My mental map of the universe has evolved over my life, partly due to new scientific discoveries and partly due to my own education. As if often the case, as you learn more, things get more complicated. The simplistic picture I had as a child was that the universe consisted of many galaxies in which there are many stars and around which there are planets, likely something similar to our own solar system. I have had to modify this model dozens of times, and perhaps I need to make another little tweak. This has to do with where planets exist.

First, galaxies are not randomly distributed throughout the universe. They are bound together in local groups, those groups are gravitationally bound into galaxy clusters, which in turn are part of superclusters which are finally organized into giant filaments, the largest gravitationally bound structures in the universe. So our universal address is – the Sol system within the Milky Galaxy, part of the Local Group within the Virgo Cluster which is part of the Laniakea Supercluster.

At some point I also learned that not all stars (and therefore, not all planets) exist within galaxies. Estimates of the number of stars within and between galaxies just overlap, so they may be equal, but the average estimates indicate that likely 1-10% of all stars are not in galaxies. They are wandering between galaxies, mostly within galaxy clusters. The first intergalactic star was discovered in 1997. It is likely that most such stars were formed within galaxies (you need clouds of gas and gravitational disturbances for stars to form) but then were flung out because of gravitational interactions with other objects, such as a black hole. Two galaxies colliding can also spray their stars throughout the cluster. It is also very likely that such stars would retain their planets.

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In Isaac Asimov’s The Caves of Steel he imagines a future in which most of humanity lives in gigantic cities, extending many levels underground. This leaves the majority of the Earth’s surface for industrial farming, necessary to feed all those densely populated cities. If you take a similar strategy, however, and keep the population at sustainable numbers, this could maximize land for natural ecosystems and also minimize the environmental footprint of the average person. So if we are going to plan our civilization around environmental sustainability we would not necessarily need Asimovian megacities, but we could concentrate the population in cities and in densely developed areas around cities, and leave large stretches of land in between undeveloped. This is far better than endless suburban sprawl.

But of course, we are not starting from scratch, and our current layout was not planned by some central committee but evolved organically. Pragmatically, the question we need to ask is – where do we go from here. Cities are growing dynamic things, so we can use city development to move in a certain desirable direction, even if we can’t tear it all down and start anew. This means it is important to study what the best city planning and development would be going forward.

When most people think of city planning to reduce the carbon emissions of transportation, the first thing that comes to mind is planning city centers so that they are walkable/bikeable and to provide public transportation, in order to minimize reliance on fossil-fuel burning cars. This also has the advantage of reducing city traffic, which can be a nightmare. However, a recent study suggests that, while important, this may be of secondary concern with respect to impact on CO2 emissions. For many cities, especially those with a single concentrated hub (as opposed to multicentric cities, like LA), the most impactful strategy might be the densification of a ring surrounding the city center. The range of this ring depends on the city, but is something like 10-20 km for a typical large city, but can extend to 40 km. Increased density can be accomplished with infill development, as many such zones are only moderately developed leaving lots of room for densification.

The idea is that the workplaces will be concentrated in the city center, and the workers will live in the ring around the city center, minimizing their commute distance. This could have a significant impact on the average commute distance that people have and therefore their transportation carbon footprint. This approach would work better for some cities than others, depending on geography. This plan could also maximize the impact of public transport, like buses and trains, dedicated to bringing people back and forth from this densified ring to the city center.

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Understanding, at a deep level, the differences between legitimate science and pseudoscience is increasingly critical in our modern world. Science, in my opinion, is perhaps the most powerful tool humans have collectively developed for understanding the universe in which we find ourselves. (I would clarify that it is complementary with philosophy which is important to ensure that we are thinking clearly, rigorously, and consistently.) Pseudoscience pretends to be scientific but is essentially doing it wrong. There are many underlying reasons for the existence of pseudoscience – it is sometimes just poor quality science due to poor training or sloppy technique, it may result from a motivation to achieve a desired result rather than letting the empirical chips fall where they may, researchers may not appreciate their own biases, or it may be part of a dedicated campaign motivated by profit, politics, ideology, religion, culture, or just wishful thinking.

I spend a lot of time studying and writing about certain classic pseudosciences because I think they are especially instructive, and acupuncture is definitely on the short list.  I just wrote about it last week, specifically about a gullible article in the NYT which has bought into the pro-acupuncture propaganda. That piece resulted in lots of feedback, some of which doubled-down or extended the pseudoscientific arguments often made for acupuncture, so I wanted to reply to some of those and further clarify my position.

One common feature of pseudoscience is the use of vague or fluctuating definitions. Science requires unambiguous definitions, which is why it so often relies on technical jargon which evolves to be incredibly precise. This is one of the things I love about science, and why I think everyone should study it to some degree, at least enough to become functionally scientifically literate. Science forces you to think clearly, precisely, and consistently. If we take a seemingly simple question, for example, such as “does acupuncture work”, we first have to operationally define “acupuncture” and also “work”. You also have to include – work for what? I am usually careful to do so when addressing this question.

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It is old news that all the tech we now live with is constantly gathering data about us. It is important, however, not to become complacent about this or to assume the situation cannot or is not getting worse. Pretty much every piece of digital technology that we interact with likely is gathering some personal information about you which is used to target advertising and to sell to third parties. Regulations in most countries are inadequate and fail to keep up with technological changes.

One of the latest venues to soak up information about you may be surprising – your car. Cars are increasingly computerized, and they typically collect driving behavior data – how fast your drive, how hard you break, and how tight you make turns. But also, some vehicles have cameras facing the driver which means they can detect your behavior visually. Sometimes this is sold as a safety feature, to tell if you are too sleepy or inebriated to drive. Sometimes this is part of a system to get your insurance company to reduce your rates if you think you are a safe driver. But often it is done without disclosure. Recent GM was found guilty of collecting and selling such data without the permission of the user, and was banned for doing so for five years. But many other car manufacturers also do this.

All they really have to do is bury some disclosure deep in the user agreement, which functionally nobody reads, and they are covered. You may have the ability to opt-out of such data selling. Of course, putting the burden on the end user to find and read any such disclosures and then go through the steps necessary to opt out of data selling is a huge problem. In fact insurance companies will buy data from car companies and then use that data to increase your insurance premiums, without you opting into any of it.

The basic fact is that collecting data from users, packaging that data and then selling it to third parties is a huge industry. It is estimated that globally this is a $240 billion industry. When that kind of money is on the line, companies are going to do everything they can to capitalize on it, while avoiding legal issues by either flying under the radar or hiding behind legal fig leaves (like the buried consumer disclosures). They will also use that money to lobby the government to let them continue to do so, or even to mandate certain things that will help this industry. For example, some car monitoring technology is sold as a safety feature, and it can legitimately be used for this purpose. Others are convenience features, like GPS. But once all the sensors and cameras are in place, they will soak up all the data they can – because that data is worth billions.

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Dozens of people have e-mailed me about a recent article in the New York Times Magazine on the interstitium. The reason is not because of the interstitium itself, but because it is being used to retcon an alleged explanation for acupuncture.  The discussion of the interstitium itself is fine, but then the author veered off into gratuitous pseudoscience.

The interstitium was proposed in 2018 based on a study showing that the interstitial connective tissue spaces around organs and other tissues in the body are not separate spaces but all appear to be connected. Essentially the authors propose this is a body-wide fluid filled space in the body through which fluids flow and communicate. This adds to the other fluid systems in the body, such as the lymphatic system (which drains excess fluid from tissues), the circulatory system (which distributes oxygen and nutrients and carries away waste), and the spinal fluid system (which is inside and surrounds the central nervous system). Later studies have supported the evidence for one continuous interstitial space.

Of course, the more we know about how the body works the better we will be able to understand disease processes and design treatments. Cancer cells, for example might spread through the interstitium, resulting in distant metastasis.

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If I were a UFO enthusiast, someone who believes that some UFOs (now UAPs) are aliens visiting the Earth and that the US government knows this and is covering it up, I would be really disappointed. I might engage in some serious motivated reasoning to convince myself that the recent release of documents by the Pentagon was somehow dramatic, but down deep how could you escape crushing devastation. Lucy once again put the football out there for Charlie Brown and then yanked it.

Trump wrote that his administration will, “begin the process of identifying and releasing Government files related to alien and extraterrestrial life, unidentified aerial phenomena (UAP), and unidentified flying objects (UFOs).” The Pentagon announced they will make their files available to the public. The NASA director applauded this transparency. The media is now hyping up these
“never before seen” documents.

And the result – an absolute nothingburger. We get more indistinct blobs, dots of light, blurry nothings, camera artifacts, and stories of people seeing dots of light. The Pentagon acknowledges – there is no evidence that any of these things are alien phenomena, but also that some of the blobs and lights have not been fully explained. So we are exactly where we have always been – there is no compelling or unambiguous evidence of aliens. Believers can weave whatever anomaly-hunting stories they want from the terrible evidence. Skeptics will continue to point out that the evidence does not tell us anything. Conspiracy theorists will continue to argue that the government is covering up the “real” evidence, and this data dump must be a false flag.

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Richard Dawkins is a public intellectual of some renown, although not without his controversies. So it is noteworthy when he writes an article claiming that the chatbot Claude is likely conscious. I found the article fascinating, not because I agree with his core claim or feel that he has contributed anything significant to the conversation, but because it seems to represent a scholar and deep thinker writing about a topic in which he lacks specific expertise. I also see no evidence in the article that he engaged meaningfully, or at least adequately, with a topic expert. As a result he makes some thoughtful and instructive errors.

He begins with a discussion of the Turing test, which has long been discussed as an early thought experiment about how we might determine if an AI is actually conscious. Dawkins essentially accepts the Turing test and write:

“It was one thing to grant consciousness to a hypothetical machine that — just imagine! — could one day succeed at the Imitation Game. But now that LLMs can actually pass the Turing Test? “Well, er, perhaps, um… Look here, I didn’t really mean it when, back then, I accepted Turing’s operational definition of a conscious being…””

He feels saying that LLMs have passed the Turing test but still not accepting them as conscious is moving the goalpost. However, the Turing test was never generally accepted by AI experts or philosophers as a true test of consciousness. Rather, it was understood that such a test really is only a measure of a machine’s ability to imitate human speech. I wrote about it in 2008, writing: “Ever since Alan Turing proposed his test it has provoked two still relevant questions: what does it mean to be intelligent, and what is the Turing test actually testing.” I went on to write:

“But I can imagine a day in the not-too-distant future when such AI can pass a Turing test. The algorithms will have to become much more complex, allow for varying answers to the same question, and make what seem to be abstract connections which take the conversation is new and unanticipated directions. You can liken computer AI simulating conversation to computer graphics (CG) simulating people. At first they appeared cartoonish, but in the last 20 years we have seen steady progress. Movement is now more natural, textures more subtle and complex. One of the last layers of realism to be added was imperfection. CG characters still seem CG when they are perfect, and so adding imperfections adds to the sense of reality. Similarly, an AI conversation might want to sprinkle some random quirkiness into the responses.

The questions is – will sophisticated-enough algorithms running on powerful-enough computers ever be conscious? What Loebner is saying, and I agree, is that the answer is no. Something more is needed.”

Basically, the limitation of the Turing test is that it is looking only at output, and therefore there is no way to distinguish the output of true consciousness from a really good simulation. This is not a new idea, and no one is moving the goalpost. We need to know something about how a computer is working to conclude whether or not it is conscious. What LLM experts will tell you is that these chatbots are just really good autocompletes – they are mimicking language, and since language is how we communicate thoughts, this creates the powerful illusion that they are mimicking thought, but they aren’t. They do not think, they do not truly understand.

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I came across a few news items that I could possibly write about today and couldn’t decide which to cover, so I will write about all of them, since they all relate to renewable energy. The first is a new study comparing direct air capture (DAC) to installing new wind and solar. This is a direct comparison between these two options, to see which provides the most bang for the buck.

DAC involves taking CO2 directly out of the atmosphere in order to mitigate carbon release through burning fossil fuels. If this technology were sufficiently efficient it could be hugely useful in reducing future climate change. This is the only approach that can potentially have a negative carbon footprint, actually reducing the amount of CO2 in the atmosphere. Other technologies simply reduce the amount released. This negative carbon factor is highly attractive since it could theoretically zero out our carbon release and even take us back in time to an atmosphere with less CO2. Right now, it should be noted, we are not only continuing to release massive amounts of CO2 into the atmosphere, the amount continues to increase. In 2025 the world emitted 38.1 billion tonnes, of carbon, a 1.1% increase over 2024.

But there are problems with DAC – it is currently not very efficient and is not scalable enough to have enough of an impact. Also, the efficiency of DAC depends heavily on how you power it – if you connect it to the grid and there is some fossil fuel energy on that grid, you may actually increase CO2 rather than decreasing it. Ideally DAC would be powered entirely by low carbon energy sources. This is why critics of DAC argue that it simply makes no sense to deploy this technology before we have decarbonized the energy sector, which we should do first.

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I thought everyone needed one more thing to worry about, so here you go: evolving AI. When I hear this phrase I think of two things. The first are AI systems designed to simulate organic evolution. The second are artificially intelligent systems that are capable of evolving themselves. That latter one is the type you need to worry about.

Systems that simulate evolution already exist – Avida, Biogenesis,  Grovolve, Tierra, Framsticks: and others. They basically have some code that competes for some resource or to complete some task and the code randomly mutates and reproduces. That’s it, all you need for an evolution simulation. Code can compete for computer resources, or be a physics simulator with digital creature trying to move quickly across terrain. These are sometime gamified for entertainment, but are also used for serious research, to study patterns within evolutionary systems. I would love to see these kinds of systems get more and more sophisticated, even to the point of reasonably simulating living systems. Such systems could be used to test hypotheses about evolution – and would also disprove a lot of silly creationist talking points.

But now we are talking about evolvable AI – AI systems that are capable of developing themselves through evolutionary processes. A new paper in PNAS discusses the potential power and risks of such systems. They echo they kinds of issues that have been explored in science fiction for decades. The authors write: “Evolvable AI (eAI), i.e., AI systems whose components, learning rules, and deployment conditions can themselves undergo Darwinian evolution, may soon emerge from current trends in generative, agentic, and embodied AI.” The results, they argue, have not been adequately addressed when discussing the potential risks of rapidly developing AI ability.

The authors distinguish two types of evolving AI – breeder systems and ecological systems. In breeder scenarios the programmers are in control of the process, selecting which code to “breed” and evaluating the outcome. This process is like a digital version of domestication, and has the potential, if done wisely, to maintain control. In fact, systems can be bred to have greater predictability and control. There are still risks here. So far humanity has not bred an animal to be more intelligent than humans. This could theoretically happen with AI, resulting in emergent behavior not specifically selected for that could get out of the control of human programmers.

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It is long past time the US eliminated gerrymandering, the drawing of district lines specifically for the purpose of favoring one political party, across the board. This requires either a 50 state agreement, or action at the federal level. This has been a problem since near the beginning of our democracy, and seems to be getting worse. We are now in the middle of a mid-decade tit-for-tat rash of gerrymandering that is extremely anti-democratic, so it’s a good time to raise this as an issue voters should definitely understand and prioritize.

As a quick aside – this is not a “political” blog, which does not mean that I never discuss political issues or topics with a political dimension. It partly means that I try my best to by non-partisan, and to avoid purely political value-judgements. I recognize this is an impossible ideal – we all have our biases and perspectives that color our thinking on topics in subtle ways. But we can try. Also, this is not a strictly science blog, it covers science, critical thinking, and media savvy, which are part of what we call scientific skepticism. Recently I started a video podcast, Political Reality, with co-host Andrea Jones Roy, who is a political scientist, for the purpose of applying scientific skepticism to political topics. This is also not a partisan show, and is mostly part civics lesson and part fact-checking. With that in mind, I thought I would write about what science and critical thinking have to say about gerrymandering, given that this is a topic in the news recently, although not as much as I think it should be. We also did cover this topic on Political Reality.

The term gerrymander dates back to 1812 when Massachusetts Governor Elbridge Gerry redistricted his state’s representative districts in order to favor his party, the Democratic Republicans. One of the districts looked like a salamander, leading the Boston Gazette to quip that it was really a “Gerry-mander”, and the name stuck. (Ironically, the two parts of that term, gerry and mander, both kinda sound like they mean “rig”, but the word has nothing to do with that.) Since then all political parties have used gerrymandering to gain unfair advantage. This stems from some features of US politics.

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