The Nobel Prize is often awarded not to researchers who made a huge discovery about how the world works, but to those who have developed techniques that improve research itself. The downstream effects of such developments are likely to be dozens or hundreds, even thousands, of discoveries This is the case for the 2026 Nobel Prize in physiology or medicine – awarded to Karl Deisseroth, Peter Hegemann, and Georg Nagel for their work developing optogenetics. This is a powerful tool that allows us to create a functional map of the brain.

The story begins with Hegemann who in the 1990s was studying the free swimming algae, Chlamydomonas. The algae swims towards light, which anyone can reproduce by shining a light on a petri dish full of the green organisms. They have a small eyespot which can detect light, but Hegemann was fascinated with how quickly they were able to respond to a light signal. This lead him to hypothesize that the algae may be using a different method for turning light into a signal that can affect their behavior than vertebrate eyes. When he attached electrodes to Chlamydomonas and shined a light on them, he was able to detect an electrical signal in half a millisecond – incredibly fast. This is simply too fast to conduct any signal, so he thought it must be the same protein that detects the light and creates the resulting electrical pulse. Now all he had to do was identify that protein.

A Japanese group had already mapped the DNA of Chlamydomonas, and of the genes identified, two had properties that might fit the bill. But this was now a bit out of the field of expertise of Hegemann, so he contacted Nagel for help and sent him the code for the two candidate genes. Nagel injected the genes into separate batches of frog eggs, which began to express the proteins. He could then study how the proteins functioned, and was able to confirm Hegemann’s hypothesis. Both proteins, named channelrhodopsin-1 and channelrhodopsin-2, were ion channels that respond to light by opening up, allowing positive ions to flow through,  and creating an electrical potential. Channelrhodopsin-2 was particularly powerful, creating an electrical signal in 0.2 milliseconds. They were able to incorporate the gene for this protein into human and hamster kidney cells and show that they become light sensitive.

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Stephen J Gould (still my favorite science essayist) wrote an excellent article in 1985 (Red Wings in the Sunset, later published in his book, Bully for Brontosaurus) about artist and naturalist Abbott Handerson Thayer. Thayer wrote about how animals use coloration as camouflage – what he called “cryptic coloration”. His ideas were solid, but he made a classic mistake that scientists sometimes make, overapplying their key discovery. Thayer argued that all animal coloration is cryptic. For example, he argued that flamingos are pink because it hides them in the setting sun (hence the title of the essay). This is a transparently absurd argument, and it shows how Thayer tried to shoehorn all evidence into his preferred and absolute narrative. It is better to assume that nature is complex, and all explanations are at best partial (unless proven otherwise).

Animal coloration, in fact, can serve many different purposes, only one of which is camouflage. Thayer also struggled with the male peacock, for example. Butterflies appear to be another example. Actually, many butterflies are camouflaged on the underside of their wings, so that when they are at rest with their wings up they tend to blend into their surroundings. But the top side of their wings are often very colorful and not camouflaged at all.

One assumption is that the brightly colored part of their wings is to attract mates. This may be true, but that does not mean the coloration does not serve another function. Often animals use visual cues when choosing their mates that are markers for health and success. As evidence that butterfly wing color may be serving a survival benefit, if you look at birds that feed on insects during flight, they target dully-colored moths much more than brightly colored butterflies, even though the butterflies should be easier to see.

A recent study tests the hypothesis that the brightly colored and patterned top side of butterfly wings may have evolved to produce an optical illusion to confuse predators. The idea of using optical illusions as visual protection in animals is not new. For example, zebra stripes allow zebras to hide in the herd, confusing predators as to where one zebra ends and another begins. Stripes on zebras and snakes may also serve to confuses predators about their direction of motion, but this hypothesis has not been tested previously. Continue Reading »

Dog owners (of which I am one) tend to develop a strong sense that their dogs understand what they are saying. Research actually supports this conclusion. In a 2016 study dogs were able to respond to praise words without intonation, to intonation with neutral words, but most strongly to praise words with intonation. Dogs seemed to understand the words themselves – even absent the intonation. This does not mean they understand words the way we do, or that they process language the way humans do. But they can recognize words and associate them with something at least good or bad.

There is also evidence that dogs have more neurons and a somewhat higher neuronal density for a carnivore (although nothing like racoons, which are the real outliers in terms of neuronal density). And there is evidence that dogs evolved to socialize with humans, showing clear differences even from wolves raised with humans. Dogs also evolved greater expressiveness, (like the eyebrow thing that they can do and no wolves), which further supports the notion that their domestication involved them gaining more smarts so that they can socialize and communicate with humans.

Such studies are always just partial slices of reality, and often raise even more questions. One question that a recent study seeks to address is whether or not a dog’s increased ability to parse words is innate or learned, and what are the mechanisms for this. The research paradigm they used was to look at human and dog brains with an EEG while they were listening to fake speech, to see how well their brains synchronized with different parts of speech. Synchronizing means that the EEG shows a repeating rhythm that is the same as a repeating rhythm in the speech. They were specifically interested in consonant bias – humans are the only animals tested so far whose brains respond more to consonants than vowels (not even our closest primate relatives do). This consonant bias likely results from the fact that consonants tend to be more distinctive in parsing words than vowels, so our brains pay attention more to the consonants when trying to discern words. Put another way, there is no reason for a brain to synchronize more with consonants than vowels unless it was trying to parse speech. So it is a very interesting question, whether dog brains show a consonant bias.

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This is a tiny bit of good news it what has been a horrific story for American science over the last almost two years. Trump has backtracked on his attempts to politicize science funding at the NIH, and throughout the federal government. He did this apparently in reaction to bipartisan pushback from Congress. This, however, does not mean that there hasn’t already been profound damage and that further meddling will not happen.

To put all this into context, it is important to understand the infrastructure of scientific research that has been developed in the US, largely since WWII. The federal government created a number of institutions, the biggest of which is the NIH with an annual budget of $48 billion, to fund scientific research. This money largely went to universities through specific grants. This grant money is the life-blood of many research institutions – it pays for the research itself but also the infrastructure that allows the research to happen. Grants are awarded by merit, determined by panels of relevant experts. In exchange the funding organizations can impose many quality control rules on grant receiving institutions, labs, and researchers. You have to maintain a suite of certifications, and the results of your research have to serve some definable purpose and be accessible to the taxpayers who ultimately fund the research.

The NIH and other granting institutions are creatures of Congress – they were created by Congress, who determines their mission and provide their funding. Generally Congress determines their general goals, and then allows appointed experts to execute those goals by determining things like which grants get funded. This system has worked fabulously well over the last 70 years or so, allowing America to lead the world in scientific development. It is arguably one of the main reasons the US today is the superpower that it is. Here is a good discussion by Neil deGrasse Tyson on the power of this research infrastructure and the damage that has resulted from tampering with it.

As a general rule this arrangement, of politicians and funders determining broad goals and experts determining how to best execute those goals, works well. Historically whenever politicians try to meddle in those details, to “micromanage” what experts should be doing, the results range from bad to catastrophic. The classic historical example is Lynsenkoism in the former Soviet Union. Briefly (read the linked Wikipedia entry for more info) Trofim Lysenko did not believe in Mendelian genetics or natural selection, but favored what is now called a more Lamarkian approach. His science was favored by Stalin because it aligned better with Soviet ideology, and so he was elevated in power and those scientists who disagreed with him vanished. The result was mass starvation and a hollowing out of the science of genetics in Russia, something that they have still not fully recovered from. For this reason, whenever political ideology inappropriately meddles in the conduction of science, it is referred to as Lysenkoism. Arguably, that is exactly what the Trump administration is doing.  Continue Reading »

A recent article in the New York Times is, unfortunately, terrible journalism that ends up shilling for a fraudulent and exploitative industry. The author, Sarah Wildman, tells of her previous encounter with a medium through her unnamed cousin. Part of the subject was Wildman’s 14 year-old daughter who had died the year before. This is a massive conflict that compromises her objectivity. No editor should have let this article go through. Wildman herself admits. “Also, I did not really want to debunk it.” She wanted to believe – that makes her a vulnerable target, not an objective journalist.

The essence of the article is that it really doesn’t matter if mediums are genuine or not. They allow people to feel a connection to their deceased loved-ones, and there is research evidence that such a feeling of connection can be emotionally useful. But the research she cites is about people remembering their loved-ones, thinking about them, looking at pictures and videos, etc. It is not about receiving messages from them through mediums. There is some research looking specifically at mediums, but it is all non-controlled and retrospective (this is exploratory preliminary research). What this research also shows is that feeling comfort from a medium is largely dependent on believing that the communication is real – you have to believe that the mediums are genuinely psychic. And that is a huge problem.

I would therefore challenge Wildman’s core conclusion, that mediums are necessarily or uniquely helpful. People can be assisted in their bereavement through other means that do not involve giving money to an industry built on grift and exploitation. There are also methods that do not require people to believe in things that are not real. In fact, people who seek mediums often feel shame for doing so, and hide what they have done for fear of being dismissed. Wildman blames scientists and skeptics for this. She wrote:

“The study of these occurrences and their impact on grief has long been prejudiced by exactly what you’d expect: the surety of scientists and skeptics that all of it — mediums, sensations, signs — might be about as real as a dime-store Ouija board, a 19th-century séance or, worse, a momentary psychotic episode.”

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When you hear the phrase, “Science is socially constructed,” what does that mean to you? If you are a philosopher of science, you likely have a deep and nuanced understanding of exactly what that means. Unfortunately, in my experience, many scientists and science communicators do not fully appreciate what it means, and may even have severe misconceptions about it. I am not a philosopher, but I have studied a lot of philosophy and always try to align my understanding with the experts. I also have an interest in many different sciences, which I think is extremely helpful in understanding the socially constructed nature of science.

Let me first say what is not meant by that phrase – it does not mean that science is not real, that the findings of science are not true in some meaningful way, or that all methods of understanding the universe are equivalent. This is not relativism or post-modernism.

What it does mean is that science is something that people do. That may seem obvious, even trivial, but often people speak of “science” as if it is an entity unto itself. I know that we often use shorthand for convenience, but it is good to occasionally think about how that shorthand may bias how we think about things, and reflect those biases. When people say things like, “listen to the science” it makes it seem as if “science” is something that can speak with its own voice. It isn’t. Similarly someone might say, “look at the facts” as if the facts can interpret themselves. They can’t. People are always in the loop. People actively construct science out of observations and hypotheses and theories. There are institutions of science, different cultures within science, and different perspectives.

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For many science fiction fans the computer voice on the Enterprise is iconic, and is, in fact, “the” voice of any computer. Majel Barrett deliberately gave the computer a stilted, monotone, mechanical voice to reflect the fact that it was a non-sentient machine. But also, it reflected the technology at the time. Voice synthesizers had been around since the VODER, first debuted at the 1939 World’s Fair. They produce an electronic-sounding definitely artificial voice. Many people are also familiar with this electronic voice through Stephen Hawking’s voice synthesizer.

But now we have the technology to create artificial speech that sounds convincingly human – the voice sounds natural and not electronic and contains all the inflection and nuance of natural speech. Sesame is one example, and you can try the demo for yourself. The app adds pauses, breaths, “ums”, and other fillers to sound more natural. Essentially it is now simply not possible to tell an AI generated voice from a human generated voice. I recently rewatched Star Trek TOS and paid close attention to the futurism in the series and was struck by how dated all concepts of AI were in the show. We can already produce natural-sounding human speech, so why is the Enterprise’s computer so mechanical 200 years in the future?

However – there is another explanation (this is obviously a retcon having nothing to do with the original intent of the creators). More recently it occurred to me that we might consider the mechanical nature of the computer voice a deliberate feature, not a bug of technological limitations. In the episode Tomorrow is Yesterday the ship’s computer had recently been overhauled on Cygnet XIV and was given a female personality with a more natural sounding (albeit humorously annoying) voice. We can therefore say that the technology existed and the mechanical voice was at least partly a deliberate choice. At this point you are probably thinking, “So what?”

Well, one of the features of LLM-based chatbots is that they are getting increasingly good at imitating human interactions through language, whether with text or speech. This creates the powerful illusion that they are sentient and feeling, when they clearly are not. I know this point can be debated – how can we be sure they are not sentient? I maintain they are simply not built to be sentient. As an example, the same technology can be used to make pictures, but no one argues that Midjourney is sentient. So for the sake of discussion let’s stipulate LLMs are not sentient.  Continue Reading »

Solar power as a source of electricity has been on a meteoric rise, and has crossed some encouraging milestone recently. Every way you look at it, solar is booming. This is driven primarily by the decrease in the cost of adding solar power, so let’s start there.

The Berkley Lab has been tracking the cost of solar power in the US for years, so they are a reliable source of information. They get direct data from the actual price paid by consumers, and break down the cost of panels, the cost of installation, and all the “soft” costs that  are part of the industry. In 2009 the total cost of installing solar between $7 – 8.70 per watt. An average US residential home installs a 6-8 kW system, with the average increasing over the years. In 2025 the average size was 7.7 kW. Let’s use a 7 kW system, at $8 per watt, that’s $56,000 total installation cost. Today the average price of installed residential solar in the US is $3.6 per watt (but it is $3.0 if you pay upfront, $4.50 if you finance). So that same 7 kW system now costs $25,200. (If you pay for it outright, the cost drops to $21,000.) So over the last two decades the total const of installed residential solar has dropped by about 60%. This is without considering any tax breaks or incentives.

The reason for the decrease is partly that solar panels themselves are cheaper, and they are more efficient, so a 7kW system requires fewer panels. Installation costs have decreased by about 50% over this time, largely due to economies of scale. However, the US still has higher soft costs for solar than many other industrialized nations, and this is mostly a matter of the regulatory system. So there is some regulatory efficiency to be gained. Solar companies themselves also have some possible efficiency gains. Fortunately, the net price for solar continues to go down, with another 50-60% decrease in total price possible even without further technology gains. Commercial and grid scale solar is even cheaper – with large non-residential installations down to $2.4 per watt.

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On Sunday August 30, 2026, NASA successfully launched the Nancy Grace Roman space telescope aboard a Space X Falcon Heavy. This telescope has amazing capabilities and will add to our ability to study and understand the universe, with a special focus on dark energy, black holes, and exoplanets. However, the best way to look at such telescopes is as part of a system of instruments, each with complementary trade-offs in abilities. But there are also some genuine technological advancements in this instrument.

The telescope is named after Nancy Grace Roman, who was NASAs first Chief Astronomer and NASA’s first female executive. She is also consider the “Mother of the Hubble” telescope for her critical role in planning and securing funding for the Hubble telescope.

Let’s start with the technical details. The Roman telescope was launched on a journey 1 million miles to the L2 Lagrange point between the Earth and Sun. Lagrange points are locations within overlapping gravitational fields, like the gravitational fields of the Earth and Sun, where there is a balance between the pull of both fields and the centrifugal force of orbiting itself. . The result is net zero forces on an object at a Lagrange point (of which there are 5 between Earth and the Sun). It does functionally act like a gravitational well itself. Roman will be orbiting the L2 point in what’s called a quasi-halo orbit, a large loop that ranges from about 117,000 to 500,000 miles away. The James Webb telescope is in a similar orbit around the same L2 point, but their orbits will keep them safely separated from each other. It will take Roman three months to arrive in its orbit around L2.

The primary mirror of Roman is a wide field infrared camera, with a 2.4 meter lens (the same size as Hubble’s). However, one of Roman’s primary features is that this lens is wide field – it can see 100 times as much of the sky in clear focus at the same time as the Hubble. It has a 300 megapixel camera with which to capture this information. This means it can gather a ton of data quickly, surveying the sky 1000 times faster than the Hubble. It will gather 50 times more data in 5 years than Hubble has in the last 30 years.

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There is a lot of legitimate concern over the net effects of students using AI to do their school work. If you let AI do your writing, then you will not learn how to write, for example. Or worse – if you let AI think for you, then you will not learn how to think. I have spoken with many teachers at many levels about this and they range from those who take diligent steps to minimize AI “cheating”, to the other end where some throw up their arms and essentially say, “if students us AI to cheat, they are just cheating themselves out of an education.” For me the bigger question is – what happens to society if we raise a generation intellectually crippled by their dependence on AI?

Certainly, the educational system at every level needs to deal with the reality of AI, and needs to protect the legitimacy of their methods for evaluating students. But also, schools need to face the reality of a world with AI and prepare their students for that world, which may require leveraging AI itself as a teaching tool. In fact, using AI as part of the teaching itself may secondarily solve (or at least mitigate) the AI-cheating problem. For example, if you develop a workflow that includes human and AI elements, then use of AI is already baked in and accounted for. Ideally, the outcome will be optimal when the human adds value to the process and uses AI effectively. The outcome should be better than AI alone, or human alone, could generate. And if you have to show your work throughout the workflow, it becomes hard to fake your contribution to the process.

I don’t think researchers were even thinking of the AI-cheating problem when they developed this AI assisted workflow to teach undergraduates how to develop proper research questions. They were trying to more effectively teach students how to develop research questions. But their model may provide a useful template in many other educational contexts, and also help the AI-cheating problem.

Here is the problem they were trying to address – undergraduate students often have a difficult time developing useful research questions. It’s a lot harder than you might think – “Students can usually name a topic they care about, but often lack strategies for turning that interest into a question that is grounded in evidence, scoped to what is feasible, and aligned with available methodologies.” This has long been identified as a bottleneck in undergraduate research education. Developing a solid research question is often the most critical step in any research, and is often the most challenging thing to do.  Continue Reading »