There may also be another disparity. There are many people who, even if you offer them the vaccine, will not take it. And that’s partly because of the distrust. There is a much higher level of distrust among Latino and Black Americans, partly because of historical mistreatment.
Q: How are you seeing mistrust affect global vaccination disparities more globally?
A: When we think about mistrust on a global scale, that may be partly because of how the pharmaceutical industry prices things and how they have patents. Some countries may be thinking, “These companies from the US or Europe are really trying to sell us their expensive vaccines. But we can’t really afford them for our population in the first place because they are patented, and we are not allowed to just make a generic version of it.” They may be thinking, “These companies are just trying to take advantage of us.” And there certainly have been examples of lower-income countries that have been exploited by the pharmaceutical industry.
Inequitable vaccine allocation definitely will disrupt the supply chain for all, including the wealthiest nations that have come to depend on cheap sources of labor.
In Indonesia, for example, this happened with H5N1. Whenever there’s an outbreak, if you’re a WHO member, you send samples to a WHO lab and they try to find out about this particular virus or disease. Based on genetic material sent from Indonesia, scientists developed therapeutics for H5N1 and tried to sell them back to Indonesia. Then Indonesia thought, “Okay, these were our samples. Should there not have been collaboration? You’re using them to sell drugs back to us.”
Q: Does the US have a moral obligation to send people to other countries to help with vaccinations?
A: One of the problems is that we’re not able to train enough people in the local places. For Covax or other kinds of international collaboration, it’s not about sending people so much as it’s about how do we help them build up their own infrastructure? Even financial resources for training courses or other kinds of ways to beef up their own human resources. Because you can imagine we’d go, and then we’d leave, and they’re not any better in terms of infrastructure.
Q: How would it affect higher-income countries if other, lower-income countries don’t receive their vaccines until later? Recent research says, for example, that if poor countries don’t get vaccines, it will disrupt the economy for everyone.
A: While it’s still likely that at the human level, people in the most vulnerable countries will suffer more, inequitable vaccine allocation definitely will disrupt the supply chain for all, including—perhaps even especially—the wealthiest nations that have come to depend on cheap sources of labor. If supplying nations have lots of people being sick, or they have to shut down, [there are] no workers to process or transport the raw materials, or to manufacture and deliver the products. People in these countries also can’t travel or spend money, which can greatly affect international hotel chains, airlines, and hospitality industries as well.
This would apply within a high-income country too. If undocumented workers, farm workers, homeless people, and others in low-wage jobs can’t get vaccinated, they can’t work to keep the supply chain going. So restaurants, entertainment industries, etc. would suffer. If they can’t pay the rent or mortgage or have extra money, that also affects the rest of the economy.
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.
