Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.
Fast for Longevity, with Less Hunger, with Dr. Valter Longo
You’ve probably heard about intermittent fasting, where you don’t eat for about 16 hours each day and limit the window where you’re taking in food to the remaining eight hours.
But there’s another type of fasting, called a fasting-mimicking diet, with studies pointing to important benefits. For today’s podcast episode, I chatted with Dr. Valter Longo, a biogerontologist at the University of Southern California, about all kinds of fasting, and particularly the fasting-mimicking diet, which minimizes hunger as much as possible. Going without food for a period of time is an example of good stress: challenges that work at the cellular level to boost health and longevity.
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If you’ve ever spent more than a few minutes looking into fasting, you’ve almost certainly come upon Dr. Longo's name. He is the author of the bestselling book, The Longevity Diet, and the best known researcher of fasting-mimicking diets.
With intermittent fasting, your body might begin to switch up its fuel type. It's usually running on carbs you get from food, which gets turned into glucose, but without food, your liver starts making something called ketones, which are molecules that may benefit the body in a number of ways.
With the fasting-mimicking diet, you go for several days eating only types of food that, in a way, keep themselves secret from your body. So at the level of your cells, the body still thinks that it’s fasting. This is the best of both worlds – you’re not completely starving because you do take in some food, and you’re getting the boosts to health that come with letting a fast run longer than intermittent fasting. In this episode, Dr. Longo talks about the growing number of studies showing why this could be very advantageous for health, as long as you undertake the diet no more than a few times per year.
Dr. Longo is the director of the Longevity Institute at USC’s Leonard Davis School of Gerontology, and the director of the Longevity and Cancer program at the IFOM Institute of Molecular Oncology in Milan. In addition, he's the founder and president of the Create Cures Foundation in L.A., which focuses on nutrition for the prevention and treatment of major chronic illnesses. In 2016, he received the Glenn Award for Research on Aging for the discovery of genes and dietary interventions that regulate aging and prevent diseases. Dr. Longo received his PhD in biochemistry from UCLA and completed his postdoc in the neurobiology of aging and Alzheimer’s at USC.
Show links:
Create Cures Foundation, founded by Dr. Longo: www.createcures.org
Dr. Longo's Facebook: https://www.facebook.com/profvalterlongo/
Dr. Longo's Instagram: https://www.instagram.com/prof_valterlongo/
Dr. Longo's book: The Longevity Diet
The USC Longevity Institute: https://gero.usc.edu/longevity-institute/
Dr. Longo's research on nutrition, longevity and disease: https://pubmed.ncbi.nlm.nih.gov/35487190/
Dr. Longo's research on fasting mimicking diet and cancer: https://pubmed.ncbi.nlm.nih.gov/34707136/
Full list of Dr. Longo's studies: https://pubmed.ncbi.nlm.nih.gov/?term=Longo%2C+Valter%5BAuthor%5D&sort=date
Research on MCT oil and Alzheimer's: https://alz-journals.onlinelibrary.wiley.com/doi/f...
Keto Mojo device for measuring ketones
Silkworms with spider DNA spin silk stronger than Kevlar
Story by Freethink
The study and copying of nature’s models, systems, or elements to address complex human challenges is known as “biomimetics.” Five hundred years ago, an elderly Italian polymath spent months looking at the soaring flight of birds. The result was Leonardo da Vinci’s biomimetic Codex on the Flight of Birds, one of the foundational texts in the science of aerodynamics. It’s the science that elevated the Wright Brothers and has yet to peak.
Today, biomimetics is everywhere. Shark-inspired swimming trunks, gecko-inspired adhesives, and lotus-inspired water-repellents are all taken from observing the natural world. After millions of years of evolution, nature has quite a few tricks up its sleeve. They are tricks we can learn from. And now, thanks to some spider DNA and clever genetic engineering, we have another one to add to the list.
The elusive spider silk
We’ve known for a long time that spider silk is remarkable, in ways that synthetic fibers can’t emulate. Nylon is incredibly strong (it can support a lot of force), and Kevlar is incredibly tough (it can absorb a lot of force). But neither is both strong and tough. In all artificial polymeric fibers, strength and toughness are mutually exclusive, and so we pick the material best for the job and make do.
Spider silk, a natural polymeric fiber, breaks this rule. It is somehow both strong and tough. No surprise, then, that spider silk is a source of much study.The problem, though, is that spiders are incredibly hard to cultivate — let alone farm. If you put them together, they will attack and kill each other until only one or a few survive. If you put 100 spiders in an enclosed space, they will go about an aggressive, arachnocidal Hunger Games. You need to give each its own space and boundaries, and a spider hotel is hard and costly. Silkworms, on the other hand, are peaceful and productive. They’ll hang around all day to make the silk that has been used in textiles for centuries. But silkworm silk is fragile. It has very limited use.
The elusive – and lucrative – trick, then, would be to genetically engineer a silkworm to produce spider-quality silk. So far, efforts have been fruitless. That is, until now.
We can have silkworms creating silk six times as tough as Kevlar and ten times as strong as nylon.
Spider-silkworms
Junpeng Mi and his colleagues working at Donghua University, China, used CRISPR gene-editing technology to recode the silk-creating properties of a silkworm. First, they took genes from Araneus ventricosus, an East Asian orb-weaving spider known for its strong silk. Then they placed these complex genes – genes that involve more than 100 amino acids – into silkworm egg cells. (This description fails to capture how time-consuming, technical, and laborious this was; it’s a procedure that requires hundreds of thousands of microinjections.)
This had all been done before, and this had failed before. Where Mi and his team succeeded was using a concept called “localization.” Localization involves narrowing in on a very specific location in a genome. For this experiment, the team from Donghua University developed a “minimal basic structure model” of silkworm silk, which guided the genetic modifications. They wanted to make sure they had the exactly right transgenic spider silk proteins. Mi said that combining localization with this basic structure model “represents a significant departure from previous research.” And, judging only from the results, he might be right. Their “fibers exhibited impressive tensile strength (1,299 MPa) and toughness (319 MJ/m3), surpassing Kevlar’s toughness 6-fold.”
A world of super-materials
Mi’s research represents the bursting of a barrier. It opens up hugely important avenues for future biomimetic materials. As Mi puts it, “This groundbreaking achievement effectively resolves the scientific, technical, and engineering challenges that have hindered the commercialization of spider silk, positioning it as a viable alternative to commercially synthesized fibers like nylon and contributing to the advancement of ecological civilization.”
Around 60 percent of our clothing is made from synthetic fibers like nylon, polyester, and acrylic. These plastics are useful, but often bad for the environment. They shed into our waterways and sometimes damage wildlife. The production of these fibers is a source of greenhouse gas emissions. Now, we have a “sustainable, eco-friendly high-strength and ultra-tough alternative.” We can have silkworms creating silk six times as tough as Kevlar and ten times as strong as nylon.
We shouldn’t get carried away. This isn’t going to transform the textiles industry overnight. Gene-edited silkworms are still only going to produce a comparatively small amount of silk – even if farmed in the millions. But, as Mi himself concedes, this is only the beginning. If Mi’s localization and structure-model techniques are as remarkable as they seem, then this opens up the door to a great many supermaterials.
Nature continues to inspire. We had the bird, the gecko, and the shark. Now we have the spider-silkworm. What new secrets will we unravel in the future? And in what exciting ways will it change the world?