Maitri

Emily Tamkin in New Statesman:

This is not the usual bland political ­memoir offering yet another story of finding the American dream. In part, this is because Ilhan Omar is not another dull politician. That much is obvious from the waves she has made in Washington, DC since becoming a member of Congress in 2019. Omar, from Minnesota, and Rashida Tlaib, from Michigan, are the first two ­Muslim women elected to Congress. Along with two other women of colour, who are also in their first term, Alexandria ­Ocasio-Cortez and Ayanna Pressley, they are known as “the squad”. The 181-year-old ban on wearing ­religious head-wear in the House chamber was changed in 2019, allowing Omar to wear her hijab on the floor of Congress. While ­running for Congress, Omar admits, she was worried that this rule would keep her from being able to serve.

Omar quickly gained attention for her progressive stances, aggressive questioning of foreign policy hawks, and contentious statements on Israel (she apologised for one of these comments, a 2019 tweet that suggested Republican support for Israel was “all about the Benjamins, baby” – ie, financially motivated). And she has been relentlessly attacked from all sides, from the far right to the centre left. Donald Trump ranted about Omar in a rally in 2019, prompting the crowd to start chanting, “Send her back.” Omar was born in Mogadishu, Somalia, in 1982. She was the baby of the family; “a particularly tiny child” and a tomboy. She lost her mother at a very young age, but even so her family lived what sounds like a happy, chaotic but colourful life – until war came and they left for the US. Omar’s book gives an insight into one of the likely future leaders of Democratic politics. Some of the details are unexpected: I didn’t think the person, living or dead, she would choose to meet would be Margaret Thatcher: “Time and time again,” Omar writes, “she showed up in rooms filled with men and didn’t have to do much to lead them to decide that she should be in charge.”

More here.

When Deep Blue defeated world chess champion Garry Kasparov in 1997, it may have seemed artificial intelligence had finally arrived. A computer had just taken down one of the top chess players of all time. But it wasn’t to be.

Though Deep Blue was meticulously programmed top-to-bottom to play chess, the approach was too labor-intensive, too dependent on clear rules and bounded possibilities to succeed at more complex games, let alone in the real world. The next revolution would take a decade and a half, when vastly more computing power and data revived machine learning, an old idea in artificial intelligence just waiting for the world to catch up.

Today, machine learning dominates, mostly by way of a family of algorithms called deep learning, while symbolic AI, the dominant approach in Deep Blue’s day, has faded into the background.

Key to deep learning’s success is the fact the algorithms basically write themselves. Given some high-level programming and a dataset, they learn from experience. No engineer anticipates every possibility in code. The algorithms just figure it.

Now, Alphabet’s DeepMind is taking this automation further by developing deep learning algorithms that can handle programming tasks which have been, to date, the sole domain of the world’s top computer scientists (and take them years to write).

In a paper recently published on the pre-print server arXiv, a database for research papers that haven’t been peer reviewed yet, the DeepMind team described a new deep reinforcement learning algorithm that was able to discover its own value function—a critical programming rule in deep reinforcement learning—from scratch.

Surprisingly, the algorithm was also effective beyond the simple environments it trained in, going on to play Atari games—a different, more complicated task—at a level that was, at times, competitive with human-designed algorithms and achieving superhuman levels of play in 14 games.

DeepMind says the approach could accelerate the development of reinforcement learning algorithms and even lead to a shift in focus, where instead of spending years writing the algorithms themselves, researchers work to perfect the environments in which they train.

Pavlov’s Digital Dog

First, a little background.

Three main deep learning approaches are supervised, unsupervised, and reinforcement learning.

The first two consume huge amounts of data (like images or articles), look for patterns in the data, and use those patterns to inform actions (like identifying an image of a cat). To us, this is a pretty alien way to learn about the world. Not only would it be mind-numbingly dull to review millions of cat images, it’d take us years or more to do what these programs do in hours or days. And of course, we can learn what a cat looks like from just a few examples. So why bother?

While supervised and unsupervised deep learning emphasize the machine in machine learning, reinforcement learning is a bit more biological. It actually is the way we learn. Confronted with several possible actions, we predict which will be most rewarding based on experience—weighing the pleasure of eating a chocolate chip cookie against avoiding a cavity and trip to the dentist.

In deep reinforcement learning, algorithms go through a similar process as they take action. In the Atari game Breakout, for instance, a player guides a paddle to bounce a ball at a ceiling of bricks, trying to break as many as possible. When playing Breakout, should an algorithm move the paddle left or right? To decide, it runs a projection—this is the value function—of which direction will maximize the total points, or rewards, it can earn.

Move by move, game by game, an algorithm combines experience and value function to learn which actions bring greater rewards and improves its play, until eventually, it becomes an uncanny Breakout player.

Learning to Learn (Very Meta)

So, a key to deep reinforcement learning is developing a good value function. And that’s difficult. According to the DeepMind team, it takes years of manual research to write the rules guiding algorithmic actions—which is why automating the process is so alluring. Their new Learned Policy Gradient (LPG) algorithm makes solid progress in that direction.

LPG trained in a number of toy environments. Most of these were “gridworlds”—literally two-dimensional grids with objects in some squares. The AI moves square to square and earns points or punishments as it encounters objects. The grids vary in size, and the distribution of objects is either set or random. The training environments offer opportunities to learn fundamental lessons for reinforcement learning algorithms.

Only in LPG’s case, it had no value function to guide that learning.

Instead, LPG has what DeepMind calls a “meta-learner.” You might think of this as an algorithm within an algorithm that, by interacting with its environment, discovers both “what to predict,” thereby forming its version of a value function, and “how to learn from it,” applying its newly discovered value function to each decision it makes in the future.

LPG builds on prior work in the area.

Recently, researchers at the Dalle Molle Institute for Artificial Intelligence Research (IDSIA) showed their MetaGenRL algorithm used meta-learning to learn an algorithm that generalizes beyond its training environments. DeepMind says LPG takes this a step further by discovering its own value function from scratch and generalizing to more complex environments.

The latter is particularly impressive because Atari games are so different from the simple worlds LPG trained in—that is, it had never seen anything like an Atari game.

Time to Hand Over the Reins? Not Just Yet

LPG is still behind advanced human-designed algorithms, the researchers said. But it outperformed a human-designed benchmark in training and even some Atari games, which suggests it isn’t strictly worse, just that it specializes in some environments.

This is where there’s room for improvement and more research.

The more environments LPG saw, the more it could successfully generalize. Intriguingly, the researchers speculate that with enough well-designed training environments, the approach might yield a general-purpose reinforcement learning algorithm.

At the least, though, they say further automation of algorithm discovery—that is, algorithms learning to learn—will accelerate the field. In the near term, it can help researchers more quickly develop hand-designed algorithms. Further out, as self-discovered algorithms like LPG improve, engineers may shift from manually developing the algorithms themselves to building the environments where they learn.

Deep learning long ago left Deep Blue in the dust at games. Perhaps algorithms learning to learn will be a winning strategy in the real world too.

Update (6/27/20): Clarified description of preceding meta-learning research to include  prior generalization of meta-learning in RL algorithms (MetaGenRL).

Image credit: Mike Szczepanski / Unsplash

Microsoft will stop relying on Amazon to help it run the popular Minecraft video game. CNBC reports: The shift represents an obvious way for Microsoft to cut back on payments to one of its toughest competitors and promote its own product. Amazon Web Services rules the market for public cloud infrastructure for running software from afar through vast data centers, and Microsoft has been working to take share with its Azure cloud. Azure is growing faster than many other parts of Microsoft, helping it lean less on longstanding properties like Windows and Office. Moving more of its own software to Azure can help Microsoft make the case to customers that it doesn't look anywhere else for computing, storage and networking resources to deliver its online services. That's an important consideration, because Amazon can tell customers that its sprawling e-commerce business consumes resources from AWS. The use of AWS for Minecraft for a version called Realms -- virtual places for small groups to gather and play the open-world game together -- dates to 2014. Months after AWS published a blog post about how Mojang, the game developer behind Minecraft, had chosen to tap AWS for Realms, Microsoft announced that it would acquire Mojang for $2.5 billion. It would not have been right to make Mojang get off AWS immediately after the acquisition, Matt Booty, the head of studios at Microsoft, suggested in a recent interview. Now there is an end in sight for the dependence on a rival. "We'll be fully transitioned to Azure by the end of the year," the Microsoft spokesperson wrote.

Read more of this story at Slashdot.

Ed Yong in The Atlantic:

Seven years ago, the White House was bracing itself for not one pandemic, but two. In the spring of 2013, several people in China fell sick with a new and lethal strain of H7N9 bird flu, while an outbreak of MERS—a disease caused by a coronavirus—had spread from Saudi Arabia to several other countries. “We were dealing with the potential for both of those things to become a pandemic,” says Beth Cameron, who was on the National Security Council at the time.

Neither did, thankfully, but we shouldn’t mistake historical luck for future security. Viruses aren’t sporting. They will not refrain from kicking you just because another virus has already knocked you to the floor. And pandemics are capricious. Despite a lot of research, “we haven’t found a way to predict when a new one will arrive,” says Nídia Trovão, a virologist at the National Institutes of Health. As new diseases emerge at a quickening pace, the only certainty is that pandemics are inevitable. So it is only a matter of time before two emerge at once.

“We have to prepare for a pandemic to happen at any time, and ‘any time’ can be when we’re already dealing with one pandemic,” Cameron told me.

More here.

Starting in 1970, Soviet scientists began drilling into the Earth as far as they could possibly go. The project ended in 1992 at 12,262 meters due to temperatures of 356 °F that far down into the Earth's crust. The Kola Superdeep Borehole remains the deepest artificial hole on Earth.

Ammar Kalia in The Guardian:

Nusrat Fateh Ali Khan’s voice is quite unlike any other. At turns heavy and hulkingly powerful, yet also nimble and pointedly precise, his vocalisations have come to epitomise not only the tradition of the Sufi qawwali but the art of singing itself.

The qawwali is an Islamic devotional music designed to bring its performers and audience to a state of rapture and trance-like communion with the divine. Born of a 600-year-old line of qawwali singers, Khan’s grasp of music as a form of spiritual communication was acute. For the few thousand attendees at the Womad festival in 1985 witnessing Khan perform for the first time outside of south Asia, their experience would have been one of unexpected transcendence.

Peter Gabriel had begun the festival only three years earlier as a western showcase of music from around the world, as well as that of his peers. The first edition, held in the Somerset town of Shepton Mallet, saw performances by Gabriel, Indian sitar player Imrat Khan and free jazz trumpeter Don Cherry. Poor access to the festival site and low attendance almost sunk Womad in its first year, but a well-timed reunion concert for Gabriel’s old band Genesis kept them afloat.

More here.

Cancelled pipelines and green ambitions point to declining role in energy mix

Most of the world's hydrogen is produced with a high associated carbon footprint. But there are alternative, low-carbon methods for producing hydrogen.

Combine animal stem cells with nutrients, salts, pH buffers, and growth factor in science-fiction-like vats and you get actual meat without growing the whole animal. It’s called cultivated meat production and will probably meet the world’s meat requirements in a decade or so.

Google is expanding its skills certification program to help more people land tech jobs like Data Analyst, Project Manager, and UX designer.

Google just announced that it is expanding its skills certification program to help more people land high-paying tech jobs without a college degree.

Read Full Story

Older adults adopt tech they find useful and resist tech they don’t.

"I hear a woman in the crowd yell out, 'Don't kill him.' And in that second, I realize that she's talking about me," Vauhxx Booker tells NPR.

(Image credit: Jeremy Hogan/SOPA Images/LightRocket via Gett)

Emily: In the last installment of our Summer of Bad Books Club, I asked what characteristics a Good Woman has in the world of My Sweet Audrina. The ways in which VC Andrews and, by extension, Audrina, attempts to classify women’s behavior is fascinating, deeply flawed, and, contradictorily, a bizarrely accurate…

Read more...

When you start a new job, you’ll probably be instructed to go to Human Resources (HR) if you ever experience any sort of harassment at work—but this path hasn’t always been the most useful one for employees. In fact, according to a new survey from Zenefits, one out of five workers do not trust their HR departments,…

Read more...

Velodyne Lidar founder David Hall is finally taking the San Jose-based company public through a reverse merger that leaves him and existing investors at the steering wheel. Here's how much the company — and insiders' stakes — is expected to be worth.

While the art world likes to present itself as inclusive and progressive, it is plagued by institutional racism, tokenism, and a general lack of diversity. Next Tuesday, July 14, six women artists of Southeast and East Asian descent will come together to frankly discuss their experiences and what they hope to push forward with their work. The artists, most of whom are based in the Bay Area and Los Angeles, are Pearl C Hsiung, Maia Ruth Lee, Astria Suparak, Stephanie Syjuco, Hồng-Ân Trương, and Christine Tien Wang. The host and main organizer of the event, stephanie mei huang, is also a Los Angeles-based artist.

“The yellow woman’s body, historically rendered either invisible or as ‘object,’ is now catapulted into hypervisibility amidst xenophobic questions of contagion, virility, and a history of scapegoatism,” reads the description of the event, which is aptly titled “hyper(in)visibility.”

In putting together this panel, huang hopes to create a “space of solidarity” for Asian women — something she says has “seldom been given.” Some of the questions she plans to pose include “How have COVID-19 and BLM affected your understanding of your racial, ethnic, and national identity?” and “How do we want to be seen? Is it possible to be seen the way we want to be seen?”

These six artists have a wide collective range of work, exploring the environment, food politics, race representation, immigrant histories, and much more. But huang sees some common ground, particularly a “sense of imposed discomfort.” She surmises that this may be because “as racialized and gendered bodies, we somehow are always experiencing that discomfort.” As an example, she cites Wang’s I just want to be a white girl paintings. 

The “hyper(in)visibility” panel was originally going to be held in late June in partnership with the Vancouver Art Gallery (VAG), but huang decided to withdraw after the museum appointed a white man as CEO and director. The appointment incited local controversy because “VAG has no Black representation on their board in addition to an overwhelmingly amount of white senior positions.” VAG’s response was “insufficient,” according to huang, and she “did not want to support an institution that was tone deaf to the urgent calls of the BLM uprising and BIPOC actions.”

She added, “I did not want myself or the panelists to yet again be forced into tokenized positions as women of color by an institution that reinforces the status quo of racism in the Americas or uses Asian people as a wedge between other people of color and whiteness.” As artists and art workers rally to inspire institutional change, the panel — now hosted by the Contemporary Calgary — has absorbed a whole new relevant layer of discussion.

When: Tuesday, July 14, 1:30 pm (PDT)
Where: Zoom

More info at Contemporary Calgary.

A collection of intriguing images based on data from ESA's Herschel and Planck space telescopes show the influence of magnetic fields on the clouds of gas and dust where stars are forming.

A quarter-billion years ago, huge volcanic eruptions burned coal, leading to the worst extinction in Earth’s history. Here’s how scientists hunted down the evidence.

The ethics of AI is a hot topic at the minute, particularly with the ongoing controversies around facial recognition software. Now mathematicians have developed a model that can help businesses spot when commercial AI systems might make shady choices in the pursuit of profits.

Modern AI is great at optimizing—finding the shortest route, the perfect pricing sweet spot, or the best distribution of a company’s resources. But it’s also blind to a lot of the context that a human making similar decisions would be cognizant of, particularly when it comes to ethics.

As an example, most people realize that while jacking the price of a medicine up during a health crisis would boost profits, it would also be morally indefensible. But AI has no sense of ethics, so if put in charge of pricing strategy this might seem like a promising approach.

In fact, in a recent paper in Royal Society Open Science, researchers showed that AI tasked with maximizing returns is actually disproportionately likely to pick an unethical strategy in fairly general conditions. Fortunately, they also showed it’s possible to predict the circumstances in which this is likely to happen, which could guide efforts to modify AI to avoid it.

The fact that AI is likely to pick unethical strategies seems intuitive. There are plenty of unethical business practices that can reap huge rewards if you get away with them, not least because few of your competitors dare use them. There’s a reason companies often bend or even break the rules despite the reputational and regulatory backlash they could face.

Those potential repercussions should be of considerable concern to companies deploying AI solutions, though. While efforts to build ethical principles into AI are already underway, they are nascent and in many contexts there are a vast number of potential strategies to choose from. Often these systems make decisions with little or no human input and it can be hard to predict the circumstances under which they are likely to choose an unethical approach.

And in fact, the authors of the paper have proven mathematically that AI designed to maximize returns is disproportionately likely to pick an unethical strategy, something they dub the “unethical optimization principle.” Fortunately, they say it’s possible for risk managers or regulators to estimate the impact of this principle to help detect potential unethical strategies.

The key is to focus on the strategies likely to provide the biggest returns, as these are the ones the optimization process is likely to settle on. The authors recommend ranking strategies by their returns and then manually inspecting the highest-ranked ones to determine if they’re ethical or not.

This will not only weed out the unethical strategies most likely to be adopted, they say, but will also help develop intuition about the way the AI approaches the problem and therefore have a better understanding of where to look for other problematic strategies.

The hope is that this would make it possible to then redesign the AI to avoid these kinds of strategies. If that’s not possible, the authors recommend analyzing the strategy space to estimate how likely it is that the AI will choose unethical solutions.

What they found is that if the probability of extreme returns for a small number of strategies is high, there are statistical techniques that could help estimate the risk that the AI will choose an unethical one. But if the probability of returns is evenly distributed, then it’s highly likely the optimal strategy will be unethical, and companies shouldn’t allow the system to make decisions without human input.

Even when it’s possible to estimate the risk, the authors still say it’s unwise to put too much faith in these predictions. And they suggest it may actually be necessary to instead re-think how AI operates so that unethical strategies are automatically weeded out at the training stages.

How exactly that would happen is far from clear, so for the time being it seems like it might be a good idea to keep humans in the loop for most AI decision-making.

Image Credit: Arek Socha from Pixabay

The U. S. tight oil rig count about 25% of what is needed to maintain 2019 levels of tight and total oil production.

We’ve had fun with Dilbert creator Scott Adams here on the Mary Sue before. Really, at this point, we shouldn’t be surprised that a man whose entire identity nowadays seems to be making an unfunny, dated three-panel comic and being a condescending ass on social media is, well, being an ass on social media. And yet, here we are, because the potent combination of toxic masculinity, classism, ingrained racism, and overall white male fragility that Scott Adams exemplifies is worth examining.

Let’s first examine Adams’ painful whiteness, and by extension, his perceived victimhood in relation. Adams recently claimed that his Dilbert cartoon was canceled because he was white. Because that’s a thing that happens. Twitter was swift to dunk on him for what was both obviously a lie and a statement that feeds into the false narrative of white victimhood.

Ah so? So … you were lying when you said this? pic.twitter.com/hGhvfnEDxS

— Robert Clarke-Chan (@999RPMs) June 29, 2020

Or perhaps the fact that your show was the fifth lowest-rated program on broadcast TV during the 1999-2000 season had a little something to do with it, you dime-store Cathy Guisewite. https://t.co/L8t0FrPjWS pic.twitter.com/BYEnuxTx6p

— Anthony Crupi (@crupicrupicrupi) June 29, 2020

Imagine admitting being white and mediocre couldn’t save your Hollywood career. https://t.co/RTxLuugttf

— Joelle Monique ✍🏾 (@JoelleMonique) June 29, 2020

That last tweet from Joelle Monique sums it up perfectly. A mediocre white dude like Adams would never attribute his success to his privilege, but that same privilege has made him so oblivious to his mediocrity that he blames fake persecution, due to whiteness, for his failures.

This tracks really well, because Scott Adams is a very insecure little man, as we can see from his 2016 essay on the Democratic National Convention, which he saw as an attack directly on his manhood—no, really. Here’s a quote:

I watched Keys tell the world that women are the answer to our problems. True or not, men were probably not feeling successful and victorious during her act.

Let me say this again, so you know I’m not kidding. Based on what I know about the human body, and the way our thoughts regulate our hormones, the Democratic National Convention is probably lowering testosterone levels all over the country. Literally, not figuratively. And since testosterone is a feel-good chemical for men, I think the Democratic convention is making men feel less happy.

Yes. Scott Adams felt that the celebration of the potential to elect the first woman president was emasculating, and that’s because the toxic masculinity that Scott Adams represents isn’t the most obvious kind. He’s not out there being violent or reckless to show he’s a man; he’s just doggedly defending his place at the top of the heap and thinks feminism means women will treat men as badly as men have treated women for millennia. Think of hyper-masculine toxic masculinity as Sriracha mayo, and this as a tub of Hellmann’s that’s been left on a picnic table for so long that even the flies are avoiding it.

This white male fragility has a lot to do with classism, as well (and of course, class divides are rooted in white supremacy, because it’s all related). Adams has a habit of refusing to engage with any sort of debate on Twitter, or anywhere, and instead just insults people as failed artists or musicians. That’s certainly funny, because he himself has made his fortune as an artist of sorts, and yet won’t have a debate or even a competition with anyone that threatens his worldview.

Take, for example, what happened today, when Bill Sienkiewicz called out Adams’ latest BS. (Today’s BS flavor, by the way, is saying that if Joe Biden is elected, Republicans will be hunted, and “you” will be dead within a year. Way to show you’re a secure, normal guy and not a paranoid racist, Scott).

Good for you, Scott. I mean that. Explain all your other claims & stances pls. What side of history do you want to be on? You’re portraying yourself as a social observer,unappreciated truth-teller & as a victim of reverse racism, yet it all reeks of entitled self absorption. https://t.co/7BppIumGNG

— Bill Sienkiewicz (@sinKEVitch) July 2, 2020

And proudly so. Also a human being trying to make a small difference for good. Too bad you say Artists like it’s a bad thing; I seem to recall you being one too-at least for a time. What the hell happened to you, man? Btw: way to avoid explaining or answering ANY of my questions. https://t.co/97k6XjMrQv

— Bill Sienkiewicz (@sinKEVitch) July 2, 2020

When fellow comics legend and queen of Twittering Gail Simone proposed Sienkiewicz and Adams settle this with a draw-off for charity … Adams was silent on accepting, because it’s pretty clear he’s an insecure, petty guy that doesn’t want to be shown as inferior to another artist.

And that’s what’s at the core of Scott Adams and so many other white men like him performing for each other. They’re insecure and afraid because they know they got where they are because cis, straight, white men are exclusively allowed to fail upwards. They’ve never had to confront their own unremarkableness, not to mention their complicity in systems of white supremacy and patriarchy. They think equality means oppression.

But fear isn’t an excuse, Mr. Adams. Your brand of white male victimhood might seem boring and unimportant, like you, but you are a man with a platform, and when you perpetuate that narrative, you empower other racists and sexists. You make it seem fine to dismiss people based on their race or sex rather than engage with challenging ideas. You set an example for all the trolls that follow you that it’s not white supremacy and sexism that’s the enemy; it’s the social justice warriors and, snort, the artists.

And I know that this sort of piece won’t change Scott Adams’ mind. I know he’ll probably read this and scoff and call me a failed artist, as well. (I hate to tell you, Scott, that I’m not just a failed artist. I’m also a lawyer, and my Juris Doctor cum Laude could beat up your MBA any day.) But that’s fine. What matters here is confronting and breaking down the insidious ways in which Scott Adams and his ilk stand in the way of progress.

Fragile white men who are afraid that if women and BIPOC people get any sort of power, we’ll hunt them for sport and burn all their boring comics are just as dangerous to progress as some jerk with a Confederate flag tattoo—perhaps they’re even more dangerous, because they dress up their hate in a sneering veneer of superiority and disdain for anyone that dares to question them.

But we do question them, and hopefully, if we do it enough, one day, they’ll be as distant a memory as the time when anyone cared about Dilbert.

(image: Pexels)

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The lights in the sky above us—the sun, the moon, and the panoply of countless stars—have surely been a source of wonder since long before recorded history. Ingenious efforts to measure distances to them began in earnest in the 3rd and 4th centuries B.C., and astronomers and astrophysicists today, with high-powered telescopes and computers, still ponder the universe and attempt to tease out answers to millennia-old questions.

But one of the most significant discoveries in this inquiry was not made with a high-powered telescope or a computer, or by anyone peering at the sky. Two hundred years ago, Joseph von Fraunhofer, a Bavarian glassmaker and researcher, experimented in his laboratory with simple equipment and detected dark lines in the spectrum of sunlight. He had no way of knowing that this curious discovery would allow future scientists to calculate the distances of stars and precipitate one of the most momentous advances in the history of all science—the recognition that the universe is expanding.

Joseph Fraunhofer was born on March 6, 1787, in Straubing, in lower Bavaria. On both his father’s and his mother’s sides, his forebears had had links to glass production for generations. Joseph, the youngest of 11 children, likely worked in his father’s shop. When Joseph was 10, his mother died; his father died a year or two later, and Joseph’s guardians sent him to Munich to apprentice with the glassmaker Philipp Anton Weichselberger, who produced mirrors and decorative glass for the court. This should have been an enviable apprenticeship, but Weichselberger was a harsh master who gave his apprentices menial tasks and taught them little about glassmaking. He prevented Joseph from reading the science books he loved by refusing him a reading lamp at night and forbade his attending the Sunday classes that offered Munich apprentices some education outside the trade.

Joseph endured two years of this misery, but then his story took a turn that could have come from a Charles Dickens novel. Weichselberger’s house collapsed, burying Joseph underneath. His rescue was dangerous and took several hours, giving prince-elector Maximilian IV time to arrive on the scene. The accident made Joseph the city’s hero, and a still-existing woodcut in Munich’s Deutsches Museum shows Maximilian, arms outspread, welcoming the boy back to life. Maximilian invited Joseph to his castle and put him in the care of his advisor, industrialist Joseph von Utzschneider. Utzschneider, realizing that this lucky young man was bright and had a thirst for knowledge, supplied Joseph with books on mathematics and optics.

Maximilian gave Joseph a generous gift that was sufficient to buy him out of his apprenticeship and purchase an optical grinding machine. Then Joseph set up a small business engraving visiting cards, which failed to supply him with a living. Without a source of income, and perhaps realizing that an apprentice was not wise to depart from the established route into his craft, he returned to Weichselberger, working for him during the week and for an optician, Joseph Niggl, on Sundays. Weichselberger still did not allow him his reading lamp.

Eventually, Utzschneider took things in hand, saw to it that the boy was supplied with books and the time and light to read them, and arranged for Ulrich Schiegg, a Benedictine pastor with considerable scientific interest and education, to mentor him. When Utzschneider judged that Joseph was sufficiently prepared, he recruited him to work in Utzschneider’s own Optical Institute in Benediktbeurern, where Joseph assisted in the manufacture of telescope lenses and surveying instruments. When he was still in his early 20s, Utzschneider put him in total charge of the glass works at the Institute.

The improvement of lenses for telescopes and surveying instruments was a major goal of the Institute, and it was not long after his arrival that Fraunhofer began to focus on more basic research that underlay this effort, research having to do with the nature of light and its refraction. In 1807, at age 20, he submitted his first major scientific paper.

In 1814, at age 27, Fraunhofer was working in his laboratory to make more accurate measurements of the manner in which different types and configurations of glass refract light. The fact that a prism transforms ordinary white light into a rainbow of colors had been known since antiquity. But the assumption had been that the colors are somehow in the prism. Isaac Newton, in the 1660s, had shown that white light is composed of colors that spread out in an ordered sequence—the spectrum—red, orange, yellow, green, blue, indigo, and violet. Different wavelengths of light are responsible for the different colors. The longer the wavelengths, the further toward the “red” end of the spectrum. The shorter the wavelengths, the further toward the violet or “blue” end.

Though modern science finds minute variations in the speed of light in a vacuum or empty space, for most purposes it’s safe to assume that the speed in such situations does not vary. Not so for the speed of light moving from one medium to another (air to water, for example). The “refractive index” of a medium indicates how the speed of light moving through that medium differs from the speed of light as it moves through another.

When a beam of white light passes through a prism, the colors in the light do not all bend equally, because the refractive index of a material (in this case, whatever the prism is made of) differs slightly for different wavelengths of light. The shorter the wavelength, the greater the strength of the refraction. As the white light splits into visible colors, red light bends least; violet light, most.

The fact that a prism transforms ordinary white light into a rainbow of colors had been known since antiquity. But the assumption had been that the colors are in the prism.

One obstacle Fraunhofer and other researchers of his time faced was that the colors in the spectrum are not sharply separated from one another. Looking closely at the spectrum produced by light emerging from a prism, a researcher cannot judge precisely where red changes to yellow, for example. The colors blend off one into the next. Experiment after experiment proved unsuccessful in solving this problem, but among Fraunhofer’s attempts there was one result that particularly intrigued him.

Using as his light source a flame made by burning alcohol and sulfur, he saw that when this light passed through his prism, the result was a clearly defined bright line in the orange region of the spectrum. His curiosity aroused, Fraunhofer repeated the experiment using the sun as his source of light, to find whether the spectrum would show similar lines. Newton had studied the spectrum of light by allowing sunlight to enter through a small round hole in a shutter, pass through a prism, and fall on a screen. For Newton’s round hole in the shutter, Fraunhofer substituted a narrow slit, and for Newton’s screen he substituted a surveying instrument designed to measure angles, known as a theodolite telescope.

As he reported, “Looking in this spectrum for the bright line that I had found in a spectrum of artificial light, I discovered instead an infinite number of vertical lines, of different thicknesses. These are darker than the rest of the spectrum, some of them entirely black.” The lines remained the same when he adjusted the window-shutter slit or made various adjustments to the spacing of his equipment, ruling out the possibility that the lines were a product of his experimental apparatus. They were a property of solar light itself.

Building on NewtonIsaac Newton studied the way a prism splits white light into all the colors of the rainbow, known as a spectrum. Fraunhofer recreated Newton’s experiment and discovered the dark lines.MilanB/Shutterstock

In groundbreaking papers, Fraunhofer announced his discovery that the spectrum of light from the sun is interrupted by many dark lines, and that these lines are present in all sunlight, both direct and reflected from other objects on Earth or from the moon and the planets. He labeled the ten most prominent lines in the solar spectrum and eventually reported that he had found 574 lines.

Continuing to investigate, Fraunhofer detected dark lines also appearing in the spectra of several bright stars, but in slightly different arrangements. He ruled out the possibility that the lines were produced as the light passes through the Earth’s atmosphere. If that were the case they would not appear in different arrangements. He concluded that the lines originate in the nature of the stars and sun and carry information about the source of light, regardless of how far away that source is. Fraunhofer did not know what that information would be, how the lines would serve the future, or that “Fraunhofer lines” would become a household term in science.

Fraunhofer was a busy and effective entrepreneur, and under his leadership the Institute became a leading manufacturer of telescopes. He wrote in his memoirs that, “In making the experiments… I have considered principally their relations to practical optics. My leisure did not permit me to make any [other experiments] or to extend them farther. The path that I have taken… has furnished interesting results in physical optics, and it is therefore greatly hoped that skillful investigators of nature would condescend to give them some attention.” They certainly would!

Each of the lines represents a particular element and the strength of a line is related to the abundance of that element. 

Yet in his own lifetime, Fraunhofer failed to receive as much recognition as he deserved from his peers. Eminent researchers such as Hans Christian Ørsted and John Herschel visited him at the Institute, but others regarded him as a mere artisan, or were offended by the excessive secrecy practiced at the Institute to protect its monopoly.

Bavaria eventually chose to celebrate her native son. In 1821, after heated debate over his complete lack of academic training, the Royal Bavarian Academy of Sciences appointed him “extraordinary visiting member.” Two years later he became curator of their physics collection. In 1822, the University of Erlangen awarded the self-schooled Fraunhofer an honorary doctorate. In 1824, Fraunhofer became von Fraunhofer when King Maximilian I Joseph dubbed him a Knight of the Order of Civil Service of the Bavarian Crown. The city of Munich marked the occasion by giving him relief from paying city taxes.

Portraits depict von Fraunhofer as a well-appointed, lively man, but he was always somewhat frail. His work in the glass furnaces with poisonous lead oxide probably contributed to his death, in June 1826, from “lung tuberculosis.” He was 39.

Utzschneider, evidently thinking about Fraunhofer’s work with telescopes at the Institute, eulogized him with the words “He brought us closer to the stars.” He might more accurately have said that his young friend had given us an essential leg-up on the journey to find how astoundingly far away the stars are, for von Fraunhofer had indeed found the hidden code in starlight.

The Busy Entrepreneur and Researcher Joseph von Fraunhofer demonstrating an instrument that he used in his investigation of light and refraction.Photogravure from a painting by Richard Wimmer. Wikimedia Commons.

Until the beginning of the 19th century, the chemical and physical make-up of stars had appeared to be unobtainable knowledge. However, in mid-century, there began to be serious challenges to that assumption when researchers such as Anders Ångström, Léon Foucault, and Sir George Stokes recognized that a pair of the lines Fraunhofer had detected in the sun’s spectrum were the same wavelength as a pair of lines seen in the laboratory in the spectrum of sodium. Clearly the sun must contain sodium.

In the late 1850s, a young pair of researchers—physicist Gustav Kirchhoff and chemist Robert Bunsen (of the Bunsen burner)—confirmed that the lines Fraunhofer had discovered are signatures of different chemical elements in the sun’s atmosphere. William Huggins in 1863 followed up on their work and on Fraunhofer’s study of star spectra and recognized that elements present on Earth and in the sun are also present in stars. As Huggins, wrote, “Within this unraveled starlight exists a strange cryptography. In the hands of an astronomer, a prism has now become more potent in revealing the unknown than even was said to be “Agrippa’s magic glass.” By looking at the pattern of Fraunhofer’s lines and noting where they occur within the spectrum, it is possible to discern the chemical composition of a star.

Underlying this picture, we now better understand that nuclear reactions in the central region of a star generate energy, mostly in the form of photons, that travels outward toward the exterior of the star. On the journey through some layers of the star, highly ionized atoms that make up the star’s fluid matter absorb and re-emit the photons. The radiation eventually flows into interstellar space, preserving the image of the last layer in which that activity took place, with some wavelengths of the light now missing from that image. The missing wavelengths (in effect, missing colors) show up as black lines in the spectrum, called “absorption” lines. Each of the lines represents a particular element and the strength of a line is related to the abundance of that element. The size and shape of a line is related to the temperature, pressure, and turbulent motion in the fluid matter of the star.

The process of using Fraunhofer lines to help sort stars into categories began in the 1860s when Father Angelo Secchi, in Rome at the Observatory of the Roman College, now the Vatican Observatory, divided stars into types based on the relative prominence and width of their spectral lines. Until the late 18th century, researchers had thought that it might be possible to calculate the distances to stars by comparing how bright they appear from Earth. The idea had been based on the knowledge that the apparent brightness of a light (how bright it appears to you) decreases with distance in a mathematically dependable way summed up in Isaac Newton’s inverse square law. If you have two identical 100-watt light bulbs and place one twice as far from you as the other, the farther bulb will appear to be only a fourth as bright as the nearer. Unfortunately, calculation like this hadn’t helped for stars, for stars are not all of equal “wattage.” Their “absolute magnitudes” (close-up or “intrinsic” brightnesses) vary enormously. The hope remained, however, that if stars belong to different categories, the knowledge of those categories might help us know their absolute magnitudes.

The most dramatic role that Fraunhofer lines played was in the discovery that the universe is expanding.

The sorting became more complicated when Edward C. Pickering and colleagues at the Harvard College Observatory began a process in which spectra were focused on a photographic plate. As research continued, it turned out that the overwhelming abundance of stars can be placed in a very few categories, suggesting that the range of compositions of stars is rather small. In the 1920s, Cecilia Payne, in her doctoral dissertation at Harvard, established that even in this small range of different spectral patterns, the differences we observe are a result of the temperatures of the stars, not because their compositions differ very greatly. With a more sophisticated understanding of atomic structure and the causes of the lines, stars could be meaningfully classified according to surface temperature.

The trick in calculating the distances to stars was to find an independent measure of their absolute magnitudes. Today a table known as the Hertzsprung-Russell diagram provides that. If you know a star’s spectral type (from the study of its spectral lines), allowing for certain assumptions, you can read the star’s absolute magnitude off the diagram. Knowing the star’s absolute magnitude, you can calculate its distance by measuring its apparent magnitude and using Newton’s inverse-square law.

The most dramatic role that Fraunhofer lines played in the 20th century was in the discovery that the universe is expanding. If a light source is moving toward us, light waves coming from it are squashed together. The lines in its spectrum are shifted toward the blue end (“blue-shifted”). If the source is moving away, they are stretched out. The lines in the spectrum are shifted toward the red end (“red-shifted”). In the late 1920s, Edwin Hubble and Milton Humason, studying such shifts, discovered that except for galaxies clustered close to our own Milky Way galaxy, every galaxy in the universe appears to be receding from Earth. In fact, on the large scale, every galaxy is receding from every other. The amount of the shift of the lines in its spectra is an indicator of the speed at which a galaxy is approaching or receding.

The discovery that the farther away galaxies are, the faster they are receding was convincing evidence that the universe is expanding. As Caleb Scharf, Director of Columbia University Astrobiology Center, puts it, “When [Fraunhofer] first split sunlight finely enough to see its complex spectrum he was laying the groundwork for scientists like Edwin Hubble who split the light of distant galaxies and realized that the cosmos is a dynamic beast.”

The lenses and telescopes von Fraunhofer designed and built 200 years ago were equal or superior to any others produced at the time. His inventions and innovations made them easier to use and more effective. These practical accomplishments were not incidental to, nor merely a distraction from, his experimental work. They were essential to its success. Seldom have technological and theoretical genius been so well paired, nor that pairing more essential for the future of knowledge. He gave us a tool to measure the distances to the stars and nebulae—a crucial rung on the ladder to modern measurements of the size of the universe.

Kitty Ferguson is the author of nine books of popular science, including Measuring the Universe, and most recently, a biography of Stephen Hawking.

References

Aller, Lawrence H. Atoms, Stars and Nebulae Cambridge University Press, 3rd Edition (1991).

Danielson, D. The Book of the Cosmos: Imagining the Universe from Heraclitus to Hawking Perseus Publishing (2000).

Jackson, M. Spectrum of Belief: Joseph von Fraunhofer and the Craft of Precision Optics The MIT Press (2000).

Wolfgang, J.  Fraunhofer in Benediktbeuern Glassworks and Workshop Burton, Van Iersel & Whitney GmbH (2008).

This article was originally published in our “Light” issue in March, 2014.

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Microsoft suspended its advertising on Facebook and Instagram in the U.S. in May and recently expanded that to a global pause, according to an internal chat transcript seen by Axios. From a report: Unlike the many advertisers who recently joined a Facebook boycott,, Microsoft is concerned about where its ads are shown, not Facebook's policies. But the move still means yet another big advertiser is not spending on Facebook right now. "Based on concerns we had back in May we suspended all media spending on Facebook/Instagram in the US and we've subsequently suspended all spending on Facebook/Instagram worldwide," Microsoft CMO Chris Capossela said in an internal Yammer post, responding to an employee's question. The transcript did not specifically say what content Microsoft objected to its ads appearing next to, but as examples of "inappropriate content" it cited examples of "hate speech, pornography, terrorist content, etc."

Read more of this story at Slashdot.

Zwift, a platform for racing bikes from the comfort of your home, has taken off amid the coronavirus pandemic and is even powering this year’s virtual Tour de France. Next stop? The Olympics.

A friend wants to go for a ride. He has been cycling forever, and I’ve taken the last 20 years off, so he chooses the trail—a 2,000-foot climb up a small mountain. Moments after agreeing, I realize this is a terrible mistake.

Read Full Story

Recent declines in demand for petroleum products have led commercial crude oil inventories in the United States to reach an all-time high of 541 million barrels as of the week ending June 19, which is 5 million barrels more than the previous record set in late March 2017, according to data in the U.S. Energy Information Administration's (EIA) Weekly Petroleum Status Report.

A high school computer science teacher claims there's an "unacknowledged failure" of America's computer science (CS) classes at the high school and junior high school level. "Visit classrooms and you'll find students working with robotic sensors, writing games and animations in Scratch, interfacing with Arduino microcontrollers, constructing websites, and building apps with MIT App Inventor... "Look underneath the celebratory and self-congratulatory remarks, however, and you'll find that, although contemporary secondary education is quite good at generating initial student interest, it has had much less success at sustaining that engagement beyond a few weeks or months, and has frankly been ineffectual in terms of (a) measurable learning for the majority of students; (b) boosting the number of students who take a second CS course, either in high school or college; and (c) adequately preparing students for CS college study." Long-time Slashdot reader theodp writes: In " A New Pedagogy to Address the Unacknowledged Failure of American Secondary CS Education ," high school computer science teacher Scott Portnoff argues that a big part of the problem is the survey nature of today's most popular high school CS course offerings — Exploring Computer Science (ECS) and AP Computer Science Principles (AP CSP) — both of whose foundational premise is that programming is just one of many CS topics. "Up until a decade ago," Portnoff explains, "introductory high school computer science classes were synonymous with programming instruction, period. No longer." This new status quo in secondary CS education, Portnoff argues, resulted from baseless speculation that programming was what made Java-based AP CS A inaccessible, opposed to, say, an uninspiring or pedagogically ineffective version of that particular curriculum, or a poorly prepared instructor. It's quite a departure from the 2011 CSTA K-12 Computer Science Standards, which made the case for the centrality of programming in CS education ("Pedagogically, computer programming has the same relation to studying computer science as playing an instrument does to studying music or painting does to studying art. In each case, even a small amount of hands-on experience adds immensely to life-long appreciation and understanding"). This teacher believes that programming languages are acquired rather than learned, just like any other human language — and concludes the solution is multi-year courses focused on one programming language until proficiency is fully acquired. For this reason, for the last seven years he's also been making his students memorize small programs, and then type them out perfectly, arguing that "the brain subconsciously constructs an internal mental representation of the syntax rules implicitly by induction from the patterns in the data."

Read more of this story at Slashdot.

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Have you noticed that, in art, very few things exist or are created in a vacuum? In other words, every choice you make has an effect on all the other choices you have made or will make when designing and creating original works of art. So, if you are coming to my blog for the first time, you may want to read the last three weeks of posts first because each successive article builds off the last.

Last week we talked about color value and this week we’re going to talk about how you can change the value along with something called saturation. This will be a little heavy on terminology but it’s easy stuff and by the time you’re done reading, you will have quite the sophisticated color vocabulary.

I also want to speak for just a moment on the reason you would want to do this deep dive into color and design. Whether you create your own colors or simply choose colors from pre-mixed options, your choices are best ruled by your understanding of the characteristics of color. Of course, understanding color characteristics is essential in color mixing but choosing and identifying color requires the same knowledge especially when creating color palettes, analyzing your work (or the work of others), and correcting or improving your color choices.

Working with color, like anything else in design, is about the relationship between colors and between all the design elements. In design, we work with likeness and disparity. That’s really what all relationships are about, aren’t they? Think about your spouse or your best friend or the coworkers you like to hang around with. You have something in common, some area of your life that overlaps that you can share. But you also have differences. These differences make the relationship interesting, encourages curiosity and conversation, and allows each of you to fulfill different roles in the relationship. That’s how design works as well, including between colors.

So, if you keep in mind that these conversations are about those design relationships, I think you’ll start to see just how useful and essential these immersive color lessons are regardless of whether you makes your own colors, pick available colors, or simply want a better understanding of the art that you enjoy.

Saturation is Not Value

Now, let’s talk about value versus saturation. For some reason, these two concepts get confused a lot even though they are quite different. As you learned last week, value is the lightness or darkness of a color. Saturation, however, is about how intense the color is or how close it is to the unadulterated hue or “key” color, at least in regard to pigment. (This is dealt with a little bit differently when it comes to mixing light in RGB. Just thought you ought to know that in case you come across a definition that talks about saturation, brightness, and luminosity. That’s RGB stuff.)

 

So, let’s take a pure blue as an example of both high saturation and dark value. Take a look at the color wheel. True blue, in its most saturated and vivid form there on the outside ring of the color wheel, is far darker than pure yellow. You could make that blue as light in value as yellow by adding a lot of white to it but that would also change its saturation because the addition of white takes away from the purity of the hue, right? The addition of white in a color is called tint.

Now let’s take that yellow. If you wanted it to be as dark in value as the blue, you could add a lot of black, so much so that it would probably look gray with little yellow to be gleaned. This would both darken the value and desaturate it, a lot. The addition of black to a color is known as shade.

So that’s the thing with adding black or white to a color. It will desaturate a color but it also will make it lighter or darker in value. I bet that doesn’t fully clarify why value and saturation are so different since adding white or black changes the lightness or darkness (value) as well as the intensity of a color (saturation). Well, here’s the thing – you can, on the other hand, change the saturation without changing the value, just not with black or white.

Let’s look at the color red for moment. On the CMY color wheel, you can see that opposite red is cyan. They look to be about the same midrange color value, right? If you add a bit of cyan to the red that will reduce the saturation or purity of the red by altering its hue but it will not make a noticeable change to its value. If you got yourself one of those CMY color wheels, you’ll see on the front side there that each ring getting closer to the center shows what happens when you add 10%, 20%, 30%, or 40% of each hue’s complementary color. That kind of mix tones down the color which is why it is called a tone.

You can also tone down a color without changing its value by adding a gray that is the same value as the color. In fact, a fully desaturated color would be just gray. Or you can mix in a lighter or darker gray to make the color lighter or darker while toning it down but without muddying the key with its complement. A gray mixed with a color is also called tone.

So, you see, changing saturation can, but does not always, change value but changing the value will necessarily change the saturation of a hue, making it less pure. This is true for color mixing or even using digital photo editing (and is why I warned you last week not to use saturation options in photo editing to look at values in grayscale, because value is not taken into account.)

 

Your Bright, New, Shiny Color Vocabulary

Congratulations! You probably didn’t realize it but you just completed a major step in your color education. If you’ve read all the posts, you have learned (or refreshed your understanding of) the three most important aspects of color – Hue, Value, and Saturation.

And, now, with this article, you’ve come to know the three primary ways to change a color. Let’s review because it’s kind of cool to realize how much you’ve soaked up.

The three primary characteristics of color:

Hue – the key and name of a color.

Value – the lightness or darkness of a color.

Saturation – how pure or how adulterated a color is due to the addition of white, black, gray or a complementary color.

The three primary ways of adjusting color in pigments:

Tint – the addition of white to a color.

Shade – the addition of black to a color.

Tone – the addition of gray or a complementary hue to a color.

Look at that! You have six color terms that are going to help you tremendously in color mixing, choosing palettes, and analyzing work. But let’s spend a little more time with those last three just to be sure you got them well seated in your creative little brains.

 

Color Quiz

Okay, let’s put your new knowledge to the test. Take a look at the opening image and the images below and find the pure hue (just visually – you don’t have to name it) and then determine the variation of that hue was accomplished with tints, shades, and/or tone. We’ll chat about them after you have a chance to come up with your own thoughts.

Carved wooden vessel by Louise Hibbert

 

A polymer bracelet by Judy Belcher.

 

Well, what did you come up with? Some of these examples are not so straightforward but I find them very interesting.

First of all, Pikalda’s glass beads that open this post have a saturated blue as its key color while the other color variations, aside from the black and white accents, are the key blue with white added so they are tinted versions of the key color. Pretty easy to see that, right?

With Louise Hibbert’s wooden vessel, the key is a kind of violet and, I’m sure you guessed it, the gradation to the nearly black tips is the result of adding black, in other words, creating shades of the hue. But there are also diluted versions of the hue where she lets the wood show through towards the center. Is that a tint because it makes it lighter or a tone becuase it isn’t quite white that has been added?

Well, think in terms of the color elements here. Since the violet color is translucent, it visually mixes with the color of the wood, a pale cream, which is a tint of yellow. This actually makes that diluted violet a tone because the change in color is not due to the addition of just white or just black and it’s a color that muddies the key color even if just a little. It’s true that yellow is not the direct complement of violet – that would be a yellow-green – but you can actually tone down a color with something close to its complement too. We’ll get more into those complexities when we get deeper into color mixing so you can just stash that info away for later if you like.

Now, in Judy Belcher’s bracelet, it gets even a bit more complicated because, in truth, the fully saturated hue is not present. That would be bright lime green but the key color has been toned down with variations of gray. In fact, the entire bracelet is a series of lime green tones with nothng else but some white. Some tones are due to a very light gray addition, others to a few different middle grays and the darkest green would be a tone with a dark gray. Being able to spot the key in something like this takes practice but not a lot. It might just take the following little exercises.

 

For Further Study

Okay, so there are a couple ways you can further concrete your, hopefully, not too hard-earned knowledge. These are both fun and easy and take 10-15 minutes each to do.

Color Wheel Studies

First of all, if you bought yourself that CMY color wheel I suggested – or even if you didn’t – you can see tones, tints and shades set up on this handy color tool with approximate percentages that one would mix to achieve these colors from a key. Here is a video that the Color Wheel Company put together to explain how to use their color wheel tool while making note of where these items are on it so you can familiarize yourself with them just by looking over your color wheel. Clicking on the image takes you to the purchase page but scroll down to find the videos.

Isn’t crazy just how much information they put on this little paper tool? Keep in mind that those percentages for the tones, tints and shades are approximate because in the real world, our materials have varying amounts of pigment so adding 10% of one complement to a color could make a dramatic change while adding 10% of a complement to another color may make almost no change. You’ll start to get a sense of the stronger and weaker colors (and brands) if you do the exercise below and as we work through color mixing in July.

 

Mix it Up

Studying the color wheel is an easy and quick way to see the difference between tone, tint, and shade but the best way to not only remember the terminology and what it means but to really understand how saturation, tint, shade, and tone work in color is to mix it up.

So, grab some clay in one fully saturated key color. Pick your favorite or grab one of the primaries – cyan, magenta, or yellow. You also need a bit of your chosen color’s complement plus black and white. Roll out each clay on your thickest pasta machine setting and, using a single punch cutter, punch out portions of clay from each sheet. (You can also do this with paint – you won’t be “punching” out your portions but, instead, you’ll be picking up dabs of paint.)

1. At the top of a piece of paper, write Tint, Shade, Complement Tone, and Gray Tone as column headers
2. Put one portion of your key color under each column header. This will be a starting point for each color as we desaturate it.
3. Punch out two portions of your key color and mix it with two portions of white until well mixed. Sheet the clay and punch out one portion of this mix. Put it under the tint column with space enough between it and the key color for another portion.
4. Take one of those mixed portions and one of the key color and mix that. Punch a portion out of this new mix and place it between the previous mix and the key color.
5. Take the last portion of the first mix and mix it with a portion of white. Punch out a portion of this very light mix and line it up in the column under the middle mix, followed by a portion of whites to complete a column of tints from key color to white.

At this point you have three desaturated tint versions of the key color. These are not a lot of steps between the key color and white but it will give you an idea of what white does to a fully saturated color. If you are game before creating a wider range of this tint sampler, you can double the amount for each of the three mixes we just did so you can mix additional portions and create four more steps, one between each of the five portions in the tint column.

6. Now go through the exact same process, creating 3 or 7 mixes, as you prefer, but instead of white …
   1. … make a column using black to build a range under the Shade header. You may want to use 2-3 times as much key color as black for your middle shade to get a better gradation since black is very strong, as you can see in my example. I used twice as much key color and all the mixes are still awfully dark.
   2. … use the complementary color to create a range under the Complementary Tone column.
   3. … mix a gray (I used twice as much white as black to get my middle gray) to add to the key color to create a range under the Gray Tone column.

You will probably notice, as you mix, that sometimes the progression from the key color to the color you mix in is not very even or regular. For instance, if your key color is particularly dark in value such as the Ultramarine blue, the jump between the last mix and white may seem quite a bit different, like it could use another mix in between. You are, of course, welcome to change up the portions of color in your mixes to make a more regularly graduated range. This will, however, demonstrate that the amount of pigment in different colors of clay and between brands can differ and so some colors will dominate in a mix. You’ll need to use more of the weaker color to make the range gradations more even. But making a perfectly graduated range is not the purpose of this exercise. The idea is that you make the mixes, see the changes in color, and associated with the terminology.

Now why am I so adamant about you learning the terminology? Well, in July, as we learn about color mixing and palette choices, being able to verbalize the common and contrasting characteristics in a set of colors will be key to making beautiful, intentional color choices. Plus, you can impress friends, family, and complete strangers with sophisticated color banter!

So, relax and mix up some colors. It’s easy and often surprising how the colors come out. I have found more than one “new favorite color” doing these kinds of exercises. You just might find a inspiring new color or two as well!

 

Wondering about my references to Intention? Or how to support this content?

If you enjoy these articles, you can help me keep the lights on by making a purchase of any of the publications I have on the Tenth Muse Arts website or by making a one-time or monthly contribution here.

Read the set of articles on Intention in the February edition of the Virtual Art Box or catch up on the concept of marks, lines, and shape with a purchase of one or more of the original Virtual Art Box offerings. They are all on SALE, 25% off right now – no promo code needed. I have also put all books on sale at 20% off for the next couple weeks so it’s a great time to fill up your library.

Your purchases help support this free content as well as giving you a stronger base for the conversations we will have going forward.

If your budget doesn’t allow such support, that’s perfectly okay. I just hope this is supporting your creative journey giving you more joy in your work. if it does, just let people know this is freely available so I can support even more folks.

 

My Weird Low Pressure Week

Hopefully there aren’t too many mistakes here. I need to beg your forgiveness if there are. My brain has literally been shorted as I gave blood this past week and got tested to see if I am a antibody plasma donor candidate to help out COVID-19 patients but my naturally very low blood pressue has yet to recover so I feel very dingy and am sometimes dizzy still, 5 days later. I never could give blood in Colorado due to the high elevation and even lower blood pressure up there but they thought I’d be fine down here. Well, guess not. We learn something new all the time!

So, I probably can’t give plasma eithere but I am still going to do all I can during this rough time to help others and, as part of that, maybe you will allow me to ask a little favor. I know this has gotten a little political here in the states but thsi is not about politics … I would just like to ask that when you are out, and it has been recommended where you live, you can show your love and concern for your community by the simple act of wearing a mask. I wear one everywhere even though I’ve already had this bug so I am supposedly immune and can’t pass it on. But people are scared and worried and wearing a mask shows you care, even if you question the validity of the science that says it will save others from getting sick. We need all the consideration and caring we can put out there right now, don’t you think?

Ok, that is my public service announcement for the day. I hope you are all staying well and will find joy in a creative and colorful week!

 

Adrian Bardon in Scientific American:

Bemoaning uneven individual and state compliance with public health recommendations, top U.S. COVID-19 adviser Anthony Fauci recently blamed the country’s ineffective pandemic response on an American “anti-science bias.” He called this bias “inconceivable,” because “science is truth.” Fauci compared those discounting the importance of masks and social distancing to “anti-vaxxers” in their “amazing” refusal to listen to science.

It is Fauci’s profession of amazement that amazes me. As well-versed as he is in the science of the coronavirus, he’s overlooking the well-established science of “anti-science bias,” or science denial.

Americans increasingly exist in highly polarized, informationally insulated ideological communities occupying their own information universes.

More here.

Samanth Subramanian in Politico:

Trawling through the news archives, I found predictions of “the new normal” — the post-pandemic world — from as early as the first week of March. At the time, the United Kingdom hadn’t yet gone into lockdown; neither had France, India or Spain. In the United States, President Donald Trump had just about stopped declaring that the virus would miraculously disappear.

Roughly 3,400 people had died as of March 6 but you could still fly from London to New York. The contours of the months to come were fuzzy and indistinct, and yet there we were, making forecasts about life after the coronavirus.

The situation today is, in relative terms, not hugely different. Several governments don’t yet know when and how they will move out of lockdown. We don’t know who will be left immune after this spell of sickness, or if there will be a vaccine, or if there will be a second wave of COVID-19 this winter, or if the virus will mutate, or when it’ll be possible to travel freely across the world once again.

But even in the midst of this flux and uncertainty, we are toiling away at more predictions.

More here.