Color Tech

Do you know the difference between Blood Red, Brick Red, Candy Apple Red, Fire Engine Red, Orange Red, Rose Red, Ruby Red, and Tuscan Red? You can thank modern chemistry for that, because before 1800, there was only one Red.*

Color as we know it came from the Industrial Revolution, and by extension the chemical revolution at the tail end of it. Keep in mind that petroleum, the blood of the gods that infused the Industrial Revolution, is made of huge and complex chemicals that can be broken down into thousands of other chemicals. This provided the raw materials for an explosion in color technology.

(Most colors, as well as fragrances, were discovered by accident while scientists tried to find ever-inventive ways to exploit the waste products of coal use.)

At the same time, and for the same petro-logical reasons, the West began its descent into today's disposable culture. Plastics, paints, glazes and dyes began coating every object we came in contact with. The consumer culture was swiftly educated by color-coded crayons, lipstick and home telephones.

A mature, discriminating populace now takes for granted that the rainbow can be divided and subdivided ad infinitum and projected onto any object imaginable. But our technicolor world has downstream problems; it's poisoning local waterways and ecosystems.

This is where biomimetic color comes in. Twenty years ago, biomimetic color was mostly limited to imitating the structural color of peacock tails or butterfly wings. Today, biomimetic color means genetically engineering bacteria to make the colors for us.

For example, researchers might look for genetic codes that produce color, like the blue in a Blue Jay, then Frankenstein those genes into the bacteria, feed it some garbage, and forget about it.

There's even a company doing structural color by making bacteria that change their dimensions or their positions. This would make them be able to change colors depending on their microscale surface textures, and in real-time (i.e., an lcd video, i.e., the screens of the future will be literally alive).

The point here is that we're trying to make things that aren't reliant on petroleum or even petroleum waste products, and bioengineering allows us to still use someone else's waste stream, since the raw materials are now basically E. coli and carbohydrates.

E. coli is a standard choice of organism in these Frankensteining endeavors. So basically E. coli will be to the biotech revolution what the steam engine was to the industrial revolution?

*As a veteran art teacher, I can say that at one point I could differentiate and name, by sight, every pencil in Prismacolor's 150-color pencil set; I could tell their "yellow-orange" from their "orange-yellow" and the 20% French Grey from the 30% French Grey.

Post Script:
Totally unrelated to E. coli machines, this is a nanonscale metal-etching laser that makes the surface a "selective absorber" of light, which means you can make it absorb and reflect any color of the spectrum you want; this is a kind of structural color similar to iridiscent insect skin etc.

Review of The Color Revolution by Regina Lee Blaszcyzk, 2012
https://networkaddress.blogspot.com/2015/07/the-color-revolution.html

The first color we learn to see is Red:
Cultural Evolution of Basic Color Terms
Network Address, 2012
https://networkaddress.blogspot.com/2012/07/cultural-evolution-of-basic-color-terms.html

Green is one of the last colors we "evolved" to see;
And Japan thanks Crayola for being able to now see the color Green:
Seeing Red
Network Address, 2013
https://networkaddress.blogspot.com/2013/06/seeing-red.html

Plasmonic pixels could be used to make non-fading paint
Jun 2016, phys.org
http://phys.org/news/2016-05-plasmonic-pixels-non-fading.html

Color-changing materials could be used to detect structural failure in energy-related equipment
July 2016, phys.org
http://phys.org/news/2016-07-color-changing-materials-failure-energy-related-equipment.html

Partially Unrelated Post Script:
Yarn created from skin cells can be woven into human textiles
Feb 2020, phys.org
https://phys.org/news/2020-02-yarn-skin-cells-woven-human.html

Heal and replace damaged body parts and organs using a fabric that is grown using human skin-producing cells.

Notes:
Lasers etch a 'perfect' solar energy absorber
Feb 2020, phys.org
https://phys.org/news/2020-02-lasers-etch-solar-energy-absorber.html

Making beautiful colours without toxic chemicals
Feb 2020, BBC News
https://www.bbc.com/news/business-51007426

Here's a few companies working in the new world of color:
San Francisco biotech firm Tinctorium, France's Pili and UK-based Colorifix, University of Cambridge and Dutch biotechnology company Hoekmine

Butterfly-inspired nanotech makes natural-looking pictures on digital screens
June 2020, phys.org
https://phys.org/news/2020-06-butterfly-inspired-nanotech-natural-looking-pictures-digital.html


Also first demonstration of black/grey colors in structural color display

On the Brains of Machines

This picture is kind of like an infrared camera but for algorithms.

It's a heat map for the eyeballs of a computer; what is it looking at, what are its clues?

In this case, it's looking at the water, not at the ship, in order to identify the image as a ship. (We're also assigning agency to this thing, in case anyone's keeping track.)

Neural nets are a big deal these days, but they come with a new problem. We don't know what they're doing, because the thing that makes them so special is that they figure out their own algorithm. (Agency again.) Computer programmers are not writing the programs; the networks write the programs using trial and error. Machine Learning is another name for this idea of iterative development.

There's a lot of people who would like to know what's going on in there, mostly to see how these things are getting their answers, and to make sure that the algorithms don't cheat to get their answers. Some learn bad habits, like detecting "ships" in pictures with water (which means they're good at detecting water, not ships), or by skimming metadata, which means they're good at classifying metadata, not pictures of stuff. These heat maps, and more importantly the forensics-like algorithms that inform them, are very helpful. They let us see inside the brains of the machine.

***

Speaking of disembodied brains, here's the artificial synapse. It uses a new type of hardware memory system that works more like a brain does, in an array, where they can do their computing business simultaneously. Neuromorphic computing.

And if you want to grow those artificial synapses in a 3-D tissue culture (brains in a dish), call these guys.

Cerebral organoids -- they're more for studying how the brain works than they are about making artificial brains. At least they're not using human brains, right?

Wrong; there are ethical concerns that these organoids might develop consciousness, or have already developed consciousness. 

Notes:

What is it like being a brain in a computer?
Clarifying how artificial intelligence systems make choices
Mar 2019, phys.org

Sebastian Lapuschkin et al, Unmasking Clever Hans predictors and assessing what machines really learn, Nature Communications (2019). DOI: 10.1038/s41467-019-08987-4

Fast, efficient and durable artificial synapse developed
Apr 2019, phys.org

Elliot J. Fuller et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing, Science (2019). DOI: 10.1126/science.aaw5581

Researchers grow active mini-brain-networks
Jun 2019, phys.org

Stem Cell Reports, Sakaguchi et al.: "Self-organized synchronous calcium transients in a cultured human neural network derived from cerebral organoids"
https://www.cell.com/stem-cell-reports/fulltext/S2213-6711(19)30197-3
DOI: 10.1016/j.stemcr.2019.05.029

The Distribution of Progress

The Spirit of Truth

In 2006, the internet becomes public access tv on steroids with the dawn of YouTube. In case you forgot:

The Spirit of Truth

Ten years later...

In India, many see fake news on YouTube thanks to cheap data plans
Mar 2018, NBC

In India, a country holding 1/3 the world's people, the internet has finally taken hold. And by that we mean they're watching YouTube and getting infected by fake viruses and engineered memes.

And it looks a lot different than the same moment in other parts of the developed world about ten years ago. It's good to remember comparisons like this, because this is how progress really works.

When we imagine the future, we see it through the eyes of our own culture. But the world is a big place, and not everyone is traveling at the same speed at the same time.

Uploading our brains to the datasphere and Christopher Columbusing Mars will not happen next year. If the whole world was made of South Korea and NASA, then yes, that might be the case. But as a whole, we are worlds apart. And the speed of progress is not distributed equally across the planet.

Hype Cycle

http://blogs.gartner.com

generally good reference for those who like to imagine the future...

Gartner's 2012 Hype Cycle for Emerging Technologies Identifies "Tipping Point" Technologies That Will Unlock Long-Awaited Technology Scenarios
August 16, 2012
http://www.gartner.com/it/page.jsp?id=2124315