The news: Researchers in the UK have calculated that its contact tracing app may have prevented around 600,000 cases of covid-19. The announcement is good news for the system—which underwent serious teething problems—and a step forward for exposure notification systems more generally.
What they found: The study, by a team of Oxford researchers, modeled the impact of 1.5 million notifications that had been sent by the UK’s NHS app between October 1 and December 31, when almost 2 million people were infected with covid-19. Their analysis showed that each person who tested positive and used the app to alert others sent out an average of 4.4 notifications; without this intervention, they projected, there would have been between 200,000 and 900,000 more cases.
The data show, as we have reported previously, that even modest use of such apps can have a significant impact: “For each 1% increase in users,” said the researchers, “we estimate the number of cases will drop by between 0.8% and 2.3%”
That’s good news for those who have been trying to understand the effectiveness of such apps, something that has been notoriously difficult to measure. Raphael Yahalom, a researcher at MIT’s Sloan School who has been studying apps like these throughout the pandemic, says that the paper “represents the most comprehensive systematic analysis to date of a large-scale deployment—and so, the most compelling evidence of efficacy.”
Why it matters: It’s difficult to study whether contact tracing apps work because privacy concerns have made analytics especially challenging, says Jenny Wanger, director of programs for Linux Foundation Public Health. Many covid apps use the Google-Apple protocol, which is a system that keeps users anonymous. That protects user privacy so well that it’s difficult for central health authorities or researchers to track back information or see patterns in the alerts.
To get around this, the UK study looked at how many notifications got sent and compared the data with what scientists know about the behavior of the virus itself. Without knowing exactly who received the messages, the researchers were able to model whether the app was making a difference.
That approach won’t work in every country with a covid app, though. Among other things, it requires some sort of centralized health system to track notifications. The US, for example, lacks a national, central database and instead uses a patchwork of state apps, although that could change with the Biden administration.
Still, now that this technology is almost a year old, we could see more studies on whether digital contact tracing is working. Yahalom says there are more efforts under way, and a Swiss study was released earlier in February (although he cautions that it’s hard to compare these studies directly).
Why we need to know: Exposure notification apps have had a tough time. In countries where they are voluntary, apps have struggled with low uptake and privacy concerns. But knowing that they’re effective may encourage some people to decide to download and use one. More data could lead to more investment and more downloads, says Wanger, whose work supports the development and analysis of exposure notification apps. And more users means more broken chains of transmission.
This story is part of the Pandemic Technology Project, supported by the Rockefeller Foundation.
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.
