The state of modern science and technology is truly amazing, much more so than the fake stuff that people like to spread around. Gravitational waves have opened up an entirely new type of astronomy, a way to explore the universe through very subtle ripples in spacetime produce by powerful gravitational events. Einstein predicted the existence of gravitational waves in 1916, but it took decades to develop the technology to actually detect them. Their existence was inferred from neutron star observations in 1974, but they were not directly detected until 2015, almost a century after their prediction.

The Laser Interferometer Gravitational Wave Observer (LIGO) uses the interference at the intersection of two lasers at right angles to each other to detect tiny fluctuations in spacetime. Each laser travels through an arm 4 kilometers long. It is sensitive enough to detect changes 1/10,000 the diameter of a proton.

Using LIGO many gravitational wave events have been detected, all involving the merger of massive bodies – some combination of neutron stars and black holes. A new study, however, uses computer simulations to predict another potential source of gravitational waves – collapsars.

What are collapsars? They result from the death of rapidly spinning large stars, 15-20 solar masses. When they run out of fuel to keep their cores burning they rapidly collapse under their massive gravity, and then they explode from all that matter crashing into itself. This results in the formation of a black hole at the core, surrounded by a lot of mass that is leftover. This mass swirls rapidly around the black hole and is quickly consumed, within minutes. This large rapidly moving mass is what causes the gravitational waves – at least that is what is predicted by the current model. d

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It’s been less than two years (November 2022) since ChatGPT launched. In some ways the new large language model (LLM) type of artificial intelligence (AI) applications have been on the steep part of the improvement curve. And yet, they are still LLMs with the same limitations. In the last two years I have frequently used ChatGPT and other AI applications, and often give them tasks just to see how they are doing.

For a quick review, LLMs are AIs that are trained on vast amounts of information from the internet. They essentially predict the next work chunk in order to build natural-sounding responses to queries. Their responses therefore represent a sort-of zeitgeist of the internet, building on what is out there. Responses are therefore necessarily derivative, but can contain unique combinations of information. This has lead to a so-far endless debate about how truly creative LLMs can be, or if they are just stealing and regurgitating content from human creators.

What I am finding is that LLMs are getting better at doing what they do, but have not broken out of the limitations of this regurgitation model. Here is a good example from the New York Times – an author (Curtis Sittenfeld) wrote a short story based on the same prompt given to ChatGPT, and published both to see if readers could tell the difference. For me, I knew right away which story was AI. The author’s story was interesting and engaging. ChatGPTs story bored me before the end of the first paragraph. It was soulless and mechanical. It reminded me of a bad story written by a freshman in high school. It got the job done, and used some tired and predictable literary devices, but failed to engage the reader and lacked any sense of taking the reader on an emotional journey.

This reinforced for me what I suspected from my own interactions – LLMs are getting better at being LLMs, but have not broken out of their fundamental limitations.

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Solar power is on the upswing. In 2023, 407–446 GW of solar power was installed globally, bringing the total to 1.6 TWdc. To put this into perspective, this was 55% of new power capacity added to energy production. For the first time, a renewable energy source contributed the most to new capacity. In 2024 so far solar is 75% of new capacity. In the US this was 60% of new power generation (capacity is the potential to make energy at any given time, while generation or production is the actual energy produced). In 2023 solar made up 5.5% of world energy production.

The reason for the increase in solar is that it’s the cheapest form of new energy. According to the International Energy Agency (IEA), solar electricity costs $30–60/MWh in Europe and the US, and $20–40/MWh in China and India. Solar is also the safest, with the fewest numbers of deaths per TWh produced (0.02, compared to the worst, 32.72 for brown coal). Because solar is clean and environmentally friendly, making solar cheaper and more efficient will only enhance its advantages. As is often discussed here, there are other considerations to the overall strategy of energy production, such as intermittency, grid storage, and grid upgrades, but we are not close, at least globally, to running into significant issues. We can take solar from the current 5.5% to at least 30% without too much issue, and can push higher with some infrastructure investment.

Along the lines of making solar power better and cheaper – let’s talk about Luminescent Solar Concentrators (LSCs). If we want to make solar power more efficient there are a few approaches. We could make the conversion of photons to electrons (the photovoltaic effect) more efficient. Right now commercial silicon solar panels have an efficiency of 22-24%, which is pretty good. The theoretical limit of silicon is about 29%, and newer materials, like perovskite, have an even higher potential efficiency.

Another approach is to have some kind of layered solar panel, where each layer may have efficiencies in the 20% range but the different layers have different peak efficiencies in terms of wavelength (color) of light, and there are multiple chances for each photon to be absorbed and converted into energy. Yet another approach is a solar collector – bringing more photons to the photovoltaic cell. Mirrors, for example, are an efficient way of redirecting light to a photovoltaic cell.

LSCs are a method of solar concentration. They use luminescent material to absorb light and then re-emit that light (luminesce). This has several advantages. First, LSCs are efficient at collective diffuse light. Solar panels work best with direct light, and the more direct the better. This is why there is a huge advantage to orienting a panel toward the sun, and for large installations even following the sun with movable panels. But LSCs don’t care – they can collect diffuse or scattered light from any direction with equal efficiency. They can then re-emit that light at a specific wavelength (color), and that light can be directed through a process called total internal reflection. This is like a fiber optic cable, where all light that hits the interface at the surface of the cable is reflected internally, so it travels down the cable and never leaves it.

What this means is that you can have a system of LSCs that are arranged to gather diffuse or direct light from any direction. These LSCs then give off light which travels down a fiber to a photovoltaic where the light is made into electricity. The LSCs are semi-transparent and can be of different colors. If you are thinking of a frond of leaf-like LSCs, then you are on the right track. LSCs are like leaves, and they can be arranged just like leaves, connecting to central fibers and brining energy to the photovoltaic device. In fact, trees have evolved to have a very efficient arrangement of leaves. They absorb light, but also scatter light so that it can be collected by other leaves.

LSCs can already be cost effective, compared to solar panels. They are cheaper to make – mostly just glass or plastic with a luminescent covering. PVs, by contrast, are relatively expensive for the same surface area. So it is more cost effective to have cheap LSCs covering a wide area and bringing light to the more expensive PVs. They also avoid the need for expensive tracking systems. Further, they can be made to be more modular, easier to upgrade and replace. The cost competitiveness improved further with larger area covered, and greater variability in light intensity and scattering.

LSCs are still on the steep part of the technology curve. A recent study presents ways to make them even more efficient, by reducing the size of each LSC and bunching them in a leaf-like structure. The hope is that this approach will make LSCs more scalable, cost effective, and efficient. There already is an LSC industry, but it is small compared to the PV industry. We may, however, be getting close to a shift.

It’s possible that in the future we will not have large fields of solar panels tracking the sun and collecting energy, but fields of artificial trees covered with leaf-like LSCs collecting sunlight and directing it down to their trunks where PVs turn the photons into energy.

When we talk publicly about the effects of human activity on the climate should we refer to “global warming”, “climate change”, the “climate crisis” or to “climate justice”? Perhaps we should also be more technical and say specifically, “anthropogenic climate change”. This kind of question is often referred to as “framing”, meaning that we need to be thoughtful about how we frame topics for science communication and open discussion.

I remember about two decades ago when the concept of “framing” was really introduced into the skeptical community. There was a lot of pushback, because the practice was considered to be deceptive, and more aligned with political persuasion than science communication. This criticism was unfounded, in my opinion, largely because it is naive. It assumes, falsely, that you can communicate without framing. In reality you are framing your messages whether you know it or not, so you might as well be conscious and thoughtful about it.

To get into more detail, what is meant by framing is the overall approach to a topic in terms of major perspectives and considerations. For example, we can frame a discussion on GMOs as purely a scientific question – what does the evidence say about the risks and benefits of genetic engineering technology? We can also frame the topic as one of regulation – how should governments regulate GMOs? Or we can focus on corporate behavior and power. Often, the explicit framing I take on this blog, the framing focusses on critical thinking, pseudoscience, and conspiracy theories. How do we think logically and make sense of all the claims and information?

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The cultural effects of the COVID pandemic can still be felt reverberating through society. One of the positive effects, in my opinion, was the sudden boost to remote technology – connecting remotely for meetings and other uses through Zoom or a similar application. This development has been a little controversial, but I think on the whole has been a net positive, especially as we move into the era of voluntary remote connections and hybrid meetings.

Prior to the pandemic having a hybrid or remote meeting was still the exception rather than the rule. We were slowly progressing in this direction, but it was still uncommon and looked upon with suspicion. For example, my wife obtained her PhD through a hybrid online program (partly online, partly in person). This worked well and was very convenient, considering we live in CT and the program is based in Oregon. But she definitely faced some professional headwinds in terms of acceptance of the concept of online learning. At K-12 schools remote learning was essentially not a thing.

While virtual meetings were already a thing, they too were not routine. For me personally, for example, all of my meetings, lectures, rounds, and workshops were in person. There wasn’t even an option to attend remotely. This is despite the fact that we have the technology. Grand rounds, for example, was streamed to an outside location where some of our physicians work so that they could attend.

Clinically there was a lot of discussion about virtual office visits, and again we saw the beginnings of this technology and very tentative explorations. Mostly this was used to provide expert-level consultations to remote areas or local hospitals lacking such experts. For routine use, however, it was non-existent, and there were many bureaucratic obstacles, such as state licensing rules and insurance reimbursement.

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Both Lin Yu-ting of Taiwan and Imane Khelif of Algeria earned medals in female boxing competition at the 2024 Olympics. This has caused a controversy because both boxers, according to reports, have some form of DSD – difference of sex development. This means they have been caught up in the culture war regarding trans athletes, even though neither of them is technically trans. What is the science here and how should sporting competitions like the Olympics deal with it?

Both female boxers have XY chromosomes (according to the IBA). For some people this means they are male, but as is often the case, it’s more complicated than that. Let’s quickly review some basic biology regarding biological sex to put this into perspective.

Male-Female develop does begin with sex chromosomes: XX for female and XY for male. Specific genes on the X and Y chromosomes affect sexual development, partly through production of sex hormones such as estrogen and testosterone. XX individuals develop ovaries and eggs, produce high estrogen and low testosterone, and develop anatomically along a typical female path with uterus, vagina, and with puberty, female secondary sexual characteristics. XY individuals develop gonads and sperm, make high testosterone, and develop along a typical male path with descended testes, penis and with puberty, male secondary sexual characteristics. All of this is part of biological sex. But also there is the potential for differences every step of the way. In addition, there are other chromosomal arrangements possible.  By some estimates about 1 in 300 people have some difference of sex development.

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About half of Americans, when asked, report that they don’t read the user manual for new technical devices they acquire. Although I suspect that many people are like me – I read them sometimes, and then only partly. If there is a “quick user guide” I will often look at that. These are helpful quick summaries with just the critical bits of information you need. But often I give it a go by myself and then only consult the guide for troubleshooting. I do this partly because I want to see how intuitive the device or app is.

But of course there are situations where this approach is not adequate, especially when assembling something complex or using an entirely unfamiliar and complicated piece of technology. In these situations I find, most of the time (with a few happy exceptions) that the instructions are terrible. Sometimes they were clearly written by someone for whom English is a second language. Or the instructions are entirely pictoral (I guess to be language neutral). Other times it seems they were written by engineers who lack the ability to communicate effectively their arcane craft to the general public. I think this is why many people try to bypass the instructions – they are often terrible and frustrating.

In industry, however, this option may not exist. Further, the instructions may need to be highly technical, which is great if you are already an expert but may be challenging for most users.

This is where artificial intelligence enters the picture. Large language model AIs, like Chat GPT, can “read” material and then answer questions about that material, or even give a summary. I have used Chat GPT to analyze a scientific study, then asked it to find specific information within the study or explain certain findings, and it does pretty well. The idea, therefore, is to feed an entire technical user manual into an LLM and then ask it specific questions or have it give a summary or perhaps step-by-step guide.

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Let me start out by saying that I think the answer to that question is no – but this requires lots of clarification. This was, however, the discussion here, while although poorly informed, does raise some interesting questions. This is a Tik Tok video of a popular podcast which is mainly personalities chatting. The host, Logan, asks the question of whether or not it is possible that Jesus was essentially a con artist – a charismatic speaker who essentially started a cult of personality, and may or may not have believed his own rhetoric about being the son of God.

I think the question touches on something interesting, although historical context is critical. As I have discussed before, I think the evidence for a historical Jesus is thin. In the end, it doesn’t really matter because what is clear is that the mythology of Jesus evolved in a typical way involving all the elements known to fuel such mythologies. There were many stories of Jesus which are mutually exclusive, involving wildly different archetypes and story details. The themes followed the mythology themes that were already prominent in that time and place. The story evolved in a pattern of obvious embellishment. Eventually a canon was imposed from the top down, and all other versions became heresy and actively destroyed. What is left is almost entirely mythology, and the question of whether or not the life of a real person is in the mix is mostly irrelevant (from a historical point of view).

Unfortunately this renders the Logan conversation mostly irrelevant also, one giant non sequitur. Everyone in the conversation assumes that the details in the New Testament are historically accurate (if not the interpretation of those details), but that assumption is not justified. So the conversation takes the form of – could those details be the result of a charismatic con artist, or do they require an actual son of God.

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The topic of genetically modified organisms (GMOs) is a great target for science communication because public attitudes have largely been shaped by deliberate misinformation, and the research suggests that those attitudes can change in response to more accurate information. It is the topic where the disconnect between scientists and the public is the greatest, and it is the most amenable to change.

The misinformation comes in several forms, and one of those forms is the umbrella claim that GMOs have been bad for farmers in various ways. But this is not true, which is why I have often said that people who believe the misinformation should talk to farmers. The idea is that the false claims against GMOs are largely based on a fundamental misunderstanding of how modern farming works.

There is another issue here, which falls under another anti-GMO strategy – blaming GMOs for any perceived negative aspects of the economics of farming. Like in many industries, farm sizes have grown, and small family farms (analogous to mom-and-pop stores) have given way to large corporate owned agricultural conglomerates. This is largely due to consolidation, which has been happening for over a century (long before GMOs). It happens because larger farms have an economy of scale – they can afford more expensive high technology farm equipment. They can spread out their risk more. They are more productive. And when a small farm owner retires without a family to leave it to, they tend to consolidate with a larger farm. Also, government subsidies tend to favor larger farms.

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Surveys are always tricky because how you ask a question can have a dramatic impact on how people answer. But it is useful to ask the exact same question over a long period of time, because that can indicate how public attitudes are changing. This is one of the benefits of Gallup, which was founded in 1935 and is dedicated to high quality and representative polls. They have been asking the following question since 1982:

“Which of the following statements comes closest to your views on the origin and development of human beings — 1) Human beings have developed over millions of years from less advanced forms of life, but God guided this process, 2) Human beings have developed over millions of years from less advanced forms of life, but God had no part in this process, 3) God created human beings pretty much in their present form at one time within the last 10,000 years or so?”

It’s an imperfect way to ask these questions – the “less advanced life forms” is not really accurate, and the questions all assume or imply the existence of God. But by asking “which one comes closest” it does capture the essence of this issue. Option 3 is basically young-Earth creationism, option 2 is pure scientific evolution, and option 1 is everything else. From my view as a skeptic and science communicator, the results of this survey are dismal but also encouraging. At the start of the survey in 1982 the numbers were stark: 1 – 38%, 2 – 9%, and 3 – 44% (the rest undecided). Therefore 82% of Americans endorsed some form of creationism, and only 9% were willing to say that life resulted from evolution acting all by itself.

The most recent poll from this perspective is encouraging: 1 – 34%, 2 – 24%, and 3 – 37%. There is still a plurality endorsing young-Earth creationism, but those endorsing scientific evolution is up to 24%. These numbers also track with surveys on religion in the US. The young-Earth creationism figure is about the same as the number of Americans who identify as some kind of evangelical (something between 30 and 39%). Admittedly, this number can be squirrely depending on how you define “evangelical” and ask the question, but broadly defined, the numbers track. The scientific evolution numbers also track with those who answer on surveys that they are religiously unaffiliated, also now in the 20’s.

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