The researchers’ analysis also suggests that Labeled Faces in the Wild (LFW), a data set introduced in 2007 and the first to use face images scraped from the internet, has morphed multiple times through nearly 15 years of use. Whereas it began as a resource for evaluating research-only facial recognition models, it’s now used almost exclusively to evaluate systems meant for use in the real world. This is despite a warning label on the data set’s website that cautions against such use.
More recently, the data set was repurposed in a derivative called SMFRD, which added face masks to each of the images to advance facial recognition during the pandemic. The authors note that this could raise new ethical challenges. Privacy advocates have criticized such applications for fueling surveillance, for example—and especially for enabling government identification of masked protestors.
“This is a really important paper, because people’s eyes have not generally been open to the complexities, and potential harms and risks, of data sets,” says Margaret Mitchell, an AI ethics researcher and a leader in responsible data practices, who was not involved in the study.
For a long time, the culture within the AI community has been to assume that data exists to be used, she adds. This paper shows how that can lead to problems down the line. “It’s really important to think through the various values that a data set encodes, as well as the values that having a data set available encodes,” she says.
A fix
The study authors provide several recommendations for the AI community moving forward. First, creators should communicate more clearly about the intended use of their data sets, both through licenses and through detailed documentation. They should also place harder limits on access to their data, perhaps by requiring researchers to sign terms of agreement or asking them to fill out an application, especially if they intend to construct a derivative data set.
Second, research conferences should establish norms about how data should be collected, labeled, and used, and they should create incentives for responsible data set creation. NeurIPS, the largest AI research conference, already includes a checklist of best practices and ethical guidelines.
Mitchell suggests taking it even further. As part of the BigScience project, a collaboration among AI researchers to develop an AI model that can parse and generate natural language under a rigorous standard of ethics, she’s been experimenting with the idea of creating data set stewardship organizations—teams of people that not only handle the curation, maintenance, and use of the data but also work with lawyers, activists, and the general public to make sure it complies with legal standards, is collected only with consent, and can be removed if someone chooses to withdraw personal information. Such stewardship organizations wouldn’t be necessary for all data sets—but certainly for scraped data that could contain biometric or personally identifiable information or intellectual property.
“Data set collection and monitoring isn’t a one-off task for one or two people,” she says. “If you’re doing this responsibly, it breaks down into a ton of different tasks that require deep thinking, deep expertise, and a variety of different people.”
In recent years, the field has increasingly moved toward the belief that more carefully curated data sets will be key to overcoming many of the industry’s technical and ethical challenges. It’s now clear that constructing more responsible data sets isn’t nearly enough. Those working in AI must also make a long-term commitment to maintaining them and using them ethically.
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.
