The 24-hour vigil started just after 8 a.m. US Eastern Time on June 3—more or less on schedule, and without any major disruptions.
The event, hosted on Zoom and broadcast live on other platforms such as YouTube, was put together by Chinese activists to commemorate the Tiananmen Square Massacre, Beijing’s bloody clampdown on a student-led pro-democracy movement that took place on June 4, 1989.
The fact that it could take place wasn’t certain: organizers were worried that they’d see a repeat of last year, when Zoom, the Californian videoconferencing company, shut down three Tiananmen-related events including theirs after a request from the Chinese government. The company even temporarily suspended the accounts of the coordinators, despite the fact that all of them were located outside of mainland China and four of them were in the US.
Zoom’s actions led to an investigation and lawsuit filed by the Department of Justice in December. “We strive to limit actions taken to only those necessary to comply with local laws. Our response should not have impacted users outside of mainland China,” Zoom wrote in a statement posted to its website, in which it admitted that it “fell short.”
It was one of the most extreme examples of how far western technology companies will go to comply with China’s strict controls on online content.
A suite of suppression
This kind of self-censorship is standard for Chinese technology companies, who—unlike American businesses shielded by rules such as Section 230—are held responsible for user content by Chinese law.
Every year, a few days ahead of sensitive dates like the anniversary of the 1989 crackdown, the Chinese internet—which is already strictly surveilled—becomes even more closed than normal. Certain words are censored on various platforms. Commonly used emojis, like the candle, start disappearing from emoji keyboards. Usernames on different platforms can’t be changed. And speech that may have been borderline acceptable during other times of the year may result in a visit from state security.
In 2020, Zoom shut down three Tiananmen-related events after a request from the Chinese government—despite the fact that all of them were located outside of mainland China. In December the Department of Justice filed a lawsuit against the company.
This year, such suppression is stretching even further. Following the passage of a new national security law in Hong Kong that severely curtails speech—despite monthsof protests—commemoration events there and in neighboring Macau have been officially banned. (Last year 24 people were charged for ignoring a similar ban, including one of the movement’s most prominent leaders, democracy activist Joshua Wong, who is still in jail and was recently sentenced to a further 10 months.
Covid is playing its part too: a large public event planned in Taiwan has also been canceled, for example, due to a strict lockdown after a new wave of covid-19 infections.
A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.
For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.
That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng.
Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.
The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.
KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels.
When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.
The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.
—June Kim
Medical microrobots that travel inside the body are (still) on their way
The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.
We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.
We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).
Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now.
New drugs for Alzheimer’s disease
Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause.
Sustainable aviation fuel
Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.
Solar geoengineering
One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now.