An Electrifying Idea For Roads
Starting this summer, the public buses in the Oberhaching suburb of Munich, Germany, won’t have to be plugged in to charge overnight anymore. Stefan Schelle, the mayor of Oberhaching, is taking advantage of the fact that an innovative startup has its offices in his community: Magment, short for “magnetizing cement,” will install its underground charging pad in the coming months. As soon as that happens, the buses will charge while they wait at the city’s main station or while stored at their overnight quarters.
In his light-filled office, Magment’s co-founder and CEO, Mauricio Esguerra, demonstrates how the new technology works: The lights on his black model car only flash when he puts the miniature Porsche directly atop the induction plate. “This works just like when you charge your iPhone on its charging pad or heat a pot on an induction range. People don’t have to be afraid of magnetic fields or anything like that,” says the 60-year-old Colombia-born entrepreneur. “The induction only gets activated when the storage battery is placed directly on top.
Patented by Esguerra, the “magnetizing concrete” is able to target the charge quite precisely. The batteries will be mounted in a box underneath the vehicles such as the retrofitted public buses. “Look, here’s one passing by,” says Esguerra, pointing out the window as a blue city bus rides past his office.
An invention finds its purpose
Esguerra grew up in Bogotá, studied physics at the Technical University Munich where he fell in love with a German woman, and started a family in her home country. For 15 years, he developed magnetic products, including the magnetizing cement, for Siemens, Europe’s largest industrial manufacturing company. The patent belonged to Siemens, of course. “But there were hardly any electric vehicles yet,” Esguerra says, “and Siemens didn’t quite know what to do with this invention.”
Esguerra changed companies a few times but, in 2015, he got an offer from Siemens. The patent for the magnetizing cement was expiring and Siemens wasn’t interested in keeping it. Would he, as the inventor, want it back? “I did not hesitate a second,” Esguerra remembers with a smile. “I’m a magnetician at heart.” That same year, he founded Magment to finally make use of the technology he created 20 years ago.
To demonstrate how his cement is made, he opens the lid of a plastic bucket filled with cement powder. Mixed in are fingernail-sized black pieces, so-called ferrites, mainly consisting of three ceramic oxides: iron, nickel and zinc. Conventionally, they are used in electronics such as cell phones, computers and cables. Molded in concrete, ferrites create a magnetic field that can transport charge to a vehicle, potentially eliminating range anxiety for EV drivers.
Molded in concrete, ferrites create a magnetic field that can transport charge to a vehicle, potentially eliminating range anxiety for EV drivers.
Magment
“Ferrites have extremely high rejection rates,” Esguerra adds. “It’s comparable to other ceramics: As soon as there is a small tear or crack, the material is rejected. We are talking about a rejection pile of 500,000 tons per year worldwide. There are mountains of unused materials.”
Exactly this fact was the starting point of his research at Siemens: “What can we do with this energy-intensive material? Back then, it was crushed up and mixed into the cement for building streets, without adding any function.” Today, too, the Magment material can simply be mixed with the conventional material and equipment of the cement industry. “We take advantage of the fact that we don’t have to build factories and don’t have high transportation costs."
In addition to saving resources, recycled ferrite also makes concrete more durable.
No plugs, no charging breaks
A young intern in the office next door winds cables around a new coil. These coils will later be lowered underground in a box, connected to the grid and encased in magnetizing concrete. The recipient box looks similar; it’s another coil but smaller, and it will be mounted underneath the carriage of the vehicle. For a car, the battery box would be 25 by 25 centimeters (about 10 inches), for a scooter five by five centimeters (about two inches).
Esguerra pushes an electric scooter into a cemented scooter rack next to his office. The charging pad is invisible. A faint beep is the only sign that it has started charging. “Childs play!” Esguerra says. “Even when someone puts in the scooter a little crooked, the charge still works. Our efficiency rate is up to 96 percent.” From this summer on, hotel chains in Munich will try out this system with their rental scooters, at a price of about 500 Euros per charging station.
Compared to plug-in charging, Magment’s benefits include smaller batteries that charge slower and, therefore, gentler, so they may last longer. Nobody needs to plug in the vehicles manually anymore. “Personally, I’ve had an EV for six years,” Esguerra says, “and how often does it happen that I forgot to plug it in overnight and then start out with a low charge in the morning? Once people get used to the invisible charging system, it will become the norm.“
There are also downsides: Most car companies aren’t ready for the new technology. Hyundai is the first carmaker that announced plans to equip some new models with inductive charging capability. “How many cars are electrified worldwide?” Esguerra asks and gives the answer himself: “One percent. And how many forklifts are electrified? More than 70 percent!” Therefore, Magment focuses on charging forklifts, e-scooters and buses.
Magment has focused most of its efforts on charging forklifts and other vehicle types that are entirely or predominantly electric, unlike cars.
Magment
On the morning of my visit to Esguerra’s office, a developer of the world’s third-biggest forklift manufacturer is there to inspect how the technology works on the ground. In the basement, a Magment engineer drives an electric forklift over a testbed with invisible charging coils, turning on the green charging light. Esguerra opens the interior of the forklift and points out the two batteries. “With our system, the forklift will only need one battery.” The savings, about 7,000 Euro per forklift, will pay for the installation of Magment’s charging system in warehouses, Esguerra calculates. “Less personnel and no unnecessary wait times for charging will lead to further savings,” he says.
To implement the new technology as efficiently as possible, Magment engineers began recording the transport routes of forklifts in warehouses. “It looks like spaghetti diagrams,” Esguerra explains. “Soon you get the areas where the forklifts pass or wait most frequently. This is where you install the chargers underground.” The forklifts will charge while in use, without having to pause for charging breaks. The method could also work for robots, for instance, in warehouses and distribution centers.
Roads of the future could be electric
Potential disadvantages might become apparent once the technology is more broadly in use. Therefore investors were initially reluctant, Esguerra admits. “Some are eager to be the first but most prefer to wait until the technology has been extensively used in real life.”
A clear hurdle today is that electrifying entire freeways with induction coils would cost at least 1 to 1.5 million Euros per kilometer. The German Department for Transportation even calculates overall costs of 14 to 47 million Euros per kilometer. So, the technology may only make sense for areas where vehicles pass or dwell the longest, like the Oberhaching train station or a busy interstate toll booth.
And yet, Magment is ramping up to compete with other companies that build larger inductive charging pads. The company just finished the first 20 meters of a testbed in Indiana, in partnership with the Purdue University and the Indiana Department of Transportation. Magment is poised to build “the world’s first contactless wireless-charging concrete pavement highway segment,” Purdue University announced.
The project, part of Purdue’s ASPIRE (Advancing Sustainability through Powered Infrastructure for Roadway Electrification) program, is financed by the National Science Foundation. “Indiana is known as the Crossroads of America, and we’re committed to fortifying our position as a transportation leader by innovating to support the emerging vehicle technology,” Governor Eric J. Holcomb said. If testing is successful, including the concrete’s capacity to charge heavy trucks operating at higher power (200 kilowatts and above), Indiana plans to identify a highway segment to install Magment’s charging pads. The earliest would be 2023 at best.
In the meantime, buses in the Californian Antelope Valley, trams at Hollywood's Universal Studios and transit buses in Tampa, Florida, are already charging with inductive technology developed by Wave, a company spun out of Utah State University. In Michigan, Governor Gretchen Whitmer announced plans to build a test route for vehicles to charge while driving, in collaboration with the Israel-based company Electreon, and this year contracted to build the first road-based charging system in the U.S. The state is providing support through an innovative grant program.
Costs remain one of the biggest obstacles, but Esguerra’s vision includes the potential that toll roads could charge a premium for inductive charging capabilities. “And in reverse, a driver who has too much energy could feed his surplus into the grid while driving,” Esguerra dreams.
Meanwhile, Wave’s upcoming big projects are moving trucks along a route in Southern California and running a UPS route between Seattle and Portland. Wave CTO Michael Masquelier describes the inductive power transfer his company champions as “similar to a tuning fork. By vibrating that fork, you sent energy through the air and it is received by another tuning fork across the room. So it’s similar to that, but it’s magnetic energy versus sound energy.”
He hopes to partner with Magment, saying that “the magnetizing cement makes installation easier and improves the energy efficiency.” More research is needed to evaluate which company’s technology will prove to be the most efficient, practical, and cost-effective.
Esguerra’s vision includes the potential that toll roads could charge a premium for inductive charging capabilities. “And in reverse, a driver who has too much energy could feed his surplus into the grid while driving,” Esguerra dreams.
The future will soon arrive in the idyllic town of Bad Staffelstein, a quaint tourist destination in the Upper Franconia region of Germany. Visitors will be taken to and from the main station and the popular thermal bath by driverless shuttles. Together with the University of Wuppertal, the regional government of Upper Franconia wants to turn its district into “the center of autonomous driving.” Magment is about to install inductive charging pads at the shuttle stations and the thermal bath, eliminating the need for the shuttles to stop for charging times. No more drivers, no cable, no range anxiety. Masquelier believes that “wireless and autonomous driving go hand in hand.” Science fiction? It will become science reality in spring 2023.
CORRECTION: An earlier version of the story erroneously mentioned that Electreon required overhead cables.
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
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.