We’ve studied enough comets from our solar system to know that they formed during its early stages, when there was a ton of material swirling around and coalescing into individual bodies. They are made primarily from ice, but in order to survive, they have to form at a distance where the sun’s heat and radiation won’t instantly melt them. Other star systems presumably give rise to comets in the same way. The more distant they are from the star’s radiation, the more they retain their original composition and chemistry from their formation 4.5 billion years ago or so. This “pristine” quality means comets are like preserved time capsules of star systems in their infancies.
Comet dust in particular tells us what the solar system was made of when it first gave birth to comets, and the same principle can theoretically apply to interstellar comets. “Studying the composition and structure of dust particles in the dust coma of 2I/Borisov, we can make educated guesses about the formation conditions and locations of the dust,” says Bin Yang, an astronomer with the European Southern Observatory and the lead author of one of the studies.
The first paper, led by Stefano Bagnulo at Armagh Observatory and Planetarium in the UK, focuses on reflected light. Light is composed of waves, and these waves normally oscillate in many different directions at once. When these waves are polarized, however, they oscillate in one specific direction. If light is polarized by a comet’s coma (the hazy outer shell of gas and dust expelled as the comet is heated by the sun), studying this light can give information on the size and composition of the dust, which helps us understand how the comet formed—and, by extension, provides a glimpse into the history of its original star system.
The new data, collected by the Very Large Telescope based in Chile, tells us that the light reflected from Borisov and filtered through its coma is more polarized than the light from any other object that we’ve studied in the solar system. This is a sign the coma’s particles are small and very fine, which suggests they have not been much disturbed by any star’s radiation and heat (forces that would otherwise cause larger chunks to be haphazardly ejected from the surface). The authors conclude that Borisov is perhaps one of the most pristine objects ever detected. The only object whose polarization comes close is C/Hale-Bopp, perhaps the brightest comet ever observed, and certainly one of the most widely studied comets of the 20th century. Hale-Bopp is thought to have come close to the sun only once before its most recent solar flyby in 1997. So the authors think similar conditions may have given rise to both Borisov and Hale-Bopp, in two different star systems.
Meanwhile, the team led by Yang had set out to understand how Borisov formed, using the VLT as well as Chile’s Atacama Large Millimeter/submillimeter Array (ALMA) to detect heat from large particles hanging in Borisov’s coma.
According to these observations, Borisov’s coma consists of compact, millimeter-size grains—pebbles that are unusually large for a comet. These pebbles, rich in carbon monoxide and water, probably formed first in the inner region of the star system, before being transported outward and gradually mixing with various ices formed at different locations farther from the star. This “gravitational stirring,” induced by giant planets, is thought to have happened in our own solar system (it’s even thought to have helped Hale-Bopp form). Borisov basically came together as an agglomeration of material from different parts of its star system, before finding a secluded place to call home far from its parent star.
Taken together, the findings help tell us a few things. An abundance of carbon monoxide and water in the dust suggests the comet has resided in low-temperature environments (i.e., far away from a star), where those compounds could have remained cold and stable, for nearly all its life. The finding of “pristine” characteristics bolsters this idea.
The similarities between Borisov and Hale-Bopp, along with evidence that both comets’ star systems experienced gravitational stirring, suggests that the evolution of our solar system is perhaps not as unique as we might have thought. That would also suggest the conditions that give rise to a habitable planet like Earth are more common in the galaxy than imagined.
Or perhaps this is a red herring, and Borisov’s home star system is actually very exotic. Neil Dello Russo, an astronomer at Johns Hopkins University who was not involved with the study, says he was surprised at how high the carbon monoxide and water values were—higher than anything observed in comets from our solar system.
Other questions linger as well. The new findings still cannot tell us exactly when the pebbles in the coma formed, or even what they’re made from.
The biggest problem might be that the two papers seem to promote two different ideas about the particles that make up Borisov: Yang’s paper prominently unpacks the discovery of large pebbles in the coma, while Bagnulo’s paper suggests the coma is dominated by smoke-like small grains that can cause extreme polarization of light. But Michael Kelley, a comet scientist at the University of Maryland who was not involved with the new studies, believes this is likely “just a consequence of the different techniques”—each favoring the detection of one specific type of particle. Future analyses should be able to compare and combine both sets of data and reconcile them as parts of Borisov’s evolution.
Borisov is a weird object, but what is truly weird is the notion that it might hail from a star system not too different from our own. This interstellar comet might be one of the most normal visitors we’ve ever said hello to.
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.
