Into the Impossible With Brian Keating - Chaos, Covid, & Climate Change with Professor Tim Palmer (#267)

Episode Date: October 18, 2022

In his acclaimed latest book, The Primacy of Doubt: From Quantum Physics to Climate Change, How the Science of Uncertainty Can Help Us Understand Our Chaotic World, Professor Timothy Palmer argues that embracing the mathematics of uncertainty is vital to understanding ourselves and the universe around us. Whether we want to predict climate change or market crashes, understand how the brain is able to outpace supercomputers or find a theory that links quantum and cosmological physics, Palmer shows how his vision of mathematical uncertainty provides new insights into some of the deepest problems in science. The result is a revolution—one that shows that power begins by embracing what we don’t know. The Primacy of Doubt on Amazon: https://www.amazon.com/Primacy-Doubt-Quantum-Uncertainty-Understand/dp/1541619714 Timothy Palmer is the Royal Society Research Professor in Climate Physics, and a Senior Fellow at the Oxford Martin Institute at the University of Oxford. He is a mathematical physicist who has spent most of his career working on the dynamics and predictability of weather and climate. He pioneered the development of probabilistic ensemble forecasting techniques for weather and climate prediction, techniques that are now standard in weather and climate forecasting around the world. In 2021 Professor Palmer was awarded an honorary fellowship of the Institute of Physics. Professor Palmer was involved in the first five IPCC assessment reports, and was co-chair of the international scientific steering group of the World Climate Research Programme project (CLIVAR) on climate variability and predictability. Watch the video with slides here: https://youtu.be/q1cPyE9rAD4 Connect with me: 🏄‍♂️ Twitter: https://twitter.com/DrBrianKeating 📸 Instagram: https://instagram.com/DrBrianKeating  🔔 Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 📝 Join my mailing list; just click here http://briankeating.com/list ✍️ Detailed Blog posts here: https://briankeating.com/blog.php 🎙️ Listen on audio-only platforms: https://briankeating.com/podcast Subscribe to the Jordan Harbinger Show www.jordanharbinger.com/podcasts for amazing content from Apple’s best podcast of 2018! Can you do me a favor? Please leave a rating and review of my Podcast:  🎧 On Apple devices, click here, https://apple.co/39UaHlB scroll down to the ratings and leave a 5 star rating and review The INTO THE IMPOSSIBLE Podcast. 🎙️On Spotify it’s here: https://open.spotify.com/show/2G3PRMUhxGQkyQzLiiCqlf?si=8656119458df4555 🎧 On Audible it’s here : https://www.audible.com/pd/Into-the-Impossible-With-Brian-Keating-Podcast/B08K56PXJX?action_code=ASSGB149080119000H&share_location=pdp&shareTest=TestShar Other ways to rate here: https://briankeating.com/podcast -  Support the podcast on Patreon https://www.patreon.com/drbriankeating  or become a Member on YouTube- https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Learn more about your ad choices. Visit megaphone.fm/adchoices

By a concern for climate change, what I'm really arguing for is dedicated absolute top-of-the-range supercomputing, which these days is XA-scale, 10 to the 18 flops per second, floating point operations per second, dedicated to climate change. And that's not happening at the moment anywhere around the world. If I'm talking to a climate skeptic, I would say, look, you surely are as interested as I am or as a climate you know, somebody it's very concerned about climate change to know the truth. You know, how bad is it going to get? Solving the laws of physics is the only way we have to deal with this. We can answer this question. There's no laboratory experiment we can do to emulate climate chain.

Hello friends and welcome to one of the most wide-ranging discussions I've ever had on this podcast. And it's with one of the most polymathomathes. mathematical, most wide-ranging of all intellects that I've really had the honor to speak to, and it's Dr. Tim Palmer of Oxford University, who was introduced to me by his friend and collaborator and multi-time past guest on the show, Sabina Hassenfelder. They are collaborators, and around the time that Sabina came on the podcast to discuss her most recent book, Existential Physics, she then referred me kindly to Tim, and we got in touch about Tim's book, which you're going to hear about today called The Primacy of Doubt.

From quantum physics to climate change, how the science of uncertainty can help us understand our chaotic world. And we delve into all the greatest hits from chaos theory, the butterfly effect, climate change, meteorological forecasts, and why they're almost always wrong, except here in San Diego, where the easiest job is to be a weather person. And what I found so fascinating about Tim is that he doesn't stop at just quote-unquote his Nobel laureate laurels. He is a Nobel laureate in that he was the lead author on the IPCC Intergovernmental Panel on Climate Change, way back when, when he won it with renowned scientist Al Gore.

I don't think Al Gore is a scientist. I don't even know if he would call himself a scientist. We get into a little bit about it and why some of the dire predictions don't come true. what chaos theory can really tell you about science in general, but about climate specifically. And we also got into the impact of the COVID-19 pandemic and how Tim claims we need a large Hadron collider type effort but for climate change and why that is. I thought it was fascinating. We got into the brain and how the brain works and quantum mechanics, the foundations. And my favorite topic that we got into involves the transition from not just the classical world,

of inclined planes and pendula, but also the world of chaos, of viscosity, of turbulence, of hurricane force winds, and more that then have to somehow emerge, perhaps, from the quantum and correlated phenomena in the quantum realm. So it's a wide-ranging conversation we get into so many things. We left many things off the table, and we have a part two sometimes schedule. But as the primacy of doubt proves, one thing is for certain, there is, in embracing what we don't know, not just what we do know. So I want to, before we embark on this wonderful discussion, I want to ask you for your contribution,

not passing the plate, not necessarily to Venmo me a couple of Bitcoin, although that might be nice, but instead to leave me what could only be described as a free request, which is to leave a review or at least a rating, Just click a number of stars. I prefer five. But if you want to leave one, at least let me know why you left one. It's incredibly important for me that we get the word out about this podcast. And one of the ways we do so is by having ratings and reviews on Apple Podcasts on Spotify. It's easy to do. You can click below in the show notes wherever you're listening to this to find out how. And don't forget to subscribe to my YouTube channel because my super producer, Stuart Wolkow, puts in incredible background images of video and assorted links. to different documents. And you don't want to miss that either. So please, please do this,

the one favor for me. And don't forget again to subscribe to the channel and my mailing list, briankeating.com slash list. And so now we're going into the impossible with a man who used to be a general relativity theorist. And that person is Tim Palmer. Oh, by the way, how many people that study general relativity does it take to change a light bulb? Answer to one. One to hold the bulb and one to rotate space-time itself. Okay, you can't really blame me for my dad jokes. You knew what you were getting into. Anyway, enjoy.

Here we go, Into the Impossible. Any sufficiently advanced technology is indistinguishable from magic. Welcome, friends, to another wonderful edition of the Into the Impossible podcast with our guest, Tim Palmer, the Royal Society Research Professor in the Department of Physics at the University of of Oxford who pioneered the development of operational ensemble weather and climate forecasting, and in 2007 was formally recognized as having contributed to the Intergovernmental Panel on Climate Change and received a recognition for that via the Nobel Prize, the Nobel Peace Prize that he shared along with many other scientists and also former Vice President Al Gore.

And there's a lot of connection here in San Diego at the Scripps Institution of Oceanography, all sorts of good stuff, many, many good connections. But this will be part of our continuing sequence of think like a Nobel Prize winner, although I can't say that Tim is my first non-physics Nobel Prize winner. He didn't win it in physics, not yet at least, but he did at least participate in the Peace Prize. So you're my first Peace Prize co-recipient. So Tim, thank you so much for joining. joining us on this auspicious occasion of the publication of this wonderful book.

Thank you. It's a pleasure. Thank you. So we've had on a fair number of Oxonians. We've had on a fair number of businesses that focus on the biggest picture topics one could imagine. And you were actually referred to me by our mutual friend, Sabina Hossenfelder, who is a recent guest on the podcast, with whom you've written some wonderful works with. and with whom you and I co-recorded a video. This is our second video together. We did a very, very technical, expository video on the differences between British English and American English back on the first of the year or thereabout.

So we'll put a link to that somewhere up here, down there, wherever. So it's our second time together, Tim, but this time it's synchronized so we can actually have a conversation. So, as I mentioned, what I love to do is talk about books. on this podcast and i love to uh to discuss the thought process behind the book and kind of will get into your process you're remarkably productive and i want to get into your habits and we have a lot of young scientists that listen to this podcast and learning from folks like you who do so much in so many different fields from gravity to the brain to god we're going to get into it all and of course chaos and climate change i want to ask you where did the title of the book come to you from

I understand that took some figuring out, and the subtitle, which involves our chaotic world, and most importantly for us visual learners, what is the depiction, the illustration on the cover, meant to evoke in the reader's minds? Okay, well, there's quite a few questions sort of tied up there. Let me start by saying that, you know, you've talked about my, or at least mentioned my work on climate change but i've i've had a fairly i would say kind of broad type of career and i started life off after my undergraduate degree in mathematics and physics uh doing a phd in general relativity theory so i actually had started off thinking that my you know life's work would be in fundamental physics but i kind of re-evaluated things after the phd and uh you know part of

partly because I met some people by chance in the climate area. And I kind of felt that I needed to do something, let's say a bit more, you know, that could have some impact, let's say, on the human species or some parts of it at least. So, but working in weather and climate, you realize that weather and climate pretty much affect everything in life,

you know, from the economy through to health, through to whether we have enough food and water to eat and drink. So that in turn got me kind of interested in all these downstream application sciences. And I was kind of, I think it was at the beginning of, probably at the beginning of the pandemic. I was trying to think, is there a theme that kind of unifies everything that I've done? And the notion of uncertainty kind of came up. in my head. And that got me starting to think whether I could try to write a popular book, sort of loosely speaking about the science of uncertainty, but kind of discussing it through a

range of topics that I've been involved with one way or another. So, and then the actual title, the primacy of doubt, was actually taken from a book that James Glee, Glick wrote, a very famous biography of Richard Feynman. And Glick is somebody I've known because he, prior to that, wrote an absolutely wonderful book about chaos theory. It really actually, you know, he actually coined the phrase, the butterfly effect in the book. This is back in the 1980s, I think. Yeah. So I was aware of James Glick and his fantastic ability to explain things clearly.

clearly. And that got me interested in his biography of Feynman. And then I came across this quote in that biography about Feynman. And Glick says he believed, he, Feynman, believed in the primacy of doubt, not as a blemish on our ability to know, but as the essence of knowing. And I just thought that was such a wonderful phrase. You know, the notion that the human condition is one of uncertainty and our creativity comes from being, you know, uncertain about things. And that notion that the primacy of doubt, I thought that really resonated with me. And it's been a kind of, I suppose, a philosophy of mine that uncertainty, you know, is something that you have to put right up front and foremost. Also depicted on the cover reminded me of Glick's book Chaos, because it seems to have,

Well, it's a very attractive cover, let me say. So what is the object depicted on the cover? And what is its relevance to quantum physics and climate change? Right. The front cover is a depiction of what is sometimes called a strange attractor, which sounds kind of a bizarre words to use. But it's, I would say, you see, it's what you would call the geometry. of chaos. And it's something that I think, you know,

Lorenz, Ed Lawrence, who was the discoverer of this geometry of chaos, should actually himself have won a Nobel Prize. So what Lawrence discovered was three very simple mathematical equations that, you know, you could, I mean, these days they're so easy to solve. They, you know, they used as screen savers on computers. In his day, he had to actually get some pretty chunky computing to solve them. But what he found was that when he ran these equations over long periods of time on the computer and plotted the state, so there are three variables. So if you want to describe the state of

the system, you need a three, it's a point in a three-dimensional space, which you call state space. And you run that your model and you look at, you know, how do these points evolve? And you find that they evolve on this rather strange geometry, which is shown in the front cover of the book. And Lorenz was the one that realized that this geometry actually is a fractal. It's a fractal in the sense that if you zoom into it as much as you like, you'll still, you'll see the same kind of structures. appearing and reappearing. And this is completely the complete antithesis of the sort of classical Euclidean geometry that were all taught about at school and stuff, which like a sphere, the surface of a sphere, which just gets really boring if you zoom into it enough. It just looks flat and uninteresting.

So these fractals are distinguished by never running out of structure on the small scale. And actually, as a result of that, you need a different type of mathematics to really describe the geometry of these fractals. And I've my whole, you know, pretty much my life has evolved around either understanding these fractals or trying to kind of elucidate properties of them. And a lot of the work I've done on predictability of weather and climate has used Lorenz's geometry as a kind of didactic tool. But in the book, I tried to sort of go a bit further and address the question, what if the whole universe evolved on some kind of fractal in some humongously big state space,

cosmological state space? The interesting point about that is that because there are gaps in the fractal, there are places, no matter of how much you zoom in, There are places where no trajectories ever go. What it means is that worlds that you might conceive of in your head, what we call counterfactual worlds, things that didn't happen that might have happened, may actually be inconsistent with this geometry.

What I tried to show in the book, and I've written papers on this, is that this could be, I'm not saying it is, it could be a new way to understand some of the really puzzling sort of paradoxes and conceptual difficulties in quantum mechanics, paradoxically enough, perhaps. We could talk about that later. I don't want to get bogged down into too much detail.

Yeah, let's kind of start with the astonishing story of a very major character in this book. know what he looks like uh but i think his name is uh fish michael fish uh oh yeah a man in the met office where he used to work uh and it's uh concerns an event that occurred uh about uh 25 years ago almost exactly by the time the book comes out october 16th 1987 the south of england was battered by one of the most brutal storms to ever hit the country in over 300 years 15 million trees were blown down 22 people died and damn exceeding $3 billion. Worse yet, meteorologists had predicted a nice breezy day.

In the aftermath, scientists asked themselves, why was the forecast so wrong? The weather forecasters admit they got it wrong. They'd warned of a depression approaching, but they had no idea of its strength or direction. This morning, people woke up to devastation and forecast like this one in the Daily Telegraph. It said the weather would have an unsettled autumnal look. Really, to have predicted it further ahead in great detail, we'd have needed more information midday yesterday, the midday run of the computer model yesterday, what we call a numerical model, which tries to simulate the atmosphere in terms of equations. That would have needed more information at midday yesterday.

When I heard about the title, you know, the reason for the title coming from Feynman, I actually thought of two other quotes. Well, one from him, which is, uh, Science is the belief in the ignorance of experts. That's the first quote I want you to react to. And then the second one is from the famous Lev Landau, who said that cosmologists are often in error, but never in doubt. What is the role of doubt, of doubting yourself, of being uncertain? And how much should, you know, people have gained or lost confidence in Michael Fish and his ilk after failing to predict a hurricane?

and instead predicting a nice breezy day. And then subsequently we'll get into COVID and all sorts of other predictions that are also made by our fellow experts. I mean, that's that's scientist. So how do you react to Feynman's quote, science is the belief in the ignorance of experts? Do you agree with Feynman? Well, I mean, the word ignorance, you know, the word ignorance could indicate a complete lack of knowledge. And of course, experts don't have a complete lack of knowledge.

So I sometimes wonder whether the word ignorance, I've come across that quote myself, of course. I do wonder whether the word ignorance is quite the right word to use. But it's certainly the case that someone who says they're an expert on a in a field is fallible. So, you know, perhaps if we could substitute, you know, the fallibility of experts for the ignorance of experts, I'd feel a bit more comfortable because certainly experts are fallible. Yeah. Now, you know, the Michael Fish Storm, I say a few words about it, if you don't mind, because it came at a very opportune time for me, which is in the 1980s, I, I say a few words about it,

I had, you see, again, we come back to Lorenz's model of chaos. Good afternoon to you. Earlier on today, apparently a woman rang the BBC and said she heard that there's a hurricane on the way. Well, if you're watching, don't worry, there isn't. But having said that, actually, the weather will become very windy. But most of the strong winds, incidentally, will be down over Spain and across into France as well. Even back in the 1980s, people knew about, and of course, Glick's book, popularised the notion of the butterfly effect. So people were aware that because of the flaps of butterflies wings causing tornadoes in Texas to occur, you know, a week later or something, they were aware that the

weather, you know, couldn't be predicted precisely for long periods of time. That was kind of fairly well accepted. But the converse was that if you stick to within a few days, predicting just a few days into the future, then chaos is not really important. And you can make kind of categorical predictions. It will be sunny, it will be rainy, it will be stormy, or it will be dry. And that pervaded the professional community of meteorologists as well. And I used Lorenz's model to show that the degree of which, if you like, the degree of chaos in an initial condition really varies a lot depending on where you are on that fractal geometry, where you are on that strange attractor.

There are some points on the fractal attractor which are actually extremely predictable and stable. Small uncertainties don't really affect the trajectory. very much. But there are other initial conditions on other points of the attractor, which are incredibly unstable and very, very sensitive to initial conditions. And so I argued that we shouldn't be complacent about weather forecasting, even for a few days into the future, because if we hit one of these exceptionally unstable points or the atmosphere goes through one of these very unstable points, then we're in danger of making a complete hash of the forecast. And some of my colleagues were kind of skeptical about that, or they didn't think it was

something to worry about. But the 1987 storm was just so it was a fantastic example of this very, this exactly this phenomenon that, you know, the atmosphere itself is a chaotic system, but the degree of chaos varies and can vary very substantially from one week to the next. And we retrospectively ran an ensemble of forecasts. And by that I mean we run the weather forecast model 50 times with very, very slightly different initial states, flaps of butterflies' wings, as it were, introduced into the initial conditions. And for this particular sort of retrospective forecast made two days before the storm hit southern England, the spread in the ensemble was just extraordinarily large.

I mean, it was just, you know, bigger than you could imagine. And this was just a clear example, as I say, that chaos isn't some kind of fixed quantity that, you know, systems become unpredictable after 30 days or after whatever you want, some period of fixed period of time. It varies. And this is a feature. I mean, this is the second, if there are two themes which run through the book,

one is the notion of uncertainty. And the other is the notion of non-linearity. So pretty much all the systems I deal with are non-linear in some way or another. And this notion about the degree of predictability varying from one state to another is a feature of a non-linear system. that really sort of almost defines a nonlinear system.

It could be sometimes predictable, sometimes very unpredictable. So the Michael Fish Storm was kind of nature's way. I mean, I have to say nature came to my help in persuading my colleagues that this was a serious problem because, I mean, you know, there was nothing else on the news for several days. The Director General of the Met Office

was under a lot of pressure to resign. and people were just saying how, you know, we spend all these millions on the state meteorological service and they give us such rubbish, you know, advice or they can do. So this really changed the way things were done. And these days, Ensobble forecasting is used around the world. It's pretty much, you know, accepted by the current generation. And it makes a big difference in how you actually go about making decisions. We can come on to that.

But one really big area that's changed is how humanitarian and disaster relief organizations decide ahead of time when to send food and medicine and water and shelter and stuff ahead of some predicted storm, predicted extreme event, rather than in the old days where they would just wait for it to happen, the hurricane to happen or whatever, and then go in afterwards. Now they're going in ahead of time, but they use probability. to make that decision about when to go in and when not together. Your summer starts now with Memorial Day deals at the Home Depot. It's time to fire up summer cookouts with the next grill, four-burner gas grill, on special buy for only $199. And entertain all season with the Hampton Bay West Grove seven-piece outdoor dining set

for only $499. This Memorial Day get low prices guaranteed at the Home Depot. While supplies last, price invalid May 14th, 827. US only exclusions apply. See homedepot.com slash price match for details. And how does the, you know, probabilistic or statistical nature of, uh, this new method of dealing with, you know, climate or weather related issues, say, how does that differ, you know, how does that employ the, the main features of, say, chaos theory or, um, you know, dynamical, stochastic systems? In other words, I, I remember actually my freshman year, my, my, my professor

at Case Western Robert Brown. He used to, he made us read chaos in the physics, you know, course about, you know, inclined planes and mechanics and stuff. But we loved it. And he was a student of Feynman's actually at Caltech. But anyway, he, you know, it was very impressive. And we love learning about, you know, calculating fractal dimensions and showing, you know, the actually generating the butterfly or the Lorenzo tractor with different exponents. And, and of course, the Mandelbrot set. But I never got the sense that it was actually applied.

In other words, does my local, by the way, I should say that there's the extreme span of jobs exist here in San Diego. So the hardest job in the world is being a sportscaster because we've never won a championship in any major sport from the World Series of baseball to soccer to football when we used to have a team. But the easiest job to compensate is meteorologist because it's basically, you know, they throw up a picture. But I've never heard one of them say, you know, the Liapunov exponent of this fractal dimension of the perimeter of this approaching, you know, we just had a tropical depression blaster here. And it lasted, you know, three days longer than anybody predicted. We got, you know, random rain. So do people actually use, you know, the, aside from the general features of very extreme sensitivity to initial. conditions, aside from, you know, kind of scale and variant, are there any applied ways to

translate the esoteric and beautiful mathematics of Mandelbrot and Laurent to something practical where it would improve something in the daily life, whether it be the price of something, the impact of something, or the cause of something. So, yeah, can you address the actual? Well, you mentioned, you started to mention a thing called a Liyapenov exponent. So let me sort of follow up on that. Yeah. Because in the early days, you know, we were rather sort of blasé about how you would add these perturbations

to the initial conditions to generate an ensemble. And it turns out that if you add small amplitude and small scale perturbations into a weather forecast model, words you add so that a model would have a characteristic grid so all of the equations are are kind of discretized onto a grid which I mean these days is you know is around 10 kilometers but you know go go back 30 years and it's near a hundred kilometers so you the uncertainties are largest on the scales close to the grid. So you say, okay, I'll just add, I'll add some sort of noise, as it were, to the initial

conditions. What you find is that these perturbations actually don't grow at all. They're completely the opposite of what you might think from the butterfly effect. The reason is, or a key reason anyway, is that models have a lot of artificial kind of damping and what we call artificial viscosity to kind of keep them stable and so that these kind of small perturbations don't suddenly cause the model to kind of what a numerical analyst would call blow up do something completely unphysical um so we actually had to go about thinking uh quite carefully about how to construct perturbations that were consistent with our uncertainty in

the observational network, but we're also going to have the effect of growing as the forecast ran. So you mentioned the term Leophanov exponent, and that's a kind of a measure of how a perturbation grows in a chaotic system, and the largest Leophanov exponent sort of gives you the largest possible growth. So what we did, in fact, is. borrow the ideas or the techniques of chaos theory to compute Leaphanov exponents and the associated perturbations are called Leaphanov vectors. We had to do this within the kind of a, I don't want to go in again to technical details, but finite time kind of framework. But basically we use these techniques from chaos theory for computing how the, you know, how perturbations grow, you know,

And what are the most unstable directions, essentially? It's what are the most unstable directions perturbed along those rather than just randomly perturbing. So that was a kind of an idea borrowed from chaos theory that went right into the operational code. But you're right. Not everything is... And actually, one thing that's important to say,

and I kind of go into this a bit in my book, is that there actually is a difference between weather and, let's say, Lorenz's model of chaos or Mandelbrot's, you know, Attractor and all this sort of stuff, which is, and again, it relates actually to Leapenov exponents, which is what we would call the dimension of the attractor. So how many unstable directions are there? How many growing Leapenov exponents are there? And for something simple like Lorenza's model, there's just a couple. But for the weather, you know, we think there are billions and billions of billions. I mean, it's essentially we're dealing with in the real weather world, a very, very, very, very large dimensional chaotic system. And actually in the book, I go into the reason why,

despite the fact that the underlying equations, for example, the thing called the Navia Stokes equations, which are equations for fluid motion, even though they're deterministic, in other words, they don't have any random terms in the equations. Because we are dealing with a finite truncation of a system that, you know, for all practical purposes, actually has an infinite number of degrees of freedom, it's actually advantageous to add noise, add stochastic noise, you mentioned the word stochastic there, to the equations, the computational equations to represent properly all of those kind of unstable directions. So again, stochasticity is something that we've taken from the kind of more, say, theoretical ideas in chaos theory and apply them into operations.

When we look at the delightful figures in the book, and we'll flash some of those up in the animated version, I had the hardcover and the e-book, and there'll be an audiobook at some point. It's wonderfully and beautifully illustrated, and the illustrations really do convey, you know, the kind of really the relevance of this new way of thinking mathematically that, that, you know, is not that new, actually. I mean, as I said, I remember from a freshman year, you know, 30 years, 25 years ago, whatever. But nevertheless, in terms of its applications and what it can be useful for. And, of course, looming throughout the book is also the specter of another, you know, worldwide event that had global implications. And it's course of COVID-19, which we're still dealing with in various incarnations. I've just got over about of it.

And I want to ask you, you know, before we get into, you know, and without getting into political kind of discussions, because there's so much potential for that in both the climate and COVID and economics and all sorts of other issues in the book. The one safe space is when we'll talk about Bell's inequality. I don't think there's any political ramifications of that that we will be tiptoeing around minefields. But what are the similarities with something like a pandemic, aside from the fact that we had people, you know, and both, you know, they're both features of the UK or people in the UK, you know, predicting dire outcomes and then changing it, I guess another Neil Ferguson, not the one who's on this podcast a few weeks ago, but, but another one in Imperial maybe. Anyway, what could we apply from this additional way of thinking about mathematical, the

chaos and attractive states and bi-stable and so forth, sensitivity to initial conditions? What is the relevance of that to something that we've really known about for hundreds or thousands of years, a global pandemic? Is there a relevancy of this mathematical way of thinking with these kind of features that are similar to chaos theory towards a better comprehension of the spread of a pandemic like COVID-19? Yes. Maybe I could just quickly comment on the figure that you just quickly showed because that illustrates also. very nicely an example of the type of kind of intermittent chaos. This one? Well, it wasn't that. You showed a picture of planets, of a planetary orbits, yeah, of a three-body orbit, which look, yeah.

I mean, I recommend anyone to go to the actual online animation because it's just phenomenal. What you see are these four bodies rotating around each other in kind of what looked to be, almost, well, they look to be perfect ellipses. Yeah. And you watch this for a while and you say, oh, this is boring. I mean, why am I watching this? Well, and the reason you're watching it is all of a sudden, seemingly out of nowhere, the planets just spiral off to infinity.

It's like they hit this. It's exactly the same as the Michael Fish Storm. They hit this point of instability, having been, you know, potentially for millions of years, just trundling around each other, minding their own business. They hit this point of instability and they suddenly zip off to infinity. And this is just a lovely example of the same notion as the Michael Fishstorm. And, you know, I try to argue maybe the global meltdown in 2008 could in the financial meltdown was another example of that. Now, coming to COVID, I think what I was struck by was,

the analogy in some ways is actually better with climate change than weather forecasting. So I have a chapter talking about how ensembles are also used to make, well, I've got to say the word prediction, but perhaps that's not the right word to use in this context, because if you think about climate change, The reason we're concerned about climate change is that we're emitting greenhouse gases into the atmosphere. They warm the planet and they change the weather. But the extent to which the extent to which the weather does change is a function of how much we continue to emit carbon dioxide. If we suddenly, for some reason, tomorrow stopped emitting carbon dioxide, then we'd be looking at a very different climate by mid-century. than if we just carry on as per normal.

Now, we can't, you know, I can't, I don't think anyone can predict, you know, what we will decide to do about our emissions. So when you're making predictions of climate change, they're predicated on a particular assumption about whether we'll continue to emit as normal, whether we'll cut back a little bit or we'll cut back a lot and so on. But we still have to use ensembles.

So just having running one model with an increase in carbon dioxide is not going to give you a reliable result. You need an ensemble which span the uncertainties in the equations and so on. And again, you essentially have probabilistic estimates of future climate change. Now, the COVID thing is kind of similar to that in the sense that, you know, the government's governments around the world had to make decisions about whether to impose lockdowns or, you know, let people just carry on and hope for the best. And again, those are not, you know, nobody's going to be able to predict that. So what the COVID modelers do are make, and other words, they use the same words actually that climate change people do, which is projections. They're projecting

what the COVID deaths and COVID hospitalizations will be given certain different potential policy assumptions. One policy assumption is that we do nothing to restrict people's interactions. And then another one we restrict them a lot. But again, because the models are imperfect models, they're particularly imperfect, of course, when we're dealing with social, interactions and so on then ensembles and this was realized actually quite quite early on i think in in uh in uh covid modeling that uh i mean in the case of the imperial college model they would put stochastic some stochastic representations for uncertain parameters but in the u s they did something which was actually much closer to what IPCC does for the climate change assessments

which is they made use of the fact that many different institutes in the US had developed their own epidemiological models. And a few people kind of took the lead in ensuring that the predictions and projections were done in a fairly coordinated way with similar types of data output. So everything could then be analyzed in a central hub in a probabilistic way. So I wrote a... chapter about COVID because it did strike me there was remarkable kind of similarities in the science.

I mean, interestingly, there were remarkable similarities in the, I mean, maybe that's less remarkable, but in the political, I know you don't want to talk about politics, but the, you know, the people that advocated doing, you know, nothing much about climate change, we're typically the same people that advocated doing nothing much about restricting interactions in COVID. So that was kind of an interesting parallel there. But scientific, there's a lot of parallels between climate projections and COVID projections. Yeah. And I wonder... I mean, my only kind of comment at the end was that just as the world, the UN

World Meteorological Organization pretty much masterminded the production of these IPCC reports by coordinating climate modeling groups around the world. So I think perhaps the world health organization could do something similar for this type of epidemiological modeling. In other words, get groups around the world to do their projections in a coordinated way. You know, they have the, you know, so you can kind of compare and contrast them and produce probabilistic analyses and so on and so forth. So I hope that'll that'll be something WHA taken. In the book, you talk a lot about, you know, kind of a CERN for,

climate change. And, you know, I couldn't help thinking, well, maybe a CERN for, you know, some kind of global pandemic. But of course, there's a great deal of skepticism about both of these things, some in good faith, maybe some in and out without it. But I mean, how on a personal level, how do you react to, you know, I'm just looking at headlines from Apple News today, Wall Street Journal reports energy prices storing across Europe due to their reliance and their cancellation of nuclear energy, for example. Germany gets most of its energy now from coal after years of wind subsidies. Coal is reliable, so it is more beneficial. Hospitalization rates for COVID are very misleading. This is in the Atlantic. They are coming after the fact, and they get retroactively diagnosed

according to the Atlantic. Now, this is not a political show, as you know, I believe that astronomy is a safe space and cosmology and physics because we, you know, no one wakes up and says, I hate that Republican constellation over there or that Tory, that damn Tory astronaut asteroid. But Tim, how does that affect you when you see, you know, headlines like these that are casting doubt, maybe, or maybe it's where the, you know, scientific becomes political? You know, what advice do you have? Do we really need to invest more money to have global consensus and have, you know, CERN costs tens of billions of dollars to construct and tens of billions of dollars to operate. How would we go about convincing somebody, you know, your new prime minister, to invest in something like a CERN for climate

change? What would be some of the arguments or a CERN for COVID? Well, I don't know about a CERN for COVID because I'm perhaps not, you know, I'm not sure I'm the right person to talk about that, but I can certainly talk about a CERN for climate change. And the issue, you know, the issue comes back to what I talked about earlier, which is we're dealing with a very, you know, mathematically speaking, a very large dimensional chaotic system. So large dimension means it really needs a very large number of variables to simulate it well. And, you know, a typical weather forecast model these days would have several billion variables, you know, represented. And you can very easily show that if you skimp on that, then your hurricanes don't become as intense as they were before,

or your droughts don't become as long-lived as they were before. Extreme weather becomes less extreme if you start skimping on degrees of freedom. Now, if you're making a 10-day weather forecast, you can pretty much run a model with a billion or more degrees of freedom on a large, but not necessarily, absolutely top of the range supercomputer. I mean, a petaflopp computer would 10 to the 15 floating point operations per second would do pretty well. But when you come to climate where we're trying to make

these projections over 50 years or so, we're trying to say what, you know, what's the, you know, like California, what's California gonna be like in the, in 50 years time? And, you know, a key question, which, you know, for example, a key question to ask will be, how does climate change affect the likelihood of these Laninia events, you know, because Laninia is certainly a contributory factor for all the droughts and the fires and things that you've had. So it becomes a complex question.

And I very much am of the view that the sort of computing capabilities that a university group would have, or a typical national institute, you mentioned, the San Diego, Scripps Oceanographic Institute, world-leading Institute. But it doesn't have the compute power to do this type of calculation. And I would, you know, what by concern for climate change, what I'm really arguing, for is dedicated absolute top of the range supercomputing, which these days is XA scale, 10 to the 18 flaps per second, floating point operations per second, dedicated to climate change. And that's not happening at the moment anywhere around the world, but a kind of an international consortium.

Actually, we're not talking about billions. We're talking about maybe one to 200 million a year. So it's not a big deal. It's a, you know, it's a, it's a satellite program. or something like that, I don't know. So for me, it's kind of a no-brainer. It comes, you know, if I'm talking to a climate skeptic, I would say, look, you surely are as interested as I am or as a climate, you know, somebody that's very concerned about climate change, to know the truth, you know, how bad is it going to get?

This is the only way we have to solve this problem. Computing or solving the laws of physics is the only way we have to deal with this. We can answer this question. There's no laboratory experiment we can do to emulate climate change. Is it right? Is it even possible, though? I mean, you make a very, very clear distinction in the book between things that are complex systems and things that are merely complicated.

I would say like building a 787 jetliner is incredibly complicated. It's hard to do. You could take an individual, you know, a billion years to do it. But if he or she follows the instructions, has the raw materials, puts them into parts and et cetera and et cetera, it can be done. On the other hand, you know, it seems to me the smallest possible, and you should define from my audience the various definitions of complexity, so forth. But the smallest possible system that's capable of modeling global climate and or even weather is the climate itself. In other words, it's kind of an old school analog computer. And unfortunately, you get the results after the event.

But there's no letter from God that says you have to be able to predict things in the future, right? So what do you make of the fact that perhaps it's not just mere computing power? And I'm saying mere, I don't mean to denigrate it at all. and I think it's absolutely necessary. But maybe that's not enough. And could it be the situation requires a literal global-scale computer, not just a global collaboration of computing power? Well, let me just say, look, why are we trying to do this in the first place?

And I would say perhaps the number one reason is that countries around the world now are having to face the fact. that climate change is a reality and part of the spending is going to be on how to make society more resilient how to adapt to climate change you know the climate change that's already going to happen you know Pakistan great example I mean what do if these things are going to hit these floods that we've had in the last couple of weeks going to hit them every few years now with kind of unbelievable intensity. How is that country going to adapt to climate change? But there's a sort of subtle question, actually even taking Pakistan in mind, because at the moment,

we're kind of in a very much a sort of transition phase where the weather patterns are staying pretty much as they are, but the temperature is getting warmer, you know, the air, holds more water, so when it rains, it rains more. But in the next couple of decades, what we're going to start to see are more dynamical effects. So the circulation patterns are going to start to change. And this is really a key question then. If you're adapting to climate change, if you're a country in Africa or Southeast Asia, you have to decide is my greater threat drought and heat wave or is it storm and flooding and storm surge and that sort of thing.

And very different types of expenditure are going to, you know, you're going to spend money on completely different things if you think the main threat is from droughts and heat waves or if you think it's from storms and floods. So we absolutely need this information. And as you say, we can wait for it to happen. That's the kind of analogue, if you like. And the problem is, you know, again, I mentioned this in the book, that, you know, one of the, you know, at the turn of this millennium, the Clay Mathematics Institute announced the most important mathematical problems that faced the mathematical world at the turn of the millenniums,

kind of mirroring what David Hilbert did in 1900 announcing what were then the key problems. And one of the millennium problems was actually about the Navia Stokes equations. And you could interpret this problem as implying that we don't have a kind of Uber theory which will tell us if you get your model grid. down to one kilometer, say, you will simulate the climate with an accuracy of 95% or something like that. I mean, I just wish we could do that because then I could go to the politicians and bang my fist on the table and say, look, I can prove mathematically, if you give me 100 million year, I will, you know, simulate climate to 95% accuracy. And I can't do that.

But all common sense and all experience in weather forecasting on much shorter time scales tells us that as we increase the resolution of these, we reduce the size of these grids, which means we increase the resolution of the models, which means we're solving these equations more accurately, then we simulate things better. We get extremes better. We get all these things better. So all the, you know, all the, all the, all the, uh, kind of evidence is pointing to a kind of a modest investment in a, in, you know, one or, or a few international institutes that can put dedicated exoscale computing to get us down to kilometer scale grids is going to make a big change. And it seems much more. Which are pretty inaccurate at the moment. I have to say on the regional scale, you know, I, you know, I, you know, I don't know whether your listeners will want to know this. but if you are this country in Africa or Southeast Asia that I mentioned, and you look up the IPCC figure, the figure in the IPCC report for how rainfall is going to change. Is it going to get wetter in your country or drier in your country? You'd think that was a pretty basic question that everyone would want to know.

Is it going to get wetter or is it going to get drier? For pretty much, you know, most of the globe, the models just don't agree. There'll be this little crosshatch region of the duck figure, and you consult the legend, and it says, crosshatching means the models don't agree. So this is a pretty unsatisfactory, I would say, state of affairs. And it's coming because, well, it's coming because we parameterize all these processes which are important because we don't have the computing to solve the equations. exactly and the parameterizations are exceptionally uncertain and it seems to me that you know governments are willing to spend you know enormous amounts of money we just had a bill in america you know good fraction of a trillion dollars and and uh the analysis that i've seen from independent organizations it'll

reduce temperatures global temperatures by less than a tenth of a degree Fahrenheit in by the end of the century. And, you know, whereas for, as you say, you know, literally a thousandth of that amount of money, you could have, you know, exoscale computers dedicated to this. And yet I still wonder, you know, because COVID does play a role in the book and in your thought process, you know, I remember back in January 2020, I had been invited to go to China to speak in Tibet, of all places where they're building a C&B observatory and they wanted to have show it to me. And I thought, that's great and I got excited. I never been to China.

And I asked, I was at dinner at a Friday night, Shabbat dinner and one of my friends had here in San Diego. And one of my friends told me he's been to China many times and he's like, I don't think you should go there. You know, this is early mid-January. You know, because there was this COVID-19. I'd heard about it. But I was like, you know, I've heard all these things and SARS and this thing.

And yeah, okay, hopefully by April, it'll be resolved of 2020. I mean, it is January after all. The invitation, it's like, I'd look a little bit closer. Needless to say, it wasn't resolved and it might not even still be resolved. Some parts of China are locked down in the end of 2022. But given that we in the U.S. at least, and I assume in other countries as well, we have, you know, multi-trillion dollars worth of three-letter agencies, you know, from the NSA and the FBI and the CIA and the CIA and the CIA. We have all these spy agencies, all these. And they didn't even get, you know, the fact that we should order 10 cent masks for everybody or, you know, that we should start working on stuff.

And not all of that was, you know, because of who is the president or who control Congress. I think it was a failure of these huge, you know, sclerotic organizations to translate their discoveries or maybe they didn't know. And that terrifies me more. If you pay somebody a trillion dollars, the very damn least they better know is that, well, you know, China just built this hospital in eight days that can hold 10,000 people. Why would they do that? I mean, forget about the micro biology of the organism, just a practical on the ground fact. We didn't do any of that. And my friend knew all of that in January of 2020. He's not like some spy for the government. So, you know, I guess the question I always have is how do we translate, you know, politicians are very quick, as you know, to clamor for, you know, their particular scientific findings.

if it agrees with their persuasion politically. But how do we actually get them to listen to people like you and your colleagues to say, look, no, we don't need to like, you know, outlaw gas powered cars in America, you know, in 2020, 35. What we need is $200 million a year for 10 years to get better forecasting so that we can adapt as humans are much better at adapting than we are predicting. So how do you bridge that gap? I mean, you have a share of a Nobel Prize, which is sitting in your, office there. How do we get them to listen, not just to the science that furthers their political goals, but to listen to the true science, the good science that you and your colleagues are doing?

I mean, it's a tough, it's a tough problem. I mean, as, you know, as a scientist, you know, I can't make decisions. And I, in a way, you know, all I can do as a scientist is, is lay out the science as clearly as possible. And we just have to hope that the politicians get it. I mean, the politicians did get it eventually, at least here in the UK, they did get it with COVID, although they were very slow on the uptake. I think the science, of course, the science was pretty uncertain in the initial phase, because we would, I mean, this COVID was an unusual.

I mean, the thing that made it difficult was the fact that a lot of people were asymptomatic. They were carrying it and they could spread it, but they didn't really have the symptoms themselves. So unlike normal or more kind of familiar types of diseases, you know, where you get ill and there you automatically go to bed and you isolate, this was one where lots of people were getting the COVID virus that weren't symptomatic of. of having problems. So there was a lot of challenges for the science in the early phase. But I think in this case, the politicians, at least here in the UK, they did get it. We had a lot of committees and things. But yeah, I mean, how do I?

I mean, I'm very active in at the moment trying to sell this CERN for climate change idea. I have to say, one of the friends. frustrations is that some, maybe this was like the real CERN back in the 50s, I don't know, but some of my colleagues, I think, you know, they are nervous that if a new institute was funded, the money would come from their institute's budget. So they might, you know, let's say if we're talking about San Diego, you know, if the US decided to go down this route, would, you know, would the Scripps Institute find that its budget,

you know, was being paired off a little bit to help pay for this? And that creates some, I would say, kind of nervousness amongst some of the community. And this is a problem because when I talk to, you know, the really big scientific advisors in the UK. I mean, the question they immediately asked me, do you, does your community speak with one voice? And I would say, well, you know, there are some people who,

and they would stop me mid-sentence, and they would say, come back where they speak with fun voice. That's never going to happen. And that, that, so that, that's, that's an issue, which, you know, which, which, which, which, which, which, which, which, which, which, trying to kind of get over trying to kind of convince that are friends that because we'll still need the kind of what I would call the conventional models you know there's loads of really important climate work for example understanding paleo climate variations I mean that

that you know that involves running models for thousands tens of thousands of years there's no way we're going to be able to run a one kilometer model over 10,000 years it's inconceivable So we'll need lower resolution models for doing some of the basic science and some of the paleo-climate work and things like that. So I don't think these people actually should feel concerned that their pet models are being somehow made redundant. It's just we'll need a hierarchy.

And for the really important kind of climate policy stuff, like next week I'm going on a committee. is another example about this climate geoengineering. Should we be spraying aerosols in the stratosphere? Yeah, around now. And I would say, how would you ever make that decision if you had models that had, you know, biases and systematic errors that were as large as the signals that you're trying to simulate with this thing? It's just, it just doesn't seem plausible that we'd ever sanction such a plan B without having a very good idea, for example, whether the monsoons were going to shut off or the moisture supply into the rainforest was going to shut off.

And we're not going to do that unless we have models that can really get these regional details, you know, done correctly and accurately. So I think there's plenty of, I mean, I hope there's enough, you know, this is such an important problem. There's enough space here. for a hierarchy of models from the very simple Lorentz chaos model through to these kind of 100 kilometer scale climate models, which you would use for paleo work through to the one

kilometer models, which are going to inform policy for the next 50 years or so. There must be room for all of us, surely. So I want to transition for the second half of the conversation into a more special. speculative ideas, perhaps, but maybe not. And I couldn't escape, you know, thinking about the connection between quantum mechanics of interpretation and foundations of quantum mechanics, which are quite, quite, you know, popular and have, you know, been receiving a lot of attention lately. Many books written about it. But also, I think yours is the first book to really attempt to make a connection between, you know, as I have in my notes here, between the butterfly and the bell, you know, bell being

Bell's inequality. Right. So I wonder, first of all, if you can, you know, if you, if you don't mind kind of recapitulating, what is Bell's theorem? What is, what is real if you don't mind in a few minutes? What is the nature of Bell's inequality? A lot of people hear it. We've done interviews Adam Becker, wrote a book called What is Real about it? But a lot of people, you know, kind of, what's a big deal? You know, if I go into my sock drawer and I've got a pair of red socks and a pair of white socks, and they go on a holiday, open up my suitcase, and I've got a pair that's a red sock and a white sock. I know for damn sure in my drawer at home there's a white sock and a red sock too. So what is this about the, we should have Sabina here, you know, to actually quote, to quote the exact term that Einstein used,

Germantzachap de Shreth, wash, and fresh. I don't know. I do know that she said that this book, your book, is a whirlwind. It's partly scientific autobiography. and partly a manifesto of a visionary Tim Palmer masterfully weaves through climate change, quantum mechanics into one coherent hole. He is a revolutionary thinker way ahead of his time. So let's weave together the butterfly and the bell jar. What is the bell inequality?

What does it tell us? Is it just kind of a mathematical audit? Well, let's start with the butterfly because the butterfly effect is a pretty, it kind of conforms, I think, to what most of us think of as uncertainty. The idea being that, you know, we can't, you know, clearly we can't observe all of the butterflies in the world that flap their wings. Correct. I mean, the butterflies themselves can see their wings flapping.

They're not at all uncertain about the fact they're flapping their wings, but we humans can't see all of that. So really what the butterfly effect is saying is that, The uncertainty in a weather forecast comes because we don't have perfect information about, you know, the starting conditions for the weather forecasts. So a philosopher would call that epistemic or epistemological uncertainty. It's a kind of a lack of knowledge about the system that you're interested in. Yes. And that kind of characterizes, I would say, most, you know, most things in life that we don't know things because we can't.

know everything about the system that we're interested in. And of course, you know, Einstein, that's kind of how Einstein felt quantum uncertainty should be too. It's reflecting something that we don't know about the electrons or the photons, you know, that's going on at a smaller level than we can possibly see. But Bell's theorem, Bell's inequality, or the Belgian, whatever you want to call it, is probably the single most important, well, it's a theoretical result that has been tested experimentally, which I think most, probably most physicists would say,

kind of really, I was going to say cast out, but that's too weak a phrase. Most physicists would say it disproves Einstein's view. Right. And so basically, as you say, it's kind of based on, it's based on an idea or a word which Schrodinger came up with when he actually when he was at Oxford, which is this notion that quantum systems can be entangled. And as you say, that that gives rise to correlations. You could say it's a bit like, you know, two particles go off and one's, got a red sock and one's got a white sock or something. So if you observe the red sock, you know the other one is certainly the white sock, except that it's not like that because what Bell,

so Bell was an Irish physicist who worked at CERN, and he realized that if that was what was behind this entanglement, that one electron had a red sock on, and if you measured the red sock, you could determine that the other electron going in the other direction had a white sock. if that was all there was to it, then Bell realized that these, you could, you could look at these correlations. I mean, basically, it's about measuring the spin of particles or the polarization of photons.

And you measure them in different ways. And the correlations, you know, the result is a mathematical one where you sum up these correlations and if if uncertainty really was the fact that you you know we didn't know whether the electron was wearing its its red sock or its white sock if that's what it was then this kind of sum of correlations that bell came up with would be would would have a largest value a value of two. So you sum correlations done with different measuring, different polarizers, and you come up with this thing which says if everything is deterministic and the uncertainty is just due to the fact that we don't know everything about the electron or the photon,

then this correlation function, summing over correlations from different experiments, is kind of bounded by the number two. And you do the experiment and you find for certain settings of these polarizers this number exceeds two. So there's something gone wrong with your assumptions. And most physicists would say that the assumption that goes wrong is that uncertainty is epistemological. And that really what's happening is that the equations of quantum mechanics are intrinsically uncertain. It's not like it's like the butterflies or the electrons if the electrons are the equivalent to the butterflies The electrons don't know what they're doing the electrons are uncertain

And since quantum mechanics is a theory of everything, you know, it's describing the world It's believed at least to be a theory describing everything in the world Then everything in the world at some sense including reality is uncertain and that is the sort of I would say the sort of standard view now there is there's there's a there's a There's another way around it, but it's kind of, I mean, it's kind of just as bad, which is to say that when you measure the electron and you see it's got a white sock, that somehow makes the electron on the other side of the experiment have a red sock. So prior to you actually doing that measurement, the electron sort of had neither a white sock

or Red Sock, but doing a measurement on one particle somehow instantaneously makes the other particle have something definite. Now, that again, you know, Einstein didn't like that, quite rightly. He called it spooky action at a distance. The idea that doing something here can instantaneously change something over there. So we kind of left with, I think, a very unsatisfactory state of affairs, but the consensus view, I would say, amongst the majority of physicists who think about this problem, is that uncertainty in quantum mechanics is, so the buzzword would be ontological, rather than epistemological, ontological meaning it's kind of the notion of reality is uncertain. You said this place was steps from the water.

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And from the sort of perspective of my kind of chaos background, and this is what I like to think I'm bringing something which maybe the standard quantum community haven't quite sort of seen in this way, which we spoke a little bit about earlier, which is this notion that when you go through Bell's theorem carefully, you imagine a particle and you imagine an experimenter doing an experiment on a particle. But to prove the bell inequality, you have to assume that had the experimenter done a different measurement

to the one he or she actually did, then they would have got some definite result. And this is sometimes called counterfactual definiteness. And, you know, in life we tend to, I mean, we all use kind of counterfactual reasoning. to some extent, oh God, if only I had done this and not done that, you know, life would be so much better now. We kind of assume that it makes sense to talk about these counterfactual worlds. But I tried to argue from the point of view of chaos theory, it's not a given that a counterfactual world is consistent with the laws of physics. And we talked earlier about these fractal attractors with their gaps in them. And if you're counterfactual

world ended up in one of these gaps, that would tell you and the states of your system had to lie on the fractal, then that's telling you that counterfactual that you've just thought up is actually inconsistent with this geometry. So the short answer is, I personally think, and I have to say, I'm saying this without, you know, probably if you polled an average physicist, they might say, well, maybe it's interesting, but I don't, sure. I believe it at the moment. So this is a controversial point of view. But I think there is a way around the Bell theorem which doesn't require you to give up on this notion of a deferrent reality or have to invoke this spooky action at a distance. And that's actually why I wrote a paper

with Sabina Hosenfelder, because it comes under what some people call the superdeterminism, which is not a word I'm terribly keen on. But if it is the case that counterfactual reasoning can sometimes fail you in quantum mechanics, and as I say, I think it can do for perfectly rational and sensible reasons, then your model belongs to this class of super deterministic models. And I think that is a much more plausible way to understand quantum uncertainty. And it really, what it does then is it brings you back to what I think both Einstein and Schrodinger would have been much more happy with. And others as well, Dirac was pretty unhappy with quantum mechanics, which is to say that quantum uncertainty is actually epistemic and it's not ontological.

The laws of physics at their deepest, I think are certain and definite. And that uncertainty ultimately is our uncertainty. And what about the person practicing the epistemology or the ontology? In other words, what about the role of consciousness? As you know, your fellow Oxonian who endorsed this book wholeheartedly saying the very delightful praise that Tim Palmer's book, The Privacy of Doubt, provides a remarkably broad-ranging account of uncertainty in physics in all its various aspects. I strongly recommend this highly thought-provoking book. As you know, Roger's been on the show many times and this good friend. And he and Stuart Hammeroff were on a month or so ago on the occasion of Sir Roger's 91st birthday.

We celebrated his 90th birthday last year. And when we discussed it, we got into kind of notions of quantum mechanics in the brain. And we also explored, I asked him to steal man the many world's interpretation, which you know he doesn't believe in. And, you know, what if any role that has on or could play in what he calls orchestrate? objective reality. Now you have this collapse that is caused, as my limited understanding has it, you know, from some gravitational interactions involving spacetime curvature. You've written a lot about his conformal cyclic cosmology.

We'll maybe finish up with that in a little bit. But when we talk about the brain and quantum mechanics and determinism, what role does the observer play in your notion of or what you believe to be this sort of, you know, maybe stochastically and noisy brain that you've also had a recent paper about, and you talk about it in the book. So first of all, noise in quantum systems and biological systems. What is the role? And is it merely adding, as you said, you know, viscosity, artificial viscosity, somethings converge and don't blow up?

Or is it really a manifestation of nature and turbulence and so forth is a feature? is a feature of the brain and the noise therein needs to be comprehended as well before we even get to a quantum understanding of consciousness. What is the role of noise in consciousness, if any? Right, right. Can I just start with one point you may, because you mentioned the word collapse, collapse of the wave function. So in the model which I propose, there is no collapse of the wave function because the wave function is representing an ensemble of states on this fractal attractor. So what a quantum theorist would call measurement is actually, you know, it's like ball bearings going into one of two basins of attraction, you know,

and some of them will cluster in one basin and another set of ball bearings will cluster in a different base. And basically that is what I see as measurement. It's actually the, it's where non-linearity starts to affect the quantum world. And it's clustering trajectories in discrete clusters. Now the human, I think consciousness doesn't, and I think Roger would agree with this, actually. Consciousness doesn't play any role at all in creating, in doing quantum measurement. But where I would sort of take perhaps some departure from Roger is that I don't think there is any collapse at all of the wave function. It's just a kind of transition from a very sort of, I have a particular geometric model where you have trajectories in like a helix and they're kind of rotating like a piece of rope.

They kind of, they rotate around each other. and then measurement is a kind of splaying of the ends of the rope and bits of the rope go into one cluster of states and other bits of rope go into another cluster of state and these would be called the measurement outcomes now so so that that's just on that but on consciousness and noise so there is actually an interesting kind of story which is that I've been trying to promote the idea I think I mentioned earlier in our talk that there is a good reason to add noise to a climate model to represent the degrees of freedom you can't simulate explicitly. And you can show that adding noise actually does help improve some aspects of the climatology of the model. And in the book, I have a whole chapter where I, again, this is another area where non-linearity plays an important role. In a non-linear system, noise can be your friend. It's not your enemy. You know, typically we think of noise as a nuisance that we try to kind of minimize, but in a nonlinear system, noise can actually help amplify a signal.

And I've given several examples in the book of how that can happen. Now, I've, so I've been arguing for some years now that noise in climate models is a good thing. And I think probably most people would agree with that now. But still, to this day, we generate the noise by, you know, something I think that von Neumann originally started with, which is what's called a pseudo-random number generator. In other words, it's a piece of code, which is perfectly deterministic, but it kind of emulates randomness. And, of course, you know, it takes a certain amount of computational energy to run a pseudo-random number generator. So why not make use of noise in hardware?

So, you know, we've got all this noise of the electrons going through the chips and so on. And a colleague of mine, Krishna Palem, has pioneered this notion of producing kind of noisy chips where you turn the voltage down across the transistors. So they no longer act as completely bit reproducible deterministic systems. The point is with that is you can then produce. noise with negative energy costs because you've turned the voltage down. You're using less energy than you did before. So when I proposed this, I had a kind of commentary in nature proposing that our

supercomputers, you know, should have cabinets. Some of the cabinets, not all of them, of course, but some of them should be deliberately stochastic where we would run, you know, we turn the voltage down across the transistors. We'd be using less energy. So they'd be kind of noisy computationally. And people thought this was a bit of a weird idea. So I had to try and think of an example where, you know, in reality, the system does do this. And I think the brain is a good example of this.

So the brain, you know, the brain does phenomenal amounts of data processing on 20 watts of energy. It's typically six orders of magnitude less than a supercomputer does, uses, which is more like 20 megawatts of energy. But for the same amount of data processing. So what's going on here with 20 watts? Well, we've developed these incredibly slender neurons with very small diameters, 0.1 microns or whatever. And as a result of that, we've managed to cram 80-odd billion neurons into our brain.

So when you calculate how much, if you like, energy or power there is per neuron, I mean, it's just a microscopic amount. And the question is, is it possible that the transmission, rather, of electrical energy along axons is actually susceptible to noise? And the evidence, all the evidence is for these very slender neurons that axons that are in the human brain, they are susceptible to noise. Okay, so is that a bad thing or a good thing? And in the book, I try to argue, it's a good thing. And it's probably what's made us the kind of creative species we are.

And it's interesting. You talk about, I mean, Roger Penrose is a great example of a phenomenon, which is pretty much universal, I think, amongst, you know, people have had eureka moments. Because their eureka moment doesn't happen when they're, you know, hard at work thinking about the problem they're trying to solve. It happens in a completely kind of at a time when you're, you're multitasking when the brain, all the neurons in the brain are doing different things.

And in Roger's case, he was crossing the road, he was talking to a colleague of his, he was looking out for traffic. And then suddenly this idea, which turned out to be the thing that got in the Nobel Prize, about how to define an event horizon in a kind of generic way, came to him. And this is very common that people who have, when they have their brilliant ideas, it's when they're relaxing or just, you know, doing nothing in particular. And I think this is where the brain, where noise in the brain is most likely to have its biggest effect when we're multitasking. We're looking around us. We're hearing things.

We're walking down the road. We're perhaps talking to a colleague. Then suddenly out of nowhere we'll get some idea. Now, of course, we have to then analyze whether that crazy idea is any good or not. And nine times out of 10 or maybe 99 times out of 100, it's not much good. I mean, Michael Berry, the theoretical physicist at Bristol, has a great expression for this. He talks about these ideas that come out of your subconsciousness as claritons, like a particle. But he says the problem with claritons is that in the cold light of day, the logical analysis,

which he calls an anti-clariton, destroys the claritone, a particle, anti-particle annihilation. But it kind of makes the point that, you know, actually there are two parts to this. One is just the randomness of having these ideas, and then the other is the fact that we have a kind of a more deterministic side to our analytical processing, which kind of then decides whether the idea is any good or not. And it's the synergy between the randomness and the determinism that probably is what makes us the creative species we are. So anyway, the point is, I think that noise plays a really important role in human creation. creativity and we'd not be the species we are if if our neurons were more deterministic. So that kind of provides a good example of why having noisy hardware, you know, might be a good thing. And I would definitely think that, you know, for improving AI or the prospects of AI being really

intelligent, we probably need to go down that route. Yeah, well, you brought it up so I can't help but, you know, kind of use one of my favorite examples, which you probably know, doing your PhD with Dennis Skiamah, who's a Titan of 20th century physics and cosmology. But do you remember what Einstein called his happiest thought, Tim? Here's Einstein here. I do, but it slips my mind. I'll give you a hint, Tim. Here you go. Here's a hint.

Oh, I'm falling in the elevator. Yes, that's right. That's right. that's right I mean, you know, I'd love to know where he thought that. Do you know? Where did he have that thought? I don't, but bringing on your work.

I bet it was in the bath or Yeah, or maybe playing the violin or something. Yeah, exactly. Relevant to your statement that, you know, AI and, you know, that we'll have to incorporate some of these features, you often hear that, you know, Einstein, or sorry, that, you know, AI is going to be capable

of reproducing the works of, you know, the greatest minds and science. and even going beyond them. But that statement has always kind of been a counterfactual piece of evidence against those claims that Einstein said, my happiest thought, A, how could a computer have a notion of what makes it happy? Or two, how could such a computing device have a notion of what free fall is? I have a computing device here, you know, in my, I can make it do certain things like computer,

off the lights, turn off the plug, computer, turn off the plug. There we go. So it'll do stuff for me. It won't unplug itself, though, Tim. I try to get it to do that, and it wouldn't do that. And I won't open the pod bay doors either. I've tried it. It sounds a bit...

I tried that with Sean Carroll. It didn't work. So how optimistic should we do? You talk about quantum computing in the book. Is that potential, you know, boon for either topics of of foundations and quantum mechanics or in predicting the weather or something like that. Well, I mean, we're getting onto a big, we're getting into a big era here. I'm, I'm, you know, I'm, if we want to talk about quantum computing, okay, I've just written

the paper which I'm submitting to the bulletin of the American meteorological society. Oh, wow. About whether quantum computers will ever be used for weather and climate prediction. Wow. And I've called it the good, the bad and the noisy. because, you know, there are good points. And the good point is that, you know, I mentioned about billions of degrees of freedom,

but, you know, you don't have to have that many qubits, which are fully entangled before you have billions of degrees of freedom. So 30, I think 30 or something, 2 to the power 30, would give you roughly that sort of thing. So from that point of view, it kind of looks good, but there's a big negative, which is at the end of the day, you have to measure these 30 cubits. And it's a bit like a, you know, it's like saying instead of, you know, with a weather forecast model now, you just say, you know, print out the temperature, you know, in the US and

you'll get values for temperature at different places and stuff like that, or rainfall. But the quantum computer, you have to say, is it raining in New York? You have to ask it a question that it will just answer yes or no. And you've only got 30 of these questions to ask. Is it rainy in New York? Is it snowing in Cape Town? Is it sunny in London? And soon the computer will say, I'm sorry, you've run out of questions. That's it. And that's absolutely hopeless, you know, because the amount of data that people need across all the different applications, you know, is more than just 30 questions. So that for me is a, it is something you don't see discussed very often that, you know, there's the upside to the exponential size of Hilbert space is that you can simulate systems with relatively

few qubits with very large degrees of freedom. The downside is you better not want to know very much about your system because at the end of the day, all you can do is measure those 30 cubits. And that's just too much of a restriction. It's like saying, it's like saying, if all I ever cared about was how much the global temperature is going to increase. If all I care about is going to be two degrees warmer or four degrees warmer because of climate change. Okay, fine. But as soon as you ask questions about, is it going to rain in London? Is it going to be, you know, hotter or drier in Mozambique or something? Then it's all bets are off. It's completely hopeless. So that's one thing. The other thing I would say about AI is that AI is fantastic for interpolating, but pretty hopeless for extrapolating.

And the problem with climate change is it's a problem in extrapolation. You can't just take a look at past data and say, I know what's going to happen in 50 years time, even if you've got a super clever AI system. And, you know, so again, I think there's a role for AI. There's probably a role for quantum. but it's not a panacea. Absolutely not. Well, Tim, this has been phenomenally entertaining and elucidating educational for me,

and I know it is from my audience as well. And you've been so gracious with your time. I just want to beg your forbearance for a few more minutes, if you wouldn't mind indulging me and going into the impossible with my fantastic final four questions that I like to ask my guess. Is that okay with you, sir?

Right. Okay. So the first question, they'll involve in some way around your late countrymen, Sir Arthur C. Clark, but the first one involves your near-term future, although I hope it won't be for 50 years or more. And that involves what we call an ethical will. And even Alfred Nobel had an ethical will and his otherwise ethical component of his otherwise material will, in that the Nobel Prize was to be given to those who conferred the greatest benefit to mankind. And in that sense, he wasn't just leaving money, but he wanted to agitate towards the improvement of humanity. I want to ask you, in 50, 60 years, when you spring forth the mortal

coil that the immortal bard spoke about, what kind of wisdom, not just knowledge, but what kind of wisdom would you want to give over to the succeeding generations to come? Well, look, I tell you one thing that I feel privileged as a scientist, and I'm sure, Brian, you're probably the same as me, that we work in our own different fields, but science is very international, and you get to talk to people, you know, from completely different cultures and completely different countries. And the one thing you realize very quickly when you do that is that we all have the same problems. You know, we all have things which we're frustrated about in life, you know, getting the right kind of, you know, professional recognition, having to earn enough money to keep our families going. And, you know, all the sort of stuff, you know, it's kind of universal. And you realize, I mean, you know, I worked for many years at a European meteorological institute, which had, you know, people from every country in Europe. And, you know, we joke about, you know, you can joke about national stereotypes, you know, the Brits are pompous, so-and-sos, and they have no taste in food, decent food.

And, you know, the Americans, not the Americans, but the Germans, yeah, no, I wouldn't say anything about the American. Germans have no sense of humor, that sort of thing. So we can make jokes about that. But what you actually realize is that the differences between individuals is much, you know, even within a single culture, is much, much bigger than any kind of national stereotypical difference. Oh, yeah. And I think, you know, if I had to, it's not exactly a word of wisdom, but it just bothers me these days. And I guess I'm kind of slightly, I've got bothered by, in my own country, with Brexit, the fact that we seem to be moving away from our European partners and things more and more, that we're all basically the same.

We're all got the same issues, the same problems. And if, you know, you can find a Brit that's got a good sense of humour and you can find a Brit that's got no sense of humour. And that difference is infinitely larger than any kind of national stereotype between the Brits and the Germans or whatever it is. So it's really recognizing, if I had a piece of wisdom that science has given me, because science is so international, is that, you know, we're all basically the same. And we've, you know, the more we can, and we're the same on this, in this infinitesimally small planet that's going around the vast universe. more we can kind of come to terms with that, I think the better we'll get on as a human species. That's beautiful.

So you may know that Arthur C. Clark is famous for many pithy aphorisms. We'll get into some of them. But one of them was any sufficiently advanced technology is indistinguishable for magic. We actually open the audio version of each podcast with his actual voice. And just reminder, you guys can follow the podcast on video and on audio, Dr. Brian Keating, on YouTube and also on audio, wherever you get your podcast. And this will be available on publication day for this wonderful new book. But I want to ask you kind of in reference to Arthur's 2001 of Space Odyssey, the Sentinels, these weird kind of obelisks or monoliths that appear throughout

the movie. We don't really know what they are, maybe a time capsule. And it could be a warning about the future. But if you had access to a time capsule and you knew it would last a billion years, what would you put on it or in it? Now we're going deep into the future, well beyond your 120th year on the planet. Well, I think I would...

So I'm going to say something mathematical and I'll explain what it means. You'll know what it means. I would write the equation. It's not really an equation, but I'd write the sort of mathematical equation, which would say S-O-2 equals, sorry, S-U-2, S-U-2 equals S-O-3.

And underneath it, I would say, we know it's true, but we don't understand why it's true. And, you know, I think one of the quotes, I can't quote it exactly, from one of the greatest mathematicians of the 20th century, Michael Atier, who worked a lot with Roger Penrose. Yeah. He kind of made this point that

SU2 is a mathematical term for a group based on complex numbers. So these are numbers based on the square root of minus 1. And SO3 is a group which is about how you can rotate objects in physical space and they maintain their symmetry, like a sphere, you can rotate and it still is a sphere. Now, these two are related, but, you know, Atia himself, so don't, you know, I'm just getting this from him. He's one of the greatest mathematics. He was one of the greatest mathematicians.

He said that the complex numbers are somehow the square root of geometry, but we just don't know really what it means. And I, my kind of, all my gut instinct is that if we had to understand something that would take us further, deeper into. fundamental physics. It's really understanding what this relationship really, really, really means. And so I kind of put that in my time capsule because I hope in a million years, they would say, aha, he was right. He was right to question that because we've only understood it 10 years ago. And it really opened the door to quantum gravity and the mystery of the universe and everything. Yeah. So I'd love to talk a bit more about that. But if I write another popular book, I'm thinking I might actually, that might be kind of a theme of it. Because I feel it's really, really something that's important that we just don't quite understand what it's saying. Yeah, that is fascinating. That would be in my time capsule. I've always been fascinated by this operation that you can do in classical mechanics, which is to form a Poisson bracket. And you do the exact same thing in a quantum mechanical setting. You make a Poisson.

on bracket. And you get zero in the classical commutation, anti-commutation relations. And you get, you know, you get the square root of negative one and this funny constant, you know, Planck's constant emerges. And all you, I don't know, it must be something very deep, right? Because it's, it's saying that not only do you get non-vanishing, you don't get zero anymore, a rather important round number, as Julian Barboor used to say on the podcast. But you get this square root of negative of one. Why is that? Where does that come from? And then presumably, I don't know. I mean, you're the right person to ask, but I mean, could you keep going? Are there higher way, order ways of taking the analog of commutation relations that would give you quaternians and

octonians and Clifford algebras? And I mean, is there a whole other set of reality? We just think quantum mechanics is hard. But really, it's just the analog of, you know, the square root of negative one. And we know there's a lot more richness, like you just said, SU2. I don't know. Maybe I'm just hopelessly out of my depth here. But I find that. I think it's a great question. I'm sort of worried that if we're getting towards the end of the podcast and I start launching into something. Okay. Fair enough. Let's do that out part two. So you'll write. So I'm just going to say it's a really, I think you're, you've absolutely hit the nail on the head. It's a terrific question. And, you know, that's what we've got to focus on. All right. Great. Last two questions really

quickly. So Arthur said things like for every expert, there's an equal and opposite expert. But he also said when an distinguished but elderly scientist states that something is possible, they are almost certainly right. When they say something is impossible, they are very probably wrong. What, if anything, I'm not calling you elderly, but what, if anything, have you changed your mind on recently or most pronouncedly in your life in science. Well, okay, I'll give you an example, and I say this, you know, you've probably had the same experience as me, or maybe not. Maybe all your papers have been published, you know, with minor, only minor comments from the referees. But occasionally we have papers where the referee says,

this paper shouldn't be published, you know, should reject. And our initial reaction is to say, this guy is an idiot. You know, how did they choose? I say him, you know, him or her. I would say her, yeah, you got to be a her, yeah. But I have to say there's one occasion where I'm just so happy that the referee rejected the paper. And this was, you know, when the ozone hole was discovered

by balloon measurements over Antarctica, it kind of took everyone by surprise, because people had predicted that ozone was being destroyed by the, you know, the chlorofluorocarbons from aerosols and so on. But then, you know, since we spray most of this stuff in the northern hemisphere, surely it would be the ozone over the Arctic that would be destroyed. So I kind of thought, well, okay, this probably suggests to me that this isn't actually a human, effect, but it's something in the dynamics of the southern hemisphere climate that's causing this ozone hole. So I wrote a paper with a colleague and sent it to nature making a kind of hypothesis that it was caused by some ocean. It started off in the ocean, the southern oceans, and then the effect

propagated up in the stratosphere and the circulation changes affected the ozone and stuff. and it wasn't rigorous, but we sent it to nature, and it was rejected. And, of course, at the time, I was incensed, you know, but nature, it wouldn't. I mean, once your paper's rejected by nature, that's it, you know, forget it. But my God, am I happy that it was because this was completely wrong. I mean, it turned out that it was actually caused by human aerosol, you know, emissions. and it was a kind of complicated chemical reaction that only happened in the Southern Hemisphere

because the stratosphere is so much colder in the Southern Hemisphere than the Northern Hemisphere. So the temperatures weren't cold enough for this chemical reaction to happen in the Northern Hemisphere. So that was something where I was completely wrong and I was so happy that never made the light of day. Good. Those are the kind of errors you'd like to be wrong about.

like Einstein, his biggest blunder turned out to be a great discovery. Okay, last question, Tim, and I thank you so much. I know it's getting late there. Going backwards in time, Arthur C. Clark's third law states the only way of discovering the limits of the possible is to venture a little way past them into the impossible. That's the origin of the name of the podcast. I want to ask you, we have a lot of young scientists that listen to this show in their 20s and 30s. What advice would you give to a young Tim Palmer back in that epoch?

of your life decades ago to give you the confidence, the courage, the charisma to go into the impossible as you've done. Advice to your former self. Well, I guess there are two things. I would say one is read broadly, because I am a great believer in that sort of 90-something percent of new ideas in a particular field come because somebody has made the connection with with some technique that's used in a different field. So read broadly don't get too siloed, you know, into one particular thing. And the other thing is, which kind of comes back to what we were talking about, about the brain and creativity. I mean, if it is the case that you have your eureka moments when you are relaxing, then make sure you give yourself enough time to do that. I mean, don't spend

the whole day stuck in front of your laptop or your desktop computer or something, just kind of agonizing about some piece of code or some equation or other. You know, make sure you have time to do nothing because that's when, as long as you've got the background, you know, your brain needs the basic material there to make the connections. But the brain does its own thing in making those connections and just give it the time to do that. And if you're just all spending day and night just focusing your concentration on some scientific problem, you're not going to solve it. So, you know, don't, yeah, lead a balanced life, I would say.

Don't get, don't get sort of, you know, seduced into spending 20 hours a day focusing on some arcane problem. pick up a guitar a banjo absolutely a cricket paddle absolutely a golf club you have so many interesting instruments in the back i can't believe you're not a string theorist tim palmer uh such an incredible polymath it's true um this book uh written uh with such um i'll show you the oxford this is the oupp version got a slightly different front cover so if you're Reading this in the UK or Europe or some way, you have a slightly different cover. Different cover. Same content. Same content. Has all the ewes and the flavor and neighborhood and all those good things. It makes a worthy compliment to this book. Look, it has a butterfly on the

front of it. You appear in this book very prominently. And Sabina kindly appears on the cover, at least in endorsing with her heartiest encomium of this book. Tim, you are. a polymath incredibly impressive. I would like to spend more time at some point, maybe in the occasion of your next book or when I'm in the UK, I'm supposed to be there next year, come and visit, and I'd love to talk about, yeah, hear you play those instruments in the background. That would be such a treat. And so now I want to thank you and wish you a wonderful evening over there across the pond and hope for the best of success and congratulations in my heartiest sense on this wonderful book. It's really a joy to read. I devoured it in both,

formats and listeners should as well. Thank you, Tim. Thank you, Brian. It's very kind of you. Thank you. Any sufficiently advanced technology is indistinguishable from magic. Okay, that's a wrap on this episode and Into the Impossible Podcast. Hope you enjoyed this wide-ranging conversation with Tim and hope you'll check out some of my

past podcast with Sir Roger Penrose and with Sabina Hossenfeldar, folks that we mentioned on the show. And if you don't mind to do me a couple of quick free favors. One, join my mailing list, Brian Keating.com, slash, list. You may even win a small piece of space schmots, some space dust, a meteorite if you're among the lucky few to get selected at random. Not everybody wins, but everybody wins in a certain sense because you'll join the most magnificent minds in the multiverse on the mailing list. I summarize all these conversations, things I learn, things I want to know more about and cool

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So thank you for joining me on this Think Like a Nobel Prize edition of the Into the Impossible podcast. If you're listening to this, The Impossible, I also have another podcast where I take all the Nobel. all prize interviews only and put them all into one place. So recently we've had interviews with Huido Inbens, who won the economics, Nobel in 2021, and this is the first Peace Prize winner. I've ever had on a Tim Palmer. Wow, agitating for peace. Someone sent me a picture of my second book Into the Impossible, which is at the Nobel Peace Prize Museum all the way in Oslo. I thought it was cool. It's right next to Mother Teresa. Where I belong, damn it? I mean, who's done more for World Peace besides me? It's got to be Mother Teresa. So she's in good company. Just kidding. Just kidding.

I think the dad jokes would have scared you off by now. But no, you're still here enjoying it. Have a magical rest of your week, everybody. Thanks a lot. Ambition comes in all shapes and sizes. At First Citizens Bank, we roll with your goals because we're built for what you're building. Fit for your ambition.

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