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Podcast: The story of AI, as told by the people who invented it

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Podcast: The story of AI, as told by the people who invented it


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.

Credits:

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.

Full transcript:

[TR ID]

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…

[IMWT ID]

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 podcasts@technologyreview.com.

[MIDROLL]

[CREDITS]

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. 

[TR ID]

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The hunter-gatherer groups at the heart of a microbiome gold rush

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The hunter-gatherer groups at the heart of a microbiome gold rush


The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania. 

Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.

Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.

A changing way of life

The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.

Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.

One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.

Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.

The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.

Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost. 

But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.

—Jessica Hamzelou

Eric Schmidt has a 6-point plan for fighting election misinformation

—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures

The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.

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Navigating a shifting customer-engagement landscape with generative AI

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Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

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