The “cat-and-mouse game,” as it’s usually referred to locally, has gone viral in China this year, drawing thousands of people across the country to events every week. It’s a fun combination of a childhood game, in-person networking, the latest location-sharing technology, and meme-worthy experience. When the game first emerged in February, videos of hide-and-seek players who went wild—climbing up trees, hiding in the sewers—got millions of views on social media.
Each contest convenes dozens of people in a predetermined area, often a large city park. All of them then join a group on Amap, a Chinese Google Maps alternative, and share their live location. Among the participants, 90% are designated as “mice” and have five minutes to run and hide. Then the rest, who are “cats,” will go out and hunt down each mouse with the help of the location sharing, as well as a neon wristband that visually separates them from nonparticipants. Once caught, the mice switch teams and join the cats, so the game gets harder and harder for the remaining mice.
During a short trip to Hong Kong last month, I joined two cat-and-mouse games in the city. Both of them had about 40 participants and lasted one hour. The first park was larger and had fewer people, meaning it was prime for running and chasing; the second was crowded and smaller, which made it ideal for trying to blend in with passersby.
Being an indoor person, I’m not always a fan of group physical activities, but the two experiences went far beyond my expectations. The addition of location sharing has turned the kids’ game into a more interactive version of Pokémon Go. Trying to remain hidden in the same spot throughout the game was not possible, since the cats could always see where I was; I needed to get more creative in crafting an escape plan. I quickly learned that deception—hiding my glowing bracelet, pretending to be an innocent jogger, and avoiding checking my phone too often—was also essential to being a good mouse.
Just watching everyone’s locations in the app was an intense experience. Dozens of little avatars were floating around in the park at once, with cats gradually outnumbering mice as the game progressed. Delays and bugs were plenty, but that added to the fun and difficulty of the game. I could feel safe at one moment, seeing there were no cats around, and panic seconds later when a cat suddenly moved hundreds of feet toward me, likely because its location sharing had lagged.
As a first-timer, I did okay. For my first game, I survived as a mouse until the last few minutes, when mostly everyone else had converted to the cat side. For my second outing, I converted mid-game and caught two mice myself.
I’ll readily admit some people were much better than I was. Hong Shizhe, a 19-year-old college student, was crowned the “cat king” of the second game, having caught 11 mice by the end. “I like that you can both exercise and have fun in this activity,” Hong says. He first learned about the game through videos people shared on Chinese social media, and he has been to several games in Hong Kong and mainland China since. He told me the largest one had more than 140 participants. Once, he even took his dog to the park with him and still won the game.
His secret for success? A lot of lies and politics: “You can make a deal with the mice and have them help you find other little mice. You can also pretend to be a mouse and strike up a chat with them.”
These robots know when to ask for help
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 Download: inside the first CRISPR treatment, and smarter robots
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.
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.
5 things we didn’t put on our 2024 list of 10 Breakthrough Technologies
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.
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.