What intelligent machines can learn from a school of fish | Radhika Nagpal

109,692 views ・ 2017-10-06

TED


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In my early days as a graduate student,
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I went on a snorkeling trip off the coast of the Bahamas.
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I'd actually never swum in the ocean before,
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so it was a bit terrifying.
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What I remember the most is, as I put my head in the water
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and I was trying really hard to breathe through the snorkel,
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this huge group of striped yellow and black fish
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came straight at me ...
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and I just froze.
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And then, as if it had suddenly changed its mind,
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came towards me and then swerved to the right
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and went right around me.
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It was absolutely mesmerizing.
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Maybe many of you have had this experience.
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Of course, there's the color and the beauty of it,
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but there was also just the sheer oneness of it,
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as if it wasn't hundreds of fish
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but a single entity with a single collective mind
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that was making decisions.
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When I look back, I think that experience really ended up determining
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what I've worked on for most of my career.
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I'm a computer scientist,
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and the field that I work in is artificial intelligence.
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And a key theme in AI
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is being able to understand intelligence by creating our own computational systems
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that display intelligence the way we see it in nature.
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Now, most popular views of AI, of course, come from science fiction and the movies,
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and I'm personally a big Star Wars fan.
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But that tends to be a very human-centric view of intelligence.
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When you think of a fish school,
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or when I think of a flock of starlings,
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that feels like a really different kind of intelligence.
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For starters, any one fish is just so tiny
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compared to the sheer size of the collective,
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so it seems that any one individual
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would have a really limited and myopic view of what's going on,
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and intelligence isn't really about the individual
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but somehow a property of the group itself.
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Secondly, and the thing that I still find most remarkable,
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is that we know that there are no leaders supervising this fish school.
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Instead, this incredible collective mind behavior
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is emerging purely from the interactions of one fish and another.
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Somehow, there are these interactions or rules of engagement
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between neighboring fish
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that make it all work out.
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So the question for AI then becomes,
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what are those rules of engagement that lead to this kind of intelligence,
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and of course, can we create our own?
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And that's the primary thing that I work on with my team in my lab.
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We work on it through theory,
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looking at abstract rule systems
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and thinking about the mathematics behind it.
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We also do it through biology, working closely with experimentalists.
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But mostly, we do it through robotics,
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where we try to create our own collective systems
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that can do the kinds of things that we see in nature,
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or at least try to.
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One of our first robotic quests along this line
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was to create our very own colony of a thousand robots.
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So very simple robots,
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but they could be programmed to exhibit collective intelligence,
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and that's what we were able to do.
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So this is what a single robot looks like.
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It's quite small, about the size of a quarter,
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and you can program how it moves,
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but it can also wirelessly communicate with other robots,
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and it can measure distances from them.
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And so now we can start to program exactly an interaction,
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a rule of engagement between neighbors.
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And once we have this system,
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we can start to program many different kinds of rules of engagement
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that you would see in nature.
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So for example, spontaneous synchronization,
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how audiences are clapping and suddenly start all clapping together,
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the fireflies flashing together.
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We can program rules for pattern formation,
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how cells in a tissue
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determine what role they're going to take on
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and set the patterns of our bodies.
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We can program rules for migration,
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and in this way, we're really learning from nature's rules.
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But we can also take it a step further.
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We can actually take these rules that we've learned from nature
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and combine them and create entirely new collective behaviors
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of our very own.
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So for example,
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imagine that you had two different kinds of rules.
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So your first rule is a motion rule
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where a moving robot can move around other stationary robots.
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And your second rule is a pattern rule
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where a robot takes on a color based on its two nearest neighbors.
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So if I start with a blob of robots in a little pattern seed,
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it turns out that these two rules are sufficient for the group
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to be able to self-assemble a simple line pattern.
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And if I have more complicated pattern rules,
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and I design error correction rules,
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we can actually create really, really complicated self assemblies,
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and here's what that looks like.
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So here, you're going to see a thousand robots
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that are working together to self-assemble the letter K.
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The K is on its side.
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And the important thing is that no one is in charge.
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So any single robot is only talking to a small number of robots nearby it,
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and it's using its motion rule to move around the half-built structure
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just looking for a place to fit in based on its pattern rules.
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And even though no robot is doing anything perfectly,
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the rules are such that we can get the collective to do its goal
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robustly together.
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And the illusion becomes almost so perfect, you know --
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you just start to not even notice that they're individual robots at all,
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and it becomes a single entity,
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kind of like the school of fish.
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So these are robots and rules in two dimensions,
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but we can also think about robots and rules in three dimensions.
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So what if we could create robots that could build together?
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And here, we can take inspiration from social insects.
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So if you think about mound-building termites
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or you think about army ants,
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they create incredible, complex nest structures out of mud
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and even out of their own bodies.
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And like the system I showed you before,
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these insects actually also have pattern rules
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that help them determine what to build,
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but the pattern can be made out of other insects,
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or it could be made out of mud.
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And we can use that same idea to create rules for robots.
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So here, you're going to see some simulated robots.
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So the simulated robot has a motion rule,
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which is how it traverses through the structure,
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looking for a place to fit in,
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and it has pattern rules where it looks at groups of blocks
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to decide whether to place a block.
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And with the right motion rules and the right pattern rules,
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we can actually get the robots to build whatever we want.
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And of course, everybody wants their own tower.
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(Laughter)
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So once we have these rules,
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we can start to create the robot bodies that go with these rules.
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So here, you see a robot that can climb over blocks,
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but it can also lift and move these blocks
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and it can start to edit the very structure that it's on.
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But with these rules,
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this is really only one kind of robot body that you could imagine.
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You could imagine many different kinds of robot bodies.
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So if you think about robots that maybe could move sandbags
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and could help build levees,
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or we could think of robots that built out of soft materials
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and worked together to shore up a collapsed building --
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so just the same kind of rules in different kinds of bodies.
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Or if, like my group, you are completely obsessed with army ants,
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then maybe one day we can make robots that can climb over literally anything
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including other members of their tribe,
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and self-assemble things out of their own bodies.
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Once you understand the rules,
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just many different kinds of robot visions become possible.
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And coming back to the snorkeling trip,
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we actually understand a great deal about the rules that fish schools use.
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So if we can invent the bodies to go with that,
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then maybe there is a future
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where I and my group will get to snorkel with a fish school of our own creation.
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Each of these systems that I showed you
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brings us closer to having the mathematical and the conceptual tools
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to create our own versions of collective power,
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and this can enable many different kinds of future applications,
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whether you think about robots that build flood barriers
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or you think about robotic bee colonies that could pollinate crops
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or underwater schools of robots that monitor coral reefs,
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or if we reach for the stars and we thinking about programming
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constellations of satellites.
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In each of these systems,
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being able to understand how to design the rules of engagement
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and being able to create good collective behavior
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becomes a key to realizing these visions.
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So, so far I've talked about rules for insects and for fish
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and for robots,
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but what about the rules that apply to our own human collective?
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And the last thought that I'd like to leave you with
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is that science is of course itself
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an incredible manifestation of collective intelligence,
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but unlike the beautiful fish schools that I study,
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I feel we still have a much longer evolutionary path to walk.
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So in addition to working on improving the science of robot collectives,
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I also work on creating robots and thinking about rules
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that will improve our own scientific collective.
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There's this saying that I love:
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who does science determines what science gets done.
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Imagine a society
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where we had rules of engagement
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where every child grew up believing that they could stand here
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and be a technologist of the future,
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or where every adult
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believed that they had the ability not just to understand but to change
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how science and technology impacts their everyday lives.
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What would that society look like?
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I believe that we can do that.
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I believe that we can choose our rules,
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and we engineer not just robots
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but we can engineer our own human collective,
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and if we do and when we do, it will be beautiful.
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Thank you.
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(Applause)
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