“The most important challenge in self-driving is safety,” says Abbeel. “With a system like LINGO-1, I think you get a much better idea of how well it understands driving in the world.” This makes it easier to identify the weak spots, he says.
The next step is to use language to teach the cars, says Kendall. To train LINGO-1, Wayve got its team of expert drivers—some of them former driving instructors—to talk out loud while driving, explaining what they were doing and why: why they sped up, why they slowed down, what hazards they were aware of. The company uses this data to fine-tune the model, giving it driving tips much as an instructor might coach a human learner. Telling a car how to do something rather than just showing it speeds up the training a lot, says Kendall.
Wayve is not the first to use large language models in robotics. Other companies, including Google and Abbeel’s firm Covariant, are using natural language to quiz or instruct domestic or industrial robots. The hybrid tech even has a name: visual-language-action models (VLAMs). But Wayve is the first to use VLAMs for self-driving.
“People often say an image is worth a thousand words, but in machine learning it’s the opposite,” says Kendall. “A few words can be worth a thousand images.” An image contains a lot of data that’s redundant. “When you’re driving, you don’t care about the sky, or the color of the car in front, or stuff like this,” he says. “Words can focus on the information that matters.”
“Wayve’s approach is definitely interesting and unique,” says Lerrel Pinto, a robotics researcher at New York University. In particular, he likes the way LINGO-1 explains its actions.
But he’s curious about what happens when the model makes stuff up. “I don’t trust large language models to be factual,” he says. “I’m not sure if I can trust them to run my car.”
Upol Ehsan, a researcher at the Georgia Institute of Technology who works on ways to get AI to explain its decision-making to humans, has similar reservations. “Large language models are, to use the technical phrase, great bullshitters,” says Ehsan. “We need to apply a bright yellow ‘caution’ tape and make sure the language generated isn’t hallucinated.”
Wayve is well aware of these limitations and is working to make LINGO-1 as accurate as possible. “We see the same challenges that you see in any large language model,” says Kendall. “It’s certainly not perfect.”
These robots know when to ask for help
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 Download: inside the first CRISPR treatment, and smarter robots
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.
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.
5 things we didn’t put on our 2024 list of 10 Breakthrough Technologies
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.
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.