Welcome to I Was There When, a new oral history project from the In Machines We Trust podcast. It features stories of how breakthroughs in artificial intelligence and computing happened, as told by the people who witnessed them. In this first episode, we meet Joseph Atick— who helped create the first commercially viable face recognition system.
This episode was produced by Jennifer Strong, Anthony Green and Emma Cillekens with help from Lindsay Muscato. It’s edited by Michael Reilly and Mat Honan. It’s mixed by Garret Lang, with sound design and music by Jacob Gorski.
Jennifer: I’m Jennifer Strong, host of In Machines We Trust.
I want to tell you about something we’ve been working on for a little while behind the scenes here.
It’s called I Was There When.
It’s an oral history project featuring the stories of how breakthroughs in artificial intelligence and computing happened… as told by the people who witnessed them.
Joseph Atick: And as I entered the room, it spotted my face, extracted it from the background and it pronounced: “I see Joseph” and that was the moment where the hair on the back… I felt like something had happened. We were a witness.
Jennifer: We’re kicking things off with a man who helped create the first facial recognition system that was commercially viable… back in the ‘90s…
I am Joseph Atick. Today, I’m the executive chairman of ID for Africa, a humanitarian organization that focuses on giving people in Africa a digital identity so they can access services and exercise their rights. But I have not always been in the humanitarian field. After I received my PhD in mathematics, together with my collaborators made some fundamental breakthroughs, which led to the first commercially viable face recognition. That’s why people refer to me as a founding father of face recognition and the biometric industry. The algorithm for how a human brain would recognize familiar faces became clear while we were doing research, mathematical research, while I was at the Institute for Advanced Study in Princeton. But it was far from having an idea of how you would implement such a thing.
It was a long period of months of programming and failure and programming and failure. And one night, early morning, actually, we had just finalized a version of the algorithm. We submitted the source code for compilation in order to get a run code. And we stepped out, I stepped out to go to the washroom. And then when I stepped back into the room and the source code had been compiled by the machine and had returned. And usually after you compile it runs it automatically, and as I entered the room, it spotted a human moving into the room and it spotted my face, extracted it from the background and it pronounced: “I see Joseph.” and that was the moment where the hair on the back—I felt like something had happened. We were a witness. And I started to call on the other people who were still in the lab and each one of them they would come into the room.
And it would say, “I see Norman. I would see Paul, I would see Joseph.” And we would sort of take turns running around the room just to see how many it can spot in the room. It was, it was a moment of truth where I would say several years of work finally led to a breakthrough, even though theoretically, there wasn’t any additional breakthrough required. Just the fact that we figured out how to implement it and finally saw that capability in action was very, very rewarding and satisfying. We had developed a team which is more of a development team, not a research team, which was focused on putting all of those capabilities into a PC platform. And that was the birth, really the birth of commercial face recognition, I would put it, on 1994.
My concern started very quickly. I saw a future where there was no place to hide with the proliferation of cameras everywhere and the commoditization of computers and the processing abilities of computers becoming better and better. And so in 1998, I lobbied the industry and I said, we need to put together principles for responsible use. And I felt good for a while, because I felt we have gotten it right. I felt we’ve put in place a responsible use code to be followed by whatever is the implementation. However, that code did not live the test of time. And the reason behind it is we did not anticipate the emergence of social media. Basically, at the time when we established the code in 1998, we said the most important element in a face recognition system was the tagged database of known people. We said, if I’m not in the database, the system will be blind.
And it was difficult to build the database. At most we could build thousand 10,000, 15,000, 20,000 because each image had to be scanned and had to be entered by hand—the world that we live in today, we are now in a regime where we have allowed the beast out of the bag by feeding it billions of faces and helping it by tagging ourselves. Um, we are now in a world where any hope of controlling and requiring everybody to be responsible in their use of face recognition is difficult. And at the same time, there is no shortage of known faces on the internet because you can just scrape, as has happened recently by some companies. And so I began to panic in 2011, and I wrote an op-ed article saying it is time to press the panic button because the world is heading in a direction where face recognition is going to be omnipresent and faces are going to be everywhere available in databases.
And at the time people said I was an alarmist, but today they’re realizing that it’s exactly what’s happening today. And so where do we go from here? I’ve been lobbying for legislation. I’ve been lobbying for legal frameworks that make it a liability for you to use somebody’s face without their consent. And so it’s no longer a technological issue. We cannot contain this powerful technology through technological means. There has to be some sort of legal frameworks. We cannot allow the technology to go too much ahead of us. Ahead of our values, ahead of what we think is acceptable.
The issue of consent continues to be one of the most difficult and challenging matters when it deals with technology, just giving somebody notice does not mean that it’s enough. To me consent has to be informed. They have to understand the consequences of what it means. And not just to say, well, we put a sign up and this was enough. We told people, and if they did not want to, they could have gone anywhere.
And I also find that there is, it is so easy to get seduced by flashy technological features that might give us a short-term advantage in our lives. And then down the line, we recognize that we’ve given up something that was too precious. And by that point in time, we have desensitized the population and we get to a point where we cannot pull back. That’s what I’m worried about. I’m worried about the fact that face recognition through the work of Facebook and Apple and others. I’m not saying all of it is illegitimate. A lot of it is legitimate.
We’ve arrived at a point where the general public may have become blasé and may become desensitized because they see it everywhere. And maybe in 20 years, you step out of your house. You will no longer have the expectation that you wouldn’t be not. It will not be recognized by dozens of people you cross along the way. I think at that point in time that the public will be very alarmed because the media will start reporting on cases where people were stalked. People were targeted, people were even selected based on their net worth in the street and kidnapped. I think that’s a lot of responsibility on our hands.
And so I think the question of consent will continue to haunt the industry. And until that question is going to be a result, maybe it won’t be resolved. I think we need to establish limitations on what can be done with this technology.
My career also has taught me that being ahead too much is not a good thing because face recognition, as we know it today, was actually invented in 1994. But most people think that it was invented by Facebook and the machine learning algorithms, which are now proliferating all over the world. I basically, at some point in time, I had to step down as being a public CEO because I was curtailing the use of technology that my company was going to be promoting because the fear of negative consequences to humanity. So I feel scientists need to have the courage to project into the future and see the consequences of their work. I’m not saying they should stop making breakthroughs. No, you should go full force, make more breakthroughs, but we should also be honest with ourselves and basically alert the world and the policymakers that this breakthrough has pluses and has minuses. And therefore, in using this technology, we need some sort of guidance and frameworks to make sure it’s channeled for a positive application and not negative.
Jennifer: I Was There When… is an oral history project featuring the stories of people who have witnessed or created breakthroughs in artificial intelligence and computing.
Do you have a story to tell? Know someone who does? Drop us an email at email@example.com.
Jennifer: This episode was taped in New York City in December of 2020 and produced by me with help from Anthony Green and Emma Cillekens. We’re edited by Michael Reilly and Mat Honan. Our mix engineer is Garret Lang… with sound design and music by Jacob Gorski.
Thanks for listening, I’m Jennifer Strong.
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.
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)
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.