Transcription
[Music]
[Audience applause]
Linda Liukas: This is definitely not your typical 11:00 a.m. energy, so thank you for all being here. This is pretty much the first conference I’ve done since the pandemic, so I’m very, very excited to be back on stage.
My name is Linda Liukas. I’m a children’s book author. I’m an illustrator. I come from Helsinki, Finland, but nowadays live in Paris.
My line of work is making the world of computer science understandable for kids typically between ages five to nine, but I’ve heard about chairmen of the board for big stock companies reading these books because when you need to explain something for a six-year-old, you need to go really deep into the ideas.
I talk about coding, programming, networks, machine learning, and how computer hardware works. These books, they have been translated into 36 languages, so I’ve learned a thing or two about making technology approachable and fun and re-sparking that magic we all felt when we started in our field because, in some ways, we are the last magicians in the world. It’s that sense of magic and excitement I’m going to try to re-spark today.
Here's where I base most of my work. If coding truly is the next universal language, if our children are going to be learning English, Chinese, and JavaScript as their first foreign languages, instead of grammar classes, we ought to be teaching more poetry lessons.
What I mean with that is the idea that we don’t learn a new language only by conjugating the irregular verbs, by practicing the grammar rules. We learn a language by speaking it, by singing it, by dancing it, by flirting in it and boy do we need more poetry in the world of technology education.
Here are some of my personal idols. I look up to the folks like Maria Montessori, Jean Piaget, Seymore Papert, Loris Malaguzzi, and my native Tove Jansson. But I also look up to the pioneers of computer science.
So often in the world, we think these are two separate groups of people: those with the imagination and those with the logic; those with the soul and heart, and those with the pure skill. But this is not true.
When you look at the luminaries of computer science, they were folks with big imaginations and really whimsical personalities.
John Allen Turing was a long-distance runner and a bicyclist who bicycled with a gas mask on because he had such big hay fever in the summertime. Claude Shannon was a unicyclist who also wrote a physics paper about juggling balls. Ada Lovelace famously was the daughter of a poet and a mathematician who first discovered that computers can be spoken in a language called code. This list could go on, but for some reason, we separate the two.
It's my work to try to re-spark this connection between the people who see the world of childhood and imagination and the people who speak the language of computers. When I work with children, it’s mostly these three things I teach them.
I teach them not how to write an array or hash or how “if else” statements function in Python. I teach them curiosity. I teach them fearlessness. And I teach them a sense of wonder. I’m hoping these three things will be the things you will take away from this speech today.
My work has taken me to many different places. I started by illustrating the books about a little girl called Ruby because I’m a Ruby programmer originally. But then I started to think about how to make videos and how to create experiences that really we can touch with our fingers because that’s the way we learn about the world. We learn on our hands and knees, and we learn with our fingertips and with the tip of our tongue.
That’s meant, I’ve built the Internet and taught kids how packet switching works. We’ve practiced binary numbers with candies. And we’ve reenacted how the computer processor bosses everyone else inside of the computer around and how the RAM gets overwhelmed and so forth and so forth.
But mostly, I’ve been observing the children of today. And for me, there is no clear distinction between play that happens online and play that happens in the physical world, even though our society and a lot of folks around us tend to have this very fearful attitude towards what it happening online. When you think about sandboxes, that stateful of playground design, actually originating from here from Germany from Friedrich Froebel, who came up with the idea of a sandbox -- and even before that it was a tool for the army to plan their devices -- a very soft of natural continuum from army to Froebel to playgrounds is the digital world where we all know what a sandbox mode means today. It’s a place of creativity and a place of expression.
But again, for some reason, the way we teach these things looks like this. We don’t teach the joy and wonder and fearless that computers really deserve because they are playful devices. And that makes me really sad.
So, I’m going to propose to you three things that would teach about technology and computers for anyone under the age of 99. Because I’m a storyteller, I’ll do it in the form of A, B, C.
A, obviously, stands for the word algorithms. Most of you here in the room know what an algorithm is, but if I go to a regular educator, if I go to a policymaker, and I say, “What is an algorithm?” they get squeamish. They feel like, “Ugh, I ought to know, but I really don’t know. It has something to do with Facebook and fiancé and that scary part of the world.”
But a six-year-old, they’ll come forward, and they say, “Oh, I know what an algorithm is. It’s a step-by-step solution to solving a problem,” and that’s correct. Nothing more complex than that.
And when I start with children, I start to explain what an algorithm is by asking them to make their very first YouTube video. First, this exercise starts as any English language arts activity where they need to think about what type of a video they are making. Is it an unboxing video or a fan video or a challenge video.
Then they need to think about a beginning, a middle, and an end to their video. Then we talk a little bit about titles and summaries and tags for the video. Then they get to perform their videos.
It is through this activity that the children create a memory about algorithms. It’s by creating their own cardboard YouTube video that we get to talking about, “Hah, so there are 600 hours of new content uploaded on YouTube every one minute. How on earth is it available for everyone online?”
There is no way a human can sort through all of that, so the computer needs an algorithm. The algorithm, what it looks at, it looks at those tags you just wrote. It looks at the amount of likes your video is getting. It looks at your browsing history, at your geolocation, at the comments of the videos, and tries to make an educated guess on what kind of videos you might like.
Then we look at other services. We look at things like Google and it’s almost like this Where’s Waldo type of a game where the kids start to spot where is the algorithm hiding in a Google search. Well, obviously, in the types of ads you see and the order in which the results are shown to you.
We look at social networking sites and we try to decipher where the algorithm is. By the act of looking, by the act of noticing, kids start to get really confident. So, when we finally go on YouTube, they say really confidently that, “Oh, it’s not only the recommendation algorithm. It’s also the algorithm that guesses what your trying to type into the YouTube search bar.”
Then I tell them it’s not only that. It’s also every time you run into something weird on YouTube. How many of you have seen something that makes you feel a little bit like, “Oh, I’m not sure if this is really done for me?” Like a weird mashup video called “Surprise Play-Doh, Eggs Peppa Pig Minecraft Pocoyo,” and so forth and so forth.
Well, it’s because that video was not made for you. It was made for the algorithm. There are entire industries of companies that only produce content that is optimized for the algorithm, not for our six-year-olds. That’s why it’s so important that we’re starting to have vocabulary to discuss and describe what we see in the world.
But this obviously is not enough. If we only teach the soft side of things to our children, they will not grasp the power of an algorithm, so we need to go deeper. And I start by showing them these five numbers, and I tell them that your task is to put these numbers in order of magnitude so that the smallest is on the left-hand side and the biggest is on the right-hand side.
The kids, depending on their age, they do this in, like, I’d say, two, three minutes. And then I ask them to put these numbers in order of magnitude, and then these numbers. And by this point, the kids are complaining.
They say, “Linda, this is too boring. It takes too much time. I don’t want to do this,” and I say, “Bingo! You learned your first really important lesson,” because you don’t want to compete with a computer on a task like this.
Computers will always be faster, more efficient. They will make less mistakes than you do. You want to be the person who gives the instructions for the computer on how to do this because a computer won’t know this by themselves.
One way a computer could approach a problem like this is here. It could start from the beginning, and it could compare the two numbers together. It would say, “One is smaller than 56. Let’s keep it like this.”
It would move on to the next pair of numbers. It would compare 56 and 4 and say, “Four is smaller than 56. Let’s move it like this.” It’ll compare 56 and 70. “That looks okay. Seventy and 20, let’s swap these around.”
Then it would move all the way to the beginning and keep going on and on and on and on until the numbers were sorted. This, ladies and gentlemen, is called a bubble sort algorithm, a working horse algorithm familiar to many of you. But when you learn it with your fingertips, when you actually learn it not like this but like this, you retain a memory until you grow older.
One year, I decided to try making a pancake sorting algorithm myself with real pancakes. I know many of you might know this algorithm already. It’s when you have an unordered stack of objects that you find the biggest number and then you flip the stack around. It was a lot of fun and a lot of effort to make this happen. My fingers were so greasy after doing all of this.
But after completing the pancake sorting algorithm, I now have it in my fingertips. I have it like a spell. I will never forget the logic behind it. That wouldn’t have happened if my only entry point into a pancake sorting algorithm was this.
It is this that I’m trying to do with the computers. Grab them from their abstract interiors and make them really tangible and real. There’s nothing new for educators in this idea. It was already in the 1950s, Jean Paget famously stated that you can’t offer an entirely organized intellectual discipline for someone with a preorganized set of vocabularies and concepts. That all learning happens and is grounded in action. It’s this action that I think we sorely are lacking in the world of computer science because we need more diverse people to get excited about the power of computing.
A stood for algorithms.
B stands for Boolean logic. We’re going to get closer to the machine here. Computers, they are my favorite thing in the world. But the progress in the past 40 or 50 years with computers has made the machine very opaque.
It’s impossible to explain how computers work when you can jump 300 million transistors at the pinpoint of a pen, it’s also really impossible to understand what is actually happening inside of them. They become magical objects that most of us have a fearful relationship with.
To explore what really computers are about, I often start with drawing because language is something that we accrue over time, but drawing is something that is present with us from a very, very young age. Drawing is a fundamental way of our understanding and explaining the world before we move to the abstract world of ideas.
If I were to ask all of you to draw, “What do you imagine is inside of a computer?” for most of you this actually would be a fairly difficult task because a computer is an abstraction machine and you need to decide where your mental model starts from.
I’ve asked kids around the world, thousands of them, to do this activity. I’ve received hundreds and thousands of drawings and very loosely can group them into a couple of different buckets.
First of all, there’s always the content creators, children who draw files and apps and games that they imagine is inside of a computer. This is a useful metaphor because this is the way most of our parents and most of us grew up understanding computers, the metaphor of an operating system, files, and little folders where you put things. That’s changing, too, by the way. I just heard that kids nowadays, they don’t recognize folders anymore because they mostly use a phone.
Then there are these wonderful kids who draw these abstract networks of components, these connected ideas of elements. I think these are the future system architects. I think many of you would actually start to think about what is inside of a computer for quite abstract ideas.
Then there’s the scenographers, my personal favorite, kids who explain how a computer is a theater stage and there are little characters inside of them doing different things. They use the power of a narrative and story to explore what computers do. Obviously, this is not what is inside of a computer, but they also grasp one aspect of computing.
There’s gear gurus, kids who imagine that there are tiny gears inside of a computer. While we know that a computer is not a mechanical device like this, the kids who start with this metaphor, they grasp something about how each component of computer does a fairly simple thing. But when you combine a lot of them together, it gets more and more complicated.
Then finally, there’s the drafters, kids who draw the electrical parts of a computer, the resistors and wires and motherboards.
Through this activity, I think they all start to have a mental model of what a computer is, albeit very, very different mental models from one another. Most importantly, they discover that while computers are magical, they are not made of magic. They are made of logic, and that’s a huge thing for us to understand at a young age.
Because there are a lot of computer scientists and programmers in the room, I wanted to go a little bit deeper into the idea of what truly are computers because this was the thing that really blew my mind when I made the connection myself. Bear with me if you know this already. If you don’t know, be ready for a ride.
Bits: Inside of a computer, there are thousands of tiny switches (or millions of tiny switches) that only know how to go on and off, on and off. They either know how to pass electricity or not pass electricity. Computers are made of these bits. We all know about the one and zeros, but it didn’t still make sense to me. I didn’t understand what I was reading.
I needed to go deeper, and I discovered the logic gates. These are weird mathematical devices (part philosophy, part math, part electrical engineering) and they come in three flavors. There’s the and gate, the or gate, and the not gate. It’s only those three characters we need in order to make any computer in the world.
This here is the and gate. If you tell the and gate to statements that are true, it can always tell that they are true. This still sounds abstract, so let’s look in practice.
This is Ruby, and tells and gate, “I’m Ruby and I just fell in a puddle.” Here are my statements. Pay attention to the word “and.”
“I’m wet and I’m cold,” so the and gate says, “That’s true.” This is another way of saying the same thing, but mathematicians don’t like little doodles. They like to make numbers, so if you give the and gate one and one, the outcome is one.
If on the other hand, Ruby tries to cheat a little bit and say, “I’m warm and I’m wet,” the and gate always returns a false. This is another way of saying the exact same thing, so zero (false), one (positive or real, true), the outcome is zero.
Here are all the different permutations that the and gate offers. You can see it’s quite strict. Only if two conditions are true, it gives out a true.
Here is another way computer scientists like to look at this world, but at least for me, that was too dense. It hid a lot of the stuff that was happening behind that truth table over there.
The second character we’re going to meet is the or gate. An or gate is much more lose. It takes two statements, “I’m wet,” or “I’m cold.” That’s true. “I’m wet,” or “I’m warm,” that’s still true. “I’m warm,” or “I’m dry,” well, that’s not true. But “I’m warm,” or “I’m wet,” that’s true.
Much more relaxed. Here’s another way of looking at the character.
The final character we’re going to meet is the not gate, which is the favorite of the children because it always inverses whatever you say to it. When Ruby says, “I’m wet,” it says, “You’re dry.”
“I’m cold.”
“you’re warm.”
And here is the final way of looking at this.
We really quickly ran through all of the different characters: the and, or, and not. But this still doesn’t explain what actually happens inside of a computer, the bits and all of that stuff.
In order to get there, we also need to go and look at a completely different discipline. This is math and philosophy we’ve been talking about, the stuff of Aristotle and Leibniz and so forth. But in order to understand that these two fields emerge, we need to meet one man.
His name is Claude Shannon. He was a young electrical engineer who had two passions in his life when he was young. He had two majors. He studied very eccentric English logic and math as an undergrad in Philadelphia (or somewhere).
He ran into the work of George Boole, who was big on understanding these logic and truth tables. Then he also had a huge passion for electrical engineering, which, at the time, was still considered a magic witchcraft thing that was much more how to do with crafting than actual science.
What Shannon saw was this. He would look at an electricity circuit. I think most of us in high school have done this activity where we realize that if the switches are open, there’s no way the light functions. If the switches are closed, the light turns on.
He, in his brain, looked at the truth tables, the and gate, and looked at the electrical circuits, and he realized they are the same thing. That they have exactly the same laws that govern both of these worlds, the world of electricity and the world of math.
If you are now wondering, and gate was the really strict one. Only if both conditions are true, only if both switches are off, the light will turn on.
This is the or gate. Here’s another way of wiring an electrical circuit. Here you can see how if both of the switches are off, the light turns on. But even if one of the switches is on, the light turns on. This was the happy-go-lucky or gate that accepts all kinds of things as true.
It was this moment that made me really appreciate how far we’ve come because each computer deep down is made of a little electrical circuit. But instead of playing with wires and lights, we’re actually packaging these things together into more and more complex things. So, out of or gates by bundling them together, we build xor gate.
Then out of these different characters, we build a one bit adder until we make the ENIAC until we make more and more complex things and until we come to the modern day where we actually can jam the 300 million transistors into the pinpoint of a pen.
I won’t go into very much detail into the material side of things here, but I bet if you want to have a fascinating lunch talk, go find someone who knows a little bit about how we layer these different materials on top of one another in order to create these crazy, crazy devices that we call the computers today.
Why should we bother about this? Computers have been made so easy for us nowadays. We only need to take our phones and swipe them. We have no need to worry about all of that stuff that happens inside of a computer anymore. Why should the modern children be shown these ideas behind computers?
Because computers are everywhere around us, and this is a very lame thing to say right now. But there are hundreds of computers in every single one of our homes, and this is the last generation of children that will even remember the computer as a device that has a screen and keyboard and a mouse, the very last generation that will remember a computer that is defined by a screen.
For the next generation, computers are hidden everywhere in their daily lives. It’s in the microwave, in the doorbell, in the remote control, in the way cities operating, in the way food is delivered to them.
One of my favorite activities is this one where I bring a suite of everyday objects for the children. I ask them to pick one object, put a little on/off button on it, and imagine what the device would do if it was turned into a computer.
One of my favorite stories -- I’ve told it many times -- is the story where this little girl takes the bicycle lamp, and she puts the little on/off button on it. She goes, “If this bicycle lamp was a computer, I could go on a biking trip with my father. We could sleep in a tent, and this bicycle lamp, it could also be a movie projector.”
[Audience laughs]
Linda: And that is the moment I think we’re all looking for. The moment when we realize that the world is not finished yet. That there’s so much we can discover and invent with technology. Odds are she’s not going to be the next Steve Wozniak or the hardware hacker who creates that movie projector. But for a moment there, she believed she could be. I think that is the responsibility of all of us grownups is to pass that sense of pragmatism and optimism for the next generation.
How do you explain if you don’t want to go into the logic gate world? How do you explain how a computer works for a new generation that won’t know it by the keyboard and by the mouse?
Well, I think it’s really interesting. In order to understand the future of computing, we actually need to look at the history of computing, all the way back to John von Neumann and the 1950s and 1940s when they were building the very first computers. Von Neumann architecture famously states that a computer -- a little bit simplified -- is a device where you input data, you modify that data somehow, and out comes that modified data as the output data.
With the children, we practiced this again through physical play. We built an incredible input-output machine, and the kids become the input data. Inside of that machine, there’s a little piece of code that reads, “Come out crawling backside first.”
Around and around they go, this magical computer, until they have a deep memory that when a computer works, it doesn’t really matter what it looks like. When you sit in a car and you forget to buckle your seatbelt, in goes the input information that someone is sitting here and the seatbelt is unbuckled. Out comes the beep-beep-beep noise we hate so much.
When you go on Facebook and you like a post, in goes the input information to Facebook server that someone has liked this post. Out comes the updated like count.
It’s this idea of input process, output, I think, that is a very powerful idea when it comes to computing.
Then there’s another idea I want to quickly present. It’s the idea of a notional machine. It comes from Mark Guzdial, a famous computer science education researcher, and Benedict Du Boulay. He says that -- a little bit paraphrasing here -- instead of teaching kids to program, we want them to understand what a computer can do and what a human can do. We want them to have robust mental models of what computers are good at and what humans are good at.
That brings me to the final letter. A for algorithm. B for Boolean logic, the insane journey inside of a computer. C stands for creativity and computers.
Here’s the thing. A few years ago, I was in London, and I met a six-year-old little boy who came to me and said, “Linda, what am I going to do when I grow up when computers are going to do all the jobs?”
And I looked at him, and I said, “Oh, love. Don’t worry about that,” [laughter] but at that moment, I realized that the next generation can’t inherit our fears and our pessimism around technology.
The children of tomorrow, they deserve if not an optimistic, at least a pragmatic relationship with technology. So often, when we don’t understand and know something, we think it’s foreign.
This was true in the medieval world where you would have these maps. Every time the map, the cardiographer didn’t know something, they would say, “Here be the dragons.”
When I talk with grownups about AI, it’s always the sense of, “Here be the dragons. Don’t go here. We don’t understand this. This is scary.”
But the way I’ve started to teach what AI and machine learning is looks like this. I tell them that the world is full of data. Every time you click, click, click something online, every time you walk on the streets, data is being collected about you. That data is being processed into different kinds of products and services that try to emulate and guess the kinds of things you might need in the world.
Even though we tend to use human-centric worlds like computers that can see, move, communicate, reason, and maybe even be creative, they are all based on this idea of machine learning, of big amounts of data that are being used to create a sense of computer vision, image recognition, of natural language understanding. We’ve made very little progress in terms of general artificial intelligence or some sort of broad sense of awakening even though our popular fiction and even though our media often make it sound like that.
What in practice happens is this. If a computer scientist or a programmer wanted to know if this thing over here is a cat, in the past they would have needed to write tons and tons of instructions on what a cat looks like because you need to be very precise in order to talk to a computer.
They would have written, “A cat is an animal with two ears and a tail, and it comes in these five colors,” and these instructions would be incredibly brittle. They would break down easily and a computer wouldn’t be able to recognize, for instance, the neon cat as a cat.
What we do nowadays instead is this. We ask a computer a problem to solve like, “Is this a cat?” And we start by collecting examples of cats. Luckily, because the Internet loves cats, we had massive amounts of cat videos on YouTube. So, the researchers, they collected cat videos from YouTube, and then they fed them to the computer that built a model of what a cat video looks like.
This is the stage that usually makes people very nervous. They say, “Oh, the computer is sentient. It can recognize a cat.” But it’s not recognizing a cat. It’s looking at examples and finding patterns that a human mind can’t hold together because we can hold maybe two or three dimensions at one time, but computers can hold hundreds of dimensions of thousands of millions of examples at the same time.
It means that maybe they recognize the cat through the distinction or distance between the nose and the ear or some other thing that we don’t quite understand just yet. And we don’t really understand how the human brain recognizes a pineapple or a hairbrush, so I wouldn’t be too worried about this part.
Then the computer gives an answer to the question. But very importantly, it actually doesn’t give an answer. It gives an estimation. It gives a probability.
When you look at the whole sort of journey for machine learning, you can see that we humans are needed absolutely everywhere. We humans are the ones who ask the problems to be solved by the computer. We are the ones who are curious and creative. Computers, by themselves, are not interested in understanding if something is a cat.
We humans very much are the ones who gather the training data. We are the ones that write the algorithms the computers use to build that model. And we are the ones who assess whether the answer is enough because there are problems where it’s okay to know something for a 60% probability happens, but then there are problems in our judicial system, in our education system, in our healthcare system where we just don’t want to automate the decision-making for the machine. When you look at it this way, we humans will have a lot of work to do going forward.
But in some ways, these are the last moments that we can make sure that a lot of different people are involved in AI and machine learning. In order to do this, with the children, we have practiced the part of gathering data.
I show children four pictures of cats, and I tell them, “What is the bias? What is the prejudice the computer might learn by only having this training data set of cats?”
Kids learn really big and cool words like “training data set,” “bias,” and then they say, “Oh, they are all gray. The computer wouldn’t recognize a brown cat,” or “It wouldn’t recognize a cat with brown eyes,” “and it wouldn’t recognize a cat with a very, very short tail.”
I tell them, “That’s great. Draw a final example that makes the training data set unbiased here.”
Then we look at teacups, and again the kids start to feel very confident. They say, “It won’t recognize a Japanese teacup because it doesn’t have a handle. It wouldn’t recognize grandmother’s teacup because it has a rose in it,” and so forth and so forth.
Then I finally showed them a series of nurses. It takes them a little while to realize that not all nurses are women and if we build AI systems that fail to see the diversity we have in the world by having programmers and builders who have a very one-singled view of the world, we are failing to create a future that belongs to everyone.
This is hard to do. I’m not really advocating that this is an easy problem to do. Sometimes we practice this with the children by them having to pose a question for the computer, and then we got to like a magazine, and they cut out, for instance, they want to build a homework assignment computer. I tell them that the first task would be probably to teach the computer to recognize your handwriting.
Then they start to collect different examples of even the letter N, which comes in many flavors.
Here’s another one who wanted to make a computer or an AI system that recognizes what a happy person looks like. They cut out many pictures of happy people from Finnish magazines. Already at that point the kid realized that “Oh, these people all have blonde hair and blue eyes. That’s not very diverse. But they also realized that “Wait a moment. Finnish people never smile, actually, when they are happy.”
[Audience laughs]
Linda: We are a very, very poor to system to look into.
So, maybe these three ideas -- A for algorithms, B for Boolean logic, C for creativity and computers -- have given you a little flavor of what we could do with the world of computer science if we really, really want to broaden it up and make these ideas more approachable for different kinds of learners.
The next big thing I’m working on is actually a playground because, as I mentioned in the beginning, the idea of a sandbox that morphed from an army tool into Fortnite, I think there are a lot of ideas around computer science that we can actually take back a little bit.
In Helsinki, we’re building this public playground where anyone can come and play, where you can actually become the input data of the computer and slide out of the computer as the output data. You can practice programming logic on a trampoline, and you can just experience the big ideas of computer science as a six-year-old through play.
This is where the playground will be built in two years’ time. We’re still at the phase where we’re drawing a lot of things.
And when I went to Google, it’s really fascinating that so many of playgrounds nowadays, they have to do with castles and pirate ships. And those are important. Don’t get me wrong. But I do think that there is room for diversity also when it comes to the play spaces we create for our children.
This is a very sort of gentrifying neighborhood in Helsinki that a lot of technology companies already have their HQs here. When you kind of squint your eyes a little bit, I feel like the different buildings look a little bit like computer parts in themselves.
The best part about this project definitely has been benchmarking the amazing playgrounds out there in the world and learning about what different aspects of play mean and look like and how we could kind of incorporate these ideas into the everyday play practices of children.
It’s not only about making educational experiences. It’s also balancing out hiding and experimenting and self-expression and sliding and swinging. One of my favorite parts of this project is that when I write books and illustrate them, it’s always, “Are these books for girls?”
For sure, they are for girls, but I think it’s equally important that little boys learn to see girls as role models when it comes to computing, and I don’t think children’s books should be geared towards one sex or gender or the other.
But with the playground project, no one has ever asked, “Is this a playground only for children?” I think that is the power of public space that we feel all welcome to it.
One final thing I wanted to show to you around the playground is this idea of play in the modern era. A lot of our children, they spend a lot of their time playing online, and I ran this little survey with children in Helsinki with 600 or 700 kids.
I asked them to draw what is the most fun they’ve ever had on a computer or a phone, and most fun on the playground. What is the coolest thing they’ve built with a computer or on a phone, and what is the coolest thing they’ve built on a playground. What is the scariest thing they’ve ever seen on a playground, and what is the scariest thing they’ve ever seen on a computer.
And I’m still analyzing a lot of the results, but it’s really interesting to see that, in some ways, all of the scariest stuff has moved from the playground to the digital world. The scariest things kids had seen in a playground were a swarm of bees or some bigger boys who would throw snowballs at the children.
But I remember much more sinister things from my childhood playgrounds. Modern kids experience all of the scariest stuff online. They would talk about scary videos and prank calls and bullying and TikTok videos and so forth.
Then the other weird thing that happened was that I asked children, can--? In Finnish, we have two words. We have a word for playing and a word for gaming. And a lot of kids mentioned gaming as the thing they do on a computer and playing as the thing they do in a playground.
Then when I asked them, “Can you play on a computer and can you do gaming in the park?” most of the kids got really confused. I think it’s this kind of entanglement of these two that we aught to be able to play with our computers, create more spaces where we actually can have that sense of play and creativity and joyousness on a computer, not only directed gaming, and maybe our playgrounds should have a little bit more elements of gaming in them that will make for a more interesting future.
I honestly believe that the future of computing is not some machine that is more powerful or some VR experience or some meta thing that we put in our head. My wish for the future of computing is one where we have computers everywhere around us, and they enhance our everyday life, and they show us a different part of the world that we wouldn’t be able to access otherwise.
I read this fascinating study by Andrew Adamatzky on how he’s trying to program mushrooms because mushrooms are semiconductors, in a way. They are made of material, the fungi, that passes electricity. When you do really complex and complicated things with them, you can actually program entire swarms of mushrooms.
But not only are they sort of poor replicants of silicon. What they have is a lot of sensors that our machines don’t have. Obviously, they recognize temperature changes and moisture. But mushrooms are really good at recognizing stress levels of animals around them, stuff that computers nowadays can’t do.
Adamatzky’s team is working on thinking about how we could program entire mushrooms to monitor the health status of forests. I think this is a vision of computing I can stand behind. The idea that by harnessing the power of computers and computing, we can understand our nature and world better.
Instead of focusing on the power of computers, I would focus on the humans and stories and sort of the legacy of where computers came from and what they did to us, who were the people who built them, and what kind of ideals about the world they had. Stories, in some sense, they help us make sense of ourselves. They help us understand each other, and they help us position ourselves in a world that is changing around us. A lot of the stories around computers are very one-sided right now even though computers are these fascinating machines that are made of big ideas that have nothing to do with the computers we see nowadays.
I think all of this is important because we are entering a time where a lot of the vocabulary around computers is disappearing. A few years ago, I read this study by an Oxford University group where they had shown British children two sets of pictures. In one set of the pictures, there were Pokémon, and in one set of the pictures there were things from the natural world.
By far, the British children were much better at recognizing Pokémon, so they had more vocabulary to recognizes Pikachu than the birch tree. They were better at recognizing Bulbasaur than the badger. The researchers were worried because what happens to our understanding of the world if we don’t have vocabulary to describe it.
But I would argue the same is happening in the world of technology where we have so many of these suitcase words, words that we pack inside of a suitcase we throw from one person to another, and we never open up the suitcase, words like algorithm, bitcoin, blockchain, digitalization. I think it’s time to open up the suitcase and especially for us technical folks.
One time, a little boy came to me and said, “Linda, is the Internet a place?”
I look at him, and I say, “No. No, no, no. The Internet is this interconnected network of computers around the world. You can think of it like the information superhighway like the global village.”
Then I realized, “Oh, boy. I sound like a kid of the 1990s.” Those were my metaphors of the Internet. I’m the kid who grew up with the dial modem connect tone thingy.
For this child, the Internet is something invisible everywhere around him. Those metaphors, they won’t work anymore.
Should I explain to him that the Internet is these fiberoptic cables that go from the bottom of the sea all the way to space, and it’s this group of servers that stores all of the information about your friendships, family, and relations? Or should I talk about the protocols of the Internet, all of the software that governs how that data travels around the world nine times in a second and how there were people who made and wrote all of this software for free and publicly for everyone? Or should I talk about the memes, all of the funny cat videos and weird stuff that happens when the six billion of us can finally have a conversation together?
I think this is the challenge of technology. It’s not only the hardware. It’s not only the software. And it’s not only the societal impact that these devices and software is having on the world. Technology is all three at the same time and all three of them keep changing all the time.
Because I am a kid of the 1990s, I played a lot of Civilization, and I think it’s really fascinating the way they define technology because computers were only one part of technology. It was also hunting and democracy and archery and biology and banking.
I think there is something in my brain that got stuck in this definition and idea of technology as all kinds of human activities. I wanted to research this a little bit more, so I looked into how the Greek, who came up with pretty much everything we have in our societies nowadays, defined technology.
They said that technology is about skills and competencies alongside the tools to do a job. So, not only the tools but also the very human skills and competencies. For the Greek, agriculture was technology; democracy was technology.
Then there’s a Spanish philosopher called Jose Ortega y Gasset who says that technology is superfluities. He says -- if I understand him correctly -- that technology is our human ability to think about the future, to tell stories about what we want to do. Animals are a-technical because they are simply content living in the moment, but technology is about our human ability to tell stories.
When we pair these two ideas together, the technology is about human competencies and skills and tools, our ability to tell stories and look into the future. We’re kind of merging on a new definition of technology.
The definition I’m going to leave you with today comes from a nine-year-old little girl. It’s a few years old, and she was asked to define what is technology, who uses technology, and what is it used for. This is what she came up with.
She says, “Technology is electricity that loves. It is used to play. I use it to have a conversation with my mom. We use a WhatsApp application.” And then finally and most importantly, “People uses technology.”
Thank you very much.
[Audience applause]
