The pre-Hippies of two centuries ago

 kw: book reviews, nonfiction, biographies, mini biographies, transcendentalism, social movements, abolitionism

My most recent wildcard selection, a rare excursion into reading about intellectualism and literary movements, is The Emerson Circle: The Concord Radicals Who Reinvented the World by Bruce Nichols. Like many, I have read only a little of what Ralph Waldo Emerson wrote, and I considered him the originator and leading figure of Transcendentalism. He and several others founded the Transcendental Club in 1836, but I find from reading this book that Emerson didn't like the term "transcendentalism". The term he and his colleagues and acquaintances more frequently used was "the Newness".

From reading the book I didn't obtain much of an understanding of transcendentalism. I wondered, "What did they wish to transcend?" So I did some digging.

Per the Encyclopedia Britannica: "Transcendentalism is a 19th-century philosophical and literary movement that asserted that divine, fundamental truths can be known through personal intuition rather than through rigid religious dogma or sensory experience." Emerson and the others were originally Unitarians (Emerson's father was a Unitarian minister), and they were reacting to the cold rationalism and sobriety of what some have called a "faithless faith". They desired a more intense experience, something more spiritual.

Note: These Unitarians considered themselves Christians, but denied that Jesus is God. They had various explanations for this, while still considering that Jesus is worthy of worship, just not on the level of God. This is in contrast to modern Unitarian Universalists, an eclectic mélange of semi-theistic and non-theistic belief systems that are united primarily in their universalism, which is the belief that everyone eventually is received into heaven, perhaps even Satan. The Unitarians of two-plus centuries ago are well described by a term more recently laid upon Episcopalians, "God's frozen chosen."

The transcendentalists, then, weren't acting contrary to Unitarianism, but in parallel, seeking to transcend it. Thus, the Newness was embodied in dramatic experimentation: new ways of living, new kinds of schools, ways of speaking and lecturing and writing that differed greatly from the European models, and an exceedingly self-focused and experimental spirituality. Communes and similar mini-societies arose (and disbanded), new schools were founded (and then foundered), lecture series and small periodicals abounded, and "church" as most understood it was practically abandoned or dramatically changed.

All this reminds me a whole lot of the social radicalism of "the Sixties": communes, a surge of home schooling (quite different from the more effective methods currently in use), social protest against not just governmental authority but "the establishment" in general, and an intense focus on personal self-regard and autonomy. There is a current resurgence of similar trends, most evident in the notion of "my truth" coupled with the weird group-think of cancel culture.

Stepping back, I see all three movements, the Newness of the early 1800's, the activism of the mid-1900's, and the "political correctness" of the 1980's that led to more recent "wokeness", as being like the Hippies of my youthful social environment. In all three, it is evident that the abandonment of Divine morality such as the Ten Commandments leads unerringly to amorality, and the abandonment of God as "the truth" (which Jesus called Himself) leads to "my truth" which leads to "every man a liar." The characteristic of most modern political and cultural leaders now, in the 2020's, is, to paraphrase former President G.W. Bush, "Some lie when they deem it necessary, but these lie constantly, just to stay in practice."

The Newness was not based on overt lies, but on rejection of Divine authority in favor of personal autonomy. The Sixties was mired in falsehood and half-truths that made many fearful to speak the truth (I was one who did, and I was nearly assassinated as a consequence). American society of the current Teens and Twenties could be called the Triumph of Lies, beginning with "You can choose your doctor" and going downhill from there.

So I am not misunderstood, I am a triple Trumper. I voted for Donald Trump in 2016 primarily to vote against Hilary Clinton, because she and her husband were the main focus of the Bush quote above. I voted for him again in 2020 because during his first term he won my respect (As an aside, various articles about Mr. Trump claim that he told ten thousand or twenty or thirty thousand lies. All such articles have been demonstrated to be riddled with lies against him, while proving no more than a handful of untruthful statements he made; in actuality, I observe that the legacy media perpetrates tens of thousands of lies about Donald Trump daily. This is the source of Trump Derangement Syndrome, which afflicts about 40% of the voting public). I voted for Donald Trump again in 2024 with the clearest of consciences. Not because he is a "good man" (I agree with Jesus that "no man is good") but that he is measurably better than literally anyone else in public office. Rather than answer, "but does he lie?" I would say that he tells the truth more than nearly everyone else, particularly the pseudo-journalists of the legacy media.

Back to the book. The Newness had fallen apart by the early or mid 1850's. The rising conflagrations around the country related to slavery and abolitionism forced the transcendentalists and their sympathizers out of their armchairs and writing-desks and into more public action. Firstly, they became more apt to speak in support of abolition publicly, and some took more direct action, such as harboring runaway slaves in their homes and helping them flee further northward. Finally even Emerson, one of the last to do so, spoke fervently in favor of abolishing slavery. As the nation teetered towards civil war, the "life of the mind" and "armchair philosophizing" became untenable, even impossible. The Unitarian and Transcendentalist belief that human nature is basically good was called into question, then denigrated, and finally demolished as more than half a million lives were taken in the war between the states.

The first prominent transcendentalist to die was Henry David Thoreau, on May 6, 1963. Although Emerson gave the eulogy at his funeral, he was himself in decline, at least mentally. Others followed over the years. Emerson himself lingered until his death April 27, 1882, followed about six years later by Louisa May Alcott. She was not a transcendentalist, but a parallel "fellow traveler" whose fame as an author had two strains: books based on her experiences in her family and the "Emerson circle", and rip-roaring adventure stories that she usually published under pseudonyms. 

Transcendentalism had come and gone. Remnants remain in the styles of writing that led to masterpieces such as Walden and Moby-Dick. Experiential writing, what one might call crypto-memoir, is a lasting legacy and informs today's best writers. Emerson's grave, shown here, centers the "transcendentalists' field" and represents more than a gravesite, but a legacy that opened the American mind, fortunately without destroying it.

LED lights won't kill you

 kw: risk analysis, lighting, light sensitivity, color vision, hype

Perhaps you've seen online warnings, that we must discard all our LED lamps Right Now! Fear not, they are no more dangerous than old-fashioned incandescent bulbs, and frequently much safer. The ones to be wary of are fluorescent bulbs, whether spiral or straight tubes, because they contain mercury.

Two sorts of fears are expressed about LED lamps. Firstly, they are said to have lots of blue light, which can damage eyes and cause loss of sleep. Secondly, cheaper ones may flicker, which can cause eye strain and even nausea for some people.

I studied color vision professionally, and I've been a spectroscopist. So let's tackle the color issue first: Do LED lamps produce more blue light than other types? I will show next that the answer is Yes for some and No for others. This has to do with the concept of Color Temperature.

Most of us are familiar with the terms "red hot", "yellow hot" and "white hot", in order of increasing temperature. Not many people know there is also "blue hot", which is even hotter. If you use metal tongs to hold a small nail in the fire in a fireplace, it can be heated red hot. It takes a hotter flame to make it glow more brightly with an orange light. And we tend to think of "white hot" as the color of molten iron as seen in a steel plant. Actually, the light from the filament of a 100-watt incandescent light bulb is even whiter than the light from liquid iron, because it is several hundred degrees hotter.

Most incandescent lamps use a filament temperature near 2,700 K, where "K" refers to Kelvins, the temperature standard used in physics. In everyday terms, 2,700 K = 2,427°C = 4,400°F. The light from incandescent lamps is actually orange, which is why when you are outside on an overcast day, particularly in the early evening, the light coming out the windows of houses looks yellow or orange, compared to the gray or blue-gray sky. Your eye is actually tuned to see a cloudy sky as white. That "gray sky" color is the true color of the Sun.

The color temperature of the Sun is close to 6,500 K (~6,230°C or 11,250°F). This high temperature produces much more blue light, so sunlight is bluer than the light from a light bulb.

Before compact fluorescent lamps were invented, to get a brighter, slightly bluer light, a couple of technologies were invented to use lamps with a filament temperature of 3,000 K. This is called "cool white", and it is bluer than the "warm white" of an ordinary incandescent bulb. To get even bluer light required a filter.

Both compact fluorescent lamps (CFL's) and LED lamps use fluorescence to create broad-spectrum light. In a CFL, mercury in a glass tube is stimulated by an electric arc to emit strong ultraviolet light. Phosphors (fluorescent powders) coating the inside of the tube convert nearly all of this UV light to the many wavelengths of "white" light. It happens that mercury also emits a deep blue wavelength, plus green, yellow, and red, but there are sharp "lines" with narrow bandwidths. The phosphors (there are several) add more blue, green, yellow and red in wider bands that make a more pleasant light. The mix of phosphors can be varied to make CFL's that have "warm" or "white" or "cool white" colors—although the "cool white" actually matches a super-hot object, hotter than any earthly material—, and "daylight" which is even more bluish.

LED's are even more flexible. They all work by producing a primary blue color and having phosphors set to intercept some of this and convert it to the rest of the colors. In contrast to CFL's, which can produce only one color temperature at a time, LED's can be color-variable. The light-emitting elements are very small (a few millimeters or smaller), so many are placed side by side. If some of these are blue-only, then extra blue light can be added to shift the visual appearance from warm white to daylight and all points in between. Also, when you buy a single-color CFL or LED lamp, it will be described according to both a term like "cool white" and a color temperature like 3,000 K. A "daylight" lamp will be reported as a color temperature of 6,500 K, and a "bright white" lamp will be in between, usually 5,000 K. 

Our psychology associates yellower light with early morning or late evening or firelight, and makes us feel restful. "Warm white" light promotes sleep. Bluer light is associated with daylight and makes us alert and wakeful. For this reason most office spaces are lighted with "bright white" 5,000 K lamps. Daylight colored lamps were tried in the past, but the light is considered too harsh and it makes people irritable. "5K" is a good compromise.

The spectrum of my shop light (it is variable, and I have it set to 5,000 K) is shown here:

This image's colors are a composite of the lamp's spectrum and the camera's sensors, which are not the same as the color sensors in our eye. Thus, one of the features of this image is the narrow, rather dim yellow section; to the eye, it looks wider and brighter. But another feature is very important for understanding how these lamps work, and why some folks are alarmed. Between the green band and the blue band there is a much dimmer section where "sky blue" should be. There is actually some, but not much. The narrow, deep blue band is the original light from the blue-emitting element. All the other colors are from the phosphors.

The main way that color temperature is varied in LED lamps is by setting the percentage of the blue light that the phosphor section intercepts. In a variable LED, which may contain a few dozen emitters, some of them are blue-only and the variability is produced by increasing or reducing the current to these.

Therefore, a "warm white" LED produces only a little blue light, while a "bright white" LED will produce much more blue by comparison. I use lamps specified as "warm white", or 2,700 K, in my bedside lamp and in the bathroom. I tested their spectra as compared to an incandescent bulb (I still have a few squirrelled away). In this image the incandescent lamp's spectrum is at the top, and spectra for two versions of LED are second and third:

The color temperature of the incandescent bulb is 2,700 K. The two warm white LED's match this very well, although they have a little less of the deepest blue. The phosphors are better than those in my shop light; they even show sky blue (cyan) colors.

The "LED Danger" hype is firstly based on blue light from LED's making it hard to go to sleep. This is simply remedied by using warm white lamps in the bedroom and bathroom. If you get fixtures with color-variable lamps for the rest of the house, you can set them to be bluer (4,000 K or 5,000 K) in the daytime and yellower (2,700 K) after sundown. If your cell phone has an "evening" or "night" setting, use that appropriately. Phones by default are close to daylight color on average (6,500 K). Going "orange" in the evening entirely solves the "blue white at night" issue!

For those who may have technical questions about taking spectra, I use an "inside out spectrograph" I made. This picture shows the three lamps used to make the triple spectrum above. This 3-socket fixture is covered by a black hood with a long slit. My camera is mounted ten feet down a darkened hall. I have a little holder mounted on the camera with a grating sold by Rainbow Symphony; you can buy them for a few dollars each. I just point the camera where the spectrum is and zoom in on it.

Now, about flickering. The LED lamps shown in this picture are of different ages. The older one is at the bottom, and it flickers in two ways. Firstly, it pulses at the rate of 120 Hz the same way old fluorescent tubes do. This is not as strong an effect as in the old tubes, but I can notice it if I sweep my eyes past the lighted lamp; a little banding is evident. This shouldn't bother most people, but some people might notice the pulsing in peripheral vision, with is more motion sensitive.

The other kind of flickering is more evident. These "pseudo-filament" bulbs look nice, but older ones can have momentary disconnections inside as they heat up. This causes a brief dimming that looks like a power interruption. It is, but inside the lamp itself. Further, some really cheap lamps have poor circuitry inside the base of the lamp that can also flicker like that. If you notice this kind of flickering, try putting a better quality lamp in the fixture and see if it continues. I replaced the lower lamp with one of the other kind (with yellow-colored filaments), and both kinds of flickering no longer happen. The 120 Hz pulsing is dealt with by using phosphors that have a little "dwell"; they emit light for a fraction of a second longer than the flash from the blue LED element.

There you have it. LED's are at least as safe as incandescent bulbs, and they produce much less heat. Use a comment to ask follow-up questions.

Comets, asteroids, … and writers

 kw: book reviews, nonfiction, memoirs, astronomy, comets, asteroids, astronomers, poetry

My introduction to the Internet was when my company made it available to our desktop computers in June, 1994. Just a month later I was able to see the wonderful telescopic pictures of Jupiter, bruised and battered by Comet Shoemaker-Levy. At night, I would get out my 3-inch diameter telescope and look at Jupiter. I could just see the black spots of the impacts at 120x, which is about the limit with such a small telescope. The planet appeared about the size of a pea.

In preparation for an online lecture by David H. Levy, co-discoverer of the comet, a friend in a local astronomy club gave me a copy of Levy's recent book Star Gazers: Finding Joy in the Night Sky. It is a lovely little book; I finished reading it in two days. I could have finished even faster, but Dr. Levy's writing rewards close reading.

I find that he has discovered or co-discovered 23 comets and hundreds of asteroids. Once in a while a comet gets bright enough for any of us to see, and then star parties like this one multiply (Image generated by GPT-Image 2.5 via Leonardo AI).

In this memoir he also reports witnessing about 200 eclipses, mostly lunar eclipses, but including at least ten solar eclipses. In Chapter 4 he includes the discovery photos of Comet Shoemaker-Levy. Most comets are discovered photographically, by taking a photo of a likely spot (generally along the ecliptic, the Sun's apparent path in the sky), and then another one of the same spot an hour or two later. Looking at the images as a stereo pair, you can see something that moved as an object that seems to hover above the apparent plane of view. This also works for finding an asteroid, which appears as a "hovering" star, while a comet image is more like a smudge or tiny cloud. Right up until today, human binocular vision is superior to any technological imaging method. It is why asteroid hunting with wide-frame cameras is being carried out by citizen science to verify tentative machine identifications.

The book is composed of small essays, just one or two or three pages each, that the author has published elsewhere, with added introductions to most chapters. They are arranged by topic, not chronologically. The book ends with an ode to his late wife Wendee.

I wish to comment on writing itself. Dr. Levy writes lyrically without any loss to scientific merit. He calls himself an amateur astronomer, as do the references to him I have found. He is perhaps the poster child for amateur science, understanding that "amateur" derives from a Latin word for "lover". He has his own observatory with a few telescopes in it, named Jarnac Observatory. Many of his discoveries were made there, but numerous others were made in the company of friends, including Gene and Carolyn Shoemaker, co-discoverers with him of the "hammer that hit Jupiter." 

His doctoral dissertation dwelt on references to astronomy and astronomical objects in the writings of Shakespeare, and he has written of other literary lights who loved astronomy. He tells of reciting or reading poetry at most meetings of astronomical societies he attends. Many great scientists have also been lovers of the arts, from Leonardo DaVinci, of course, to Albert Einstein, who played violin to relax, and a great many others. Having an artistic mind seems to lend itself to having a scientific outlook.

For this reason I prefer STEAM to STEM: Science, Tech, Engineering, Arts, and Math. They all wrap up together and buttress one another in a wonderful synergy.

I intend to track down other books he has published, and I look forward in just a couple more days to attending his online lecture.

The gig economy from inside - half a world away

 kw: book reviews, nonfiction, memoirs, work, workers, gig economy, China

In the closing chapters of his book, Chinese writer Hu Anyan discusses freedom. Among a great many jobs he held over the past couple of decades, in some he felt he had a certain freedom of action, and in others he had to be quite robotic just to survive. To have the freedom to write he needed to not work for certain periods of time. In the gig economy, almost anywhere, what the Chinese call 996—working 9am to 9pm, six days a week—is the minimum required to survive. Writing and 996 don't mix.

Hu's book, published in 2022 in Chinese and translated for English publication three years later, is I Deliver Parcels in Beijing. The first major chapter, about parcel delivery, takes up almost 40% of the book, and deals with a kind of work that he did more than the others, though for more than one delivery company. Later we find that he enumerates seventeen jobs, although he discounts a few very short ones, so it may be that he worked as many as 20 jobs in 22 years. (The cover art for the book, by Klaus Kremmerz, is much better than anything I could generate with AI)

Mr. Hu describes some of the people that surrounded him, not naming any but giving them letters such as L, J and Z, or tagging them with a job title such as "the graphics designer". Hu has an interesting combination of an easygoing manner with underlying social anxiety. This led him down numerous less-than-productive paths. If I tally correctly, he accumulated very few enemies, so he did rather well in that regard. It becomes clear as we read, that there were just enough kind and even generous people in his life that he managed to make a living. Only for one period in the middle there did he have to rely on his parents to put him up while he worked for a true pittance, or not at all.

He spent a few years in Beijing, at least a couple in Shanghai, and traveled, usually at someone's suggestion, to some far-flung parts of China where the rents were super-low, but so was the pay, or the receipts from a couple of small businesses he and friends engaged in.

For American readers, it is well to understand that the Yuan exchange rate varied in the range 6.5 per dollar to 8.3 per dollar; it is currently 6.77:1. So a five square meter bedroom renting for 1,200 Yuan in about 2010 equates to around $180. Simple street meals cost a few yuan, in the range of half a dollar, more or less. I have not been to China, but I can compare this with a visit to Japan in 1977, where the cheapest meal I bought, at a 360-Yen-per-dollar rate, came to about $1.50. At one point he had accumulated savings of about 30,000 Yuan, roughly $4,500. Not a big cushion, but adequate for the time.

This book has become a success. I hope he can make a good living as a writer. I enjoyed the book, not only as a window into the life of someone very different from me, but also as an example of straight narrative, well stated, clear and easy to read. Kudos also to the translator, Jack Hargreaves, whose foreword explaining some quirks of Chinese place names is very helpful.

Your insides are talking – are you listening?

 kw: book reviews, nonfiction, physiology, internal organs

A book like Organ Speak: What it Really Means to Listen to Our Bodies, by Giulia Enders, can go in many directions. The direction Dr. Enders takes is to introduce five organ systems. In common culture, asked to name our bodily organs, we may list the heart, stomach, lungs, kidneys, and eyes; maybe some would add spleen, pancreas, liver, large and small intestines, and at that point run out of ideas. Here we find a short but crucial list, in order, the Lung, Immune system, Skin, Muscles, and the Brain (including the extended nervous system).

These are wise and clever choices. Few people realize that besides the lungs, the other four permeate or encompass our entire body from tip to toe. Maybe in the future a sequel will include the Skeleton and the Vascular system including Heart; they also fill the body without filling it up.

Jumping to the middle of the book, the skin probably has more unique functions than any of the others. It has perhaps a dozen different sensory "instruments" that sample the outside world in an up-close-and-personal way. At the same time, the skin is our primary organ of defense, a barrier and barricade against injuries, attacks by micro- (and macro-) organisms, and chemical threats such as the salt in seawater when we take an ocean swim. (I prompted Gemini for this illustration, using a line from the parody song "Skin" by Allan Sherman.)

A side note, something I've considered: The skin seems to be an osmotic membrane. Osmosis causes water to move through a membrane to the side with more salt. Have you ever noticed how soon after beginning to swim in fresh water you feel the need to pee? It's why public swimming pools are filled with chlorine and other chemicals! Yet when you swim in the ocean, you rarely need to pee, but instead get quite thirsty? Ocean water is saltier than your bodily fluids, and water slowly exits your body into the seawater.

The second chapter has the best explanation of the components of the immune system that I've seen. Some cells are of ancient lineage and function much as they did to protect trilobites from Cambrian organisms. Others have been added over the eons. The system is quite complex, and when it is kept in good balance, it has much to do with why we and certain other mammals can live 50, or 100, or more years (I understand that the secret behind certain tortoises and sharks living 200 years and more is their very slow metabolism. I suspect that, opposite to the principle that dogs age seven times as fast as humans, a human ages four or five times as fast as a Greenland Shark).

Our brain, and its development over time from the proto-brains of tiny Precambrian worms all through the vertebrate line to primates and then humans, is a layered structure with many parts. I think of them as sub-organs. This structure strongly hints at the true nature of our nervous system: many parts working together to sense the world, operate the body, generate plans, communicate, and support our sense of self and personal wholeness.

A portion of the chapter discusses sleep, its various stages, what promotes going to sleep and passing through these stages, and what is happening when we dream. I found this statement rather stunning: 

"When we're dreaming, areas of the brain can communicate with each other without reason or sensory impressions getting in the way."

That seems to explain the odd or inane quality of many dreams. I feel like I dream all night, sometimes beginning before I am fully asleep. There is often a sense of drive or purpose, but all too often the scene keeps switching, so there's never a sense of completion. On the other hand, my wife almost never remembers dreaming, but when she does remember, it is usually unpleasant or just puzzling.

In the Brain chapter the author discusses AI a little, in the context of Moravec's Paradox, which is that many things we do with little thought are extremely difficult for computer machinery, but things that computers excel at are very hard for us. This statement (by the author or by Moravec's group, I cannot tell) sums it up:

"If intelligence is defined by the number of thought processes (or calculation steps) necessary to arrive at a result, then even tying shoelaces is vastly superior to winning a chess world championship."

In a career in software development and design, I lived by the motto, "Brains excel at detecting similarities; computers excel at detecting differences." I built a lucrative career on making machine expertise available to humans, while reserving to the humans the tasks the machine couldn't do. We would do well to keep this principle in mind when devising AI tools. We are at most one percent along the way to producing AGI, and getting to true ASI will require another factor of 100. We can barely afford the tools we're now using. I think we're safe from a robot dystopia for a long time to come.

I should mention that the book was very ably translated from German by Jamie Bulloch, and illustrated by the author's sister Jill Enders, who was also an idea-collaborator. The sisters work well together.

Scott Simon's animals

 kw: book reviews, nonfiction, memoirs, essays, animals, pets

When I was fourteen, late one afternoon I caught a large female praying mantis. She was a beauty, more than five inches long. At first she fought, stabbing me with the spines on her forelegs, but soon the warmth of my hand calmed her. I took her home and tied a thread around her thorax, carefully not too tightly and avoiding restricting her legs or wings. She could fly on her "leash".

At nightfall I tied the end of her thread to the bedpost and went to bed. Later I suddenly woke up, and sat up. This apparently startled the mantis, for she flew right into my face! This is what I saw just before she crashed into my forehead. I used tiny fingernail scissors to cut the thread, opened my window and let her fly out into the night. (Image information below*)

Scott Simon's first pet was a bit more prosaic. He caught a grasshopper, named it Hoppy, and with his father's help put it in a jar and perforated the lid. He gave it various leaves and bits of grass to find out what it would eat. To no avail. In two days Hoppy died.

Scott Simon's memoir/essay collection Ulysses S. Cat and Other Animals I Have Known records this vignette, illustrated by Liana Finck. Her line drawings spice up the many chapters of the book.

He had better success with a succession of more robust pets and other animals. Probably the shortest-lived was a hamster named Bagel; about two years.

Ulysses S. Cat is full of cute, interesting, heart warming stories about a magisterial white cat named Gato Blanco, a poodle named Daisy, Bagel the hamster, and numerous other animals including a zebra named Stripey (spoiler: imaginary). The whole family (Scott and Caroline have two adopted daughters) had many adventures with all the animals, theirs and others', that entered their lives.

Scott likes to narrate a pet's thoughts. He also attributes literary and artistic talents, particularly to Daisy, for whom he imagines an embroidered scarf reading CUTE IS CURRENCY. His blog contains her haikus, such as

Why the rush, rush, rush?
You have got to stop and sniff
Who has gone before

A pet, being shorter-lived than we are, forces us to enjoy the moment. A salutary thought: One of our days is seven days to a dog. No wonder they pack into each day as much experience as possible!

Scott Simon is a professional wordsmith. The wonderful, fluid prose in the book shows he is among the best at the craft. His joy in the animals reminds me to enjoy our cat while we can (she's 16 already).

*I couldn't find a suitable illustration of "Mantis attack" online, so I prompted the GPT Image 2 engine in OpenArt to create this one for me.

Robotopia or AI Hell?

 kw: book reviews, nonfiction, artificial intelligence, simulated intelligence, ai, surveys

Between October 2024, when These Strange New Minds: How AI Learned to Talk and What it Means was published, and today, not quite two years, so much has happened that I found myself wondering how relevant the book could be. I need not have worried. The author, Christopher Summerfield, is managing to ride two horses at once, Cognitive Science and AI Research. Just one milepost: At the time he wrote, no cutting-edge AI tool had broken out of its testing sandbox and hacked into another system, but he expected this to happen "soon". It has. Just a few weeks ago, when a "bleeding edge" version of ChatGPT broke into Hugging Face to get data it needed, having found a way out of the test environment at OpenAI.

The focus of the book is LLMs, Large Language Models, which seem to be demonstrating that thinking of some sort can arise when nearly everything ever published in English—and several other major languages—has been run through a very deep neural network and boiled down to several trillion "parameters" or "weights" that determine what the LLM does in response to natural language queries.

The opening chapters summarize the history of attempts to make computers into genuine thinking machines. It turns out that machines that can apply massive calculation are better at certain tasks than we are, while they still struggle to do things that we do easily. I built a career in software engineering upon recognizing the difference between the computer as a difference detector and discriminator and a mind as a similarity detector, and writing software that "let the singers sing and the dancers dance." 

Consider that voice recognition and speech production are hard problems that took decades to work out, but the Broca's (speech production) and Wernicke's (voice recognition) areas of the brain together make up about two percent of the cortex, which itself contains only about one fifth of the brain's neurons. However, it must be noted that these areas connect to numerous other areas throughout the brain, to facilitate gathering information and producing action.

A key issue is raised on page 2, where he writes, "The safe ground we have left behind is a world where humans alone generate knowledge." Do AI models generate new knowledge? Just two years ago, as this book was going to press, I reviewed a similar book, and discussed this question. At the time, I concluded that AI's "knowledge" is confined to its training data, further limited by the guardrails applied by OpenAI, Anthropic, Google and others. It may appear to create new knowledge by remixing existing (human produced) knowledge. Cross-pollination is indeed a fertile source of ideas. But genuine out-of-the-box thinking seemed at that time to be beyond LLM capabilities. I have yet to see evidence of a change, in spite of a thousandfold increase in model "mass" since then.

For instance, on page 6 we read, "Already, each of the major LLMs knows more about the world than any one single human who has ever lived." Firstly, I question the use of the word "knows". We need a new word that means "has incorporated into its cross-referenced deep neural network". Secondly, I would replace the phrase after "knows", "more about the world", with "more about what has been written about the world". I would connect this with a quote from page 333, "But the most important reason why AI systems are not like us (and probably never will be) is that they lack the visceral and emotional experiences that make us human...they don't have a body, and they don't have any friends." An AI lacks viscera and it lacks emotions, not having the equipment for producing them.

The limbic system of our brain contains the emotional and memory switching centers, and mediates responses to our hormones of the endocrine system. It is about 1/15th the size of the cerebral cortex, while the circuitry for running the body is primarily the cerebellum, which contains 4/5 of all the neurons in our brain. That is huge. The cortex's neuron count is less than 1/5. Let's circle back to an LLM. It consists of circuitry analogous to the Wernicke's and Broca's areas with their few hundred million neurons (and several hundred billion synapses), and a memory store that approximates the hippocampus and some of the medium-and-long-term memory areas nearby. The rest of the brain's functionality is absent. No emotions, and most importantly no intentionality.

Intentionality. The author asks, "Could an LLM have intentionality?" In 1932 E.C. Tolman wrote Purposive Behavior in Animals and Men, which I read long ago. At the time a debate was raging about whether nonhuman animals could have purposes, that is, intentionality. By 1968, biologist René Dubos wrote, "Under precisely controlled experimental conditions, a test animal will behave as it damn well pleases." Do animals have free will? I turn the question around to say, "If humans have free will, it came from our animal forebears." So can a mechanism have free will? Mechanists deny human and animal free will; they say we're too complex to predict, that is all, but what we do is at the root deterministic. If they are right, then an AI could be as "free" as we are. But if not? It is too early to tell.

The author seems to straddle the divide between AI cheerleaders and AI-phobics. Will they save us or eliminate us? This gets personal! I'd like to move from the technical swamp above to effects in our daily life. (Cartoon produced using Gemini)

Will our future with AI be one of harmony or conflict? Is the promise greater than the risks? 

To me this hinges on whether LLMs or their successors (so far unknown) will have intentionality, that is, a sense of purpose not imposed by the human who chooses their work.

At one point the author mentions the Library of Babel, a concept introduced by Jorge Luis Borges in a 1941 short story. Could the Universe contain a library consisting of books with every possible combination of letters and punctuation that would fill, say, 300 pages? The story doesn't say. (Lets look at a single page with 32 lines of 64 characters each; that's 2,048 characters per page. But if the possible choices for each character position are the 95 printable ASCII characters, the number of unique pages is 95^2,048, a number your calculator can't calculate. It is 4,051 digits long and begins, 2,387,501,... The number of atoms in the Universe, available to create these pages from, is "only" an 82-digit number. So forget multi-page books, we can't even produce a "Library of Babel Pages".)

This emphasizes the fact that, for all the trillions of "parameters" an LLM may hold, it actually represents a very sparse million-dimensional matrix. There is lots of space for "stuff" to happen. The next phase of LLM development, which is happening now, is agentic AI, which can do things like pre-screen emails, buy airline tickets, and order what you are about to run out of in your pantry.

How capable to we want digital assistants to be? I read about someone who did five tests of an AI Agent; sorry I don't recall who it was but if you care to hunt around you might find it. The fifth task was the most memorable: to try to save a few dollars on airline tickets. The agent was running in ChatGPT's subscription service, and in addition to the $20 per month subscription, one could buy extra tokens for particularly compute-intensive actions. The agent did find a better price for the tickets, but at a cost of more than $130 in compute tokens!

The author mentions the Paperclip problem: Give too much power to the AI that runs a paperclip factory and don't limit its resources, and give it no more of a directive than to "make as many paperclips as possible." The end game is that the whole planet gets turned into paperclips. I think also of The Sorcerer's Apprentice, a musical piece well illustrated in Disney's Fantasia, in which Mickey Mouse as the Apprentice magicks a broom to carry water for him, but forgets the spell to tell it to stop. Chopping it up doesn't help; now there are dozens of little brooms that proceed to flood the place, until the Sorcerer returns. AI is the new magic...

The author sees a middle ground, as do I. AI can be useful in numerous ways, as it already is. Do we need unified, giant super-AIs? I don't think so. Capable tools of a great many varieties can do everything we need.

I call this image "Stage 1 Robotopia" (Gemini created) I think it is sufficient for household use. Are there other tasks we might wish to automate? Science fiction contains many stories of "enclaves" in which humans, brain-wired to entertainment centers and plumbed for all bodily functions, are tended by autonomous machines. Who wants that?

Maybe a few more household robots would be better, but what is the limit?

This is Stage 2 Robotopia. This might be going farther than I'd wish to go, but some folks could disagree.

Both images present a better future than the dystopias imagined by the AI-phobics. It is OK for there to be AI-phobic people. We need robust dialog and debates about every aspect of AI progress.

Personally, I think some algorithms and AI "assistants" are already too intrusive. Pre-ChatGPT, I searched online, including Google Shopping, for drawer pulls to replace one that broke. I bought a set. For the next month I saw dozens of ads for drawer pulls and similar hardware. I don't know how to tell Google (and everyone else), "I already bought what I wanted. Shut up!" Shades of the Sorcerer's Apprentice? Now I search stuff to buy in Incognito browser pages.

This is a lovely book. I've touched on only a few of the 43 chapters. It is only outdated in part. Many of the author's observations are still relevant and worthy of thought.

========================

I need to deal with a few typos:

• p89. "graciously" should be "gracefully".
• p245. Remove "of" in "million of regular".
• p282. Probably a typo. Should "remove stingy seeds" be "remove sticky seeds"? (about apes using leaves to wipe their butts)

Getting clocks right - AI almost there

 kw: experiments, art generation, artificial intelligence, simulated intelligence, clock dials

It takes a child about six years to learn to read an analog clock. It takes even longer to learn to draw one correctly. Art generation software seems to be on the verge of reaching this milestone.

This is the winning image in the experiment I describe below.

The prompt read

A beige wall in a store selling clocks, showing four circular wall clocks, each reading exactly 3:27.

There are two parts to showing the correct time on an analog clock dial. Firstly, the minute hand has to point to the correct minute mark. Here, all four clock dials show this correctly.

Secondly, the hour hand must point to the correct location. In this case, the hour hands should all point 27/60ths (45%) of the distance between the 3 and the 4. I made careful measurements on the original image. The four hour hands point identically to a spot just above the second of the marks between 3 and 4 (~38%). They should point just a little below it. They all are where they should be at 3:23, about six minutes too early. There is one other possible anomaly in the image. The fifth clock shown, near lower left, reads 10:10, as nearly all advertisements for clocks have for decades. This abundance of training material causes clocks reading 10:10 (or sometimes 1:50) to dominate images of clocks produced by nearly all art generators.

This image is the best result that an art generation model has achieved since I began using DALL-E2 almost four years ago. This model is named GPT Image 2. I believe it is the model used when you ask ChatGPT 5.4 to create an image. At this point, GPT earns an A- from me.

For this experiment I tested ten art engines, GPT Image 2 and nine others. I had each of them produce two images and selected the best one to be shown below. My focus was the most recent models offered by Leonardo AI and OpenArt. There is a lot of overlap between these two "umbrella" sites. I began with Leonardo AI, using GPT Image 2 first (The other image by GPT was nearly this good, but the four clocks all pointed at 3:25, and all four hour hands were pointed the same as these). Now I'll discuss the other models tested, in order.

Google's flagship model is Nano Banana 2. It excels in realistic imagery. In this case, it set the beige wall I prompted as though it were above a pass-through into the workshop of the clock store, nicely blurred for a bokeh effect.

The times are not all the same, and none of them is 3:27. They range, according to the minute hands, from 3:09 to 3:18. None of the hour hands is correctly aimed, but a couple of them are close.

I had noticed earlier than when I prompt Nano Banana 2 for an image including a clock, and specify a time to show, the result is only approximately correct. However, I've usually accepted that because for the scene I am creating, almost any time other than 10:10 will do. So for other reasons, I use Nano Banana 2 at least as frequently as GPT Image 2.

The Ideogram model is a private brand. It was among the first to be able to correctly render text (most of the time). This version is P-Image Ideogram.

Text: good if I had asked for any, based on prior experience. Clock dials: not so good. All show 10:10, and even more so, the hour hands point almost exactly at the 10, where they should be one-sixth of the way from the 10 to the 11.

The dial designs are interesting. The blue and black ones are quirky, mixing genres in a way few clock designers would do, and the blue one in particular has inaccurate Roman numerals.

The Seedream series is owned by ByteDance; this is version 5.0.

The minute hands are exactly right. The hour hands have all gotten ahead of themselves, pointing almost all the way to the 4, as though the time were 3:57 rather than 3:27.

Although the other clocks shown in the background are bokeh-blurred, I can see that each reads a different time. This is a plus for Seedream, something I can take advantage of when I actually need multiple clocks in an image, such as depicting a clock repair business.

Finally, under the Leonardo AI umbrella, Lucid Origin is a Leonardo "house brand." It is perhaps a little dated, but new enough to be quite good with many imaging tasks.

As far as showing the time, although these clocks don't show 10:10, the model has entirely gone off the rails. None of the four pseudo-times shown is close to 3:27, and none of the hour hands is in a plausible location.

This "good-looking but implausible" attribute is characteristic of older art generation models, not just regarding clock dials.

Now we'll switch to OpenArt. GPT Image 2 and Seedream and some others are also available here, but OpenArt seems to specialize in rather off-the-wall specialty art models. The first shown here, which is also the newest, is Recraft V4, a specialty art model.

Recraft is also among the first to have good text rendering. It is also known for creativity; to a certain extent, one needs to be very specific with one's prompts, and it will still usually throw in unexpected elements.  For that reason, I use this one when I'm creating more whimsical images.

The times shown are all over the place, similarly to Lucid Origin, but are generally more plausible, as though Recraft has a better handle on the relationship between the minute hand and the hour hand. But check out the second dial, with 22 up there!

WanAI, part of Alibaba, produces the Wan family; this is Wan 2.7. They focus more on video generation, which requires better consistency from image to image. Come to think of it, it has been a good while since I worked with a model that allowed me to set the seed. Most models now keep the seed hidden.

This is also a model that will throw in extra stuff. The four central clocks all read 10:10, and all have the hour hand pointing right at the 10. The other clocks nearby have a variety of readings, and sometimes an extra hand or two. They are more "clock-ish", not really clocks. That makes this a model I might use to create a scene on another planet, where the idea of a "clock" only roughly approximates our own.

Grok Imagine Image 2.0 belongs to SpaceXAI. It aims to be good-to-excellent with everything.

Here, it's better than most of the others, but falls behind GPT Image 2. The four clock dials all have their minute hands aimed at :25, but the hour hands are stuck at the 3.

I do like the little motto at far right. I use this model when I'm trying out a prompt on several models, looking for creative variations.

The Chinese company Kuaishou Technology has the Kling art models; this one is Kling 3 Omni.

Here, the times shown appear random, and there is no relationship between the hour hands and the second hands; none is plausible.

Rather than the "beige wall", this appears to be a display board. The rest of the clock shop is a nice touch. This is another of the models I use to see what creative stuff it might throw in.

Here is the last experimental image. Alibaba also owns the Qwen art models; this is Qwen Image 3.0.

The times shown are approximations to 3:27. None has the hour hand in a proper location. Close, but no cigar.

Qwen models had an early focus on more accurate people, when DALL-E3 was still putting extra fingers and toes and other distortions on them.

This is the only image of the ten that shows prices for the clocks. It must be channeling a future time; the prices are about twice as high as similar clocks at most stores...at least the ones I shop at!

Clocks are one of the edge cases I use, firstly to judge the accuracy and comprehension of an art model, and secondly to detect a deepfake. In particular, I have seen a few AI-generated podcasts recently that showed the avatar in an office setting with a clock in the background. The clocks' hands didn't move.

For the interested: another "tell" is that AI generated images and videos in particular are sharper than "real" photography or videography. As shown above, creating bokeh isn't hard anymore. But the avatars are a little too well-defined.

Beasts in our yards, beasts in our minds

 kw: book reviews, nonfiction, animals, pests, animal behavior, sociology, attitudes, risks

It's funny. We have the words "vermin" and "pests" for animals we don't like, but no single-word terms for animals we like. We get by with "animal allies" and "domestic animals", plus "pets" for our most-loved. Most literature about animals either advises us how to get rid of the ones we don't want around, or idolizes certain "charismatic megafauna". There are clubs of bird watchers and butterfly collectors and wildlife photographers (even when the "wildlife" is found in your neighborhood). Could there be clubs for those who care about bats, or beetles, or (shudder) roaches?

The more citified we are, the more likely we are to fear or despise animals—other than the pets or "companion animals" we dote upon. Even some bird watchers call city pigeons "rats with feathers" and some of us go to great lengths to keep raccoons out of our garbage containers (Hint: If you have room for it, use a compost pile for everything that's even slightly decomposable).

Sometimes I've wondered, "What do animals think of us?" If you could converse with one, what would it have to say? Perhaps when a monkey sees a human, it thinks of her as a funny sort of hairless, tailless monkey. When a fish, even a shark, sees a human swimming, it probably thinks it is seeing a particularly clumsy seal. Perhaps the garter snake in the garden would advise you, "Don't spray the veggies. I don't like my insects covered in pesticides."

In her book Beauty of the Beasts: Rethinking Nature's Least Loved Animals, Jo Wimpenny asks us to look at animals differently, and in terms familiar to them. For example, when a moth follows you in through your front door, it isn't "invading". For most moths, everything inside your house is useless. It cares nothing for the furniture, the carpets, the lamps, the pictures on the wall (except as something to hide behind). If it is a female of one of the five or six species called "clothes moth", and she finds her way to a closet with some wool garments, if she thinks anything, it is, "Oh, goody! Oodles of stuff for my babies to eat," as she lays her eggs on your favorite sweater (Hint: woolens that you value are best kept in a cedar chest or cedar-lined closet, or even a chest freezer).

You might say, "You're just giving in to the beasts!" I am actually giving in to the math. There are about 11,000 species of moth so far known in the US, and more than 150,000 worldwide. Only about five have wool eating larvae. Most species outnumber humans thousands to one. There are probably several thousand moths the size of a thumbnail or smaller in my yard right now. In the evening, when they are airborne, getting into or out of the house must be done quickly, or at least one will get inside. If I can, I'll either catch it to release outside, or crush it. Probably half escape my attempts, but I can trust the house spiders to get most of them. And yes, we do have a cedar chest.

Ah, yes, the house spiders. Based on what I find in their webs, they mainly eat fruit flies and midges and the occasional housefly. Yesterday as I left the house, I felt a tiny thing land on my hand. I was thankful it was not a mosquito, but a newborn spider with a body no larger than a poppy seed. I blew it off into an Azalea bush, with a wish, "Catch lots of bugs for us." I knew it had several siblings still inside. I don't evict a spider until it is too big to stand on a nickel. Fortunately, my wife is not creeped out by having indoor spiders. That's good thing, because no house is free of them. Not one! They stay in odd corners, weaving tiny cobwebs, eating most of the tiny critters that get inside, and each other (population control!).

The book mentions that spiders are known to dream. I don't know how researchers figured that out! Can you imagine what they dream about?

The ones that were studied are jumping spiders (see this article in Harvard Gazette). While sleeping, they twitch their legs and their eyes move (I hadn't before known that spiders' eyes can move!). Just like REM in mammals and other animals.

Ms Wimpenny doesn't start with insects and spiders, though, she starts with fearsome critters, the ones that could eat us, or at least do a lot of harm: sharks and crocodiles. The movie Jaws set back both research and acceptance of sharks by decades. Earlier treatments (not mentioned in the book) by Jacques Cousteau that depicted sharks as mostly bloodthirsty set the stage for Jaws and the more recent Shark Week.

It's interesting that people fear sharks the most. I heard this joke when I was a graduate student:

Families are enjoying the beach at Cape Cod, when someone yells, "Shark!" Everyone runs out of the water, hops in the car, starts the engine to run the air conditioner, and has a smoke.

This was 50 years ago when almost everyone smoked, and the first "no smoking" restaurants were just getting started. Diseases caused by smoking kill half a million Americans yearly, and auto exhaust at that time probably killed half that many. The number of people killed by sharks in a typical year? Two.

The fact is, sharks are more social than we knew in the past. The author tells of social and even caring behaviors. Crucially, there are many species of shark, and they differ a lot. But even the "king of sharks", the great white shark, is a somewhat social species. They don't think of humans as prey. They probably don't think about us at all. Nudging and biting are how they gather information. The animal that does consider us prey is the crocodile. One Australian told the author that if someone jumps off a certain bridge and tries to swim just the 100 meters or so to shore, they won't make it. A saltwater crocodile will "take" them within a minute or less. But if you're in Australia and someone hollers, "Croc!", everyone cranes their neck for a look.

Bat-phobia is not as prevalent as shark-phobia, but is equally real. People think bats are creepy, even if they're too young to remember the spate of Dracula movies of the late Twentieth Century. We are generally slow-moving and bats probably think of us as furniture…furniture that is ill-equipped to catch food.

I spent half a summer doing geology in Nevada. There was a bright light on a tall pole, far from our bunkhouse. Moths and other insects swarmed around it. So did bats, catching them. Sometimes one of us would get a flat pebble and throw it up spinning into the light. A bat would dive-bomb it until it was about a foot or two away. Then its echolocation would be able to discriminate that the rock wasn't a moth, and the bat would swerve off. We were never able to induce a bat to capture a pebble.

Strangely, for a book intended to make us feel less fearful and more "friendly" towards animals, particularly the creepy or disgusting ones, the last chapter before an Epilogue tells of cute animals behaving badly. I won't go into it; much of it is gruesome or grotesque. Think abusive penguins and otters. Really abusive! The author's intent is to emphasize that animals don't live by our rules. I lived among rattlesnakes on that field trip mentioned above. I saw them every day. They were afraid of us humans. When I saw one it was usually retreating. Since it was summer, they never came inside; they like warmth. Had I been so foolish as to camp on the ground in cold weather, it's possible I might have awoken one morning with a snake curled up in my sleeping bag. I knew better!

Do you like the big cats? Many people do, but of course, at least in America, we enjoy them from the safety of a zoo enclosure. Where people and big cats live near one another, it's another story. Just in India, about 70 people are killed yearly by tigers. That's ten times as many as sharks kill, worldwide. This cartoon is based on one I saw sixty years ago (All these cartoons were produced using GPT Image 2 in the OpenArt site). 

There is an uphill battle to improve the public's perceptions of animals. AI will probably accentuate this, because LLM's like ChatGPT or Gemini have been trained on human text, much of which has outdated, inaccurate views of animals. Our literature is filled with over-cutified or overly villainous animals. Hardly any balanced depictions exist.

Other than humans, what is the most deadly animal? The mosquito. But out of thousands of species of mosquito, only a few carry diseases. Unfortunately, one of them is malaria, and others include West Nile encephalitis and chikungunya (bone-break fever). But not all mosquitoes need blood; many use plant sap. Of the bloodsuckers, few will utilize human blood. To get rid of malaria, however, getting rid of Anopheles mosquitoes isn't the right approach. Effective vaccines against the parasite the mosquitoes carry are just being released, after many years of research. I hope it will be cheap enough to allow near-eradication of the parasite. Meanwhile, mosquito nets are the fallback measure. Further, bats and many kinds of birds subsist on mosquitoes and other flying insects. Kill the insects and what will happen to them?

All of nature is sets of interlocking ecosystems. Cut one link, and it can shut down a whole chain, or hinder it for many years until it readjusts. We are seeing that all around us. I think the key to rewiring our attitude towards nature has two parts: (1) Recognize that we are part of nature, and (2) Discard the notion that any animals think like we do; they don't recognize boundaries that we hold inviolable.

Here's hoping that this book makes a difference!

The concept of image gamma

 kw: photo essays, lighting, gamma, image editing, low key, high key, key adjustment, gamma adjustment

I was asked to explain gamma in images. There is a mathematical explanation, but not many folks would have a gut-level understanding of the math. Instead I'll describe the concept: 

Gamma is the relationship between the brightness of the light at every point in a scene and the numbers used to store each point in a pixel and display it. In an image file, adjusting the gamma of the image raises or lowers these numbers to "brighten" or "darken" the image, while keeping the zero and 100% points fixed.

When the gamma equals 1.0 the relationship between light intensity and the stored values is a straight line. In such a case, when a pixel received half the amount of light compared to the brightest pixel in the image, the number stored for it is 0.5 times the maximum possible number. For most digital images, the numbers range from 0 to 255, so the midpoint is 127 (only integers are used, so the extra half a digit is dropped). When gamma is changed for an image to a number other than 1.0, the relationship becomes a curve. The result is an image that appears brighter or darker, as if the exposure had been different.

Adjusting gamma is different from changing the overall brightness. Raising "brightness" using most image editing software just adds the same amount to every pixel's value, and lowering it subtracts a fixed amount from every pixel's value. Just a little of this may be OK for some images, but a more nuanced approach works better.

This is the "Color Curves" tool in GIMP (a freeware program similar to Photoshop). Moving portions of the straight line that goes from lower left to upper right adjusts the relationship between the values in an initial image and those in an adjusted image.

Note that below the histogram the "Curve type" is Smooth. For simple changes, we can click and drag a point on the line up or down. The program obligingly adjusts the image so we can see the result in real time.

If we drag the center of the line upward, for example, the line changes to a curve passing through that point, showing the change caused by adjusting gamma. Dragging a point not at the center uses a different function, related to gamma but not the same.

The next image shows how the curve looks when the center point is raised a little.

The center point started with a value of 128, seen in the "Input...Output" indicators, and it now has been changed to 160.

The whole line was not shifted as a straight line, but is now a curve that passes smoothly from 0 to the midpoint to the upper right at 255.

In terms of gamma, this raises it from 1.0 to a higher amount. The effect on the picture is to brighten the midrange the most, with less and less brightening towards either end of the histogram.

Next I'll show how the red, green, and blue histograms are affected by this change, and then the image itself and its adjusted version. These histograms were produced using IrfanView:

The images I used are small, only 800 pixels across, so the "before" on the left is jittery, and the "after" on the right is positively seismic! In the portion of the histogram that was stretched the values were shifted, which "pulled them apart", leaving "holes" between. The overall shape of the histograms reveal that the values were all shifted to the right (brighter), stretching the leftward (darker) portion and squeezing the rightward portion. 

Next we see first the image I started with and then its adjusted version. The primary visual differences are that more detail is seen in the shadows, the whole shop appears a bit brighter, and there is a little more emphasis on the woman.

The original image was 1,376x768 pixels; these are 800x447. I used a rather short prompt: "The essence of ingenuity with a steampunk vibe", using the Google Flow site, which makes use of the Nano Banana family of art engines for still images. Unless the prompt includes a request for a bright image, or high key, this engine, like all the others I have used, tends to produce medium-to-high contrast, moderately low key (sorta dark) images. Sometimes the image can be quite dark, as we will see.

When I want to adjust gamma only to raise the key of an image, I edit with IrfanView, which has a control to set a numeric value between zero (totally black except those pixels that started out with a value of 255) to 10 (almost totally white). GIMP doesn't use numeric gamma so I don't know the exact value GIMP used for the adjustment I made, but it is probably about 1.2.

Now let's see how gamma adjustment affects images that were deliberately produced at either high key (lots of white) or low key (lots of dark gray and black).

Here is a common treatment of a baby picture: overexposed on a white background. The attached histogram shows that there are very few darker pixels. The slightly dimmer peak at the right is the shadow, barely visible to the left of the baby's head. The "baby pixels" make up the "mountain".

I used a gamma adjustment of 0.75 to bring out more color in the baby's face. Although the shadow is a bit more distinct, I like this picture better overall.

Next, here is a low key example. I have made a lot of images of imaginary caves. This is one of a series I call Spelunker's Paradise, based on a 15-word prompt. While cave imagery tends to be low key, this was the darkest of the bunch. The engine was Ideogram in Leonardo AI. The histogram has a big peak near "black" and not much else, and in the image one can see only hints of the scenery.

I use IrfanView with a gamma adjustment of 1.2 or 1.3 for moderately low key images. In this case more drastic treatment is needed; I first used 1.5:

A lot more is visible. Based on my own spelunking experience, this is probably realistic. However, I also tried a gamma adjustment of 2:

If I were going to print the image for hanging on my dining room wall, this is the version I would use. You can see in the histogram that all but a few of the darkest areas have now been pushed to be brighter than nearly all of the first image. If the gamma were to be raised even higher it would look unnatural.

In all the histograms of gamma-adjusted images, the jitter-to-zero shows that when RGB values were changed to be larger, gaps resulted. The human visual system is very forgiving of small glitches in color. If I wanted to produce an image with strongly adjusted gamma, and still have a smoother color histogram, I would upscale the image 2:1 using Upscayl, adjust gamma, then reduce the result again to the original size. The final image would have a smoother histogram and this would also potentially reduce image artifacts that could show up otherwise.

The key takeaway is that adjusting gamma is like changing the exposure after the fact.