This is what the latest generation of robotics companies like Covariant and Osaro specialize in, a technology that didn’t become commercially viable until late 2019. Right now such robots are most skilled at simple manipulation tasks, like picking up objects and placing them in boxes, but both startups are already working with customers on more complicated sequences of motions, including auto-bagging, which requires robots to work with crinkly, flimsy, or translucent materials. Within a few years, any task that previously required hands to perform could be partially or fully automated away.
Some companies have already begun redesigning their warehouses to better capitalize on these new capabilities. Knapp, for example, is changing its floor layout and the way it routes goods to factor in which type of worker—robot or human—is better at handling different products. For objects that still stump robots, like a net bag of marbles or delicate pottery, a central routing algorithm would send them to a station with human pickers. More common items, like household goods and school supplies, would go to a station with robots.
Derik Pridmore, cofounder and CEO at Osaro, predicts that in industries like fashion, fully automated warehouses could come online within two years, since clothing is relatively easy for robots to handle.
That doesn’t mean all warehouses will soon be automated. There are millions of them around the world, says Michael Chui, a partner at the McKinsey Global Institute who studies the impact of information technologies on the economy. “Retrofitting all of those facilities can’t happen overnight,” he says.
One of the first Covariant-enabled robotic arms that Knapp piloted in a warehouse in Berlin, Germany.
Nonetheless, the latest automation push raises questions about the impact on jobs and workers.
Previous waves of automation have given researchers more data about what to expect. A recent study that analyzed the impact of automation at the firm level for the first time found that companies that adopted robots ahead of others in their industry became more competitive and grew more, which led them to hire more workers. “Any job loss comes from companies who did not adopt robots,” says Lynn Wu, a professor at Wharton who coauthored the paper. “They lose their competitiveness and then lay off workers.”
But as workers at Amazon and FedEx have already seen, jobs for humans will be different. Roles like packing boxes and bags will be displaced, while new ones will appear—some directly related to maintaining and supervising the robots, others from the second-order effects of fulfilling more orders, which would require expanded logistics and delivery operations. In other words, middle-skilled labor will disappear in favor of low- and high-skilled work, says Wu: “We’re breaking the career ladder, and hollowing out the middle.”
But rather than attempt to stop the trend of automation, experts say, it’s better to focus on easing the transition by helping workers reskill and creating new opportunities for career growth. “Because of aging, there are a number of countries in the world where the size of the workforce is decreasing already,” says Chui. “Half of our economic growth has come from more people working over the past 50 years, and that’s going to go away. So there’s a real imperative to increase productivity, and these technologies can help.
“We also just need to make sure that the workers can share the benefits.”
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.
