'Forever chemicals' destroyed by simple new method
PFAS, a group of manufactured chemicals commonly used since the 1940s, are called "forever chemicals" for a reason. Bacteria can't eat them; fire can't incinerate them; and water can't dilute them. And, if these toxic chemicals are buried, they leach into surrounding soil, becoming a persistent problem for generations to come.
Now, Northwestern University chemists have done the seemingly impossible. Using low temperatures and inexpensive, common reagents, the research team developed a process that causes two major classes of PFAS compounds to fall apart, leaving behind only benign end products.
The simple technique potentially could be a powerful solution for finally disposing of these harmful chemicals, which are linked to many dangerous health effects in humans, livestock and the environment.
"PFAS has become a major societal problem," said Northwestern's William Dichtel, who led the study. "Even just a tiny, tiny amount of PFAS causes negative health effects, and it does not break down. We can't just wait out this problem. We wanted to use chemistry to address this problem and create a solution that the world can use. It's exciting because of how simple—yet unrecognized—our solution is."
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The real AI fight
Tonight (November 27), I'm appearing at the Toronto Metro Reference Library with Facebook whistleblower Frances Haugen.
On November 29, I'm at NYC's Strand Books with my novel The Lost Cause, a solarpunk tale of hope and danger that Rebecca Solnit called "completely delightful."
Last week's spectacular OpenAI soap-opera hijacked the attention of millions of normal, productive people and nonsensually crammed them full of the fine details of the debate between "Effective Altruism" (doomers) and "Effective Accelerationism" (AKA e/acc), a genuinely absurd debate that was allegedly at the center of the drama.
Very broadly speaking: the Effective Altruists are doomers, who believe that Large Language Models (AKA "spicy autocomplete") will someday become so advanced that it could wake up and annihilate or enslave the human race. To prevent this, we need to employ "AI Safety" – measures that will turn superintelligence into a servant or a partner, nor an adversary.
Contrast this with the Effective Accelerationists, who also believe that LLMs will someday become superintelligences with the potential to annihilate or enslave humanity – but they nevertheless advocate for faster AI development, with fewer "safety" measures, in order to produce an "upward spiral" in the "techno-capital machine."
Once-and-future OpenAI CEO Altman is said to be an accelerationists who was forced out of the company by the Altruists, who were subsequently bested, ousted, and replaced by Larry fucking Summers. This, we're told, is the ideological battle over AI: should cautiously progress our LLMs into superintelligences with safety in mind, or go full speed ahead and trust to market forces to tame and harness the superintelligences to come?
This "AI debate" is pretty stupid, proceeding as it does from the foregone conclusion that adding compute power and data to the next-word-predictor program will eventually create a conscious being, which will then inevitably become a superbeing. This is a proposition akin to the idea that if we keep breeding faster and faster horses, we'll get a locomotive:
https://locusmag.com/2020/07/cory-doctorow-full-employment/
As Molly White writes, this isn't much of a debate. The "two sides" of this debate are as similar as Tweedledee and Tweedledum. Yes, they're arrayed against each other in battle, so furious with each other that they're tearing their hair out. But for people who don't take any of this mystical nonsense about spontaneous consciousness arising from applied statistics seriously, these two sides are nearly indistinguishable, sharing as they do this extremely weird belief. The fact that they've split into warring factions on its particulars is less important than their unified belief in the certain coming of the paperclip-maximizing apocalypse:
https://newsletter.mollywhite.net/p/effective-obfuscation
White points out that there's another, much more distinct side in this AI debate – as different and distant from Dee and Dum as a Beamish Boy and a Jabberwork. This is the side of AI Ethics – the side that worries about "today’s issues of ghost labor, algorithmic bias, and erosion of the rights of artists and others." As White says, shifting the debate to existential risk from a future, hypothetical superintelligence "is incredibly convenient for the powerful individuals and companies who stand to profit from AI."
After all, both sides plan to make money selling AI tools to corporations, whose track record in deploying algorithmic "decision support" systems and other AI-based automation is pretty poor – like the claims-evaluation engine that Cigna uses to deny insurance claims:
https://www.propublica.org/article/cigna-pxdx-medical-health-insurance-rejection-claims
On a graph that plots the various positions on AI, the two groups of weirdos who disagree about how to create the inevitable superintelligence are effectively standing on the same spot, and the people who worry about the actual way that AI harms actual people right now are about a million miles away from that spot.
There's that old programmer joke, "There are 10 kinds of people, those who understand binary and those who don't." But of course, that joke could just as well be, "There are 10 kinds of people, those who understand ternary, those who understand binary, and those who don't understand either":
https://pluralistic.net/2021/12/11/the-ten-types-of-people/
What's more, the joke could be, "there are 10 kinds of people, those who understand hexadecenary, those who understand pentadecenary, those who understand tetradecenary [und so weiter] those who understand ternary, those who understand binary, and those who don't." That is to say, a "polarized" debate often has people who hold positions so far from the ones everyone is talking about that those belligerents' concerns are basically indistinguishable from one another.
The act of identifying these distant positions is a radical opening up of possibilities. Take the indigenous philosopher chief Red Jacket's response to the Christian missionaries who sought permission to proselytize to Red Jacket's people:
https://historymatters.gmu.edu/d/5790/
Red Jacket's whole rebuttal is a superb dunk, but it gets especially interesting where he points to the sectarian differences among Christians as evidence against the missionary's claim to having a single true faith, and in favor of the idea that his own people's traditional faith could be co-equal among Christian doctrines.
The split that White identifies isn't a split about whether AI tools can be useful. Plenty of us AI skeptics are happy to stipulate that there are good uses for AI. For example, I'm 100% in favor of the Human Rights Data Analysis Group using an LLM to classify and extract information from the Innocence Project New Orleans' wrongful conviction case files:
https://hrdag.org/tech-notes/large-language-models-IPNO.html
Automating "extracting officer information from documents – specifically, the officer's name and the role the officer played in the wrongful conviction" was a key step to freeing innocent people from prison, and an LLM allowed HRDAG – a tiny, cash-strapped, excellent nonprofit – to make a giant leap forward in a vital project. I'm a donor to HRDAG and you should donate to them too:
https://hrdag.networkforgood.com/
Good data-analysis is key to addressing many of our thorniest, most pressing problems. As Ben Goldacre recounts in his inaugural Oxford lecture, it is both possible and desirable to build ethical, privacy-preserving systems for analyzing the most sensitive personal data (NHS patient records) that yield scores of solid, ground-breaking medical and scientific insights:
https://www.youtube.com/watch?v=_-eaV8SWdjQ
The difference between this kind of work – HRDAG's exoneration work and Goldacre's medical research – and the approach that OpenAI and its competitors take boils down to how they treat humans. The former treats all humans as worthy of respect and consideration. The latter treats humans as instruments – for profit in the short term, and for creating a hypothetical superintelligence in the (very) long term.
As Terry Pratchett's Granny Weatherwax reminds us, this is the root of all sin: "sin is when you treat people like things":
https://brer-powerofbabel.blogspot.com/2009/02/granny-weatherwax-on-sin-favorite.html
So much of the criticism of AI misses this distinction – instead, this criticism starts by accepting the self-serving marketing claim of the "AI safety" crowd – that their software is on the verge of becoming self-aware, and is thus valuable, a good investment, and a good product to purchase. This is Lee Vinsel's "Criti-Hype": "taking press releases from startups and covering them with hellscapes":
https://sts-news.medium.com/youre-doing-it-wrong-notes-on-criticism-and-technology-hype-18b08b4307e5
Criti-hype and AI were made for each other. Emily M Bender is a tireless cataloger of criti-hypeists, like the newspaper reporters who breathlessly repeat " completely unsubstantiated claims (marketing)…sourced to Altman":
https://dair-community.social/@emilymbender/111464030855880383
Bender, like White, is at pains to point out that the real debate isn't doomers vs accelerationists. That's just "billionaires throwing money at the hope of bringing about the speculative fiction stories they grew up reading – and philosophers and others feeling important by dressing these same silly ideas up in fancy words":
https://dair-community.social/@emilymbender/111464024432217299
All of this is just a distraction from real and important scientific questions about how (and whether) to make automation tools that steer clear of Granny Weatherwax's sin of "treating people like things." Bender – a computational linguist – isn't a reactionary who hates automation for its own sake. On Mystery AI Hype Theater 3000 – the excellent podcast she co-hosts with Alex Hanna – there is a machine-generated transcript:
https://www.buzzsprout.com/2126417
There is a serious, meaty debate to be had about the costs and possibilities of different forms of automation. But the superintelligence true-believers and their criti-hyping critics keep dragging us away from these important questions and into fanciful and pointless discussions of whether and how to appease the godlike computers we will create when we disassemble the solar system and turn it into computronium.
The question of machine intelligence isn't intrinsically unserious. As a materialist, I believe that whatever makes me "me" is the result of the physics and chemistry of processes inside and around my body. My disbelief in the existence of a soul means that I'm prepared to think that it might be possible for something made by humans to replicate something like whatever process makes me "me."
Ironically, the AI doomers and accelerationists claim that they, too, are materialists – and that's why they're so consumed with the idea of machine superintelligence. But it's precisely because I'm a materialist that I understand these hypotheticals about self-aware software are less important and less urgent than the material lives of people today.
It's because I'm a materialist that my primary concerns about AI are things like the climate impact of AI data-centers and the human impact of biased, opaque, incompetent and unfit algorithmic systems – not science fiction-inspired, self-induced panics over the human race being enslaved by our robot overlords.
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space
Image:
Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg
CC BY 3.0
https://creativecommons.org/licenses/by/3.0/deed.en
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Paywall free version! LEGALLY paywall free version, even!
“Nearly any material can be used to turn the energy in air humidity into electricity, scientists found in a discovery that could lead to continuously producing clean energy with little pollution.
The research, published in a paper in Advanced Materials, builds on 2020 work that first showed energy could be pulled from the moisture in the air using material harvested from bacteria. The new study shows nearly any material can be used, like wood or silicon, as long as it can be smashed into small particles and remade with microscopic pores. But there are many questions about how to scale the product.
“What we have invented, you can imagine it’s like a small-scale, man-made cloud,” said Jun Yao, a professor of engineering at the University of Massachusetts at Amherst and the senior author of the study. “This is really a very easily accessible, enormous source of continuous clean electricity. Imagine having clean electricity available wherever you go.”
That could include a forest, while hiking on a mountain, in a desert, in a rural village or on the road.
The air-powered generator, known as an “Air-gen,” would offer continuous clean electricity since it uses the energy from humidity, which is always present, rather than depending on the sun or wind. Unlike solar panels or wind turbines, which need specific environments to thrive, Air-gens could conceivably go anywhere, Yao said.
Less humidity, though, would mean less energy could be harvested, he added. Winters, with dryer air, would produce less energy than summers.
The device, the size of a fingernail and thinner than a single hair, is dotted with tiny holes known as nanopores. The holes have a diameter smaller than 100 nanometers, or less than a thousandth of the width of a strand of human hair.
The tiny holes allow the water in the air to pass through in a way that would create a charge imbalance in the upper and lower parts of the device, effectively creating a battery that runs continuously.
“We are opening up a wide door for harvesting clean electricity from thin air,” Xiaomeng Liu, another author and a UMass engineering graduate student, said in a statement.
While one prototype only produces a small amount of energy — almost enough to power a dot of light on a big screen — because of its size, Yao said Air-gens can be stacked on top of each other, potentially with spaces of air in between. Storing the electricity is a separate issue, he added.
Yao estimated that roughly 1 billion Air-gens, stacked to be roughly the size of a refrigerator, could produce a kilowatt and partly power a home in ideal conditions. The team hopes to lower both the number of devices needed and the space they take up by making the tool more efficient. Doing that could be a challenge.
The scientists first must work out which material would be most efficient to use in different climates. Eventually, Yao said he hopes to develop a strategy to make the device bigger without blocking the humidity that can be captured. He also wants to figure out how to stack the devices on top of each other effectively and how to engineer the Air-gen so the same size device captures more energy.
It’s not clear how long that will take.
“Once we optimize this, you can put it anywhere,” Yao said.
It could be embedded in wall paint in a home, made at a larger scale in unused space in a city or littered throughout an office’s hard-to-get-to spaces. And because it can use nearly any material, it could extract less from the environment than other renewable forms of energy.
“The entire earth is covered with a thick layer of humidity,” Yao said. “It’s an enormous source of clean energy. This is just the beginning in making use of that.””
-via The Washington Post, 5/26/23
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Building a transistor out of treated wood
A team of organic chemists and engineers from Linköping University and KTH Royal Institute of Technology, both in Sweden, has demonstrated that working transistors can be made from treated wood. The results have been published in Proceedings of the National Academy of Sciences.
Transistors are devices that switch or amplify electrical signals in a larger device. Scientists have, over the years, learned to make them ever smaller—currently, billons of them can fit on a single computer chip. Most transistors are limited to use in certain materials—those on a chip, for example, exist on a base of the semiconducting material, silicon. In this new effort, the team in Sweden looked into the possibility of creating transistors that could be used in bioelectronic products, or even purely plant-based devices. To test the possibility, they created a transistor out of wood and a few other materials.
The team tested a variety of tree types and found that balsa seemed to suit their needs best due to its strength, permeability and low density. They started by bathing small strips of the wood in a chemical bath to remove some of its lignin, making it more porous. Then they forced a conductive type of plastic called PEDOT:PSS into the small vessels of the wood normally used for water transport, which coated the vessel walls.
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