Category: Science & Tech

Though many doctors today get help from robots for procedures ranging from hernia repairs to coronary bypasses, those are used to assist surgeons, not replace them. This new research marks progress toward robots that can operate more autonomously on very intricate, complicated tasks like suturing. The lessons learned in its development could also be useful in other fields of robotics.

“From a robotics perspective, this is a really challenging manipulation task,” says Ken Goldberg, a researcher at UC Berkeley and director of the lab that worked on the robot.  

One issue is that shiny or reflective objects like needles can throw off a robot’s image sensors. Computers also have a hard time modeling how “deformable” objects, like skin and thread, react when poked and prodded. Unlike transferring a needle from one human hand to another, moving a needle between robotic arms is an immense challenge in dexterity.

The robot uses a pair of cameras to take in its surroundings. Then, having been trained on a neural network, it is able to identify where the needle is and use a motion controller to plan all six motions involved in making a stitch. 

Though we’re a long way from seeing these sorts of robots used in operating rooms to sew up wounds and organs on their own, the goal of automating part of the suturing process holds serious medical potential, says Danyal Fer, a physician and researcher on the project. 

“There’s a lot of work within a surgery,” Fer says, “and oftentimes, suturing is the last task you have to do.” That means doctors are more likely to be fatigued when doing stitches, and if they don’t close the wound properly, it can mean a longer healing time and a host of other complications. Because suturing is also a fairly repetitive task, Goldberg and Fer saw it as a good candidate for automation.

“Can we show that we actually get better patient outcomes?” Goldberg says. “It’s convenient for the doctor, yes, but most importantly, does this lead to better sutures, faster healing, and less scarring?”

That’s an open question, since the success of the robot comes with caveats. The machine made a record of six complete stitches before a human had to intervene, but it could only complete an average of about three across the trials. The test wound was limited to two dimensions, unlike a wound on a rounded part of the body like the elbow or knuckle. Also, the robot has only been tested on “phantoms,” a sort of fake skin used in medical training settings—not on organ tissue or animal skin.

I spend an inordinate amount of time thinking about transportation in general, since it’s one of the biggest areas we need to clean up to address climate change: it accounts for something like a quarter of global emissions. And the vehicles that we use to shuttle around to work, school, and the grocery store in many parts of the world are a huge piece of the problem.

Last week, MIT Technology Review hosted an event where my colleagues and I dug into a conversation about the future of batteries and the materials that go into them. We got so many great questions, and we answered quite a few of them (subscribers should check out the recording of the full event here).

But there were still a lot of questions, particularly about EVs, that we didn’t get to, so let’s take a look at a few. (I’ve edited these for length and clarity, but they came from subscribers, so thank you to everyone who submitted!)

Why is there not a bigger push for plug-in hybrids during the transition to full EVs? Could those play a role?

Hybrids are sometimes relegated to the fringes of the EV discussion, but I think they’re absolutely worth talking about. 

Before we get into this, let’s get a couple of terms straight. All hybrid vehicles use both an internal-combustion engine that burns gasoline and a battery, but there are two key types to know about. Plug-in hybrids can be charged up using an EV charger and run for short distances on electricity. Conventional hybrids have a small battery to help recapture energy that would otherwise be wasted, which boosts gas mileage, but they always run on gasoline.

Any technology that helps reduce emissions immediately can help address climate change, and even a conventional hybrid will cut emissions by something like 20%. 

Personally, I think plug-in hybrids in particular are a great option for people who can’t commit to an EV just yet. These vehicles often have a range of around 50 miles on electricity, so if you’re commuting short distances, nearly all your driving can be zero-emissions. 

Plug-ins aren’t the perfect solution, though. For one thing, the vehicles may have higher rates of problems than both EVs and gas-powered vehicles, and they need a bit more maintenance. And some studies have shown that plug-in hybrids don’t tend to get the full emissions benefits advertised, because people use the electric mode less than expected.

Mark: That’s a great question. And first, I would say across JPMorgan Chase, we do view this as an investment. And every time I talk to a senior leader about the work we do, I never speak of expenses. It is always investment. And I do firmly believe that. At the end of the day, what we’re trying to do is build an analytic factory that can deliver AI/ML at scale. And that type of a factory requires a really sound strategy, efficient platforms and compute, solid governance and controls, and incredible talent. And for an organization of any scale, this is a long-term investment, and it’s not for the faint of heart. You really have to have conviction to do this and to do this well. Deploying this at scale can be really, really challenging. And it’s important to ensure that as we’re thinking about AI/ML, it’s done with controls and governance in place.

We’re a bank. We have a responsibility to protect our customers and clients. We have a lot of financial data and we have an obligation to the countries that we serve in terms of ensuring that the financial health of this firm remains in place. And at JPMorgan Chase, we’re always thinking about that first and foremost, and about what we actually invest in and what we don’t, the types of things we want to do and the things that we won’t do. But at the end of the day, we have to ensure that we understand what’s going on with these technologies and tools and the explainability to our regulators and to ourselves is really, really high. And that really is the bar for us. Do we truly understand what’s behind the logic, what’s behind the decision-ing, and are we comfortable with that? And if we don’t have that comfort, then we don’t move forward.

We never release a solution until we know it’s sound, it’s good, and we understand what’s going on. In terms of government relations, we have a large focus on this, and we have a large footprint across the globe. And at JPMorgan Chase, we really are focused on engaging with policymakers to understand their concerns as well as to share our concerns. And I think largely we’re united in the fact that we think this technology can be harnessed for good. We want it to work for good. We want to make sure it stays in the hands of good actors, and it doesn’t get used for harm for our clients or our customers or anything else. And it’s a place where I think business and policymakers need to come together and really have one solid voice in terms of the path forward because I think we’re highly, highly aligned.

Laurel: You did touch on this a bit, but enterprises are relying on data to do so many things like improving decision-making and optimizing operations as well as driving business growth. But what does it mean to operationalize data and what opportunities could enterprises find through this process?

Mark: I mentioned earlier that one of the hardest parts of the CDAO job is actually understanding and trying to determine what the priorities should be, what types of activities to go after, what types of data problems, big or small or otherwise. I would say with that, equally as difficult, is trying to operationalize this. And I think one of the biggest things that have been overlooked for so long is that data itself, it’s always been critical. It’s in our models. We all know about it. Everyone talks about data every minute of every day. However, data has been oftentimes, I think, thought of as exhaust from some product, from some process, from some application, from a feature, from an app, and enough time has not been spent actually ensuring that that data is considered an asset, that that data is of high quality, that it’s fully understood by humans and machines.

And I think it’s just now becoming even more clear that as you get into a world of generative AI, where you have machines trying to do more and more, it’s really critical that it understands the data. And if our humans have a difficult time making it through our data estate, what do you think a machine is going to do? And we have a big focus on our data strategy and ensuring that data strategy means that humans and machines can equally understand our data. And because of that, operationalizing our data has become a big focus, not only of JPMorgan Chase, but certainly in the Chase business itself.

We’ve been on this multi-year journey to actually improve the health of our data, make sure our users have the right types of tools and technologies, and to do it in a safe and highly governed way. And a lot of focus on data modernization, which means transforming the way we publish and consume data. The ontologies behind that are really important. Cloud migration, making sure that our users are in the public cloud, that they have the right compute with the right types of tools and capabilities. And then real-time streaming, enabling streaming, and real-time decision-ing is a really critical factor for us and requires the data ecosystem to shift in significant ways. And making that investment in the data allows us to unlock the power of real-time and streaming.

Laurel: And speaking of data modernization, many organizations have turned to cloud-based architectures, tools, and processes in that data modernization and digital transformation journey. What has JPMorgan Chase’s road to cloud migration for data and analytics looked like, and what best practices would you recommend to large enterprises undergoing cloud transformations?

The others were amazed. Some were perturbed. “Everybody has to make this decision for themself,” Stone told me. “The Pearse Resurgence is not a place to experiment. When you go in there, you should be using gear and techniques that you know are going to work at that depth. You don’t want to be doing physiological experiments at 300 meters’ depth. That’s what killed all of the other divers who went beyond 200 meters’ depth. So my advice to Harry and anybody else who wants to play this game is the same as what I gave Exley: Go. To. A chamber. Simulate this first.”

“The group was sort of split,” Menduno told me. “I mean, everybody was supportive of Harry, but there were some people in the group that thought: You’re gonna die. Some of the people in the group were upset and worried that their friend was going to go off and do this thing and potentially die.”

Around the first corner of the Pearse Resurgence, the light disappears, as though the dark walls, black marble striated with veins of gray quartz, have absorbed it. The cave sometimes narrows so much that if you stood, you could touch the ceiling. Other parts billow out into enormous chambers. At one point, jagged fingers of rock bristle from the walls. Other, deeper parts of the cave are smooth and almost perfectly round, broken only by dark fissures that lead to unexplored tunnels. 

As each section of the cave gets discovered, it receives a name. Going down in February 2023, Harris and Challen passed through the Nightmare Crescent, the Needlebender, the Gargleblaster, Weaver’s Ledge, the Big Room, and finally the Brooklyn Exit. The water was 6 °C and perfectly clear. Aside from the brief hisses and clicks of the rebreathers—the crackle of the solenoid triggering, the sigh of gases being pumped through the loop—there was an otherworldly silence.

At 120 meters, the cave opens up onto a plateau that drops off into an abyss. “At that point it’s like standing on the precipice,” Harris told me. “And it feels like you are really beginning the journey.” 

The abyss takes you down 50 meters through a vertical tunnel. By 170 meters, Harris could track where he was on the map in his head, following familiar rock formations. They wanted to preserve their energy and prevent carbon dioxide buildup in their joints, so they limited their movement, relying on underwater scooters to move. They slowly tied off at different points in the descent, working around ropes left behind from dives past, some of which had been installed by Doolette 20 years before. 

At 230 meters, Harris had done something nobody had done before—swimming freely to unimaginable depths and breathing in hydrogen.

Harris remembers that even though his mind was absorbed with their strict plan, hypervigilant to any strange noises from his rebreather that could mean failure, he took a moment to pause, thinking: “What if I never got to see this again?” 

At 200 meters, Harris introduced the hydrogen. For the next 30 meters he gauged his body’s reaction. He was calm, clearheaded, but even more, he noticed that the light tremors in his hands he usually got at this depth, an early sign of high-pressure nervous syndrome, had disappeared. He looked to Challen, who was using helium, as he tied off the rope: his dive partner’s hands had a visible tremor. 

Late last year he wrote a book, called Taming Silicon Valley, which is coming out this fall. It is his manifesto on how AI should be regulated, but also a call to action. “We need to get the public involved in the struggle to try to get the AI companies to behave responsibly,” he says. 

There’s a bunch of different things people can do, ranging from boycotting some of the software until people clean up their act to choosing electoral candidates around their tech policies, he says. 

Action and AI policy are needed urgently, he argues, because we are in a very narrow window during which we can fix things in AI. The risk is that we make the same mistakes regulators made with social media companies. 

“What we saw with social media is just going to be like an appetizer compared to what’s going to happen,” he says. 

Around 12 000 steps later, we’re back at Granville Island’s Public Market. I’m starving, so Marcus shows me a spot that serves good bagels. We both get the lox with cream cheese, eat it outside in the sun, and then part ways.  

Later that day, Marcus sends out a flurry of tweets about Sora. He’s seen enough evidence to call it: “Sora is fantastic, but it is akin to morphing and splicing, rather than a path to the physical reasoning we would need for AGI,” he wrote. “We will see more systemic glitches as more people have access. Many will be hard to remedy.” 

Don’t say he didn’t warn you. 

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Not everyone is fully on board. Some theorists worry that the approach will only yield more false alarms from the collider—more tentative blips in the data like “two-sigma bumps,” so named for their low level of statistical certainty. These are generally flukes that eventually disappear with more data and analysis. Koren is concerned that this will be even more the case with such an open-ended technique: “It seems they want to have a machine that finds more two-sigma bumps at the LHC.” 

Nachman told me that he received a lot of pushback; he says one senior physicist told him, “If you don’t have a particular model in mind, you’re not doing physics.” Searches based on specific models, he says, have been amazingly productive—he points to the discovery of the Higgs boson as a prime example—but they don’t have to be the end of the story. “Let the data speak for themselves,” he says.

Building bigger machines

One thing particle physicists would really like in the future is more precision. The problem with protons is that each one is actually a bundle of quarks. Smashing them together is like a subatomic food fight. Ramming indivisible particles like electrons (and their antiparticles, positrons) into one another results in much cleaner collisions, like the ones that take place on a pool table. Without the mess, researchers can make far more precise measurements of particles like the Higgs. 

An electron-positron collider would produce so many Higgs bosons so cleanly that it’s often referred to as a “Higgs factory.” But there are currently no electron-­positron colliders that have anywhere near the energies needed to probe the Higgs. One possibility on the horizon is the Future Circular Collider (FCC). It would require digging an underground ring with a circumference of 55 miles (90 kilometers)—three times the size of the LHC—in Switzerland. That work would likely cost tens of billions of dollars, and the collider would not turn on until nearly 2050. There are two other proposals for nearer-term electron-positron colliders in China and Japan, but geopolitics and budgetary issues, respectively, make them less appealing prospects. 

D. LUCCHESI ET AL.

Physicists would also like to go to higher energies. “The strategy has literally never failed us,” Homiller says. “Every time we’ve gone to higher energy, we’ve discovered some new layer of nature.” It will be nearly impossible to do so with electrons; because they have such a low mass, they radiate away about a trillion times more energy than protons every time they loop around a collider. But under CERN’s plan, the FCC tunnel could be repurposed to collide protons at energies eight times what’s possible in the LHC—about 50 years from now. “It’s completely scientifically sound and great,” Homiller says. “I think that CERN should do it.” 

Could we get to higher energies faster? In December, the alliteratively named Particle Physics Project Prioritization Panel (P5) put forward a vision for the near future of the field. In addition to addressing urgent priorities like continued funding for the HL-LHC upgrade and plans for telescopes to study the cosmos, P5 also recommended pursuing a “muon shot”—an ambitious plan to develop technology to collide muons. 

The idea of a muon collider has tantalized physicists because of its potential to combine both high energies and—since the particles are indivisible—clean collisions. It seemed well out of reach until recently; muons decay in just 2.2 microseconds, which makes them extremely hard to work with. Over the past decade, however, researchers have made strides, showing that, among other things, it should be possible to manage the roiling cloud of energy caused by decaying muons as they’re accelerated around the machine. Advocates of a muon collider also tout its smaller size (10 miles), its faster timeline (optimistically, as early as 2045), and the possibility of a US site (specifically, Fermi National Laboratory, about 50 miles west of Chicago).

There are plenty of caveats: a muon collider still faces serious technical, financial, and political hurdles—and even if it is built, there is no guarantee it will discover hidden particles. But especially for younger physicists, the panel’s endorsement of muon collider R&D is more than just a policy recommendation; it is a bet on their future. “This is exactly what we were hoping for,” Homiller says. “This opens a pathway to having this exciting, totally different frontier of particle physics in the US.” It’s a frontier he and others are keen to explore. 

Dan Garisto is a freelance physics journalist based in New York City.

 “Europa is my favorite body in the solar system,” Quick confesses. But she notes that other ocean worlds also offer promising places to look for signs of life. Those include Enceladus, a small moon of Saturn that, like Europa, has an icy crust with an ocean beneath. Images from the Cassini mission in 2005 revealed that geysers on the south pole of Enceladus spew water and organic molecules into space, feeding Saturn’s outermost ring. 

However, Europa is bigger than Enceladus and is more likely to have a surface covered in icy plates that move in a way similar to Earth’s plate tectonics. This sort of activity would help combine the ingredients for life. Ganymede, another Jovian moon and the solar system’s largest, also likely has a liquid ocean, but sandwiched between two ice layers; without an interface between water and minerals, life is less probable. Other possible places to look include Titan, Saturn’s biggest moon, which also probably hides a liquid-water ocean beneath an ice crust. (Quick is an investigator on Dragonfly, a mission to explore Titan, scheduled to launch in 2028.) 

To look for the signs and signals of habitability, Clipper will use nine primary instruments. These will take pictures of the surface, look for water plumes, use ground-penetrating radar to measure the icy shell and search for the ocean below, and take precise measurements of the magnetic field. 

The spacecraft will pass close enough to the moon to sample its thin atmosphere, and it will use mass spectrometry to identify molecules in the gases it finds there. Another instrument will enable scientists to analyze dust from the surface that has been kicked into the atmosphere by meteorite collisions. With any luck, they’ll be able to tell if that dust originated from below—from the enclosed ocean or subsurface lakes trapped in the ice—or from above, as fragments that migrated from the violent volcanoes on the nearby moon Io. Either scenario would be interesting to planetary geologists, but if the molecules were organic and came from below, they would help build the case that life could exist there.

ESA’s Juice mission has a similar suite of instruments, and scientists from the two teams meet regularly to plan for ways to jointly exploit the data when it starts coming in—five or six years from now. “This is really very good for scientists in the planetary community,” says Lorenzo Bruzzone, a telecommunications engineer at the University of Trento who leads the Juice mission’s radar tool team. He’s long been involved in efforts to get to Europa and the rest of the Jovian system. 

Because Juice will visit the other ocean-bearing Galilean moons, Bruzzone says, data from that mission can be combined with Clipper’s to generate a more comprehensive picture of the geological processes and potential habitability of all the ocean worlds. “We can analyze the differences in subsurface geology to better understand the evolution of the Jupiter system,” he says. Those differences may help explain, for example, why three of the Galilean moons formed as icy worlds while the fourth, Io, became a volcanic hellscape. 

Jupiter’s radiation has the potential to interfere with every measurement, turning a meaningful signal into a mess of digital snow, like static on a television screen.

To make sure those instruments work when they get there, engineers and designers for both missions have had to contend with a raft of challenges. Many of them revolve around energy: Europa receives only a fifth as much sunlight as Earth. Clipper addresses the problem with gargantuan solar panels, which will span 30 meters when fully extended. (An earlier proposal for a mission to Europa included nuclear batteries, but that idea was expensive, and it was ultimately scrapped.) 

The situation is urgent. In July of last year, Panama declared a state of animal health emergency amid outbreaks of cattle screwworm throughout the country. And this February, more than 200 cases of screwworm attacks on animals were reported in Costa Rica, prompting the government to declare an emergency as well. In Uruguay, screwworm flies cost the livestock industry $40 million to $154 million a year. Agricultural export is the linchpin of Uruguay’s economy—over 80% of the goods the nation exports are agricultural products. Beef, which accounts for 20% of that, is worth $2.5 billion a year. 

That makes the country’s search for new tools to combat the pests even more critical, says Carmine Paolo De Salvo, a rural development expert at the IDB. “The [Uruguayan] government is under constant pressure to do something about it,” he says.

Scientists have been trying to tackle screwworms for decades. One method, known as the sterile insect technique (SIT), was developed by researchers at the US Department of Agriculture in the 1950s. SIT involves sterilizing male screwworm flies with radiation. Then, using airplanes, the DNA-damaged males are dropped on the area of infestation. When they mate with wild female flies, the eggs that are produced do not hatch, slowing population growth and preventing the spread of the parasite.

That approach has worked in many countries, including parts of Central America, freeing livestock and wildlife by the millions from the painful grip of the pests. In the US, an area-wide eradication program using SIT worked so well that in 1966, the USDA declared screwworm eradicated within the nation’s borders. The benefits to the livestock industry were immense: producers saved up to $900 million, and the health of both wild and farm animals improved. 

Even with sterile males, eradicating screwworms remains a stubborn challenge, however. To prevent the screwworms from returning, the US—along with Central and South American countries—still runs a permanent barrier zone of sterile flies on the Panama-Colombia border, requiring a continuous supply of billions of flies every year. This effort is too expensive, and it’s simply not powerful enough to eradicate screwworm in South America, where the pests are firmly established and difficult to surveil, researchers say. So the search has been on for alternative tools.

COURTESY OF ALEJO MENCHACA

It was Kevin Esvelt, a pioneering leader in CRISPR gene-drive systems, who first turned the team on to the idea of using one. Esvelt had been experimenting with engineering localized versions of gene drives to target Lyme disease in the US when he met the team of Uruguayan researchers on a tour of the MIT Media Lab. Shortly after that meeting, Esvelt was on a plane to Uruguay, where he met Menchaca and convinced Uruguayan officials to initiate a gene drive project to eradicate screwworms. This would have the advantage over SIT because while SIT reduces the number of successful births, the infertility conferred by the gene drive passes through multiple generations.

The team is looking to use an approach that Scott has successfully developed for livestock pests. In a recent study, Scott and his team tested it on the spotted-wing drosophila, an invasive fly that attacks soft-skinned fruit. The gene drive they developed for that study carried an edited version of the so-called doublesex gene, which is essential for the fly’s reproduction. In caged trials, they combined the engineered fly population with a population that didn’t have the gene edits, mimicking a real-world release. They found that the gene drive was copied at a rate of 94% to 99%—beyond the efficiency they had expected. ​“It was the first really efficient-homing gene drive for suppression of an agricultural pest,” says Scott. He hopes that a similar technique will work with screwworms and allow researchers to perform safer tests.

It won’t be a quick process. Assembling the gene-drive system, testing it, and securing approvals for field release could take many years, says Jackson Champer, a researcher at Peking University in Beijing, who is not part of the Uruguayan team. “It’s not an easy task; there have been many failed attempts at gene drives.”