In late February, as coronavirus took hold in Europe, governments around the world were still resisting lockdowns. In Facebook's artificial intelligence lab in Paris, data scientists and machine learning researchers were dumbstruck.
"I mean, they are data nerds, right?" Antoine Bordes, managing director of Facebook AI Research, told Protocol. "There [were] really a lot of people [here], heads down in the data, who knew this [pandemic] was coming."
One researcher, Bordes recalls, skeptical of governmental responses to the pandemic, decided to do some modeling on the weekend using data grabbed from a New York Times COVID-19 tracker. He recalled their response on returning to the office: "Hmm, it doesn't look good."
So those researchers did what researchers do: They dug into the data, they played around and they started working out what could be usefully done. "I mean 60 people, roughly, they dropped what they were doing to start [COVID-19] projects," Bordes said.
"It didn't happen because the firm, the CTO, Mark Zuckerberg [or] me said 'We need to do something about COVID,' you know … it really started from the bottom, with a lot of people saying 'We want to act.'"
Fast forward to today, and Facebook's AI lab has built software that forecasts the spread of COVID-19 at the county level in the U.S., an early version of which was used to predict demand on hospitals in New York during that first peak of cases. It's also developed tools to detect COVID-19 misinformation (or at least some of it; that remains an unfathomably difficult task to automate), as well as identify posts offering or requesting help, so others can more easily connect with the right people via the Facebook Community Hub. And it has bigger plans to continue its forecasting across Europe.
But even at Facebook, the home of moving fast and breaking things, getting to this stage wasn't straightforward. Projects like this can normally take months or years, not weeks, to complete. And the flip side of having a team full of data nerds is that they all have ideas: At least 80 to 90 people provided ideas for projects, Bordes estimated.
He needed to work out how to impose some order on the enthusiasm. And quickly.
"I had a meeting with my boss, Jerome Pesenti, the VP of AI, and a lot of the AI leadership," Bordes said. "And it was [obvious that] OK, two things: First, we feel the pandemic is very serious and we want to do something about it as an organization … we don't know how but we want to do something."
He added: "The second one was: If we don't coordinate, we're going to do small things and nobody's going to have an impact." Even worse, the execs feared that there was the possibility that too many projects might lead to conflicting and contradictory information. "Sometimes you want to help, you want to have a humanitarian action, and actually by coming up with the wrong path, or the wrong way [of doing something], you're actually doing more harm than good," he said.
"I went to the team and I said, 'OK, in the next three days, you're going to come back and propose all the ideas of projects you have,'" Bordes said. At this stage, there was no prioritization: The team just wanted a comprehensive list of what everyone was thinking about and working on — everything from projects assessing the causality of lockdowns on the spread of COVID-19 to algorithms that might help dissuade people from touching their face.
As ideas crystallized into 10 to 15 projects, theme leaders were appointed. ("Some of them made it through, some of them didn't — that's research," Bordes said.) Then, his role became more about liaising with those leaders while finding external partners to work with.
Those kinds of collaborations have been a big deal for the team throughout this process: Bordes says he wouldn't let any of the forecasting work leave the company unless his team had worked with epidemiologists, because Facebook isn't expert in medical sciences. "We don't ever claim we're right," Bordes said.
The process of identifying stronger research themes gradually continued, with the team slowly building larger and larger teams on its areas of focus. The forecasting effort, for example, ultimately wound up being a 10-person team on top of the original researcher: people working on the legal issues of sourcing data, releasing the models as open-source software, liaising with universities, or working on communications. And all the time, the team was trying to "do it in, like, two weeks, not six months," Bordes said.
Perhaps the project the group seems proudest of is its attempt tot predict the spread of the virus. That work is actually based on a research project led by Maximilian Nickel, an AI researcher based in New York, which explores how messages propagate through social graphs. As the pandemic hit, Nickel and his team were working on models that were actually borrowed from epidemiology, so they were able to repurpose their work and use it to predict the spread of the virus in New York and New Jersey "within a couple of weeks," Bordes said.
Then, it was time to check it was actually useful. "We knocked on the doors of the epidemiology labs, and we showed [them] the forecasts, and said, 'OK, is this going to be useful?'" Bordes said. "And the epidemiology team at NYU and Cornell ... they really were impressed by the quality of these forecasts … so they took them and used them in their own epidemiology models." Subsequently, those forecasts were used by New York and New Jersey during the first wave to try to allocate health care resources.
That was six months ago, and the team's kept working since. It's extended its modeling across the entire U.S., forecasting the spread of COVID-19 at the county level at one, two and three weeks out — data it shares on the Humanitarian Data Exchange.
There is, of course, no shortage of COVID-19 predictions: Facebook's is one of many, all based on different data sets and assumptions, all offering slightly different predictions. "As a standalone model, I don't think it's unique," said Youyang Gu, an independent data scientist who has been building his own predictions and tracking those built by others. "But if it leads to more people using [or] accessing the forecasts, then that can lead to beneficial impacts."
Now, Facebook's working in collaboration with the Polytechnic University of Catalonia to do the same thing in Europe, but that's a task made much harder by the fact that European countries all report data in different formats.
Even trickier is the idea of extending the work beyond North America and Europe, where data collection and sharing is far less organized. The team has open-sourced its model, but without the right data, it's hard for Facebook to do much more with these algorithms for some of the most vulnerable nations in the world.
"We wish we could," Bordes said.