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He got Facebook hooked on AI. Now he can’t fix its misinformation addiction

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He got Facebook hooked on AI. Now he can't fix its misinformation addiction


Within a year his team had developed these models, as well as the tools for designing and deploying new ones faster. Before, it had taken Quiñonero’s engineers six to eight weeks to build, train, and test a new model. Now it took only one.

News of the success spread quickly. The team that worked on determining which posts individual Facebook users would see on their personal news feeds wanted to apply the same techniques. Just as algorithms could be trained to predict who would click what ad, they could also be trained to predict who would like or share what post, and then give those posts more prominence. If the model determined that a person really liked dogs, for instance, friends’ posts about dogs would appear higher up on that user’s news feed.

Quiñonero’s success with the news feed—coupled with impressive new AI research being conducted outside the company—caught the attention of Zuckerberg and Schroepfer. Facebook now had just over 1 billion users, making it more than eight times larger than any other social network, but they wanted to know how to continue that growth. The executives decided to invest heavily in AI, internet connectivity, and virtual reality.

They created two AI teams. One was FAIR, a fundamental research lab that would advance the technology’s state-of-the-art capabilities. The other, Applied Machine Learning (AML), would integrate those capabilities into Facebook’s products and services. In December 2013, after months of courting and persuasion, the executives recruited Yann LeCun, one of the biggest names in the field, to lead FAIR. Three months later, Quiñonero was promoted again, this time to lead AML. (It was later renamed FAIAR, pronounced “fire.”)

“That’s how you know what’s on his mind. I was always, for a couple of years, a few steps from Mark’s desk.”

Joaquin Quiñonero Candela

In his new role, Quiñonero built a new model-development platform for anyone at Facebook to access. Called FBLearner Flow, it allowed engineers with little AI experience to train and deploy machine-learning models within days. By mid-2016, it was in use by more than a quarter of Facebook’s engineering team and had already been used to train over a million models, including models for image recognition, ad targeting, and content moderation.

Zuckerberg’s obsession with getting the whole world to use Facebook had found a powerful new weapon. Teams had previously used design tactics, like experimenting with the content and frequency of notifications, to try to hook users more effectively. Their goal, among other things, was to increase a metric called L6/7, the fraction of people who logged in to Facebook six of the previous seven days. L6/7 is just one of myriad ways in which Facebook has measured “engagement”—the propensity of people to use its platform in any way, whether it’s by posting things, commenting on them, liking or sharing them, or just looking at them. Now every user interaction once analyzed by engineers was being analyzed by algorithms. Those algorithms were creating much faster, more personalized feedback loops for tweaking and tailoring each user’s news feed to keep nudging up engagement numbers.

Zuckerberg, who sat in the center of Building 20, the main office at the Menlo Park headquarters, placed the new FAIR and AML teams beside him. Many of the original AI hires were so close that his desk and theirs were practically touching. It was “the inner sanctum,” says a former leader in the AI org (the branch of Facebook that contains all its AI teams), who recalls the CEO shuffling people in and out of his vicinity as they gained or lost his favor. “That’s how you know what’s on his mind,” says Quiñonero. “I was always, for a couple of years, a few steps from Mark’s desk.”

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How Twitter’s “Teacher Li” became the central hub of China protest information

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How Twitter’s “Teacher Li” became the central hub of China protest information


It’s hard to describe the feeling that came after. It’s like everyone is coming to you and all kinds of information from all over the world is converging toward you and [people are] telling you: Hey, what’s happening here; hey, what’s happening there; do you know, this is what’s happening in Guangzhou; I’m in Wuhan, Wuhan is doing this; I’m in Beijing, and I’m following the big group and walking together. Suddenly all the real-time information is being submitted to me, and I don’t know how to describe that feeling. But there was also no time to think about it. 

My heart was beating very fast, and my hands and my brain were constantly switching between several software programs—because you know, you can’t save a video with Twitter’s web version. So I was constantly switching software, editing the video, exporting it, and then posting it on Twitter. [Editor’s note: Li adds subtitles, blocks out account information, and compiles shorter videos into one.] By the end, there was no time to edit the videos anymore. If someone shot and sent over a 12-second WeChat video, I would just use it as is. That’s it. 

I got the largest amount of [private messages] around 6:00 p.m. on Sunday night. At that time, there were many people on the street in five major cities in China: Beijing, Shanghai, Chengdu, Wuhan, and Guangzhou. So I basically was receiving a dozen private messages every second. In the end, I couldn’t even screen the information anymore. I saw it, I clicked on it, and if it was worth posting, I posted it.

People all over the country are telling me about their real-time situations. In order for more people not to be in danger, they went to the [protest] sites themselves and sent me what was going on there. Like, some followers were riding bikes near the presidential palace in Nanjing, taking pictures, and telling me about the situation in the city. And then they asked me to inform everyone to be cautious. I think that’s a really moving thing.

It’s like I have gradually become an anchor sitting in a TV studio, getting endless information from reporters on the scene all over the country. For example, on Monday in Hangzhou, there were five or six people updating me on the latest news simultaneously. But there was a break because all of them were fleeing when the police cleared the venue. 

On the importance of staying objective 

There are a lot of tweets that embellish the truth. From their point of view, they think it’s the right thing to do. They think you have to maximize the outrage so that there can be a revolt. But for me, I think we need reliable information. We need to know what’s really going on, and that’s the most important thing. If we were doing it for the emotion, then in the end I really would have been part of the “foreign influence,” right? 

But if there is a news account outside China that can record what’s happening objectively, in real time, and accurately, then people inside the Great Firewall won’t have doubts anymore. At this moment, in this quite extreme situation of a continuous news blackout, to be able to have an account that can keep posting news from all over the country at a speed of almost one tweet every few seconds is actually a morale boost for everyone. 

Chinese people grow up with patriotism, so they become shy or don’t dare to say something directly or oppose something directly. That’s why the crowd was singing the national anthem and waving the red flag, the national flag [during protests]. You have to understand that the Chinese people are patriotic. Even when they are demanding things [from the government], they do it with that sentiment. 

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Your microbiome ages as you do—and that’s a problem

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Your microbiome ages as you do—and that’s a problem


These ecosystems appear to change as we age—and these changes can potentially put us at increased risk of age-related diseases. So how can we best look after them as we get old? And could an A-grade ecosystem help fend off diseases and help us lead longer, healthier lives?

It’s a question I’ve been pondering this week, partly because I know a few people who have been put on antibiotics for winter infections. These drugs—lifesaving though they can be—can cause mass destruction of gut microbes, wiping out the good along with the bad. How might people who take them best restore a healthy ecosystem afterwards?

I also came across a recent study in which scientists looked at thousands of samples of people’s gut microbe populations to see how they change with age. The standard approach to working out what microbes are living in a person’s gut is to look at feces. The idea is that when we have a bowel movement, we shed plenty of gut bacteria. Scientists can find out which species and strains of bacteria are present to get an estimate of what’s in your intestines.

In this study, a team based at University College Cork in Ireland analyzed data that had already been collected from 21,000 samples of human feces. These had come from people all over the world, including Europe, North and South America, Asia, and Africa. Nineteen nationalities were represented. The samples were all from adults between 18 and 100. 

The authors of this study wanted to get a better handle on what makes for a “good” microbiome, especially as we get older. It has been difficult for microbiologists to work this out. We do know that some bacteria can produce compounds that are good for our guts. Some seem to aid digestion, for example, while others lower inflammation.
 
But when it comes to the ecosystem as a whole, things get more complicated. At the moment, the accepted wisdom is that variety seems to be a good thing—the more microbial diversity, the better. Some scientists believe that unique microbiomes also have benefits, and that a collection of microbes that differs from the norm can keep you healthy.
 
The team looked at how the microbiomes of younger people compared with those of older people, and how they appeared to change with age. The scientists also looked at how the microbial ecosystems varied with signs of unhealthy aging, such as cognitive decline, frailty, and inflammation.
 
They found that the microbiome does seem to change with age, and that, on the whole, the ecosystems in our guts do tend to become more unique—it looks as though we lose aspects of a general “core” microbiome and stray toward a more individual one.
 
But this isn’t necessarily a good thing. In fact, this uniqueness seems to be linked to unhealthy aging and the development of those age-related symptoms listed above, which we’d all rather stave off for as long as possible. And measuring diversity alone doesn’t tell us much about whether the bugs in our guts are helpful or not in this regard.
 
The findings back up what these researchers and others have seen before, challenging the notion that uniqueness is a good thing. Another team has come up with a good analogy, which is known as the Anna Karenina principle of the microbiome: “All happy microbiomes look alike; each unhappy microbiome is unhappy in its own way.”
 
Of course, the big question is: What can we do to maintain a happy microbiome? And will it actually help us stave off age-related diseases?
 
There’s plenty of evidence to suggest that, on the whole, a diet with plenty of fruit, vegetables, and fiber is good for the gut. A couple of years ago, researchers found that after 12 months on a Mediterranean diet—one rich in olive oil, nuts, legumes, and fish, as well as fruit and veg—older people saw changes in their microbiomes that might benefit their health. These changes have been linked to a lowered risk of developing frailty and cognitive decline.
 
But at the individual level, we can’t really be sure of the impact that changes to our diets will have. Probiotics are a good example; you can chug down millions of microbes, but that doesn’t mean that they’ll survive the journey to your gut. Even if they do get there, we don’t know if they’ll be able to form niches in the existing ecosystem, or if they might cause some kind of unwelcome disruption. Some microbial ecosystems might respond really well to fermented foods like sauerkraut and kimchi, while others might not.
 
I personally love kimchi and sauerkraut. If they do turn out to support my microbiome in a way that protects me against age-related diseases, then that’s just the icing on the less-microbiome-friendly cake.

To read more, check out these stories from the Tech Review archive:
 
At-home microbiome tests can tell you which bugs are in your poo, but not much more than that, as Emily Mullin found.
 
Industrial-scale fermentation is one of the technologies transforming the way we produce and prepare our food, according to these experts.
 
Can restricting your calorie intake help you live longer? It seems to work for monkeys, as Katherine Bourzac wrote in 2009. 
 
Adam Piore bravely tried caloric restriction himself to find out if it might help people, too. Teaser: even if you live longer on the diet, you will be miserable doing so. 

From around the web:

Would you pay $15,000 to save your cat’s life? More people are turning to expensive surgery to extend the lives of their pets. (The Atlantic)
 
The World Health Organization will now start using the term “mpox” in place of “monkeypox,” which will be phased out over the next year. (WHO)
 
After three years in prison, He Jiankui—the scientist behind the infamous “CRISPR babies”—is attempting a comeback. (STAT)
 
Tech that allows scientists to listen in on the natural world is revealing some truly amazing discoveries. Who knew that Amazonian sea turtles make more than 200 distinct sounds? And that they start making sounds before they even hatch? (The Guardian)
 
These recordings provide plenty of inspiration for musicians. Whale song is particularly popular. (The New Yorker)
 
Scientists are using tiny worms to diagnose pancreatic cancer. The test, launched in Japan, could be available in the US next year. (Reuters)

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The Download: circumventing China’s firewall, and using AI to invent new drugs

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The Download: circumventing China’s firewall, and using AI to invent new drugs


As protests against rigid covid control measures in China engulfed social media in the past week, one Twitter account has emerged as the central source of information: @李老师不是你老师 (“Teacher Li Is Not Your Teacher”). 

People everywhere in China have sent protest footage and real-time updates to the account through private messages, and it has posted them, with the sender’s identity hidden, on their behalf.

The man behind the account, Li, is a Chinese painter based in Italy, who requested to be identified only by his last name in light of the security risks. He’s been tirelessly posting footage around the clock to help people within China get information, and also to inform the wider world.

The work has been taking its toll—he’s received death threats, and police have visited his family back in China. But it also comes with a sense of liberation, Li told Zeyi Yang, our China reporter. Read the full story.

Biotech labs are using AI inspired by DALL-E to invent new drugs

The news: Text-to-image AI models like OpenAI’s DALL-E 2—programs trained to generate pictures of almost anything you ask for—have sent ripples through the creative industries. Now, two biotech labs are using this type of generative AI, known as a diffusion model, to conjure up designs for new types of protein never seen in nature.

Why it matters: Proteins are the fundamental building blocks of living systems. These protein generators can be directed to produce designs for proteins with specific properties, such as shape or size or function. In effect, this makes it possible to come up with new proteins to do particular jobs on demand. Researchers hope that this will eventually lead to the development of new and more effective drugs. Read the full story.

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