Pang says he spent three weeks trying to sign into VAMS, but he constantly ended up in the dashboard for patients instead of clinic administrators. In the meantime, his staff was vaccinating hundreds of people a day and keeping track of their information on paper forms. The college set up a bank of volunteers to sit in a room and copy all the information into VAMS.
Eventually, the local hospital helped him get signed into the system. The clinic used it for three days. On the last day, 20 new volunteers came in ready to work. But they’d already signed into VAMS to get their mandatory shots, and there was no way to switch them from patient accounts to staff ones.
The next day, they went back to paper.
“A good system is easier to use than it is not to use. If people are writing this on paper, there’s something wrong,” says Stone. “How are you going to do 100 million shots in 100 days and have someone enter it all in by hand?”
“There is zero way it’ll happen without help”
“VAMS is fussy. There’s days when VAMS works, and days when VAMS doesn’t work,” says Courtney Rowe, a pediatric urologist at Connecticut Children’s Medical Center, who has been volunteering to monitor people for reactions after their shots. She takes it as an opportunity to help people get set up for their second appointments. “I basically function as tech support,” she says.
Online sign-ups are especially challenging for older people, perhaps the worst group to beta-test a new system. Many seniors probably lost their internet access when libraries and senior centers closed; only 59% have broadband connections at home, according to a 2019 Pew survey. While many states offer phone lines for making appointments, people around the country have complained about endless waits.
“There’s days when VAMS works, and days when VAMS doesn’t work.”
“It won’t work on Internet Explorer; it only works in Chrome. The ‘Next’ button is all the way down and to the right, so if you’re on a cell phone, you literally can’t see it,” says Rowe. “In the first round, people using VAMS mostly had advanced degrees. If you’re 75 and someone asks you to log into VAMS, there is zerowayit’ll happen without help.”
After I spoke with Rowe, Connecticut opened up vaccinations to anyone over 70. Her prediction came true immediately. On the first day of a new vaccination clinic in Vernon, Connecticut, 204 vaccines were ready but only 52 seniors had made appointments in VAMS.
“Our residents, and those from around the state that we’re serving at this clinic, are frustrated, angry, and confused by the ineffectiveness of this registration system,” town administrator Michael Purcaro said at a press conference.
Elderly people aren’t the only ones who will struggle if vaccination requires online sign-up. Language barriers will become a significant problem, especially for non-native English speakers doing high-risk essential work. People in rural or poor urban areas often have limited access to the internet in the first place, a problem disproportionately affecting the same Black and Latino communities that have suffered the worst traumas of the pandemic.
“There are some real equity concerns,” says Stone. “What happens when you go to a city and 20% of the population can’t get the notices?”
So what went wrong? In an email, a CDC spokesperson defended the system and said that appointments are not randomly canceled, despite what many clinicians have claimed: the problem, she said, was user error. She also outlined several fixes that have been made in response to feedback. VAMS now includes warnings when administrators do something that might change patient appointments, for example.
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.
