Ethic, Hedge, and Adila, the leader of the revolution, plot out how they can steal an artifact called the Node of Power.
It’s being used to run a heavily fortified train that runs all around the country, providing supplies to settlements and facilities.
This armored behemoth undergoes a complex and unpredictable unloading procedure— a procedure which is displayed, in detail, on a screen within the engine car.
Right means the train will go one car length forward, and left means the train will go the same distance backwards.
While unloading, the train frequently moves back and forth, so a typical sequence might look like this.
Also within the engine car is a button that can only be pressed once.
When pressed, it lets down the force field over the artifact for 10 seconds.
The engine car is tiny and designed for a robot.
Of your team, only Hedge can fit.
The members of the resistance have positioned a crane over the train tracks that can pluck the artifact once it’s exposed.
They’ll know when to lower the crane by sight.
But the only way Hedge can determine the train’s position and know when to lower the force field is by analyzing the unloading procedure, because he’ll be inside the windowless engine car.
Hedge can’t program himself though, so it’s up to Ethic to tell him what to do.
The artifact is in the car 10 positions behind the one that’s directly under the crane at the start.
What instructions can Ethic give Hedge so that he hits the button at just the right moment?
Here’s a hint to get you started.
The key to this problem, as with many programming challenges, is to reframe the information in a way that a computer can work with.
A computer doesn’t know what a train is, nor does it need to.
It can, however, work with variables.
Try making a variable that tracks the train’s position.
How will it change as the train moves?
Let’s start by breaking this problem into two objectives.
The first is to know where the train will be as it carries out its instructions.
The second is to hit the button when the train is in just the right position.
For the first objective it’ll help to think of the train as a big number line.
Let’s make 0 the car with the node, 1 the car in front of it, and so on.
That means car 10 is under the crane at the start.
When the train moves one car right, car 9 is under the crane.
So a right arrow can be thought of as “subtract 1.”
And when the train moves left from there, 10 is back under the crane, making a left arrow the same as “add 1."
Let’s set our train position variable to 10, since that’s where we start.
We can now use a loop to read the instructions one at a time, adding or subtracting as we go, to track which car is under the crane.
The nice thing about setting up the variable this way is that it tells us how far the node is from the crane.
So as soon as the variable hits 0, Hedge should hit the button.
And here’s what happens.
Ethic gets into position on the crane while Hedge rushes off and slips into the engine car unnoticed, just before the train lurches to life.
It rolls 3 cars back.
1 forward, another 4 back.
Then so far forward Ethic loses track before it reverses once more.
When the artifact finally rolls into position, Adila lowers the crane, hoping Ethic and Hedge got it right.
At the last possible moment, the force field sputters and falls.
Ethic swoops in, and lifts the Node of Power to freedom.
When Ethic gives the node to Hedge for safe keeping, something incredible happens.
The artifact shimmers to life with a vision of the past: When the crystal was unearthed, no one could make the console inside work.
The government put out a call for people to try their luck with it, one at a time.
Ethic loved to figure out what made things tick, so she signed up.
Within moments at the console, something clicked into place, and she created her first robot.
The government hired Ethic as chief robotics engineer on the spot.
Within a year her creations ran almost every aspect of society, and the nation and its people thrived, no longer needing to toil in the fields and factories.
The vision ends, and Hedge detects the second artifact in the 198forest, to the southeast.
Luckily, the train is going there next, and has just enough reserve fuel for the trip.
Ethic and Hedge smuggle themselves aboard and find a hiding spot for the long journey ahead.
(Music) (Applause)
Every day, every week, we agree to terms and conditions.
And when we do this, we provide companies with the lawful right to do whatever they want with our data and with the data of our children.
Which makes us wonder: how much data are we giving away of children, and what are its implications?
I'm an anthropologist, and I'm also the mother of two little girls.
And I started to become interested in this question in 2015 when I suddenly realized that there were vast -- almost unimaginable amounts of data traces that are being produced and collected about children.
So I launched a research project, which is called Child Data Citizen, and I aimed at filling in the blank.
Now you may think that I'm here to blame you for posting photos of your children on social media, but that's not really the point.
The problem is way bigger than so-called "sharenting."
This is about systems, not individuals.
You and your habits are not to blame.
For the very first time in history, we are tracking the individual data of children from long before they're born -- sometimes from the moment of conception, and then throughout their lives.
You see, when parents decide to conceive, they go online to look for "ways to get pregnant," or they download ovulation-tracking apps.
When they do get pregnant, they post ultrasounds of their babies on social media, they download pregnancy apps or they consult Dr. Google for all sorts of things, like, you know -- for "miscarriage risk when flying" or "abdominal cramps in early pregnancy."
I know because I've done it -- and many times.
And then, when the baby is born, they track every nap, every feed, every life event on different technologies.
And all of these technologies transform the baby's most intimate behavioral and health data into profit by sharing it with others.
So to give you an idea of how this works, in 2019, the British Medical Journal published research that showed that out of 24 mobile health apps, 19 shared information with third parties.
And these third parties shared information with 216 other organizations.
Of these 216 other fourth parties, only three belonged to the health sector.
The other companies that had access to that data were big tech companies like Google, Facebook or Oracle, they were digital advertising companies and there was also a consumer credit reporting agency.
So you get it right: ad companies and credit agencies may already have data points on little babies.
But mobile apps, web searches and social media are really just the tip of the iceberg, because children are being tracked by multiple technologies in their everyday lives.
They're tracked by home technologies and virtual assistants in their homes.
They're tracked by educational platforms and educational technologies in their schools.
They're tracked by online records and online portals at their doctor's office.
They're tracked by their internet-connected toys, their online games and many, many, many, many other technologies.
So during my research, a lot of parents came up to me and they were like, "So what?
Why does it matter if my children are being tracked?
We've got nothing to hide."
Well, it matters.
It matters because today individuals are not only being tracked, they're also being profiled on the basis of their data traces.
Artificial intelligence and predictive analytics are being used to harness as much data as possible of an individual life from different sources: family history, purchasing habits, social media comments.
And then they bring this data together to make data-driven decisions about the individual.
And these technologies are used everywhere.
Banks use them to decide loans.
Insurance uses them to decide premiums.
Recruiters and employers use them to decide whether one is a good fit for a job or not.
Also the police and courts use them to determine whether one is a potential criminal or is likely to recommit a crime.
We have no knowledge or control over the ways in which those who buy, sell and process our data are profiling us and our children.
But these profiles can come to impact our rights in significant ways.
To give you an example, in 2018 the "New York Times" published the news that the data that had been gathered through online college-planning services -- that are actually completed by millions of high school kids across the US who are looking for a college program or a scholarship -- had been sold to educational data brokers.
Now, researchers at Fordham who studied educational data brokers revealed that these companies profiled kids as young as two on the basis of different categories: ethnicity, religion, affluence, social awkwardness and many other random categories.
And then they sell these profiles together with the name of the kid, their home address and the contact details to different companies, including trade and career institutions, student loans and student credit card companies.
To push the boundaries, the researchers at Fordham asked an educational data broker to provide them with a list of 14-to-15-year-old girls who were interested in family planning services.
The data broker agreed to provide them the list.
So imagine how intimate and how intrusive that is for our kids.
But educational data brokers are really just an example.
The truth is that our children are being profiled in ways that we cannot control but that can significantly impact their chances in life.
So we need to ask ourselves: can we trust these technologies when it comes to profiling our children?
Can we?
My answer is no.
As an anthropologist, I believe that artificial intelligence and predictive analytics can be great to predict the course of a disease or to fight climate change.
But we need to abandon the belief that these technologies can objectively profile humans and that we can rely on them to make data-driven decisions about individual lives.
Because they can't profile humans.
Data traces are not the mirror of who we are.
Humans think one thing and say the opposite, feel one way and act differently.
Algorithmic predictions or our digital practices cannot account for the unpredictability and complexity of human experience.
But on top of that, these technologies are always -- always -- in one way or another, biased.
You see, algorithms are by definition sets of rules or steps that have been designed to achieve a specific result, OK?
But these sets of rules or steps cannot be objective, because they've been designed by human beings within a specific cultural context and are shaped by specific cultural values.
So when machines learn, they learn from biased algorithms, and they often learn from biased databases as well.
At the moment, we're seeing the first examples of algorithmic bias.
And some of these examples are frankly terrifying.
This year, the AI Now Institute in New York published a report that revealed that the AI technologies that are being used for predictive policing have been trained on "dirty" data.
This is basically data that had been gathered during historical periods of known racial bias and nontransparent police practices.
Because these technologies are being trained with dirty data, they're not objective, and their outcomes are only amplifying and perpetrating police bias and error.
So I think we are faced with a fundamental problem in our society.
We are starting to trust technologies when it comes to profiling human beings.
We know that in profiling humans, these technologies are always going to be biased and are never really going to be accurate.
So what we need now is actually political solution.
We need governments to recognize that our data rights are our human rights.
(Applause and cheers) Until this happens, we cannot hope for a more just future.
I worry that my daughters are going to be exposed to all sorts of algorithmic discrimination and error.
You see the difference between me and my daughters is that there's no public record out there of my childhood.
There's certainly no database of all the stupid things that I've done and thought when I was a teenager.
(Laughter) But for my daughters this may be different.
The data that is being collected from them today may be used to judge them in the future and can come to prevent their hopes and dreams.
I think that's it's time.
It's time that we all step up.
It's time that we start working together as individuals, as organizations and as institutions, and that we demand greater data justice for us and for our children before it's too late.
Thank you.
(Applause)