Podcasts about brownian

Lancelot 'Capability' Brown is known throughout the world as the master of the English landscape garden. A visionary who created carefully curated, idyllic and natural-looking landscapes at many of the greatest country houses in England, Brown is one of those true 'greats' after whom a whole style is named; the 'Brownian' landscape continues to inspire and entrance gardeners, landscape designers and punters alike.But Brown's style wasn't without controversy... many felt that his reckless sweeping away of traditional formal gardens and parterres (replacing them with lawns, ha-has and rolling parks) was the height of vandalism.In this episode, Geoff gives Rory a whistle-stop overview of the life of Capability Brown from his humble birth to sudden death. We discuss the controversies surrounding Brown's style, and reflect on his extraordinary genius and long-term legacy.Please join us for a 'walk in the [Capability Brown] park' and if you like this episode please like it and write us a review. Please also send in questions for our soon-to-be-resurrected Q&A episodes!

“To navigate proof, we must reach into a thicket of errors and biases. We must confront monsters and embrace uncertainty, balancing — and rebalancing —our beliefs. We must seek out every useful fragment of data, gather every relevant tool, searching wider and climbing further. Finding the good foundations among the bad. Dodging dogma and falsehoods. Questioning. Measuring. Triangulating. Convincing. Then perhaps, just perhaps, we'll reach the truth in time.”—Adam KucharskiMy conversation with Professor Kucharski on what constitutes certainty and proof in science (and other domains), with emphasis on many of the learnings from Covid. Given the politicization of science and A.I.'s deepfakes and power for blurring of truth, it's hard to think of a topic more important right now.Audio file (Ground Truths can also be downloaded on Apple Podcasts and Spotify)Eric Topol (00:06):Hello, it's Eric Topol from Ground Truths and I am really delighted to welcome Adam Kucharski, who is the author of a new book, Proof: The Art and Science of Certainty. He's a distinguished mathematician, by the way, the first mathematician we've had on Ground Truths and a person who I had the real privilege of getting to know a bit through the Covid pandemic. So welcome, Adam.Adam Kucharski (00:28):Thanks for having me.Eric Topol (00:30):Yeah, I mean, I think just to let everybody know, you're a Professor at London School of Hygiene and Tropical Medicine and also noteworthy you won the Adams Prize, which is one of the most impressive recognitions in the field of mathematics. This is the book, it's a winner, Proof and there's so much to talk about. So Adam, maybe what I'd start off is the quote in the book that captivates in the beginning, “life is full of situations that can reveal remarkably large gaps in our understanding of what is true and why it's true. This is a book about those gaps.” So what was the motivation when you undertook this very big endeavor?Adam Kucharski (01:17):I think a lot of it comes to the work I do at my day job where we have to deal with a lot of evidence under pressure, particularly if you work in outbreaks or emerging health concerns. And often it really pushes the limits, our methodology and how we converge on what's true subject to potential revision in the future. I think particularly having a background in math's, I think you kind of grow up with this idea that you can get to these concrete, almost immovable truths and then even just looking through the history, realizing that often isn't the case, that there's these kind of very human dynamics that play out around them. And it's something I think that everyone in science can reflect on that sometimes what convinces us doesn't convince other people, and particularly when you have that kind of urgency of time pressure, working out how to navigate that.Eric Topol (02:05):Yeah. Well, I mean I think these times of course have really gotten us to appreciate, particularly during Covid, the importance of understanding uncertainty. And I think one of the ways that we can dispel what people assume they know is the famous Monty Hall, which you get into a bit in the book. So I think everybody here is familiar with that show, Let's Make a Deal and maybe you can just take us through what happens with one of the doors are unveiled and how that changes the mathematics.Adam Kucharski (02:50):Yeah, sure. So I think it is a problem that's been around for a while and it's based on this game show. So you've got three doors that are closed. Behind two of the doors there is a goat and behind one of the doors is a luxury car. So obviously, you want to win the car. The host asks you to pick a door, so you point to one, maybe door number two, then the host who knows what's behind the doors opens another door to reveal a goat and then ask you, do you want to change your mind? Do you want to switch doors? And a lot of the, I think intuition people have, and certainly when I first came across this problem many years ago is well, you've got two doors left, right? You've picked one, there's another one, it's 50-50. And even some quite well-respected mathematicians.Adam Kucharski (03:27):People like Paul Erdős who was really published more papers than almost anyone else, that was their initial gut reaction. But if you work through all of the combinations, if you pick this door and then the host does this, and you switch or not switch and work through all of those options. You actually double your chances if you switch versus sticking with the door. So something that's counterintuitive, but I think one of the things that really struck me and even over the years trying to explain it is convincing myself of the answer, which was when I first came across it as a teenager, I did quite quickly is very different to convincing someone else. And even actually Paul Erdős, one of his colleagues showed him what I call proof by exhaustion. So go through every combination and that didn't really convince him. So then he started to simulate and said, well, let's do a computer simulation of the game a hundred thousand times. And again, switching was this optimal strategy, but Erdős wasn't really convinced because I accept that this is the case, but I'm not really satisfied with it. And I think that encapsulates for a lot of people, their experience of proof and evidence. It's a fact and you have to take it as given, but there's actually quite a big bridge often to really understanding why it's true and feeling convinced by it.Eric Topol (04:41):Yeah, I think it's a fabulous example because I think everyone would naturally assume it's 50-50 and it isn't. And I think that gets us to the topic at hand. What I love, there's many things I love about this book. One is that you don't just get into science and medicine, but you cut across all the domains, law, mathematics, AI. So it's a very comprehensive sweep of everything about proof and truth, and it couldn't come at a better time as we'll get into. Maybe just starting off with math, the term I love mathematical monsters. Can you tell us a little bit more about that?Adam Kucharski (05:25):Yeah, this was a fascinating situation that emerged in the late 19th century where a lot of math's, certainly in Europe had been derived from geometry because a lot of the ancient Greek influence on how we shaped things and then Newton and his work on rates of change and calculus, it was really the natural world that provided a lot of inspiration, these kind of tangible objects, tangible movements. And as mathematicians started to build out the theory around rates of change and how we tackle these kinds of situations, they sometimes took that intuition a bit too seriously. And there was some theorems that they said were intuitively obvious, some of these French mathematicians. And so, one for example is this idea of you how things change smoothly over time and how you do those calculations. But what happened was some mathematicians came along and showed that when you have things that can be infinitely small, that intuition didn't necessarily hold in the same way.Adam Kucharski (06:26):And they came up with these examples that broke a lot of these theorems and a lot of the establishments at the time called these things monsters. They called them these aberrations against common sense and this idea that if Newton had known about them, he never would've done all of his discovery because they're just nuisances and we just need to get rid of them. And there's this real tension at the core of mathematics in the late 1800s where some people just wanted to disregard this and say, look, it works for most of the time, that's good enough. And then others really weren't happy with this quite vague logic. They wanted to put it on much sturdier ground. And what was remarkable actually is if you trace this then into the 20th century, a lot of these monsters and these particularly in some cases functions which could almost move constantly, this constant motion rather than our intuitive concept of movement as something that's smooth, if you drop an apple, it accelerates at a very smooth rate, would become foundational in our understanding of things like probability, Einstein's work on atomic theory. A lot of these concepts where geometry breaks down would be really important in relativity. So actually, these things that we thought were monsters actually were all around us all the time, and science couldn't advance without them. So I think it's just this remarkable example of this tension within a field that supposedly concrete and the things that were going to be shunned actually turn out to be quite important.Eric Topol (07:53):It's great how you convey how nature isn't so neat and tidy and things like Brownian motion, understanding that, I mean, just so many things that I think fit into that general category. In the legal, we won't get into too much because that's not so much the audience of Ground Truths, but the classic things about innocent and until proven guilty and proof beyond reasonable doubt, I mean these are obviously really important parts of that overall sense of proof and truth. We're going to get into one thing I'm fascinated about related to that subsequently and then in science. So before we get into the different types of proof, obviously the pandemic is still fresh in our minds and we're an endemic with Covid now, and there are so many things we got wrong along the way of uncertainty and didn't convey that science isn't always evolving search for what is the truth. There's plenty no shortage of uncertainty at any moment. So can you recap some of the, you did so much work during the pandemic and obviously some of it's in the book. What were some of the major things that you took out of proof and truth from the pandemic?Adam Kucharski (09:14):I think it was almost this story of two hearts because on the one hand, science was the thing that got us where we are today. The reason that so much normality could resume and so much risk was reduced was development of vaccines and the understanding of treatments and the understanding of variants as they came to their characteristics. So it was kind of this amazing opportunity to see this happen faster than it ever happened in history. And I think ever in science, it certainly shifted a lot of my thinking about what's possible and even how we should think about these kinds of problems. But also on the other hand, I think where people might have been more familiar with seeing science progress a bit more slowly and reach consensus around some of these health issues, having that emerge very rapidly can present challenges even we found with some of the work we did on Alpha and then the Delta variants, and it was the early quantification of these.Adam Kucharski (10:08):So really the big question is, is this thing more transmissible? Because at the time countries were thinking about control measures, thinking about relaxing things, and you've got this just enormous social economic health decision-making based around essentially is it a lot more spreadable or is it not? And you only had these fragments of evidence. So I think for me, that was really an illustration of the sharp end. And I think what we ended up doing with some of those was rather than arguing over a precise number, something like Delta, instead we kind of looked at, well, what's the range that matters? So in the sense of arguing over whether it's 40% or 50% or 30% more transmissible is perhaps less important than being, it's substantially more transmissible and it's going to start going up. Is it going to go up extremely fast or just very fast?Adam Kucharski (10:59):That's still a very useful conclusion. I think what often created some of the more challenges, I think the things that on reflection people looking back pick up on are where there was probably overstated certainty. We saw that around some of the airborne spread, for example, stated as a fact by in some cases some organizations, I think in some situations as well, governments had a constraint and presented it as scientific. So the UK, for example, would say testing isn't useful. And what was happening at the time was there wasn't enough tests. So it was more a case of they can't test at that volume. But I think blowing between what the science was saying and what the decision-making, and I think also one thing we found in the UK was we made a lot of the epidemiological evidence available. I think that was really, I think something that was important.Adam Kucharski (11:51):I found it a lot easier to communicate if talking to the media to be able to say, look, this is the paper that's out, this is what it means, this is the evidence. I always found it quite uncomfortable having to communicate things where you knew there were reports behind the scenes, but you couldn't actually articulate. But I think what that did is it created this impression that particularly epidemiology was driving the decision-making a lot more than it perhaps was in reality because so much of that was being made public and a lot more of the evidence around education or economics was being done behind the scenes. I think that created this kind of asymmetry in public perception about how that was feeding in. And so, I think there was always that, and it happens, it is really hard as well as a scientist when you've got journalists asking you how to run the country to work out those steps of am I describing the evidence behind what we're seeing? Am I describing the evidence about different interventions or am I proposing to some extent my value system on what we do? And I think all of that in very intense times can be very easy to get blurred together in public communication. I think we saw a few examples of that where things were being the follow the science on policy type angle where actually once you get into what you're prioritizing within a society, quite rightly, you've got other things beyond just the epidemiology driving that.Eric Topol (13:09):Yeah, I mean that term that you just use follow the science is such an important term because it tells us about the dynamic aspect. It isn't just a snapshot, it's constantly being revised. But during the pandemic we had things like the six-foot rule that was never supported by data, but yet still today, if I walk around my hospital and there's still the footprints of the six-foot rule and not paying attention to the fact that this was airborne and took years before some of these things were accepted. The flatten the curve stuff with lockdowns, which I never was supportive of that, but perhaps at the worst point, the idea that hospitals would get overrun was an issue, but it got carried away with school shutdowns for prolonged periods and in some parts of the world, especially very stringent lockdowns. But anyway, we learned a lot.Eric Topol (14:10):But perhaps one of the greatest lessons is that people's expectations about science is that it's absolute and somehow you have this truth that's not there. I mean, it's getting revised. It's kind of on the job training, it's on this case on the pandemic revision. But very interesting. And that gets us to, I think the next topic, which I think is a fundamental part of the book distributed throughout the book, which is the different types of proof in biomedicine and of course across all these domains. And so, you take us through things like randomized trials, p-values, 95 percent confidence intervals, counterfactuals, causation and correlation, peer review, the works, which is great because a lot of people have misconceptions of these things. So for example, randomized trials, which is the temple of the randomized trials, they're not as great as a lot of people think, yes, they can help us establish cause and effect, but they're skewed because of the people who come into the trial. So they may not at all be a representative sample. What are your thoughts about over deference to randomized trials?Adam Kucharski (15:31):Yeah, I think that the story of how we rank evidence in medicines a fascinating one. I mean even just how long it took for people to think about these elements of randomization. Fundamentally, what we're trying to do when we have evidence here in medicine or science is prevent ourselves from confusing randomness for a signal. I mean, that's fundamentally, we don't want to mistake something, we think it's going on and it's not. And the challenge, particularly with any intervention is you only get to see one version of reality. You can't give someone a drug, follow them, rewind history, not give them the drug and then follow them again. So one of the things that essentially randomization allows us to do is, if you have two groups, one that's been randomized, one that hasn't on average, the difference in outcomes between those groups is going to be down to the treatment effect.Adam Kucharski (16:20):So it doesn't necessarily mean in reality that'd be the case, but on average that's the expectation that you'd have. And it's kind of interesting actually that the first modern randomized control trial (RCT) in medicine in 1947, this is for TB and streptomycin. The randomization element actually, it wasn't so much statistical as behavioral, that if you have people coming to hospital, you could to some extent just say, we'll just alternate. We're not going to randomize. We're just going to first patient we'll say is a control, second patient a treatment. But what they found in a lot of previous studies was doctors have bias. Maybe that patient looks a little bit ill or that one maybe is on borderline for eligibility. And often you got these quite striking imbalances when you allowed it for human judgment. So it was really about shielding against those behavioral elements. But I think there's a few situations, it's a really powerful tool for a lot of these questions, but as you mentioned, one is this issue of you have the population you study on and then perhaps in reality how that translates elsewhere.Adam Kucharski (17:17):And we see, I mean things like flu vaccines are a good example, which are very dependent on immunity and evolution and what goes on in different populations. Sometimes you've had a result on a vaccine in one place and then the effectiveness doesn't translate in the same way to somewhere else. I think the other really important thing to bear in mind is, as I said, it's the averaging that you're getting an average effect between two different groups. And I think we see certainly a lot of development around things like personalized medicine where actually you're much more interested in the outcome for the individual. And so, what a trial can give you evidence is on average across a group, this is the effect that I can expect this intervention to have. But we've now seen more of the emergence things like N=1 studies where you can actually over the same individual, particularly for chronic conditions, look at those kind of interventions.Adam Kucharski (18:05):And also there's just these extreme examples where you're ethically not going to run a trial, there's never been a trial of whether it's a good idea to have intensive care units in hospitals or there's a lot of these kind of historical treatments which are just so overwhelmingly effective that we're not going to run trial. So almost this hierarchy over time, you can see it getting shifted because actually you do have these situations where other forms of evidence can get you either closer to what you need or just more feasibly an answer where it's just not ethical or practical to do an RCT.Eric Topol (18:37):And that brings us to the natural experiments I just wrote about recently, the one with shingles, which there's two big natural experiments to suggest that shingles vaccine might reduce the risk of Alzheimer's, an added benefit beyond the shingles that was not anticipated. Your thoughts about natural experiments, because here you're getting a much different type of population assessment, again, not at the individual level, but not necessarily restricted by some potentially skewed enrollment criteria.Adam Kucharski (19:14):I think this is as emerged as a really valuable tool. It's kind of interesting, in the book you're talking to economists like Josh Angrist, that a lot of these ideas emerge in epidemiology, but I think were really then taken up by economists, particularly as they wanted to add more credibility to a lot of these policy questions. And ultimately, it comes down to this issue that for a lot of problems, we can't necessarily intervene and randomize, but there might be a situation that's done it to some extent for us, so the classic example is the Vietnam draft where it was kind of random birthdays with drawn out of lottery. And so, there's been a lot of studies subsequently about the effect of serving in the military on different subsequent lifetime outcomes because broadly those people have been randomized. It was for a different reason. But you've got that element of randomization driving that.Adam Kucharski (20:02):And so again, with some of the recent shingles data and other studies, you might have a situation for example, where there's been an intervention that's somewhat arbitrary in terms of time. It's a cutoff on a birth date, for example. And under certain assumptions you could think, well, actually there's no real reason for the person on this day and this day to be fundamentally different. I mean, perhaps there might be effects of cohorts if it's school years or this sort of thing. But generally, this isn't the same as having people who are very, very different ages and very different characteristics. It's just nature, or in this case, just a policy intervention for a different reason has given you that randomization, which allows you or pseudo randomization, which allows you to then look at something about the effect of an intervention that you wouldn't as reliably if you were just digging into the data of yes, no who's received a vaccine.Eric Topol (20:52):Yeah, no, I think it's really valuable. And now I think increasingly given priority, if you can find these natural experiments and they're not always so abundant to use to extrapolate from, but when they are, they're phenomenal. The causation correlation is so big. The issue there, I mean Judea Pearl's, the Book of Why, and you give so many great examples throughout the book in Proof. I wonder if you could comment that on that a bit more because this is where associations are confused somehow or other with a direct effect. And we unfortunately make these jumps all too frequently. Perhaps it's the most common problem that's occurring in the way we interpret medical research data.Adam Kucharski (21:52):Yeah, I think it's an issue that I think a lot of people get drilled into in their training just because a correlation between things doesn't mean that that thing causes this thing. But it really struck me as I talked to people, researching the book, in practice in research, there's actually a bit more to it in how it's played out. So first of all, if there's a correlation between things, it doesn't tell you much generally that's useful for intervention. If two things are correlated, it doesn't mean that changing that thing's going to have an effect on that thing. There might be something that's influencing both of them. If you have more ice cream sales, it will lead to more heat stroke cases. It doesn't mean that changing ice cream sales is going to have that effect, but it does allow you to make predictions potentially because if you can identify consistent patterns, you can say, okay, if this thing going up, I'm going to make a prediction that this thing's going up.Adam Kucharski (22:37):So one thing I found quite striking, actually talking to research in different fields is how many fields choose to focus on prediction because it kind of avoids having to deal with this cause and effect problem. And even in fields like psychology, it was kind of interesting that there's a lot of focus on predicting things like relationship outcomes, but actually for people, you don't want a prediction about your relationship. You want to know, well, how can I do something about it? You don't just want someone to sell you your relationship's going to go downhill. So there's almost part of the challenge is people just got stuck on prediction because it's an easier field of work, whereas actually some of those problems will involve intervention. I think the other thing that really stood out for me is in epidemiology and a lot of other fields, rightly, people are very cautious to not get that mixed up.Adam Kucharski (23:24):They don't want to mix up correlations or associations with causation, but you've kind of got this weird situation where a lot of papers go out of their way to not use causal language and say it's an association, it's just an association. It's just an association. You can't say anything about causality. And then the end of the paper, they'll say, well, we should think about introducing more of this thing or restricting this thing. So really the whole paper and its purpose is framed around a causal intervention, but it's extremely careful throughout the paper to not frame it as a causal claim. So I think we almost by skirting that too much, we actually avoid the problems that people sometimes care about. And I think a lot of the nice work that's been going on in causal inference is trying to get people to confront this more head on rather than say, okay, you can just stay in this prediction world and that's fine. And then just later maybe make a policy suggestion off the back of it.Eric Topol (24:20):Yeah, I think this is cause and effect is a very alluring concept to support proof as you so nicely go through in the book. But of course, one of the things that we use to help us is the biological mechanism. So here you have, let's say for example, you're trying to get a new drug approved by the Food and Drug Administration (FDA), and the request is, well, we want two trials, randomized trials, independent. We want to have p-values that are significant, and we want to know the biological mechanism ideally with the dose response of the drug. But there are many drugs as you review that have no biological mechanism established. And even when the tobacco problems were mounting, the actual mechanism of how tobacco use caused cancer wasn't known. So how important is the biological mechanism, especially now that we're well into the AI world where explainability is demanded. And so, we don't know the mechanism, but we also don't know the mechanism and lots of things in medicine too, like anesthetics and even things as simple as aspirin, how it works and many others. So how do we deal with this quest for the biological mechanism?Adam Kucharski (25:42):I think that's a really good point. It shows almost a lot of the transition I think we're going through currently. I think particularly for things like smoking cancer where it's very hard to run a trial. You can't make people randomly take up smoking. Having those additional pieces of evidence, whether it's an analogy with a similar carcinogen, whether it's a biological mechanism, can help almost give you more supports for that argument that there's a cause and effect going on. But I think what I found quite striking, and I realized actually that it's something that had kind of bothered me a bit and I'd be interested to hear whether it bothers you, but with the emergence of AI, it's almost a bit of the loss of scientific satisfaction. I think you grow up with learning about how the world works and why this is doing what it's doing.Adam Kucharski (26:26):And I talked for example of some of the people involved with AlphaFold and some of the subsequent work in installing those predictions about structures. And they'd almost made peace with it, which I found interesting because I think they started off being a bit uncomfortable with like, yeah, you've got these remarkable AI models making these predictions, but we don't understand still biologically what's happening here. But I think they're just settled in saying, well, biology is really complex on some of these problems, and if we can have a tool that can give us this extremely valuable information, maybe that's okay. And it was just interesting that they'd really kind of gone through that kind process, which I think a lot of people are still grappling with and that almost that discomfort of using AI and what's going to convince you that that's a useful reliable prediction whether it's something like predicting protein folding or getting in a self-driving car. What's the evidence you need to convince you that's reliable?Eric Topol (27:26):Yeah, no, I'm so glad you brought that up because when Demis Hassabis and John Jumper won the Nobel Prize, the point I made was maybe there should be an asterisk with AI because they don't know how it works. I mean, they had all the rich data from the protein data bank, and they got the transformer model to do it for 200 million protein structure prediction, but they still to this day don't fully understand how the model really was working. So it reinforces what you're just saying. And of course, it cuts across so many types of AI. It's just that we tend to hold different standards in medicine not realizing that there's lots of lack of explainability for routine medical treatments today. Now one of the things that I found fascinating in your book, because there's different levels of proof, different types of proof, but solid logical systems.Eric Topol (28:26):And on page 60 of the book, especially pertinent to the US right now, there is a bit about Kurt Gödel and what he did there was he basically, there was a question about dictatorship in the US could it ever occur? And Gödel says, “oh, yes, I can prove it.” And he's using the constitution itself to prove it, which I found fascinating because of course we're seeing that emerge right now. Can you give us a little bit more about this, because this is fascinating about the Fifth Amendment, and I mean I never thought that the Constitution would allow for a dictatorship to emerge.Adam Kucharski (29:23):And this was a fascinating story, Kurt Gödel who is one of the greatest logical minds of the 20th century and did a lot of work, particularly in the early 20th century around system of rules, particularly things like mathematics and whether they can ever be really fully satisfying. So particularly in mathematics, he showed that there were this problem that is very hard to have a set of rules for something like arithmetic that was both complete and covered every situation, but also had no contradictions. And I think a lot of countries, if you go back, things like Napoleonic code and these attempts to almost write down every possible legal situation that could be imaginable, always just ascended into either they needed amendments or they had contradictions. I think Gödel's work really summed it up, and there's a story, this is in the late forties when he had his citizenship interview and Einstein and Oskar Morgenstern went along as witnesses for him.Adam Kucharski (30:17):And it's always told as kind of a lighthearted story as this logical mind, this academic just saying something silly in front of the judge. And actually, to my own admission, I've in the past given talks and mentioned it in this slightly kind of lighthearted way, but for the book I got talking to a few people who'd taken it more seriously. I realized actually he's this extremely logically focused mind at the time, and maybe there should have been something more to it. And people who have kind of dug more into possibilities was saying, well, what could he have spotted that bothered him? And a lot of his work that he did about consistency in mass was around particularly self-referential statements. So if I say this sentence is false, it's self-referential and if it is false, then it's true, but if it's true, then it's false and you get this kind of weird self-referential contradictions.Adam Kucharski (31:13):And so, one of the theories about Gödel was that in the Constitution, it wasn't that there was a kind of rule for someone can become a dictator, but rather people can use the mechanisms within the Constitution to make it easier to make further amendments. And he kind of downward cycle of amendment that he had seen happening in Europe and the run up to the war, and again, because this is never fully documented exactly what he thought, but it's one of the theories that it wouldn't just be outright that it would just be this cycle process of weakening and weakening and weakening and making it easier to add. And actually, when I wrote that, it was all the earlier bits of the book that I drafted, I did sort of debate whether including it I thought, is this actually just a bit in the weeds of American history? And here we are. Yeah, it's remarkable.Eric Topol (32:00):Yeah, yeah. No, I mean I found, it struck me when I was reading this because here back in 1947, there was somebody predicting that this could happen based on some, if you want to call it loopholes if you will, or the ability to change things, even though you would've thought otherwise that there wasn't any possible capability for that to happen. Now, one of the things I thought was a bit contradictory is two parts here. One is from Angus Deaton, he wrote, “Gold standard thinking is magical thinking.” And then the other is what you basically are concluding in many respects. “To navigate proof, we must reach into a thicket of errors and biases. We must confront monsters and embrace uncertainty, balancing — and rebalancing —our beliefs. We must seek out every useful fragment of data, gather every relevant tool, searching wider and climbing further. Finding the good foundations among the bad. Dodging dogma and falsehoods. Questioning. Measuring. Triangulating. Convincing. Then perhaps, just perhaps, we'll reach the truth in time.” So here you have on the one hand your search for the truth, proof, which I think that little paragraph says it all. In many respects, it sums up somewhat to the work that you review here and on the other you have this Nobel laureate saying, you don't have to go to extremes here. The enemy of good is perfect, perhaps. I mean, how do you reconcile this sense that you shouldn't go so far? Don't search for absolute perfection of proof.Adam Kucharski (33:58):Yeah, I think that encapsulates a lot of what the book is about, is that search for certainty and how far do you have to go. I think one of the things, there's a lot of interesting discussion, some fascinating papers around at what point do you use these studies? What are their flaws? But I think one of the things that does stand out is across fields, across science, medicine, even if you going to cover law, AI, having these kind of cookie cutter, this is the definitive way of doing it. And if you just follow this simple rule, if you do your p-value, you'll get there and you'll be fine. And I think that's where a lot of the danger is. And I think that's what we've seen over time. Certain science people chasing certain targets and all the behaviors that come around that or in certain situations disregarding valuable evidence because you've got this kind of gold standard and nothing else will do.Adam Kucharski (34:56):And I think particularly in a crisis, it's very dangerous to have that because you might have a low level of evidence that demands a certain action and you almost bias yourself towards inaction if you have these kind of very simple thresholds. So I think for me, across all of these stories and across the whole book, I mean William Gosset who did a lot of pioneering work on statistical experiments at Guinness in the early 20th century, he had this nice question he sort of framed is, how much do we lose? And if we're thinking about the problems, there's always more studies we can do, there's always more confidence we can have, but whether it's a patient we want to treat or crisis we need to deal with, we need to work out actually getting that level of proof that's really appropriate for where we are currently.Eric Topol (35:49):I think exceptionally important that there's this kind of spectrum or continuum in following science and search for truth and that distinction, I think really nails it. Now, one of the things that's unique in the book is you don't just go through all the different types of how you would get to proof, but you also talk about how the evidence is acted on. And for example, you quote, “they spent a lot of time misinforming themselves.” This is the whole idea of taking data and torturing it or using it, dredging it however way you want to support either conspiracy theories or alternative facts. Basically, manipulating sometimes even emasculating what evidence and data we have. And one of the sentences, or I guess this is from Sir Francis Bacon, “truth is a daughter of time”, but the added part is not authority. So here we have our president here that repeats things that are wrong, fabricated or wrong, and he keeps repeating to the point that people believe it's true. But on the other hand, you could say truth is a daughter of time because you like to not accept any truth immediately. You like to see it get replicated and further supported, backed up. So in that one sentence, truth is a daughter of time not authority, there's the whole ball of wax here. Can you take us through that? Because I just think that people don't understand that truth being tested over time, but also manipulated by its repetition. This is a part of the big problem that we live in right now.Adam Kucharski (37:51):And I think it's something that writing the book and actually just reflecting on it subsequently has made me think about a lot in just how people approach these kinds of problems. I think that there's an idea that conspiracy theorists are just lazy and have maybe just fallen for a random thing, but talking to people, you really think about these things a lot more in the field. And actually, the more I've ended up engaging with people who believe things that are just outright unevidenced around vaccines, around health issues, they often have this mountain of papers and data to hand and a lot of it, often they will be peer reviewed papers. It won't necessarily be supporting the point that they think it's supports.Adam Kucharski (38:35):But it's not something that you can just say everything you're saying is false, that there's actually often a lot of things that have been put together and it's just that leap to that conclusion. I think you also see a lot of scientific language borrowed. So I gave a talker early this year and it got posted on YouTube. It had conspiracy theories it, and there was a lot of conspiracy theory supporters who piled in the comments and one of the points they made is skepticism is good. It's the kind of law society, take no one's word for it, you need this. We are the ones that are kind of doing science and people who just assume that science is settled are in the wrong. And again, you also mentioned that repetition. There's this phenomenon, it's the illusory truth problem that if you repeatedly tell someone someone's something's false, it'll increase their belief in it even if it's something quite outrageous.Adam Kucharski (39:27):And that mimics that scientific repetition because people kind of say, okay, well if I've heard it again and again, it's almost like if you tweak these as mini experiments, I'm just accumulating evidence that this thing is true. So it made me think a lot about how you've got essentially a lot of mimicry of the scientific method, amount of data and how you present it and this kind of skepticism being good, but I think a lot of it comes down to as well as just looking at theological flaws, but also ability to be wrong in not actually seeking out things that confirm. I think all of us, it's something that I've certainly tried to do a lot working on emergencies, and one of the scientific advisory groups that I worked on almost it became a catchphrase whenever someone presented something, they finished by saying, tell me why I'm wrong.Adam Kucharski (40:14):And if you've got a variant that's more transmissible, I don't want to be right about that really. And it is something that is quite hard to do and I found it is particularly for something that's quite high pressure, trying to get a policymaker or someone to write even just non-publicly by themselves, write down what you think's going to happen or write down what would convince you that you are wrong about something. I think particularly on contentious issues where someone's got perhaps a lot of public persona wrapped up in something that's really hard to do, but I think it's those kind of elements that distinguish between getting sucked into a conspiracy theory and really seeking out evidence that supports it and trying to just get your theory stronger and stronger and actually seeking out things that might overturn your belief about the world. And it's often those things that we don't want overturned. I think those are the views that we all have politically or in other ways, and that's often where the problems lie.Eric Topol (41:11):Yeah, I think this is perhaps one of, if not the most essential part here is that to try to deal with the different views. We have biases as you emphasized throughout, but if you can use these different types of proof to have a sound discussion, conversation, refutation whereby you don't summarily dismiss another view which may be skewed and maybe spurious or just absolutely wrong, maybe fabricated whatever, but did you can engage and say, here's why these are my proof points, or this is why there's some extent of certainty you can have regarding this view of the data. I think this is so fundamental because unfortunately as we saw during the pandemic, the strident minority, which were the anti-science, anti-vaxxers, they were summarily dismissed as being kooks and adopting conspiracy theories without the right engagement and the right debates. And I think this might've helped along the way, no less the fact that a lot of scientists didn't really want to engage in the first place and adopt this methodical proof that you've advocated in the book so many different ways to support a hypothesis or an assertion. Now, we've covered a lot here, Adam. Have I missed some central parts of the book and the effort because it's really quite extraordinary. I know it's your third book, but it's certainly a standout and it certainly it's a standout not just for your books, but books on this topic.Adam Kucharski (43:13):Thanks. And it's much appreciated. It was not an easy book to write. I think at times, I kind of wondered if I should have taken on the topic and I think a core thing, your last point speaks to that. I think a core thing is that gap often between what convinces us and what convinces someone else. I think it's often very tempting as a scientist to say the evidence is clear or the science has proved this. But even on something like the vaccines, you do get the loud minority who perhaps think they're putting microchips in people and outlandish views, but you actually get a lot more people who might just have some skepticism of pharmaceutical companies or they might have, my wife was pregnant actually at the time during Covid and we waited up because there wasn't much data on pregnancy and the vaccine. And I think it's just finding what is convincing. Is it having more studies from other countries? Is it understanding more about the biology? Is it understanding how you evaluate some of those safety signals? And I think that's just really important to not just think what convinces us and it's going to be obvious to other people, but actually think where are they coming from? Because ultimately having proof isn't that good unless it leads to the action that can make lives better.Eric Topol (44:24):Yeah. Well, look, you've inculcated my mind with this book, Adam, called Proof. Anytime I think of the word proof, I'm going to be thinking about you. So thank you. Thanks for taking the time to have a conversation about your book, your work, and I know we're going to count on you for the astute mathematics and analysis of outbreaks in the future, which we will see unfortunately. We are seeing now, in fact already in this country with measles and whatnot. So thank you and we'll continue to follow your great work.**************************************Thanks for listening, watching or reading this Ground Truths podcast/post.If you found this interesting please share it!That makes the work involved in putting these together especially worthwhile.I'm also appreciative for your subscribing to Ground Truths. All content —its newsletters, analyses, and podcasts—is free, open-access. I'm fortunate to get help from my producer Jessica Nguyen and Sinjun Balabanoff for audio/video tech support to pull these podcasts together for Scripps Research.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don't hesitate to post comments and give me feedback. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years.A bit of an update on SUPER AGERSMy book has been selected as a Next Big Idea Club winner for Season 26 by Adam Grant, Malcolm Gladwell, Susan Cain, and Daniel Pink. This club has spotlighted the most groundbreaking nonfiction books for over a decade. As a winning title, my book will be shipped to thousands of thoughtful readers like you, featured alongside a reading guide, a "Book Bite," Next Big Idea Podcast episode as well as a live virtual Q&A with me in the club's vibrant online community. If you're interested in joining the club, here's a promo code SEASON26 for 20% off at the website. SUPER AGERS reached #3 for all books on Amazon this week. This was in part related to the segment on the book on the TODAY SHOW which you can see here. Also at Amazon there is a remarkable sale on the hardcover book for $10.l0 at the moment for up to 4 copies. Not sure how long it will last or what prompted it.The journalist Paul von Zielbauer has a Substack “Aging With Strength” and did an extensive interview with me on the biology of aging and how we can prevent the major age-related diseases. Here's the link. Get full access to Ground Truths at erictopol.substack.com/subscribe

If you love playing white noise for sleeping at night, you may really love this brown noise ambience! This brown noise (or Brownian noise!) is super smooth and a lot deeper than our typical white noise to sleep, really helping to cover up outside distractions that keep you awake. It even kind of resembles the noise from fan sounds for sleeping, since the brown noise produces a sleep sound with wind-like qualities. Brown noise can also help you focus better while studying or working, quieting busy thoughts and allowing your mind to concentrate on your tasks. Whether you are struggling to get some sleep at night or need a reliable study aid, brown noise has you covered! Here are some great products to help you sleep! Relaxing White Noise receives a small commission (at no additional cost to you) on purchases made through affiliate links. Thanks for supporting the podcast!⁠⁠Baloo Living Weighted Blankets⁠⁠ (Use code 'relaxingwhitenoise10' for 10% off)At Relaxing White Noise, our goal is to help you sleep well. This episode is eight hours long with no advertisements in the middle, so you can use it as a sleeping sound throughout the night. Listening to our white noise sounds via the podcast gives you the freedom to lock your phone at night, keeping your bedroom dark as you fall asleep.⁠⁠Check out the 10-Hour version on YouTube⁠⁠⁠⁠Contact Us for Partnership Inquiries⁠⁠Relaxing White Noise is the number one online destination for white noise and nature sounds to help you sleep, study or soothe a baby. With more than a billion views across YouTube and other platforms, we are excited to now share our popular ambient tracks on the Relaxing White Noise podcast. People use white noise for sleeping, focus, sound masking or relaxation. We couldn't be happier to help folks live better lives. This podcast has the sound for you whether you use white noise for studying, to soothe a colicky baby, to fall asleep or for simply enjoying a peaceful moment. No need to buy a white noise machine when you can listen to these sounds for free. Cheers to living your best life!DISCLAIMER: Remember that loud sounds can potentially damage your hearing. When playing one of our ambiences, if you cannot have a conversation over the sound without raising your voice, the sound may be too loud for your ears. Please do not place speakers right next to a baby's ears. If you have difficulty hearing or hear ringing in your ears, please immediately discontinue listening to the white noise sounds and consult an audiologist or your physician. The sounds provided by Relaxing White Noise are for entertainment purposes only and are not a treatment for sleep disorders or tinnitus. If you have significant difficulty sleeping on a regular basis, experience fitful/restless sleep, or feel tired during the day, please consult your physician.⁠⁠Relaxing White Noise Privacy Policy⁠⁠© Relaxing White Noise LLC, 2025. All rights reserved. Any reproduction or republication of all or part of this text/visual/audio is prohibited.

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Nalini AnantharamanGéométrie spectraleCollège de FranceAnnée 2024-2025Colloque - Géométries aléatoires et applications - Yilin Wang : The Brownian Loop Measure on Riemann Surfaces and Applications to Length SpectraIntervenant :Yilin WangIHESRésuméThe goal of this talk is to showcase how we can use stochastic processes to study the geometry of surfaces. More precisely, we use the Brownian loop measure to express the lengths of closed geodesics on a hyperbolic surface and zeta-regularized determinant of the Laplace-Beltrami operator. This gives a tool to study the length spectra of a hyperbolic surface and we obtain a new identity between the length spectrum of a compact surface and that of the same surface with an arbitrary number of additional cusps. This is a joint work with Yuhao Xue (IHES).

Work habits, FMOD, and brown noise figure prominently in this episode of the GAH, with Alex in particular berating Vince for his poor attempts at work-life balance while Mike maintains order with some helpful tips. Other notable mentions include: Elden Ring (Video Game / FromSoftware)Pacific Drive (Video Game / Ironwood Studios)Solas (Irish-American Musical Band) Featuring: Alex May and Vincent Diamante Recorded April 26, 2024

Stephen Wolfram answers questions from his viewers about the future of science and technology as part of an unscripted livestream series, also available on YouTube here: https://wolfr.am/youtube-sw-qa Questions include: What scientific breakthroughs would you like to see in 2024? - Whatever happened to graphene? Is it still a viable product of future technologies? - Could we build "bio-vehicles," e.g. instead of batteries, use synthetic adipose tissue, which is ~50–100 times more mass efficient per kWh? (Is there a future in bio-batteries?) - Based on the level of computational advances this last decade, with the trend only showing even more of the same, do you think that traditional engineering disciplines will be relegated to OpenLLM? - Do you think we'll see mass-producible, room-temperature superconductors in the next decade? - ​It has been suggested that AI will displace coders/programmers. Do you think AI might also replace many physical and chemical experiments? - Any thoughts on "zero-knowledge proofs," i.e. the ability to make proofs without revealing details? - Given that some of our greatest accomplishments as a species have happened when we mimic nature, how important do you think biomimetics will be going forward? - Can you see a time when the discovery of new mathematical theorems and axioms will be generated from AIs? - When Betelgeuse explodes, will humans be okay? - Do you think smart textiles/computing fabrics will take off or be viable? Would you wear, say, a sweater to hear instead of a hearing aid? - But things like math, geometry and especially tessellation have patterns that are universally implicit and can be interpreted as interesting by their own existence, and not just by the view of humanity. - Is there a way we can use Brownian motion at a molecular scale as a type of fingerprint for nano-sensors to create things that are piracy-proof? - Why are the axioms of mathematics necessarily the ones that are effective at describing things we see as well? - What do things like dreams and "higher states of consciousness" spoken about in Eastern philosophies tell us about ourselves as observers? - Would it be easy to have an AI remaster old movies, both real ones and cartoons, so we can watch all the old gems in high-end graphics? - "Interesting" is defined by a "coolness" threshold. - Since the scientific paradigm was a major cause for the Enlightenment, can we expect the (multi-)computational paradigm to kick off a socio-philosophical paradigm of comparable importance? - If someone invented calculus in the Stone Age, it would probably have not been used for anything... Do you think there are some ideas that may be "rediscovered" because they have a better use?

I am properly losing track of what I am reading every week in the diary section.Even since we finished Volume II of The Invisible Man (1998-2014) I have been scrabbling around on my hands and knees (figuratively speaking of course) under the lid of my MacBook trying to locate odd bits of stuff to read out.And because I keep stumbling on stuff, we have lost all sense of chronology in terms of what we have included and what can still be used. Which is why I sent Ant an email on Wednesday and asked him to look at a couple of pages to try and ascertain if we had used them already. I couldn't be sure, and as it turned out neither could he, but we both plumped for the 'travelling to Lille' extract as being the safer option - so that is what you are getting.And as far as the rest of the episode is concerned, well it's just the other Mr.H being brilliant. Nuff said.Love'n'railcardshTCD Merch StoreBecome Purple and support the showThe Invisible Man Volume 1: 1991-1997The Invisible Man Volume2: 1998-2014FacebookInstagramWebsite

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Rapid capability gain around supergenius level seems probable even without intelligence needing to improve intelligence, published by Towards Keeperhood on May 6, 2024 on LessWrong. TLDR: 1. Around Einstein-level, relatively small changes in intelligence can lead to large changes in what one is capable to accomplish. 1. E.g. Einstein was a bit better than the other best physi at seeing deep connections and reasoning, but was able to accomplish much more in terms of impressive scientific output. 2. There are architectures where small changes can have significant effects on intelligence. 1. E.g. small changes in human-brain-hyperparameters: Einstein's brain didn't need to be trained on 3x the compute than normal physics professors for him to become much better at forming deep understanding, even without intelligence improving intelligence. Einstein and the heavytail of human intelligence 1905 is often described as the "annus mirabilis" of Albert Einstein. He founded quantum physics by postulating the existence of (light) quanta, explained Brownian motion, introduced the special relativity theory and derived E=mc from it. All of this. In one year. While having a full-time job in the Swiss patent office. With the exception of John von Neumann, we'd say those discoveries alone seem more than any other scientist of the 20th century achieved in their lifetime (though it's debatable). Though perhaps even more impressive is that Einstein was able to derive general relativity. Einstein was often so far ahead of his time that even years after he published his theories the majority of physicists rejected them because they couldn't understand them, sometimes even though there was experimental evidence favoring Einstein's theories. After solving the greatest open physics problems at the time in 1905, he continued working in the patent office until 1908, since the universities were too slow on the uptake to hire him earlier. Example for how far ahead of his time Einstein was: Deriving the theory of light quanta The following section is based on parts of the 8th chapter of "Surfaces and Essences" by Douglas Hofstadter. For an analysis of some of Einstein's discoveries, which show how far ahead of his time he was, I can recommend reading it. At the time, one of the biggest problems in physics was the "Blackbody spectrum", which describes the spectrum of electromagnetic wavelengths emitted by a Blackbody. The problem with it was that the emitted spectrum was not explainable by known physics. Einstein achieved a breakthrough by considering light not just as a wave, but also as light quanta. Although this idea sufficiently explained the Blackbody spectrum, physicists (at least almost) unanimously rejected it. The fight between the "light is corpuscles" and "light is a wave" faction had been decided a century ago, with a clear victory for the "wave" faction. Being aware of these possible doubts, Einstein proposed three experiments to prove his idea, one of which was the photoelectric effect. In the following years, Robert Millikan carried out various experiments on the photoelectric effect, which all confirmed Einstein's predictions. Still, Millikan insisted that the light-quanta theory had no theoretical basis and even falsely claimed that Einstein himself did not believe in his idea anymore. From Surfaces and Essences (p.611): To add insult to injury, although the 1921 Nobel Prize in Physics was awarded to Albert Einstein, it was not for his theory of light quanta but "for his discovery of the law of the photoelectric effect". Weirdly, in the citation there was no mention of the ideas behind that law, since no one on the Nobel Committee (or in all of physics) believed in them! [1][...] And thus Albert Einstein's revolutionary ideas on the nature of light, that most fundamental and all-...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Rapid capability gain around supergenius level seems probable even without intelligence needing to improve intelligence, published by Towards Keeperhood on May 6, 2024 on LessWrong. TLDR: 1. Around Einstein-level, relatively small changes in intelligence can lead to large changes in what one is capable to accomplish. 1. E.g. Einstein was a bit better than the other best physi at seeing deep connections and reasoning, but was able to accomplish much more in terms of impressive scientific output. 2. There are architectures where small changes can have significant effects on intelligence. 1. E.g. small changes in human-brain-hyperparameters: Einstein's brain didn't need to be trained on 3x the compute than normal physics professors for him to become much better at forming deep understanding, even without intelligence improving intelligence. Einstein and the heavytail of human intelligence 1905 is often described as the "annus mirabilis" of Albert Einstein. He founded quantum physics by postulating the existence of (light) quanta, explained Brownian motion, introduced the special relativity theory and derived E=mc from it. All of this. In one year. While having a full-time job in the Swiss patent office. With the exception of John von Neumann, we'd say those discoveries alone seem more than any other scientist of the 20th century achieved in their lifetime (though it's debatable). Though perhaps even more impressive is that Einstein was able to derive general relativity. Einstein was often so far ahead of his time that even years after he published his theories the majority of physicists rejected them because they couldn't understand them, sometimes even though there was experimental evidence favoring Einstein's theories. After solving the greatest open physics problems at the time in 1905, he continued working in the patent office until 1908, since the universities were too slow on the uptake to hire him earlier. Example for how far ahead of his time Einstein was: Deriving the theory of light quanta The following section is based on parts of the 8th chapter of "Surfaces and Essences" by Douglas Hofstadter. For an analysis of some of Einstein's discoveries, which show how far ahead of his time he was, I can recommend reading it. At the time, one of the biggest problems in physics was the "Blackbody spectrum", which describes the spectrum of electromagnetic wavelengths emitted by a Blackbody. The problem with it was that the emitted spectrum was not explainable by known physics. Einstein achieved a breakthrough by considering light not just as a wave, but also as light quanta. Although this idea sufficiently explained the Blackbody spectrum, physicists (at least almost) unanimously rejected it. The fight between the "light is corpuscles" and "light is a wave" faction had been decided a century ago, with a clear victory for the "wave" faction. Being aware of these possible doubts, Einstein proposed three experiments to prove his idea, one of which was the photoelectric effect. In the following years, Robert Millikan carried out various experiments on the photoelectric effect, which all confirmed Einstein's predictions. Still, Millikan insisted that the light-quanta theory had no theoretical basis and even falsely claimed that Einstein himself did not believe in his idea anymore. From Surfaces and Essences (p.611): To add insult to injury, although the 1921 Nobel Prize in Physics was awarded to Albert Einstein, it was not for his theory of light quanta but "for his discovery of the law of the photoelectric effect". Weirdly, in the citation there was no mention of the ideas behind that law, since no one on the Nobel Committee (or in all of physics) believed in them! [1][...] And thus Albert Einstein's revolutionary ideas on the nature of light, that most fundamental and all-...

On May 2, 2024 we spoke with Skirmantas Janusonis on the peculiar morphology and spatial distribution of the serotonin innervation of the brain, and his idea that it can be described using the mathematics of fractional Brownian motion. We consider the kind of developmental mechanisms that could be responsible. Guest: Skirmantas Janusonis, Associate Professor, Department of Psychological and Brain Sciences, University of California, Santa Barbara. Participating: Fidel Santamaria, Department of Neuroscience, Developmental and Regenerative Biology, UTSA Host: Charles Wilson, Department of Neuroscience, Developmental and Regenerative Biology, UTSA Thanks to Jim Tepper for original music

 In this episode, Shekerah and Fatu have a delightful conversation with Jishad Kumar, a theorist and researcher. As a theorist he uses concrete assumptions and models to solve problems which can then be further investigated with in depth experimentation. Jishad's journey into theoretical physics started accidentally when he discovered Brownian motion is related to particle motion and has nothing to do with the color brown. From there, he extensively read books and scientific articles building a very solid knowledge base for his graduate studies. But, Jishad had a difficult start in his research career; he struggled with his first project assignment and did not have good support from this research advisor. During this difficult period, however, he found guidance from another advisor who encouraged him to conquer his fear and gave him the motivation to continue with the assignment and ultimately succeed. Things continued to progress and his confidence grew as he was also able to design a very impressive doctoral research project examining superconductivity. Looking back, Jishad sees this initial research experience as very formative in his research journey, and he is grateful for this. “I cannot stay away from science,” he explains as he also looks back and reflects on all the challenges and triumphs of the journey. Currently Jishad's research focuses on applications of quantum thermo-dynamics, such as quantum heat exchange, and he looks forward to future real-world applications and innovations from this research. His long term goals include setting up his own research lab with students and teaching. To hear more about Jishad's work tune into the latest episode.     Tune into this episode to hear Jishad discuss:His start in theoretical physics in a pre-wikipedia worldKeeping motivation on his journey even with several setbackFuture theoretical research goals and aspirations Reach out to Jishad:LinkedIn - www.linkedin.com/in/drjishadkumarIf you enjoyed this episode, be sure to also check out: From Postdoc to Assistant Professor - The WorkAccidental Discovery of the MicrowaveQuantum Biology with Clarice Aiello - The Work Reach out to Fatu:www.linkedin.com/in/fatubmTwitter: @thee_fatu_band LoveSciencePodcast@gmail.com Reach out to Shekerah:www.linkedin.com/in/shekerah-primus and LoveSciencePodcast@gmail.com Music from Pixabay: Future Artificial Intelligence Technology 130 by TimMoorMusic from https://freemusicarchive.org/music/Scott_Holmes: Hotshot by ScottHolmesMusic

Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations to be now used as all kinds of signals and information. Transforming Noise examines the historical origin of modern attempts to understand, control, and use noise. Its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. This book explores the process of engineers and physicists turning noise into an informational concept, starting from the rise of sound reproduction technologies such as the phonograph, telephone, and radio in the 1900s-20s until the theory of Brownian motions for random fluctuations and its application in thermionic tubes of telecommunication systems. These processes produced different theoretical treatments of noise in the 1920s-30s, such as statistical physicists' studies of Brownian fluctuations' temporal evolution, radio engineers' spectral analysis of atmospheric disturbances, and mathematicians' measure-theoretic formulation. Finally, it discusses the period during and after World War II and how researchers have worked on military projects of radar, gunfire control, and secret communications and converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. To physicists, mathematicians, electrical engineers, and computer scientists, this book offers a historical perspective on themes highly relevant in today's science and technology, ranging from Wi-Fi and big data to quantum information and self-organization. This book also appeals to environmental and art historians to modern music scholars as the history of noise constitutes a unique angle to study sound and society. Finally, to researchers in media studies and digital cultures, Transforming Noise demonstrates the deep technoscientific historicity of certain notions - information, channel, noise, equivocation - they have invoked to understand modern media and communication. Interview by Pamela Fuentes historian and editor of New Books Network en español Communications officer- Institute for the History and Philosophy of Science and Technology, University of Toronto Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations to be now used as all kinds of signals and information. Transforming Noise examines the historical origin of modern attempts to understand, control, and use noise. Its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. This book explores the process of engineers and physicists turning noise into an informational concept, starting from the rise of sound reproduction technologies such as the phonograph, telephone, and radio in the 1900s-20s until the theory of Brownian motions for random fluctuations and its application in thermionic tubes of telecommunication systems. These processes produced different theoretical treatments of noise in the 1920s-30s, such as statistical physicists' studies of Brownian fluctuations' temporal evolution, radio engineers' spectral analysis of atmospheric disturbances, and mathematicians' measure-theoretic formulation. Finally, it discusses the period during and after World War II and how researchers have worked on military projects of radar, gunfire control, and secret communications and converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. To physicists, mathematicians, electrical engineers, and computer scientists, this book offers a historical perspective on themes highly relevant in today's science and technology, ranging from Wi-Fi and big data to quantum information and self-organization. This book also appeals to environmental and art historians to modern music scholars as the history of noise constitutes a unique angle to study sound and society. Finally, to researchers in media studies and digital cultures, Transforming Noise demonstrates the deep technoscientific historicity of certain notions - information, channel, noise, equivocation - they have invoked to understand modern media and communication. Interview by Pamela Fuentes historian and editor of New Books Network en español Communications officer- Institute for the History and Philosophy of Science and Technology, University of Toronto Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/history

Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations to be now used as all kinds of signals and information. Transforming Noise examines the historical origin of modern attempts to understand, control, and use noise. Its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. This book explores the process of engineers and physicists turning noise into an informational concept, starting from the rise of sound reproduction technologies such as the phonograph, telephone, and radio in the 1900s-20s until the theory of Brownian motions for random fluctuations and its application in thermionic tubes of telecommunication systems. These processes produced different theoretical treatments of noise in the 1920s-30s, such as statistical physicists' studies of Brownian fluctuations' temporal evolution, radio engineers' spectral analysis of atmospheric disturbances, and mathematicians' measure-theoretic formulation. Finally, it discusses the period during and after World War II and how researchers have worked on military projects of radar, gunfire control, and secret communications and converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. To physicists, mathematicians, electrical engineers, and computer scientists, this book offers a historical perspective on themes highly relevant in today's science and technology, ranging from Wi-Fi and big data to quantum information and self-organization. This book also appeals to environmental and art historians to modern music scholars as the history of noise constitutes a unique angle to study sound and society. Finally, to researchers in media studies and digital cultures, Transforming Noise demonstrates the deep technoscientific historicity of certain notions - information, channel, noise, equivocation - they have invoked to understand modern media and communication. Interview by Pamela Fuentes historian and editor of New Books Network en español Communications officer- Institute for the History and Philosophy of Science and Technology, University of Toronto Learn more about your ad choices. Visit megaphone.fm/adchoices

Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations to be now used as all kinds of signals and information. Transforming Noise examines the historical origin of modern attempts to understand, control, and use noise. Its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. This book explores the process of engineers and physicists turning noise into an informational concept, starting from the rise of sound reproduction technologies such as the phonograph, telephone, and radio in the 1900s-20s until the theory of Brownian motions for random fluctuations and its application in thermionic tubes of telecommunication systems. These processes produced different theoretical treatments of noise in the 1920s-30s, such as statistical physicists' studies of Brownian fluctuations' temporal evolution, radio engineers' spectral analysis of atmospheric disturbances, and mathematicians' measure-theoretic formulation. Finally, it discusses the period during and after World War II and how researchers have worked on military projects of radar, gunfire control, and secret communications and converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. To physicists, mathematicians, electrical engineers, and computer scientists, this book offers a historical perspective on themes highly relevant in today's science and technology, ranging from Wi-Fi and big data to quantum information and self-organization. This book also appeals to environmental and art historians to modern music scholars as the history of noise constitutes a unique angle to study sound and society. Finally, to researchers in media studies and digital cultures, Transforming Noise demonstrates the deep technoscientific historicity of certain notions - information, channel, noise, equivocation - they have invoked to understand modern media and communication. Interview by Pamela Fuentes historian and editor of New Books Network en español Communications officer- Institute for the History and Philosophy of Science and Technology, University of Toronto Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations to be now used as all kinds of signals and information. Transforming Noise examines the historical origin of modern attempts to understand, control, and use noise. Its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. This book explores the process of engineers and physicists turning noise into an informational concept, starting from the rise of sound reproduction technologies such as the phonograph, telephone, and radio in the 1900s-20s until the theory of Brownian motions for random fluctuations and its application in thermionic tubes of telecommunication systems. These processes produced different theoretical treatments of noise in the 1920s-30s, such as statistical physicists' studies of Brownian fluctuations' temporal evolution, radio engineers' spectral analysis of atmospheric disturbances, and mathematicians' measure-theoretic formulation. Finally, it discusses the period during and after World War II and how researchers have worked on military projects of radar, gunfire control, and secret communications and converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. To physicists, mathematicians, electrical engineers, and computer scientists, this book offers a historical perspective on themes highly relevant in today's science and technology, ranging from Wi-Fi and big data to quantum information and self-organization. This book also appeals to environmental and art historians to modern music scholars as the history of noise constitutes a unique angle to study sound and society. Finally, to researchers in media studies and digital cultures, Transforming Noise demonstrates the deep technoscientific historicity of certain notions - information, channel, noise, equivocation - they have invoked to understand modern media and communication. Interview by Pamela Fuentes historian and editor of New Books Network en español Communications officer- Institute for the History and Philosophy of Science and Technology, University of Toronto Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sound-studies

Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations to be now used as all kinds of signals and information. Transforming Noise examines the historical origin of modern attempts to understand, control, and use noise. Its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. This book explores the process of engineers and physicists turning noise into an informational concept, starting from the rise of sound reproduction technologies such as the phonograph, telephone, and radio in the 1900s-20s until the theory of Brownian motions for random fluctuations and its application in thermionic tubes of telecommunication systems. These processes produced different theoretical treatments of noise in the 1920s-30s, such as statistical physicists' studies of Brownian fluctuations' temporal evolution, radio engineers' spectral analysis of atmospheric disturbances, and mathematicians' measure-theoretic formulation. Finally, it discusses the period during and after World War II and how researchers have worked on military projects of radar, gunfire control, and secret communications and converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. To physicists, mathematicians, electrical engineers, and computer scientists, this book offers a historical perspective on themes highly relevant in today's science and technology, ranging from Wi-Fi and big data to quantum information and self-organization. This book also appeals to environmental and art historians to modern music scholars as the history of noise constitutes a unique angle to study sound and society. Finally, to researchers in media studies and digital cultures, Transforming Noise demonstrates the deep technoscientific historicity of certain notions - information, channel, noise, equivocation - they have invoked to understand modern media and communication. Interview by Pamela Fuentes historian and editor of New Books Network en español Communications officer- Institute for the History and Philosophy of Science and Technology, University of Toronto Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations to be now used as all kinds of signals and information. Transforming Noise examines the historical origin of modern attempts to understand, control, and use noise. Its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. This book explores the process of engineers and physicists turning noise into an informational concept, starting from the rise of sound reproduction technologies such as the phonograph, telephone, and radio in the 1900s-20s until the theory of Brownian motions for random fluctuations and its application in thermionic tubes of telecommunication systems. These processes produced different theoretical treatments of noise in the 1920s-30s, such as statistical physicists' studies of Brownian fluctuations' temporal evolution, radio engineers' spectral analysis of atmospheric disturbances, and mathematicians' measure-theoretic formulation. Finally, it discusses the period during and after World War II and how researchers have worked on military projects of radar, gunfire control, and secret communications and converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. To physicists, mathematicians, electrical engineers, and computer scientists, this book offers a historical perspective on themes highly relevant in today's science and technology, ranging from Wi-Fi and big data to quantum information and self-organization. This book also appeals to environmental and art historians to modern music scholars as the history of noise constitutes a unique angle to study sound and society. Finally, to researchers in media studies and digital cultures, Transforming Noise demonstrates the deep technoscientific historicity of certain notions - information, channel, noise, equivocation - they have invoked to understand modern media and communication. Interview by Pamela Fuentes historian and editor of New Books Network en español Communications officer- Institute for the History and Philosophy of Science and Technology, University of Toronto

This week, Louie and Sam are joined by Dr Clare Wallace on a quest to understand Brownian motions, the ways we learn maths and how connected the world really is. 

This sleep meditation begins with a mindful progressive body scan, making sure your body is fully relaxed, one part at a time, followed by soothing encouragement to  think happy thoughts.... recalling positively wonderful memories of past times, or dreaming of "heart-warming thoughts of fanciful imaginings"... anything to take your mind away from anxious thoughts and worry.  With hair brushing ASMR sound at the end of the recording.   Please Note: This recording might cause ringing in the ears due to low level Brownian noise. If so, do not continue to listen. It's here! https://sleep-like-a-log.com - Grab your FREE 14 NIGHT SLEEP LOG FANCY 4 NEW AD-FREE, ANXIETY REDUCING Episodes per Month?SUBSCRIBE to this show for just £2.99 monthly, at:Apple Podcasts   https://podcasts.apple.com/us/podcast/sleep-like-a-log/id1677920774Please be assured that anything suggested to you, other than what your moral, ethical and legal compass would allow, will not be absorbed successfully and will be rejected by your mind.FeedbackPlease email: support@sleep-like-a-log.comDisclaimer / WarningDO NOT drive, operate heavy machinery, or use this video when it is not safe for you to become drowsy and/or fall asleep. All videos are for entertainment or psycho-educational purposes only. Therefore, no videos on this channel should be used as a substitute for clinical professional advice or support.Please consult your GP before listening to this recording.Written and Spoken by Clare Bailey, Counselling Psychotherapist, Author and Hypnotherapist (BA Hons) MBACP DHP Acc.Hyp

IN THE HALL, I threw myself into the usual chaos of kids hurrying for their lockers before catching their buses for home, bumping against one another, rebounding, bumping into someone else, bouncing with a Brownian shuffle. ... Get full access to The Personal History, Adventures, Experiences & Observations of Peter Leroy at peterleroy.substack.com/subscribe

Experience the comforting blend of Brown Noise's deep tones with the timeless serenity of falling rain. This episode offers a tranquil backdrop designed to envelop listeners in a cocoon of relaxation. Before you drift off, remember to set a sleep timer for a seamless night's rest. Let the profound frequencies of Brown Noise meet the gentle cadence of rain, forming an aural retreat tailor-made for deep relaxation. Brownian noise, often referred to as "Brown Noise," is characterized by a power density that decreases 6 decibels per octave with increasing frequency. This results in a deep, low rumble sound profile, dominated by lower frequencies. Its soothing quality is often likened to the distant roar of a waterfall or the constant hum of a large city, making it a favorite among sound enthusiasts seeking deeper, richer soundscapes for relaxation or sleep Sleep Sounds YouTube: here sleep sounds, natural sleep sounds, sleep soothing sounds, ocean sounds for sleeping, thunderstorm sounds for sleep, rain and thunder sounds for sleeping, free sleep sounds, sounds to sleep by ocean or storm sounds to sleep to, our podcast has you covered. Get bonus content on Patreon Hosted on Acast. See acast.com/privacy for more information.

Support me by becoming wiser and more knowledgeable – check out Albert Einstein's collection of books for sale on Amazon here: https://amzn.to/3Vx9eY1 If you purchase a book through this link, I will earn a 4.5% commission and be extremely delighted. But if you just want to read and aren't ready to add a new book to your collection yet, I'd recommend checking out the ⁠⁠⁠Internet Archive⁠⁠⁠, the largest free digital library in the world. If you're really feeling benevolent you can buy me a coffee or donate over at ⁠https://ko-fi.com/theunadulteratedintellect⁠⁠. I would seriously appreciate it! __________________________________________________ Albert Einstein (14 March 1879 – 18 April 1955) was a German-born theoretical physicist, widely held to be one of the greatest and most influential scientists of all time. Best known for developing the theory of relativity, he also made important contributions to quantum mechanics, and was thus a central figure in the revolutionary reshaping of the scientific understanding of nature that modern physics accomplished in the first decades of the twentieth century. His mass–energy equivalence formula E = mc2, which arises from relativity theory, has been called "the world's most famous equation". He received the 1921 Nobel Prize in Physics "for his services to theoretical physics, and especially for his discovery of the law of the photoelectric effect", a pivotal step in the development of quantum theory. His work is also known for its influence on the philosophy of science. In a 1999 poll of 130 leading physicists worldwide by the British journal Physics World, Einstein was ranked the greatest physicist of all time. His intellectual achievements and originality have made Einstein synonymous with genius. In 1905, a year sometimes described as his annus mirabilis (miracle year), Einstein published four groundbreaking papers. These outlined a theory of the photoelectric effect, explained Brownian motion, introduced his special theory of relativity—a theory which addressed the inability of classical mechanics to account satisfactorily for the behavior of the electromagnetic field—and demonstrated that if the special theory is correct, mass and energy are equivalent to each other. In 1915, he proposed a general theory of relativity that extended his system of mechanics to incorporate gravitation. A cosmological paper that he published the following year laid out the implications of general relativity for the modeling of the structure and evolution of the universe as a whole. The middle part of his career also saw him making important contributions to statistical mechanics and quantum theory. Especially notable was his work on the quantum physics of radiation, in which light consists of particles, subsequently called photons. For much of the last phase of his academic life, Einstein worked on two endeavors that proved ultimately unsuccessful. Firstly, he fought a long rearguard action against quantum theory's introduction of fundamental randomness into science's picture of the world, objecting that "God does not play dice". Secondly, he attempted to devise a unified field theory by generalizing his geometric theory of gravitation to include electromagnetism too. As a result, he became increasingly isolated from the mainstream of modern physics. Born in the German Empire, Einstein moved to Switzerland in 1895, forsaking his German citizenship (as a subject of the Kingdom of Württemberg) the following year. In 1897, at the age of seventeen, he enrolled in the mathematics and physics teaching diploma program at the Swiss Federal polytechnic school in Zürich, graduating in 1900. In 1901, he acquired Swiss citizenship, which he kept for the rest of his life. In 1903, he secured a permanent position at the Swiss Patent Office in Bern. Full essay transcript here Audio source ⁠here⁠⁠ Full Wikipedia entry ⁠here⁠ Albert Einstein's books ⁠here --- Support this podcast: https://podcasters.spotify.com/pod/show/theunadulteratedintellect/support

In this short podcast, Bryan talks about filtration and IAQ, especially as they relate to virus control. He also answers the age-old question: “Can filters capture viruses?” While it may seem like particle size matters when it comes to filter efficacy, filters are not nets that strain air particles and prevent pollutants from passing through. When we talk about particles, we tend to focus on ones that are 0.3 microns in diameter, which tend to be medium-sized particles. Viruses tend to be among the smallest particles that we aim to control when it comes to IAQ. Filter media are crisscrossed fibers that catch particles in different ways. Inertial impaction is one means of stopping particles from passing through; the initial impact stops the particles from passing through. Interception happens when particles graze filter fibers and get stuck. Electrostatic attraction relies on energy to attract and catch particles. Diffusion happens when smaller particles move more erratically due to Brownian motion and get caught in the filter media.  Viruses are among those smaller particles. Smaller particles' erratic motion makes them more likely to collide with the filter media, so they aren't necessarily harder to catch. Higher MERV ratings are associated with higher capture efficiencies. HEPA filters surpass the MERV scale and have also been proven to filter viruses out of the air, but we rarely use true HEPA filtration in residential HVAC because they are too restrictive for total system airflow. We can use bypass HEPA filtration to filter the air without creating a massive restriction at the unit. Large filter-back returns with 2” filters can help catch more particles with a greater surface area without tanking the static pressure.   Learn more about the HVACR Training Symposium or buy a virtual ticket today at https://hvacrschool.com/symposium. If you have an iPhone, subscribe to the podcast HERE, and if you have an Android phone, subscribe HERE. Check out our handy calculators HERE.

Experience a restful and rejuvenating sleep with our 12-hour compilation of soothing brown noise. Brown noise, also known as Brownian noise or red noise, is a type of audio signal that has a gentle and deep sound spectrum. It is characterized by a smooth and consistent frequency range, making it ideal for promoting relaxation and enhancing sleep. Our carefully crafted brown noise track is designed to create a calming and comforting environment that can help you fall asleep faster and achieve a deeper sleep. The gentle and constant sound of brown noise helps mask disruptive background sounds and distractions, allowing your mind and body to unwind and enter a state of tranquility. Whether you struggle with insomnia, have difficulty falling asleep, or simply want to enhance the quality of your sleep, our 12-hour brown noise compilation is here to provide you with a serene auditory experience throughout the night. Lie back, close your eyes, and let the soothing brown noise envelop you in its peaceful embrace. Allow the gentle sound to lull you into a state of deep relaxation and promote a more restful sleep experience. fall asleep faster, deeper sleep, 12 hours, soothing brown noise, restful, rejuvenating sleep, gentle and deep sound, audio signal, smooth frequency range, promote relaxation, enhance sleep, calming, comforting environment, mask disruptive background sounds, distractions, unwind, tranquility, insomnia, difficulty falling asleep, quality of sleep, auditory experience, night, lie back, close your eyes, peaceful embrace, lull, deep relaxation, restful sleep. Support our mission of spreading relaxation and wellness by rating and reviewing our podcast on your preferred platform. Your feedback helps us improve and enables others to discover the benefits of our soothing sounds. Enhance your listening experience by subscribing to our ad-free version, immersing yourself in uninterrupted tranquility.  Clicking Here  Join our community of relaxation seekers and embark on a journey of self-discovery. Subscribe, rate, and review Meditation Sounds today and unlock a world of serenity and rejuvenation.  Email List Support this podcast  https://www.meditationsoundspodcast.com Say goodbye to stubborn belly fat with our revolutionary product! Our formula is designed to target and dissolve unwanted fat, leaving you with a slimmer, more toned midsection. Try it now and experience the results for yourself. #dissolvebellyfat #slimandtoned http://bit.ly/3jV1Ip1 Learn more about your ad choices. Visit megaphone.fm/adchoices

Experience a restful and rejuvenating sleep with our 12-hour compilation of soothing brown noise. Brown noise, also known as Brownian noise or red noise, is a type of audio signal that has a gentle and deep sound spectrum. It is characterized by a smooth and consistent frequency range, making it ideal for promoting relaxation and enhancing sleep. Our carefully crafted brown noise track is designed to create a calming and comforting environment that can help you fall asleep faster and achieve a deeper sleep. The gentle and constant sound of brown noise helps mask disruptive background sounds and distractions, allowing your mind and body to unwind and enter a state of tranquility. Whether you struggle with insomnia, have difficulty falling asleep, or simply want to enhance the quality of your sleep, our 12-hour brown noise compilation is here to provide you with a serene auditory experience throughout the night. Lie back, close your eyes, and let the soothing brown noise envelop you in its peaceful embrace. Allow the gentle sound to lull you into a state of deep relaxation and promote a more restful sleep experience. fall asleep faster, deeper sleep, 12 hours, soothing brown noise, restful, rejuvenating sleep, gentle and deep sound, audio signal, smooth frequency range, promote relaxation, enhance sleep, calming, comforting environment, mask disruptive background sounds, distractions, unwind, tranquility, insomnia, difficulty falling asleep, quality of sleep, auditory experience, night, lie back, close your eyes, peaceful embrace, lull, deep relaxation, restful sleep. Support our mission of spreading relaxation and wellness by rating and reviewing our podcast on your preferred platform. Your feedback helps us improve and enables others to discover the benefits of our soothing sounds. Enhance your listening experience by subscribing to our ad-free version, immersing yourself in uninterrupted tranquility.  Clicking Here  Join our community of relaxation seekers and embark on a journey of self-discovery. Subscribe, rate, and review Meditation Sounds today and unlock a world of serenity and rejuvenation.  Email List Support this podcast  https://www.meditationsoundspodcast.com Say goodbye to stubborn belly fat with our revolutionary product! Our formula is designed to target and dissolve unwanted fat, leaving you with a slimmer, more toned midsection. Try it now and experience the results for yourself. #dissolvebellyfat #slimandtoned http://bit.ly/3jV1Ip1 Learn more about your ad choices. Visit megaphone.fm/adchoices

Discover inner peace and tranquility with the soothing sounds of brown noise. Brown noise, also known as Brownian noise or red noise, is a gentle and consistent sound that can help you relax, focus, and find a sense of calm. Unlike white noise that has an equal distribution of frequencies, brown noise has a deeper and more soothing quality. It resembles the sound of a soft waterfall or the rustling of leaves in a gentle breeze. Its steady and uniform characteristics create a soothing and comforting ambiance that can drown out distractions and promote relaxation. As you listen to the gentle hum of brown noise, allow yourself to let go of stress and tension. Feel the sound enveloping you, creating a cocoon of tranquility. Let it wash away the worries of the day and guide you to a state of inner peace. The soothing nature of brown noise can be particularly helpful during meditation, yoga, or other relaxation practices. It acts as a backdrop, helping to quiet the mind and create a serene atmosphere for deep introspection and reflection. Whether you're seeking a moment of calm amidst a busy day, a way to unwind and de-stress, or a tool to enhance your meditation practice, the soothing brown noise is here to support you. Let it embrace you in its comforting embrace and guide you towards a state of deep relaxation and inner peace. inner peace, tranquility, soothing sounds, brown noise, Brownian noise, red noise, relax, focus, calm, gentle, consistent, soft waterfall, rustling of leaves, steady, uniform, ambiance, distractions, relaxation, stress, tension, wash away, worries, meditation, yoga, relaxation practices, quiet the mind, serene atmosphere, deep introspection, reflection, unwind, de-stress, meditation practice, support, comforting embrace, deep relaxation. Support our mission of spreading relaxation and wellness by rating and reviewing our podcast on your preferred platform. Your feedback helps us improve and enables others to discover the benefits of our soothing sounds. Enhance your listening experience by subscribing to our ad-free version, immersing yourself in uninterrupted tranquility.  Clicking Here  Join our community of relaxation seekers and embark on a journey of self-discovery. Subscribe, rate, and review Meditation Sounds today and unlock a world of serenity and rejuvenation.  Email List Support this podcast  https://www.meditationsoundspodcast.com Say goodbye to stubborn belly fat with our revolutionary product! Our formula is designed to target and dissolve unwanted fat, leaving you with a slimmer, more toned midsection. Try it now and experience the results for yourself. #dissolvebellyfat #slimandtoned http://bit.ly/3jV1Ip1 Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we present the soft and soothing sound of brown noise. Brown noise, also known as Brownian noise, is a type of random sound that has a relaxing effect on the mind and body. The gentle, constant sound of brown noise provides a comforting background that can help you fall asleep faster and sleep more deeply. The episode features only natural soundscapes with no dialogue or voice, allowing you to fully immerse yourself in the calming sounds of nature and let your worries drift away. Whether you're struggling with insomnia or just need a way to unwind after a stressful day, "Calming Brown Noise" offers the perfect solution to help you relax and achieve a deep, restful sleep.Tags: sleep, relaxation, meditation, natural sounds, brown noise, calming, peaceful, stress relief, anxiety relief, insomnia reliefAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode, we explore the benefits of utilizing brown and pink noise at a frequency of 30Hz to enhance focus and concentration during study sessions. Brown noise, also known as Brownian noise or red noise, is a type of random signal that has a deeper and more bass-heavy sound compared to white noise. Pink noise, on the other hand, has a more balanced and soothing sound that resembles the natural frequencies found in our environment. Studies have shown that the combination of brown and pink noise at a frequency of 30Hz can have a positive impact on cognitive performance. The low-frequency sound helps mask distracting noises in the environment, creating a more conducive atmosphere for concentration and productivity. Additionally, the balanced and gentle nature of pink noise promotes a sense of relaxation, reducing stress and anxiety that can hinder focus. By incorporating brown and pink noise at 30Hz into your study routine, you can create a focused and calming auditory backdrop that helps you stay on task and absorb information more effectively. Whether you're preparing for exams, working on a project, or engaging in deep reading, this study boost technique can help optimize your cognitive abilities and improve overall productivity. Brown and pink noise for study, Focus and concentration boost, 30Hz frequency for study, Study with brown and pink noise, Enhance productivity with noise, Improve focus with sound, Concentration aid, Study environment optimization, Study sound therapy, Background noise for studying. Support our mission of spreading relaxation and wellness by rating and reviewing our podcast on your preferred platform. Your feedback helps us improve and enables others to discover the benefits of our soothing sounds. Enhance your listening experience by subscribing to our ad-free version, immersing yourself in uninterrupted tranquility.  Clicking Here  Join our community of relaxation seekers and embark on a journey of self-discovery. Subscribe, rate, and review Meditation Sounds today and unlock a world of serenity and rejuvenation.  Email List Support this podcast  https://www.meditationsoundspodcast.com Say goodbye to stubborn belly fat with our revolutionary product! Our formula is designed to target and dissolve unwanted fat, leaving you with a slimmer, more toned midsection. Try it now and experience the results for yourself. #dissolvebellyfat #slimandtoned http://bit.ly/3jV1Ip1 Learn more about your ad choices. Visit megaphone.fm/adchoices

「量子コンピュータ」シリーズの雑談回です。「あのパラドックスが量子現象名に！？”量子ゼノン効果”」「ギリシア神話からのおしゃれ命名”シジフォスクーリング”」「アリスにちなんで名付けられた”量子チェシャ猫状態”」など、今春から物理学者のLE0さんにオシャレな量子コンピュータ用語について教えてもらっています。 【目次】 0:00 素敵な物理学用語やナイト 2:36 「蛇に睨まれた蛙」 は量子ゼノン効果 10:12 卵パックで原子を絶対零度に！？ 21:16 オシャレ命名するならギリシャ神話は必須 27:57 電子はチェシャネコと同じようにふるまう 36:28 物理学者の苦しみは自分を信じられないこと 58:04 ものを忘れたければ電磁波で 1:07:31 これでマクスウェルの悪魔が分かる 1:13:39 物理学者、悪魔に情報詰めがち 【LE0さんの動画】 ◯ゆる学徒ハウス別館 https://www.youtube.com/@YuruGakutoHouseAnnex ◯ゆる物理学ラジオ 物理学者は量子の夢を見るか。量子コンピューターの現状と未来【量子コンピューター 1/4】 https://youtu.be/j91x9gL6msE 量子論について ・前編：https://youtu.be/COYO69MHV84 ・後編：https://youtu.be/hx7hj5-EUfw ◯カッコいい物理用語シリーズ（今回の台本） https://abounding-utahraptor-77e.notion.site/08445a10ec224ecf9beee895bcb4746f ◯水と学徒の相転移 https://note.com/metoro/n/nb9c6acddb7a7 【参考文献】 ◯Quantum Computation and Quantum Information https://amzn.to/3YnvfqR ◯シジフォスクーリングの画像 ・Cold atom realizations of Brownian motors - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Sisyphus-cooling-mechanism_fig6_1849003 [accessed 11 Feb, 2023] ・論文 https://www.tandfonline.com/doi/abs/10.1080/00107510512331337945 ◯不思議の国のアリス https://amzn.to/40RmHKj ◯鏡の国のアリス https://amzn.to/3IiDzCM 【サポーターコミュニティ加入はこちらから】 https://yurugengo.com/support 【親チャンネル：ゆる言語学ラジオ】 https://www.youtube.com/@yurugengo 【フランチャイズプロジェクト：ゆる学徒ハウス】 https://www.youtube.com/@yurugakuto 【おたよりフォーム】 https://forms.gle/BLEZpLcdEPmoZTH4A ※皆様からの楽しいおたよりをお待ちしています！ 【お仕事依頼はこちら！】 info@pedantic.jp 【堀元見プロフィール】 慶應義塾大学理工学部卒。専門は情報工学。WEBにコンテンツを作り散らかすことで生計を立てている。現在の主な収入源は「アカデミックに人の悪口を書くnote有料マガジン」。 Twitter→https://twitter.com/kenhori2 noteマガジン→https://note.com/kenhori2/m/m125fc4524aca 個人YouTube→https://www.youtube.com/@kenHorimoto 【水野太貴プロフィール】 名古屋大学文学部卒。専門は言語学。 某大手出版社で編集者として勤務。言語学の知識が本業に活きてるかと思いきや、そうでもない。 【姉妹チャンネル】 ◯ゆる音楽学ラジオ (https://open.spotify.com/show/7Ba89bnuEW0pyMeUbGR3oT) ◯ゆる民俗学ラジオ (https://open.spotify.com/show/2OPaWdgRVuUv5jLeFBViDU) ◯ゆる天文学ラジオ (https://open.spotify.com/show/6CGctNRBpOJmNPPSbvGV51) ◯ゆる書道学ラジオ (https://open.spotify.com/show/03kMZOoIJS9ybknZGv3zXc) ◯ゆる生態学ラジオ (https://open.spotify.com/show/7tTeHy7MjTGmrFrPGmjwMz) ◯ゆる哲学ラジオ (https://open.spotify.com/show/7t8NNVqRiisEHL4HG9tArT)

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Gradient surfing: the hidden role of regularization, published by Jesse Hoogland on February 6, 2023 on The AI Alignment Forum. Produced as part of the SERI ML Alignment Theory Scholars Program - Winter 2022 Cohort In a previous post, I demonstrated that Brownian motion near singularities defies our expectations from "regular" physics. Singularities trap random motion and take up more of the equilibrium distribution than you'd expect from the Gibbs measure. In the computational probability community, this is a well-known pathology. Sampling techniques like Hamiltonian Monte Carlo get stuck in corners, and this is something to avoid. You typically don't want biased estimates of the distribution you're trying to sample. In deep learning, I argued, this behavior might be less a bug than a feature. The claim of singular learning theory is that models near singularities have lower effective dimensionality. From Occam's razor, we know that simpler models generalize better, so if the dynamics of SGD get stuck at singularities, it would suggest an explanation (at least in part) for why SGD works: the geometry of the loss landscape biases your optimizer towards good solutions. This is not a particularly novel claim. Similar versions of the claim been made before by Mingard et al. and Valle Pérez et al.. But from what I can tell, the proposed mechanism, of singularity "stickiness", is quite different. Moreover, it offers a new possible explanation for the role of regularization. If exploring the set of points with minimum training loss is enough to get to generalization, then perhaps the role of regularizer is not just to privilege "simpler" functions but also to make exploration possible. In the absence of regularization, SGD can't easily move between points of equal loss. When it reaches the bottom of a valley, it's pretty much stuck. Adding a term like weight decay breaks this invariance. It frees the neural network to surf the loss basin, so it can accidentally stumble across better generalizing solutions. So could we improve generalization by exploring the bottom of the loss basin in other ways — without regularization or even without SGD? Could we, for example, get a model to grok through random drift? .No. We can't. That is to say I haven't succeeded yet. Still, in the spirit of "null results are results", let me share the toy model that motivated this hypothesis and the experiments that have (as of yet) failed to confirm it. The inspiration: a toy model First, let's take a look at the model that inspired the hypothesis. Let's begin by modifying the example of the previous post to include an optional regularization term controlled by λ: We deliberately center the regularization away from the origin at c=(−1,−1) so it doesn't already privilege the singularity at the origin. Now, instead of viewing U(x) as a potential and exploring it with Brownian motion, we'll treat it as a loss function and use stochastic gradient descent to optimize for x. We'll start our optimizer at a uniformly sampled random point in this region and take T=100 steps down the gradient (with optional momentum controlled by β). After each gradient step, we'll inject a bit of Gaussian noise to simulate the "stochasticity." Altogether, the update rule for x is as follows: with momentum updated according to: and noise given by, If we sample the final obtained position, x(T) over independent initializations, then, in the absence of regularization and in the presence of a small noise term, we'll get a distribution that looks like the figure on the left. Unlike the case of random motion, the singularity at the origin is now repulsive. Good luck finding those simple solutions now. However, as soon as we turn on the regularization (middle figure) or increase the noise term (figure on the right), the singulari...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Spooky action at a distance in the loss landscape, published by jhoogland on January 28, 2023 on LessWrong. Produced as part of the SERI ML Alignment Theory Scholars Program Winter 2022 Cohort. Not all global minima of the (training) loss landscape are created equal. Even if they achieve equal performance on the training set, different solutions can perform very differently on the test set or out-of-distribution. So why is it that we typically find "simple" solutions that generalize well? In a previous post, I argued that the answer is "singularities" — minimum loss points with ill-defined tangents. It's the "nastiest" singularities that have the most outsized effect on learning and generalization in the limit of large data. These act as implicit regularizers that lower the effective dimensionality of the model. Even after writing this introduction to "singular learning theory", I still find this claim weird and counterintuitive. How is it that the local geometry of a few isolated points determines the global expected behavior over all learning machines on the loss landscape? What explains the "spooky action at a distance" of singularities in the loss landscape? Today, I'd like to share my best efforts at the hand-waving physics-y intuition behind this claim. It boils down to this: singularities translate random motion at the bottom of loss basins into search for generalization. Random walks on the minimum-loss sets Let's first look at the limit in which you've trained so long that we can treat the model as restricted to a set of fixed minimum loss points. Here's the intuition pump: suppose you are a random walker living on some curve that has singularities (self-intersections, cusps, and the like). Every timestep, you take a step of a uniform length in a random available direction. Then, singularities act as a kind of "trap." If you're close to a singularity, you're more likely to take a step towards (and over) the singularity than to take a step away from the singularity. It's not quite an attractor (we're in a stochastic setting, where you can and will still break away every so often), but it's sticky enough that the "biggest" singularity will dominate your stable distribution. In the discrete case, this is just the well-known phenomenon of high-degree nodes dominating most of expected behavior of your graph. In business, it's behind the reason that Google exists. In social networks, it's similar to how your average friend has more friends than you do. To see this, consider a simple toy example: take two polygons and let them intersect at a single point. Next, let a random walker run loose on this setup. How frequently will the random walker cross each point? If you've taken a course in graph theory, you may remember that the equilibrium distribution weights nodes in proportion to their degrees. For two intersecting lines, the intersection is twice as likely as the other points. For three intersecting lines, it's three times as likely, and so on. Now just take the limit of infinitely large polygons/step size to zero, and we'll recover the continuous case we were originally interested in. Brownian motion near the minimum-loss set Well, not quite. You see, restricting ourselves to motion along the minimum-loss points is unrealistic. We're more interested in messy reality, where we're allowed some freedom to bounce around the bottoms of loss basins. This time around, the key intuition-pumping assumption is to view the behavior of stochastic gradient descent late in training as a kind of Brownian motion. When we've reached a low training-loss solution, variability between batches is a source of randomness that no longer substantially improves loss but just jiggles us between solutions that are equivalent from the perspective of the training set. To understand these dy...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Spooky action at a distance in the loss landscape, published by Jesse Hoogland on January 28, 2023 on The AI Alignment Forum. Produced as part of the SERI ML Alignment Theory Scholars Program Winter 2022 Cohort. Not all global minima of the (training) loss landscape are created equal. Even if they achieve equal performance on the training set, different solutions can perform very differently on the test set or out-of-distribution. So why is it that we typically find "simple" solutions that generalize well? In a previous post, I argued that the answer is "singularities" — minimum loss points with ill-defined tangents. It's the "nastiest" singularities that have the most outsized effect on learning and generalization in the limit of large data. These act as implicit regularizers that lower the effective dimensionality of the model. Even after writing this introduction to "singular learning theory", I still find this claim weird and counterintuitive. How is it that the local geometry of a few isolated points determines the global expected behavior over all learning machines on the loss landscape? What explains the "spooky action at a distance" of singularities in the loss landscape? Today, I'd like to share my best efforts at the hand-waving physics-y intuition behind this claim. It boils down to this: singularities translate random motion at the bottom of loss basins into search for generalization. Random walks on the minimum-loss sets Let's first look at the limit in which you've trained so long that we can treat the model as restricted to a set of fixed minimum loss points. Here's the intuition pump: suppose you are a random walker living on some curve that has singularities (self-intersections, cusps, and the like). Every timestep, you take a step of a uniform length in a random available direction. Then, singularities act as a kind of "trap." If you're close to a singularity, you're more likely to take a step towards (and over) the singularity than to take a step away from the singularity. It's not quite an attractor (we're in a stochastic setting, where you can and will still break away every so often), but it's sticky enough that the "biggest" singularity will dominate your stable distribution. In the discrete case, this is just the well-known phenomenon of high-degree nodes dominating most of expected behavior of your graph. In business, it's behind the reason that Google exists. In social networks, it's similar to how your average friend has more friends than you do. To see this, consider a simple toy example: take two polygons and let them intersect at a single point. Next, let a random walker run loose on this setup. How frequently will the random walker cross each point? If you've taken a course in graph theory, you may remember that the equilibrium distribution weights nodes in proportion to their degrees. For two intersecting lines, the intersection is twice as likely as the other points. For three intersecting lines, it's three times as likely, and so on. Now just take the limit of infinitely large polygons/step size to zero, and we'll recover the continuous case we were originally interested in. Brownian motion near the minimum-loss set Well, not quite. You see, restricting ourselves to motion along the minimum-loss points is unrealistic. We're more interested in messy reality, where we're allowed some freedom to bounce around the bottoms of loss basins. This time around, the key intuition-pumping assumption is to view the behavior of stochastic gradient descent late in training as a kind of Brownian motion. When we've reached a low training-loss solution, variability between batches is a source of randomness that no longer substantially improves loss but just jiggles us between solutions that are equivalent from the perspective of the training set. To u...

Gordon Brown's New Britain Report promised a radical examination of the state of the UK. We see if it lives up to Labour's hype. Trust us, it doesn't.The opinion polls and the recent Chester by election result suggest that Labour, either as a minority or majority, will form the Westminster government post the 2024 General Election. Where does this leave the SNP de facto referendum strategy, would this be enough to tempt the Scottish left back into the Labour fold, and would Labour call a snap indyref2?Meanwhile Ian Blackford has resigned as SNP leader at Westminster. Stephen Flynn and Alison Thewliss are the two candidates vying to take over. What difference, if any, will victory for either candidate mean?Away from the world of party politics Lesley wonders if the Yes movement can learn from, and be inspired by, the successful Eigg buy out campaign. ★ Support this podcast ★

Black-Scholes is a beautiful model driven by Brownian motion, but Brownian motion is too restrictive to accurately describe the asset prices. One way to generalize Brownian motion which is widely used in science is to allow discontinuous jumps. This lies underneath some very fancy models that financial institutions employ. Building on last week, Jacob joins Tom and Tony to go over how these jump diffusion models look under the hood.

Black-Scholes is a beautiful model driven by Brownian motion, but Brownian motion is too restrictive to accurately describe the asset prices. One way to generalize Brownian motion which is widely used in science is to allow discontinuous jumps. This lies underneath some very fancy models that financial institutions employ. Building on last week, Jacob joins Tom and Tony to go over how these jump diffusion models look under the hood.

Try our Sleep App, Slow for iPhone. https://slow-app.com/ The sleep app that lets you create your own sleep soundscapes, add-free. Get the sleep you need, download the app: https://slow-app.com/ Become a paid podcast subscriber, and unlock all add-free premium episodes: https://anchor.fm/sleeppodcast/subscribe Give feedback: Send us your sound request in a podcast review, and we will try to make it for you. Our premium sleep app: https://slow-app.com/ Get Spotify sleep playlists here: https://podlink.to/long-sleep-playlist Today's episode is devoted to Brown Noise. In science, Brownian noise, also known as Brown noise or red noise, is the type of signal noise produced by Brownian motion, hence its alternative name of random walk noise. The term "Brown noise" does not come from the color, but after Robert Brown, who documented the erratic motion for multiple types of inanimate particles in water. On this sleep podcast you will find: Relaxing nature sounds, sleep soundscapes, binaural beats, deep sleep sounds, rain sounds, ocean sounds, ocean waves, white noise machines, thunderstorms, waterfall sounds, baby sleep sounds, tinnitus masker sounds, jungle, forest sounds, relaxing music, and guided sleep meditations. We hope this channel will help you with your sleepless nights, insomnia, sleep apnea, sleep paralysis. Use this podcast as your daily sleep podcast and experience the benefits of good quality sleep. We recommend that you talk with a doctor if your sleep doesn't improve. There are many people who find it difficult to sleep at night. Can you relate? 2 AM in bed, but you just can't relax, your head is spinning… Nature sounds are one of the best ways to relax and sleep and de-stress. Use nature sounds as a way to fall asleep. Play this sleep podcast with relaxing sounds for sleep when you go to bed or just when you need some calmness in your life. Some of these relaxing sounds include rain, ocean waves, crickets, and the sound of a fireplace. Sounds have a powerful effect on our mood and emotions. They can calm us down, help us focus, and even lull us to sleep. The sounds that you listen to can have a significant impact on your sleep quality. This is because the brain associates the sound with a certain feeling or mood. So if you listen to calming sounds like rain or ocean waves, it will help you relax and fall asleep faster than if you were listening to traffic noise. Some people use white noise machines or thunderstorms as a way of blocking out distracting noises in their environment, such as tinnitus or snoring partners. Nature sounds are a great way to help you sleep better. You can use them as background noise for meditation or for sleep. The most popular type of nature sounds is rain and ocean waves. Many other types of sounds can also be used, such as thunderstorms, waterfall, and baby sleep sounds. These natural noises have been shown to help people fall asleep faster and stay asleep longer than using white noise machines or tinnitus maskers. Have a relaxing day and sleep well :) You spend 1/3 of your life sleeping so do it well. Sleep is your superpower.

Last week, we discussed a very big bacterium, one you can see with your naked eye! But back in high school we all learned that bacteria and prokaryotes in general were pretty simple cells and were definitely smaller than our cells. While we've found a lot of examples that push back against this idea, there is a fundamental truth behind it -- a simple cell has definite physical constraints on how big it can grow. What are those constraints? And how do these giant bacteria (and our own cells) get around these problems? References: https://royalsocietypublishing.org/doi/10.1098/rsif.2008.0014 http://www.math.uchicago.edu/~lawler/reu.pdf https://www.science.org/content/article/largest-bacterium-ever-discovered-has-unexpectedly-complex-cells

Did Wassily Kandinsky really invent abstract art? Randall takes Chris on a journey with many twists and turns.  *** Download slides: https://mega.nz/file/J9tGTQAC#5Oa99t7-pxmdxowHcq0pe5i5nSpKYg-Gns1MXlJtovc *** Topics discussed include:  the first abstract painting Wassily Kandinsky Hilma af Klint Helena Blavatsky automatic drawing Rudolf Steiner The Ten Largest Theosophy Sigmund Freud Adolf Hitler and the Nazis Bauhaus school Georgiana Houghton Albert Einstein the birth of the modern world *** Timeline: 1859 -- Georgiana Houghton starts making "spirit" drawings at seances 1862 -- Hilma af Klint born 1863 -- Salon des Refusés  1871 -- Houghton pays for a show in London 1874 -- Impression, Sunrise by Monet 1875 -- Helena Blavatsky cofounds the Theosophical Society, as "the synthesis of science, religion and philosophy", proclaiming that it was reviving an "Ancient Wisdom" which underlay all the world's religions.  1880 -- Hilma's 10-year-old sister dies, spurring her interest in the occult 1882 -- Hilma af Klint enrolled in Sweden' s Royal Academy of Fine Arts.  1884 -- Georgiana Houghton dies 1887 -- Hilma af Klint graduates with honors, awarded use of shared studio until 1909. Here she paints first 100 or so Paintings For the Temple.  1888 -- The Five is founded 1895 -- X-rays discovered 1895 -- Sigmund Freud publishes one of his first books, Studies on Hysteria 1896 -- Radio waves discovered, first radios 1900 1896 -- radioactivity discovered 1896 -- Hilma experiments with automatic drawing. was participating in weekly seances with The Five. * Through her work with The Five, Hilma af Klint created experimental automatic drawing as early as 1896, leading her toward an inventive geometric visual language capable of conceptualizing invisible forces both of the inner and outer worlds.[citation needed] She explored world religions, atoms, and the plant world and wrote extensively about her discoveries.[5] As she became more familiar with this form of expression, Hilma af Klint was assigned by the High Masters to create the paintings for the "Temple" – however she never understood what this "Temple" referred to.  Hilma af Klint felt she was being directed by a force that would literally guide her hand. She wrote in her notebook:  The pictures were painted directly through me, without any preliminary drawings, and with great force. I had no idea what the paintings were supposed to depict; nevertheless I worked swiftly and surely, without changing a single brush stroke.[14] * 1903 -- Kandinsky paints the Blue Rider 1904 -- Hilma af Klint joins Theosophical society 1904 -- Hilma af Klint was informed by spirit guides a great temple should be built and filled with paintings.  1905 -- Albert Einstein publishes his 4 seminal papers: photoelectric effect, Brownian motion, special relativity, and the equivalence of mass and energy. 1906 -- Klint begins automatic painting https://www.nytimes.com/2019/10/21/travel/stockholm-hilma-af-klint.html * led by a spiritual guide named Amaliel who contacted af Klint during séances and not only “commissioned” the paintings but, at least at the outset, had, she claimed, directed her hand as she painted. “The pictures were painted directly through me, without any preliminary drawings and with great force,” af Klint wrote in one of her journals of the 193 mostly abstract works known as “The Paintings for the Temple,” meditations on human life and relationships in the most elemental terms. “I had no idea what the paintings were supposed to depict, nevertheless I worked swiftly and surely without changing a single brush stroke.”  * https://www.bbc.com/culture/article/20181012-hilma-af-klint-the-enigmatic-vision-of-a-mystic Absorbing a wide array of cultural influences old and new – from Goethe's colour theories to Darwin's discoveries concerning evolution, from Car Linnaeus's botanical taxonomies to cutting-edge ideas about atomic matter and radioactivity – Af Klint set about composing for posterity an alluring eye-music that echoed back the complex psyche of her age.  * 1907 -- De Fem finishes The Ten Largest 1908 -- Hilma meets Rudolf Steiner * In 1908 af Klint met Rudolf Steiner for the first time. In one of the few remaining letters, she was asking Steiner to visit her in Stockholm and see the finished part of the Paintings for the Temple series, 111 paintings in total. Steiner did see the paintings but mostly left unimpressed, stating that her way of working was inappropriate for a theosophist. According to H.P. Blavatsky, mediumship was a faulty practice, leading its adepts on the wrong path of occultism and black magic.[18] However, during their meeting, Steiner stated that af Klint's contemporaries would not be able to accept and understand their paintings, and it would take another 50 years to decipher them. Of all the paintings shown to him, Steiner paid special attention only to the Primordial Chaos Group, noting them as "the best symbolically".[19] After meeting Steiner, af Klint was devastated by his response and, apparently, stopped painting for 4 years. Interestingly enough, Steiner kept photographs of some of af Klint's artworks, some of them even hand-coloured. Later the same year he met Wassily Kandinsky, who had not yet come to abstract painting. Some art historians assume that Kandinsky could have seen the photographs and perhaps was influenced by them while developing his own abstract path.[20] Later in her life, she made a decision to destroy all her correspondence. She left a collection of more than 1200 paintings and 125 diaries to her nephew, Erik af Klint. Among her last paintings made in 1930s, there are two watercolours predicting the events of World War II, titled The Blitz and The Fight in the Mediterranean.[21]  * https://www.theguardian.com/artanddesign/2016/feb/21/hilma-af-klint-occult-spiritualism-abstract-serpentine-gallery In 1908, after making 111 paintings, she collapsed: “She had completed a painting every third day – including the 10 huge ones. She was exhausted.” And there was further reason for despond. That same year, Steiner was lecturing in Stockholm. She invited this charismatic man to see her paintings (Mondrian petitioned Steiner too, but always in vain). She had hoped he would interpret the work. Instead he advised: “No one must see this for 50 years.” For four years after this verdict she gave up painting and looked after her sightless mother. Johan shows me a photograph of Hilma at Hanmora, looking down with tenderness, a hand on her mother's shoulder – the more sympathetic of clues to her character.  * 1910 -- first abstract by Kandinsky 1919 -- Bauhaus school founded 1923 -- Hilma writes Steiner asking him what she should do, "burn them?" She never hears back. 1925 -- Rudolf Steiner dies 1928 -- Theosophy reaches peak membership 1930s -- While studies, sketches, and improvisations exist (particularly of Composition II), a Nazi raid on the Bauhaus in the 1930s resulted in the confiscation of Kandinsky's first three Compositions. They were displayed in the State-sponsored exhibit "Degenerate Art", and then destroyed (along with works by Paul Klee, Franz Marc and other modern artists)  1932 -- Hilma af Klint's last will. In  will, Hilma keaves 1200 paintings, 26,000 pages of notes (125 notebooks), not to be shown until 20 years after her death. 1933 -- Hitler appointed chancellor of Germany 1944 -- Hilma dies of car accident. She was 82. Also Kandinsky (77), Mondrian (pneumonia, 71) 1970s -- Johan af Kilnt offers works to the Moderna Museet, they refuse. The then-director turned them down. “When he heard that she was a medium, there was no discussion. He didn't even look at the pictures.” Only in 2013 did the museum redeem itself with a retrospective. https://www.theguardian.com/artanddesign/2020/oct/06/hilma-af-klint-abstract-art-beyond-the-visible-film-documentary 1985 -- Hilma's work discovered. Distant relative of Klint finds paintings just hanging on walls of theosophical society.  1986 -- Hilma af Klint show: The Spiritual in Art, Abstract Painting 1890-1985  2013 -- Hilma af Klint Moderna Museet Stockholm show: perhaps their most popular in history 2019 -- Hilma af Klint Guggenheim show: may have been it's most popular 2020 -- Beyond the Visible: Hilma af Klint documentary *** recorded April 21, 2022 *** Visit us at https://chrisandrandall.com/

Brownian motion provides financial theorists with the basic building block for understanding prices evolving under market forces.  Today, Jacob join Tom and Tony to discuss some of the finer points of Brownian paths. We will use their symmetries to discuss the frequency of maxima and minima along the path, even if they are not identifiable at the time.

Brownian motion provides financial theorists with the basic building block for understanding prices evolving under market forces.  Today, Jacob join Tom and Tony to discuss some of the finer points of Brownian paths. We will use their symmetries to discuss the frequency of maxima and minima along the path, even if they are not identifiable at the time.

Hello beautiful lights! Welcome to the Mystery Box on this channel, Lex spun the wheel and it landed on, Robert Brown! Brown Noise is low key slept on, literally and figuratively! Named after brilliant botanist Robert Brown, who discovered Brownian Motion (random particle motion) in the early 1800s. Most people use this sound to help relax their mind. It is often used while sleeping. Even if it is used just for 10 mins a day, the benefits are wonderful. Think of the sound like tiny gold/brown particles moving through your body, cleansing you of negative energy and helping you ground. This sound makes me think of Mother Earth and her gift of healing. Grab headphones, go outside barefoot and ground with Brownian Noise! --- Support this podcast: https://anchor.fm/saint-finnikin/support

What happens when you disrupt the healthcare workforce industry while it is being disrupted by a global pandemic? Dr. Alexi Nazem, the CEO of Nomad Health, discusses his role leading Nomad to modernize the travel nursing industry right at a time of unprecedented need, extraordinary uncertainty and recurrent staffing crises. From death-defying moments to leading through breakneck growth, Nazem describes his journey doing to the travel nursing industry what Travelocity did to travel agencies. Join also for a conversation around the strengths and weaknesses of medicine's leadership training, pursuing both clinical practice and entrepreneurship, and the "Brownian motion" at the heart of company creation. --- Send in a voice message: https://anchor.fm/tdio/message

On the Money Vikings Podcast #39 - Brian Reeves from Gator Traders and The Money Vikings get together for the first time, talking about Gann Fans, Chaos Theory, Brownian movements, Andrew's Pitchfork and a few stocks we're watching like MVIS, BNGO, CWSFF/CMC, UAVS. Subscribe to Gator Traders and use code TMV20 for 20% off from 6/6 - 6/19. Where to Find us:

We're back with a quick run down of the win against Sheffield Wednesday before diving into a  preview of what (we decide) is our biggest game of the year against Leicester. We then meander through Newcastle, January transfers and Frank Lampard via Eistein's Nobel prize for Brownian motion and the war in Iraq. Not even joking.If you don't have enough nonsense in your life, subscribe wherever you get your podcasts and follow us at @BluesBrosEFC on Twitter.