With the humanitarian crisis in India worsening, immediate and aggressive measures are needed to stabilize the situation and buy time for vaccine production to ramp up. The crisis is already spreading beyond India’s borders and will require coordinated global action.
Speed is critical. As Michael Ryan of the World Health Organization noted in March 2020, “The greatest error is not to move … speed trumps perfection.” Over the past week, governments in countries including the UK, EU, Russia, and the US have pledged help, but they risk providing too little, too late.
Medical oxygen is in critically short supply in India, with an estimated daily need of 2 million oxygen cylinders far exceeding domestic production capacity. India also needs medications, hospital beds, ventilators, personal protective equipment, covid testing supplies, and other basic medical goods. More health workers may soon be needed to augment India’s own, who are currently working under immense pressure.
The US has pledged oxygen cylinders, oxygen concentrators and generation units, antiviral drugs, testing kits, and access to vaccine manufacturing supplies, and the first aid flights arrived in India on Friday, April 30. The EU has activated its Civil Protection Mechanism to ship oxygen and medications. The first aid shipments from the UK arrived on Tuesday, April 27, and included oxygen concentrators and ventilators.
Even this global aid response will not avert a historic tragedy. Projections show that we are likely to see over 12,000 daily deaths in India by mid-May, and close to 1 million total deaths by August.
REUTERS/AMIT DAVE
That’s why Indian central and state governments must immediately enact aggressive public health measures to keep the virus at bay. These could include travel restrictions, workplace and school closures, and requirements for social distancing and mask wearing, along with social and economic support for the most vulnerable populations.
Such measures have been deployed inconsistently across India, and in some cases they have been undermined by political leaders. Multiple Indian regions, including Delhi, Karnataka, and Maharashtra, have recently imposed stringent travel and movement restrictions, but there’s still no national approach.
Ramping up vaccine manufacturing capacity, too, will be key to subduing the virus in India in the longer term and slowing its spread around the world. Doing that will require a coordinated global effort between companies and governments.
Slowly, the Indian government is starting to wake up to the situation. The recent advance purchase payments will allow Bharat Biotech to double its production capacity, to 20 million doses a month, by June and reach 60 million per month by August. Similarly, the Serum Institute hopes to be producing 100 million doses a month by mid-year. But this is not a near-term solution. Unfortunately, vaccines will not solve the acute crisis, and no major stocks of vaccines are currently available to import into India. Even the US pledge to share 60 million doses of AstraZeneca vaccine globally will take months to fulfill.
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.
