The Facebook engineer was itching to know why his date hadn’t responded to his messages. Perhaps there was a simple explanation—maybe she was sick or on vacation.
So at 10 p.m. one night in the company’s Menlo Park headquarters, he brought up her Facebook profile on the company’s internal systems and began looking at her personal data. Her politics, her lifestyle, her interests—even her real-time location.
The engineer would be fired for his behavior, along with 51 other employees who had inappropriately abused their access to company data, a privilege that was then available to everyone who worked at Facebook, regardless of their job function or seniority. The vast majority of the 51 were just like him: men looking up information about the women they were interested in.
In September 2015, after Alex Stamos, the new chief security officer, brought the issue to Mark Zuckerberg’s attention, the CEO ordered a system overhaul to restrict employee access to user data. It was a rare victory for Stamos, one in which he convinced Zuckerberg that Facebook’s design was to blame, rather than individual behavior.
So begins An Ugly Truth, a new book about Facebook written by veteran New York Times reporters Sheera Frenkel and Cecilia Kang. With Frenkel’s expertise in cybersecurity, Kang’s expertise in technology and regulatory policy, and their deep well of sources, the duo provide a compelling account of Facebook’s years spanning the 2016 and 2020 elections.
Stamos would no longer be so lucky. The issues that derived from Facebook’s business model would only escalate in the years that followed but as Stamos unearthed more egregious problems, including Russian interference in US elections, he was pushed out for making Zuckerberg and Sheryl Sandberg face inconvenient truths. Once he left, the leadership continued to refuse to address a whole host of profoundly disturbing problems, including the Cambridge Analytica scandal, the genocide in Myanmar, and rampant covid misinformation.
The authors, Cecilia Kang and Sheera Frenkel
BEOWULF SHEEHAN
Frenkel and Kang argue that Facebook’s problems today are not the product of a company that lost its way. Instead they are part of its very design, built atop Zuckerberg’s narrow worldview, the careless privacy culture he cultivated, and the staggering ambitions he chased with Sandberg.
When the company was still small, perhaps such a lack of foresight and imagination could be excused. But since then, Zuckerberg’s and Sandberg’s decisions have shown that growth and revenue trump everything else.
In a chapter titled “Company Over Country,” for example, the authors chronicle how the leadership tried to bury the extent of Russian election interference on the platform from the US intelligence community, Congress, and the American public. They censored the Facebook security team’s multiple attempts to publish details of what they had found, and cherry-picked the data to downplay the severity and partisan nature of the problem. When Stamos proposed a redesign of the company’s organization to prevent a repeat of the issue, other leaders dismissed the idea as “alarmist” and focused their resources on getting control of the public narrative and keeping regulators at bay.
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.
