the numbers into context.

This figure shows the evolution of the Japanese population from 1800 to the present. It peaked around 10 years ago at about 130 million. If that doesn’t sound like much, keep in mind that Japan is only about half the size of Texas. This means its population density is currently about ten times higher than that of the United States. The Lancet paper forecasts that Japan will remain the world’s 4th largest economy even after halving its population and no one expects the population to continue shrinking forever. So the future looks nice for Japanese people, regardless of what Musk thinks.
 

What’s with Europe? The population of Germany is expected to go from currently 83 to 66 million people in 2100. Spain and Portugal will see their population cut by more than half. But this isn’t the case in all European countries, especially those up north can expect moderate increases. Norway, for example, is projected to go from currently 5.5 to about 7 million, and Sweden from currently 10 to 13 million.

But the biggest population increase will happen in currently underdeveloped areas thanks to both high fertility rates and further improvements in living conditions. For example, according to the Lancet estimates Nigeria will increase from currently 206 million to a staggering 791 million. That’s right, by 2100 there will be more Nigerians than Chinese. Niger will explode from 21 to 185 million.

Overall the largest increase will be in sub-Saharan Africa, which will go from currently 1 billion to 3 billion, but even there the fertility rate is projected to decrease below the replacement rate by the end of the century. If you want to check the fertility forecast for your country just check out the paper.

Those extrapolations assumed business as usual. But the same paper also considers an alternative scenario in which the United Nations Sustainable Development Goals for education and contraceptive are met. In that case the population would start decreasing much sooner, peak in 2046 at 8.5 billion and by the year 2100 the world population would be between 6.3 and 6.9 billion.

What do we learn from this? According to the conservative estimates for the carrying capacity of the world and extrapolations for population trends, it looks like the global population is going to peak relatively soon below carrying capacity. Population decrease is going to lead to huge changes in power structures both nationally and internationally. That’ll cause a lot of political tension and economic stress. And this doesn’t even include the risk of killing off a billion people or so with pandemics, wars, or a major economic crisis induced by climate change.

So both the doomsters and boomsters are wrong. The doomsters are wrong to think that overpopulation is the problem, but right in thinking that we have a problem. The boomsters are right in thinking that the world can host many more people but wrong in thinking that we’re going to pull it off.  

And I’m afraid Musk is right. If we’d play our cards more wisely, we could almost certainly squeeze some more people on this planet. And seeing that the most relevant ingredient to progress is human brains, if progress is what you care about, then we’re not on the best possible track.

An update on the status of superdeterminism with some personal notes

In December I put out a video on superdeterminism that many of you had asked for. I hesitated with this for a long time. As you have undoubtedly noticed, I don’t normally do videos about my own research. This is because I can’t lay out all the ifs and buts in a 10 minutes video, and that makes it impossible to meet my own scientific standards.

I therefore eventually decided to focus the video on the most common misunderstandings about superdeterminism, which is (a) that superdeterminism has something to do with free will and (b) that it destroys science. I sincerely hope that after my video we can lay these two claims to rest.

However, on the basis of this video, a person by name Bernado Kastrup chose to criticize my research. He afterwards demanded on twitter that we speak together about his criticism. I initially ignored him for several reasons.  

First of all, he got things wrong pretty much as soon as he started writing, showing that he either didn’t read my papers or didn’t understand them. Second, a lot of people pick on me because they want to draw attention to themselves and that’s a game I’m not willing to take part in. 

Third, Kastrup has written a bunch of essays about consciousness and something-with-quantum and “physicalism” which makes him the kind of person I generally want nothing to do with. Just to give you an idea, let me quote from one of his essays:

“Ordinary phenomenal activity in cosmic consciousness can thus be modelled as a connected directed graph. See Figure 1a. Each vertex in the graph represents a particular phenomenal content and each edge a cognitive association logically linking contents together.”

And here is the figure: 

Hence, in contrast to what Kastrup accused me of, the reason I didn’t want to talk to him was not that I hadn’t read what he wrote, but that I had read it.

The fourth and final reason that I didn’t want to talk to him is that I get a lot of podcast request, and I don’t reply to most of them simply because I don’t have the time.

I consulted on this matter with some friends and collaborators, and after that decided that I’d talk to Kastrup anyway. Mostly because I quite like Curt Jaimugal who offered to host the discussion and who I’d spoken with before. He’s a smart young man and if you take away nothing else from this blogpost, then at least go check out his YouTube channel which is well worth some of your time. Also, I thought that weeding out Kastrup’s understandings might help other people, too.

A week later, the only good thing I can report about my conversation with Kastrup is that he didn’t bring up free will, which I think is progress. Unfortunately, he didn’t seem to know much about superdeterminism even after having had time to prepare. He eventually ran out of things to say and then accused me of being “combative” after clearly being surprised to hear that an interaction with a single photon isn’t a measurement. Srsly. Go listen to it.

Instead of concluding that he’s out of his depth, he then wrote another blogpost in which he accused me of “misleading, hollow, but self-confident, assertive rhetoric”, claimed that “Sabine has a big mouth and seems to be willing to almost flat-out lie in order to NOT look bad when confronted on a point she doesn’t have a good counter for.” And, “Her rhetorical assertiveness is, at least sometimes, a facade that hides a surprising lack of actual substance.”

Keep in mind that this is a person who claimed to “model” the “phenomenal activity in cosmic consciousness” with 16 circles. Speak of lack of substance.

Lesson learned: I was clearly too optimistic about the possibility of rational discourse, and don’t think it makes sense to further communicate with this person.

Having said that, I gather that some people who watched the exchange were genuinely interested in the details, so I want to add some explanations that didn’t come across as clearly as I hoped they would.

First of all, the reason I am interested in superdeterminism has nothing whatsoever to do with physicalism or realism (I don’t know what these words mean to begin with). It’s simply that the collapse postulate in quantum mechanics isn’t compatible with general relativity because it isn’t local. That’s a well-defined mathematical problem and solving such problems is what I do for a living.

Note that simply declaring that the collapse isn’t a physical process doesn’t explain what happens and hence doesn’t solve the problem. We need to have some answer for what happens with the expectation value of the stress-energy-tensor during a measurement. I’m an instrumentalist; I am looking for a mathematical prescription that reproduces observations, one of which is that the outcome of a measurement is a detector eigenstate.

The obvious solution to this problem is that the measurement process which we have in quantum mechanics is an effective, statistical description of an underlying local process in a hidden variables theory. We know from Bell’s theorem (or its observed violations, respectively) that if a local theory underlies quantum mechanics then it has to violate statistical independence. That’s what is commonly called “superdeterminism”. In such theories the wave-function is an average description, hence not “real” or “physical” in any meaningful way.

So: Why am I interested in superdeterminism? Because general relativity is local. It is beyond me why pretty much everybody else wants to hold onto an assumption as problematic and unjustified as statistical independence, and is instead willing to throw out locality, but that’s the situation.

Now, the variables in this yet-to-be-found underlying theory are only “hidden” in so far as that they don’t appear in quantum mechanics; they may well be observable with suitable experiments. This brings up the question what a suitable experiment would be.

It is clear that Bell-type tests are not the right experiments, because superdeterministic theories violate Bell inequalities just like quantum mechanics. In fact, superdeterministic theories, since they reproduce quantum mechanics when averaged over the hidden variables, will give the same inequality violations and obey the same bounds as quantum mechanics. (Some people seem to find this hard to understand and try to impress me by quoting other inequalities than Bell’s. You can check for yourself that all those inequalities assume statistical independence, so they cannot be used to test superdeterminism.)

This is why, in 2011, I wrote a paper in which I propose a mostly model-independent test for hidden variables that relies on repeated measurements on non-commuting variables. I later learned from Chris Fuchs (see note at end of paper) that von Neumann made a similar proposal 50 years ago, but the experiment was never done. It still hasn’t been done.

A key point of the 2011 paper was that one does not need to make specific assumptions about the hidden variables. One reason I did this is that Bell’s theorem works the same way: you don’t need to know just what the hidden variables are, you just need to make some assumptions about their properties.

Another reason is, as I have explained in my book “Lost in Math”, that math alone isn’t sufficient to develop a new theory. We need data to develop the underlying hidden variables theory. Without that, we can only guess models and the chance that any one of them is correct is basically zero. 

This is why I did not want to develop a model for the hidden variables – it would be a waste of time. It didn’t work for phenomenology beyond the standard model and it won’t work here either. Instead, we have to identify the experimental range where evidence could be found, collect the data, and then develop the model.

Unfortunately and, in hindsight, unsurprisingly, the 2011 paper didn’t go anywhere. I think it’s just too far off the current mode of thinking in physics, which is all about guessing models and then showing that those guesses are wrong, a methodology that works incredibly badly. Nevertheless, I have since spent some time on developing a hidden variables model, but it’s going slowly, partly because I think it’s a waste of time (see above), but also because I am merely one person working four jobs while raising two kids and my day only has 24 hours.

However, in contrast to what Kastrup accuses me of, I have repeatedly and clearly stated that we do not have a satisfactory superdeterministic hidden variables model at the moment. I say this in pretty much all of my talks, it’s explicitly stated in my paper with Tim (“These approaches [...] leave open many questions and it might well turn out that none of them is the right answer.”). I also said this in my conversation with Kastrup. 

But I want to stress that the reason I (and quite possibly others too) didn’t write down a particular hidden variables model is not that it can’t be done, but that there are too many ways it could be done.

Next thing he got confused about is that two years ago, Sandro an I cooked up a superdeterministic toy model. The point of this model was not to say that it should be experimentally tested. We merely put this forward to demonstrate once and for all that superdeterministic models do not require “finetuning” or any “conspiracies”.

The toy model reproduces quantum mechanics exactly, but – in contrast to quantum mechanics – it’s local and deterministic, on the expense of violating statistical independence. Since it reproduces quantum mechanics it’s as falsifiable as quantum mechanics, hence the claim that superdeterminism somehow ruins science is arguably wrong. 

But besides this, it is a rather pointless and ad hoc toy model that I don’t think makes a lot of sense for a number of reasons (which are stated in the paper). Still, it demonstrates that of course if you want to then can define your hidden variables somehow. I should also mention that our model is certainly not the first superdeterministic hidden variables model. (See references in paper.)

There are a lot of toy models in quantum foundations like this with the purpose of shedding light on one particular assumption or another, and my model falls in this tradition. I could easily modify this model so that it would make predictions that deviate from quantum mechanics, so that one could experimentally test it. But the predictions would be wrong, so why would I do this.

Having said that, my thinking about superdeterminism has somewhat changed since 2011. I was at the time thinking about the hidden variables the way that they are usually portrayed as some kind of extra information that resides within particles. I have since become convinced that this doesn’t work, and that the hidden variables are instead the degrees of freedom of the detector. If that is so, then we do know what the hidden variables are, and we can estimate how likely they are to change. Hence, it becomes easier to test the consequences.

This is why in my later papers and in my more recent talks I mention a simpler type of experiment that works for this case – when the hidden variables are the details of the detector – specifically. I have to stress though that there are other models of superdeterminism which work differently and that can’t be tested this way.

Just what the evolution law looks like I still don’t know. I think it can’t be done with a differential equation, which is why I have been looking at path integrals. I wrote a paper about this with Sandro recently in which we propose new path-integral formalism that can incorporate the required type of evolution law. (It was just accepted for publication the other day.)

What we do in the paper is to define the formalism and show that it can reproduce quantum mechanics exactly – a finding I think is interesting in and by itself. As Kastrup said entirely correctly, there are no hidden variables in that paper. I don’t know why he thought there would be. The paper is just not about hidden variables theories. 

I have a number of ideas of how to include the detector degrees of freedom as hidden variables into the path integral. But again the problem isn’t that it can’t be done, but that there are too many ways it can be done. And in none of the ways I have tried can you still calculate something with the integral. So this didn’t really go anywhere – at least so far. It doesn’t help that I have no funding for this research.

I still think the best way forward would be to just experimentally push into this region of parameter space (small, cold system with quick repeated measurements on simple states) and see if any deviations from quantum mechanics can be found. However, if anyone reading is interested in helping with the path integral, please shoot me a note because I have a lot more to say than what’s in our papers.

Finally, I have given a number of seminars about superdeterminism, at least one of which is on YouTube here. I also some months ago did a discussion with Matt Leifer which is here. Leifer, I would say, is one of the leading people in the foundations of quantum mechanics at the moment. I may have some disagreements with him but he knows his stuff. You will learn more from him than from Kastrup.

In case you jumped over some of the more cumbersome paragraphs above, here is the brief summary. You can either go off the deep end and join people like Kastrup who complain about “physicalism”, claim that photons are observers, detectors can both click and not click at the same time, and other bizarre consequences you have to accept if you insist that quantum mechanics is fundamental. 

Or you conclude, like I have, that quantum mechanics is not a fundamental theory. In this case we just need to find the right experiment get a handle on the underlying physics.