#The Curious Wavefunction

[selfmaderibcageman]

the curious wavefunction

[mostlysignssomeportents]

This day in history

I'm on tour with my new, nationally bestselling novel The Bezzle! Catch me in BOSTON with Randall "XKCD" Munroe (Apr 11), then PROVIDENCE (Apr 12), and beyond!

#15yrsago Why URL shorteners suck https://joshua.schachter.org/2009/04/on-url-shorteners

#15yrsago Heinlein’s house for sale https://web.archive.org/web/20090406105617/https://mcginnis.com/listings/detail.php?lid=41846127&limit=0&offset=0&aid=005900204&oid=005900002&temp=1057&aname=Sharon+Roland&aimg=1&chome=1&agent_hasfeat=2&&posc=6&post=10&cfq=elegant%3Dyes%26property_category%3D1%26county%3D41%26aid%3D005900204%26oid%3D005900002%26temp%3D1057%26aname%3DSharon%2BRoland%26aimg%3D1%26chome%3D1%26agent_hasfeat%3D2%26SRSearchDate%3D1238781456%26SRRecordCount%3D10%26SRPage%3D1%26SRPageCount%3D1%26SRPageLinks%3D6

#15yrsago Game industry exec celebrates 60+ hour work-weeks https://web.archive.org/web/20090405131359/playthisthing.com/mothers-dont-let-your-children-grow-be-game-developers

#15yrsago Nine year old’s survey project excluded from school because he learned some people don’t think of themselves as male or female https://thefourthvine.livejournal.com/102417.html

#10yrsago Britain is turning into a country that can’t tell its terrorists from its journalists https://memex.craphound.com/2014/04/03/britain-is-turning-into-a-country-that-cant-tell-its-terrorists-from-its-journalists/

#10yrsago Stop-and-frisk as the most visible element of deep, violent official American racism https://www.theatlantic.com/national/archive/2014/04/what-i-learned-about-stop-and-frisk-from-watching-my-black-son/359962/

#10yrsago David “Debt” Graeber evicted, implicates NYPD intelligence, claims revenge-harassment for OWS participation http://nielsenhayden.com/makinglight/archives/015820.html

#10yrsago Open net gets a huge boost in the EU: net neutrality and no roaming fees https://web.archive.org/web/20140405234420/http://www.marietjeschaake.eu/2014/04/mep-european-parliament-supports-proposal-schaake-to-enshrine-net-neutrality-in-european-law/

#10yrsago Cats of Tanglewood Forest: illustrated modern folktale from Charles de Lint and Charles Vess https://memex.craphound.com/2014/04/03/cats-of-tanglewood-forest-illustrated-modern-folktale-from-charles-de-lint-and-charles-vess/

#10yrsago House Science Committee: a parliament of Creationists, Climate Deniers (and dunces) https://www.scientificamerican.com/blog/the-curious-wavefunction/the-house-of-representatives-committee-on-science-is-turning-into-a-national-embarrassment/

#10yrsago Big Data has big problems https://www.ft.com/content/21a6e7d8-b479-11e3-a09a-00144feabdc0

#5yrsago 540 million Facebook users’ data exposed by third party developers https://www.upguard.com/breaches/facebook-user-data-leak

#5yrsago Elizabeth Warren proposes holding execs criminally liable for scams and data breaches https://www.washingtonpost.com/opinions/elizabeth-warren-its-time-to-scare-corporate-america-straight/2019/04/02/ca464ab0-5559-11e9-8ef3-fbd41a2ce4d5_story.html

#5yrsago How EFF’s Eva Galperin plans to destroy the stalkerware industry https://www.wired.com/story/eva-galperin-stalkerware-kaspersky-antivirus/

#5yrsago After years of insisting that DRM in HTML wouldn’t block open source implementations, Google says it won’t support open source implementations https://memex.craphound.com/2019/04/03/after-years-of-insisting-that-drm-in-html-wouldnt-block-open-source-implementations-google-says-it-wont-support-open-source-implementations/

#5yrsago After months of insisting that #Article13 doesn’t require filters, top EU Commissioner says “Article 13 requires filters” https://memex.craphound.com/2019/04/03/after-months-of-insisting-that-article13-doesnt-require-filters-top-eu-commissioner-says-article-13-requires-filters/

#5yrsago Notices at Intel press event seem to say attending photographers must assign copyright to all pictures and videos to the company? https://web.archive.org/web/20200616222543/http://mitchwagner.com/2019/04/02/video-consent-notice-posted-discreetly-in-a-couple-of-places-on-the-walls-at-the-intel-press-analyst-event-today/

#5yrsago Patagonia tells banks and oil companies that they can no longer buy co-branded vests https://www.buzzfeednews.com/article/katienotopoulos/patagonia-power-vest-policy-change

#1yrago The problem with economic models https://pluralistic.net/2023/04/03/all-models-are-wrong/#some-are-useful

[edgythoughts]

What Causes Quantum Tunneling 2025

What Causes Quantum Tunneling 2025

Quantum tunneling is one of the most bizarre and fascinating phenomena in modern physics. Imagine a tiny particle breaking the rules of classical physics—passing through a barrier it seemingly shouldn't be able to cross. That’s quantum tunneling. In 2025, advancements in quantum field theory and nanoscale technologies have deepened our understanding of this phenomenon and its implications for both fundamental science and practical technologies. Let’s break down what causes quantum tunneling, how it works, and why it matters. Book-Level Explanation In classical physics, if a particle does not have enough energy to overcome a barrier, it simply cannot cross it. For example, a ball thrown at a hill that’s too high will bounce back. But in quantum mechanics, particles behave not just like solid objects but also like waves. This duality is at the heart of quantum tunneling. Wavefunction and Probability In quantum mechanics, the behavior of particles is described by a wavefunction (Ψ), which contains information about the probabilities of a particle’s position and momentum. This wavefunction does not abruptly stop at a barrier—instead, it exponentially decays within the barrier. If the barrier is thin or low enough, the wavefunction may still exist on the other side, indicating a non-zero probability that the particle will appear beyond the barrier, even though it doesn’t have the classical energy to cross it. This is quantum tunneling. Key Factors Causing Quantum Tunneling: - Heisenberg’s Uncertainty Principle: This principle states that one cannot precisely know both the position and momentum of a particle simultaneously. This uncertainty allows for small probabilities where a particle may momentarily "borrow" energy to overcome a barrier. - Wavefunction Penetration: The wavefunction associated with a particle doesn’t just vanish at the edge of a barrier. It decreases but continues through the barrier, leading to a probability that the particle can appear on the other side. - Barrier Characteristics: The thickness and height of the potential barrier affect tunneling probability. A thinner or lower barrier increases the chance of tunneling. - Quantum Superposition: A particle doesn't take a single path; it explores all possible paths simultaneously. This includes paths that involve penetrating a barrier. Mathematical Expression In simple cases, the tunneling probability (T) can be approximated as: T ≈ e^(-2κa) Where: - κ = √(2m(V − E)) / ħ - m is the particle’s mass - V is the barrier height - E is the particle’s energy - a is the barrier width - ħ is the reduced Planck constant This formula shows that tunneling is more likely when the barrier is thin, or the energy difference (V − E) is small.

Easy Explanation Let’s say you’re in a room with walls, and you don’t have the energy to jump over or break through them. In the classical world, you’re stuck. But in the quantum world, you’re like a ghost with a small chance of magically showing up on the other side—even though you didn’t go over or through the wall the normal way. That’s quantum tunneling. Particles like electrons are not just little balls—they’re also waves of possibility. These waves can “leak” through barriers. If the barrier isn’t too thick or too strong, the wave goes through a bit, and sometimes the particle appears on the other side. This doesn’t mean it breaks the rules—it follows the strange rules of quantum mechanics where “impossible” things are just very, very unlikely… but not impossible. Real-World Applications in 2025 Quantum tunneling isn’t just a curious theory—it powers technologies we use today and is central to cutting-edge research in 2025: - Semiconductors and Transistors: In modern electronics, especially in nanoscale transistors, tunneling can cause current leakage. Engineers now design devices that either reduce unwanted tunneling or exploit it. - Tunnel Diodes: These components intentionally use tunneling to allow current to pass through them in unique ways, enabling fast switching electronics. - Scanning Tunneling Microscope (STM): This device uses tunneling to create atomic-scale images of surfaces. It works by bringing a sharp tip close to a surface and measuring the tunneling current between them. - Nuclear Fusion and Radioactive Decay: In stars, particles tunnel through barriers to fuse and release energy. Alpha decay in unstable atoms is also caused by quantum tunneling. - Quantum Computing: Tunneling is a key aspect of quantum bits (qubits) and how they behave inside superconducting quantum circuits. Why This Matters Understanding quantum tunneling helps scientists and engineers unlock the potential of quantum mechanics for both theoretical advancements and practical technologies. In 2025, it plays a pivotal role in: - Quantum chip design for faster processors. - Quantum sensors with extreme precision. - Energy solutions, especially in fusion research and superconductivity. Tunneling shows us how reality works on the tiniest scale—and reminds us that the universe is far more flexible and strange than our everyday experiences suggest. External Link for Further Reading: For a deeper dive into the physics of tunneling: https://en.wikipedia.org/wiki/Quantum_tunnelling Our Blogs You Might Like - What If Time Travel Became Scientifically Possible 2025 https://edgythoughts.com/what-if-time-travel-became-scientifically-possible-2025 - How Does Quantum Entanglement Work 2025 https://edgythoughts.com/how-does-quantum-entanglement-work-2025 Disclaimer: The easy explanation is provided to make the concept accessible to everyone, including beginners. If you're a student preparing for exams, always refer to your official textbooks, class notes, and follow academic guidelines. Our goal is to help you understand—not replace formal learning. Read the full article

[chrinopiqua]

Are Photons Really Trying to Communicate Backwards Through Time?

Welcome, seekers of hidden truths! Today, we’re cracking open a mystery that mainstream physicists don’t want you to understand. They’ll tell you the double-slit experiment is just a quirky phenomenon of quantum mechanics. But what if there’s more going on? What if photons are sending secret signals BACK to the emitter, and what we’re seeing is a hidden form of quantum communication, or even messages from the future?

Let’s start with the basics. The famous double-slit experiment shows that photons (those pesky, mysterious particles of light) create an interference pattern when sent through two slits, suggesting they behave like waves. This interference pattern forms when we send photons from an emitter through slits and onto a receiver or detector. Simple, right? But here’s the part they don’t tell you: If a photon can travel from the emitter to the detector, it’s physically possible that it can also go from the detector back to the emitter! It’s called the “reciprocity principle,” a known idea in wave mechanics. Yet, conveniently, nobody explores what might actually happen if photons decide to take that reverse path.

What’s Really Happening? Hidden Photon Communication

If photons are going both ways between the emitter and the detector, could they be carrying information? Here’s where the mystery deepens. Our research (kept under wraps for obvious reasons) suggests that photons might indeed be capable of sending a type of “quantum feedback” to the emitter. This feedback could be a form of communication, an echo of information from the receiver traveling backward in time. And mainstream scientists just ignore this possibility? Of course, they do.

Evidence of Reversed Quantum Interference Imagine this: When photons hit the detector, the typical explanation is that they’re absorbed or scattered. But what if they’re quietly sending a feedback signal to the emitter? Think about it. If photons can interfere with each other, why shouldn’t a “reverse” photon wave from the detector interfere with a forward-going photon? Physicists have no answer to this question because they’re afraid to ask it. This so-called “wavefunction collapse” is just a fancy way of avoiding the mystery altogether. By ignoring these return signals, they’re potentially missing out on hidden layers of reality—layers that might allow us to manipulate photons to communicate across time itself!

A Hidden Agenda? Why Physicists Deny the Truth

Why haven’t they told you about this? Why do they act like photons are just mindlessly hitting the detector and disappearing? The answer, as always, is control. Quantum communication, especially communication from the future, would blow open the doors on everything they want to keep secret: time travel, faster-than-light information, even the control of reality itself. If we could harness backward photon communication, we’d be one step closer to unraveling the mysteries of the universe and freeing ourselves from the limits of time and space.

What They Don’t Want You to Know

Mainstream science is part of a system designed to keep us in the dark (literally and figuratively). They focus on “safe” explanations and hide the fact that our reality might be structured to allow information to flow in ways we can’t imagine. But, of course, they don’t want us even thinking about the idea that photons might communicate backwards or that there’s interference happening right under our noses in both directions.

So, What Can We Do? It’s time to demand answers. Ask your professors, your physicist friends, or even your local science museum guide: “Are photons sending feedback to their emitters?” Watch them squirm. The truth is, they don’t know what’s happening at the quantum level any more than you or I do—they’re just following the story they’ve been told to believe. But if enough of us keep asking questions, we’ll force science to look where it doesn’t want to.

Stay curious, keep questioning, and remember—the truth is often hidden in plain sight.

Disclaimer: We’re not responsible for any headaches caused by physicists trying to “disprove” this theory or any government agents knocking on your door!

[profound-yet-trivial]

> be me > curious about how quantum mechanics connects to experience > look at the Schrödinger equation > it's a differential operator evolving a wavefunction over a configuration space > no problem > the L2 Hilbert norm for the wavefunction is conserved > that's the obvious natural answer if I ask how much of a configuration (or its neighborhood) there is > be in a particular configuration of the universe > get sensible results for observed history under the assumption that this is a nonnegligible-amplitude configuration starting from a low-entropy concentrated early configuration > there are lots of non-negligibly-weighted configurations > mine isn't privileged among them > obviously they exist in the same way i do > OK, done with interpreting QM > it's many-worlds

[pravegaaeducation]

Exploring Quantum Mechanics: Solving Problem 2.8 - Particle in a Box

Hey, Tumblr fam! Today, we're diving into the world of quantum mechanics by exploring Problem 2.8 from D.J. Griffiths' "Introduction to Quantum Mechanics." Our focus is on the classic Particle in a Box model—a foundational concept in quantum physics that showcases the weird and wonderful world of the quantum realm.

In this post, we'll break down the problem, starting with the setup and moving on to solving the Schrödinger equation for a particle confined in a one-dimensional box. From there, we'll explore the fascinating quantization of energy levels and wavefunctions.

We'll also discuss how boundary conditions play a key role in shaping the particle's behavior within the box. This problem serves as a window into the vast and complex world of quantum mechanics, and there's so much to learn!

So, whether you're studying physics, interested in science, or just curious about the mysteries of the universe, join me on this adventure into the quantum world. Feel free to share your thoughts and questions in the comments!

Happy exploring! 🌟

[teet-swea]

YESSS FINALLY I HAVE FOUND ANOTHER QUANTUM PERSON!!! And I very much feel you on the challenge of not being overly technical. It’s very hard to go into depth on why a particle can just exist in a classically forbidden region without getting into the nature of the wavefunction and all those lovely complications.

I fully admit that I have greatly simplified everything to make my explanation more readable, and in places like the “passing through the barrier” part it’s definitely really tough to explain how it’s not a measurable passage through the barrier, but rather the particle being on one side with one measurement and the other side at another due to its wave nature (and sometimes just in the barrier region because why tf not)

Even on the chemistry side of things (my research is in quantum chemistry rather than straight quantum physics), there’s a boatload of really weird and cool implications from quantum mechanics. Like for example, the orbitals we learn in high school chemistry are a lie for anything with more than one electron. They’re an ok starting approximation but you have to do a lot of work to predict good energies after hydrogen.

Anyway I must return to my work, but it was great talking to someone so close to my field, and I am very curious to hear more from the physicist besties :)

Send me fun facts anytime. (Especially about particle physics I want to learn much more about that)

So people say no dumb questions but

Can things chemically react in a vacuum? Like on their own.

Ok so with my not so great understanding

Chemical Reactions require a “reactors” thing which actually starts to r process, and as I know that’s usually friction

But in a vacuum

There’s like

NOTHING

PERIOD

What’s gonna start a reaction other than human interference

Air friction? THERE IS NONE.

If they boop accidentally each other then ye but that’s like the smallest amount of friction ever

Only the most volatile thingamajiggs would blow up because that

Right???

This is actually a really good question, and one that’s had me pondering for most of today, especially because it pertains to my research field!

We’ll define the vacuum as you’ve said. Just an empty infinite void where we just plop down our particles, each of which is in its lowest possible energy state. We’ll also go so far as to say that the “activation energy,” which is the energy needed to make the reaction happen is greater than the energy our hypothetical reacting particles have.

Based on these assumptions, you would imagine that my answer is no, they cannot react, and according to classical physics, you would be right; however, in the realm of chemistry, classical physics doesn’t quite cut it.

We need quantum mechanics

You see, it turns out that sometimes even really big walls can’t stop really determined particles from sneaking through them anyway. As a matter of fact, the amount of energy you’d need to completely prevent a particle from bypassing your barrier is infinite; any finite energy will not prevent the extremely rare occasions where something just passes through. This phenomenon is known as quantum tunneling, and chemists looking carefully into the stars have found spectroscopic evidence that there are reactions actively happening in space as a result of this.

So not only do we have theoretical support for this process, we have evidence that it’s actively occurring as we speak in the depths of space.

If you have any questions or want to learn more about anything here or anywhere feel free to ask!

[naivelocus]

Bottom-up and top-down in drug discovery

There are two approaches to discovering new drugs. In one approach drugs fall in your lap from the sky. In the other you scoop them up from the ocean. Let’s call the first the top-down approach and the second the bottom-up approach.

The bottom-up approach assumes that you can discover drugs by thinking hard about them, by understanding what makes them tick at the molecular level, by deconstructing the dance of atoms orchestrating their interactions with the human body. The top-down approach assumes that you can discover drugs by looking at their effects on biological systems, by gathering enough data about them without understanding their inner lives, by generating numbers through trial and error, by listening to what those numbers are whispering in your ear.

To a large extent, the bottom-up approach assumes knowledge while the top-down approach assumes ignorance. Since human beings have been ignorant for most of their history, for most of the recorded history of drug discovery they have pursued the top-down approach. When you don't know what works, you try things out randomly. The Central Americans found out by accident that chewing the bark of the Cinchona plant relieved them of the afflictions of malaria. Through the Middle Ages and beyond, people who called themselves physicians prescribed a witches' brew of substances ranging from sulfur to mercury to arsenic to try to cure a corresponding witches' brew of maladies, from consumption to the common cold. More often than not these substances killed patients as readily as the diseases themselves.

The top-down approach may seem crude and primitive, and it was primitive, but it worked surprisingly well. For the longest time it was exemplified by the ancient medical systems of China and India – one of these systems delivered an antimalarial medicine that helped its discoverer bag a Nobel Prize for Medicine. Through fits and starts, scores of failures and a few solid successes, the ancients discovered many treatments that were often lost to the dust of ages. But the philosophy endured. It endured right up to the early 20th century when the German physician Paul Ehrlich tested 604 chemical compounds - products of the burgeoning dye industry pioneered by the Germans - and found that compound 606 worked against syphilis. Syphilis was a disease that so bedeviled people since medieval times that it was often a default diagnosis of death, and cures were desperately needed. Ehrlich's 606 was arsenic-based, unstable and had severe side effects, but the state of medicine was such back then that anything was regarded as a significant improvement over the previous mercury-based compounds.

It was with Ehrlich's discovery that drug discovery started to transition to a more bottom-up discipline, systematically trying to make and test chemical compounds and understand how they worked at the molecular level. But it still took decades before the approach bore fruition. For that we had to await a nexus of great and concomitant advances in theoretical and synthetic organic chemistry, spectroscopy and cell and molecular biology. These advances helped us figure out the structure of druglike organic molecules, they revealed the momentous fact that drugs work by binding to specific target proteins, and they also allowed us to produce these proteins in useful quantity and uncover their structures. Finally at the beginning of the 80s, we thought we had enough understanding of chemistry to design drugs by bottom-up approaches, "rationally", as if everything that had gone on before was simply the product of random flashes of unstructured thought. The advent of personal computers (Apple and Microsoft had launched in the late 70s) and their immense potential left people convinced that it was only a matter of time before drugs were "designed with computers". What the revolution probably found inconvenient to discuss much was that it was the top-down analysis which had preceded it that had produced some very good medicines, from penicillin to thorazine.

Thus began the era of structure-based drug design which tries to design drugs atom by atom from scratch by knowing the protein glove in which these delicate molecular fingers fit. The big assumption is that the hand that fits the glove can deliver the knockout punch to a disease largely on its own. An explosion of scientific knowledge, startups, venture capital funding and interest from Wall Street fueled those heady times, with the upbeat understanding that once we understood the physics of drug binding well and had access to more computing power, we would be on our way to designing drugs more efficiently. Barry Werth's book "The Billion-Dollar Molecule" captured this zeitgeist well; the book is actually quite valuable since it's a rare as-it-happens study and not a more typical retrospective one, and therefore displays the same breathless and naive enthusiasm as its subjects.

And yet, 30 years after the prophecy was enunciated in great detail and to great fanfare, where are we? First, the good news. The bottom-up approach did yield great dividends - most notably in the field of HIV protease inhibitor drugs against AIDS. I actually believe that this contribution from the pharmaceutical industry is one of the greatest public services that capitalism has performed for humanity. Important drugs for lowering blood pressure and controlling heartburn were also the beneficiaries of top-down thinking. 

The bad news is that the paradigm fell short of the wild expectations that we had from it. Significantly short in fact. And the reason is what it always has been in the annals of human technological failure: ignorance. Human beings simply don't know enough about perturbing a biological system with a small organic molecule. Biological systems are emergent and non-linear, and we simply don't understand how simple inputs result in complex outputs. Ignorance was compounded with hubris in this case. We thought that once we understood how a molecule binds to a particular protein and optimized this binding, we had a drug. But what we had was simply a molecule that bound better to that protein; we still worked on the assumption that that protein was somehow critical for a disease. Also, a molecule that binds well to a protein has to overcome enormous other hurdles of oral bioavailability and safety before it can be called a drug. So even if - and that's a big if - we understood the physics of drug-protein binding well, we still wouldn't be any closer to a drug, because designing a drug involves understanding its interactions with an entire biological system and not just with one or two proteins. In reality, diseases like cancer manifest themselves through subtle effects on a host of physiological systems involving dozens if not hundreds of proteins. Cancer especially is a wily disease because it activates cells for uncontrolled growth through multiple pathways. Even if one or two proteins were the primary drivers of this process, simply designing a molecule to block their actions would be too simplistic and reductionist. Ideally we would need to block a targeted subset of proteins to produce optimum effect. In reality, either our molecule would not bind even one favored protein sufficiently and lack efficacy, or it would bind the wrong proteins and show toxicity. In fact the reason why no drug can escape at least a few side effects is precisely because it binds to many other proteins other than the one we intended it to.

Faced with this wall of biological complexity, what do we do? Ironically, what we had done for hundreds of years, only this time armed with far more data and smarter data analysis tools. Simply put, you don't worry about understanding how exactly your molecule interacts with a particular protein; you worry instead only about its visible effects, about how much it impacts your blood pressure or glucose levels, or how much it increases urine output or metabolic activity. These endpoints are agnostic of knowledge of the detailed mechanism of action of a drug. You can also compare these results across a panel of drugs to try to decipher similarities and differences.

This is top-down drug design and discovery, writ large in the era of Big Data and techniques from computer science like machine learning and deep learning. The field is fundamentally steeped in data analysis and takes advantage of new technology that can measure umpteen effects of drugs on biological systems, greatly improved computing power and hardware to analyze these effects, and refined statistical techniques that can separate signal from noise and find trends.

The top-down approach is today characterized mainly by phenotypic screening and machine learning. Phenotypic screening involves simply throwing a drug at a cell, organ or animal and observing its effects. In its primitive form it was used to discover many of today's important drugs; in the field of anxiety medicine for instance, new drugs were discovered by giving them to mice and simply observing how much fear the mice exhibited toward cats. Today's phenotypic screening can be more fine-grained, looking at drug effects on cell size, shape and elasticity. One study I saw looked at potential drugs for wound healing; the most important tool in that study was a high-resolution camera, and the top-down approach manifested itself through image analysis techniques that quantified subtle changes in wound shape, depth and appearance. In all these cases, the exact protein target the drug might be interacting with was a distant horizon and an unknown. The large scale, often visible, effects were what mattered. And finding patterns and subtle differences in these effects - in images, in gene expression data, in patient responses - is what the universal tool of machine learning is supposed to do best. No wonder that every company and lab from Boston to Berkeley is trying feverishly to recruit data and machine learning scientists and build burgeoning data science divisions. These companies have staked their fortunes on a future that is largely imaginary for now.

Currently there seems to be, if not a war, at least a simmering and uneasy peace between top-down and bottom-up approaches in drug discovery. And yet this seems to be mainly a fight where opponents set up false dichotomies and straw men rather than find complementary strengths and limitations. First and foremost, the ultimate proof of the pudding is in the eating, and machine learning's impact on the number of approved new drugs still has to be demonstrated; the field is simply too new. The constellation of techniques has also proven itself to be much better at solving certain problems (mainly image recognition and natural language processing) than others. A lot of early stage medicinal chemistry data contains messy assay results and unexpected structure-activity relationships (SAR) containing "activity cliffs" in which a small change in structure leads to a large change in activity. Machine learning struggles with these discontinuous stimulus-response landscapes. Secondly, there are still technical issues in machine learning such as working with sparse data and noise that have to be resolved. Thirdly, while the result of a top-down approach may be a simple image or change in cell type, the number of potential factors that can lead to that result can be hideously tangled and multifaceted. Finally, there is the perpetual paradigm of garbage-in-garbage-out (GIGO). Your machine learning algorithm is only as good as the data you feed it, and chemical and biological data are notoriously messy and ill-curated; chemical structures might be incorrect, assay conditions might differ in space and time, patient reporting and compliance might be sporadic and erroneous, human error riddles data collection, and there might be very little data to begin with. The machine learning mill can only turn data grist into gold if what it's provided with is grist in the first place.

In contrast to some of these problems with the top-down paradigm, bottom-up drug design has some distinct advantages. First of all, it has worked, and nothing speaks like success. Also operationally, since you are usually looking at the interactions between a single molecule and protein, the system is much simpler and cleaner, and the techniques to study it are less prone to ambiguous interpretation. Unlike machine learning which can be a black box, here you can understand exactly what's going on. The amount of data might be smaller, but it may also be more targeted, manageable and reproducible. You don't usually have to deal with the intricacies of data fitting and noise reduction or the curation of data from multiple sources. Ultimately at the end of the day, if like HIV protease your target does turn out to be the Achilles heel of a deadly disease like AIDS, your atom-by-atom design can be as powerful as Thor's hammer. There is little doubt that bottom-up approaches have worked in selected cases, where the relevance of the target has been validated, and there is little doubt that this will continue to be the case. Now it's also true that just like with top-downers, bottom-uppers have had their burden of computational problems and failures, and both paradigms have been subjected to their fair share of hype. Starting from that "designing drugs using computers" headline in 1981, people have understood that there are fundamental problems in modeling intermolecular interactions: some of these problems are computational and in principle can be overcome with better hardware and software, but others like the poor understanding of water molecules and electrostatic interactions are fundamentally scientific in nature. The downplaying of these issues and the emphasizing of occasional anecdotal successes has led to massive hype in computer-aided drug design. But in case of machine learning it's even worse in some sense since hype from applications of the field in other human endeavors is spilling over in drug discovery too; it seems hard for some to avoid claiming that your favorite machine learning system is going to soon cure cancer if it's making inroads in trendy applications like self-driving cars and facial recognition. Unlike machine learning though, the bottom-up take has at least had 20 years of successes and failures to draw on, so there is a sort of lid on hype that is constantly waved by skeptics. Ultimately, the biggest advantage of machine learning is that it allows us to bypass detailed understanding of complex molecular interactions and biological feedback and work from the data alone. It's like a system of psychology that studies human behavior purely based on stimuli and responses of human subjects, without understanding how the brain works at a neuronal level. The disadvantage is that the approach can remain a black box; it can lead to occasional predictive success but at the expense of understanding. And a good open question is to ask how long we can keep on predicting without understanding. Knowing how many unexpected events or "Black Swans" exist in drug discovery, how long can top-down approaches keep performing well?

The fact of the matter is that both top-down and bottom-up approaches to drug discovery have strengths and limitations and should therefore be part of an integrated approach to drug discovery. In fact they can hopefully work well together, like members of a relay team. I have heard of at least one successful major project in a leading drug firm in which top down phenotypic screening yielded a valuable hit which then, midstream, was handed over to a bottom-up team of medicinal chemists, crystallographers and computational chemists who deconvoluted the target and optimized the hit all the way to an NDA (New Drug Application). At the same time, it was clear that the latter would not have been made possible without the former. In my view, the old guard of the bottom-up school has been reluctant and cynical in accepting membership in the guild for the young Turks of the top-down school, while the Turks have been similarly guilty of dismissing their predecessors as antiquated and irrelevant. This is a dangerous game of all-or-none in the very complex and challenging landscape of drug discovery and development, where only multiple and diverse approaches are going to allow us to discover the proverbial needle in the haystack. Only together will the two schools thrive, and there are promising signs that they might in fact be stronger together. But we'll never know until we try.

(Image: BenevolentAI)

— The Curious Wavefunction

[joerojasburke]

I just love this response from physicist Robert Wilson in 1969, when asked to justify spending on basic science with no foreseeable practical applications in the Cold War.

quote via The Curious Wavefunction

[hextheta]

If the state of a particle is independent of a measurement event, how can you explain the quantum zeno effect, or even the interference pattern of the double slit? There's a multitude of experiments that show that measurement/observation of a quantum system will impact the time evolution of the wavefunction. I'm genuinely curious as to what interpretation of qm you're describing.

my opinion on quantum physics is that we should stop looking into it. it's none of our business and frankly the particles seem to agree

[readeverymorning]

Richard Feynman

https://en.wikipedia.org/wiki/Richard_Feynman

https://blogs.scientificamerican.com/the-curious-wavefunction/richard-feynman-sexism-and-changing-perceptions-of-a-scientific-icon/

https://fs.blog/intellectual-giants/richard-feynman/

https://www.youtube.com/watch?v=ipRvjS7q1DI

https://www.britannica.com/biography/Richard-Feynman

[plumsworld]

Top 5 reasons why intelligent liberals don t like nuclear energy

https://blogs.scientificamerican.com/the-curious-wavefunction/top-5-reasons-why-intelligent-liberals-dont-like-nuclear-energy/

[savannahdshaw-blog1]

Caffeine’s effect on studying

Whenever I’m drinking coffee while I’m studying, I’m doing it for the sole reason for keeping my eyes open. But, the more coffee I consume, the more I wonder if it’s affecting me in more ways than one.

Researchers at John Hopkins University found that caffeine enhanced long-term memory for at least 24 hours after consumed. The researchers put on a trial that found that people who have consumed caffeine reached a deep level of memory retention.

This trial was the first to study caffeine effects in detail. John Hopkins research team plans to continue this experiment in greater depth in the future. Some aspects of the trial they want to focus on are the degree of memory retention and potential negative effects of caffeine. 

Keep up with the research here: https://hub.jhu.edu/2014/01/12/caffeine-enhances-memory/. 

Another insightful blog on the effects of caffeine is The Scientific American. Keep up with the blog here: https://blogs.scientificamerican.com/the-curious-wavefunction/should-you-drink-coffee-before-or-after-a-learning-task/.

This blog on The Scientific American takes the John Hopkins research in a different direction. It focuses on whether or not someone should drink coffee before or after learning a task. It challenges the notion that coffee before learning a task is the most beneficial. 

Ultimately, caffeine enhances our learning experience. Now go visit one of the local coffee shops!

[naivelocus]

Kurt Gödel's Open World

Today marks Kurt Gödel's one hundred and eleventh birthday. Along with Aristotle, Gödel is often considered the greatest logician in history. But I believe his influence goes much farther. In an age when both science and politics seem to be riddled with an incessant search for "truth" - often truth that aligns with one's preconceived social or political opinions - Gödel's work is a useful antidote and a powerful reminder against the illusion of certainty. Gödel was born in 1906 in Brünn, Czechoslovakia, at a time when the Austro-Hungarian empire was at its artistic, philosophical and scientific peak. Many of Gödel's contemporaries, including Ludwig Wittgenstein, distinguished themselves in the world of the intellect during this period. Gödel was born to middle class parents and imbibed the intellectual milieu of the times. It was an idyllic time, spent in cafes and lecture halls learning the latest theories in physics and mathematics and pondering the art of Klimt and the psychological theories of Freud. There had not been a major European conflict for almost a hundred years. In his late teens Gödel came to Vienna and became part of the Vienna Circle, a group of intellectuals who met weekly to discuss the foundations of philosophy and science. The guiding principle of the circle was the philosophy of logical positivism which said that only statements about the natural world that can be verified should be accepted as true. The group was strongly influenced by both Bertrand Russell and Ludwig Wittgenstein, neither of whom was formally a member. The philosopher Karl Popper, whose thinking on falsification even now is an influential part of science, ran circles around the group, although his love for them seems to be unreciprocated. It was at the tender age of 25 that young Gödel published his famous incompleteness theorem. He did this as part of his PhD dissertation, making that dissertation one of the most famous in history (as a rule, even most famous scientists don't always do groundbreaking work in graduate school). In a mere twenty-one pages, Gödel overturned the foundations of mathematics and created an edifice that sent out tendrils not just in mathematics but in the humanities, including psychology and philosophy. To appreciate what Gödel did, it's useful to take a look at what leading mathematicians thought about mathematics until that time. Both Bertrand Russell and the great mathematician David Hilbert had pursued the foundations of mathematics with conviction. In a famous address given in 1900, Hilbert had laid out what he thought were the outstanding problems in mathematics. Perhaps none of these was as important as the overarching goal of proving that mathematics was both consistent and complete. Consistency means that there exists no statement in mathematics that is both true and false at the same time. Completeness means that mathematics should be capable of proving the truth or falsity (the "truth value") of every single statement that it can possibly make.  In some sense, what Hilbert was seeking was a complete "axiomatization" of mathematics. In a perfectly axiomatized mathematical system, you would start with a few statements that would be taken as true, and beginning with these statements, you would essentially have an algorithm that would allow you derive every possible statement in the system, along with their truth value. The axiomatization of mathematics was not a new concept; it had been pioneered by Euclid in his famous text of geometry, "The Elements". But Hilbert wanted to do this for all of mathematics. Bertrand Russell had similar dreams. In one fell swoop the 25-year-old Gödel shattered this fond hope. His first incompleteness theorem, which is the most well-known, proved that any mathematical system which is capable of proving the basic theorems of arithmetic is always going to include statements whose truth value cannot be proved using the axioms of the system. You could always 'enlarge' the system and prove the truth value in the new system, but then the new, enlarged system itself would contain statements which succumbed to Gödel's theorem. What Gödel thus showed is that mathematics will always be undecidable. It was a remarkable result, one of the deepest in the annals of pure thought, striking at the heart of the beautiful foundation built by mathematicians ranging from Euclid to Riemann over the previous two thousand years. Gödel's theorems had very far-reaching implications; in mathematics, in philosophy and in human thought in general. One of those momentous implications was worked out by Alan Turing when he proved a similar theorem for computers, addressing a problem called the "halting problem". Similar to Hilbert's hope for the axiomatization of mathematics, the hope for computation was that, given an input and a computer program, you could always find out whether the program would halt. Turing proved that you could not decide this for an arbitrary program and an arbitrary input (although you can certainly do this for specific programs). In the process Turing also clarified our definitions of "computer" and "algorithm" and came up with a universal "Turing machine" which embodies a mathematical model of computation. Gödel's theorems were thus what inspired Turing's pioneering work on the foundations of computer science. Like many mathematicians who make seminal contributions in their twenties, Gödel produced nothing of comparable value later in his life. He migrated to the US in the 1930s and settled down at the Institute for Advanced Study in Princeton. There he made a new friend - Albert Einstein. From then until Einstein's death in 1955, the sight of the two walking from their homes to the institute and back, often mumbling in German, became a town fixture. Einstein afforded the privilege of being his walking companion to no one, and seemed to have considered only Gödel as his intellectual equal: in fact he held Gödel in such esteem that he was known to have said in his later years that his own work did not mean much to him, and the main reason he went to work was to have the privilege of walking home with Gödel. At least once Gödel startled his friend with a scientific insight he had: he showed using Einstein's own field equations of gravitation that time travel could be possible. Sadly, like a few other mathematical geniuses, Gödel was also riddled with mental health problems and idiosyncrasies that got worse as he grew older. He famously tried to find holes in the U.S. Constitution while taking his citizenship exam, and Einstein who accompanied him to the exam had to talk him out of trying to demonstrate to the judge how the U.S. could be turned into a dictatorship (nowadays some people have similar fears, but for different reasons). After Einstein died Gödel lost his one friend in the institute. Since early childhood he had always been a hypochondriac - often he could be seen dressed in a warm sweater and scarf even in the balmy Princeton summer - and now his paranoia about his health greatly grew. He started suspecting that his food was poisoned, and refused to accept anything not cooked by his protective wife Adele; in 1930s Vienna she had once physically protected him from Nazis, and now she was protecting him from imagined germs. When Adele was hospitalized with an illness, Kurt stopped eating completely. All attempts to soothe his fears failed, and on January 14, 1978 he died in Princeton Hospital, weighing only 65 pounds and essentially succumbing to starvation. Somehow this sublimely rational, austere man had fallen prey to a messy, frightful, irrational paranoia; how these two contradictory aspects of his faculties conspired to doom him is a conundrum that will remain undecidable. He left us a powerful legacy. What Gödel's theorems demonstrated was that not only the world of fickle human beings but also the world of supposedly crystal-clear mathematics is, in a very deep sense, unknowable and inexhaustible. Along with Heisenberg's uncertainty principle, Gödel's theorems showed us that all attempts at grasping ultimate truths are bound to fail. More than almost anyone else, Gödel contributed to the fall of man from his privileged, all-knowing position. We see his undecidability in politics and human affairs, but it is true even in the world of numbers and watertight theorems. Sadly we seem to have accepted uncertainty in mathematics while we keep on denying it in our own lives. From political demagogues to ordinary people, the world keeps getting ensnared in passionate attempts to capture and declare absolute truth. The fact that even mathematics cannot achieve this goal should give us pause. It should inculcate a sense of wonder and humility in the face of our own fallibility, and should lead us to revel in the basic undecidability of an open world, a world without end, Kurt Gödel's world. 

— The Curious Wavefunction

[naivelocus]

An open letter to my fellow industry scientists: Why the March for Science must be led by us

On April 22nd, scientists, science-lovers and people who care about evidence-based reasoning are going to participate in protests and marches around the country. The flagship march will take place in Washington DC, but there are sister marches in Boston, San Francisco, Atlanta and Raleigh, to name a few cities. There has been a lot of debate and commentary on what the objectives of the march should be, how political it should get and what it generally should or should not do. Some think that scientists should not politicize the march, others think that there is no way the march could not be political. I am participating in the march myself and wish it all success, but one thing is clear to me: the march will not succeed in its objectives if industry scientists do not participate in it in large numbers. For me, it is very clear why this is the case. First of all, a few words about the motives and reasoning of the march. The objective of the march is to send a resounding message to the politicians and people of this country about respecting scientific facts and divorcing them from political ideology. However the people who need to hear the message about science the most are Donald Trump's supporters, especially ones in the rural areas of this country. If we don't reach them, we would spend the day feeling happy and smug about ourselves, wander around with like-minded people, and come home after patting each other's backs without really having accomplished much, feeling secure in our secure worlds. We would have done almost nothing to change the mind of the average person living in rural Alabama. The only way to not have our efforts fail is to understand the details of the bridge we need to build to mend our relationship with those who may think differently. We are more similar than we think. Many people who are suspicious of science are far from dumb, but they are suspicious of certain fields of science such as climate science and evolution while being supportive of areas like space exploration. I don't think there is any evidence that the average American is against scientific research as a whole. But those who are suspicious of specific areas think that not only do these ideas infringe on their deeply held religious beliefs, but that they are part of a grand liberal agenda to ram sweeping government policies down their throats. They also think especially of academic scientists as liberal, ivory tower intellectuals who have their heads in the clouds and who don't care about the welfare of the common man. The victory of Trump was in large part a victory against these perceived intellectual elitists. Firstly, what we need to convince these people is that many of the facts unearthed by science, even in areas like climate change and evolution, are independent of the political beliefs of the people who discover these facts. That means pointing out, first of all, that there are religious and conservative scientists who work in these fields, and that these scientists also support the facts independent of their religious or political beliefs. The continuing head of the NIH, Francis Collins, is a devout Christian for instance who fully supports evolution and important fields like stem cell research. The better we can do in separating scientific facts from the beliefs of the people who find out these facts, the better we will be able to reach the people who need to know them the most. At the same time, we should admit that some scientists do politicize science, and that we need to have an honest dialogue with each other about how we can keep science as neutral as possible. We also need to admit that extreme politicization of science can take place on both the left and the right. Most importantly, however, we need to convince the people who need to hear about science the most that science is far from being limited to ivory tower academics, and to fields like evolution or climate change. Even if we completely ignored those fields, there is zero doubt that science has had a profound impact on the standard of living of the very people who are suspicious of it during the last few decades. Even if you completely took liberal academics out of the equation, science still pervades every aspect of everyone's lives. The best way to convince them of this is to move away from pure and basic science to applied science. That's where industry scientists come in. Forget, for a moment, abstract academic matters like dark energy and directed gene evolution and the fine-tuning of computer climate models, forget what Thomas Kuhn said about science, forget what stuffy scientific epistemology and ontology are all about, and focus on one stark fact: Science has directly, immeasurably and irrevocably impacted the lives of rural and urban populations alike through its research into fossil fuels, into agriculture, into infrastructure such as roads and bridges, into oil refining, into plastics and textiles, into improving water and air quality, and into lifesaving drugs and vaccines against cancer, polio and infectious diseases. Most of this innovation was made possible by the creativity and passion of industry scientists, ranging from Wallace Carothers to Gertrude Elion.  Throughout recent history, companies like Bell Labs, IBM, Lockheed, Ford, GE and Exxon have been fonts of scientific innovation and progress. This kind of science is not just sewn into the fabric of everyday American life from Boston, MA to Savannah, GA, but it should also appeal to his or her patriotic instincts, since it's what has allowed the United States to become a powerhouse of technology and finance after World War 2. Even if you are suspicious of global warming or evolutionary theory, you should be able to appreciate the profound influence science has had on your way of life by bringing you transistors, the Saturn V rocket, nitrogen fertilizer, painkillers, petroleum cracking, nylon, Ford F-150 trucks, Portland cement and the iPhone. These are not liberal or conservative inventions. These are scientific inventions. They are enabling beyond measure. Even if you think they haven't really helped lift you out of poverty, without them your fate would be unimaginable. It's only by talking about these very practical and amazing innovations that you can convince the average American of the value of science. Whether you are a Clinton supporter or a Trump supporter, a Methodist or an atheist, poor or rich, gun lover or hater, for or against abortion, it is simply impossible for your life or that of your parents and grandparents to not have been radically impacted for the better because of improved medicines, better roads and automobiles, better clothing and housing and better means of communication. At the heart of every single one of these innovations is science, firmly rooted in observable fact and independent of politics and religion. At the same time, this is where we can loop back from these highly applied advances to basic science which may sound very esoteric. For instance, Einstein's relativity is what makes GPS possible. Basic research into organic synthesis is what makes new drugs possible. And it would be impossible to understand cancer and AIDS without understanding evolution. But even if you didn't care about the process that goes into these developments, you can still care about the fruits themselves. I therefore want to issue an open appeal to my colleagues in industry. To scientists from Exxon, Dow, Pfizer, Kraft, GM, Raytheon, Genentech, GE, Monsanto, Coca Cola and the umpteen number of small startups doing research in pharmaceuticals, food science, agriculture, electronics and tech; don't just participate but lead the March for Science. Show up with your families. Come out in large numbers; petition your employers to give you a day off or take a day off nonetheless (it would have been so much better to have the march on a weekend). Since academia is a niche, show the country that you and your colleagues actually constitute the majority of scientists in this country. Leave aside your political differences and come together to show everyone how your work and that of your intellectual forefathers has profoundly changed the average American's life for the better, and how it has turned his nation into the most technologically advanced civilization on earth. Forget about politics for a moment and remember the joy that each one of you feels in the shared moment of scientific discovery, a moment completely divorced from your political beliefs. It certainly helps that unlike leading academic centers, the leading industrial research centers you work in are more uniformly distributed throughout the country and not just limited to the coasts. A strong showing of industry scientists would thus automatically disperse the science march over a much wider area.  My fellow industry scientists, here is our chance to try to convince our fellow countrymen, and especially ones with whom we have strong political disagreements, that irrespective of what they think about its political trappings, science is a fantastic truth-finding practical tool that influences and will keep influencing their way of life through its contributions to the most practical matters related to energy, transportation, housing, and healthcare. Here is our chance to convince the average American how she could be a part of this revolution, and how you are ready to do what you can to communicate not just the wonders of science to her but to enable her to participate in its fruits. Reassure her that you are willing to have an honest dialogue about how the government can do more to retrain her for this new technological age, to try to make sure that her kids can go to college and become technically enabled, to become a part of the same adventure that put a man on the moon, helped the United States win World War 2, and ended polio and smallpox.  Seen from the angle of these practical innovations, science has been the great equalizing force in American life, reaching Americans of every political stripe and disagreement. That is what makes it so special, so important for our future, so much worth fighting for, and most importantly, so much worth sharing with those who we think are so different from us. I'll see you there. — The Curious Wavefunction