Benjamen Walker's Theory of Everything - Enchanting By Numbers

Episode Date: October 6, 2014

When I was in Beijing last summer I dropped by the Microsoft research campus to talk with  Dr. Yu Zheng. He studies the air pollution in his city, and the noise pollution in mine. Using algorithms he is able to predict what kinds of noises New Yorkers are most likely to hear in their neighborhoods, take a look at his Citynoise map. His algorithms could one day help city planners curb air pollution and noise or as Christian Sandvig notes they could be used by the GPS apps on our mobile devices to keep us from walking through neighborhoods perceived to have loud people hanging around outside. Christian Sandvig studies algorithms which is hard to do, most companies like Facebook and Google don’t make their algorithms public. In a recent study he asked Facebook users to explain how they imagine the Edgerank algorithm works (this is the algorithm that powers Facebook’s news feed). Sandvig discovered that most of his subjects had no idea there even was an algorithm at work. When they learned the truth, it was like a moment out of the Matrix. But none of the participants remained angry for long. Six months later they mostly reported satisfaction with the algorithms that determine what the can and can’t see. Sandvig finds this problematic, because our needs and desires often don’t match with the needs and desires of the companies who build the algorithms. “Ada’s Algorithm” is the title of James Essinger’s new book. It tells the remarkable story about Ada Lovelace the woman who wrote the first computer program (or as James puts it – Algorithm)  in 1843. He believes Ada’s insights came from her “poetical” scientific brain. Suw Charman-Anderson, the founder of Ada Lovelace day, tells us more about this remarkable woman.

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Episodes every other week at neverpo.st and wherever you find pods. You are listening to Benjamin Walker's Theory of Everything. This installment is called Enchanting by Numbers. In my opinion, algorithms can empower people to deal with something impossible. Wu Jun works at Microsoft Research in China. He uses algorithms to tackle really big problems like the air pollution in his city, Beijing, Here you see, it's not a good day today. and the noise pollution in mine, New York City. I was usually awakened by different kind of noise when I stay in a hotel in Manhattan.

So it's not fun. Using crowd sensing and ubiquitous data, Yujin and his team built CityNoise, an online noise map. You can find a link to it at toe.prx.org. The algorithms that power this map enable New Yorkers like myself to track noise pollution over time. So if you click a particular location, let's say where you're living. Manhattan. Manhattan, let's zoom along. Right there. And you can see the deeper the color is,

the more annoying people feel about the noise. I love that I'm in Beijing talking to you about my city in New York. The primary data source for city noise is 311. This is the number that New Yorkers call when they want to complain about stuff, potholes, broken escalators, noise. According to the data of the complaint, the biggest one is loud music, party. And the second one is construction.

Third one is loud talking. That is actually the sound of the construction that's going on outside my window right now. And I could call 311 because I definitely can't record the rest of this podcast until they stop. But I'm not going to call. They're just doing their job. But if tonight that German bar downstairs hosts another late night polka party, no, I'm still not going to call. I'm just not that kind of person. And plus, I like noise.

But because of people like me, Eugene must turn to other data sources in order to get his map to work. 311 data is very good, but not perfect. The reason is the data is very sparse. We cannot guarantee we have people reporting noise situation anytime, anywhere. Sometimes maybe we don't have people there. People are not at home or people are too busy or too lazy to make a phone call. But that does not mean there's no noise situation.

City noise uses a lot of data. Traffic data about the city's busy streets and intersections, Foursquare and Facebook check-ins for popular clubs and restaurants. They've even gathered point of interest data for every neighborhood. Data about coffee shops, bars, museums, movie theaters, places where noise happens. And Yujin's algorithm uses all this data to supplement the data that he gets from 311. So his algorithm can predict what kinds of noise one is most likely to hear in a particular

neighborhood at a particular time. If you can feel the missing value, then we know everything. The semantic meaning behind this algorithm is the three metrics can be built based on additional data source, and they are not sparse at all. They are dense. There are lots of interesting things that the city of New York could do using Yujin's algorithm.

They could use it to determine where to install traffic noise barriers. They could use it to determine where to deploy more cops late at night to deal with all the people who scream, Woo! The apps on my phone, like Google Maps, could use this algorithm as well. My GPS could recommend that I not walk on streets with loud bars late at night, because loud bars often have loud drunks outside, for many a sign of trouble. My GPS could even keep me from walking down streets with late-at-night loud-talking complaints, because loud talking means people hanging out on the street.

Another sign of trouble? And I'm not being speculative here. This is already happening. So one algorithm is going to promote, I want to get there faster, so what's the fastest way that I might get there? Another algorithm might take into account, in a controversial example, the crime rates or the perceptions of crime rates in the neighborhoods that you're

passing through. So what we use on our phone to find our way from point A to point B is an algorithm and it's in a computer and there's elements of computer science in it, but there's also a bunch of normative decisions about what we want to prioritize, and all algorithms tend to be that way. Christian Sandvig is an associate professor in the communications department at the University of Michigan. He studies algorithms, but this kind of research, keeping track of the algorithms that determine what we think and do, he says, is getting harder and harder to carry out. The difficult thing about studying algorithms is that because they're happening inside a computer, and in many cases the algorithms that govern our life are happening inside some data center

somewhere, it's very difficult to determine what exactly is happening. So in our research, we talk to users about what they think algorithms are doing. You might even say that how they think about algorithms is more important than the algorithm. If you had an image in your mind of how Google works, it might lead you to choose different search terms. Or if you had an image in your mind about what you thought Facebook was doing,

it might lead you to click on different things or use different status updates. So in some ways, how you think about the algorithm is as important as the actual algorithm because it can determine what you do. Recently, Christian and a team of computer scientists asked a group of Facebook users about how they imagined the EdgeRank algorithm works.

That's the name of the proprietary algorithm that Facebook uses for its news feed. We actually designed the study to ask people about how they thought about the algorithms that filtered their Facebook news feed. So people had ideas about, for example, well, I'm always going to like my own posts because the fact that I give it one like really kind of gets it started. And that's going to make sure that other people are going to be more likely to be shown that post. Now, that's probably the saddest folk belief about how social media algorithms work and definitely not true. But some of the more common folk beliefs do have some truth to them. There are elements of product mentioning that are taken into account on Facebook that are real.

For example, I read a news story about Facebook's deals with advertisers. And the news story said that Facebook had launched an agreement where it would make some status updates that mention advertisers, it would convert those status updates into ads. And so I tried to mention a bunch of advertisers that were listed. And then I asked my friend network, hey, did you guys see any of these things that I posted? And where did you see them? And what did they look like? And can you send me a screenshot? And that's how I discovered that mentioning advertisers is a good way to get your posts pegged at the top and listed as a sponsored link. It's tempting to call this the folk method of Facebook research.

But it turns out that when it comes to studying algorithms, especially the ones companies like Google and Facebook keep under lock and key, looking over someone's shoulder is a primary research method. I mean, self-experimentation is a big frontier in understanding how algorithms work. If you look at important journalistic discoveries about platforms, you find that many of them are people who just did things like looking over the shoulder of a friend or a family member, and they looked at their friend or family member's Facebook, and they said, huh, why is that thing from me there on your Facebook? Or why isn't that thing from me there? And why does it look like that? Okay, so here's what's so mind-blowing about Christian's research, at least for me. The average participant in this study used Facebook at least

10 times a day. That was the average. So they were all serious users. And most of the participants in this study were college educated. Many even had graduate degrees. But yet the majority of these super smart, hardcore users had no idea, no idea that Facebook even employed something like an algorithm. We were stunned at the degree to which people didn't realize that the things that they saw on Facebook are filtered by Facebook. We assumed that everyone would be familiar with that.

But in fact, the majority of people that we interviewed didn't know that Facebook was filtering the things that it shows them. So I think this tells us that it's not necessarily that obvious that there's filtering going on. In a way, all of these Facebook users had their own imaginary model for how the Facebook algorithm worked, even the ones that were unaware of its existence. And these imaginary models directly influenced how these people, all of them, felt about themselves.

They didn't know that Facebook was filtering, and so when something happened on Facebook, they would make an inference about their personal relationship. And many of these were kind of tragic. You know, they'd sit in their room and cry because they said things on social media and no one answered. I mean, they would think, oh, well, I posted this to Facebook and I didn't get any likes or comments. I'm unloved. Of course, Christian and his colleagues informed all the algorithmically unaware subjects about what was really going on,

that most of their friends and family members weren't even seeing their posts on Facebook because of an algorithm. And Christian says this kind of went down like that scene in The Matrix when Morpheus shows Neo that he's just a human battery for evil machines. Stop. Let me out. Let me out! I want out!

Yeah, the Facebook study subjects were pissed off too. When we showed people that there was an algorithm, because we revealed to them, we said, hey, look, why is this post at the top for so long? Or have you ever noticed that some posts seem to be on there, even though they're not something that was posted recently? When we explained to them that they had a number of friends that were posting things that Facebook chose not to show them, a number of them were angry. But when Christian and his colleagues followed up with these subjects, six months, six months

later, they discovered that all of the anger was gone. One big change that they reported is that they actually liked Facebook more. So rather than their initial horror and anger and shock and surprise at the fact that their stuff was filtered, after they thought about it more and spent more time interacting on Facebook with the algorithm in mind, they understood why Facebook filtered things and they found that it made their feed better.

Christian's research suggests that in the future, companies like Facebook and Google could be a bit more transparent with their users about the algorithms that they use and suffer no consequences. And this troubles him because history, history tells us something entirely different. If you like computer history, you're familiar with the Sabre system. It's sometimes described as the first wide scale commercial application of computing. At the time that it was turned on, it was the largest commercial computer network in existence in the world. And it did airline reservations

It was a system that allowed ticket agents and travel agents to Search for flights and buy tickets with a special computer terminal that was provided by Sabre The funny thing about it is that the use of the system Started to notice that many of their requests resulted in improbable itineraries. So the system might recommend extremely long and expensive flight as a top result. And that seemed odd. Well, it turned out that since American Airlines built the reservation system, they had a unit at American that informally was referred to as the Screen Science Unit.

And that unit's job was to think about how they might sort the results in the airline reservation system so that American would make more money. This was something of a scandal at the time, but when the CEO of American was called before Congress to testify, he didn't understand why people were upset. He said, of course American is using the system to make money. Why would we build an airline

reservation system if we weren't going to use it to advantage our own airline. Christian Sandvig says this piece of computer history gives us what he calls Crandall's Law of Algorithms. Robert Crandall was the name of the American Airlines CEO, the guy who testified to Congress. And we should apply Crandall's law, Christian says, to all of the algorithms that we use. We should expect this behavior from all of our algorithms. Why wouldn't an algorithm be designed to advantage the company that invested millions of dollars in building it? The thing we have to watch out for are the places where what benefits the company doesn't

benefit us. The true history of the algorithm begins in the Victorian age in England. Of course, there were no computers in the Victorian year of 1833. But there were a couple of strange machines that the mathematician and inventor Charles Babbage kept in his drawing room. Babbage invented a calculating machine, a mechanical calculating machine called the difference engine, which could do basic arithmetic. And he went on to invent another machine that was much more complicated, which he called the analytical engine. Sue Charman Anderson is an author, technologist,

and the founder of Ada Lovelace Day, a celebration of the woman who wrote the world's first computer program for her friend and collaborator Charles Babbage's theoretical computer, the analytical engine. What Ada saw was that the analytical engine could do more than just calculate large tables of numbers. She saw that given the right input, it could create art and music if it was given the right algorithms to start with. This was so far ahead of its time. She was the only person in the world, on the planet, in the 19th century,

who had the insight to see what a computer could really be. She saw that the unlimited engine, which Babbage regarded as a sophisticated calculator could in fact control any kind of process that you wanted it to control. That's James Essinger. He's written a number of books about what you would call the prehistory of the computer. And the title of his new book makes it clear just how much credit he believes belongs to Ada.

I've published a book called Ada's Algorithm, how Lord Byron's daughter, Ada Lovelace, launched the digital age. Lord George Gordon Byron was already famous when he married Ada's mother, Lady Annabella Milbank. In fact, it was Lady Annabella's cousin, Lady Lamb, who made his reputation. She said he was...

Mad, bad and dangerous to know. It was definitely a doomed union. They had Ada Lovelace and then just a month or so later, the marriage basically broke up. After the separation, Byron fled his creditors and England. Ada never saw her father again. I think this had a massive impact on Annabella and her attitude towards her daughter. And so I think she was very keen to try and make sure that none of

Byron's sort of poetical madness, if you like, would infect her own daughter. That was one of the reasons she had her schooled in maths and science and why she had these eminent tutors for Ada was because she wanted that discipline to try and edge out any sort of poetic tendencies. In Ada's time, few women got the opportunity to study math and science. In fact, it was commonly believed that the female brain could not handle the stress that came with serious thinking. But Ada excelled at math.

She didn't have any airs and graces. She didn't imagine she was an amazing mathematician. She just saw herself as a competent mathematician. But she did devise this term, poetical science. And for her, poetical science, for her, clearly meant bringing the imagination to the service of science. She actually had insights into technology,

which seemed to derive from her doing precisely this, marrying both her imagination and her knowledge of science. So when she saw Babbage's plans for his analytical engine, she became fascinated with them and collaborated with him on how to describe them. If Babbage hoped to build his analytical machine, he was going to need a lot of money from the British government. Ada wanted to help him. She translated a paper about Babbage's ideas

that had been published in a Swiss academic journal. But Ada added her own notes to this translation. And it is in note G where we find her detailed instructions on how this computer would perform an equation. She writes, The science of operations, as derived from mathematics more especially, is a science of itself and has its own abstract truth and value. This science constitutes the language through which alone we can adequately express

the great facts of the natural world and those unceasing changes of mutual relationship which visibly or invisibly, consciously or unconsciously, to our immediate physical perceptions are intermittently going on in the agencies of the creation we live amidst. That was really a major step forward in the way that people thought about machines, because at the time, machines could only do exactly what you told them to do. Whereas the analytical engine was capable of actually working something out for itself, if you like.

Her idea that it could be used for creating music and creating art really shows how she situated the analytical engine in a creative sphere, in a sort of humanistic sphere, not just as this kind of strange abstract project. I don't know where it came from, this astonishing insight. Maybe it was a feminine insight. Maybe she looked at Babidi's machine and thought, this could do anything, not just calculate numbers. And that, for me, is something close to miraculous. Babbage foolishly turned down Ada's partnership offer, and he never got the money he needed to build the analytical machine. And it would be almost another hundred years before the computer revolution really took off.

It's impossible not to wonder what could have happened had Ada Lovelace and Charles Babbage stuck together, or if Ada hadn't been felled by cancer at such a young age. But I also can't help but wonder if Ada Lovelace's ideas about poetical science might be just what we need today as we plot out the algorithms that will determine what we think and what we do in the future. We don't want to forget that we have the ability to do anything. You have been listening to Benjamin Walker's Theory of Everything.

This installment is called Enchanting by Numbers. This episode was produced by myself Bill Bowen did the sound and it featured U Jun, Christian Sandvig James Essinger and Sue Charman Anderson Special thanks to Mandira Banerjee

and Joy Lo and everyone at Microsoft Beijing The Theory of Everything is a founding member of Radiotopia, the world's greatest podcast network. From PRX, the public radio exchange. Radiotopia. From PRX.