This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, . As regular readers will know, I tend to start each edition of this newsletter by telling you all about a topic that’s been on my mind—whether it’s a big news story, a fascinating trend, or just something cool I happened to hear about in my reporting. This week is a bit different. It’s my last Checkup for a while. In a matter of weeks, I’ll be starting a at MIT (which is completely unrelated to my position at Tech Review). The Checkup will live on—I’ll be passing the baton to my brilliant colleagues while I’m away! But this is a farewell from me, for now. The Checkup is not yet a year old, but we’ve covered some extremely exciting developments in medicine and biotechnology since we launched last September. We’ve come a long way since then—today, there are over 77,000 of you getting this newsletter in your inboxes every week! We’ve covered everything from to life-changing brain implants. There’s been a real mix of stories that have made me laugh, cry, and—always—think. So let’s take the opportunity to look at some story highlights from the last 10 months. The first edition of the Checkup looked at what minimally conscious brains can do. There’s some really fascinating research on the minds of people who are in what’s known as an unresponsive wakefulness state and only show unreliable flickers of awareness. Some studies suggest that people in this state can still learn. I spoke to neuroscientist John Whyte, who told me about attempts to pull minimally conscious people back into full consciousness. Some of these have involved sticking electrodes into a part of the brain that’s thought to control awareness. Others have involved drugs. I don’t think I’ll ever forget Whyte’s story about a young man he’d treated with one of these drugs. The man, who had sustained a head injury on his way home from his summer vacation, had been unconscious for three years. Within an hour of being given a drug called zolpidem, he seemed revived—he was even able to hug his parents. But the effects lasted only a few hours, . His parents opted to save the drug for special occasions. As a reporter covering health and biotech, I am hugely privileged to hear the personal stories of people who have been through incredible experiences. Another story that will stick with me is that of Ian Burkhart, who I spoke to for a more recent edition of the Checkup. Burkhart also experienced a life-changing injury in his young adulthood—a diving accident that left him with a broken neck. He was no longer able to move his limbs. A few years later, he volunteered to have an experimental device implanted in his brain. The device, which was essentially a set of 100 electrodes, was designed to record activity in a part of his brain responsible for controlling arm movement. Researchers were able to send recorded brain signals to a sleeve of electrodes on Burkhart’s arm via a computer. He was soon able to use the device to move his hand and fingers by thought alone. I first spoke to Burkhart in 2016, a couple of years after he’d had the device implanted. By that point, he was able to control his fingers well enough to play Guitar Hero. At the time, he said of the device: “.” But looming funding cuts soon threatened the project, and after an infection, he had to have the implant removed. He found this difficult, . “When I first had my spinal cord injury, everyone said: ‘You’re never going to be able to move anything from your shoulders down again,’” he said. “I was able to restore that function, and then lose it again. That was really tough.” (You can read more about the ethical implications of removing brain implants—particularly when recipients feel it has become part of them—in ). More generally, brain implants can both record brain activity and electrically stimulate parts of the brain. It’s an approach that appears to help treat some disorders, but it’s worth bearing in mind that these devices can collect intimate biological data. And while this data should be used to improve a person’s health, there’s a chance it could be used in a legal setting. Recordings from a brain device have already been used to clear someone from assault charges. In that case, recordings suggest the person was having a seizure at the time of the alleged assault. But such recordings could just as easily be used against someone, as . In another edition, I had with futurist and legal ethicist Nita Farahany about the need to protect our brain data and establish our “neurorights.” Since its inception, the Checkup has also covered some of the most exciting aspects of microbiome research. Anyone who knows me understands my fascination with the tiny bugs that live in and on us. (Former colleagues referred to me as their “poo correspondent” for my reporting on fecal transplants.) So perhaps it’s no surprise that a recent edition of this newsletter looked at what fecal analysis can tell you about your diet and your microbiome. Scientists are developing new tools that they hope will eventually allow them to . Others are working on engineering . It’s a worthwhile endeavor given just how important these microbes seem to be for our health. They even change as we age, which has led some scientists to wonder if . We’ve also explored some really tricky ethical questions that surround reproduction and parenthood as a result of new scientific advances. Scientists can now , for example. ? We can also use cells from dead people to make babies. , if ever? And then there’s . This technology could allow us to create babies with more than two parents, or none at all. Will it ? There often aren’t definitive answers to questions like these, but exploring them has been a blast. I’d like to say a great big thank you for doing that with me. Read more from Tech Review’s archive I’ve really enjoyed writing to you from reporting trips I’ve taken over the last year, especially from in Switzerland for uber-wealthy people looking to add years to their lives. And from a seaside resort in Montenegro where . While I’m away, the Checkup will live on! It will take a short break and then return to your inboxes in early August. In the meantime, I’d also like to flag the other amazing weekly newsletters written by my fabulous colleagues. Every Monday morning, Melissa Heikkilä shares her insights on the wild world of AI with subscribers of . And there’s more throughout the week. If you’re interested in batteries, concrete, lab-grown meat, and all things climate-related, Casey Crownhart’s newsletter, , is for you. Tate Ryan-Mosley has all you need to know about power, politics, and Silicon Valley in. And you can probably guess what Zeyi Yang’s informative and entertaining is all about. From around the web There’s evidence that weight-loss drugs like Wegovy work well in children—and trials in children as young as six are about to start. But taking these drugs could be a lifelong commitment, and they could be harmful for those with eating disorders. So should we ever give weight-loss drugs to kids? () Humans transmitted the coronavirus to white-tailed deer more than 100 times in late 2021 and early 2022, according to new research. The virus probably spread among the deer, mutated, and then passed back to us. () Activists are suing the Idaho government over a state law that prohibits adults from helping minors access abortions. The law was hastily cobbled together and is unconstitutional, according to the plaintiffs. () The US Food and Drug Administration has approved a daily contraceptive pill for over-the-counter use. The move should allow people to buy birth control pills without a prescription. () There are somewhere between 50 and 800 longevity clinics in the US, where clients pay as much as $100,000 for sometimes unproven treatments. () Two virologists have testified in support of their findings that the coronavirus had a “natural” origin and was not engineered in a lab. At a hearing titled “Investigating the proximal origin of a cover-up,” the scientists also said that Anthony Fauci did not exert influence over their research paper. ()
People have been using ChatGPT to help them to do their jobs since it was in November of last year, with enthusiastic adopters using it to help them write everything from marketing materials to emails to reports.. Now we have the first indication of its effect in the workplace. A new study by two MIT economics graduate students, published today in , suggests it could help reduce gaps in writing ability between employees. They found that it could enable less experienced workers who lack writing skills to produce work similar in quality to that of more skilled colleagues. Shakked Noy and Whitney Zhang recruited 453 marketers, data analysts, and college-educated professionals and got each of them to complete two kinds of tasks they’d normally undertake as part of their jobs, such as writing press releases, short reports, or analysis plans. Half were given the option of using ChatGPT to help them complete the second of the two tasks. A group of other professionals then quality-checked the results, grading the writing on a scale of 1 to 7, with 7 the best. Each piece of work was evaluated by three people working in the same professions, hired through the research platform Prolific. The writers who chose to use ChatGPT took 40% less time to complete their tasks, and produced work that the assessors scored 18% higher in quality than that of the participants who didn’t use it. The writers who were already skilled at writing were able to reduce the amount of time they spent on their work, while those who were assessed as being weaker writers produced higher-quality work once they gained access to the chatbot. “ChatGPT is just very good at producing this kind of written content, and so using it to automate parts of the writing process seems likely to save a lot of time,” says Noy, lead author of the research. “One thing that’s clear is that this is very useful for white-collar work—a lot of people will be using it, and it’s going to have a pretty big effect on how white-collar work is structured,” he adds. However, the output of ChatGPT and other generative AI models is far from reliable. ChatGPT is very good at , meaning that although workers may be able to leverage it to help them produce more work, they also run the risk of . Depending on the nature of a person’s job, those kinds of inaccuracies could have serious implications. Lawyer Steven Schwartz was by a judge last month for using ChatGPT to produce a legal brief that contained false judicial opinions and legal citations.“Technological advances are commonplace and there is nothing inherently improper about using a reliable artificial intelligence tool for assistance,” the judge, Kevin Castel, wrote. “But existing rules impose a gatekeeping role on attorneys to ensure the accuracy of their filings.” The research hints at how AI could be helpful in the workplace by acting as a sort of virtual assistant, says Riku Arakawa, a researcher at Carnegie Mellon University who studies workers’ use of large language models, and was not involved with the research. “I think this is a really interesting result that demonstrates how human-AI cooperation works really well in this kind of task. When a human leverages AI to refine their output, they can produce better content,” he adds.
This is today’s edition of , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How common chemicals could help clean up global shipping Global shipping is a big deal for the climate, accounting for 3% of the world’s greenhouse-gas emissions. Last week saw a big news announcement from the International Maritime Organization, the UN agency in charge of regulating the vessels that carry the goods we buy all over the world. On July 7, the IMO agreed to new climate goals, setting a target date of “by or around 2050” to clean up the industry’s act and reach net-zero emissions. There are checkpoints too: emissions should be at least 20% below 2008 levels by 2030. The shipping industry hasn’t had targets like this before. So how does it reach them? It’s more doable than you might think, as our climate reporter Casey Crownhart explains. . Casey’s story is from The Spark, her weekly climate and energy newsletter. to receive it in your inbox every Wednesday. If you’re interested in the shipping industry’s carbon footprint, why not check out: + How ammonia could help clean up global shipping. The fuel could provide an efficient way to store the energy needed to power large ships on long journeys. . + Why could be a crucial piece of the puzzle when it comes to reaching those net-zero goals. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk has unveiled his new startup, xAIBut his chances of overtaking OpenAI any time soon are slim. ( $)+ The new company will rely on Tesla for a lot of its resources. ( $)+ Anthropic’s new chatbot is here. () 2 What the James Webb Space Telescope has taught us Its first year of operation has opened our eyes to the wonders of the universe. ( $)+ There’s granite on the moon, apparently. ( $)+ How the James Webb Space Telescope broke the universe. () 3 What hasn’t Sam Altman invested in?Many of his 400 or so investments are benefiting from the AI boom, too. ( $)+ Has GPT-4 been secretly overhauled? It seems like it. ( $)+ Sam Altman invested $180 million into a company trying to delay death. () 4 America’s aggressive subsidies policy is paying offThe US wants future technologies to be developed at home, and the rest of the world is scrabbling to keep up. ( $)+ The $100 billion bet that a postindustrial US city can reinvent itself as a high-tech hub. () 5 Facebook and Google are tracking your tax preparationsNow, a group of lawmakers want to do something about it. () 6 Junky AI content is taking over the internetIt’s a new form of spam, and it’s everywhere. ( $)+ Junk websites filled with AI-generated text are pulling in money from programmatic ads. ()+ AI is fueling a drug addiction crisis. ( $) 7 Climate change is changing the color of the oceanIts new greener hue indicates serious disruptions in the marine food web. () 8 IT workers in Bangladesh are struggling in the heatThe country’s intense heatwave makes it virtually impossible to work in offices. ()+ Heat exposure is a deadly killer. ( $)+ The villagers fighting to survive India’s deadly heatwaves. () 9 Farming robots are getting better—and cheaper Human laborers are no longer always the best at what they do. ( $)+ How technology might finally start telling farmers things they didn’t already know. () 10 Airbnb’s party ban isn’t workingIt seems like background checks aren’t doing much to curb wild guests. ( $) Quote of the day “I look a little bit askance at signing a six month pause while you’re trying to accelerate your own effort.” —Reid Hoffman, co-founder of LinkedIn, criticizes Elon Musk’s recent call for a pause on AI development given that the billionaire has just unveiled his own AI startup, xAI, he tells . The big story Predictive policing algorithms are racist. They need to be dismantled. July 2020 Inequality and the misuses of police power don’t just play out on the streets or during school riots. For digital rights activists, the focus is now on where there is most potential for long-lasting damage: predictive policing tools and the abuse of data by police forces. A number of studies have shown that these tools perpetuate systemic racism, and yet we still know very little about how they work, who is using them, and for what purpose. All of this needs to change before a proper reckoning can take place, but a clear principle is emerging: if we can’t fix them, we should ditch them. . —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 .) + This baby learning to stand on one leg is just too cute. + Live Aid was 38 years ago today! Let’s revisit just of its iconic performances.+ is an acquired taste, but you can’t deny its buildings are striking.+ is just unapologetically himself.+ What are these up to at the bottom of the sea, exactly?
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, . I’ve been thinking a lot about boats lately, and not just because it’s been hot in New York for days and hopping into any body of water sounds incredibly refreshing right now. I’ve actually got boats on the mind because there was just big news in global shipping from the International Maritime Organization, the UN agency in charge of regulating the vessels that carry everything from tennis skirts to EV batteries around the world. On July 7, the IMO agreed to new climate goals, setting a target date of “by or around 2050” to clean up the industry’s act and reach net-zero emissions. This is a big deal for the shipping industry, which didn’t have any broadly accepted target before. But as we all know, a goal is more of a starting point than an end. So let’s take a look at the technology that companies might turn to as they chase net-zero shipping. Starting small Aside from the net-zero target, a crucial piece of the IMO agreement is a set of checkpoints along the way to 2050. These aren’t binding, but the IMO did set a target to cut emissions 20% by 2030, and 70% by 2040. Those checkpoints could be critical in spurring industry to take action, said , who was present for the IMO proceedings and is a senior director of climate at Pacific Environment, an environmental group. I was especially intrigued by that first checkpoint, because 2030 is coming up fast. (Fun fact: The first day of 2030 is actually closer to today than the last day of 2016 is.) And a 20% emissions cut for an industry that’s often called sounds like a lot. But digging into it, I was surprised to learn that there are actually several fairly straightforward avenues the industry could take to reach this target, and likely with time to spare. In fact, just slowing down ships could be enough to achieve that 20% cut in greenhouse-gas emissions. Faster ships require more fuel than slower ships, even when traveling the same distance. And other technology options are on the table too, like new fuels and devices like sails or special rotors that can harness the wind to boost ships. That trifecta could actually add up to a nearly 50% decrease in emissions by the end of the decade, according to from environmental consultancy CE Delft. I wrote all about these near-term measures that shipping could take, so . In the meantime, let’s set our sights further toward the horizon and consider what shipping might look like in 2050. Ocean-going Slowing ships down, adding wind assistance, or even adding coatings to make boats more slippery in the water will all cut down on the amount of fuel used. But that isn’t how we’re going to reduce greenhouse-gas emissions to zero. That’s because even as you get more efficient, you’ll still be using fossil fuels that produce the climate-warming emissions. So in the longer term, shipping will have to find more fundamental ways to clean up its act, like finding new power sources. Batteries will find their way into some ships, but they’ll probably be limited to shorter voyages, because most batteries today would be too bulky and heavy to carry enough energy for the longest trips. , published last year in Nature Energy, estimated that journeys of up to 1,000 kilometers (620 miles) could be economically serviced by battery-powered ships today. If batteries continue to get cheaper and pack more energy into a smaller package, that could soon stretch to 3,000 kilometers (1,860 miles) (or even longer, if environmental costs are taken into account and ships can be designed to carry more weight). But for the longest routes, we’ll likely still need to rely on fuels. One option is ammonia,. This chemical, today used as a fertilizer ingredient, could power ships in two different ways. It could be used in combustion engines, since as a non-carbon-based fuel it doesn’t produce carbon dioxide when burned. Ammonia can also be used as a way to store and transport hydrogen, which could then be used in fuel cells to power electric ships. Check out for all the details. Other companies are looking to methanol as a potential green fuel. There’s still carbon in it, so it does produce carbon emissions when burned, but the fuel can be produced using renewable electricity and carbon dioxide pulled from the atmosphere or from biological sources, so the balance of emissions could be low, or even zero. Shipping giant Maersk that it gathered enough bio-methanol for a maiden voyage from South Korea to Denmark. Availability of bio-methanol and other low-emissions fuels is still a bottleneck in the industry, but the company has ordered over a dozen methanol-powered ships. I’ll be following work on these alternative power sources, so stay tuned for more from me. And for the record, we’re closer to 2050 than we are to 1996. Related reading Check out about how the shipping industry can start making emissions cuts right now. Ammonia is a popular candidate for global shipping, but the fuel has some potential roadblocks to overcome first. about these two sides of the ammonia coin. Ships might not be the only thing powered by methanol: in China, some companies want to use it to power vehicles. My colleague Zeyi Yang . Another thing Syracuse, New York, could soon go through a time of immense change. The city is marked by poverty—and it will soon be home to four massive chip factories, which will cost a total $100 billion to build. My colleague David Rotman took a deep dive into what this could mean for the area, and what an influx of funding from the US federal government will mean for other cities across the country. Keeping up with climate Over 60,000 people died because of Europe’s summer heat waves last year. Italy, Spain, and Portugal saw the highest mortality rates. () → I wrote last year about how changing summer heat patterns will likely bring more air conditioning to the continent, and why that might be a problem. () A decades-old coal-fired power plant in North Dakota is getting retrofitted with a carbon capture system. The project will cost over $1 billion, and it could be a major test for the technology. () Toyota announced ambitions to get solid-state batteries into cars in 2027. But this is far from the first time the automaker has made promises about the technology. () → If they make it into EVs, solid-state batteries could speed charging times and boost vehicle range. () A test of an enhanced geothermal system in Utah hit a big milestone, connecting deep tunnels drilled underground. Enhanced geothermal projects could help bring renewable energy to places where traditional geothermal isn’t accessible. () There’s a pot of money at the Department of Energy with hundreds of millions of dollars in it, and the office wants to use some of it for public transit. () The Biden administration just approved a massive offshore wind farm. It’s off the coast of New Jersey and could power as many as 380,000 homes. ()
This is today’s edition of , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Bill Gates isn’t too scared about AI Bill Gates just joined the chorus of big names in tech who have weighed in on the question of risks around artificial intelligence. TL;DR? He’s not too worried, we’ve been here before.The billionaire business magnate and philanthropist made his case in a post on his personal blog GatesNotes, in which he called AI the most transformative technology any of us will see in our lifetimes—ahead of the internet, smartphones, and personal computers.His optimism is refreshing after weeks of doomsaying. Gates urges fast but cautious action to address some of the harms AI already poses to society, from elections to education to employment. The problem is that he doesn’t offer anything new. . —Will Douglas Heaven The UN just set a net-zero goal for shipping. Here’s how that could work. Ships crisscrossing the world’s oceans are vital to our global economy—everything from the bananas in your kitchen to the car in your driveway may have journeyed on one at some point. But all that travel causes pollution: the global shipping industry is responsible for over a billion tons of greenhouse-gas emissions each year, about 3% of the world’s total. A UN group called the International Maritime Organization agreed earlier this month to set a goal of net-zero greenhouse-gas emissions for global shipping by or around 2050. But experts say that there are more than enough tools available for the industry to reach, or even surpass, those new goals. . —Casey Crownhart The US-China chip war is still escalating The temperature of the US-China tech conflict just keeps rising. Last week, the Chinese Ministry of Commerce announced a new export license system for gallium and germanium, two elements that are used to make computer chips and other tech devices. By putting a chokehold on these two raw materials, China is signaling that it, in turn, can cause pain for the Western tech system and push other countries to rethink the curbs they put on China.But despite the country’s intentions, the new export controls may not have much long-term impact on other countries. And technological tensions are only getting worse. . —Zeyi Yang Zeyi’s story is from China Report, his weekly newsletter giving you the inside track on all things happening with China and tech. to receive it in your inbox every Tuesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 China-linked hackers infiltrated US government emailsExperts worry it’s part of an extensive espionage campaign against US officials. ( $)+ The attack appears to have been targeted rather than broad-brush. ( $) 2 Silk Road’s second-in-command has been jailed for 20 yearsAlmost a decade after the infamous online black market went offline. ( $) 3 Microsoft and Activision could merge as soon as this monthIt’s good news for Microsoft, bad news for those worried by tech monopolies. ( $) 4 Generative AI will tip Google Search into chaosWhile SEO clickbait suffers, what comes next could be even weirder. ( $)+ AI chatbots could end up making us work harder. ( $)+ AI’s overlooked workforce is toiling away, day after day. ()+ Why you shouldn’t trust AI search engines. () 5 This ‘biological camera’ stores images in DNA Treating DNA as hardware is a useful storage solution. () 6 What this Canadian lake tells us about climate changeIt’s a remarkable record of geological change. ()+ Global warming’s crisis can be felt everywhere. ( $)+ Restoring an ancient lake from the rubble of an unfinished airport in Mexico City. () 7 VCs are going all in on AICrypto? What crypto? ( $)+ Generative AI is changing everything. But what’s left when the hype is gone? () 8 China has successfully launched a methane-fueled rocketAnd it’s leapfrogged the US’s efforts in the process. ( $)+ Elsewhere, one of Blue Origin’s rocket engines caught fire last month. ( $) 9 A photography competition rejected a suspected AI imageEven though the flattered creator swears she took it on her iPhone. () 10 Russia’s top Wikipedia editor is launching a Kremlin-compliant rivalThis could be a precursor to the website being banned there. ( $) Quote of the day “Twitter doesn’t have an intolerant policy like Meta. Other platforms cannot replace it.” —Anas Haqqani, a Taliban thought-leader, officially endorses over Meta’s new Threads platform. The big story The code must go on: An Afghan coding bootcamp becomes a lifeline under Taliban rule December 2021 Four months after the Afghan government fell to the Taliban, 22-year-old Asad Asadullah had settled into a new routine. In his hometown in Afghanistan’s northern Samangan province, the former computer science student started and ended each day glued to his laptop screen. Since late October, Asadullah had been participating in a virtual coding bootcamp organized by CodeWeekend, a volunteer-run community of Afghan tech enthusiasts, with content donated by Scrimba, a Norwegian company that offers online programming workshops. Asadullah is one of the millions of young Afghans whose lives, and plans for the future, were turned upside down when the Taliban recaptured Afghanistan in August 2020. In such dire circumstances, a coding bootcamp may seem out of place. But for its participants, it offers hope of a better future. . —Eileen Guo 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 .) + Thanks to new data from the JWST, we can now , which is seriously cool.+ These know how to have a good time.+ Are worth it? Well, it depends what you put in them. + These are some of the most spectacular in the world.+ There may not be much evidence for , but it’s a nice idea nonetheless.
This story first appeared in China Report, MIT Technology Review’s newsletter about technology developments in China. to receive it in your inbox every Tuesday. The temperature of the US-China tech conflict just keeps rising. Last week, the Chinese Ministry of Commerce announced a new export license system for gallium and germanium, two elements that are used to make computer chips, fiber optics, solar cells, and other tech devices. Most experts see the move as China’s most significant retaliation against the West’s semiconductor tech blockade, which expanded dramatically last October when the US limited the export to China of the most cutting-edge chips and the equipment capable of making them. Earlier this year, China responded by putting Raytheon and Lockheed Martin on a list of unreliable entities and banned domestic companies from buying chips from the American company Micron. Yet none of these moves could rival the global impact of the gallium/germanium export control. By putting a chokehold on these two raw materials, China is signaling that it, in turn, can cause pain for the Western tech system and push other countries to rethink the curbs they put on China. But , China’s new export controls may not have much long-term impact. “Export control is not as effective if the technologies are available in other markets,” Sarah Bauerle Danzman, an associate professor of international studies at Indiana University Bloomington, told me. Since the technology to produce gallium and germanium is very mature, it won’t be too hard for mines in other countries to ramp up their production, although it will take time, investment, policy incentives, and maybe technological improvement to make the process more environmentally friendly. So what happens now? Half of 2023 is now behind us, and even though there have been a few diplomatic events showing the US-China relationship warming up, like trips to China made by US officials Antony Blinken and Janet Yellen,the tensions on the technological front are only getting worse. When the US instituted its chip-related export restrictions in October, it wasn’t clear how much of an impact they would have, because the US doesn’t control the entirety of the semiconductor supply chain. one of the biggest outstanding questions was the extent to which the US could persuade its allies to join the blockade. Now the US has managed to get the key players on board. In May, Japan announced that it is limiting the export of 23 types of equipment used in a variety of chipmaking processes. It even went further than the original US rules. The US limited the export of tools for making the most cutting-edge chips—those of the 14-nanometer generation and under. Japan’s restrictions extend to older, less-advanced chip generations (all the way to the 45-nanometer level), which has that production of basic chips used in everyday products, like cars, will also be affected. At the end of June, the Netherlands followed suit and announced that it will limit the export to China of deep ultraviolet (DUV) lithography machines used to pattern chips. That’s also an escalation of the previous rules, which since 2019 had only limited export of the most advanced extreme ultraviolet (EUV) lithography machines. These expanding restrictions likely prompted China to take a page from its enemies’ playbook by instituting the controls on gallium and germanium. Yellen’s visit last week shows that this back-and-forth retaliation between China and the US-led bloc is not ending anytime soon. Both Yellen and the Chinese leaders expressed their concern at the meeting about the other side’s export controls, yet neither said anything about backing down. If more aggressive actions are taken soon, we may see the tech war expand out of the semiconductor field to involve things like battery technologies. As I explained in , that’s where China would have a larger advantage. Do you believe the technological tensions between the US and China will worsen from here? Let me know your thoughts at zeyi@technologyreview.com. Catch up with China 1. Tesla is laying off some battery manufacturing workers in China as a result of the cutthroat electric-vehicle price competition in the country. () 2. China’s top EV maker, BYD, is building three new factories in Brazil to make batteries, EVs, and hybrid cars. They will be built at the location of an old Ford plant. () 3. Shenzhen, the city often seen as the Silicon Valley of China, is facing population decline for the first time in decades. () 4. Five people were arrested by the Hong Kong police for involvement in creating an online shopping app to map out local businesses that support the pro-democracy movement. () 5. There’s now an official app for learning how to do journalism in China—with online courses taught about the Marxist view of journalism, why the party needs to control the press, and how to be an “influencer-style journalist.” () 6. During her visit, Yellen sat down for dinner with six female Chinese economists. Then they were called traitors online. () 7. A new study says a rapidly growing number of scientists of Chinese descent have left the US since 2018, the year the US Department of Justice launched its “China Initiative.” (). An by MIT Technology Review published in late 2021 showed it had shifted its focus from economic espionage to “research integrity.” The initiative was officially shut down in 2022. 8. Threads, the new Twitter competitor released by Meta, hit the top five on Apple’s China app store even though Chinese users have to access the platform with a VPN. () Lost in translation On July 5, the famous Hong Kong singer CoCo Lee died by suicide after having battled depression for several years. The tragic incident again highlighted the importance of depression treatment, which is often inaccessible in China. , fewer than 10% of patients diagnosed with depression in China have received any kind of medical treatment. But in recent years, as several patents for popular Western brand-name depression drugs have expired, Chinese pharmaceutical companies have ramped up their production of local generic alternatives. There’s also a fierce race to invent home-grown treatments. Last November, the first domestically designed depression drug was approved for sale in China, marking a new era for the industry. There are 17 more domestic treatments in trials right now. One more thing Every time high-profile US visitors come to China, Chinese social media always fixates on one thing: what they ate. Apparently, Janet Yellen is a fan of the wild mushrooms from China’s southwest border, which her group ordered four times in one dinner. The specific mushroom, called Jian Shou Qing in China, is also known for having psychedelic effects if not cooked properly. Now the restaurant is cashing in by offering Yellen’s dinner choices as a set, branded the “God of Money” menu, .
Bill Gates has joined the chorus of big names in tech who have weighed in on the . The TL;DR? He’s not too worried, we’ve been here before. The optimism is refreshing after weeks of doomsaying. The billionaire business magnate and philanthropist made his case in a today. “I want to acknowledge the concerns I hear and read most often, many of which I share, and explain how I think about them,” he writes. According to Gates, “[AI is] the most transformative technology any of us will see in our lifetimes.” That puts it above the internet, smartphones and personal computers, the technology he did more than most to bring into the world. (It also suggests that nothing else to rival it will be invented in the next few decades.) Gates was one of dozens of high-profile figures to sign a a few weeks ago, which reads, in full: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” But there’s no fear-mongering in today’s blog post. In fact, existential risk doesn’t get a look in. Instead, Gates frames the debate as “longer-term” versus “immediate” risk, and chooses to focus on “the risks that are already present, or soon will be.” “Gates has been plucking on the same string for quite a while,” says David Leslie, director of Ethics and Responsible Innovation Research at the Alan Turing Institute in the UK. Gates was one of several public figures who talked about the existential risk of AI a decade ago, when deep learning first took off, says Leslie: “He used to be more concerned about superintelligence way back when. It seems like that might have been watered down a bit.” Gates doesn’t dismiss existential risk fully. He wonders what may happen “when”—not if —“we develop an AI that can learn any subject or task.” He writes: “Whether we reach that point in a decade or a century, society will need to reckon with profound questions. What if a super AI establishes its own goals? What if they conflict with humanity’s? Should we even make a super AI at all? But thinking about these longer-term risks should not come at the expense of the more immediate ones.” Gates has staked out a kind of middle ground between deep learning pioneer , who quit Google and went public with his fears about AI in May, and others like at Meta AI (who think talk of existential risk is “preposterously ridiculous” and “unhinged”) or at Signal (who thinks the fears shared by Hinton and others are “ghost stories”). It’s interesting to ask what contribution Gates makes by weighing in now, says Leslie: “With everybody talking about it, we’re kind of saturated.” Like Gates, Leslie doesn’t dismiss doomer scenarios outright. “Bad actors can take advantage of these technologies and cause catastrophic harms,” he says. “You don’t need to buy into superintelligence, apocalyptic robots or AGI speculation to understand that.” “But I agree that our immediate concerns should be in addressing the existing risks that derive from the rapid commercialization of generative AI,” says Leslie. “It serves a positive purpose to sort of zoom our lens in and say, ‘Okay, well, what are the immediate concerns?’” In his post, Gates notes that AI is already a threat to many fundamental areas of society, from elections to to . Of course, such concerns aren’t news. What Gates wants to tell us is that although these threats are serious, we’ve got this: “The best reason to believe that we can manage the risks is that we have done it before.” In the 1970s and 80s, calculators changed how students learned math, allowing them to focus on what Gates calls the “thinking skills behind arithmetic” rather than the basic arithmetic itself. He now sees apps like doing the same with other subjects. In the 1980s and 90s, word processing and spreadsheet applications changed office work—changes that were driven by Gates’s own company, Microsoft. Again, Gates looks back at how people adapted and claims that we can do it again. “Word processing applications didn’t do away with office work, but they changed it forever,” he writes. “The shift caused by AI will be a bumpy transition, but there is every reason to think we can reduce the disruption to people’s lives and livelihoods.” Similarly, with misinformation: we learned how to deal with spam, we can do the same for deepfakes. “Eventually, most people learned to look twice at those emails,” Gates writes. “As the scams got more sophisticated, so did many of their targets. We’ll need to build the same muscle for deepfakes.” Gates urges fast but cautious action to address all the harms on his list. The problem is that he doesn’t offer anything new. Many of his suggestions are tired; some are facile. Like others in the last few weeks, Gates calls for a global body to regulate AI similar to the International Atomic Energy Agency. Gates thinks this would be a good way to control the development of AI cyberweapons. But he does not say what those regulations should curtail or how they should be enforced. He says that governments and businesses need to make sure that people do not get left behind in the job market, by offering them support, such as retraining. Teachers, he says, should also be supported in the transition to a world in which apps like ChatGPT are the norm. But Gates does not specify what this support would look like. And he says that we need to get better at spotting deepfakes, or at least use tools that detect them for us. But the latest crop of tools. As generative AI improves, will the detectors keep up? Gates is right that “a healthy public debate will depend on everyone being knowledgeable about the technology, its benefits, and its risks.” But he often falls back on a conviction that AI will solve AI’s problems—a conviction that not everyone will share. Yes, immediate risks should be prioritized. Yes, we have steered through (or bulldozed over) technological upheavals before and we could do it again. But how? “One thing that’s clear from everything that has been written so far about the risks of AI—and a lot has been written—is that no one has all the answers,” Gates writes. That’s still the case.
This is today’s edition of , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Weather forecasting is having an AI moment Last week was the hottest week on record. Punishing heat waves and extreme weather events like hurricanes and floods are going to become more common as the climate crisis worsens, making it more important than ever before to produce accurate weather forecasts. AI is proving increasingly helpful with that. In the past year, weather forecasting has been having an AI moment. Using AI to predict weather has a big advantage: it’s fast. Traditional forecasting models are big, complex computer algorithms based on atmospheric physics and take hours to run. AI models can create forecasts in just seconds. But they’re unlikely to replace conventional weather prediction models anytime soon—and we don’t know if they’ll be reliable enough to predict rare and extreme weather events. . —Melissa Heikkilä Melissa’s story is from The Algorithm, her weekly AI newsletter. to receive it in your inbox every Monday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Threads is hurting TwitterTwitter’s traffic is tumbling, while Threads already has more than 100 million new users. ( $)+ Threads could make Meta a cool $8 billion in the next two years. ( $)+ Elon Musk is resorting to dirty tactics. () 2 US officials asked for a delay on their social media company contact banBut the judge has already denied one previous request to halt proceedings. ( $)+ Senators are being briefed on AI today. () 3 The EU and the US have agreed a data sharing dealIt’s taken years to thrash out, and a lot of European lawmakers still don’t like it. ( $)+ Social media companies will be relieved. () 4 China is drawing up its rules to govern AIIt’s being forced to offset rapid innovation against state control. ( $)+ China isn’t waiting to set down rules on generative AI. () 5 AI detection tools discriminate against non-native English speakersIt highlights how many AI detection systems aren’t fit for purpose. ()+ AI-text detection tools are really easy to fool. () 6 Real-time crime centers are on the riseThey collect extensive surveillance data that privacy advocates claim crosses a line. ( $)+ Marseille’s battle against the surveillance state. () 7 How to cope with climate anxiety Climate therapy is a growing field to help people cope with their fears. ( $)+ Heatwaves claimed the lives of tens of thousands of people in Europe last year. ( $) 8 Facebook is a breeding ground for illegal wildlife traffickingRare animals are exchanging hands for vast amounts. ()+ Governments are using counterterrorism measures to counter poaching. ( $) 9 The Earth is lumpy It’s far less smooth than photos taken from space would have us believe. ( $)+ Earth’s low orbit is becoming increasingly crowded. () 10 Would you pay to smash up a printer? Plenty of people do, it turns out. ( $) Quote of the day “It’s just not easy to kill everybody.” —Kjirste Morrell, a professional superforecaster, explains why fears over the threat AI poses to humanity are overblown to . The big story China’s path to modernization has, for centuries, gone through my hometown June 2021 For generations, politicians and intellectuals have sought ways to build a strong China. Some imported tools and ideas from the West. Others left for a better education, but the homeland still beckoned. Yangyang Cheng, a particle physicist at Yale Law School, is a product of their complex legacy. She grew up in Hefei, then a humble, medium-sized city in central-eastern China, which is now a budding metropolis with new research centers, manufacturing plants, and technology startups. For two of the city’s proudest sons, born a century apart, a strong homeland armed with science and technology was the aspiration of a lifetime. Cheng grew up with their stories. They teach her about the forces that propelled China’s rise, and the way lives can be squeezed by the pressures of geopolitics. . 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 .) + Learning isn’t only possible—it can even be fun.+ I wish I was this .+ If you’re a book lover, book yourself on the next available trip to .+ Think beyond Coachella—there are plenty of out there to suit everyone’s tastes.+ Is video art better than TikTok clips? .
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, . Is it hot where you are? It sure is here in London. I’m writing this newsletter with a fan blasting at full power in my direction and still feel like my brain is melting. Last week was the . It’s yet another sign that climate change is “out of control,” the UN secretary general said. Punishing heat waves and extreme weather events like hurricanes and floods are going to become more common as the climate crisis worsens, making it more important than ever before to produce accurate weather forecasts. AI is proving increasingly helpful with that. In the past year, weather forecasting has been having an AI moment. Three recent papers from Nvidia, Google DeepMind, and Huawei have introduced machine-learning methods that are able to predict weather at least as accurately as conventional methods, and much more quickly. Last week I wrote about , an AI model developed by Huawei. Pangu-Weather is able to forecast not only weather but also the path of tropical cyclones. . Huawei’s Pangu-Weather, Nvidia’s , and Google DeepMind’s , are making meteorologists “reconsider how we use machine learning and weather forecasts,” Peter Dueben, head of Earth system modeling at the European Centre for Medium-Range Weather Forecasts (ECMWF), told me for the story. ECMWF’s weather forecasting model is considered the gold standard for medium-term weather forecasting (up to 15 days ahead). Pangu-Weather managed to get comparable accuracy to the ECMWF model, while Google DeepMind claims in an non-peer-reviewed paper to have beat it 90% of the time in the combinations they tested. Using AI to predict weather has a big advantage: it’s fast. Traditional forecasting models are big, complex computer algorithms based on atmospheric physics and take hours to run. AI models can create forecasts in just seconds. But they are unlikely to replace conventional weather prediction models anytime soon. AI-powered forecasting models are trained on historical weather data that goes back decades, which means they are great at predicting events that are similar to the weather of the past. That’s a problem in an era of increasingly unpredictable conditions. We don’t know if AI models will be able to predict rare and extreme weather events, says Dueben. He thinks the way forward might be for AI tools to be adopted alongside traditional weather forecasting models to get the most accurate predictions. Big Tech’s arrival on the weather forecasting scene is not purely based on scientific curiosity, reckons Oliver Fuhrer, the head of the numerical prediction department at MeteoSwiss, the Swiss Federal Office of Meteorology and Climatology. Our economies are becoming increasingly dependent on weather, especially with the rise of renewable energy, says Fuhrer. Tech companies’ businesses are also linked to weather, he adds, pointing to anything from logistics to the number of search queries for ice cream. The field of weather forecasting could gain a lot from the addition of AI. Countries track and record weather data, which means there is plenty of publicly available data out there to use in training AI models. When combined with human expertise, AI could help speed up a painstaking process. What’s next isn’t clear, but the prospects are exciting. “Part of it is also just exploring the space and figuring out what potential services or business models might be,” Fuhrer says. Deeper Learning AI-text detection tools are really easy to fool Within weeks of ChatGPT’s launch, there were fears that students would be using the chatbot to spin up passable essays in seconds. In response to those fears, startups started making products that promise to spot whether text is written by a human or a machine. Turns out it’s relatively simple to trick these tools and avoid detection. Snake-oil alert: I’ve written about how. As my colleague Rhiannon Williams reports, new research found that most of the tools that claim to be able to spot such text perform poorly. Researchers tested 14 detection tools and found that while they were good at spotting human-written text (with 96% accuracy on average), that fell to 74% for AI-generated text, and even lower, to 42%, when that text had been slightly tweaked. . Bits and Bytes AI companies are facing a flood of lawsuits over privacy and copyrightWhat America lacks in AI regulation, it makes up for in multimillion-dollar lawsuits. In late June, a California law firm a class action lawsuit against OpenAI, claiming that the company violated the privacy of millions of people when it scraped data from the internet to train its model. Now, actor and comedian for scraping her copyrighted work into their AI models. These cases, along with existing , could set an important precedent for how AI is developed in the US. OpenAI has introduced a new concept: “superalignment” It’s a bird … It’s a plane … It’s superalignment! OpenAI is assembling a team of researchers to work on “superintelligence alignment.” That means they’ll focus on solving the technical challenges that would be involved in controlling AI systems that are smarter than humans. On one hand, I think it’s great that OpenAI is working to mitigate the harm that could be done by the superintelligent AI it is trying to build. But on the other hand, such AI systems remain wildly hypothetical, and existing systems cause plenty of harm today. At the very least, I hope OpenAI comes up with more effective ways to control this generation of AI models. () Big Tech says it wants AI regulation, so long as users bear the bruntThis story gives a nice overview of the lobbying happening behind the scenes around the AI Act. While tech companies say they support regulation, they are pushing back against EU efforts to impose stricter rules around their AI products. () How elite schools like Stanford became fixated on the AI apocalypseFears about existential AI risk didn’t come from nowhere. In fact, as this piece explains, it’s a billionaire-backed movement that’s recruited an army of elite college students to its cause. And they’re keen to capitalize on the current moment. ()
Software engineers and their ability to deliver are critical to a business’ success. They help organizations keep pace with innovation and respond to disruptive forces. Even companies in industries that are not traditionally considered tech, such as agriculture or financial services, recognize the need for software engineers and are actively seeking to hire talented individuals. Software engineers are responsible for an ever-growing list of demands through the software development cycle. Their working environment is more complex due to proliferation of complex multicloud infrastructure, tooling, and applications. While they command good salaries and professional esteem, the job takes its toll. Additionally, ever-evolving cyber threats and the constant need to stay ahead in terms of continual scanning and early detection add to the complexity. A 2021 poll conducted by Haystack Analytics found were suffering burnout, driven by increasing demands on their time and inefficient processes. A 2022 survey by LaunchDarkly showed continued burn-out and retention challenges for software engineers, with a key frustration. Challenges are more acute in organizations saddled with technical debt, heritage applications, and legacy infrastructure. “The engineer’s job has become extremely hard, but with one of the largest tech footprints and investments, JPMorgan Chase has a unique opportunity and responsibility to lead the industry in a paradigm shift toward minimizing the cognitive load for engineers and multiplying their productivity to accelerate the value we deliver to our customers and clients,” says Sandhya Sridharan, global head of Engineering Platforms and Experience at JPMorgan Chase. Building an intuitive interface As part of its modernization journey, JPMorgan Chase is building in a highly integrated self-service engineer platform designed to empower and enable the company’s 43,000+ person engineering community, with the goal of amplifying experience, engagement, and productivity. The firm’s approach is driven by four strategic imperatives. First is a unified interface. This is a personalized, data-driven experience that gives engineers ownership and has a self-service dynamic, which is a change from business-as-usual. “An engineer platform must simplify an engineer’s day-to-day tasks by providing the right level of contextual abstraction along with the appropriate tooling and resources,” explains Sridharan. “This needs to happen within the context of an integrated development environment where engineers spend most of their time providing complete visualization of their build and deployment pipelines.” The second imperative is to be cloud focused. The public cloud offers scalability, which improves speed, agility, and cost. The majority of software developer tooling is primarily available in, and built for, public cloud platforms, which can be more reliable and resilient than on-premise infrastructures. Engineers can quickly take advantage of best-of-breed capabilities, including observability tools, while adopting strategies like canary deployment (releasing first to a small subset of users) that help accelerate time to market. “If we were on-premises, we would not have the flexibility for elastic scale and it wouldn’t be cost effective,” notes Sridharan. The third imperative is to be data-driven, which is core to an industry as complex and fast-moving as financial services. The platform equips engineers with the right data, insights, and recommendations to enable real-time detection and resolution, and to track progress. It also provides telemetry, which can help personalize the engineer experience to individual needs. “Data will power everything we do and inform our decision making as we continue to evolve and improve the platform to best support the needs of our engineers,” Sridharan elaborates. This platform also offers a more robust system for governance and security. Software failures are inevitable, but what matters is whether a platform provides the capability to quickly detect failures and recover. JPMorgan Chase’s platform includes observability tooling that can detect problems and auto-remediate or rollback the change that caused it, reducing outage time for end users. Observability and automation are especially important in heavily regulated sectors, like finance, in terms of audit evidence. “We need to have full traceability of every transaction and changes that go into production,” notes Sridharan. “This not only equips our engineers with detailed insights and trends, but it also saves them several days and weeks of effort anytime we are audited, as the platform provides a full audit report with the click of a button.” Engineer experience and competing for talent JPMorgan Chase’s upgrade of its engineering platform improves productivity, efficiency, and security. Just as important is helping the company compete for engineering talent by offering a vastly more efficient working environment than software engineers might find elsewhere. The firm’s goal is to be the most attractive engineering destination, and given the consistent competition for good talent, it’s more important than ever to offer engineers a world-class working environment with minimal friction. “Engineering excellence and a highly intuitive platform are critical for us to not only retain our top talent, but also continue to attract the best talent in the industry,” says Sridharan. By abstracting both the controls and the infrastructure complexities, JPMorgan Chase’s platform allows engineers to focus on high-value productivity, rather than being mired in coordination or laborious processes. It abstracts controls by automatically factoring in the relevant workflows and processes, such as differing regulatory environments, which the platform incorporates through its policy and rules engine. This allows engineers to focus on what they do best: build high value applications which are scalable, robust, and resilient. As many companies move to multicloud or hybrid cloud environments, some workloads are best conducted via one cloud hyperscaler platform or another. However, choosing which one shouldn’t tax an engineer’s time. The platform directs workflows into the right infrastructure based on factors like regulation and capacity. Abstracting all of this complexity, while providing context where needed, means developers can focus on building the right application to generate customer value. “To make JPMorgan Chase the most attractive engineering destination, we need to be able to not only remove friction every step of the way, but we need to build a solid foundation that evolves and simplifies the day-to-day life of a software engineer, which will help accelerate our business outcomes,” concludes Sridharan. This article is for informational purposes only and it is not intended as legal, tax, financial, investment, accounting or regulatory advice. Opinions expressed herein are the personal views of the individual(s) and do not represent the views of JPMorgan Chase & Co. The accuracy of any statements, linked resources, reported findings or quotations are not the responsibility of JPMorgan Chase & Co. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This is today’s edition of , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How China is fighting back in the semiconductor exports war China has been on the receiving end of semiconductor export restrictions for years. Now, it’s striking back with the same tactic. On July 3, the Chinese Ministry of Commerce announced that the export of gallium and germanium, two elements used in producing chips, solar panels, and fiber optics, will soon be heavily restricted. Exports of the materials will need to be approved by the government, and Western companies that rely on them could have a hard time securing a supply. Even though the news immediately sent the price of gallium and germanium up, the curbs are not likely to hit the US as hard as American export restrictions have hit China. Zeyi Yang, our China reporter, explains why. . Zeyi’s story is part of MIT Technology Review Explains, our section in which our writers untangle the complex, messy world of technology to help you understand what’s coming next. Check out the on everything from lab-grown meat to how to log off. Why everyone is mad about New York’s AI hiring law Last week, a law about AI and hiring went into effect in New York City, and everyone is up in arms about it. It’s one of the first AI laws in the country, and so the way it plays out will offer clues about how AI policy and debate might take shape in other cities. AI hiring regulation is part of the AI Act in Europe, and other states in the US are considering similar bills to New York’s. But the law has been met with significant controversy—with public interest groups and civil rights advocates saying it isn’t enforceable and extensive enough. . —Tate Ryan-Mosley This story is from The Technocrat, Tate’s weekly newsletter on tech policy and power. to receive it in your inbox every Friday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Google’s medical chatbot is being used in hospitals Which raises a lot of questions about the kind of data it’ll have access to. ( $)+ Character.AI’s chatbots have gained a legion of passionate fans. ( $)+ Artificial intelligence is infiltrating health care. We shouldn’t let it make all the decisions. () 2 Threads wants to make the internet a friendlier placeBut users are already pushing its policies to their limits. ()+ And brands have already swamped Threads with banal content. ( $)+ Elon Musk has done the impossible: made Mark Zuckerberg seem cool. ( $)+ It’s already surpassed more than 100 million users. () 3 Volunteers are assembling low-tech bombs in UkraineThey’re cheap, easy to make, and deadly. ()+ Tech is central to rebuilding Ukraine. ( $) 4 Global negotiations over deep-sea mining kick off todayProposals to mine the seabed for precious metals are seriously controversial. ()+ Countries are divided over how extensive the mining should be. ( $)+ These deep-sea “potatoes” could be the future of mining for renewable energy. () 5 The FDA has approved a new treatment for Alzheimer’s While it’s not a cure, it could slow the disease’s progression. () 6 Air taxis can’t take off any time soonThe industry’s futuristic promises have raced ahead of regulatory controls. ()+ These aircraft could change how we fly. () 7 Who’s paying for those weird Twitter ads?Many of the bizarre gadget ads seem to be traced back to a single company. ( $) 8 Shein sells practically everything nowAnd it’s Amazon that’s feeling the crunch. ( $)+ Chinese app Temu is mirroring Amazon… possibly a little too closely. ( $)+ This obscure shopping app is now America’s most downloaded. () 9 Meat is getting seriously weirdPlant-based, lab-grown, unicorn steak? ( $)+ Lab-grown meat just reached a major milestone. Here’s what comes next. () 10 How Hollywood de-aged Indiana JonesWith a liberal sprinkling of AI, and a whole lot of Harrison Ford footage. ( $) Quote of the day “Hey @zuck you should go to space just to really make him mad lol.” —Fast food company Wendy’s wades into the row brewing between Mark Zuckerberg and Elon Musk in a cheeky post. The big story How big science failed to unlock the mysteries of the human brain August 2021 In September 2011, Columbia University neurobiologist Rafael Yuste and Harvard geneticist George Church made a not-so-modest proposal: to map the activity of the entire human brain at the level of individual neurons and detail how those cells form circuits. That knowledge could be harnessed to treat brain disorders like Alzheimer’s, autism, schizophrenia, depression, and traumatic brain injury, and would help answer one of the great questions of science: How does the brain bring about consciousness? A decade on, the US project has wound down, and the EU project faces its deadline to build a digital brain. Did it achieve anything? Or have we spent a decade and billions of dollars chasing a vision that remains as elusive as ever? . —Emily Mullin 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 .) + It sounds like director Greta Gerwig went all out making the Barbie movie—.+ How delicious took over the US. + Woah—here’s what a looks like.+ These (particularly the banana pudding) look delicious.+ Video game Destiny has a track record of making , and its latest release does not disappoint.
This article is from The Technocrat, MIT Technology Review’s weekly tech policy newsletter about power, politics, and Silicon Valley. To receive it in your inbox every Friday, . Last week, a law about AI and hiring went into effect in New York City, and everyone is up in arms about it. It’s one of the first AI laws in the country, and so the way it plays out will offer clues about how AI policy and debate might take shape in other cities. AI , and other states in the US are considering similar bills to New York’s. The use of AI in hiring has for the way it automates and entrenches existing racial and gender biases. AI systems that evaluate candidates’ facial expressions and language have been shown to prioritize white, male, and abled-bodied candidates. The problem is massive, and many companies use AI at least once during . US Equal Employment Opportunity Commission chair Charlotte Burrows said that as many as four out of five companies use automation to make employment decisions. NYC’s Automated Employment Decision Tool law, which came into force on Wednesday, says that employers who use AI in hiring have to tell candidates they are doing so. They will also have to submit to annual independent audits to prove that their systems are not racist or sexist. Candidates will be able to request information from potential employers about what data is collected and analyzed by the technology. Violations will result in fines of up to $1,500. Proponents of the law say that it’s a good start toward regulating AI and mitigating some of the harms and risks around its use, even if it’s not perfect. It requires that companies better understand the algorithms they use and whether the technology unfairly discriminates against women or people of color. It’s also a fairly rare regulatory success when it comes to AI policy in the US, and we’re likely to see more of these specific, local regulations. Sounds sort of promising, right? But the law has been . Public interest groups and civil rights advocates say it isn’t enforceable and extensive enough, while businesses that will have to comply with it argue that it’s impractical and burdensome. Groups like the Center for Democracy & Technology, the Surveillance Technology Oversight Project (S.T.O.P.), the NAACP Legal Defense and Educational Fund, and the New York Civil Liberties Union argue that the law is and risks leaving out many uses of automated systems in hiring, including systems in which . What’s more, it’s not clear exactly what independent auditing will achieve, as the auditing industry is currently so immature. BSA, an influential tech trade group whose members include Adobe, Microsoft, and IBM, to the city in January criticizing the law, arguing that third-party audits are “not feasible.” “There’s a lot of questions about what type of access an auditor would get to a company’s information, and how much they would really be able to interrogate about the way it operates,” says Albert Fox Cahn, executive director of S.T.O.P. “It would be like if we had financial auditors, but we didn’t have generally accepted accounting principles, let alone a tax code and auditing rules.” Cahn argues that the law could produce a false sense of security and safety about AI and hiring. “This is a fig leave held up as proof of protection from these systems when in practice, I don’t think a single company is going to be held accountable because this was put into law,” he says. Importantly, the mandated audits will have to evaluate whether the output of an AI system is biased against a group of people, using a metric called an “impact ratio” that determines whether the tech’s “selection rate” varies across different groups. The audits won’t have to seek to ascertain how an algorithm makes a decision, and the law skirts around the “explainability” challenges of complex forms of machine learning, like deep learning. As you might expect, that omission is also a hot topic for debate among AI experts. In the US we’re likely to see much more AI regulation of this sort—local laws that take on one particular application of the technology—as we . And it’s in these local fights that we can understand how AI tools, safety mechanisms, and enforcement are going to be defined in the decades ahead. Already, New Jersey and California are . (Read more of our coverage on .) What I am reading this week
Celebrities riding the crypto wave are now dealing with the crash of reality, and some, like Tom Brady, are in hotter water than others. I loved about the “humiliating reckoning facing the actors, athletes and other celebrities who rushed to embrace the easy money and online hype of cryptocurrencies,” as authors Erin Griffith and David Yaffe-Bellany write. Russia is vying for more geopolitical control over the internet, writes . Russia submitted a resolution ahead of the United Nations meeting in Geneva that would address how the internet is governed globally. This wonky space is actually quite interesting to watch, as China and Russia have both tried to rewrite global digital rules. A US federal judge , saying that their attempts to remove and report online content (such as misinformation) would violate the First Amendment. The ruling, which will most certainly be challenged, feels more like a political play than a meaningful change to content moderation policy.
What I learned this week Syracuse, New York, is thanks to a new semiconductor manufacturing facility for chipmaker Micron and a $100 billion investment. The funding is part of President Biden’s plan to revitalize domestic industrial policy with the help of tech jobs. David Rotman, our editor at large, writes, “Now Syracuse is about to become an economic test of whether, over the next several decades, the aggressive government policies—and the massive corporate investments they spur—can both boost the country’s manufacturing prowess and revitalize regions like upstate New York.” It’s a phenomenal story, and I’d highly recommend you take the time to read it this weekend!
China has been on the receiving end of semiconductor export restrictions for years. Now, it is striking back with the same tactic. On July 3, the Chinese Ministry of Commerce announced that the export of gallium and germanium, two elements used in producing chips, solar panels, and fiber optics, will soon be subject to a license system for national security reasons. That means exports of the materials will need to be approved by the government, and Western companies that rely on them could have a hard time securing a consistent supply from China. The move follows years of restrictions by the US and Western allies on exports of cutting-edge technologies like high-performing chips, lithography machines, and even . The policies have created a bottleneck for China’s tech growth, especially for a few major companies like Huawei. China’s announcement is a clear signal it aims to retaliate, says Kevin Klyman, a technology researcher on the Avoiding Great Power War Project at the Harvard Kennedy School’s Belfer Center for Science and International Affairs. “Every day the technology war is getting worse,” Klyman says. “This is a notable day that accelerated things further.” But even though they immediately sent the price of gallium and germanium up, China’s new curbs are not likely to hit the US as hard as American export restrictions have hit China. These two raw materials, though they are important, still have relatively niche applications in the semiconductor industry. And while China dominates gallium and germanium production, other countries could ramp up their own production and export enough to substitute for the supply from China. Here’s a quick look at where things stand and what comes next. What are gallium and germanium? What are they used for? Gallium and germanium are two chemical elements that are commonly extracted along with more familiar minerals. Gallium is usually produced in the process of mining zinc and alumina, while germanium is acquired during zinc mining or separated from brown coal. “Beijing likely chose gallium and germanium because both are important for semiconductor manufacturing,” says Felix Chang, a senior fellow at the Foreign Policy Research Institute. “That is especially true for germanium, which is prized for its high electrical conductivity. Meanwhile, gallium has unusual crystallization properties that lead to some useful alloying effects.” Gallium is used in the manufacture of radio communication equipment and LED displays, while germanium is widely used in fiber optics, infrared optics, and solar cells. These applications also make them useful components in modern weapons. Currently, about 60% of the world’s germanium and 90% of the world’s gallium is produced in China, according to the . But because China doesn’t have the capacity to turn these materials into later-stage semiconductor or optical products, a big chunk of it is exported to companies in Japan and Europe. What’s the immediate impact? The new export license regime will start being implemented on August 1. Right after it was announced, purchase orders began swarming into Chinese gallium and germanium producers. The stockpiling has raised the price of the two materials, . AXT, an American maker of semiconductor wafers, quickly responded to say that its China-based subsidiary would apply for an export license to maintain business as usual. It’s important to remember that this is not a ban but a licensing system, which means the impact will depend on how difficult it is to secure an export license. “We see no evidence that no licenses will be granted. They will not be granted to US defense contractors, I imagine,” says Klyman, who notes that American defense companies Raytheon and Lockheed Martin were the first two names added to China’s newly established “unreliable entity list” earlier this year. But the ability to control who can be granted the permits will give China more leverage in trade negotiations with other countries, particularly those—like Japan and Korea—that rely on such imports for their own semiconductor industries. Why is China announcing these restrictions now? The US government lobbying allies to join forces in restricting China from sourcing high-end chipmaking equipment , and the results are showing. In June, both Japan and the Netherlands announced their decisions to restrict the export of chip-related materials and equipment to China. China certainly is feeling the pressure, and its attempts to negotiate with the US on the restrictions have been unsuccessful. Many experts point to the China visit of Janet Yellen, the US secretary of the treasury, which happened last week, as the major reason these export controls were announced when they were. “Beijing was … sending a signal before the Yellen visit that China will play the game of controlling exports in key sectors of concern to the US government,” says Paul Triolo, a senior vice president for China and technology policy lead at the consultancy Albright Stonebridge Group. Control of gallium and germanium is one of the tools Beijing wields to push the US and its allies back to the negotiation table. There’s also a strategic concern that holding onto these critical materials could serve China’s interests if a conflict breaks out, says Xiaomeng Lu, director of geotechnology practice . “Russia has been pretty much blocked out of the global tech ecosystem at this point … but they still have oil, they still have food, and that’s how they survived. That’s the worst-case scenario Chinese leadership keep at the back of their mind,” Lu says. “If the worst-case scenario happens, we need to hold the raw materials that we have in our reserve as much as possible.” What will happen to the gallium and germanium supply chain? The Chinese government may be seizing stronger control of the supply chain for now, but the added uncertainty of the licensing regime will cause foreign importers of gallium and germanium to look elsewhere for a more reliable supply. Most people agree that these export restrictions may not be beneficial to China in the long run. “My read is that the US government is happy about this move,” says Klyman. “This forces suppliers to diversify their supply of gallium, germanium, and other critical minerals, and it will cause markets to reinterpret the value of mining in North America and other regions.” Mining companies in Congo and Russia to increase production of germanium to meet demand. Some Western countries, including the US, Canada, Germany, and Japan, also produce these materials, but ramping up production could be difficult. The mining process causes significant pollution, which was one of the reasons production was offshored to China in the beginning. “The West will have to accelerate its innovation of new processes to separate and purify rare-earth metals. Otherwise, it may have to relax the environmental regulations that constrain traditional separation and purification techniques in the West,” says Chang. Could China’s export controls be as successful as the American ones? Probably not. Germanium and gallium can be mined elsewhere. But cutting-edge technologies are more restricted in their availability; the EUV lithography machines that the US wanted barred from export to China, for example, are . “Export control is not as effective if the technologies are available in other markets,” says Sarah Bauerle Danzman, an associate professor of international studies at Indiana University Bloomington. The US also has other advantages that make export control work more efficiently, she says, like the international importance of the dollar. The US chip curbs have an extraterritorial effect because companies fear being sanctioned if they don’t comply. They could be excluded from receiving payments in US dollars. For China, the export controls could hurt its own economy, Bauerle Danzman adds, because it relies more on export trade than that of the US. Restricting Chinese companies from working with the rest of the world will undermine their business. “Unless [China] is going to get Japan and South Korea and the EU to agree to not trade with the US, in order for it to really execute on a strategy like this, it not only has to stop exports to the US—it has to stop exports to basically everywhere,” she says. Has China restricted the export of critical raw materials before? This is not the first time China has tried to restrict the export of raw materials. In 2010, it reduced the allotment of rare-earth elements available for export by 40%, citing an interest in environmental conservation. The same year, the country was accused of unofficially banning rare-earth exports to Japan over a territorial dispute. Rare-earth elements are used in manufacturing a variety of products, including magnets, motors, batteries, and LED lights. The quota was later challenged by the US, EU, and Japan in a World Trade Organization dispute. China’s environmental protection justifications didn’t convince the settlement panel. It ruled against China and asked it to roll back the restrictions, which happened in 2015. This time, the Japanese government it could raise the issue with WTO, but China likely won’t need to worry about it as much as the last time. With the rise in trade protectionism and self-preserving supply-chain policies during the pandemic era, the organization has increasingly lost its authority among member countries. “Today, WTO is less relevant, and China is trying to find a more nuanced policy argument to back up their actions.” says Lu. It doesn’t need to look far. In December, China with the WTO around the US semiconductor export controls, calling them “politically motivated and disguised restrictions on trade.” In a , the US delegate to the WTO said every country has the authority to take measures it considers “necessary to the protection of its essential security interests,” an argument that China can easily use for itself. Will China have more export controls in the future? China most likely won’t stop at gallium and germanium when it comes to export controls. Wei Jianguo, a former Chinese vice minister of commerce, in the state-owned publication China Daily as saying that “this is just the beginning of China’s countermeasures, and China’s toolbox has many more types of measures available.” Gallium and germanium, while important, don’t represent the worst pain China could inflict on the raw materials front. “It’s giving the global system a little pinch, showing that we have the capability to cause a bigger pain sometime down the road,” says Lu. That could come if China chooses to clamp down again on the export of rare-earth elements. Or the materials used in making —lithium, cobalt, nickel, graphite. Because these materials are used in much greater quantities, it’s more difficult to find a substitute supply in a short time. They are the real trump card China may hold at the future negotiation table.
This is today’s edition of , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI-text detection tools are really easy to fool The news: As soon as ChatGPT launched, there were fears that students would use the chatbot to churn out passable essays. In response, startups started creating products that promise to spot whether text was written by a human or a machine. The problem is, it’s relatively simple to trick these tools and avoid detection, according to new research. How it unfolded: Researchers tested 14 commonly-used AI-detection tools, and found that while they detected human-written text consistently, they struggled to pick up ChatGPT-generated text that had been slightly rearranged by humans or obfuscated by a paraphrasing tool. This suggests that all students need to do is slightly adapt the essays the AI generates to evade detection. Why it matters: Experts believe that overreliance on AI detection tools could lead to incorrect accusations of cheating, which could have dire consequences for students’ academic career. . —Rhiannon Williams Read more about how AI will change education by my colleague Will. Covid hasn’t entirely gone away—here’s where we stand —Jessica Hamzelou My colleague has just come down with covid-19. The onset of symptoms was rapid, and she described it as “like being hit by a freight train.” “How very retro of you,” another colleague commented. Another replied: “This is still a thing?” As a health reporter who has been covering covid since the early days, I am still asked this question on a fairly regular basis. And more than three years in, there are still some big, unanswered questions when it comes to covid. We still don’t really know where this particular coronavirus came from, and our understanding of long covid is still patchy at best. But we do know it continues to cause infections, disease—and death. . Jessica’s story is from The Checkup, her weekly newsletter giving you the inside track on all things biotech. to receive it in your inbox every Thursday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Twitter is threatening to sue Meta over Threads It claims that Meta poached former Twitter workers to create the “copycat” app. ()+ Impressively, Threads sign-ups have surged past the 30 million mark. ( $)+ It’s also already hosting a whopping 95 million posts. ()+ Threads has what other Twitter rivals don’t: a built-in user base. ( $) 2 Oceangate has suspended all operationsIt had another two trips to the Titanic penciled in for June 2024. ( $) 3 Reddit has even more demands for its moderatorsMods are being told to remove NSFW labels that make a subreddit ineligible for advertising. () 4 ChatGPT is losing usersVisits to its website dropped by 10% last month. ( $)+ Meanwhile, Alibaba has created a new AI image generator. ()+ The inside story of how ChatGPT was built from the people who made it. () 5 Tesla’s New York State solar-panel factory is a flopEight years and $1 billion later, its output is woeful. ( $)+ At least solar cell technology is coming on in leaps and bounds. ( $) 6 Brazil gig reviewers are competing for jobsThe country’s dire economy means that even gigs filming fake reviews are much sought-after. () 7 Compostable plastic is still just plasticWithout the means to compost it properly, it’s effectively useless. ( $) 8 Apple’s Vision Pro headset is a pain to manufactureSony, which is making its tiny displays, is feeling the pressure. ( $) 9 These enthusiasts are competing to make the worst computer game Pear-Shaped and Crap Football are just some of the notable past entries. () 10 You can now listen to deep spaceTransforming space images into musical sequences is a new way to appreciate them. ( $)+ How sounds can turn us on to the wonders of the universe. () Quote of the day “I cringe every time I get that weekly screen time report from my iPhone.” —Evadne Eddins, 29, is feeling the effects of app fatigue, as she struggles to juggle all her different social media platforms, she tells . The big story Dementia content gets billions of views on TikTok. Whose story does it tell? February 2022 A dementia diagnosis can instantly change how the world sees someone. The internet, at its best, can help make the reality of living with dementia more visible. And for some, the internet is the only place they can connect with others going through the same thing. But among the popular #Dementia hashtag on TikTok, it’s easy to find viral videos in which care partners mock dementia patients and escalate arguments with them on camera. Creators have not settled on the ethics of making public content about someone who may no longer be able to consent to being filmed. Meanwhile, people who are themselves living with dementia are raising their own questions about consent, and emphasizing the harms caused by viral content that perpetuates stereotypes or misrepresents the full nature of the condition. . —Abby Ohlheiser 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 .) + How won the evolutionary arms race.+ Get your weekend off to a great start with this of Grimes’ Oblivion.+ Would you try ?+ Here’s a useful guide for what to do, and crucially, not to do on a .+ I could really do with some right about now.
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, . We’re well into summer here in the Northern Hemisphere. For a parent of two young children, that means ice creams, water fountains, picnics, and—inevitably—coughs and colds. My eldest told me she was feeling poorly this morning, and the youngest crawled into my bed to cough in my face. It’s not just kids, of course. My colleague has just come down with covid-19. The onset of symptoms was rapid, and she described it as “like being hit by a freight train.” “How very retro of you,” another colleague commented. Another replied: “This is still a thing?” As a health reporter who has been covering covid since the early days, I am still asked this question on a fairly regular basis. So this week let’s take a look at exactly where we stand with covid. It’s worth pointing out that there are still some big, unanswered questions when it comes to covid-19. For a start, . Most scientists believe it must have jumped from an animal host to humans at a market in Wuhan, China. But some maintain that it could have leaked from a lab. My colleague Antonio Regalado has explored the question in his five-part podcast series,. What we do know is that covid-19 spread all around the world in 2020. On January 9 of that year, Chinese authorities determined that a mysterious cluster of pneumonia-like illnesses was caused by a novel coronavirus. The first death was reported days later. Since then, . The true figure is and the use of helped slow the spread of the disease. But even that aimed to keep the virus out of entire countries couldn’t stem the spread. To date, there have been over 767 million confirmed cases. eventually helped us get the virus under control, at least to some degree. As of June 27, over 13.4 billion vaccine doses have been administered globally. These days, the number of reported cases is much, much lower. On July 3, the WHO reported 143,898 weekly cases of covid-19. People are still getting infected, but that’s a massive decline from July 3 last year, when the figure was 6.3 million. Some of that difference in numbers may be due to changes in how often people test and the declining availability of free tests around the world. Those of us who are vaccinated can still get infected, but if and when we do, our symptoms should be less severe. That, along with the lack of free tests, mean it’s likely that far fewer people are testing for covid-19 when they start to get sick. As I was clearing out my home earlier this week, I came across a box of covid tests. They’re old now—we’ve had most of them for over two years. About half of them have expired, so they’re no longer reliable. But what should we do with the others? Reassuringly, older, non-expired tests do still seem to be picking up new variants of the virus (although it’s worth bearing in mind that we don’t know how future variants might evolve). But they’ve never been 100% accurate, and they still aren’t. (Antonio reviewed a few of the tests back in 2021 and .) A published a couple of days ago found that symptomatic people should really take two tests, 48 hours apart. And people who think they might have been infected but don’t have symptoms should test three times. A couple of months ago, the WHO declared that covid was no longer a public health emergency of international concern. Which sounds great, until you realize it’s because Oh, and it’s still a pandemic. There can still be huge spikes in case numbers, like last winter, when the WHO recorded over 44 million cases on December 19. And while deaths have thankfully declined, they do still happen. The most recent data we have suggests that 497 people died of covid in the week ending July 3. Deaths were much higher in January of this year, with 20,000 to 40,000 every week. Again, those are just the recorded covid deaths. The real numbers are likely to be higher. Personally, I’m not as worried about covid-19 as I was during the early days of the pandemic. That’s partly because I’m fully vaccinated and have already had covid at least twice. I’m also fortunate enough not to have a condition that makes me vulnerable to severe disease. But the elephant in the room is —another hotly contested topic. (There has been a particularly intense debate surrounding long covid in children, as I covered .) The condition continues to cause lasting pain and suffering to an unknown but significant number of people. Scientists believe it’s possible to develop the condition after any infection with the coronavirus. So I’m keeping my unexpired tests for now, just in case. Read more from Tech Review’s archive mRNA vaccines helped us through the pandemic. But they could also help defend against many other infectious diseases, offer universal protection against flu, and even treat cancer, as I covered in exploring what’s next for this technology. Some are taking a different approach: attempting to create a universal, nanoparticle-based covid vaccine that protects against multiple variants, as Adam Piore last year. Shi Zhengli is the scientist at the center of the lab leak controversy, having spent years at the Wuhan Institute of Virology researching coronaviruses that live in bats. Jane Qiu covered her story in . We can track the spread of new coronavirus variants in wastewater, as Antonio in 2019. Scientists are working on drugs that stave off the effects of aging. And they’ve been testing those drugs as treatments for covid-19, as I last year. From around the web Mealworm burgers? Fermented fungi facon? Here are seven sources of protein that don’t involve farming animals. () Incidentally, my colleague Casey Crownhart recently explored the environmental impact of cultured meat. Unsurprisingly, it’s not straightforward. () There’s more evidence to show that brain stimulation can improve memory in people with brain injuries. () I covered the use of a “memory prosthesis” in people with brain damage , last year. Injections of a single protein appear to have improved the cognitive function of older monkeys. Scientists hope it might rejuvenate the brains of old people, too. () Nine months before the US Supreme Court overturned Roe v. Wade, Texas enacted a ban on abortions after six weeks of pregnancy. Nearly 10,000 extra births followed. ()
From traditional manufacturing companies using AI in robots to build smart factories to tech startups developing automated customer service and chatbots, AI is becoming pervasive across industries. “AI is no longer just in assistant mode, but is now playing autonomous roles in robotics, driving, knowledge generation, simulating our hands, feet, and brains,” says Lan Guan, global lead for data and AI at Accenture. This episode is part of our “Building the future” podcast series. It’s a multi-episode series focusing on how organizations, researchers, and innovators are meeting our evolving global challenges. We understand the importance of inclusive conversations and have chosen to highlight the work of women on the cutting edge of technological innovation, and business excellence. Researchers are similarly unlocking the value of AI through machine learning and robots that are developed to augment rather than replace human capabilities across manufacturing, health care, and space exploration. The robots of the past were kept in cages on factory floors and in labs, but this new era of AI-enabled robotics allows humans to work interdependently with robots to boost productivity, increase quality of work, and enable greater flexibility, says Julie Shah, professor in the department of aeronautics at MIT. Shah is also the co-lead of the Work of the Future Initiative at MIT. “Sometimes it can feel as though the emergence of these technologies is just going to sort of steamroll and work and jobs are going to change in some predetermined way because the technology now exists,” says Shah. “But we know from the research that the data doesn’t bear that out actually.” Although there are longstanding concerns about AI taking jobs and vastly changing workforces across the globe, Guan and Shah paint a picture of a future where AI empowers and supports human innovation. Taking a human-centered approach enables human invention and ingenuity to be augmented by AI and AI-enabled technologies. A critical question Shah asks throughout her research is: “How do we develop these technologies such that we’re maximally leveraging our human capability to innovate and improve how we do our work?” With more and better collected data, researchers and organizations alike have the opportunity to learn from the future when deploying AI and robotics. From ethics to varied use cases, the AI landscape is constantly shifting and decisions that academics and enterprises make now can have long term ramifications. A strategic practice of foresight offers a solution of envisioning multiple futures and forming strategies in the present. “I’m very optimistic about all these amazing aspects of flexibility, resilience, specialization, and also generating more economic profit, economic growth for the society aspect of AI,” says Guan. “As long as we walk into this with eyes wide open so that we understand some of the existing limitations, I’m sure we can do both of them.” This episode of Business Lab is sponsored. Related reading , Accenture Full transcript Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. This episode is part of our “Building the future” series. We’re focusing on how organizations, researchers, and innovators are meeting our evolving global challenges. We understand the importance of inclusive conversations and have chosen to highlight the work of women on the cutting edge of technological innovation and business excellence.Our topic today is artificial intelligence. Advances in AI and robotics help not just explore the unknown but surface new possibilities and innovations. And with more and better data, AI, and robotics, researchers and organizations have an opportunity to learn from the future. But for the near term, enterprises are taking advantage of current capabilities and technologies to be smarter from manufacturing to beyond.Two words for you: smart robots. My guests are Lan Guan, who is the global lead for data and AI at Accenture. Julie Shah is a professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology. She also co-leads the Work of the Future Initiative at MIT. This episode of Business Lab is sponsored. Welcome Lan and Julie. Julie Shah: Thank you so much.Lan Guan: Thank you for having us.Laurel: Lan, let’s start off with you. How would you describe the current state of AI? How are Accenture’s customers using it? Lan: Sure. Let me start by giving you lay of the land of what’s happening in artificial intelligence the more in the enterprise and industry kind of context. I think from my perspective, AI has been making significant headlines within the last two, three years, right? And the recent advances in machine learning and deep learning have made it possible to build models, I mean gigantic models, that are capable of automating a variety of human tasks including what we call using AI for perceptual data, right? Perceptual data meaning speech and vision recognition, natural language processing, or even doing reasoning. So, it’s just incredible to see some of these models perform at levels higher than what humans are capable of. So, I think that’s pretty amazing. Also, another thing that I’m seeing: AI is no longer just in assistant mode, but is now playing autonomous roles in robotics, driving, knowledge generation, simulating our hands, feet, and brains. I think that AI has become very pervasive. And then playing these autonomous roles, this is something that I’ve seen across different industries. But we also recognize AI is still young and data hungry. There’s still so much more to be done to make it more robust, explainable and fair, responsible in many, many different ways. Additionally, there’s still so many challenges, right? Limitations for business leaders to overcome before we can achieve true artificially generated intelligence where a machine can actually perform any intellectual task that a human can. So, I’ve seen many clients from basically all industries with different maturities approach us to implement or advance their AI journey, some in experimentation and others who are actually already high achievers in AI. Let me give you one example. In traditional industries like manufacturing, companies are adopting AI in robots to build smart factories. One digital native client in e-commerce has asked us to help them provide ultra-personalized experiences to meet growing customer taste and demands. We have even delved into animal precision health with the client where we created models to monitor cows’ lactation cycles, predict their milk production based on their genetics, and even project how fast they can reproduce through natural and artificial insemination. So very quickly, I gave you examples of how AI has become pervasive and very autonomous across multiple industries. This is a kind of trend that I am super excited about because I believe this brings enormous opportunities for us to help businesses across different industries to get more value out of this amazing technology.Laurel: Julie, your research focuses on that robotic side of AI, specifically building robots that work alongside humans in various fields like manufacturing, healthcare, and space exploration. How do you see robots helping with those dangerous and dirty jobs?Julie: Yeah, that’s right. So, I’m an AI researcher at MIT in the Computer Science & Artificial Intelligence Laboratory (CSAIL), and I run a robotics lab. The vision for my lab’s work is to make machines, these include robots. So computers become smarter, more capable of collaborating with people where the intention is to be able to augment rather than replace human capability. And so we focus on developing and deploying AI-enabled robots that are capable of collaborating with people in physical environments, working alongside people in factories to help build planes and build cars. We also work in intelligent decision support to support expert decision makers doing very, very challenging tasks, tasks that many of us would never be good at no matter how long we spent trying to train up in the role. So, for example, supporting nurses and doctors and running hospital units, supporting fighter pilots to do mission planning.The vision here is to be able to move out of this sort of prior paradigm. In robotics, you could think of it as… I think of it as sort of “era one” of robotics where we deployed robots, say in factories, but they were largely behind cages and we had to very precisely structure the work for the robot. Then we’ve been able to move into this next era where we can remove the cages around these robots and they can maneuver in the same environment more safely, do work in the same environment outside of the cages in proximity to people. But ultimately, these systems are essentially staying out of the way of people and are thus limited in the value that they can provide.You see similar trends with AI, so with machine learning in particular. The ways that you structure the environment for the machine are not necessarily physical ways the way you would with a cage or with setting up fixtures for a robot. But the process of collecting large amounts of data on a task or a process and developing, say a predictor from that or a decision-making system from that, really does require that when you deploy that system, the environments you’re deploying it in look substantially similar, but are not out of distribution from the data that you’ve collected. And by and large, machine learning and AI has previously been developed to solve very specific tasks, not to do sort of the whole jobs of people, and to do those tasks in ways that make it very difficult for these systems to work interdependently with people.So the technologies my lab develops both on the robot side and on the AI side are aimed at enabling high performance and tasks with robotics and AI, say increasing productivity, increasing quality of work, while also enabling greater flexibility and greater engagement from human experts and human decision makers. That requires rethinking about how we draw inputs and leverage, how people structure the world for machines from these sort of prior paradigms involving collecting large amounts of data, involving fixturing and structuring the environment to really developing systems that are much more interactive and collaborative, enable people with domain expertise to be able to communicate and translate their knowledge and information more directly to and from machines. And that is a very exciting direction. It’s different than developing AI robotics to replace work that’s being done by people. It’s really thinking about the redesign of that work. This is something my colleague and collaborator at MIT, Ben Armstrong and I, we call positive-sum automation. So how you shape technologies to be able to achieve high productivity, quality, other traditional metrics while also realizing high flexibility and centering the human’s role as a part of that work process.Laurel: Yeah, Lan, that’s really specific and also interesting and plays on what you were just talking about earlier, which is how clients are thinking about manufacturing and AI with a great example about factories and also this idea that perhaps robots aren’t here for just one purpose. They can be multi-functional, but at the same time they can’t do a human’s job. So how do you look at manufacturing and AI as these possibilities come toward us? Lan: Sure, sure. I love what Julie was describing as a positive sum gain of this is exactly how we view the holistic impact of AI, robotics type of technology in asset-heavy industries like manufacturing. So, although I’m not a deep robotic specialist like Julie, but I’ve been delving into this area more from an industry applications perspective because I personally was intrigued by the amount of data that is sitting around in what I call asset-heavy industries, the amount of data in IoT devices, right? Sensors, machines, and also think about all kinds of data. Obviously, they are not the typical kinds of IT data. Here we’re talking about an amazing amount of operational technology, OT data, or in some cases also engineering technology, ET data, things like diagrams, piping diagrams and things like that. So first of all, I think from a data standpoint, I think there’s just an enormous amount of value in these traditional industries, which is, I believe, truly underutilized.And I think on the robotics and AI front, I definitely see the similar patterns that Julie was describing. I think using robots in multiple different ways on the factory shop floor, I think this is how the different industries are leveraging technology in this kind of underutilized space. For example, using robots in dangerous settings to help humans do these kinds of jobs more effectively. I always talk about one of the clients that we work with in Asia, they’re actually in the business of manufacturing sanitary water. So in that case, glazing is actually the process of applying a glazed slurry on the surface of shaped ceramics. It’s a century-old kind of thing, a technical thing that humans have been doing. But since ancient times, a brush was used and hazardous glazing processes can cause disease in workers.Now, glazing application robots have taken over. These robots can spray the glaze with three times the efficiency of humans with 100% uniformity rate. It’s just one of the many, many examples on the shop floor in heavy manufacturing. Now robots are taking over what humans used to do. And robots and humans work together to make this safer for humans and at the same time produce better products for consumers. So, this is the kind of exciting thing that I’m seeing how AI brings benefits, tangible benefits to the society, to human beings.Laurel: That’s a really interesting kind of shift into this next topic, which is how do we then talk about, as you mentioned, being responsible and having ethical AI, especially when we’re discussing making people’s jobs better, safer, more consistent? And then how does this also play into responsible technology in general and how we’re looking at the entire field?Lan: Yeah, that’s a super hot topic. Okay, I would say as an AI practitioner, responsible AI has always been at the top of the mind for us. But think about the recent advancement in generative AI. I think this topic is becoming even more urgent. So, while technical advancements in AI are very impressive like many examples I’ve been talking about, I think responsible AI is not purely a technical pursuit. It’s also about how we use it, how each of us uses it as a consumer, as a business leader.So at Accenture, our teams strive to design, build, and deploy AI in a manner that empowers employees and business and fairly impacts customers and society. I think that responsible AI not only applies to us but is also at the core of how we help clients innovate. As they look to scale their use of AI, they want to be confident that their systems are going to perform reliably and as expected. Part of building that confidence, I believe, is ensuring they have taken steps to avoid unintended consequences. That means making sure that there’s no bias in their data and models and that the data science team has the right skills and processes in place to produce more responsible outputs. Plus, we also make sure that there are governance structures for where and how AI is applied, especially when AI systems are using decision-making that affects people’s life. So, there are many, many examples of that.And I think given the recent excitement around generative AI, this topic becomes even more important, right? What we are seeing in the industry is this is becoming one of the first questions that our clients ask us to help them get generative AI ready. And simply because there are newer risks, newer limitations being introduced because of the generative AI in addition to some of the known or existing limitations in the past when we talk about predictive or prescriptive AI. For example, misinformation. Your AI could, in this case, be producing very accurate results, but if the information generated or content generated by AI is not aligned to human values, is not aligned to your company core values, then I don’t think it’s working, right? It could be a very accurate model, but we also need to pay attention to potential misinformation, misalignment. That’s one example.Second example is language toxicity. Again, in the traditional or existing AI’s case, when AI is not producing content, language of toxicity is less of an issue. But now this is becoming something that is top of mind for many business leaders, which means responsible AI also needs to cover this new set of a risk, potential limitations to address language toxicity. So those are the couple thoughts I have on the responsible AI.Laurel: And Julie, you discussed how robots and humans can work together. So how do you think about changing the perception of the fields? How can ethical AI and even governance help researchers and not hinder them with all this great new technology?Julie: Yeah. I fully agree with Lan’s comments here and have spent quite a fair amount of effort over the past few years on this topic. I recently spent three years as an associate dean at MIT, building out our new cross-disciplinary program and social and ethical responsibilities of computing. This is a program that has involved very deeply, nearly 10% of the faculty researchers at MIT, not just technologists, but social scientists, humanists, those from the business school. And what I’ve taken away is, first of all, there’s no codified process or rule book or design guidance on how to anticipate all of the currently unknown unknowns. There’s no world in which a technologist or an engineer sits on their own or discusses or aims to envision possible futures with those within the same disciplinary background or other sort of homogeneity in background and is able to foresee the implications for other groups and the broader implications of these technologies.The first question is, what are the right questions to ask? And then the second question is, who has methods and insights to be able to bring to bear on this across disciplines? And that’s what we’ve aimed to pioneer at MIT, is to really bring this sort of embedded approach to drawing in the scholarship and insight from those in other fields in academia and those from outside of academia and bring that into our practice in engineering new technologies. And just to give you a concrete example of how hard it is to even just determine whether you’re asking the right question, for the technologies that we develop in my lab, we believed for many years that the right question was, how do we develop and shape technologies so that it augments rather than replaces? And that’s been the public discourse about robots and AI taking people’s jobs. “What’s going to happen 10 years from now? What’s happening today?” with well-respected studies put out a few years ago that for every one robot you introduced into a community, that community loses up to six jobs.So, what I learned through deep engagement with scholars from other disciplines here at MIT as a part of the Work of the Future task force is that that’s actually not the right question. So as it turns out, you just take manufacturing as an example because there’s very good data there. In manufacturing broadly, only one in 10 firms have a single robot, and that’s including the very large firms that make high use of robots like automotive and other fields. And then when you look at small and medium firms, those are 500 or fewer employees, there’s essentially no robots anywhere. And there’s significant challenges in upgrading technology, bringing the latest technologies into these firms. These firms represent 98% of all manufacturers in the US and are coming up on 40% to 50% of the manufacturing workforce in the U.S. There’s good data that the lagging, technological upgrading of these firms is a very serious competitiveness issue for these firms.And so what I learned through this deep collaboration with colleagues from other disciplines at MIT and elsewhere is that the question isn’t “How do we address the problem we’re creating about robots or AI taking people’s jobs?” but “Are robots and the technologies we’re developing actually doing the job that we need them to do and why are they actually not useful in these settings?”. And you have these really exciting case stories of the few cases where these firms are able to bring in, implement and scale these technologies. They see a whole host of benefits. They don’t lose jobs, they are able to take on more work, they’re able to bring on more workers, those workers have higher wages, the firm is more productive. So how do you realize this sort of win-win-win situation and why is it that so few firms are able to achieve that win-win-win situation?There’s many different factors. There’s organizational and policy factors, but there are actually technological factors as well that we now are really laser focused on in the lab in aiming to address how you enable those with the domain expertise, but not necessarily engineering or robotics or programming expertise to be able to program the system, program the task rather than program the robot. It’s a humbling experience for me to believe I was asking the right questions and engaging in this research and really understand that the world is a much more nuanced and complex place and we’re able to understand that much better through these collaborations across disciplines. And that comes back to directly shape the work we do and the impact we have on society.And so we have a really exciting program at MIT training the next generation of engineers to be able to communicate across disciplines in this way and the future generations will be much better off for it than the training those of us engineers have received in the past.Lan: Yeah, I think Julie you brought such a great point, right? I think it resonated so well with me. I don’t think this is something that you only see in academia’s kind of setting, right? I think this is exactly the kind of change I’m seeing in industry too. I think how the different roles within the artificial intelligence space come together and then work in a highly collaborative kind of way around this kind of amazing technology, this is something that I’ll admit I’d never seen before. I think in the past, AI seemed to be perceived as something that only a small group of deep researchers or deep scientists would be able to do, almost like, “Oh, that’s something that they do in the lab.” I think that’s kind of a lot of the perception from my clients. That’s why in order to scale AI in enterprise settings has been a huge challenge.I think with the recent advancement in foundational models, large language models, all these pre-trained models that large tech companies have been building, and obviously academic institutions are a huge part of this, I’m seeing more open innovation, a more open collaborative kind of way of working in the enterprise setting too. I love what you described earlier. It’s a multi-disciplinary kind of thing, right? It’s not like AI, you go to computer science, you get an advanced degree, then that’s the only path to do AI. What we are seeing also in business setting is people, leaders with multiple backgrounds, multiple disciplines within the organization come together is computer scientists, is AI engineers, is social scientists or even behavioral scientists who are really, really good at defining different kinds of experimentation to play with this kind of AI in early-stage statisticians. Because at the end of the day, it’s about probability theory, economists, and of course also engineers.So even within a company setting in the industries, we are seeing a more open kind of attitude for everyone to come together to be around this kind of amazing technology to all contribute. We always talk about a hub and spoke model. I actually think that this is happening, and everybody is getting excited about technology, rolling up their sleeves and bringing their different backgrounds and skill sets to all contribute to this. And I think this is a critical change, a culture shift that we have seen in the business setting. That’s why I am so optimistic about this positive sum game that we talked about earlier, which is the ultimate impact of the technology.Laurel: That’s a really great point. Julie, Lan mentioned it earlier, but also this access for everyone to some of these technologies like generative AI and AI chatbots can help everyone build new ideas and explore and experiment. But how does it really help researchers build and adopt those kinds of emerging AI technologies that everyone’s keeping a close eye on the horizon?Julie: Yeah. Yeah. So, talking about generative AI, for the past 10 or 15 years, every single year I thought I was working in the most exciting time possible in this field. And then it just happens again. For me the really interesting aspect, or one of the really interesting aspects, of generative AI and GPT and ChatGPT is, one, as you mentioned, it’s really in the hands of the public to be able to interact with it and envision multitude of ways it could potentially be useful. But from the work we’ve been doing in what we call positive-sum automation, that’s around these sectors where performance matters a lot, reliability matters a lot. You think about manufacturing, you think about aerospace, you think about healthcare. The introduction of automation, AI, robotics has indexed on that and at the cost of flexibility. And so a part of our research agenda is aiming to achieve the best of both those worlds.The generative capability is very interesting to me because it’s another point in this space of high performance versus flexibility. This is a capability that is very, very flexible. That’s the idea of training these foundation models and everybody can get a direct sense of that from interacting with it and playing with it. This is not a scenario anymore where we’re very carefully crafting the system to perform at very high capability on very, very specific tasks. It’s very flexible in the tasks you can envision making use of it for. And that’s game changing for AI, but on the flip side of that, the failure modes of the system are very difficult to predict.So, for high stakes applications, you’re never really developing the capability of doing some specific task in isolation. You’re thinking from a systems perspective and how you bring the relative strengths and weaknesses of different components together for overall performance. The way you need to architect this capability within a system is very different than other forms of AI or robotics or automation because you have a capability that’s very flexible now, but also unpredictable in how it will perform. And so you need to design the rest of the system around that, or you need to carve out the aspects or tasks where failure in particular modes are not critical.So chatbots for example, by and large, for many of their uses, they can be very helpful in driving engagement and that’s of great benefit for some products or some organizations. But being able to layer in this technology with other AI technologies that don’t have these particular failure modes and layer them in with human oversight and supervision and engagement becomes really important. So how you architect the overall system with this new technology, with these very different characteristics I think is very exciting and very new. And even on the research side, we’re just scratching the surface on how to do that. There’s a lot of room for a study of best practices here particularly in these more high stakes application areas.Lan: I think Julie makes such a great point that’s super resonating with me. I think, again, always I’m just seeing the exact same thing. I love the couple keywords that she was using, flexibility, positive-sum automation. I think there are two colors I want to add there. I think on the flexibility frame, I think this is exactly what we are seeing. Flexibility through specialization, right? Used with the power of generative AI. I think another term that came to my mind is this resilience, okay? So now AI becomes more specialized, right? AI and humans actually become more specialized. And so that we can both focus on things, little skills or roles, that we’re the best at.In Accenture, we just recently published our point of view, “.” Within the point of view, we laid out this, what I call the ACCAP framework. It basically addresses, I think, similar points that Julie was talking about. So basically advice, create, code, and then automate, and then protect. If you link all these five, the first letter of these five words together is what I call the ACCAP framework (so that I can remember those five things). But I think this is how different ways we are seeing how AI and humans working together manifest this kind of collaboration in different ways.For example, advising, it’s pretty obvious with generative AI capabilities. I think the chatbot example that Julie was talking about earlier. Now imagine every role, every knowledge worker’s role in an organization will have this co-pilot, running behind the scenes. In a contact center’s case it could be, okay, now you’re getting this generative AI doing auto summarization of the agent calls with customers at the end of the calls. So the agent doesn’t have to be spending time and doing this manually. And then customers will get happier because customer sentiment will get better detected by generative AI, creating obviously the numerous, even consumer-centric kind of cases around how human creativity is getting unleashed.And there’s also business examples in marketing, in hyper-personalization, how this kind of creativity by AI is being best utilized. I think automating—again, we’ve been talking about robotics, right? So again, how robots and humans work together to take over some of these mundane tasks. But even in generative AI’s case is not even just the blue-collar kind of jobs, more mundane tasks, also looking into more mundane routine tasks in knowledge worker spaces. I think those are the couple examples that I have in mind when I think of the word flexibility through specialization.And by doing so, new roles are going to get created. From our perspective, we’ve been focusing on prompt engineering as a new discipline within the AI space—AI ethics specialist. We also believe that this role is going to take off very quickly simply because of the responsible AI topics that we just talked about.And also because all this business processes have become more efficient, more optimized, we believe that new demand, not just the new roles, each company, regardless of what industries you are in, if you become very good at mastering, harnessing the power of this kind of AI, the new demand is going to create it. Because now your products are getting better, you are able to provide a better experience to your customer, your pricing is going to get optimized. So I think bringing this together is, which is my second point, this will bring positive sum to the society in economics kind of terms where we’re talking about this. Now you’re pushing out the production possibility frontier for the society as a whole. So, I’m very optimistic about all these amazing aspects of flexibility, resilience, specialization, and also generating more economic profit, economic growth for the society aspect of AI. As long as we walk into this with eyes wide open so that we understand some of the existing limitations, I’m sure we can do both of them.Laurel: And Julie, Lan just laid out this fantastic, really a correlation of generative AI as well as what’s possible in the future. What are you thinking about artificial intelligence and the opportunities in the next three to five years?Julie: Yeah. Yeah. So, I think Lan and I are very largely on the same page on just about all of these topics, which is really great to hear from the academic and the industry side. Sometimes it can feel as though the emergence of these technologies is just going to sort of steamroll and work and jobs are going to change in some predetermined way because the technology now exists. But we know from the research that the data doesn’t bear that out actually. There’s many, many decisions you make in how you design, implement, and deploy, and even make the business case for these technologies that can really sort of change the course of what you see in the world because of them. And for me, I really think a lot about this question of what’s called lights out in manufacturing, like lights out operation where there’s this idea that with the advances and all these capabilities, you would aim to be able to run everything without people at all. So, you don’t need lights on for the people.And again, as a part of the Work of the Future task force and the research that we’ve done visiting companies, manufacturers, OEMs, suppliers, large international or multinational firms as well as small and medium firms across the world, the research team asked this question of, “So these high performers that are adopting new technologies and doing well with it, where is all this headed? Is this headed towards a lights out factory for you?” And there were a variety of answers. So some people did say, “Yes, we’re aiming for a lights out factory,” but actually many said no, that that was not the end goal. And one of the quotes, one of the interviewees stopped while giving a tour and turned around and said, “A lights out factory. Why would I want a lights out factory? A factory without people is a factory that’s not innovating.”I think that’s the core for me, the core point of this. When we deploy robots, are we caging and sort of locking the people out of that process? When we deploy AI, is essentially the infrastructure and data curation process so intensive that it really locks out the ability for a domain expert to come in and understand the process and be able to engage and innovate? And so for me, I think the most exciting research directions are the ones that enable us to pursue this sort of human-centered approach to adoption and deployment of the technology and that enable people to drive this innovation process. So a factory, there’s a well-defined productivity curve. You don’t get your assembly process when you start. That’s true in any job or any field. You never get it exactly right or you optimize it to start, but it’s a very human process to improve. And how do we develop these technologies such that we’re maximally leveraging our human capability to innovate and improve how we do our work?My view is that by and large, the technologies we have today are really not designed to support that and they really impede that process in a number of different ways. But you do see increasing investment and exciting capabilities in which you can engage people in this human-centered process and see all the benefits from that. And so for me, on the technology side and shaping and developing new technologies, I’m most excited about the technologies that enable that capability. Laurel: Excellent. Julie and Lan, thank you so much for joining us today on what’s been a really fantastic episode of The Business Lab. Julie: Thank you so much for having us. Lan: Thank you.Laurel: That was Lan Guan of Accenture and Julie Shah of MIT who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review overlooking the Charles River.That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. You can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Giro Studios. Thanks for listening. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This is today’s edition of , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The $100 billion bet that a postindustrial US city can reinvent itself as a high-tech hub On a day in late April, a small drilling rig sits at the edge of the scrubby overgrown fields of Syracuse, New York, taking soil samples. It’s the first sign of construction on what could become the largest semiconductor manufacturing facility in the United States. The CHIPS and Science Act, passed last year with bipartisan congressional support, was widely viewed by industry leaders and politicians as a way to secure supply chains, and make the United States competitive again in semiconductor chip manufacturing. Now Syracuse is about to become an economic test of whether, over the next several decades, the aggressive government policies—and the massive corporate investments they spur—can both boost the country’s manufacturing prowess and revitalize regions like upstate New York. And it all begins with an astonishingly expensive and complex kind of factory called a chip fab. . —David Rotman Inside a high-tech cement laboratory Cement is a climate nightmare. The material, which is basically the glue that holds concrete together, accounts for about 8% of global emissions. It requires super-high temperatures to make, meaning you have to burn fossil fuels in the process. Secondly, there are chemical reactions involved in transforming minerals into working cement, and those release carbon dioxide. But it might not have to be that way. Sublime Systems, a Boston-based startup, is working to clean up cement. Casey Crownhart, our climate reporter, took a trip to their lab to learn more. . Casey’s story is from The Spark, her weekly climate newsletter. to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Meta’s new Threads app is now live The company’s optimistic it can host friendly, public conversations. ( $)+ At least Twitter’s given them a blueprint for what not to do. ( $)+ More than 10 million users joined the platform in the first few hours. ( $) 2 The AI race between China and the US is overhypedChina’s strict state controls mean it’s often forced to follow America’s lead. ( $) 3 US spies are buying citizens’ private dataThe warrantless mass surveillance could be prevented with a simple Act amendment. ( $)+ The US military has been experimenting with chatbots. ( $) 4 Celebrities were left humiliated by crypto’s collapseBut their pain is probably small potatoes compared to ordinary investors’. ( $)+ London is happy to offer refuge to American crypto victims. ( $) 5 Our summers are going to keep getting hotterAnd they’re likely to start sooner, too. ()+ The record for the hottest ever day has been broken twice this week. ( $)+ We have to start rapidly adopting renewables to help make a difference. ( $)+ How heat could solve climate problems. () 6 US officials canceled a meeting with Facebook about the 2024 electionJust one day after the Biden administration was told not to contact social media firms. ( $) 7 Two authors have filed a lawsuit against OpenAIThey claim their novels were unlawfully used to train ChatGPT. ()+ OpenAI’s hunger for data is coming back to bite it. () 8 Nickel is vital for building EV batteriesIndonesia is testing out new methods for extracting it from the soil. ( $)+ China has a stranglehold on its output, though. ( $)+ Meet the new batteries unlocking cheaper electric vehicles. () 9 Wimbledon is using AI announcers for its tennis gamesYou have to search to find them, though. ( $) 10 India is growing closer to NASA A potential human spaceflight partnership could be on the cards. () Quote of the day “May this platform have good vibes, strong community, excellent humor, and less harassment.” —Congresswoman Alexandria Ocasio-Cortez hopes Threads sidesteps the toxicity of its rivals, she explains in her first post on the new platform, reports. The big story What cities need now April 2021 Urban technology projects have long sought to manage the city. The latest, “smart city” projects, have much in common with previous iterations. Again and again, these initiatives promise novel “solutions” to urban “problems.” After a decade of pilot projects and flashy demonstrations, though, it’s still not clear whether smart city technologies can actually solve or even mitigate the challenges cities face. What is clear, however, is that technology companies are increasingly taking on administrative and infrastructure responsibilities that governments have long fulfilled.If smart cities are to avoid exacerbating urban inequalities, we have to take a long, hard look at how cities have fared so far. . —Jennifer Clark 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 .) + The correct way to tie your shoes? has an answer.+ Aww: a whole new species of has been discovered in a deep-sea nursery + A works a whole lot better than I ever would have expected.+ If you’re finding it too hot to cook for long, these should be just the ticket.+ The is back! Thanks Ryan Gosling.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, . A few weeks ago, I found myself in a room where fluorescent lights reflected off the stainless steel tanks lining the walls. The setup reminded me of an exceedingly high-tech craft brewery. I wasn’t at a cider tasting, but on a visit to Sublime Systems, a Boston-based startup working to clean up one of the world’s toughest climate challenges: cement. Today, making cement involves a whole lot of fossil fuels, and this one material accounts for about 8% of global emissions. But it might not have to be that way. So for the newsletter this week, come along with me to see what the startup is up to, and how its process could change the way we build. Concrete progress First things first: let’s get definitions straight here. Cement is basically the glue that holds concrete together. A finished building, sidewalk, or sculpture is made of concrete, which is about 10% cement by weight. I’ve written before about the challenges of cleaning up cement production— To sum it up briefly, cement is a climate nightmare for two main reasons. One, the process used to make cement requires super-high temperatures (I’m talking over 1,500 °C, or 2,700 °F), which today basically means you have to burn fossil fuels in the process. Second, there are chemical reactions involved in transforming minerals into working cement, and those release carbon dioxide. Sublime’s answer is to use electrochemistry. The company’s cofounders, Yet-Ming Chiang and Leah Ellis, both made their mark in the battery world before turning to building materials. While at MIT, the duo developed a set of chemical reactions powered by electricity that can transform minerals into the cement we know and love today. They cofounded Sublime Systems in 2020. What I was most interested in during my visit was seeing how the company is taking lab results and transforming them to work at a much larger scale. BOB O’CONNOR/SUBLIME SYSTEMS
Building up I started my day in a tree-shaded courtyard at Sublime’s offices, chatting with Ellis, who is the CEO. She walked me through the progression of the startup’s cement technology, starting in the lab at MIT where she did her postdoctoral fellowship. Things started out small: the first time she and a labmate made cement, it was about the same volume as a single die. Years later, that small scale is almost inconceivable when you look around the company’s pilot facility. The ceilings feel dozens of feet high, and I wouldn’t be able to get my arms around the tanks that line the room. This facility started up in November 2022, recalls Mike Corbett, Sublime’s head of engineering. The team moved quickly to build it, going from design to execution in about nine months. The company is doing something entirely new by bringing electrochemistry to cement production. But they’ve been able to leverage technology from other industries, like mining and chemical production, to find equipment that will work for what they’re trying to do. “You can usually beg and borrow from other industries to solve similar technical problems,” Corbett says. The result is part brewery, part mining operation, part cement production. The pilot line is a huge upgrade from the early days, but as Ellis put it, in the grand scheme of the industry, it’s still “a cement plant for ants.” A concrete truck can hold about 20 tons of concrete mix, which includes about two tons of cement. At a rate of 100 tons per year, it would take Sublime’s facility roughly a week to make enough cement for just one truck. The next step for the startup is to build a demonstration facility producing around 100 tons per day. “That’s the size where you’re no longer invisible to the cement world,” Ellis says. The current goal is to have that facility running in 2025. After that, there’s yet another step: commercial scale, at about a million tons a year. The world has a huge appetite for cement, and Sublime is working to scale its production to meet it. While the material is basically invisible to us today, its climate impact is huge, and only likely to grow. “Everybody uses and owns cement, but they don’t see it,” Ellis says. So keep your eyes peeled, both for the cement around you and for more on this topic from me. Related reading For more on the technical details of electrochemical cement making, along with another route that involves injecting carbon dioxide into the material, check out Leah Ellis was on our list of 35 innovators under 35 in 2021. You can read Another Boston-area startup wants to bring electricity to heavy industry, but its target is steelmaking. Read STEPHANIE ARNETT/MITTR
Another thing Lab-grown meat was just approved for sale in the US. In theory, that should be a huge win for the climate. But there are still many, many questions remaining about just how much cultivated meat is going to be able to help cut emissions from our food. For these new products to be a climate solution, companies making them will need to scale up production and find ways to avoid expensive, energy-intensive processes. You can dive into the details of what we know about lab-grown meat and climate change Keeping up with climate Tweaking microbes could help improve our health. It could also help the fight against climate change by taking aim at the ones that make methane inside cows and other livestock. () The global average temperature hit a new high on Tuesday, beating a record that had been set … the day before. It’s hot out there, y’all. () → Extreme weather is taking a toll on the reliability of natural-gas plants in the US. () Unfortunately, we can’t solve climate change with rooftop solar alone. Panels on roofs could meet about a third of power demand, at the absolute maximum. () Meet the startups vying to be the Tesla of heat pumps, by making them attractive and convenient. () China is installing wind and solar at an astonishing pace. The country is on track to hit its goals for 2030 years ahead of schedule. () The smoke from wildfires in Canada is going to be a staple of life in the US for the foreseeable future. () Of course climate change has a hand in the Sriracha shortage. ()
When it comes to the ability to generate, arrange, and analyze content, generative AI is a gamechanger—one with transformative social and economic potential. As a technology that is democratized—one that doesn’t simply exist in a faraway lab or tech community in Silicon Valley, for instance—generative AI lowers the barriers to participation. In the age of generative AI, anyone can be a creator. But this also entails a profound workforce shift, changing the processes of production within the economy and, in turn, the types of tasks that are undertaken and the skills needed to succeed. This year, Microsoft commissioned global tech advisory firm Access Partnership, working alongside local partners including the Analytics Association of the Philippines, the Federation of Indian Chambers of Commerce & Industry (FICCI), and the Center for Global Communications (GLOCOM) in Japan, to conduct country-level research on the potential economic impact of generative AI across Asia. The research estimates a potential boost to productive capacity of , , and alone, with studies ongoing in Malaysia, Indonesia and South Korea. These country findings are consistent with other global studies—for instance, a recent report by McKinsey estimates to the global economy. The potential economic growth is so large because generative AI has implications for most types of work: its impact can be thought of as comparable to that of digitalization in general, rather than that of a specific product. In particular, this huge injection of productivity will arise from three channels—generative AI’s potential to unleash creativity, accelerate discovery, and enhance efficiency. While we cannot predict the future, it is likely that generative AI will serve as a “copilot” that augments people’s ability to perform their roles, thereby leading an evolution of tasks within roles rather than eliminating jobs altogether. For example, the Access Partnership research projects that 45% of workers in India will potentially use generative AI for up to 20% of regular work activities. So, what exactly are the potential implications for industries, jobs, and skills? Unleashing creativity Think of it as a digital update on the Renaissance. Given generative AI’s ability to provide outputs in a variety of formats—text, images, video, audio, computer code, and synthetic data—Asia is likely to see an explosion of new content. “While innovation will continue to need a human spark, generative AI can play a role in supporting the creative process,” says Ahmed Mazhari, president of Microsoft Asia. By learning from large amounts of input data, generative AI can help create new content or simply reduce the time and cost involved in conceptualization. The technology has the potential to open up new possibilities and use cases in fields such as journalism, academia, creative arts, marketing, and product design—from the reporter seeking to quickly drum up story ideas to the brand strategist brainstorming concepts and the researcher looking for a rough draft to then sharpen and customize. Industry uses already abound: Coca-Cola, for example, has announced the use of generative AI to create personalized ad copy at scale, while Deloitte has found a 20% increase in code development speed. Generative AI also stands to turbocharge the gig economy and solo entrepreneurship. For example, in India, where the number of individual creators is already on the rise, a survey of more than 1,600 freelancers found that 47% were using generative AI tools regularly and more than 50% reported a positive impact on their productivity. Meanwhile, as the Philippines strives to become Asia’s leading creative economy by 2030, generative AI can play a key role in professionalizing the work of the country’s freelancers. Accelerating discovery The second way generative AI can deliver major economic impact is by accelerating the process of scientific and educational discovery. That might include reducing the cost of research—the technology’s capabilities to interrogate vast data sets, for example, can help develop and test hypotheses quickly and more cost-efficiently. That, in turn, can reduce the time required to design new medicines from years to weeks. Based on Access Partnership’s analysis, roles such as biochemists and biophysicists, astronomers, biologists, bioinformatics scientists, and computer and information research scientists are likely to have the greatest share of their tasks transformed by generative AI. Another economic benefit is generative AI’s role in improving educational outcomes. For example, the Japanese government recently announced plans to allow students from elementary to high school limited use of generative AI to facilitate in-class discussions and artistic activities. Taiwan’s Ministry of Education has brought in a . In India, the Integrating AI and Tinkering with Pedagogy (AIoT) program was launched last year to upgrade the curriculum at 50 schools. The technology can also streamline class preparation and curriculum planning, enabling teachers to create personalized learning experiences based on an algorithmic analysis of student learning patterns and preferences. According to Access Partnership, this application of generative AI will lead to especially significant reprioritization of work activities for teachers in areas such as biological sciences, nursing, physics, geography, architecture, and computer science. Enhancing efficiency A third major area of economic impact involves enhancing workplace efficiency through generative AI’s ability to digest and summarize vast amount of information. The technology helps to make big data more interpretable and useful for decision-making, especially in industries that rely on large amounts of data or involve complex tasks, such as financial services, professional services, scientific research, and ICT. But equally, generative AI tools offer productivity benefits for workers in administrative fields—lessening their workloads and enabling them to refocus on higher-level or more interpersonally challenging work. In Asia, there is a major opportunity for the business process outsourcing industry—so pivotal to many economies—to be an early mover in seizing potential efficiency gains. From automating workflows to real-time multilingual customer support, Mazhari believes that “given likely intensifying competition for global market share, leveraging the possibilities from generative AI may become an important competitive advantage.” In the Philippines, for example, he suggests the industry could refocus on specialist areas such as medical transcript preparation, as well as knowledge-based processes such as software development and market research. What next? While generative AI brings opportunities for all Asian economies, the transition also has to be carefully managed. Early movers can play a crucial role in shaping policies, regulations, and an environment that encourages innovation, investment, and responsible use. Accountability has to be a core principle, to ensure that machines remain subject to effective oversight by people. Therefore, as generative AI capability grows, more organizations will need workers who oversee the reliable, fair, and ethical use of the technology. “There will still need to be human judgment to account for potential algorithmic bias, as well as person-to-person interaction to manage important stakeholder relationships,” Mazhari explains. History suggests that technological advancements lead to the creation of new jobs and long-term economic growth, including the development of roles that can’t even be imagined today. Across Asia, the goal should be to ensure these opportunities are equitably distributed, along with investments to ensure the workforce is adequately prepared. To thrive in a world of generative AI, people will have to apply the technology across a range of situations and work tasks. In both India and the Philippines, there are important initiatives underway to improve digital literacy across the whole population. However, a collaborative approach from government, industry and education providers is essential. “Skilling programs exist today in pockets across Asia, but too many people are severely underserved because of race, gender, geography, displacement, or other barriers,” Mazhari says. The fact that 61% of students do not receive any digital literacy education at school in ASEAN countries means it is imperative that swift action is taken now—to ensure this technology’s economic impact in the future. This content was produced by Microsoft. It was not written by MIT Technology Review’s editorial staff.
This is today’s edition of , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. This is how AI will transform how science gets done —by Eric Schmidt, former CEO of Google, and current co-founder of philanthropic initiative Schmidt Futures With the advent of AI, science is about to become much more exciting — and in some ways unrecognizable. The reverberations of this shift will be felt far outside the lab and will affect us all. If we play our cards right with sensible regulation and proper support for innovative uses of AI to address science’s most pressing issues, AI can rewrite the scientific process.We can build a future where AI-powered tools will both save us from mindless and time-consuming labor—and also propose creative inventions and discoveries, encouraging breakthroughs that would otherwise take decades. . The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 A judge restricted Biden officials’ ability to contact social media firms The ruling is a victory for the Republicans who’ve accused the White House of censorship. ( $)+ Agencies aren’t allowed to flag specific posts to the platforms. ( $)+ But they can get in contact to raise the alarm on illegal activity. () 2 Monday was the hottest day ever recordedUnsurprisingly, recent heat waves are likely to be a contributing factor. ( $) 3 Video games are filling up with AI-generated contentWhich is creating a copyright quagmire. ( $)+ AI models spit out photos of real people and copyrighted images. () 4 China’s EVs are still only massive in ChinaEurope is where the industry needs to succeed to hit the big time globally. ()+ How did China come to dominate the world of electric cars? () 5 India is throwing its hat into the chip-making arenaAnd it’s working to a very ambitious deadline. ( $)+ China’s move to ban chip metals could disrupt the entire industry. ( $)+ The chip patterning machines that will shape computing’s next act. () 6 Abortion rights will matter in the 2024 Republican primariesAnd pro-choice campaigners are mobilizing. ( $)+ Texas is trying out new tactics to restrict access to abortion pills online. () 7 Hong Kong residents are supporting jailed protestorsBy mailing them memes and letters. ()+ Democracy advocates have bounties on their heads. ( $) 8 The hydrogen industry is boomingBut the results aren’t quite living up to its potential. ( $)+ When hydrogen will help climate change—and when it won’t. () 9 Your next horoscope could be written by AI Astrologer is yet one more job it could turn its hand to. ( $)+ Just don’t take it too seriously, okay? () 10 These Taylor Swift fans are livestreaming her showsFor the thousands of disappointed Swifties who didn’t manage to snag tickets. ( $) Quote of the day “The United States Government seems to have assumed a role similar to an Orwellian ‘Ministry of Truth.'” —Judge Terry Doughty, who has barred White House officials and some government agencies from contacting social media firms over “content containing protected free speech,” explains his reasoning in a 155-page ruling, reports the . The big story This US company sold iPhone hacking tools to UAE spies September 2021When the United Arab Emirates paid over $1.3 million for a powerful and stealthy iPhone hacking tool in 2016, the monarchy’s spies—and the American mercenary hackers they hired—put it to immediate use. The tool exploited a flaw in Apple’s iMessage app to enable hackers to completely take over a victim’s iPhone. It was used against hundreds of targets in a vast campaign of surveillance and espionage whose victims included geopolitical rivals, dissidents, and human rights activists. MIT Technology Review can confirm the exploit was developed and sold by an American firm named Accuvant—shedding new light on the role played by American companies and mercenaries in the proliferation of powerful hacking capabilities around the world. . —Patrick Howell O’Neill 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 .)+ If you love tech, be sure to get stuck into these .+ This is pretty spectacular.+ That said, there’s probably a reason why .+ Japan’s beckoning cat has a long and illustrious history.+ I love this little boy’s .