Mar 17 2020

Being Anti-intellectual During a Pandemic

In the past I have written a defense of elitism and expertise, and articles exploring the phenomenon of anti-intellectualism. For those who reject science this is a core issue – they must attack expertise, reject consensus, and defend populism as their justification for promoting the idea that the consensus of scientific opinion is wrong. They do so with the same tired and rejected arguments they have for decades, which I guess is in line with their anti-intellectualism.

Recently Michael Egnor, who writes for the anti-science Discovery Institute, and with whom I have tangled before, wrote a stunning defense of anti-intellectualism. He marshaled all the old tropes, which I have already dealt with, but I felt it was especially poignant in the middle of a pandemic. We are actually seeing in real time the consequences of science-denial, of rejecting the advice of experts and basing opinions on your “hunches”, and of approaching reality with a general attitude of anti-expertise populism.

The core of Egnor’s anti-intellectual attack is the notion that – those scientists have been wrong before. First – of course they have. Science is not a crystal ball. It is a set of methods for slowly, painstakingly working out how reality functions. It is full of false hypotheses, dead-ends, mistakes, and occasional brilliance. But mostly it’s careful tedious work, which is then put through the meat-grinder of peer-review. Science is messy, which is why I spend perhaps the majority of my time writing here and on SBM discussing the messiness of science, the pitfalls, the institutional failures, and the changes that many think will help make the institutions of science incrementally better.

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Mar 16 2020

Perpetual Flying Machine

I’m a sucker for perpetual motion machines. I don’t mean that I think they work – they don’t – but they are often intriguing contraptions out of some cyberpunk fantasy. They are also often a bit of a puzzle. How are they supposed to work, and why don’t they? That free energy or perpetual motion machines don’t work is a given, because of the laws of thermodynamics. Energy has to come from somewhere, so for each such claim it’s a fun game to figure out where the energy is actually coming from. This game also helps dispel any notion of continuous or free energy.

A new perpetual motion claim is revealed in an article in the Rob Report. The claims is for an electric plane that will fly mostly with the energy generated by the friction of the flying itself. The idea is that the plane will have rechargeable electric batteries that are used for take-off and landing. But while in flight, the batteries will be recharged by vibrations and the flexing of the wings. The inventor, Michal Bonikowski, who calls his project Eather One, hopes this will yield enough energy to keep the plane flying indefinitely.

The problem with this concept, as with all perpetual motion concepts, is the second law of thermodynamics. Every time you change energy from one state to another, at least a little bit is lost. You can never have 100% efficiency. So the energy you use to propel the plane forward will have to be greater than the energy you harvest from pushing through the air. If you design a mechanism (as in the concept art) for harvesting air friction, the extra resistance from the mechanism will cause the plane to slow by more than using that energy to propel it will increase its speed. The entire process will be a net negative. You would be better off optimizing aerodynamics.

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Mar 13 2020

Extreme Depth of Focus Lens

This is definitely one of those “Holy Sh*!” technology breakthroughs; a game-changer that will likely have many more implications than you can take in immediately. Researchers have demonstrated that with the “judicious design of a multi-level diffractive lens (MDL)” they can create a single flat (one thousandth of an inch thick) thin lens with an extreme depth of focus – four orders of magnitude greater than traditional lenses. Let that sink in.

The depth of focus (or depth of field for objects not at infinity) is the range over which objects are in focus. You can adjust the depth of field on a camera by changing the aperture, with smaller aperture settings having a deeper depth of field. But you still need to focus the camera to bring the desired image into sharp focus. This requires that the camera can change the distance between the lens and the sensor, and modern cameras may use multiple lenses. Good cameras also use multiple lenses for different colors (wavelengths of visible light) to make sure all the colors are focusing the same way.

Now imagine if all this could be replaced with a single very thin flat lens. That is what the researchers have done. They accomplish this by using nanostructures on the glass to control the path of the light. The lens can simultaneously focus objects at different distances, and also light of different colors. What we have now is a proof of concept, and of course we need to see what an an actual commercial camera using this technology will be like. But if the published results pan out, there are several immediately obvious implications.

The first is much smaller, cheaper, lighter camera lenses. This will be great for cell phones and other tiny electronic devices with cameras. Medical devices such as those used for endoscopic surgery would also benefit from smaller lenses. Any situation where size and weight are at a premium would benefit – such as drone cameras.

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Mar 12 2020

Smallest Dinosaur Ever

Published by under Evolution
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We like extremes, partly because they help define the borders of reality. It helps our mental model of the world when the know the biggest, smallest, hottest, lightest, or fastest of something. It’s also just fascinating to see how extreme some things can get. For this reason there has been a fascination with which dinosaur is the biggest – how big did these animals get. The record is currently held by Argentinasaurus, a long-necked sauropod, weighing between 77 and 110 tons. Meanwhile, the record for the smallest known dinosaur is microraptor, a bird-like dinosaur only 40 cm long. Well, that is until the latest discovery.

Scientists report the discovery of the head of a bird-like dinosaur trapped in amber. The species has been named Oculudentavis khaungraae and is about the size of a bee hummingbird, the smallest living bird. The specimen is trapped in amber from Myanmar, and is dated at 99 million years old. The amber preserved some soft tissue, including its tongue. The specimen is interesting on multiple levels.

First, it reflects the extreme of vertibrate miniaturization. It’s difficult to cram all the sensory organs into a tiny skull, and species that evolve to become so small have to find solutions. In this case the eye socket anatomy appears different than hummingbirds and other tiny birds. Rather than a rim of bone, the socket is more spoon-shaped. The anatomy also suggests a small opening for light, which further implies the species was diurnal.

The mouth sports a surprising number of teeth, making the creature look like a predator. At that size is likely fed on insects. So we have a bird-like dinosaur the size of a tiny hummingbird that hunted insects during the day. The anatomy, such as fusion of the skull, also suggests this was an adult, so not just a juvenile specimen to explain its small size.

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Mar 10 2020

Day Was Shorter 70 Million Years Ago

What does an extinct mollusk have to do with the Moon? This is one of those amazing science stories that ties together multiple disciplines and lines of evidence into one elegant narrative. In this case a detailed analysis of a 70 million year old mollusk shell has given scientists a critical piece of information that will help them model the Earth-Moon system.

Let’s start with the Moon – astronomers know that the Moon is moving farther away from the Earth at a constant rate, 3.82 centimeters per year. We can precisely measure this because the Apollo missions left corner reflectors on the surface of the Moon, and we can shoot lasers off those reflectors and measure the round-trip travel time. Because scientists have also precisely measured the speed of light, we can use this round-trip time to calculate the exact distance between the laser on Earth and the reflector on the Moon.

Why is the Moon moving away from the Earth? In a word – tides. Tidal forces from nearby large objects causes a bulge to form. We are most familiar with this phenomenon because of the bulge in the ocean caused mostly by the Moon (and to a lesser degree the Sun) which we experience locally as a rising and falling of the sea. The tidal bulge on the Earth is slightly ahead of the Moon in its orbit, because the Earth is spinning faster than the Moon. This leading bulge tugs slightly on the Moon, accelerating it into a higher orbit farther from the Earth. This represents a transfer of momentum from the Earth to the Moon via gravity, which not only moves the Moon farther away, but slows down the rotation of the Earth (and the conservation of angular momentum is obeyed).

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Mar 09 2020

Using Neural Networks for Image Sensing

A new study published in Nature details the use of a neural network on a 2-dimensional computer chip that by itself can be trained to recognize specific images within nanoseconds. This is more of a proof of concept than something with direct immediate applications, but let’s talk about that concept.

To back all the way out – evolution represents hundreds of millions of years of tinkering with multi-cellular structures, and even longer when talking about biochemistry. This is a natural laboratory that has developed some elegant designs, and at the very least can serve as a useful source of inspiration for modern technology. That is the concept of neural networks, designing computers to work more like a vertebrate brain. Specifically, the “neurons” in a neural network are not just binary, on or off, but rather can fire with various degrees of strength. Further, their firing affects the activity of those neurons they are connected to. Computer hardware with networks designed on these basic principles are called  artificial neural networks (ANN). They hold the promise of not only faster and more powerful computing, but are designed to learn (which is why they are so often associated with artificial intelligence).

Another principle at work here is top-down vs bottom-up processing, another concept that has increasingly been incorporated into AI. If we go all the way back to the early days of AI the basic idea was to create high level computer intelligence that could solve problems with the top down, with deep understanding. That goal, now referred to as general AI, is still a ways off. But meanwhile AI has advanced considerably through more of a bottom-up approach, using algorithms to sift data in increasingly sophisticated and adaptable ways. We now have deep learning AI and other specific processes that can produce impressive results without any general AI “understanding” what it is doing.

One question is – will we be able to build a general AI out of these limited AI components? Is it just a matter of building in enough sophistication and complexity? We won’t know until we do it, but if living organisms are any guide, I think there is reason to be positive. Specifically – that is basically how our brains work.

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Mar 05 2020

Organic and Flow Batteries – Hype or Promise

It’s important to recognize that we currently do not know with any confidence the path forward that our energy infrastructure will take. This is why we have to spread our bets out on as many technologies as possible – we don’t know which ones will be the most successful. Many people place their hopes on battery technology, and there is no doubt that batteries are a great energy storage medium and will play a critical role in our energy future. But batteries are not a simple panacea, and we may run into important limits. This is why we need new battery technology.

The demand for batteries is likely to increase significantly. Electric cars depend on batteries, and therefore putting millions of EVs onto the road means necessarily putting millions of batteries on the road as well. Also, batteries are one possible solution to home and grid energy storage, which will be necessary if we want to maximize renewable energy sources like wind and solar. Current lithium-ion battery tech is great, and is getting incrementally better all the time, but it has limitations. One significant limitation is the availability of lithium and cobalt which are necessary for their manufacture.

Cobalt, for example, comes mostly from the DRC, an unstable country, and it comes mostly as a byproduct of copper and nickle mining. Global supplies are expected to fall short of global demand, and if there is a surge in Li-ion batteries this will only get worse. Lithium is more complicated, and we are not really sure what the worldwide supply is. For now there is no problem, but there is widespread concern that lithium supply will not keep up with demand as EVs take to the streets. We also do not currently have the ability to recycle lithium into a pure enough state to reuse in batteries.

What battery tech is on the horizon that will potentially change the game for batteries? For now, continued incremental improvements in Li-ion battery technology are important. We need to squeeze as much function out of the raw materials as possible, with greater capacity, and longer charge-discharge lifespans. Right now Tesla boasts million-mile batteries for its EVs. Increasing the lifespan further will decrease the need for new batteries as replacements. Batteries from retired EVs can also be repurposed for grid storage, where it wont’ matter if their range has decreased.

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Mar 03 2020

The Brains of Newborns

Most regular readers of this blog should know by now that the answer to nature vs nurture is – yes. Both are involved in neural development and function, and interact in a complex way. Further, while I still find myself reverting to it on occasion, the metaphors of hardware and software don’t quite work with the brain. Rather, the brain is wetware, which is a dynamic combination of both. A new study, looking at the visual cortex of young newborns, adds more information to this basic understanding.

Generally what people are trying to refer to with the term “hardwired” are patterns in the human brain determined largely by genetics, rather than any environmental input. But the fundamental misunderstanding that comes along as baggage with that metaphor is the notion that genes carry a blueprint of the brain – they don’t. They encode rules for how to develop a brain, and those rules include environmental input. So you cannot disentangle the two. Even newborns developed in an environment – the environment of the womb, with the brain also receiving feedback from the body of the fetus itself.

Newborns do come out of the womb with some behaviors, albeit a limited basic repertoire. They need to sleep, feed, and interact. They have limited movement, hearing, and vision, and a similarly limited range of emotions and mental states. But perhaps their biggest behavioral ability is learning – they are sponges that absorb information from their environment. The question is – how much of their trajectory from this infantile state to an adult state is determined by the pathways that are already laid out in their brains, and how much is determined by their environment? Both clearly have a dramatic effect, and interact with each other.

The purpose of the current study was to investigate what the visual cortex of newborns is like, and how they compare to adults. Vision is a pretty basic function, and we would expect the brain to be primed for it. We also know that deprived of visual stimulation, this part of the brain will fail to develop, or will be coopted for other uses. Using fMRI the researchers found that the basic adult structure of the visual cortex of infants was already present. We knew from prior research that there are two brain regions that talk to each other and are involved in facial recognition. Similarly, there are two brain regions that are wired together and function to visualize places. In infants (average age 27 days), the two face regions were active and synchronized together, while the two place regions were also active and synchronized together. This pattern was identical to that of adult brains – although not as robust.

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Mar 02 2020

A Claim for Dinosaur Proteins and DNA

Another paper has been published in the simmering controversy over whether or not proteins, and even DNA, can survive millions of years in well-preserved dinosaur (non-avian dinosaurs, that is) fossils. The paper looks at cartilage from a duck-billed dinosaur, a young Hypacrosaurus stebingeri. The authors claim:

“…microstructures morphologically consistent with nuclei and chromosomes in cells within calcified cartilage. We hypothesized that this exceptional cellular preservation extended to the molecular level and had molecular features in common with extant avian cartilage. Histochemical and immunological evidence supports in situ preservation of extracellular matrix components found in extant cartilage, including glycosaminoglycans and collagen type II. Furthermore, isolated Hypacrosaurus chondrocytes react positively with two DNA intercalating stains.”

Let me say right away that these claims are controversial, but what would they mean if true? If we could examine the structure of proteins and DNA from >65 million years ago, in well-preserved dinosaur fossils, then the world of molecular biology would extend back to that era. Molecular examination has had a significant impact on paleontology – but it has limits. So far the oldest DNA sequenced from a fossil is from a 700,000 year old horse frozen in ice. The oldest protein so far confirmed is from a rhino 1.7 million years old. This means that if the current claims are true, DNA can survive in fossils 100 times longer than the current record would indicate.

This is also not the only source of information from which to estimate the lifespan of DNA. Researchers have examined DNA from Moa specimens in New Zealand, over a span of about 8,000 years. This allowed them to estimate the half-life of DNA, the time over which about half the bonds would be broken. Their estimate – 521 years. This means that all the chemical bonds in a DNA molecule would be gone after 6.8 million years, but having any fragments along enough to sequence would be gone after about one million years. This aligns nicely with the evidence from actual fossils. So claiming DNA from >65 million years would be extraordinary, to say the least. This is why most scientists remain skeptical of these claims.

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Feb 28 2020

Astronomers Detect Largest Explosion Ever

Published by under Astronomy
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We can quibble about whether or not the Big Bang should be considered an explosion, or whether it happened “in the universe.” It was the expansion of spacetime that is the universe. In any case, astronomers have detected what they think is the biggest explosion (at least discovered so far) since the big bang –  in the Ophiuchus galaxy cluster, 390 million light years from us. The explosion is essentially a bubble with a diameter the size of 15 Milky Way galaxies – about 1.5 million light years across. That’s five time bigger than the previous record holder.

Astronomers first suspected something was going on when they discovered a big X-ray bubble. They report:

It was discovered in the Chandra X-ray image by Werner and collaborators, who considered a possibility of it being a boundary of an AGN-inflated bubble located outside the core, but discounted this possibility because it required much too powerful an AGN outburst.

An AGN is an active galactic nuclei – more on that below. So they initially discounted it because it was too big, but they then followed up with radio observation, and found an identical radio bubble, confirming that this was a real fossil of an ancient explosion, centered around an AGN. So what’s going on here?

Well, astronomers are not sure. The do not know exactly what may have caused some a massively energetic event. But let’s give some background on AGNs – these are supermassive black holes (SMBH) in the centers of galaxies. Most galaxies have them, including our own. But some supermassive black holes are more super massive than others – getting up to billions of solar masses. More importantly to their activity, some of the black holes are feeding, which means that gas and dust are actively swirling around the event horizon forming an accretion disc and then plunging into the incredible gravity well of the black hole. All that gravity represents an unimaginable amount of energy, and when that gas and dust falls in it swirls around at relativistic speeds – near the speed of light.

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