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 bookThe 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.
“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.
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.
Story by Freethink
Try burning an iron metal ingot and you’ll have to wait a long time — but grind it into a powder and it will readily burst into flames. That’s how sparklers work: metal dust burning in a beautiful display of light and heat. But could we burn iron for more than fun? Could this simple material become a cheap, clean, carbon-free fuel?
In new experiments — conducted on rockets, in microgravity — Canadian and Dutch researchers are looking at ways of boosting the efficiency of burning iron, with a view to turning this abundant material — the fourth most common in the Earth’s crust, about about 5% of its mass — into an alternative energy source.
Iron as a fuel
Iron is abundantly available and cheap. More importantly, the byproduct of burning iron is rust (iron oxide), a solid material that is easy to collect and recycle. Neither burning iron nor converting its oxide back produces any carbon in the process.
Iron oxide is potentially renewable by reacting with electricity or hydrogen to become iron again.
Iron has a high energy density: it requires almost the same volume as gasoline to produce the same amount of energy. However, iron has poor specific energy: it’s a lot heavier than gas to produce the same amount of energy. (Think of picking up a jug of gasoline, and then imagine trying to pick up a similar sized chunk of iron.) Therefore, its weight is prohibitive for many applications. Burning iron to run a car isn’t very practical if the iron fuel weighs as much as the car itself.
In its powdered form, however, iron offers more promise as a high-density energy carrier or storage system. Iron-burning furnaces could provide direct heat for industry, home heating, or to generate electricity.
Plus, iron oxide is potentially renewable by reacting with electricity or hydrogen to become iron again (as long as you’ve got a source of clean electricity or green hydrogen). When there’s excess electricity available from renewables like solar and wind, for example, rust could be converted back into iron powder, and then burned on demand to release that energy again.
However, these methods of recycling rust are very energy intensive and inefficient, currently, so improvements to the efficiency of burning iron itself may be crucial to making such a circular system viable.
The science of discrete burning
Powdered particles have a high surface area to volume ratio, which means it is easier to ignite them. This is true for metals as well.
Under the right circumstances, powdered iron can burn in a manner known as discrete burning. In its most ideal form, the flame completely consumes one particle before the heat radiating from it combusts other particles in its vicinity. By studying this process, researchers can better understand and model how iron combusts, allowing them to design better iron-burning furnaces.
Discrete burning is difficult to achieve on Earth. Perfect discrete burning requires a specific particle density and oxygen concentration. When the particles are too close and compacted, the fire jumps to neighboring particles before fully consuming a particle, resulting in a more chaotic and less controlled burn.
Presently, the rate at which powdered iron particles burn or how they release heat in different conditions is poorly understood. This hinders the development of technologies to efficiently utilize iron as a large-scale fuel.
Burning metal in microgravity
In April, the European Space Agency (ESA) launched a suborbital “sounding” rocket, carrying three experimental setups. As the rocket traced its parabolic trajectory through the atmosphere, the experiments got a few minutes in free fall, simulating microgravity.
One of the experiments on this mission studied how iron powder burns in the absence of gravity.
In microgravity, particles float in a more uniformly distributed cloud. This allows researchers to model the flow of iron particles and how a flame propagates through a cloud of iron particles in different oxygen concentrations.
Existing fossil fuel power plants could potentially be retrofitted to run on iron fuel.
Insights into how flames propagate through iron powder under different conditions could help design much more efficient iron-burning furnaces.
Clean and carbon-free energy on Earth
Various businesses are looking at ways to incorporate iron fuels into their processes. In particular, it could serve as a cleaner way to supply industrial heat by burning iron to heat water.
For example, Dutch brewery Swinkels Family Brewers, in collaboration with the Eindhoven University of Technology, switched to iron fuel as the heat source to power its brewing process, accounting for 15 million glasses of beer annually. Dutch startup RIFT is running proof-of-concept iron fuel power plants in Helmond and Arnhem.
As researchers continue to improve the efficiency of burning iron, its applicability will extend to other use cases as well. But is the infrastructure in place for this transition?
Often, the transition to new energy sources is slowed by the need to create new infrastructure to utilize them. Fortunately, this isn’t the case with switching from fossil fuels to iron. Since the ideal temperature to burn iron is similar to that for hydrocarbons, existing fossil fuel power plants could potentially be retrofitted to run on iron fuel.
Tom Oxley is building what he calls a “natural highway into the brain” that lets people use their minds to control their phones and computers. The device, called the Stentrode, could improve the lives of hundreds of thousands of people living with spinal cord paralysis, ALS and other neurodegenerative diseases.
Leaps.org talked with Dr. Oxley for today’s podcast. A fascinating thing about the Stentrode is that it works very differently from other “brain computer interfaces” you may be familiar with, like Elon Musk’s Neuralink. Some BCIs are implanted by surgeons directly into a person’s brain, but the Stentrode is much less invasive. Dr. Oxley’s company, Synchron, opts for a “natural” approach, using stents in blood vessels to access the brain. This offers some major advantages to the handful of people who’ve already started to use the Stentrode.
The audio of this episode improves about 10 minutes in. (There was a minor headset issue early on, but everything is audible throughout.) Dr. Oxley’s work creates game-changing opportunities for patients desperate for new options. His take on where we're headed with BCIs is must listening for anyone who cares about the future of health and technology.
In our conversation, Dr. Oxley talks about “Bluetooth brain”; the critical role of AI in the present and future of BCIs; how BCIs compare to voice command technology; regulatory frameworks for revolutionary technologies; specific people with paralysis who’ve been able to regain some independence thanks to the Stentrode; what it means to be a neurointerventionist; how to scale BCIs for more people to use them; the risks of BCIs malfunctioning; organic implants; and how BCIs help us understand the brain, among other topics.
Dr. Oxley received his PhD in neuro engineering from the University of Melbourne in Australia. He is the founding CEO of Synchron and an associate professor and the head of the vascular bionics laboratory at the University of Melbourne. He’s also a clinical instructor in the Deepartment of Neurosurgery at Mount Sinai Hospital. Dr. Oxley has completed more than 1,600 endovascular neurosurgical procedures on patients, including people with aneurysms and strokes, and has authored over 100 peer reviewed articles.
Synchron website - https://synchron.com/
Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients (paper co-authored by Tom Oxley) - https://jamanetwork.com/journals/jamaneurology/art...
More research related to Synchron's work - https://synchron.com/research
Tom Oxley on LinkedIn - https://www.linkedin.com/in/tomoxl
Tom Oxley on Twitter - https://twitter.com/tomoxl?lang=en
Tom Oxley website - https://tomoxl.com/
Novel brain implant helps paralyzed woman speak using digital avatar - https://engineering.berkeley.edu/news/2023/08/novel-brain-implant-helps-paralyzed-woman-speak-using-a-digital-avatar/
Edward Chang lab - https://changlab.ucsf.edu/
BCIs convert brain activity into text at 62 words per minute - https://med.stanford.edu/neurosurgery/news/2023/he...
Leaps.org: The Mind-Blowing Promise of Neural Implants - https://leaps.org/the-mind-blowing-promise-of-neural-implants/