Welcome When I was there, From a new oral history project We believe in machines Podcast. It tells stories of how artificial intelligence and computing have been successful, as their eyewitnesses have told them. In this first episode, we meet Joseph Atik who helped create the first commercially effective face recognition system.
The episode is produced with the help of Jennifer Strong, Anthony Green and Emma Silkins Lindsay Muscato. It was edited by Michael Riley and Matt Honan. It is blended by Garrett Lang, with sound design and music by Jacob Gorski.
Jennifer: I’m Jennifer Strong, the host We believe in machines.
I want to tell you about something that we have been working behind the scenes for some time here.
It’s called When I was there.
It’s an oral history project that tells the story of how much success has been made in artificial intelligence and computing … as their witnesses have said.
Joseph Atik: And when I entered the room, it saw my face, pulled it out of the background and said: “I can see Joseph” and the moment the hair on his back … I thought something had happened. We were witnesses.
Jennifer: We’re working with someone who helped create the first face recognition system that was commercially viable … in the 90’s …
I’m Joseph Atik. Today, I am the Executive Chairman of ID for Africa, a humanitarian organization that focuses on giving people in Africa a digital identity so that they can access services and exercise their rights. But I wasn’t always in the humanities. Since I received my PhD in Mathematics, I have achieved some fundamental successes together with my colleagues, leading to the first commercially effective facial recognition. That’s why people refer to me as the father of facial recognition and the founder of the biometric industry. When we were at Princeton’s Institute for Advanced Study, when we were doing research, mathematical research, the algorithm of how the human brain would recognize familiar faces became clear. But you have no idea how to implement such a thing.
Programming and Failure and Programming and Failure It was a long time. And one night, very early in the morning, in fact, we finalized a version of the algorithm. To get the run code we have submitted the source code for the compilation. And as we got out, I stepped out to go to the washroom. And then when I went back to the room and the source code was compiled by the machine and came back. And usually it runs automatically after you compile and when I enter the room, it sees a man moving around the room and it sees my face, pops out of the background and pronounces it: “I see Joseph.” And the moment where the back hair – I thought something had happened. We were witnesses. And I started calling the other people in the lab and each of them would come into the room.
And it will say, “I’m looking at Norman. I’ll look at Paul, I’ll look at Joseph.” And we want to run around the house to see how many spots it can make in the room. The work eventually led to a success, although theoretically, no additional success was required, just how we can implement it and finally see that the power of action is very, very fruitful and satisfying. The team, not a research team, focused on putting all those capabilities on a PC platform, and that was the birth, the birth of truly commercial face recognition, I put it, in 1994.
My anxiety started very quickly. I have seen a future where there is no place to hide with the proliferation of cameras everywhere and the commoditization of computers and the processing capacity of computers. And so in 1998, I lobbied the industry and I said, we need to put together policies for responsible use. And I felt good for a while, because I felt like we got it right. I feel like we have established a responsible usage code that is whatever the implementation. However, that code did not survive the test of time. And the reason behind this is that we have not predicted the rise of social media. Originally, when we established the code in 1998, we said that the most important element of a face recognition system is a database of people tagged. We said, if I’m not in the database, the system will go blind.
And creating databases was difficult. At most we could make 10,000, 15,000, 20,000 because each image had to be scanned and entered by hand – in the world we live in today, we are now in a system where we have allowed the animal to get out of the bag and feed it billions of faces. And help by tagging themselves. Um, of course, yes, I know this, why it’s something known in advance. And at the same time, there’s no shortage of familiar faces on the internet because you can just scrap, as has happened recently with some companies. And so I started panicking in 2011, and I wrote an op-ed article that the time has come to press the panic button because the world is moving in a direction where face recognition is going to be ubiquitous and faces are going to be available everywhere in the database.
And at the time people said I was a skeptic, but today they understand exactly what is happening today. And so where do we go from here? I am lobbying for legislation. I advocate for a legal framework that makes it your responsibility to use someone else’s face without your consent. And so it is no longer a technical problem. We cannot contain this powerful technology through technology. There must be some kind of legal framework. We can’t let technology go too far ahead of us. In front of our values, beyond what we think is acceptable.
The issue of compliance becomes one of the most difficult and challenging issues when it comes to working with technology, just giving someone notice doesn’t mean it’s enough. I have to agree. They need to understand what this means. And not just to say, well, we signed up and it was enough. We told people, and if they didn’t want to, they could go anywhere.
And I have also found that it is so easy to be tempted by glamorous technical features that it can give us short-term benefits in life. And then from the line, we admit that we left out something that was very valuable. And in that time, we’ve sensitized the population and we’ve reached a point where we can’t pull back. That’s what I’m worried about. I am concerned about this face recognition through the work of Facebook and Apple and others. I’m not saying it’s all illegal. Much of this is valid.
We have come to a place where ordinary people may be blessed and they may become sensitive because they see it everywhere. And maybe in 20 years, you’ll be out of your house. You will no longer have the expectation that you will not. It will not be recognized by the dozens of people on the path you will cross. I think the public at that time will be very apprehensive because the media will start reporting on cases where people were shouted at. People were targeted, even people were picked up and kidnapped on the basis of their property on the streets. I think it’s a lot of responsibility in our hands.
And so I think the question of consent will continue to haunt the industry. And unless this question is going to be a result, it probably won’t be solved. I think we need to set limits on what can be done with this technology.
My career has also taught me that being too far ahead is not a good thing because facial recognition, as we know it today, was actually invented in 1994. But most people think that it was invented by Facebook and machine learning algorithms are now widespread all over the world. I basically, at some point, had to resign as public CEO because I was reducing the use of technology that my company was going to promote because of the risk of negative consequences for humanity. So I think 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 being successful. No, you should go to full strength, achieve more success, but we should be honest with ourselves and basically warn the world and policy makers that this progress has pluses and minuses. And so, in using this technology, we need some kind of guidance and framework to ensure that it is for a positive application and not a negative one.
Jennifer: When I was there … A verbal history project of the stories of those who have seen or created success in artificial intelligence and computing.
Tell a story? Do you know anyone who does? Give us an email at firstname.lastname@example.org.
Jennifer: This episode was taped in New York City in December 2020 and was produced by me with the help of Anthony Green and Emma Silicon. We are edited by Michael Reilly and Matt Honan. Our mix engineer is Garrett Lang … with sound design and music by Jacob Gorski.
Thanks for listening, I’m Jennifer Strong.