A few weeks ago, Michelle Watson woke up to a deafening, steadily oscillating screech. “What the heck is that noise?” she wondered.
She went outside to her yard and saw hundreds of beady-eyed insects enrobed in a thick shell of gold emerging out of the ground and crawling up the trees. What Watson was seeing was the emergence of thousands of Brood X cicadas, part of a billions-strong insect swarm that has lain dormant for 17 years before arising to “scream,” mate— all over about three thunderous weeks.
Watson had spent the past 20 years in Las Vegas, but moved to the Blue Ridge Mountains in Georgia last year. She’d seen social media posts about the cicadas, which emerge once in a generation across a huge swath of the eastern United States, but figured they were just the usual summer bugs that she’d heard her entire life. “I thought, ‘What’s the big deal?’” she says.
Faced with an onslaught of bizarre creatures, though, she suddenly understood what the big deal was—and did what any modern human would do: She Googled it. Within minutes, she had downloaded Cicada Safari, a cicada-tracking app.
“We’re getting 16,000 photos a day, and at this rate, we are very likely to get half a million observations.”
Apps like iNaturalist, PictureThis, and PlantIn have become popular respites from the pandemic. Many of these apps act as a digital resource, and allow users to submit photos and video for scientific study. Their success inspired Cicada Safari’s creator Gene Kritsky, an entomologist and biology professor at Mount St. Joseph University, to create his own service as a way of tracking Brood X.
Crowdsourcing has long been a way of gathering information for an event that only happens once in a generation, says Kritsky. Researchers in 1858 wrote to newspaper editors urging them to get readers to write in with observations, while postcards were popular in the first half of the 20th century. By the late 1980s, Kritsky was using a telephone hotline that would often get so drowned in tips that the tape on his voicemail machine would get jammed. In 2004, during the last emergence of Brood X, he urged people to send in observations via email with photos attached. He received about 1,000.
Cicada Safari app allows users to track sightings of cicadas on a map, as well as take photos of insects they spot and submit them to the app. And it is riding a wave, with nearly 180,000 downloads as of publication — not bad for a piece of software that most people won’t use beyond the three-week lifespan of the insects.
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.
