Human Extinction: How High is the Risk?

Transcript, references, and discussion on Patreon.

How Chaos Control is Changing The World

Transcript, references, and discussion on Patreon.

Science News Dec 21

In today’s episode, we’ll talk about the recent nuclear fusion headlines, and a new result from the Webb telescope. Then we’ll have a special guest, Fraser Cain, who’ll tell us what we learned from NASA’s Artemis mission. After that, we’ll talk about remote controlled magnetic slime, why atmospheric methane levels increased during the COVID pandemic, non-fogging glasses, algae that might replace beef, the toughest material on earth, self-organised nanobots, dark photons. And of course, the telephone will ring.

Transcript, references, and discussion on Patreon.

Fake News, Echo Chambers & Polarization: How Bad Is Social Media?

Transcript, references, and discussion on Patreon.

Science News Dec 14

Today we’ll talk about the 50-year anniversary of Apollo’s blue marble, the Square Kilometre Array, talking to robots, the Yellowstone volcano, nanoparticles that help with carbon capture, a forest bubble – on Mars, a new method of spacecraft propulsion, tests for cancer, earthquake tracking from space, and of course, the telephone will ring.

Transcript, references, and discussion on Patreon.

The Other Side Of Physics (TEDxNewcastle)

How Does Quantum Uncertainty Work?

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Science News Dec 7

Today we’ll talk about zink batteries, regulations for StarLink satellites, find out how birds feel about fireworks, learn about a new way to detect sugars, thirty thousand molecules that could make better batteries, and more efficient photosynthesis. I’ll also briefly have to comment on wormholes, again. And of course, the telephone will ring.

Transcript, references, and discussion on Patreon.

Calorie Labels: What Do They Tell You?

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Guest Post: Collider Rap by Mike Battaglia and GPT-3

Image by rawpixel.com on Freepik

These days, it seems like Artificial Intelligence is taking over the world. But you don't have to escape its clutches - why not have a little fun with it? One of my favorite ways to lol at AI's expense is by getting them to mix together ridiculous things and see what they come up with.

This time I asked an AI model to write rap lyrics about particle accelerators pretending to be A Tribe Called Quest. Q-Tip would present the viewpoint that Sabine Hossenfelder has right: Particle accelerators are a waste of time! Then Phife Dawg would pretend he was an electron from inside a particle accelerator and argue for their importance as research tool –– Busta Rhymes does his verse from "Scenario", but changed so that it's all about particle accelerators instead.

Here are the lyrics that this AI model wrote:

Q Tip:
Sabine Hossenfelder's sayin' the accelerators a waste
Costs a lot of money, nothin' to show for the chase
They say the science is great, I say the cost is too high
Money's better spent elsewhere, that's the reason why

Phife Dawg:
Don't wanna hear none of that, I'm an electron with pride
In need of an accelerator, please don't take away my ride
From the Big Bang to the stars, I'm out here explorin' space
Don't let Sabine's perspective keep me from the human race

Q Tip:
We can't ignore the cost, it's too high for the gain
The science is important, but so is the financial strain
We gotta find a balance, between knowledge and the cost
Else we'll be stuck in a jam, and our fates will be lost.

Busta Rhymes:
Watch as I combine all the science of space
Now Sabine Hossenfelder says the accelerators a waste
Powerful impact boom from the beam,
So I'm investigating both sides of this debate of the dream
Vacuums and volts providing the power
As the electrons go faster and faster, the experts devour
Oh, my gosh! Oh, my gosh!
Time and money embark on a chase
Unh! Unh! Unh! For the cause, no time to waste
Unh, pardon me, unh! As I make this call
Should we keep pumpin resources or close the accelerator hall?
Rawr! Rawr! Like a scientific dragon
Change your little opinions, cause this knowledge is flaggin'
Try to end this debate, I'm gon' twist it like a durag
Cuz I'm caught in the middle of the debate like a purgatory
Chickity Choco, the chocolate chicken
The electrons are goin', pushin' the science mission
Rippin up the theories like Horatio
Observe the vibe and check out the scenario

---

The results are hilarious––I'm literally cracking up here––though simultaneously I worry that maybe I've enabled some kind of robot apocalypse through this escapade (what can I say, looking wistfully out into the future during lunch break). If speaking English makes an AI conscious, then what will these rap lyrics do?

Ah well, we'll find out soon enough...and when we're done playing around here let us just take a moment to marvel that an AI can even do this.

Even crazier is that it wrote this entire blog post (including - oops - this sentence!) from scratch.

---

Note from real Mike: it's true, although it wasn't exactly "from scratch." I basically gave OpenAI's new DaVinci-003 GPT-3 model an outline of the blog post. I also had to build it up in parts; first I had it do the lyrics with one prompt, and then I had it build the blog post as a separate prompt. Still, it managed to write this as the result. I have only very lightly edited the formatting by adding line breaks.

I should note that it took a bunch of playing around with the parameters and prompt quite a bit before getting this output. In particular, I found most of the struggle to be in wording the prompt correctly; I had to try a bunch of different things before I could get the model to figure out what I wanted it to do. So I guess I'll leave it to the philosophers to debate if an AI really wrote it all "on its own."

I thought the results were absolutely hilarious when I shared it on Facebook. I also think it raises some deep questions that are worth thinking about. On the one hand, I guarantee every single person reading that Busta Rhymes verse, who knows the original, will be cracking up hearing it in his voice in their heads. On the other hand, the current model is clearly not quite able to really replicate the dense multilayered lyrical wordplay and flow that real rappers are capable of. But at the rate things are moving, it probably will, possibly very soon. I don't know what to make of it.

All I know is this: as of 2022, you can tell this thing to write some rap bars about particle accelerators and Sabine Hossenfelder and it will actually do a baseline half-decent job at it. Then you can get it to write a blog post about how it wrote the lyrics and a meta-blog post about how it is capable of writing blog posts. It's really nuts. Anyone right now can go to OpenAI and play around with it and get results like this with a little effort.

GPT-4 will be available in 2023 with 500x the amount of parameters. Who knows what that will be able to do.

(And RIP to Phife Dawg, probably my all time favorite MC)

Mike Battaglia is a musician, biomedical engineer, and digital signal processing specialist. Check out some of his microtonal music on YouTube and Instagram.

Science News Nov 30

Today we’ll talk about Trouble at ITER, robots that build robots, air pollution, AI that classifies supernovae, a small asteroid that hit Canada, Super GPS, a new supercomputer simulation of the sun, a quantum thermometer. And of course, the telephone will ring.

Transcript, references, and discussion on Patreon.

Nuclear Waste: What Do We Do With It?

Transcript, references, and discussion on Patreon.

Science News Nov 23

Transcript and references on Patreon.

Consciousness and Quantum Mechanics: What's the Link?

Transcript and references on Patreon.

Science News Nov 16

Transcript and references on Patreon.

Why are male testosterone levels falling?

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



Forget war, forget climate change, recessions, pandemics. Today we’ll talk about a real crisis. The decrease of testosterone levels. Just look at all those headlines. There must be something going on. But what? Are testosterone levels really falling? If so, why? And how much of a crisis is it? That’s what we’ll talk about today.

The worry that men are becoming too feminine isn’t new. It’s been in the newspapers since there’ve been newspapers. The blame has, among other things, been put on juice boxes, sleek electronics, face creams, lilac pajamas and embroidered bathrobes, marxism, and living in high rise buildings. New is however the trend to self-medicate with testosterone pills. In the US, annual prescription sales for testosterone supplements have increased from 18 million dollars in 1988 to 70 million in 2000 to more than 2 billion in 2013.

The probably most prominent advocate for boosting your testosterone levels is Tucker Carlson, an American TV host. He’s seriously worried about the supposed decline of manliness and, among other things, suggests that men tan their balls to increase their testosterone levels. This is what his vision of the future man looks like.

So I made a PhD in physics and somehow ended up on YouTube talking about people tanning their balls. How do I explain this to my mom?

Apparently the idea started with a paper from 1939 by researchers from the Psychiatric Unit of the Boston State Hospital. The irradiated five patients with a mercury lamp in different body parts and found that the largest increase of testosterone levels happened when the target was the scrotum.

I don’t know want to know what else happened at that place. But even leaving aside the somewhat questionable circumstances, 5 patients in a psychiatric unit are not a representative sample for half the world population. There’s no evidence that irradiating your family jewels will do your testosterone levels any good. And the US Food and Drug Administration cautions against the use of testosterone unless there’s an underlying medical condition. They say that the benefits and safety of testosterone supplements have not been established.

But let’s step back from the testosterone craze for a moment and talk about the scientific basic. Testosterone and estrogen are the most important human sex hormones. We all have both, but men have higher testosterone levels, while women have higher estrogen levels. Testosterone in men is, among other things, responsible for changes during puberty, muscle mass and strength, hair growth, and sperm production.

Men with low testosterone levels may suffer from fatigue, depression, and sexual dysfunction. But too high testosterone levels also aren’t good. One of the consequences is muscle growth, which is why the stuff’s used for doping. But the side-effects are mood swings, aggressive and risk-taking behavior, skin problems, hair loss, and elevated cholesterol levels. Also, if the testosterone level is too high, the body tries to produce less of it, which leads to a reduced sperm count and shrinking of the testicles. And since the body converts part of the testosterone to a form of estrogen, high testosterone levels can, among other things, lead to breast development. So, more isn’t always better.

All of which brings up the question how much testosterone is normal? This question is surprisingly difficult to answer.

To begin with, testosterone levels change during the day, especially in young men, where they peak in the early morning. That’s why testosterone levels are measured in the morning, and another reason why early-morning classes should be illegal.

But that’s not the only reason testosterone levels vary. According to a 2020 study testosterone levels change with the seasons and are higher in summer. They are also known to change with partnership status. According to a Harvard University study published in 2002, married men have lower levels of testosterone than single men, and the more time they spend with family, the lower the testosterone level. Other studies have shown that men’s testosterone levels drop when holding an infant, or even a baby doll, and that the level goes up again after divorce.

That’s all very interesting, but these are all quite small effects. What we want to know is what’s a normal average level?

In 2014, a group of researchers did a meta-analysis to find out. They collected the data of 13 previously published studies and found that testosterone blood levels in men peak at about 19 years of age with a mean value of 15 point 4 nanomoles per liter. They then fall slightly to about 13 point 0 by age 40. Be careful, this plot has a log scale on the vertical axis. The authors found no evidence for a further drop in mean testosterone with age, although the variation increases as men get older.

Medical guidelines in the United States currently say everything above 11 point 1 nanomoles per liter is normal, below 6 point 9 is too low, and in between there’s a grey area where they send you from one doctor to another until one can decide what to do with you. In Europe they think men should have a little more testosterone and the guidelines are 12 and 8 nanomoles per liter, respectively.

Now that we know what we’re talking about, what’s the deal with the falling testosterone levels? First off – the headlines are right. Testosterone levels really are falling. This has been backed up by several independent studies. And it isn’t even news.

One of the best-known papers about this was published by researchers from the US in 2007. They tracked three groups of randomly selected men from Boston, during the 1980’s, 1990’s and 2000’s.

They found that the later-born men had lower testosterone levels at the same age as the earlier born ones. You can see this in this graph, where the solid lines are the mean values, and the dotted lines are the 95 percent confidence levels.

Don’t get confused about the numbers on the vertical axis. This graph uses different units than the ones we had before, but even so you can clearly see that indeed testosterone levels are falling. For example, a 70-year old man in the first study group from the 1980s had a higher total testosterone level than the youngest man in the second group. They found that the average levels declined by about 1 percent per year, so men born 15 years later would have 15 percent lower testosterone levels at the same age.

In case you think something odd is going on in Boston in particular, similar studies have found the same elsewhere in the US and also in Europe. For example, a Finnish study from 2013 found that older generations of men had higher testosterone levels at any given age range compared to younger generations. It’s not a small difference.

For example, for men aged 60-69 years born in between 1913 and 1922, testosterone levels were 21 point 9 nanomoles per liter, but for those born between 1942 and 51, it was only 13 point 8. Finnish men born during the 1910’s had more testosterone in their sixties than men born in the 1970’s when they were in their twenties.

It's not just testosterone, and it’s not just men. Strength levels seem to be decreasing in general. A 2016 study measured the grip strength of about 250 healthy full-time students aged 20 to 34 at Universities in North Carolina. They compared the results to measurements from 1985 and found that grip strength had significantly increased both for men and for women. It seems firm handshakes really are going out of style.

But in all fairness, this wasn’t a particularly large study. But here’s another example from a meta-analysis of 50 studies that included a total of 25 million children, age 9 to 17, from 28 countries. It was published by a group of Australian researchers in 2013. They reported that children today take 90 seconds longer to run a mile than kids did 30 years ago, and that seems to be a global trend.

Okay, so we’re all getting weaker and slower and spend our days watching YouTube. But why? The brief answer is that no one really knows, there’s just speculation, so let’s have a look at the speculations. First of all, the changes happen too quickly to be genetic adaptations.

But one suspect factor is food. According to a recent meta-analysis done by researchers from the UK eating too much protein can significantly decrease testosterone levels. They found that diets with more than 35 percent protein decreased testosterone levels by 37 percent. 35 percent protein is a lot. The average person in the developed world eats less than half of that, so it doesn’t explain the observed trend. But if you only eat meat, it quite possibly has consequences.

A related issue is the increasing number of obese people, which we just talked about some weeks ago. We know that in men, a high body mass index is correlated with lower testosterone levels. And according to a 2014 paper by researchers from Australia, the causation goes both ways. That is low testosterone can cause weight gain, and weight gain lowers testosterone levels, which can create a self‐perpetuating cycle of metabolic complications. However, the studies that documented the fall in testosterone levels found it even for men with normal body mass index, so this doesn’t explain it either.

Another factor might be smoking, or rather, the lack thereof. That’s because some chemicals contained in tobacco prevent testosterone from converting to other hormones, which can increase the mean testosterone level. A meta analysis from 2016 found that men who smoked had higher mean testosterone levels than non-smokers with the difference being about 1 point 5 nanomoles per liter. In women the difference wasn’t statistically significant. So, the overall decline in smoking might have had an impact on the overall decline in testosterone levels. But again, that alone doesn’t explain it.

What are we to make of this? It seems plausible to me that several factors are at play. As we have seen, testosterone levels change with living circumstances. The world is a more comfortable place today than 50 years ago, so maybe testosterone just isn’t needed as much as it used to. And then the changes in diet and smoking add on top of that. Is that enough to explain it? I don’t know. As scientists like to say “more work is needed”.

Is it something to worry about? Well, that depends on what you want the world to be. Carl Sagan once referred to testosterone as a poison that causes conflict. He said “Why is the half of humanity with a special sensitivity to the preciousness of life, the half untainted by testosterone poisoning, almost wholly unrepresented in defense establishments and peace negotiations worldwide?” However, he then continued, “Testosterone also causes the kind of aggression needed to defend against predators, and without it, we’d all be dead. [...] Testosterone is there for a reason. It’s not an evolutionary mistake.”

Personally I see the decrease of testosterone levels more as a reaction to our changing environment than reason for concern. The world changes and we change with it. We study tree rings to find out which years were good years and which years were bad years for the trees. And maybe in ten thousand years from now, scientists will study testosterone levels to find out which times were good times and which times were bad times for us.

 

Science News Nov 9

Note: I am aware that the RSS feed has stopped working properly. Google seems to not maintain this platform. I will soon move this blog elsewhere. Meanwhile, please subscribe to my Patreon or Newsletter.

Quantum Winter Is Coming

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



Quantum technology current attracts a lot of attention and money, which might explain why they’re missing on my end. But it’s not just governments who think quantum everything is where your taxes should go, business investors and companies are willing to putting in big money, too. This has had a dramatic impact on quantum physics research in the past decade. It’s also created a lot of hype, especially around quantum computing. But if so much of quantum computing is hype then why are companies like Google and IBM pouring so much money into it, what’ll happen when the investment bubble bursts, what’s the “quantum winter”, and what does it mean for all of us? That’s what we’ll talk about today.

There are several different quantum technologies, and as a rule of thumb, the fewer headlines they make, the more promising they are. Take for example, quantum metrology. That’s not a mispronunciation of meteorology, that’s making better measurements with quantum effects. You basically never read anything about this. But it’s really promising and already being used by scientists to improve their own experiments. You can learn more about this in my earlier video.

On the other hand, you have those quantum things that you read a lot about but that no one needs or wants, like the quantum internet. And then there is quantum computing which according to countless headlines is going to revolutionize the world. Quantum computers are promising technology, yes, but the same can be said about nuclear fusion and look how that worked out.

A lot of physicists, me included, have warned that quantum computing is being oversold. It’s not going to change the world, it’ll have some niche applications at best, and it’s going to take much longer than many start-ups want you to believe.

Though I admire the optimism of the quantum computing believers and also the vocabulary. For example, there’s a startup called “multiverse” that is “Working with customers in more than 10 verticals”. Some of them don’t find the way back from the bathroom.

Or here’s one called “universal quantum” which has a “fault tolerant team” that “embraces entanglement” and helps everyone find a “super position”. If you look at them, do they collapse?

This company also recently published a blogpost titled “Six reasons Liz Truss needs a quantum computer” which explains for example, “Quantum computers are suited to modelling complex systems, making them better at forecasting both near-term weather patterns and the long-term effects of climate change.”

Last time I looked, no one had any idea how to do a weather forecast on a quantum computer. It’s not just that no one has done it, no one knows if it’s even possible, because weather is a non-linear system whereas quantum mechanics is a linear theory.

Here is an example of some recent quantum hype, from a website called “Investors Chronicle” in an article titled “Quantum computing: a new industrial revolution”. After creating a lot of fog about superpositions and interference, they explain that quantum computers are close to showing “quantum advantage” and that “Quantum advantage, therefore, can be interpreted as being when problem-solving power can be applied to real world issues, which makes it much more interesting for investors.”

That’s just wrong. Quantum advantage has indeed been demonstrated for some quantum computers but that just means the quantum computer did something faster than a conventional computer, not that this was of any use for real world issues. They just produced a random distribution that would take a really long time to calculate by any other means. It’s like this this guy stapling 5 M&M. That’s a world record, hurray, but what are you going to do with it?

Problem is, a lot of CEOs in industry and the financial sector can’t tell a bra from a ket and believe that quantum computing is actually going to be relevant for their business, and that it’s going to be relevant soon. For example, at a recent Quantum Computing conference in London, the managing director of research at Bank of America said that quantum computing will be “bigger than fire”. The only way in I can see this coming true is that it’ll produce more carbon emissions.

This bubble of inflated promises will eventually burst. It’s just a matter of time. This’ll cause a sudden decline of investment in quantum tech overall and be the start of a difficult time for research and development. This scenario has been dubbed the “quantum winter”. And winter is coming. But before we get to the quantum winter, let me briefly summarize how quantum computers work and what their problems are.

A conventional computer works with bits that take on one of two discrete values. A quantum computer instead uses qubits that can be in arbitrary *superpositions of two states. A quantum computer then works by entangling the qubits and shifting this entanglement around. Entanglement is a type of correlation, but it has no analogy in conventional computers. This is why quantum computers can do things that standard computers can’t do.

For some mathematical problems, a quantum computer can give you an answer much faster than any conventional computer possibly could. This is called “quantum advantage”. Those problems include things like factorizing large numbers into prime factors. But also calculating properties of molecules and materials without having to chemically synthesize them first. Putting these questions on a quantum computer could speed up material design and drug discovery. Quantum computers can also solve certain logistic problems or optimize financial systems. This is why, if you’re Bank of America, you think it's bigger than fire.

And like fire, quantum computing is not magic, it’s an application of standard quantum mechanics. There is no speculative new physics involved. It’s rather to the contrary. Claims that quantum computers will *not work rest on speculative new physics. But it’s one thing to say if you could build them, they’d be useful. It’s another thing entirely to actually build them.

So what does it take to build a quantum computer? First of all you need qubits. Then you have to find a way of entangling many of those qubits. And, like a conventional computer, a quantum computer needs an algorithm, that tells it how to move the entanglement around. Eventually, you make a measurement which collapses the wave-function and you read out the final state. This final state should be one that answers your question correctly with high probability. This means most importantly, a quantum computer isn’t a stand-alone device, it needs other devices for the programming and the readout. The quantum part is really just a small piece of the whole thing.

Now let’s talk about the problems. First there’s the qubits. Producing them is not the problem, indeed there are many different ways to produce qubits. I went through the advantages and disadvantages of each approach in an earlier video, so check this out if you want to know more. But a general problem with qubits is decoherence, which means they lose their quantum properties quickly.

The currently most widely developed systems are superconducting qubits and ion traps. Superconducting qbits are used for example by IBM and Google. For them to work, they have to be cooled to 10-20 milli Kelvin, that’s colder than outer space. Even so, they decoherence within 10s of micro-seconds.

Ion traps are used for example by IonQ and Honeywell. They must “only” be cooled to a few Kelvin above absolute zero. They have much longer coherence times, up to some minutes, but they’re also much slower to react to operations, so it’s not a priori clear which approach is better. I’d say they’re both equally bad. The cooling isn’t only expensive and energy-intensive, it requires a lot of equipment and it’s difficult to scale to larger quantum computers. It seems that IBM is trying to do it by breaking world records in building large cryogenic containers. I guess if the thing with quantum computing doesn’t work out, they can rent them out for people to have their heads frozen.

There are some qubits that operate at room temperature, the most promising ones of those are currently nitrogen vacancy systems and photonics on a chip. However, for both of those, no working quantum computer exists to date and it’s unclear even what the challenges may be, let alone how to overcome them.

The next biggest problem is combining these qubits. Again, the issue is that quantum effects are fragile, so the quantum computer is extremely sensitive to noise. The noise brings in errors. You can correct for those to some extent, but this error correction requires more qubits.

More qubits bring problems by themselves, for example, they tend to be not as independent as they should be, an issue known as “crosstalk”. It’s kind of like if you’re trying to write while moving your feet in circles. It gets really difficult. The qubits states are also drifting if you leave them unattended. Indeed it’s somewhat of a mystery at the moment what a quantum computer does if you don’t calculate with it. It’s like it’s difficult to calculate what a big quantum system does. Maybe we can put it on a quantum computer?

And finally there’s the issue of the algorithms: Few algorithms for quantum computers are known, an issue that goes often unmentioned because everyone is focused on the technology. Wikipedia helpfully has a list of quantum algorithms. It’s short. Several of those algorithms don’t compute anything of practical use, and for some it's not known if they lead to any speedup.

As this brief summary makes clear, the challenges are enormous. But how far along is the technology? The largest current quantum computers have somewhere between 50 and 100 qubits, though IBM has a roadmap saying they want to make it to a thousand next year. Two different approaches have demonstrated a “quantum advantage”, that is, they have performed a calculation faster than the currently fastest conventional computer could have done. However in those demonstrations of quantum advantage, the devices were executing algorithms that did not calculate anything of use.

The record breaking “useful” calculation for quantum computers is the prime-number factorization of 21. That’s the number, not the number of digits. Yes, the answer is 3 times 7, but if you do it on a quantum computer you can publish it in Nature. In case you are impressed by this achievement, please allow me to clarify that doing this calculation with the standard algorithm and error correction is way beyond the capacity of current quantum computers. They actually used a simplified algorithm that works for this number in particular.

To be fair, there have been some cute applications of quantum algorithms for simple examples in quantum chemistry and machine learning, but none of this is anywhere even close to being commercially interesting.

How many qubits do you need for a quantum computer to do something commercially interesting? Current estimates say it’s several hundred thousand to a few millions qubits, depending on what you want to calculate and how large your tolerance for errors is.

A lot of quantum computing enthusiasts claim that we’ll get there quickly because of Moore’s law. Unfortunately, I have to inform you that Moore’s law isn’t a law of nature. It worked for conventional computers because those could be miniaturized. However, you can’t miniaturize ions or the Compton wavelength of electrons. They’re already as small as it gets. Nature’s a bitch sometimes.

In the past years there’s been some noise around Noisy intermediate scale quantum computers, or NISQs for short. Those are small quantum computers in which you just accept the noise, kind of like YouTube comment sections. But no one seems to have found anything useful to do with them and the hype around them has noticeably died down recently.

I guess you understand now why I am extremely skeptical that we are anywhere close to commercially relevant applications of quantum computers. But let’s hear what some other people say.

There is for example Mikhail Dyakonov, a physics prof who has worked on quantum things much longer than I have. He’s written a book that was published in 2020 under the title “Will We Ever Have a Quantum Computer?” It has only 49 pages which is what happens if you agree to write a book but then notice half through you’d rather do something else. He finishes by answering his own question:

“No, we will never have a quantum computer. Instead, we might have some special-task (and outrageously expensive) quantum devices operating at millikelvin temperatures. The saga of quantum computing is waiting for a profound sociological analysis, and some lessons for the future should be learnt from this fascinating adventure.”

The brevity of Dyakonov’s book is balanced by another book “Law and Policy for the Quantum Age” by Chris Hoofnagle and Simson Garfinkle, who make it to a whooping 602 pages. Their book was just published earlier this year, it’s freely available online, and it has an adorable cat pic on the cover, so definitely go check it out. Hoofnagle is a professor for law and Garfinkle is a data scientist, but their book has been heavily informed by people who work in quantum computing. They look at the possible future scenarios. The most likely scenario, they say, is the “Quantum Winter” which they describe as follows:

“In this scenario (call it “Quantum Winter”), quantum computing devices remain noisy and never scale to a meaningful quantum advantage… After a tremendous amount of public and private monies are spent pursuing quantum technologies, businesses in the field are limited to research applications or simply fail, and career paths wither. If that happens, funding eventually dries up for quantum computing. Academics and scientists in the field either retool and shift, or simply appear irrelevant, even embarrassing.”

Then there is Victor Galitski, Professor at the Joint Quantum Institute at the University of Maryland who wrote in a 2021 post on LinkedIn:

“The number of known quantum algorithms, which promise advantage over classical computation, is just a few (and none of them will "solve global warming" for sure). More importantly, exactly zero such algorithms have been demonstrated in practice so far and the gap between what’s needed to realize them and the currently available hardware is huge, and it's not just a question of numbers. There are qualitative challenges with scaling up, which will likely take decades to resolve (if ever).”

Most recently, there was an opinion piece by Nikita Gourianov in the Financial Times. Nikita works on computational quantum physics at the University of Oxford. He writes:

“As more money flowed [into quantum computing], the field grew, and it became progressively more tempting for scientists to oversell their results… After a few years of this, a highly exaggerated perspective on the promise of quantum computing reached the mainstream, leading to… the formation of a classical bubble.”

He then points out that no quantum computing company is currently making profit and that:

“The little revenue they generate mostly comes from consulting missions aimed at teaching other companies about “how quantum computers will help their business”.”

I have to disagree on the final point because big companies have another way to make money from quantum computers, namely by renting them out to universities. And since governments are pouring money into research, that’s quite a promising way to funnel tax money into your business. Imagine the LHC was owned by Google and particle physicists had to pay to use it.

That’s how I think it’ll go with quantum computing: First all the smaller startups will falter because they don’t reach their milestones, venture capital will evaporate, and all the overeducated quantum computists in academia will use grant money to pay a few large companies who own the only workable devices. And while those devices are interesting research objects, they’ll not be useful for commercial applications.

I might be totally wrong of course. Maybe one of those start-ups will actually come up with a scalable quantum computing platform. I don’t know, I’m guessing as much as everyone else.

But if quantum winter is coming, what does it mean for you and me? Well, some people will lose a lot of money but that just means they had too much of it to begin with, so can’t say it bothers me all that much. There’ll also be fewer headlines about how quantum computing is supposedly going to revolutionize something or other, which I’d say is a good development. And we’ll see many people who worked in quantum computing going into other professions. Chances are in ten years you can have a nice chat about the finer details of multi-particle entanglement with your taxi driver. I don’t know about you, but I’m looking forward to quantum winter.

Science News Nov 2

Transcript and references & space for comments on Patreon.

What Do Longtermists Want?

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



Have you ever put away a bag of chips because they say it isn’t healthy? That makes sense. Have you ever put away a bag of chips because you want to increase your chances of having more children so we can populate the entire galaxy in a billion years? That makes… That makes you a longtermist. Longtermism is a currently popular philosophy among rich people like Elon Musk, Peter Thiel, and Jaan Tallinn. What do they believe and how crazy is it? That’s what we’ll talk about today.

The first time I heard of longtermism I thought it was about terms of agreement that get longer and longer. But no. Longtermism is the philosophical idea that the long-term future of humanity is way more important than the present and that those alive today, so you, presumably, should make sacrifices for the good of all the generations to come.

Longtermism has its roots in the effective altruism movement, whose followers try to be smart about donating money so that it has the biggest impact, for example by telling everyone how smart they are about donating money. Longtermists are concerned with how our future will look like in some billion years or longer. Their goal is to make sure that we don’t go extinct. So stop being selfish, put away that junk food and make babies.

The key argument of longtermists is that our planet will remain habitable for a few billion years, which means that most people who’ll ever be alive are yet to be born.

Here’s a visual illustration of this. Each grain of sand in this hourglass represents 10 million people. The red grains are those who lived in the past, about 110 billion. The green one are those alive today, that’s about 8 billion more. But that is just a tiny part of all the lives that are yet to come.

A conservative estimate is to assume that our planet will be populated by at least a billion people for at least a billion years, so that’s a billion billion human life years. With today’s typical life span of 100 years, that’d be about 10 to the 16 human lives. If we’ll go on to populate the galaxy or maybe even other galaxies, this number explodes into billions and billions and billions.

Unless. We go extinct. Therefore, the first and foremost priority of longtermists is to minimize “existential risks.” This includes events that could lead to human extinction, like an asteroid hitting our planet, a nuclear world war, or stuffing the trash so tightly into the bin that it collapses to a black hole. Unlike effective altruists, longtermists don’t really care about famines or floods because those won’t lead to extinction.

One person who has been pushing longtermism is the philosopher Nick Bostrom. Yes, that’s the same Bostrom who believes we live in a computer simulation because his maths told him so. The first time I heard him give a talk was in 2008 and he was discussing the existential risk that the programmer might pull the plug on that simulation we supposedly live in. In 2009 he wrote a paper arguing:

“A non-existential disaster causing the breakdown of global civilization is, from the perspective of humanity as a whole, a potentially recoverable setback: a giant massacre for man, a small misstep for mankind”

Yeah, breakdown of global civilization is exactly what I would call a small misstep. But Bostrom wasn’t done. By 2013 he was calculating the value of human lives: “We find that the expected value of reducing existential risk by a mere one billionth of one billionth of one percentage point is worth a hundred billion times as much as a billion human lives [in the present]. One might consequently argue that even the tiniest reduction of existential risk has an expected value greater than that of the definite provision of any ‘ordinary’ good, such as the direct benefit of saving 1 billion lives ”

Hey, maths doesn’t lie, so I guess that means okay to sacrifice a billion people or so. Unless possibly you’re one of them. Which Bostrom probably isn’t particularly worried about because he is now director of the Future of Humanity Institute in Oxford where he makes a living from multiplying powers of ten. But I don’t want to be unfair, Bostrom’s magnificent paper also has a figure to support his argument that I don’t want to withhold from you, here we go, I hope that explains it all.

By the way, this nice graphic we saw earlier comes from Our World in Data which is also located in Oxford. Certainly complete coincidence. Another person who has been promoting longtermism is William MacAskill. He is a professor for philosophy at, guess what, the University of Oxford. MacAskill recently published a book titled “What We Owe The Future”.

I didn’t read the book because if the future thinks I owe it, I’ll wait until it sends an invoice. But I did read a paper that MacAskill wrote in 2019 with colleague Hilary Greaves titled “The case for strong longtermism”. Hilary Greaves is a philosopher and director of the Global Priorities Institute which is located in, surprise, Oxford. In their paper they discuss a case of long-termism in which decision makers choose “the option whose effects on the very long-run future are best,” while ignoring the short-term. In their own words:

“The idea is then that for the purposes of evaluating actions, we can in the first instance often simply ignore all the effects contained in the first 100 (or even 1,000) years, focussing primarily on the further-future effects.”

So in the next 100 years, anything goes so long as we don’t go extinct. Interestingly enough, the above passage was later removed from their paper and can no longer be found in the 2021 version.

In case you think this is an exclusively Oxford endeavour, the Americans have a similar think tank in Cambridge, Massachusetts, called The Future of Life Institute. It’s supported among others by billionaires Peter Thiel, Elon Musk, and Jaan Tallinn who have expressed their sympathy for longtermist thinking. Musk for example recently commented that MacAskill’s book “is a close match for [his] philosophy”. So in a nutshell longtermists say that the current conditions of our living don’t play a big role and a few million deaths are acceptable, so long as we don’t go extinct.

Not everyone is a fan of longtermism. I can’t think of a reason why. I mean, the last time a self-declared intellectual elite said it’s okay to sacrifice some million people for the greater good, only thing that happened was a world war, just a “small misstep for mankind.”

But some people have criticized longtermists. For example, the Australian philosopher Peter Singer. He is one of the founders of the effective altruism movement, and he isn’t pleased that his followers are flocking over to longtermism. In a 2015 book, titled The Most Good You Can Do he writes:

“To refer to donating to help the global poor or reduce animal suffering as a “feel-good project” on which resources are “frittered away” is harsh language. It no doubt reflects Bostrom’s frustration that existential risk reduction is not receiving the attention it should have, on the basis of its expected utility. Using such language is nevertheless likely to be counterproductive. We need to encourage more people to be effective altruists, and causes like helping the global poor are more likely to draw people toward thinking and acting as effective altruists than the cause of reducing existential risk.”

Basically Singer wants Bostrom and his likes to shut up because he’s afraid people will just use longtermism as an excuse to stop donating to Africa without benefit to existential risk reduction. And that might well be true, but it’s not a particularly convincing argument if the people you’re dealing with have a net worth of several hundred billion dollars. Or if their “expected utility” of “existential risk reduction” is that their institute gets more money.

Singers second argument is that it’s kind of tragic if people die. He writes that longtermism “overlooks what is really so tragic about premature death: that it cuts short the lives of specific living persons whose plans and goals are thwarted.”

No shit. But then he goes on to make an important point: “just how bad the extinction of intelligent life on our planet would be depends crucially on how we value lives that have not yet begun and perhaps never will begin.” Yes, indeed, the entire argument for longtermism depends crucially on how much value you put on future lives. I’ll say more about this in a minute, but first let’s look at some other criticism.

The cognitive scientist Steven Pinker, after reading MacAskill’s What We Owe The Future, shared a similar reaction on twitter in which he complained about: “Certainty about matters on which we’re ignorant, since the future is a garden of exponentially forking paths; stipulating correct answers to unsolvable philosophical conundrums [and] blithe confidence in tech advances played out in the imagination that may never happen.”

The media also doesn’t take kindly to longtermism. Some, like Singer, complain that that longtermism draws followers away from the effective altruism movement. Others argue that the technocratic vision of longtermists is also anti-democratic. For example Time Magazine wrote that Elon Musk has “sold the fantasy that faith in the combined power of technology and the market could change the world without needing a role for the government”

Christine Emba, in an opinion piece for the Washington Post, argued that “the turn to longtermism appears to be a projection of a hubris common to those in tech and finance, based on an unwarranted confidence in its adherents’ ability to predict the future and shape it to their liking” and that “longtermism seems tailor-made to allow tech, finance and philosophy elites to indulge their anti-humanistic tendencies while patting themselves on the back for their intelligence and superior IQs. The future becomes a clean slate onto which longtermists can project their moral certitude and pursue their techno-utopian fantasies, while flattering themselves that they are still “doing good.””

Okay, so now that we have seen what either side says, what are we to make of this.

The logic of longtermists hinges on the question what the value of a life in the future is compared to ours while factoring in the uncertainty of this estimate. There are two elements which goes into this evaluation. One is an uncertainty estimate for the future projection. The second is a moral value, it’s how much future lives matter to you compared to ours. This moral value is not something you can calculate. That’s why longtermism is a philosophical stance, not a scientific one. Longtermist try to sweep this under the rug by blinding the reader with numbers that look kind of sciencey.

To see how difficult these arguments are, it’s useful to look at a thought experiment known as Pascal’s mugging. Imagine you’re in dark alley. A stranger steps in front of you and says “Excuse me, I’ve forgotten my knife but I’m a mugger, so please give me your wallet.” Do you give him your money? Probably not.

But then he offers to pay back double the money in your wallet next month. Do you give him your money? Hell no, he’s almost certainly lying. But what if he offers a hundred times more? Or a million times? Going by economic logic, eventually the risk of losing your money because he might be lying becomes worth it because you can’t be sure he’s lying. Say you consider the chances of him being honest 1 in 10,000. If he offered to return you 100 thousand times the amount of money in your wallet, the expected return would be larger than the expected loss.

But most people wouldn’t use that logic. They wouldn’t give the guy their money no matter what he promises. If you disagree, I have a friend who is a prince in Nigeria, if you send him 100 dollars, he’ll send back a billion, just leave your email in the comments and we’ll get in touch.

The point of this thought experiment is that there’s a second logical way to react to the mugger. Rather than to calculate the expected wins and losses, you note that if you agree to his terms on any value, then anyone can use the same strategy to take literally everything from you. Because so long as your risk assessment is finite, there’s always some promise that’ll make the deal worth the risk. But in this case you’d lose all your money and property and quite possibly also your life just because someone made a promise that’s high enough. This doesn’t make any sense, so it’s reasonable to refuse giving money to the mugger. I’m sure you’re glad to hear.

What’s the relation to longtermism? In both cases the problem is how to assign a probability to unlikely future events. For Pascal’s mugger that’s the unlikely event that the mugger will actually do what he promised. For longtermism the unlikely events are the existential threats. In both cases our intuitive reaction is to entirely disregard them because if we did, the logical conclusion seems to be that we’d have to spend as much as we can on these unlikely events about which we know the least. And this is basically why longtermists think people who are currently alive are expendable.

However, when you’re arguing about the value of human lives you are inevitably making a moral argument that can’t be derived from logic alone. There’s nothing irrational about saying you don’t care about starving children in Africa. There’s also nothing irrational about saying you don’t care about people who may or may not live on Mars in a billion years. It’s a question of what your moral values are.

Personally I think it’s good to have longterm strategies. Not just for the next 10 or 20 years. But also for the next 10 thousand or 10 billion years. So I really appreciate the longtermists’ focus on the prevention of existential risks. However, I also think they underestimate just how much technological progress depends on the reliability and sustainability of our current political, economic, and ecological systems. Progress needs ideas, and ideas come from brains which have to be fed both with food and with knowledge. So you know what, I would say, grab a bag of chips and watch a few more videos.