The Download: Driverless cars’ AI plan, and stretching cells with a robotic shoulder

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The big new idea for making self-driving cars that can go anywhere

Four years ago, Alex Kendall sat in a car on a small road in the British countryside and took his hands off the wheel. The car, equipped with a few cheap cameras and a massive neural network, veered towards the verge. When it did, Kendall grabbed the wheel for a few seconds to correct it. The car veered again; Kendall corrected it. It took less than 20 minutes for the car to learn to stay on the road by itself, he says.

This was the first time that reinforcement learning—an AI technique that trains a neural network to perform a task via trial and error—had been used to teach a car to drive from scratch on a real road. It was a small step in a new direction—one that a new generation of startups believe just might be the breakthrough that makes driverless cars an everyday reality.

Branding themselves AV2.0, these startups are betting that smarter, cheaper tech will let them overtake current market leaders. Indeed, Kendall’s firm Wayve says it wants to be the first company to deploy driverless cars in 100 different cities. 

But is this yet more hype from an industry that’s been drinking its own Kool-Aid for years? Read the full story.

—Will Douglas Heaven

Watch a robotic shoulder practice twisting and stretching human cells

A robotic shoulder that stretches, presses, and twists lab-grown human tendon tissue could pave the way for more successful tissue grafts.

Though the field of tissue engineering is still mostly experimental, skin cells, cartilage, and even a windpipe grown from samples of human cells have been implanted in patients. But growing usable human tendon cells—which need to stretch and twist—has proved trickier.

Over the past two decades, scientists have encouraged engineered tendon cells and tissue to grow and mature by repeatedly stretching them in one direction. However, this approach has so far failed to produce fully functional tissue grafts that could be used in human bodies.

Humanoid robots could be used to make engineered tendon tissue that is more like the real thing. Read the full story—and watch the robot in action.

—Rhiannon Williams

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 New privacy-focused apps are ill-equipped to cope with moderation demands
It makes maintaining the balance between privacy and policing more complicated. (WP $)
+ How to have honest conversations with children about the Texas shooting. (The Atlantic $)

2 Big Tech’s lobbying efforts are paying off
Democrats are wary of backing antitrust legislation for fear of losing their slim majority. (Politico)
+ Industry lobbyists have successfully weakened privacy regulation efforts, too. (The Markup)
+ What does breaking up Big Tech really mean? (MIT Technology Review)

3 The comforting sensation of touch is tough to replicate
But scientists are trying their best with sensors and prostheses. (National Geographic $) 
+ Inventing soft things to solve hard problems. (MIT Technology Review)

4 Our obsession with restoring nature is unhelpful 🌲
Living ecosystems are not meant to be static environments, so why do we treat them like they are? (New Statesman $)
+ “A Trillion Trees” is a great idea—that could become a dangerous climate distraction. (MIT Technology Review)

5 A Japanese toy company owns a stake in 4Chan
And sexualized anime figures could explain why. (Wired $)
+ The malicious rumor a trans person was responsible for the Texas shooting started on 4Chan. (Rolling Stone)

6 Students are afraid of being accused of cheating by an algorithm 
Schools are placing too much trust in systems which can make flawed judgments. (NYT $)

7 How birth control could change in a post-Roe world
Including “night before” pills and sperm-prohibiting gels. (Neo.Life)
+ Data harvesting is likely to worsen if abortion is banned. (FT $)
+ Activists are helping Texans get access to abortion pills online. (MIT Technology Review)

8 Africa’s market traders are thriving thanks to supply chain startups
Enabling them to order products in bulk without having to travel. (FT $)
+ Major blockchain projects are flooding into the continent, too. (Quartz)

9 ​​How to keep up with the news without getting overwhelmed
Turning off notifications is a good place to start. (WP $)

10 An AI has painted a disturbing portrait of the Queen
And critics are less than impressed with the result. (The Guardian)
+ The dark secret behind those cute AI-generated animal images. (MIT Technology Review)

The big story

Why you don’t really know what you know
October 2020

What does it really mean to know anything? How well can we understand the world when so much of our knowledge relies on evidence and argument provided by others?

These questions matter not only to scientists. Many other fields are becoming more complex, and we have access to far more information and informed opinions than ever before. Yet at the same time, increasing political polarization and misinformation are making it hard to know whom or what to trust. Read the full story.

—Matthew Hutson

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ If I wasn’t excited enough for Jurassic World Dominion hitting movie theaters next month, this list of the top 20 dinosaur movies has tipped me over the edge.
+ The Sims’ answer to the Addams Family has been given a glamorous makeover.
+ There sure are a lot of monkeys scattered throughout the history of art.
+ An ambitious couple are hoping to eat their way around the world by recreating meals from every country—in alphabetical order.
+ This love letter to cities is a reminder of what’s great about urban living.

The big new idea for making self-driving cars that can go anywhere

Four years ago, Alex Kendall sat in a car on a small road in the British countryside and took his hands off the wheel. The car, equipped with a few cheap cameras and a massive neural network, veered to the side. When it did, Kendall grabbed the wheel for a few seconds to correct it. The car veered again; Kendall corrected it. It took less than 20 minutes for the car to learn to stay on the road by itself, he says.

This was the first time that reinforcement learning—an AI technique that trains a neural network to perform a task via trial and error—had been used to teach a car to drive from scratch on a real road. It was a small step in a new direction—one that a new generation of startups believes just might be the breakthrough that makes driverless cars an everyday reality.

Reinforcement learning has had enormous success producing computer programs that can play video games and Go with superhuman skill; it has even been used to control a nuclear fusion reactor. But driving was thought to be too complicated. “We were laughed at,” says Kendall, founder and CEO of the UK-based driverless-car firm Wayve.

Wayve now trains its cars in rush-hour London. Last year, it showed that it could take a car trained on London streets and have it drive in five different cities—Cambridge (UK), Coventry, Leeds, Liverpool, and Manchester—without additional training. That’s something that industry leaders like Cruise and Waymo have struggled to do. This month Wayve announced it is teaming up with Microsoft to train its neural network on Azure, the tech giant’s cloud-based supercomputer.

Investors have sunk more than $100 billion into building cars that can drive by themselves. That’s a third of what NASA spent getting humans to the moon. Yet despite a decade and a half of development and untold miles of road testing, driverless tech is stuck in the pilot phase. “We are seeing extraordinary amounts of spending to get very limited results,” says Kendall.

That’s why Wayve and other autonomous-vehicle startups like Waabi and Ghost, both in the US, and Autobrains, based in Israel, are going all in on AI. Branding themselves AV2.0, they’re betting that smarter, cheaper tech will let them overtake current market leaders.

Hype machines

Wayve says it wants to be the first company to deploy driverless cars in 100 cities. But is that yet more hype from an industry that’s been getting high on its own supply for years?  

“There is way too much overselling in this field,” says Raquel Urtasun, who led Uber’s self-driving team for four years before leaving to found Waabi in 2021. “There’s also a lack of acknowledgment of how difficult the task is in the first place. But I don’t believe that the mainstream approach to self-driving is going to get us to where we need to be to deploy the technology safely.”

That mainstream approach dates back at least to 2007 and the DARPA Urban Challenge, when six teams of researchers managed to get their robotic vehicles to navigate a small-town mock-up on a disused US Air Force base.

Waymo and Cruise launched on the back of that success, and the robotics approach taken by the winning teams stuck. That approach treats perception, decision-making, and vehicle control as different problems, with different modules for each. But this can make the overall system hard to build and maintain, with errors in one module bubbling over into others, says Urtasun. “We need an AI mindset, not a robotics mindset,” she says.

Here’s the new idea. Instead of building a system with multiple neural networks and wiring these together by hand, Wayve, Waabi, and others are each building one large neural network that figures out the details by itself. Throw enough data at the AI and it learns to convert input (camera or lidar data about the road ahead) into output (turning the wheel or hitting the brakes), much like a kid learning to ride a bike.

Going straight from input to output like this is known as end-to-end learning, and it’s what GPT-3 did for natural-language processing and AlphaZero did for Go and chess. “In the last 10 years it’s caused so many seemingly insolvable problems to get solved,” says Kendall. “End-to-end learning pushed us forward to superhuman capabilities. Driving will be no different.”

Like Wayve, Waabi is using end-to-end learning. It isn’t (yet) using real vehicles, however. It is developing its AI almost fully inside a super-realistic driving simulation, itself controlled by an AI driving instructor. Ghost also adopts an AI-first approach, building driverless tech that not only navigates roads but learns to react to other drivers.    

200,000 small problems

Autobrains is betting on an end-to-end approach too, but does something different with it. Instead of training one large neural network to handle everything a car might encounter, it is training many smaller networks—hundreds of thousands, in fact—to handle a very specific scenario each.

“We’re translating the hard AV problem into hundreds of thousands of smaller AI problems,” says Igal Raichelgauz, the company’s CEO. Using one large model makes the problem more complex than it actually is, he says: “When I’m driving, I’m not trying to understand every pixel on the road. It’s about extracting contextual cues.”

Autobrains takes the sensor data from a car and runs it through an AI that matches the scene to one of many possible scenarios: rain, pedestrian crossing, traffic light, bicycle turning right, car behind, and so on. By watching a million miles of driving data, Autobrains says, its AI has identified around 200,000 unique scenarios, and the company is training individual neural networks to handle each of them.

The firm has been partnering with car manufacturers to test its technology and has just got hold of a small fleet of its own vehicles.

Kendall thinks that what Autobrains is doing might work well for advanced driver-assist systems, but he does not see it having an advantage over his own approach. “When tackling the full self-driving problem, I’d expect that they would be just as challenged by the complexity faced in the real world,” he says.

Cruise control

Either way, should we count on this new wave of firms to chase down the front-runners? Unsurprisingly, Mo ElShenawy, executive vice president of engineering at Cruise, isn’t convinced. “The state of the art as it exists today is not sufficient to get us to the stage where Cruise is at,” he says.

Cruise is one of the most advanced driverless-car firms in the world. Since November it has been running a live robotaxi service in San Francisco. Its vehicles operate in a limited area, but anyone can now hail a car with the Cruise app and have it pull up to the curb with nobody inside. “We see a real spectrum of reactions from our customers,” says ElShenawy. “It’s super exciting.”

Cruise has built a vast virtual factory to support its software, with hundreds of engineers working on different parts of the pipeline. ElShenawy argues that the mainstream modular approach is an advantage because it lets the company swap in new tech as it comes along.

He also dismisses the idea that Cruise’s approach won’t generalize to other cities. “We could have launched in a suburb somewhere years ago, and that would have painted us into a corner,” he says. “The reason we’ve picked a complex urban environment, such as San Francisco—where we see hundreds of thousands of cyclists and pedestrians and emergency vehicles and cars that cut you up—was very deliberate. It forces us to build something that scales easily.”

But before Cruise drives in a new city, it first has to map its streets in centimeter-level detail. Most driverless car companies use these kinds of high-definition 3D maps. They provide extra information to the vehicle on top of the raw sensor data it gets on the go, typically including hints like the location of lane boundaries and traffic lights, or whether there are curbs on a particular stretch of street.

These so-called HD maps are created by combining road data collected by cameras and lidar with satellite imagery. Hundreds of millions of miles of roads have been mapped in this way in the US, Europe, and Asia. But road layouts change every day, which means map-making is an endless process.

Many driverless-car companies use HD maps created and maintained by specialist firms, but Cruise makes its own. “We can re-create cities—all the driving conditions, street layouts, and everything,” says ElShenawy.

This gives Cruise an edge against mainstream competitors, but newcomers like Wayve and Autobrains have ditched HD maps entirely. Wayve’s cars have GPS, but they otherwise learn to read the road using sensor data alone. It may be harder, but it means they are not tied to a particular location.

For Kendall, this is the key to making driverless cars widespread. “We are going to be slower to get into our first city,” he says. “But once we get to one city, we can just scale everywhere.”

For all the talk, there’s a long way to go. While Cruise’s robotaxis are driving paying customers around San Francisco, Wayve—the most advanced of the new crop—has yet to test its cars without a safety driver. Waabi doesn’t even use real cars.

Still, these new AV2.0 firms have recent history on their side: end-to-end learning rewrote the rules of what’s possible in computer vision and natural-language processing.  So their confidence is not misplaced. “If everybody goes in the same direction and it’s the wrong direction, we’re not going to solve this problem,” says Urtasun. “We need a diversity of approaches, because we haven’t seen the solution yet.”

Watch a robotic shoulder practice twisting and stretching human cells

A robotic shoulder that stretches, presses, and twists lab-grown human tendon tissue could pave the way for more successful tissue grafts.

Though the field of tissue engineering is still mostly experimental, skin cells, cartilage, and even a windpipe grown from samples of human cells have been implanted in patients so far. 

But growing usable human tendon cells—which need to stretch and twist—has proved trickier. Over the past two decades, scientists have encouraged engineered tendon cells and tissue to grow and mature by repeatedly stretching them in one direction. However, this approach has so far failed to produce fully functional tissue grafts that could be used clinically, in human bodies.   

A new study, published in Communications Engineering today, shows how humanoid robots could be used to make engineered tendon tissue that is more like the real thing.

“The clinical need is clearly there,” says Pierre-Alexis Mouthuy from the University of Oxford, who led the team. “If we can create grafts in vitro that can be of good enough quality to use in clinics, that would be really helpful for improving outcomes in patients. Any improvement would be more than welcome.”

The first step involved redesigning the test chamber that houses the cells, known as a bioreactor, to attach it to a humanoid robot shoulder that can bend, push, pull, and twist cells in the same way musculoskeletal tissues would.

While traditional bioreactors resemble rigid boxes, the team created a flexible one in which human fibroblast cells—elongated cells found in connective tissues—are grown on a soft plastic scaffold suspended between two rigid blocks. They attached this chamber to the robotic shoulder, which spent half an hour a day over 14 days replicating the kinds of raises and rotation movements a human would make.

Afterwards, the cells in the bioreactor were found to have reproduced more rapidly than samples that had not been stretched, and they expressed genes differently—although the researchers don’t know yet how that would translate to the quality of the graft. The team plans to investigate how cells grown in their new bioreactor compare with those grown in traditional stretch bioreactors.  

“Using robots for tissue engineering creates much more realistic biomechanical stimulations, which I see as a breakthrough,” says Dana Damian, a lecturer at the University of Sheffield, who was not involved in the study. “The next step is to establish that robot involvement shows a clear improvement over using conventional bioreactors.”

The technology could be used to produce tissue to repair tears in the rotator cuff tendons, a very common shoulder issue that can arise from a sports injury or a disease such as tendinitis, which is the most common cause of shoulder pain in adults. Typically, surgeons use sutures to reattach broken tendons to the bone, a repair that fails in around 40% of cases because of poor tissue healing. Tissue grafts grown using stimulation from humanoid robots might heal more successfully. 

The technique is still some way from producing a fully functional tendon tissue graft, but the researchers say a similar approach could have other applications as well–creating better muscles or ligaments in bioreactors, for example. And the robots could be made to match the patient’s own physiology, personalizing the tissue they produce, the team suggests.

Correction: The name of the journal has been amended.

The Download: Locking up carbon with corn, and the path to greener steel

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Inside Charm Industrial’s big bet on corn stalks for carbon removal

In recent weeks, a crew of staffers from a company called Charm Industrial have been working on the edge of Kansas corn fields, moving rolled bales of stalks, leaves, husks, and tassels up to a semi-trailer.

Inside, a contraption called a pyrolyzer uses high temperatures in the absence of oxygen to break down the plant material into a mix of biochar and bio-oil. This oil is pumped into EPA-regulated deep wells used for industrial waste, or into salt caverns. Charm says it solidifies there, locking away carbon for thousands to millions of years that would otherwise go back into the air as farmers burn crop remains or leave them to rot.

The San Francisco startup has been sequestering carbon this way for the past two years, working on behalf of companies including Microsoft. Late last year, it announced that the process has safely locked up nearly the equivalent of 5,500 tons of CO2 so far, claiming that’s the largest amount of long-term carbon removal delivered to date. 

But there are still plenty of questions  about how reliable, scalable, and economical this approach will prove to be. Read the full story.

—James Temple

How Charm Industrial hopes to use crops to cut steel emissions

Charm Industrial is also exploring whether the same bio-oil could be used to cut emissions from iron and steelmaking, pursuing a new technical path for cleaning up the dirtiest industrial sector.

The approach could be welcome news to companies compelled to explore cleaner production methods, amid hefty emissions and increasingly strict climate policies. Read the full story.

—James Temple

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The Texas gunman detailed his plans in Facebook messages before the shooting
Meta said the direct messages weren’t discovered until after the tragedy. (WP $)
+ Repeated mass shootings is a problem unique to America. (New Yorker $)
+ AI-powered metal detectors are a controversial solution. (WP $)
+ Three false claims surrounding the shooting are circulating online. (NYT $) 

2 Twitter has been fined for sharing users’ phone numbers
It allowed numbers and email addresses to inform targeted advertising. (Variety)
+ Elon Musk needs more money if he wants to buy the company. (FT $)

3 A quantum network has successfully teleported information
Good news for the future of a super-secure quantum internet. (New Scientist $)
+ We still don’t know if quantum computers will ever live up to their potential. (Vox)
+ Quantum computing has a hype problem. (MIT Technology Review)

4 Carbon removal companies are betting big on kelp 
Seaweed seems an attractive natural solution to our growing carbon problem. (The Atlantic $)
+ But eager companies may be rushing ahead of the science. (MIT Technology Review)

5 Dyson is turning its attention to household chore robots
And wants them in our homes by 2030. (The Guardian)
+ This tiny robotic crab is smaller than a flea. (TechCrunch)

6 The obvious flaw in our hunt for alien life 👽
Our assumptions that they live as we do could be blinding us to real clues. (The Economist $)
+ The best places to find extraterrestrial life in our solar system, ranked. (MIT Technology Review)

7 Improving plant-based food is an uphill struggle 🌿🍔
While vegan startups have boomed in recent years, not all their offerings are good. (Vox)
+ And the appeal of plant-based dishes isn’t international, either. (Economist $)
+ Battles over patents isn’t making it easy for the alt-meat industry. (Sifted)
+ Your first lab-grown burger is coming soon—and it’ll be “blended”. (MIT Technology Review)

8 Teens are running social media coping workshops to help their peers
Their lived experience can be more valuable than programs run by adults. (Time $)

9 China’s short-video obsession is giving faded celebrities a second shot at fame
Shopping channel-style livestreams are extremely lucrative—for a lucky few. (Rest of World)
+ TikTok can be a scary place if you’re pregnant. (LA Times)

10 Meet the artist who’s been making computer art since the 1950s
And it’s taken him until now to gain the recognition he deserves. (Elephant)

Quote of the day

“If we enter a real recession, NFTs are going to be the first to go.” 

—David Hsiao, chief executive of the crypto magazine Block Journal, isn’t optimistic about the lasting value of NFTs in an economic downturn, he tells the Washington Post.

The big story

How Facebook and Google fund global misinformation
 
November 2021

Last fall, a collection of internal documents known as the Facebook Papers reaffirmed what civil society groups have been saying for years: Facebook’s algorithmic amplification of inflammatory content, combined with its failure to prioritize content moderation outside the US and Europe, has fueled the spread of hate speech and misinformation, dangerously destabilizing countries around the world. 

But there’s a crucial piece missing from the story. Facebook isn’t just amplifying misinformation—it’s also funding it.

Tech giants are paying millions of dollars to the operators of clickbait pages, bankrolling the deterioration of information ecosystems around the world, an MIT Technology Review investigation found. Read the full story.

—Karen Hao

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Clubbing in the metaverse still sounds awful, I’m sorry.
+ In fact, who needs the metaverse, when you’ve got DJ Gandalf on the decks?
+ Nothing but best wishes for this happy couple.
+ An essential investigation into why we’re all such big fans of delicious shawarmas.
+ I enjoyed this look back at cult Japanese magazine FRUiTS’ coolest images—which shut down because there were “no more cool kids left to photograph.”

How Charm Industrial hopes to use crops to cut steel emissions

Charm Industrial has gained attention for its unusual approach to storing away carbon dioxide: converting plant matter into bio-oil that it then pumps into deep wells and salt caverns. (See related story.)

But the San Francisco startup is now exploring whether that oil could be used to cut emissions from iron and steelmaking as well, pursuing a new technical path for cleaning up the dirtiest industrial sector.

The iron and steel industry produces about 4 billion tons of carbon emissions each year, accounting for around 10% of all energy-related climate pollution, according to a 2020 report by the International Energy Agency. Those figures have risen sharply this century, driven by rapid economic growth in China and elsewhere.

The hefty emissions and increasingly strict climate policies in some areas, including Canada and the European Union, have started to compel some companies to explore cleaner ways of producing these essential building blocks of the modern world. 

The Swedish joint venture Hybrit delivered the first commercial batch of green steel to Volvo last year. This partnership between the steel giant SSAB, the state-owned power company Vattenfall, and the mining company LKAB, used a manufacturing method that relied on carbon-free hydrogen in lieu of coal and coke. Other companies are exploring the use of facilities with equipment that captures carbon dioxide or, like Boston Metal, implementing entirely different electrochemical methods. 

Charm is evaluating still another approach. In the back corner of the company’s warehouse, employees have been using a narrow metal contraption, known as a reformer, to react the company’s bio-oil with hot steam and oxygen. That produces what’s known as syngas, which is mostly a mix of carbon monoxide and hydrogen.

pouring bio oil
Bio-oil produced from crop remains.
CHARM INDUSTRIAL

That could potentially be swapped into one method of producing iron and steel.

The most common form of steel production starts with a blast furnace, which heats iron ore, limestone, and coke, a form of coal, to temperatures above 1,500 ˚C. The resulting carbon-laden metal, known as “pig iron,” then moves into a second furnace, where oxygen is blown into it, impurities are removed, and other materials are added to produce various grades of steel.

Emissions occur at every stage of this process, including the extraction and production of iron, coal, and coke; the combustion of fuels to run the furnaces; and the chemical reactions that occur within them.

But about 7% of non-recycled steel today is produced in a different type of furnace, using what’s known as a direct-reduction method. It usually relies on natural gas to strip oxygen atoms off the iron oxide ore in a shaft furnace. This produces what’s known as sponge iron, which basically just needs to be melted and mixed with other materials. That second step can be done in what’s known as an electric arc furnace, which can run on carbon-free power from solar, wind, geothermal, or nuclear plants.

The method already produces fewer emissions than the blast furnace approach. But it can be made cleaner still by replacing the natural gas with alternatives, including carbon-free hydrogen in Hybrit’s case or syngas produced from crop remains in Charm’s.

Because those crops sucked the carbon that goes into the syngas out of the air, the process shouldn’t emit any more than it removes, CEO Peter Reinhardt says. If it were done in a facility with added equipment to capture emissions, it could even produce a form of negative-emissions steel, removing more than it releases, he says.

Reinhardt says the company is in discussion with several steelmakers about carrying out demonstration  projects, exploring how it could deploy bio-oil-based syngas as a cleaner way of producing iron. 

Charm is considering several market approaches, including selling syngas directly to steel manufacturers or producing its own hot briquetted iron, a product one step beyond sponge iron. Any company with an electric arc furnace could use that to produce steel. Most steel in the US is produced from recycled materials using such furnaces.

Charm Employees gathered
Charm staffers at the company’s headquarters.
CHARM INDUSTRIAL

Charm, however, will face some obvious challenges. 

First, the steel industry has decades of experience with existing manufacturing methods and has sunk massive amounts of capital into them. 

“The blast furnace is one of the most energy-efficient machines that has ever been invented,” says Rebecca Dell, director of the industry program at ClimateWorks. “We’ve spent well over a century optimizing its performance. So people aren’t going to change without a real good reason.”

That reason will almost certainly need to include strict public policy mandates or incentives in more nations, particularly the US, China, India, and other fast-growing economies, as well as demand from customers looking to operate in greener ways. 

Another question is how much this approach will do to address emissions. There has been a long history of companies claiming that bioenergy sources would produce more climate-friendly products than turned out to be true once the entire process was carefully scrutinized. Corn-based ethanol is a famous case.

“We’ve had some very bad experiences with bioenergy sources … and we need to learn the lessons from those,” Dell says. “So that’s a claim that needs to be interrogated. Even if it might be low carbon, it’s almost never carbon neutral.”

It’s also not clear if Charm’s approach will prove to be the most affordable or attractive way of cutting emissions from steel, given the other efforts underway. 

But as Dell notes, iron and steel pollution is such a big and urgent problem that we’re likely to need a variety of solutions and should be spreading out our technological bets.

Inside Charm Industrial’s big bet on corn stalks for carbon removal

In recent weeks, a crew of staffers from a company called Charm Industrial have been working on the edge of Kansas corn fields, moving rolled bales of stalks, leaves, husks, and tassels up to a white semi-trailer.

Inside, a contraption called a pyrolyzer uses high temperatures in the absence of oxygen to break down the plant material into a mix of biochar and bio-oil. The former will eventually go back into fields, adding carbon and nutrients to the soil. 

But the company pumps the oil down EPA-regulated deep wells used for industrial waste, or into salt caverns left behind by oil and gas companies. Charm says it solidifies there, locking away carbon for thousands to millions of years that would otherwise go back into the air as farmers burn crop remains or leave them to rot.

Companies like Microsoft, Shopify, and Stripe pay Charm $600 for every ton of carbon it puts underground, either to offset their own emissions or to help build up an industry that will need to play a critical part in tempering climate change, by pulling huge amounts of greenhouse gas out of the air and storing it away.

The San Francisco startup has been sequestering carbon this way for the past two years. Late last year, the company announced that the process has safely locked up nearly the equivalent of 5,500 tons of CO2 so far, claiming that’s the largest amount of long-term carbon removal delivered to date. But it’s a tiny sliver of the billions of tons per year that climate scientists warn the world may need to suck up in the coming decades to pull the warming planet back into a safer zone. And there are plenty of questions and concerns about how reliable, scalable, and economical this approach will prove to be.

The company has edged ahead of others mainly because it’s taking a simple approach. It leans on agricultural crops to capture the carbon and uses existing formations for storage. And Charm doesn’t have to construct big projects, sidestepping some of the development, permitting, and capital challenges that startups like Climeworks or Carbon Engineering have encountered as they try to build carbon-sucking factories.

But an early lead in a field that scarcely exists doesn’t necessarily say much about how the company will fare as the market develops. Notably, the next generation of direct-air-capture plants coming online are meant to remove a million tons a year, 180 times more than Charm has achieved so far.

The company will also face some obvious challenges as it scales, including the rising costs of shipping waste between fields and wells, competing demands for the agricultural byproducts it relies on, and questions around how much net carbon its approach ultimately removes.

In addition, the company will face the same risk as other young companies in carbon removal and storage: they’re gambling that large corporations will be willing to continue footing the high bill for cleaning up the atmosphere, and that governments will enact the necessary policies to build up the costly sector.

Corn stalks to carbon credits

Charm’s CEO, Peter Reinhardt, 32, previously led Segment, a customer data software company that Twilio acquired in 2020 for $3.2 billion. He started looking into carbon removal as a way of offsetting Segment’s emissions, initially exploring possibilities like funding rainforest protection.

In 2018, Reinhardt and three others cofounded Charm (a mashup of “char” and “farm”) to build a business around what they saw as a more promising approach. The initial plan was to gasify biomass, a similar process to pyrolysis but done at higher temperatures, to produce biochar and hydrogen. They expected the latter to be the real moneymaker.

But the company found that picking up the biomass and transporting it to a centralized gasification facility was far too expensive, because biomass is “too fluffy.” It’s bulky, heavy, and unwieldy, increasing the cost of handling and moving it—a painful lesson that biofuels companies learned more than a decade ago.

In 2020, Charm’s chief scientist, cofounder Shaun Meehan, had a bright idea: if the company was willing to do what Reinhardt describes as “half-assed gasification,” yielding bio-oil instead of hydrogen, the equipment could fit into the back of a semi-trailer. Then the company could pull right up to farms and carry out the process at the edge of the fields.

Now Charm, which has about 30 employees, pays farmers to allow it to pick up unwanted plant materials left after harvesting. It’s also looking at carrying out the same process with trees and plants removed from forests—say, for fire prevention or in the aftermath of droughts. Separately, the company has begun to explore whether it can use the resulting bio-oil to clean up steel and iron production, the dirtiest industrial sector (see related story).

The business model would make no sense in any other time (and perhaps it doesn’t in this one). But a growing number of companies are willing to pay the high cost of carbon removal and storage as a way of balancing out their own emissions, to help support the emerging market, or as a form of climate philanthropy. So far, around 40 organizations have purchased tons of removal from the company.

Charm’s CEO Peter Reinhardt at the company’s San Francisco headquarters.
WINNI WINTERMEYER

Reinhardt says the company expects to eventually drive down the cost to $50 a ton of removed and stored carbon dioxide as it scales up its operations. For one, it plans to build up a fleet of semi-trailers equipped with high-capacity, fast pyrolyzers developed in-house. Eventually, the company hopes to also create a type of combine harvester with a pyrolyzer unit that can pick up and convert agricultural remains wherever they fall in the fields, saving the costs of collecting, bundling, and moving the material.

Tough economics

Charm’s approach to carbon removal and storage offers several advantages relative to other methods, observers say. 

It promises to lock away the carbon for very long periods, while options like planting trees or altering farming methods to hold more carbon in soil can be quickly reversed when trees die or fields are tilled. It prevents emissions that would otherwise occur in many circumstances.

And it can reduce some of the air pollution associated with agricultural burning—which, for instance, California farmers are allowed to do during certain periods to dispose of orchard prunings, trees, weeds, and more. 

The company seems to be “serving the market in an innovative way that meets multiple needs through one intervention,” Lauren Gifford, a postdoctoral researcher at the University of Arizona who focuses on carbon offsets and climate governance, said in an email.

CarbonPlan, a San Francisco nonprofit that evaluates the integrity of carbon removal methods, also rates Charm’s described approach highly.

But the company’s carbon accounting and economics could depend a lot on the particular crops or trees in question, and on what farmers or foresters would have done with the plant remains otherwise.

stages in the process to convert biomass to bio oil
Charm uses pyrolysis to convert plant matter into bio-oil, biochar and ash.
WINNI WINTERMEYER

Corn growers, for instance, rely on significant amounts of crop leftovers. They leave it on their fields to prevent erosion and retain water, and plow it under to add nutrients and carbon back to the soil.

The optimal amount to keep is difficult to determine: it depends on the crop rotation, soil conditions, weather patterns, the slope of the field, and other factors. But farmers generally take a conservative approach to avoid the cost of synthetic nutrient additives, says Chad Hart, a professor of economics at Iowa State University. 

“They try to leave as much on the ground as possible,” he says.

The leftovers they do bale up are often sold locally as supplemental feed for cattle, or as livestock bedding.

The question is: how scalable will Charm’s approach be over the long term if farmers already use and sell much of this material? 

Hart adds that transporting bio-oil between farms mostly in the Great Plains and salt caverns clustered in the South could be a significant expense.

“Will the carbon market structure support that?” he asks.

In addition, growing demand for agricultural remains could push up the price. Other companies are using them to produce fuels or electricity within plants designed to capture any resulting emissions. These include LanzaJet, Mote Hydrogen, and a joint venture between Chevron, Schlumberger New Energy, Microsoft, and Clean Energy Systems.

Well safety

There are also questions around Charm’s reliance on US salt caverns and injection wells, which have repeatedly leaked in the past, despite oversight and regulations.

The bio-oil produced from plant materials has a different chemistry from the petroleum and natural gas currently stored in salt caverns, and years of work might be required to demonstrate that it can be safely and permanently sequestered, says Saeed Salehi, a professor of petroleum and geological engineering at the University of Oklahoma, who focuses on well integrity and geological carbon storage.

“I don’t think we have enough data or established field administration practices to say this will be 100% safe or that we’re fully aware of the whole risks,” he says.

He believes that Charm will also need to go through extended permitting processes with the EPA or other regulators before it can inject large quantities of bio-oil into those caverns.

Reinhardt disputes the transportation concerns, saying there are plenty of US wells or formations that “should be convertible to bio-oil injection,” including many in the Midwest and Great Plains.

A semi-trailer at the company’s headquarters.
WINNI WINTERMEYER

He adds that much of Charm’s technical work to date has been on the carbon sequestration side, including analyses to determine the subsurface chemistry and geology best suited to solidifying and locking away the bio-oil. 

But he stresses that bio-oil is a dense fluid that already fits within the regulatory process for the types of wells and caverns Charm has in mind, and that the company is following best practices and EPA requirements to prevent leakage. To help the company “develop the appropriate pathways for safe and permanent injection,” he said in an email, it has hired consultants who’ve permitted, built, and operated injection wells in the past.

“We will of course continue to invest in field measurements, lab measurements, and computational work to always be continuing to improve our understanding of what’s going on in the subsurface,” Reinhardt added.

Carbon accounting

Then there’s the issue of tallying the climate benefits and costs.

How much net carbon the process stores depends on what would otherwise have happened to the plant material. For instance, crops that are plowed under and trees that are turned into timber can also store carbon for certain periods. What’s more, Charm is producing its own emissions—for example, by using diesel to initiate the pyrolysis process and hauling around bio-oil with trucks.

And the math only gets more complicated on larger scales. If Charm and other companies buy up large amounts of corn leftovers, cattle farmers might have to switch to other sources of low-cost feed, including crops grown for that purpose. If the market gets really heated, it could even create economic incentives for farmers to expand their operations.

Charm staffers have been converting bales of corn stover from Kansas farms in recent weeks.
CHARM INDUSTRIAL

The company would need to account for any emissions released or land converted as a result. 

Reinhardt says that Charm will only take half the agriculture material on any given field, and he notes that putting the resulting biochar and ash into the fields will improve soil health. He adds that competing uses of corn remains depend on the region, but that much of it isn’t sold or plowed under, leaving it to rot and release carbon dioxide. 

But he stresses that Charm will properly account for alternative uses, land-use changes, and these other factors.

The company’s internal carbon math estimates that when the company is using its own pyrolyzers, the process will generally remove the equivalent of 0.85 tons of carbon dioxide for every ton of biomass. Reinhardt says Charm will improve those figures over time by switching to carbon-neutral syngas instead of diesel to initiate the pyrolysis process, optimizing its pyrolyzers for converting plant matter to bio-oil, and eventually transitioning to electric trucks.

The role of government

Robert Höglund of Marginal Carbon AB, a consulting firm specializing in carbon removal and climate policy, says Charm’s customers are paying a lofty $600 a ton today to help “kick-start” the approach, betting that the company will be able to drive down costs. But he says it’s not clear whether Charm’s method will prove to be among the most effective, scalable, or affordable over time, or the best use of this biomass as the need grows for ever more renewable energy sources.

It’s also unlikely that corporations will continue to buy up enough carbon removal to reach the billions of tons per year that could eventually be required, both to stabilize the planet’s temperatures and to sustain the businesses emerging to pull greenhouse gas out of the air.

In effect, investors and startups are betting that governments will enact laws that subsidize, incentivize, or mandate these practices. Reinhardt, for one, acknowledges that government policy will be crucial for building up the carbon removal markets that will allow his company and others to thrive.

He says Charm is working to educate lawmakers in California and Washington, DC, calling for greater support of the nascent sector as well as rules that are technology neutral while researchers and companies explore a variety of paths. 

“Corporate buyers like Microsoft, Stripe, and Shopify will only get to so much scale, and then regulation will need to step in,” Reinhardt said in an email, adding: “So much innovation has happened in the space, and we just need to unlock it.”

The Download: Google’s AI cuteness overload, and America’s fight for gun control

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The dark secret behind those cute AI-generated animal images

Another month, another flood of weird, wonderful and cute images generated by an artificial intelligence. In April, OpenAI showed off its new picture-making neural network, DALL-E 2, which could produce remarkable high-res images of almost anything it was asked to. 

Now, just a few weeks later, Google Brain has revealed its own image-making AI, called Imagen. And it performs even better than DALL-E 2: it scores higher on a standard measure for rating the quality of computer-generated images and the pictures it produced were preferred by a group of human judges.

But like OpenAI did with DALL-E, Google is going all in on cuteness. Both firms promote their tools with a series of pictures filled with anthropomorphic animals doing adorable things: a fuzzy panda dressed as a chef making dough, a corgi sitting in a house made of sushi, a teddy bear swimming the 400m butterfly at the Olympics—and it goes on.

This cuteness hides a darker side to these tools, one that the public doesn’t get to see because it would reveal the ugly truth about how they are created. Read the full story—and see more pictures created by Imagen—here.

—Will Douglas Heaven

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The Uvalde school shooting is strengthening urgent calls for gun control
Lawmakers are pushing for an end to America’s gun violence epidemic. (The Guardian)
+ President Biden asks when America is going to stand up to the gun lobby. (Politico)
+ Texas has some of the most lenient gun laws in the country. (NYT $)

2 Social media is woefully underprepared to archive evidence of war crimes
And pressures to remove gruesome content quickly is complicating the issue. (Coda Story)

3 Remote educational apps tracked children and hoarded their data 
They were then targeted with ads.(WP $)

4 The price of electronics is likely to rise
Along with the cost of anything else that relies on semiconductors. (CNBC)
+ The great chip crisis threatens the promise of Moore’s Law. (MIT Technology Review)

5 Facial recognition is retraumatizing revenge porn survivors
PimEyes’ paid-for service creates a database of searchable images, some of which are sexually explicit. (CNN)
+ A horrifying AI app swaps women into porn videos with a click. (MIT Technology Review)

6 Tech hasn’t delivered on its promise to make us more productive
And experts are divided over whether it ever will. (NYT $)
+Tech alone is rarely enough to create significant benefits. (MIT Technology Review)

7 Social media challenges have become modern day relics
No, TikTok dances don’t count. (The Atlantic $)
+ There’s a fine line between following a trend and plagiarizing. (Vox) 

8 Forging Australia’s digital driving licenses is scarily easy
It takes well under an hour. (Ars Technica)
+ California should take note before it starts testing digital licenses. (LA Times

9 Musicians claim they’re being pressured by their labels to go viral on TikTok
But their objections have also been labeled as attempts to go viral. (Fast Company)

10 How you manage stress can lower your biological age
Which is the age of your cells and organs, not your chronological “birth” age. (WSJ $)
+ Aging clocks aim to predict how long you’ll live. (MIT Technology Review)

Quote of the day

“The only way to protect your customers’ location data from such outrageous government surveillance is to not keep it in the first place.”

—An open letter from 42 Democrat lawmakers urges Google CEO Sundar Pichai to stop collecting location data that could be used to identify people seeking abortions, according to CNBC.

The big story

Artificial general intelligence: Are we close, and does it even make sense to try?

A machine that could think like a person has been the guiding vision of AI research since the earliest days—and remains its most divisive idea. Artificial General Intelligence, or AGI, has become a common buzzword for human-like or superhuman AI, as well as a catchall for the hopes and fears surrounding an entire technology. But is it a reckless, misleading dream—or the ultimate goal? Read the full story.

—Will Douglas Heaven

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Cool fact of the day— dolphins can identify their friends by taste.
+ France’s first fondue championship looks like an absolute riot.
+ Some kind-hearted paddleboarders rescued a stranded deer found in a cave in England.
+ The top item of clothing Americans bought during the pandemic? Turns out it was socks.
+ Hold the front page—naps are officially good for you.

The dark secret behind those cute AI-generated animal images

Another month, another flood of weird and wonderful images generated by an artificial intelligence. In April, OpenAI showed off its new picture-making neural network, DALL-E 2, which could produce remarkable high-res images of almost anything it was asked to. It outstripped the original DALL-E in almost every way.

Now, just a few weeks later, Google Brain has revealed its own image-making AI, called Imagen. And it performs even better than DALL-E 2: it scores higher on a standard measure for rating the quality of computer-generated images, and the pictures it produced were preferred by a group of human judges.

“We’re living through the AI space race!” one Twitter user commented. “The stock image industry is officially toast,” tweeted another.

Many of Imagen’s images are indeed jaw-dropping. At a glance, some of its outdoor scenes could have been lifted from the pages of National Geographic. Marketing teams could use Imagen to produce billboard-ready advertisements with just a few clicks.

But as OpenAI did with DALL-E, Google is going all in on cuteness. Both firms promote their tools with pictures of anthropomorphic animals doing adorable things: a fuzzy panda dressed as a chef making dough, a corgi sitting in a house made of sushi, a teddy bear swimming the 400-meter butterfly at the Olympics—and it goes on.

There’s a technical, as well as PR, reason for this. Mixing concepts like “fuzzy panda” and “making dough” forces the neural network to learn how to manipulate those concepts in a way that makes sense. But the cuteness hides a darker side to these tools, one that the public doesn’t get to see because it would reveal the ugly truth about how they are created.

Most of the images that OpenAI and Google make public are cherry-picked. We only see cute images that match their prompts with uncanny accuracy—that’s to be expected. But we also see no images that contain hateful stereotypes, racism, or misogyny. There is no violent, sexist imagery. There is no panda porn. And from what we know about how these tools are built—there should be.

It’s no secret that large models, such as DALL-E 2 and Imagen, trained on vast numbers of documents and images taken from the web, absorb the worst aspects of that data as well as the best. OpenAI and Google explicitly acknowledge this.   

Scroll down the Imagen website—past the dragon fruit wearing a karate belt and the small cactus wearing a hat and sunglasses—to the section on societal impact and you get this: “While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized [the] LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes. Imagen relies on text encoders trained on uncurated web-scale data, and thus inherits the social biases and limitations of large language models. As such, there is a risk that Imagen has encoded harmful stereotypes and representations, which guides our decision to not release Imagen for public use without further safeguards in place.”

It’s the same kind of acknowledgement that OpenAI made when it revealed GPT-3 in 2019: “internet-trained models have internet-scale biases.” And as Mike Cook, who researches AI creativity at Queen Mary University of London, has pointed out, it’s in the ethics statements that accompanied Google’s large language model PaLM and OpenAI’s DALL-E 2. In short, these firms know that their models are capable of producing awful content, and they have no idea how to fix that. 

For now, the solution is to keep them caged up. OpenAI is making DALL-E 2 available only to a handful of trusted users; Google has no plans to release Imagen.

That’s fine if these were simply proprietary tools. But these firms are pushing the boundaries of what AI can do and their work shapes the kind of AI that all of us live with. They are creating new marvels, but also new horrors— and moving on with a shrug. When Google’s in-house ethics team raised problems with the large language models, in 2020 it sparked a fight that ended with two of its leading researchers being fired.

Large language models and image-making AIs have the potential to be world-changing technologies, but only if their toxicity is tamed. This will require a lot more research. There are small steps to open these kinds of neural network up for widespread study. A few weeks ago Meta released a large language model to researchers, warts and all. And Hugging Face is set to release its open-source version of GPT-3 in the next couple of months. 

For now, enjoy the teddies.

The Download: Clearview AI’s hefty fine, and countries’ monkeypox preparation

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The walls are closing in on Clearview AI

Controversial facial recognition company Clearview AI has been fined more than $10 million by the UK’s data protection watchdog for collecting the faces of UK citizens from the web and social media. The firm was also ordered to delete all of the data it holds on UK citizens.

The move by the UK’s Information Commissioner’s Office (ICO) is the latest in a string of high-profile fines against the company as data protection authorities around the world eye tougher restrictions on its practices.

Clearview AI boasts one of the world’s largest databases of 20 billion images of people’s faces that it has scraped off the internet from publicly available sources, such as social media, without their consent. Clients such as police departments pay for access to the database to look for matches.

But data protection authorities around the Western world have found this to be a clear violation of privacy. Now they are beginning to work together to clamp down—and fines may just be the beginning. Read the full story.

—Melissa Heikkilä

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Production of the smallpox vaccine is being ramped up
Dozens of countries have inquired about supplies of the shot, which protects against monkeypox. (WSJ $)
The US has more than 100 million doses stockpiled. (NYT $)
+ Conspiracy theories blaming the US for the outbreak are circulating in China. (Bloomberg $)
There’s no evidence to suggest the monkeypox virus is becoming more infectious. (NYT $) 

2 Data’s wild west era is coming to an end
While countries are divided on how widely it should be shared, everyone agrees on its value. (NYT $)
+ GDPR hasn’t stopped data brokers from hoarding our information. (Wired $)

3 Mark Zuckerberg’s grand plan to appear politically neutral backfired
His $419 million donation fueled the false theory that the 2020 election was rigged. (Protocol)
He’s also being sued over the Cambridge Analytica data scandal. (WP $)
+ Meta will give researchers more information on political ad targeting. (NYT $)
Facebooktroll farms reached 140 million Americans a month before the election. (MIT Technology Review)

4 Marshes are struggling against rising water levels
While some plants are suffering, others will thrive—for now, at least. (Wired $)
How rising groundwater caused by climate change could devastate coastal communities. (MIT Technology Review)

5 Maybe we’re spreading disinformation about disinformation
The phrase has become such a catch-all, we’re losing sight of what it actually means. (Slate $) 
+ How Facebook and Google fund global misinformation. (MIT Technology Review)

6 Facebook’s customer service is notoriously terrible
Leaving disgruntled users with no way to seek help for their problems. (WSJ $)

7 Humans aren’t going extinct any time soon
But our ability to adapt and learn from mistakes is crucial to our future survival. (CNET)

8 Mexico City’s gig economy is helping medical workers treat patients
Allowing them to carry out tests and vaccinations at home. (Rest of World)

9 It’s time to break up with email 📧
“If it’s important, they’ll get back to me” is a good philosophy to adopt. (WSJ $)

10 Google’s text-to-image AI is pretty impressive
But it isn’t quite as advanced as OpenAI. (TechCrunch)
This horse-riding astronaut is a milestone in AI’s journey to make sense of the world. (MIT Technology Review)

Quote of the day

“You can totally make a fortune in crypto. I would never say you can’t, but you are betting that you are going to be a better shark than all the sharks that built the shark pool.”

—David Gerard, author, explains to Wired why the volatile nature of crypto means the odds are generally stacked against investors.

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ A Nintendo DS has been spotted filming at a My Chemical Romance concert, though the footage it captured is…not great.
+ Did you hate Matrix Resurrections? Here’s an explanation why.
+ I love working out which tracks a song has sampled—this website is a comprehensive library explaining who’s sampled who.
+ This Twitter user poses an excellent question.
+ While this video of a seagull stealing an entire pizza is amazing, the behind the scenes is even better.
+ A child actor who starred in Jaws has become a police chief on the Massachusetts island where most of the movie was filmed.
+ This journey inside one of the deepest caves in the world is awe-inspiring, if a bit scary.