Building with the communityWe believe in building with the Deaf community, not just for it. Deaf perspectives have shaped every stage of this project — from conceptualization by Sam Sepah, a Deaf Googler, to data collection with Deaf partners, evaluation in Deaf user studies, and impact assessment of the technology with Deaf experts.To guide responsible real-world deployment, we established the AI Sign Language Advisory Committee (AISLAC), bringing together many global Deaf organizations and subject-matter experts. Through this participatory governance model, the communities most impacted by our technology directly influence our development priorities. We co-authored a joint impact report for the release of SL2T 1.0 in Gboard and Live Transcribe, transparently detailing the technology’s capabilities and current limitations — a collaborative approach we plan to continue for all major sign language releases.Looking aheadSL2T builds upon decades of foundational research across academia and industry, but bringing ASL input to users’ phones is only the beginning. Google’s mission is to organize the world’s information and make it universally accessible and useful. Achieving universal accessibility means reaching full parity with spoken and written languages. Our team is working to expand this technology into additional sign languages, sign language generation, and frontier AI capabilities. We look forward to sharing our progress responsibly in order to make access through sign languages standard across the digital landscape.You can experience SL2T in Gboard and Live Transcribe first on Pixel 11, with more devices coming soon — all at no additional cost.AcknowledgementsThis work was done jointly by teams from Google DeepMind and Android. The core team who developed the SL2T model is: Garrett Tanzer, Benoit Brard, Elizabeth Clark, Tim Dozat, Sebastian Ebert, Dan Garrette, Manfred Georg, Vicky Holgate, Shankar Kumar, Mohammad Saboorian, Miloš Stanojević, Megh Umekar, John Wieting, Andy Zhang, and Chris Dyer.The Android team who integrated the model into Gboard and Live Transcribe is: Ausmus Chang, Sai Aditya Chitturu, Dayle Chiu, Anna Chou, Ajay Dudani, Angana Ghosh, Alex Huang, Joanne Kim, Ed Lee, Thomas Lin, James Su, Yanchao Su, and Sharlene Yuan.We are grateful for additional support from Anelia Angelova, Abhishek Bapna, Sara Basson, Glenn Cameron, Scott Crowell, Trevor Cohn, Noah Fiedel, Zoubin Ghahramani, Raia Hadsell, Tom Hudson, Alexander Hauerslev Jensen, Kazuya Kawakami, Peike Li, Liam McCafferty, Caroline Pantofaru, Abhinav Parashar, Christopher Patnoe, Laura Rimell, Sam Sepah, Thad Starner, Dave Uthus, and Biao Zhang.Many thanks also go to those who participated in early stage testing of our models.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How we picked 35 of the world’s top young scientists and engineers On September 8, MIT Technology Review will reveal its 2026 list of Innovators Under 35, recognizing 35 young people from around the world who are doing groundbreaking scientific work and building clever technical fixes for sticky problems. By finding the top young innovators globally and learning what they’re focused on in their work, we aim to give readers a sense of what advances to expect in the years to come.  As a newsroom, we also use this exercise to help us spot rising talent and get to know some of the best early-career researchers in the fields that we cover.
This year, we received 550 nominations. Find out how we whittled them down to 35 of the young innovators shaping the future of technology, and check out last year’s list. —Amy Nordrum
How the “censorship-industrial complex” is changing the internet and US policy —Eileen Guo I first heard the term “censorship-industrial complex” on April 15, 2025.  That’s when I got the tip that a small office in the US State Department, which focused on monitoring and countering foreign disinformation from the likes of Russia, Iran, and China, was facing imminent shutdown—the next day.  And the reason? The office was accused of serving as the department’s central hub in the so-called censorship-industrial complex—a sprawling constellation of government agencies, academics, civil society groups, and Big Tech platforms allegedly conspiring to suppress conservative and populist speech online under the guise of combating disinformation.  I broke the story on April 16. But for me, it was just the start of a deep reporting rabbit hole into an idea that had moved from the fringes of the right-wing internet into the Trump administration. For more on what the narrative means for the internet, read my story here. MIT Technology Review Narrated: Montana’s plan to become an experimental medical hub just pushed forward At the end of July, any biotech company in Montana with an experimental drug gained a clear path to selling it to consumers. Companies whose drugs have been through preliminary testing—sometimes in as few as 10 healthy people—can pay $12,500 to apply to a newly established review board. Once approved, they can set their own prices and sell the drugs through experimental treatment clinics, the first of which is likely to open around the end of this year. Montana’s latest right-to-try legislation is unique. While similar laws elsewhere limit access to people with terminal illness, Montana’s system is theoretically open to anyone who gives informed consent and can pay. That includes people desperate for treatments for rare diseases. It also includes those interested in longevity and drugs pitched as preventive therapies.

—Jessica Hamzelou This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. 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 have hit Taiwan in an “unprecedented” AI attackThey used open-source agents to compromise government websites. (FT $)+ UK military drones were found sending a signal to China. (Cybernews)+ Taiwan’s “silicon shield” could be weakening. (MIT Technology Review) 2 Wall Street firms are paying $100,000 a month to get Trump posts firstTrump Media said more than 10 firms have signed up for the service. (CNN)+ It offers faster access to market-moving posts on Truth Social. (BBC)+ Trump Media also lost $238 million as crypto holdings fell. (CNBC) 3 ICE plans to give officers gloves that can deliver painful electric shocksIt’s set to spend up to $20 million to buy thousands of the devices. (AP News)+ A switch turns them from normal gloves into “electrical mode.” (Guardian) 4 Spotify will label AI artists and stop recommending themThe platform is cracking down on fake performers. (Guardian)+ “AI personas” will appear on artist profiles and track listings. (NYT $) 5 Social media spurred a deadly migrant surge from Morocco to SpainDisinformation encouraged thousands to attempt the crossing. (NYT $) 6 Anthropic’s Claude is adding watermarks to AI text and imagesIt could guarantee votes are counted and kept anonymous. (Axios)7 Drugs that mimic the brain’s wakefulness signal are taking offOrexin drugs could treat sleep disorders, ADHD and addiction. (Economist $)+ But psychedelics are falling short in clinical trials. (MIT Technology Review) 8 Cargo thieves have turned to violence to steal AI hardwareShipments have disappeared after their escorts were attacked. (Wired $)9 Scientists may have found the elusive glueball, a particle made of forceA Chinese collider has produced the strongest evidence yet. (Science)10 A firm selling “100% human-written, never AI” research is entirely AIThe reviewers on the Research Gold site are AI-generated. (404 Media) Quote of the day “I think the fourth wave of slop will be when there’s no longer any meaningful quality hit in slop, when the average piece of slop is better than the best human in that field.”  —Kevin Roose, a technology columnist at The New York Times, tells the Pivot podcast what the next stage of AI slop will look like.
One More Thing PATRICK LEGER Are we ready to hand AI agents the keys? We’re starting to give AI agents real autonomy, and we’re not prepared for what could happen next. Any action that can be captured by text is potentially within the purview of AI agents—which is why they can cause so much mischief.
“The great paradox of agents is that the very thing that makes them useful—that they’re able to accomplish a range of tasks—involves giving away control,” says Iason Gabriel, a senior staff research scientist at Google DeepMind who focuses on AI ethics. Researchers warn that agents could misinterpret goals, leak sensitive information, fall victim to prompt-injection attacks, and exploit software vulnerabilities at scale. And there’s no foolproof way to guarantee that they’ll act as their developers intend.

Next month, on September 8, MIT Technology Review will reveal its 2026 list of Innovators Under 35, recognizing 35 young people from around the world who are doing groundbreaking scientific work and building clever technical fixes for sticky problems.  By finding the top young innovators globally and learning what they’re focused on in their work, we aim to give readers a sense of what advances to expect in the years to come. As a newsroom, we also use this exercise to help us spot rising talent and get to know some of the best early-career researchers in the fields that we cover.  The editors of MIT Technology Review published the first Innovators Under 35 list in 1999, and it’s become a beloved annual tradition alongside our lists of 10 Breakthrough Technologies, 10 Climate Tech Companies to Watch, and (new this year) 10 Things That Matter in AI Right Now.  The people we’ve featured through the years have gone on to shape the tech industry and our broader culture, from Lisa Su (featured in 2002), whose stunning turnaround of AMD has built it into one of the top chipmakers worldwide, to Daniel Ek (featured in 2012), who cofounded Spotify (which we described at the time as “a jukebox in the cloud”). Subscribers can browse all the past honorees in this database. 
Selecting the 2026 Innovators was a monthslong endeavor. This year, we received 550 nominations, both from staff and via our public nomination process. From those entries, our editors selected 110 semifinalists. We looked for candidates who were setting out to solve big problems or answer pressing scientific questions in their work, and who had already made clear progress toward their goals.  All semifinalists then completed an application to help us learn more about them. They collected reference letters, uploaded videos, and submitted résumés. Forty-four expert judges then helped us evaluate these applications. Some of these judges are former Innovators themselves. Many have returned year after year to volunteer their time, energy, and expertise to the judging process. We’re grateful for their efforts. 
In the end, our editors reviewed all of the judges’ scores and comments and selected the 35 winners. Each works in one of four categories: biotechnology, artificial intelligence, computing and robotics, and climate and energy.  “These Innovators represent some of the best aspects of science and technology research—pushing forward bold ideas to improve the future for everyone,” says Costa Samaras, a 2026 judge who is also the director of Carnegie Mellon’s Scott Institute for Energy Innovation.  The 2026 list of Innovators will be available to MIT Technology Review subscribers on our site on September 8. To access the package when it comes out online, you can subscribe now via this link. It will also be published in the September/October issue, which will be available on newsstands worldwide on August 31.  Do you know someone who deserves a spot on next year’s list? Nominations for the 2027 competition will open by early December. Check back then or sign up for our daily newsletter The Download to stay in the loop. 

I first heard the term “censorship-industrial complex” on April 15, 2025.  That’s when I got the tip that a small office in the U.S. State Department, which focused on monitoring and countering foreign disinformation from the likes of Russia, Iran, and China, was facing imminent shutdown—the next day.  And the reason? R/FIMI, as the office was called, was accused of serving as the State Department’s central hub in the so-called censorship-industrial complex—a sprawling constellation of government agencies, academics, civil society groups, and Big Tech platforms allegedly conspiring to suppress conservative and populist speech online under the guise of combating disinformation.  I broke the story around 10:30AM on April 16, (and broke more in the weeks that followed) but for me, it was just the start of a deep reporting rabbit-hole into an idea that had moved from the fringes of the right-wing Internet into the mainstream, championed and spread by a network of well-funded conservative media platforms and non-profits, and finally as a sort of prevailing logic behind much of the second Trump administration’s domestic and foreign policy.  But this isn’t just a policy story. The weaponization of ideas about censorship also affects the billions of people globally who get information, or interact with each other, online—which is to say, all of us.  For more on what the narrative means for the Internet, read my story here.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. These startups are chasing the next big thing in LLMs Nine years after Google researchers introduced the transformer, this family of neural networks has become the engine inside every major large language model. But transformers are starting to show their age.  As LLMs get bigger and better, transformers have become a bottleneck. Their dense attention mechanism becomes increasingly expensive as the amount of text grows, and they’re not great at keeping track of a lot of information at once. Here are four new ideas for how to solve the transformer problem—innovations that could change LLMs for good, making them faster, far more efficient, and (maybe) even smarter.
—Will Douglas Heaven This story is from MIT Technology Review’s What’s Next series, which looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
AI professors are negotiating the new realities of academic research —Grace Huckins Last week, I headed to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI.  The fellows list is a who’s who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I’d interviewed previously or whose research I admired. It’s a weird time for university AI researchers, who make up most of the AI2050 group. Read Grace’s story to find out why, and what could be coming next. This story is from The Algorithm, our weekly AI newsletter. Sign up 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 Nvidia has secured $500 billion from Wall Street for AI infrastructureIt’s struck deals with BlackRock, Goldman Sachs, and four others. (BBC)+ Showing the pull of AI compute for ⁠institutional investors. (Reuters $)+ And that AI infrastructure is becoming a new asset class. (CNBC) 2 Mark Zuckerberg’s new manifesto says open-source AI can save the USIt presents a utopian vision of personalized “superintelligence.” (Guardian)+ And arrived the same day as Meta’s new, open-source model. (NYT $)+ Zuckerberg said he plans to launch more of these models. (WSJ $)+ And pit Meta against Chinese open-weight developers. (SCMP) 3 Bernie Sanders has called on Silicon Valley to “pause AI development”He noted that AI giants have pledged to do this if necessary for safety.+ And warned that lawmakers will step in if no action is taken. (Guardian)+ House Democrats are already pressing AI leaders over rogue models. (WP $)+ A populist backlash is building against AI. (MIT Technology Review) 4 A US court will allow thousands of social media lawsuits to proceedThe suits target addictive mechanisms used by Meta, TikTok, Google, and Snapchat. (Axios)+ They claim the platforms are designed to hook young users. (Reuters $)+ Can we repair the internet? (MIT Technology Review) 5 Unitree’s IPO is more than 8,000 times oversubscribed by retailThe Chinese humanoid firm raised $900 million ahead of its listing. (Reuters $)+ Its pricing for the Shanghai IPO values the company at $9 billion. (FT $)6 Flock’s car-tracking cameras are facing a bipartisan backlashThe surveillance network has spread rapidly across the US. (NYT $)+ Flock also plans to chase shoplifters with drones. (MIT Technology Review) 7 China is breaking up AI relationshipsBeijing has introduced new rules for emotionally interactive AI. (Rest of World)+ It’s surprisingly easy to fall for a chatbot. (MIT Technology Review) 8 An AI tool claims to pick the best 1% of scientific papersBut researchers doubt that AI can reliably judge scientific quality. (Nature)9 The AI slop backlash is workingIt’s pushing platforms to restrict AI-generated content. (Wired $)10 An 82-year-old rejected $26 million to turn her farm into a data centerShe criticised the environmental impacts of data centers. (Fortune) Quote of the day

“It is not too late to avoid disaster. Stop building machines that humans cannot control.”  —Senator Bernie Sanders urges Sam Altman, Dario Amodei, and Mark Zuckerberg to pause all AI development in a letter. One More Thing The race to make the perfect baby is creating an ethical mess A new field of science is using genetic sequencing to predict what kind of person an embryo might become. Some parents turn to these tests to avoid devastating genetic disorders, while a much smaller group are willing to pay tens of thousands of dollars to optimize for intelligence, appearance, and personality. Customers, however, may not be getting what they’re paying for. Genetics experts have highlighted the potential deficiencies of this testing for years, while its underlying assumptions have made these companies a political lightning rod.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last week, I headed 30 miles south of San Francisco to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI. The fellows list is a who’s who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I’d interviewed previously or whose research I admired. (Full disclosure: I received a science communication award funded by Schmidt Sciences in 2024.)  It’s a weird time for university AI researchers, who make up most of the AI2050 group. In the past four years, AI research has reoriented around large language models, and its cutting edge has moved from academic institutions to private companies. Universities simply can’t afford the GPUs required to train and run frontier models, and even if they could, Anthropic and OpenAI aren’t letting anyone else see the inner details of Claude or ChatGPT. In a conversation over lunch, Nika Haghtalab, a computer science professor at UC Berkeley, said that being an AI academic these days was like being a biologist in a world in which private companies had exclusive control over the gene-editing tool CRISPR. Experts outside the frontier labs can study how ChatGPT and Claude behave, but they can’t do any detailed research on the design and training of those tools, nor can they steer that design or training themselves.
 The AI2050 program does offer fellows some funding that they can use to buy GPUs, which some researchers I spoke with said was a major benefit of participating in the program. But money remains a pressing concern, especially given the reduction of federal scientific funding in the United States. Even for researchers who don’t run local models themselves, the cost of repeatedly querying OpenAI’s, Anthropic’s, and Google’s models in order to study them rigorously can be prohibitive. Rather than focusing on advancing capabilities, many fellows aim their attention at questions that are unlikely to be addressed by Anthropic or OpenAI. “I try not to work on problems that I think are gonna be solved by a tech company,” says Anjalie Field, a computer science professor at Johns Hopkins. Companies need to make money, and research questions that have little promise of profit might not be worth investing in—especially if their answers might make the companies look bad. Recently, for example, Field conducted a study in which she found that language models give less sophisticated responses to prompts that are phrased in ways more commonly used by women than by men. It’s difficult to imagine that kind of research coming out of Anthropic or OpenAI.
There’s also a huge group of AI academics who don’t work with LLMs at all. Many of them are scientists who build specialized AI models that can analyze data, make useful predictions, or even simulate entire physical systems. Those researchers aren’t necessarily competing with the frontier labs—Google DeepMind’s AlphaFold team, which built a Nobel Prize–winning model that predicts the structures of proteins, was disbanded last month. But they face plenty of their own challenges. At the convening, several voiced concerns about how the widespread ignorance of non-LLM AI was affecting their work. Researchers who build specialized AI tools to help address climate change, for example, sometimes struggle to advocate for their work when so many people believe that “AI” means “energy-guzzling LLMs.” All these challenges are changing the landscape of academia: Several prominent academics have recently taken leave from their universities to join frontier labs, and many AI2050 fellows hold industry positions alongside their academic jobs. And in the past six months, yet another threat has emerged. OpenAI’s models have solved a number of real research problems in mathematics, and some experts are worried that humans might not have a future in pure math. One fellow I spoke with said that she was concerned about the mental health of her mathematician peers. But it’s not all doom and gloom. For one thing, empirical science may prove much more difficult to automate than mathematics, because collecting data is an intrinsically slow process. And some researchers see AI mathematicians and scientists as a boon rather than a threat—including Tim Dettmers, a computer scientist at Carnegie Mellon who works to make AI models faster and cheaper to run. AI scientists won’t replace humans, Dettmers says. On the contrary, they could make human scientists far more efficient, so that he and his peers have the chance to pursue all the wild and inspired ideas they might otherwise never have gotten around to. And scientists are a resilient sort. The very resource constraints that prevent them from training frontier models also push them to discover new ways to make models smaller and more efficient, or to explore completely new architectures. If the next big AI breakthrough comes not from a major company but from a scrappy academic lab, I won’t be shocked.