Science: Research Rabbit Hole: Geology of Bermuda

I can’t even remember how it happened (perhaps it was triggered by sending a postcard to the French Overseas Department Réunion, which is an island off the coast of Madagascar, two days ago?), but I suddenly, very much needed to know how Bermuda, which lies in very isolated waters off the eastern coast of the United States, came to exist.

Answer: it is volcanic.

Bermuda Pedestal

The Bermuda Pedestal is an oval geological feature in the northern Atlantic Ocean containing the topographic highs of the Bermuda Platform, the Plantagenet (Argus) Bank, and the Challenger Bank. The pedestal is 50 km (31 mi) long and 25 km (16 mi) wide at the 100 fathom line (-185 m), while the base measures 130 km by 80 km at -4200 m.

I don’t think of the east coast of the US as volcanic generally, and while it is quite a distance from shore, it still feels like a surprise. A theory of a Bermuda Hotspot is uncertain.

I know our Pacific Ring of Fire isn’t the only site of tectonic plate volcanism, but outside of Iceland (which is quite wildly and unmistakably and actively volcanic), “Atlantic” and “volcanic” aren’t ideas that go together for me.

If Réunion did plant the conceptual seed of this need to know, it is likely because (yes) it is also volcanic, and the island has not one but TWO volcanoes: one dormant and the other quite active.

Piton de la Fournaise

Piton de la Fournaise ( French for “Peak of the Furnace”) is a shield volcano on the eastern side of Réunion island (a French department) in the Indian Ocean. It is currently one of the most active volcanoes in the world, along with Kīlauea in the Hawaiian Islands, Stromboli and Etna in Italy and Mount Erebus in Antarctica.

(Until sending this Réunion bound mail off, my prior association with Réunion was that a confirmed piece of missing flight plane MH-370 washed up there. )

Science/Culture: Enthusiasm for both David Bowie and Nudibranchs

Tumblr is a site famous for sites/pages dedicated to a single topic, with great enthusiasm. A friend shared this link, in which a fan of David Bowie and the glamorous nudibranchs (which are soft, festive molluscs), found a way to match particular outfits of Bowie’s with a corresponding nudibranch. (And here I’ve just been using software to identify wildflowers!)

I find the site adorable. The author, Hannah Weller, is obtaining appropriate source credits for the images, which is always a good thing!

Bowiebranchia

Pantone predicted this. and now, perhaps the most vital work I will ever do: using colordistance to objectively prove which David Bowie outfit most closely matches a given sea slug.

She is a marine biologist, and of course you can follow her on Twitter.

Hannah Weller

The latest Tweets from Hannah Weller (@hannahiweller). 🐠 PhD candidate @elbrainerd lab, studying how behavior ↔️ morphology by way of mouthbrooding fishes🐟 image processing enthusiast 🤖 ginger nut 🍪. Providence, RI

Science: Research Rabbit Hole: Comb Jellies

I like jelly fish, and I am not ashamed!

They are a beautiful feature of many aquariums here on the West Coast of the USA, and so this isn’t surprising. You can often find children AND adults staring, mesmerized by the peaceful movement of jellyfish in a tank with a vivid background, pretty lighting, and a slight current to keep the jellies swimming. [soft sigh here]

But I hadn’t heard of a “comb jelly,” until this article appeared in the UK Guardian: Warty comb jelly, scourge of fisheries, also eats its young. (Note to self: don’t call your comb jelly mother on mother’s day – it could lead to trouble!)

So that led to this Wikipedia article:

Ctenophora

Ctenophora (; singular ctenophore, or ; from Ancient Greek: κτείς, kteis, ‘comb’ and φέρω, pherō, ‘to carry’; commonly known as comb jellies) comprise a phylum of invertebrate animals that live in marine waters worldwide. They are notable for the groups of cilia they use for swimming (commonly referred to as “combs”), and they are the largest animals to swim with the help of cilia.

So I learned that Ctenophora are different from cnidarians (jellyfish, among others), and somehow, wound up reading about salps, which are also not jellyfish (I swear, my search was not, “not jellyfish,”), and which also look really awkward to swim into when they form long, slippery, transparent chains. The photos are wild:

Salp

A salp (plural salps) or salpa (plural salpae or salpas) is a barrel-shaped, planktic tunicate. It moves by contracting, thus pumping water through its gelatinous body, one of the most efficient examples of jet propulsion in the animal kingdom. The salp strains the pumped water through its internal feeding filters, feeding on phytoplankton.

I still like my local jellyfish (which I understand a little better), but knowing that there are salps further north along our coast, and that they are carbon-fixing, means I’ll keep an eye out for information about them on future marine biology research tagents.

Books: Department of Mind-Blowing Theories by Tom Gauld

Cover of Tom Gauld’s latest book

Department of Mind-Blowing Theories
by Tom Gauld
published by Drawn + Quarterly
2020

This is a charming book of science cartoons, which had previously appeared in New Scientist magazine, collected here by the excellent comic/graphic novel publishers at Drawn + Quarterly. They are subtle, funny, brainy cartoons with really fantastic contraptions, many explosions, heartless thesis committees, and at least one appearance of Cthulhu.

This book is for you if: you wish the text message “LOL!” really stood for “Let’s Observe Lobsters!”

(Speaking of LOL, I did laugh out loud at the bear cave strip, and several others.)

I want all of the contraptions.

Science: Jupiter

This is so beautiful, I can hardly stand it:

Astronomers capture new images of Jupiter using ‘lucky’ technique

Astronomers have captured some of the highest resolution images of Jupiter ever obtained from the ground using a technique known as “lucky imaging”. The observations, from the Gemini North telescope on Hawaii’s dormant volcano Mauna Kea, reveal lightning strikes and storm systems forming around deep clouds of water ice and liquid.

Science: Coronavirus Vaccine Approaches

As someone who has long conversations with a biologist friend about protein sciences, I have many opportunities to discuss and ask about science. Well, biology pal PYT came through by recommending this excellent feature in the April 30th issue of the science magazine Nature.

The race for coronavirus vaccines: a graphical guide

More than 90 vaccines are being developed against SARS-CoV-2 by research teams in companies and universities across the world. Researchers are trialling different technologies, some of which haven’t been used in a licensed vaccine before. At least six groups have already begun injecting formulations into volunteers in safety trials; others have started testing in animals.

News/Humor: Do Not Apply Lava To Your Skin

This started out as a sort of joke, but morphed into an excuse to learn about archaea!! 🙂

Can Lava Kill The Coronavirus? An Investigation

I was recently asked, via email, if lava can kill the new coronavirus. It can, but there’s a good reason why no-one is using it in the fight against the ongoing pandemic: nothing else would survive the encounter with molten rock either.

Archaea are interesting prokaryotes, and I’m happy this inspired me to read more about them – not just about the extremophiles, but (via Wikipedia) about their abundance just about everywhere, including inside us.

News: SF COVID-19 Data

My City believes in data! And it even develops graphics to display it so it can be easily interpreted and visualized.

This is a serious topic, and I appreciate how the City is making such an effort to be sure we UNDERSTAND it.

San Francisco COVID-19 Data and Reports

San Francisco’s response to the coronavirus emergency is grounded in data, science and facts. Data are an important tool to help San Franciscans see the whole picture of coronavirus in our community. It can help us all do our part and see over time how the situation is changing.

Advocacy: Algorithmic Justice League

After reading about all of the biases that can be introduced into AI systems which can have life-and-death influences on real humans in real life, I remembered reading a GREAT interview with the founder of the Algorithmic Justice League, who is helping people understand, and hopefully avoid, the dangers of blindly trusting software that may have profound flaws.

Prevent incorrectly trained software from mathwashing bad ideas!

AJL’s website is worth a visit:

Algorithmic Justice League – Unmasking AI harms and biases

Join the Algorithmic Justice League in the movement towards equitable and accountable AI. In today’s world, AI systems are used to decide who gets hired, the quality of medical treatment we receive, and whether we become a suspect in a police investigation.

Book: You Look Like a Thing and I Love You by Janelle Shane

Cover of You Look Like a Think and I Love You by Janelle Shane

You Look Like a Thing and I Love You
by Janelle Shane, Ph.D.
published by Voracious (Little, Brown and Company)
2019

I’ve enjoyed Janelle Shane’s site, aiweirdness.com, for some time, and when she mentioned that she had published a book on the same themes, I couldn’t resist it.

What are her themes? Machine learning, mostly, and how difficult it is to train a neural network to do what you really want it to do. You THINK you are training your software to recognize cancerous lumps, and it does well with your training data, but it doesn’t work so well in real life. In retrospect, you trained it with images of cancerous lumps that have rulers next to them to show the size of the lump, while no one cares (or measures) what size benign lumps are. Your program relied on the rulers to know whether or not a lump is cancerous: ruler = yes, no ruler = no. You invented… a RULER-DETECTOR.

Why I am reading about this geeky, specialist topic? I have to deal with the limitations of “AI”s of various designs all the time. Voicemail hell? That’s a not-very-intelligent program imitating an AI, possibly with AI voice recognition. Applying for a job? Software is screening my resume. Getting a laboratory test? Software may be screening that for me, too!

If you’ve ever gotten into an argument with your phone, you know that these programs are… not perfect. Depending on whether you have a high or low voice, they may not seem to work at all. My father is still amused that one of his friends couldn’t get her voice assistant on her phone to understand ANYTHING she said, but my father (who sounds like Darth Vader) could ALWAYS be understood. Why? Because it was trained this way.

Janelle Shane finds amusing ways to talk about how neural networks and other near-AI programs work, what they are good at, why they fail at so many tasks, and how the data sets they train on can make them vulnerable to manipulation.

You will laugh, as I did, as an AI trained to generate metal band names learns to generate ice cream flavors! You’ll laugh often, really: Ms. Shane has some good stories, and good quotes from people who fought to teach their AI something specific, and their AI interpreted them literally and won. The challenges she sets up for the simple neural nets she build are VERY FUNNY.

It isn’t just jokes and witty examples: you won’t laugh at the idea of a navigation-bot telling you to drive TOWARD a fire (because there is less traffic in that direction!), nor at racial and gender biases that oblivious employees train software with, nor at the fact that image recognition programs that train on the same free (manipulatable) data sets can be mis-trained to see things that aren’t visible / obvious / correct to humans.

Maybe there’s a rare but catastrophic bug that develops, like the one that affected Siri for a brief period of time, causing her to respond to users saying “Call me an ambulance” with “Okay, I’ll call you ‘an ambulance’ from now on.”

Excerpt From: Janelle Shane. “You Look Like a Thing and I Love You.” Apple Books. https://books.apple.com/us/book/you-look-like-a-thing-and-i-love-you/id1455076486

It is good (and refreshing) to truly think about the serious implications of our rush to be dependent upon machines, and the hazy way we think that machines are neutral decision makers, when nearly every application we have developed for them is not neutral in inputs, programming, or impact.