Future Tech Will Give You The Benefits Of City Life Anywhere: Julio Gil

Today, more than half of the world's population lives in cities. The urbanization process started in the late 1700s and has been increasing since then. The prediction is that by 2050, 66 percent of the population will live in cities and the United Nations, the World Health Organization, the World Economic Forum, are warning us, if we don't plan for the increased density, current problems in our cities, like inequality, congestion, crime can only get worse. As a result, urban planners and city developers are putting a lot of effort and creativity in designing our future, denser, bigger cities.

But I have a different opinion. I think urbanization is actually reaching the end of its cycle, and now people are going to start moving back to the countryside. And you may think, "But what about the trend?" Well, let me tell you, socioeconomic trends don't last forever. You know, 12,000 years ago everybody was perfectly happy roaming the land, hunting and gathering. And then, the trend changes, and the new thing is to live in a farm and have cattle, until it changes again. When we get to the industrial revolution. Actually, that is what started the urbanization process. And you know what triggered it? Steam power, machines, new chemical processes -- in two words, technological innovation. And I believe technology can also bring the end of this cycle.

I've been working on innovation for most of my career. I love it. I love my job. It allows me to work with drones, with 3D printers and smart glasses, and not just those you can buy in the shop but also prototypes. It's a lot of fun sometimes. Now, some of these technologies are opening new possibilities that will radically change the way we did things before and in a few years, they may allow us to enjoy the benefits of city life from anywhere. Think about it. If you could live in a place with a lower crime rate and more space and a lower cost of living and less traffic, of course many people would want that, but they feel they don't have a choice. You have to live in the city.

Well, in the past, people moved to the cities not because they loved the city itself but for the things you could have in a city, more job opportunities, easier access to services and goods and a rich social life. So let's dive deeper.

More jobs and career opportunities. Is that still true today, because the office people are starting to realize that working in the office and being in the office may not be the same thing anymore. According to a study by Global Workplace Analytics, more than 80 percent of the US workforce would like to work from home. And do you know how much it costs for a company to even have an office? 11,000 dollars per employee per year. If only half of those workers would telework even 50 percent of the time, the savings in the states would exceed 500 billion dollars, and it could reduce greenhouse gases by 54 million tons. That is the equivalent of 10 million cars off the streets for a whole year. But even though most people would want to telework, current technology makes the experience isolating. It's not comfortable. It doesn't feel like being there. But that is going to change by the convergence of two technologies: augmented reality and telepresence robots.

Augmented reality already today allows you to take your office environment everywhere with you. All you need is a wearable computer, a pair of smart glasses, and you can take your emails and your spreadsheets with you wherever you go. And video conferences and video calls have become very common these days, but they still need improvement. I mean, all those little faces on a flat screen, sometimes you don't even know who is talking.

Now, we already have something way better than static video calls: your average telepresence robot. I call it tablet on a stick.

You can control, you can move around, you can control what you're looking at. It's way better, but far from perfect. You know how they say that most human communication is nonverbal? Well, the robot doesn't give you any of that. It looks like an alien. But with advances in augmented reality, it will be easy to wrap the robot in a nice hologram that actually looks and moves like a person. That will do it. Or else, forget the robot. We go full VR, and everybody meets in cyberspace. Give it a couple of years and that will feel so real, you won't tell the difference.

So what was the next reason why people move to cities? Access to services and goods. But today you can do all that online. According to a study made by comScore, online shoppers in the US last year did more than half of their retail purchases online, and the global market for e-commerce is estimated to be at two trillion dollars. And it's expected to reach 2.38 by the end of 2017,according to eMarketer.

Now, from a logistics standpoint, density is good for deliveries. Supplying goods to a shopping mall is easy. You can send big shipments to the shop, and people will go there, pick it up and take it home themselves. E-commerce means we need to ship one and have them home delivered. That's more expensive. It's like the difference between having a birthday party for 20 people or bringing a piece of the cake to each of your 20 friends at their place. But at least in the city, they live close to each other. Density helps. Now, e-commerce deliveries in the countryside, those take forever. The truck sometimes needs to drive miles between one address and the next one. Those are the most expensive deliveries of all.

But we already have a solution for that: drones. A vehicle carrying a squadron of drones. The driver does some of the deliveries while the drones are flying back and forth from the truck as it moves. That way, the average cost for delivery is reduced, and voila: affordable e-commerce services in the countryside. You will see: the new homes of our teleworkers will probably have a drone pod in the yard. So once the final mile delivery is not a problem, you don't need to be in the city to buy things anymore. So that's two.

Now, what was the third reason why people move to cities? A rich social life. They would need to be in the city for that these days. Because people these days, they make friends, they chat, gossip and flirt from the comfort of their sofa.

And while wearing their favorite pajamas.

There are over two billion active social media users in the world. In a way, that makes you think like we are connected no matter where we are. But OK, not completely. Sometimes you still need some real human contact. Ironically, the city, with its population density, is not always the best for that. Actually, as social groups become smaller, they grow stronger. A recent study made in the UK by the Office for National Statistics showed a higher life satisfaction rating among people living in rural areas. So as people settle in the countryside, well, they will buy local groceries, fresh groceries, foodstuff, maintenance services. So handymen, small workshops, service companies will thrive. Maybe some of the industrial workers from the cities displaced by the automation will find a nice alternative job here, and they will move too. And as people move to the countryside, how is that going to be? Think about autonomous, off-the-grid houses with solar panels, with wind turbines and waste recycling utilities, our new homes producing their own energy and using it to also power the family car. I mean, cities have always been regarded as being more energy-efficient, but let me tell you, repopulating the countryside can be eco too.

By now, you're probably thinking of all the advantages of country living.

I did it myself. Six years ago, my wife and I, we packed our stuff, we sold our little apartment in Spain, and for the same money we bought a house with a garden and little birds that come singing in the morning.

It's so nice there. And we live in a small village, not really the countryside yet. That is going to be my next move: a refurbished farmhouse, not too far from a city, not too close. And now we'll make sure to have a good spot for drones to land.

But hey, that's me. It doesn't have to be you, because it would seem like I'm trying to convince somebody to come join us in the country. I'm not.

I don't need more people to come.

I just think they will once they realize they can have the same benefits the city has. But if you don't like the country, I have good news for you, too. Cities will not disappear. But as people move out, a lower density will help them recover a better flow and balance.

Anyway, I guess now you have some thinking to do. Do you still think you need to live in the city? And more importantly, do you want to?

Source: https://www.ted.com/talks/julio_gil_future_tech_will_give_you_the_benefits_of_city_life_anywhere/transcript?utm_source=tedcomshare&utm_medium=email&utm_campaign=tedspread--a

The Era Of Blind Faith In Big Data Must End: Cathy O'Neil

Algorithms are everywhere. They sort and separate the winners from the losers. The winners get the job or a good credit card offer. The losers don't even get an interview or they pay more for insurance. We're being scored with secret formulas that we don't understand that often don't have systems of appeal. That begs the question: What if the algorithms are wrong?

To build an algorithm you need two things: you need data, what happened in the past, and a definition of success, the thing you're looking for and often hoping for. You train an algorithm by looking, figuring out. The algorithm figures out what is associated with success. What situation leads to success?

Actually, everyone uses algorithms. They just don't formalize them in written code. Let me give you an example. I use an algorithm every day to make a meal for my family. The data I use is the ingredients in my kitchen, the time I have, the ambition I have, and I curate that data. I don't count those little packages of ramen noodles as food.

My definition of success is: a meal is successful if my kids eat vegetables. It's very different from if my youngest son were in charge. He'd say success is if he gets to eat lots of Nutella. But I get to choose success. I am in charge. My opinion matters. That's the first rule of algorithms.

Algorithms are opinions embedded in code. It's really different from what you think most people think of algorithms. They think algorithms are objective and true and scientific. That's a marketing trick. It's also a marketing trick to intimidate you with algorithms, to make you trust and fear algorithms because you trust and fear mathematics. A lot can go wrong when we put blind faith in big data.

This is Kiri Soares. She's a high school principal in Brooklyn. In 2011, she told me her teachers were being scored with a complex, secret algorithm called the "value-added model." I told her, "Well, figure out what the formula is, show it to me. I'm going to explain it to you." She said, "Well, I tried to get the formula, but my Department of Education contact told me it was math and I wouldn't understand it."

It gets worse. The New York Post filed a Freedom of Information Act request, got all the teachers' names and all their scores and they published them as an act of teacher-shaming. When I tried to get the formulas, the source code, through the same means, I was told I couldn't. I was denied. I later found out that nobody in New York City had access to that formula. No one understood it. Then someone really smart got involved, Gary Rubinstein. He found 665 teachers from that New York Post data that actually had two scores. That could happen if they were teaching seventh grade math and eighth grade math. He decided to plot them. Each dot represents a teacher.

What is that?

That should never have been used for individual assessment. It's almost a random number generator.

But it was. This is Sarah Wysocki. She got fired, along with 205 other teachers, from the Washington, DC school district, even though she had great recommendations from her principal and the parents of her kids.

I know what a lot of you guys are thinking, especially the data scientists, the AI experts here. You're thinking, "Well, I would never make an algorithm that inconsistent." But algorithms can go wrong, even have deeply destructive effects with good intentions. And whereas an airplane that's designed badly crashes to the earth and everyone sees it, an algorithm designed badly can go on for a long time, silently wreaking havoc.

He founded Fox News in 1996. More than 20 women complained about sexual harassment. They said they weren't allowed to succeed at Fox News. He was ousted last year, but we've seen recently that the problems have persisted. That begs the question:What should Fox News do to turn over another leaf?

Well, what if they replaced their hiring process with a machine-learning algorithm? That sounds good, right? Think about it. The data, what would the data be? A reasonable choice would be the last 21 years of applications to Fox News. Reasonable. What about the definition of success? Reasonable choice would be, well, who is successful at Fox News? I guess someone who, say, stayed there for four years and was promoted at least once. Sounds reasonable. And then the algorithm would be trained. It would be trained to look for people to learn what led to success, what kind of applications historically led to success by that definition. Now think about what would happen if we applied that to a current pool of applicants. It would filter out women because they do not look like people who were successful in the past.

Algorithms don't make things fair if you just blithely, blindly apply algorithms. They don't make things fair. They repeat our past practices, our patterns. They automate the status quo. That would be great if we had a perfect world, but we don't. And I'll add that most companies don't have embarrassing lawsuits, but the data scientists in those companies are told to follow the data, to focus on accuracy. Think about what that means. Because we all have bias, it means they could be codifying sexism or any other kind of bigotry.

Thought experiment, because I like them: an entirely segregated society -- racially segregated, all towns, all neighborhoods and where we send the police only to the minority neighborhoods to look for crime. The arrest data would be very biased. What if, on top of that, we found the data scientists and paid the data scientists to predict where the next crime would occur? Minority neighborhood. Or to predict who the next criminal would be? A minority. The data scientists would brag about how great and how accurate their model would be, and they'd be right.

Now, reality isn't that drastic, but we do have severe segregations in many cities and towns, and we have plenty of evidence of biased policing and justice system data. And we actually do predict hotspots, places where crimes will occur. And we do predict, in fact, the individual criminality, the criminality of individuals. The news organization ProPublica recently looked into one of those "recidivism risk" algorithms, as they're called, being used in Florida during sentencing by judges. Bernard, on the left, the black man, was scored a 10 out of 10. Dylan, on the right, 3 out of 10. 10 out of 10, high risk. 3 out of 10, low risk. They were both brought in for drug possession. They both had records, but Dylan had a felony but Bernard didn't. This matters, because the higher score you are, the more likely you're being given a longer sentence.

What's going on? Data laundering. It's a process by which technologists hide ugly truths inside black box algorithms and call them objective; call them meritocratic. When they're secret, important and destructive, I've coined a term for these algorithms: "weapons of math destruction."

They're everywhere, and it's not a mistake. These are private companies building private algorithms for private ends. Even the ones I talked about for teachers and the public police, those were built by private companies and sold to the government institutions. They call it their "secret sauce" -- that's why they can't tell us about it. It's also private power. They are profiting for wielding the authority of the inscrutable. Now you might think, since all this stuff is private and there's competition, maybe the free market will solve this problem. It won't. There's a lot of money to be made in unfairness.

Also, we're not economic rational agents. We all are biased. We're all racist and bigoted in ways that we wish we weren't, in ways that we don't even know. We know this, though, in aggregate, because sociologists have consistently demonstrated this with these experiments they build, where they send a bunch of applications to jobs out, equally qualified but some have white-sounding names and some have black-sounding names, and it's always disappointing, the results -- always.

So we are the ones that are biased, and we are injecting those biases into the algorithms by choosing what data to collect, like I chose not to think about ramen noodles -- I decided it was irrelevant. But by trusting the data that's actually picking up on past practices and by choosing the definition of success, how can we expect the algorithms to emerge unscathed? We can't. We have to check them. We have to check them for fairness.

The good news is, we can check them for fairness. Algorithms can be interrogated, and they will tell us the truth every time. And we can fix them. We can make them better. I call this an algorithmic audit, and I'll walk you through it.

First, data integrity check. For the recidivism risk algorithm I talked about, a data integrity check would mean we'd have to come to terms with the fact that in the US, whites and blacks smoke pot at the same rate but blacks are far more likely to be arrested -- four or five times more likely, depending on the area. What is that bias looking like in other crime categories, and how do we account for it?

Second, we should think about the definition of success, audit that. Remember—with the hiring algorithm? We talked about it. Someone who stays for four years and is promoted once? Well, that is a successful employee, but it's also an employee that is supported by their culture. That said, also it can be quite biased. We need to separate those two things. We should look to the blind orchestra audition as an example. That's where the people auditioning are behind a sheet. What I want to think about there is the people who are listening have decided what's important and they've decided what's not important, and they're not getting distracted by that. When the blind orchestra auditions started, the number of women in orchestras went up by a factor of five.

Next, we have to consider accuracy. This is where the value-added model for teachers would fail immediately. No algorithm is perfect, of course, so we have to consider the errors of every algorithm. How often are there errors, and for whom does this model fail? What is the cost of that failure?

And finally, we have to consider the long-term effects of algorithms, the feedback loops that are engendering. That sounds abstract, but imagine if Facebook engineers had considered that before they decided to show us only things that our friends had posted.

I have two more messages, one for the data scientists out there. Data scientists: we should not be the arbiters of truth. We should be translators of ethical discussions that happen in larger society.

And the rest of you, the non-data scientists: this is not a math test. This is a political fight. We need to demand accountability for our algorithmic overlords.

Source:  

https://www.ted.com/talks/cathy_o_neil_the_era_of_blind_faith_in_big_data_must_end/transcript?utm_source=tedcomshare&utm_medium=email&utm_campaign=tedspread--a#t-64712