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(bees buzzing)
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Many things are impressive about the honeybee.
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When you work this closely, you see their intelligence,
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you see their individuality,
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you see their collective behavior,
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you see the structures they've built,
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you see the organization of that society.
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You can't do anything but admire it, you can't.
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They are the most beautiful, phenomenal creatures.
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They really are.
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(soft classical music)
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This bee has learned that if it moves
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that yellow ball into the yellow circle,
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the well beneath the ball fills up with nectar
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and it gets a drink,
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and all of that intelligence, all of that smarts,
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come somehow from the bee brain,
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and I want to understand this bee level of intelligence.
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We have a jumbo jet, we have a bumble bee,
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we have an osprey, I could not say
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which one is a better flier than the other
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because they're different.
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Putting the envelope around what intelligence is
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is extremely difficult,
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and I think what will help us frame that envelope
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is if we can study the diversity.
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If we study intelligence, not just in humans,
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but in other living things, potentially even other machines,
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we can tidy up that definition of what intelligence is
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and where we draw the boundary on
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what's intelligent and what's not.
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My project is particularly focusing on honeybee intelligence
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because it gives us such an informative lens,
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sort of informative, comparative lens,
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on the intelligence of other animals, including humans.
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Things like complex learning, complex memory,
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complex navigation, complex assessment,
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we'll learn some evolved solutions for that,
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and we can then ask is the human brain
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doing this in a similar way.
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We have these tiny little animals with really minute brains.
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They have a million neurons.
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It's minute compared to a human brain.
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(soft classical music)
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The honeybee brain is very small,
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but it would be wrong to characterize it
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as a simple system.
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We do still have 1 million neurons in a bee brain,
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and they are organized in quite beautiful
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different structural regions that interact and
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intersect in very complex ways.
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People normally think they're very clever as groups
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but simply rather stupid individually,
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and nothing could be further from the truth.
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Honeybees have been documented to find
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their way home from 12 kilometers away.
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In a routine foraging flight,
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bees will fly 5 or 6 kilometers,
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which doesn't sound much, but when you scale that by
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the size of an individual bee,
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that's a really huge distance.
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Our own machine learning and AR algorithms
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for navigation aren't that sophisticated or reliable.
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But what stands out as a unique feature of the honeybee
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would have to be its symbolic dance language.
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When they dance, the vigor with which they shake their butt
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and how many times they dance is the quality of
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the sugar reward they have found.
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They are transforming information about
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distance and direction to things in the real world,
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to these remote food sources,
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into a single vector that they can then
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signal through a dance,
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so the dance is a readout of this
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subjective evaluation of how good
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that reward was for the bee.
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It's the tail wag for a bee.
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For me, the bee was in this unique position
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where its behavior was complex enough to be interesting,
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but its newer biology in its brain was simple enough
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that we could study it.
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The honeybees really are spectacular learners.
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They learn very fast and very robustly.
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As an example, if we give a honeybee
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something simple to learn,
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like this odor is associated with nectar,
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this odor's where you find nectar,
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it will learn that on one trial.
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If you give it three trials,
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it will learn that for the rest of its lifetime,
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so that's very fast acquisition of
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relationships between information.
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They can even learn things that we would consider
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to be abstract concepts,
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things that we would call learning of sameness,
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learning of difference.
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Honeybees able to do that.
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That hasn't been shown in
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any other invertebrate that I know of.
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A statement, I don't know, is an example of metacognition.
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You're assessing a circumstance,
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and you're coming to the conclusion that
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you don't have enough information to address that,
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or to answer that.
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If we'd look comparatively across the literature,
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in many tests, even these tests of very simple learning,
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or even tests of very complex learning,
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we see the bees learning faster than rats.
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I don't have an answer for you as to why that is yet.
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It fascinates me.
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We have an organism that where our assumption is,
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this is smarter, and yet in a whole battery of tests,
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at learning, tests of memory, tests of spatial cognition,
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the bees are outperforming the rats.
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If the bee is solving a task that
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we think demonstrates metacognition,
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how can an animal with just one million neurons do that?
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It forces us to rethink our assumptions.
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What is the minimal computational architecture
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that could do this.
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A computational model is, it's building
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a circuit diagram of the brain in a virtual world,
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and we can then make it a dynamic system
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that we can feed input to.
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(soft electronic music)
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It will process the input in the way
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that we think that the honeybee brain is processing it,
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and it will give us an output.
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We can analyze that output in terms of,
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well, is this system doing what the bee's doing?
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If it is, maybe our model is close to reality.
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We can do exactly the same with bits of mammalian brain,
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and that means that we can actually compare
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what are superficially very, very different-looking systems.
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We've done something that no one else has done,
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in that we've taken an abstract concept,
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and we have given you a neuron-by-neuron connected circuit.
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If we can model the bee brain,
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we can take insights from those models
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and translate them directly into technological applications.
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We're building drones that can fly in
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a comparable way to a bee,
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but not exactly the same as a bee.
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You know, with only a million neurons
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in the bee brain, they were already well in advance
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of our own abilities in artificial intelligence
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and robotics, so really what we'd like to do is
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try and make silicon versions of bee brains,
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or at least of the aspects of the bee brains
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that generate behavior we find useful for our own robots,
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so especially around navigation.
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I thought if we could just reverse engineer the bee brain,
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that could actually try
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and really advance the state-of-the-art.
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Bees have evolved for millions of years
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to be fantastic autonomous behavioral control systems.
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They're really robust, they're really reliable,
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they're amazing navigators across very large distances.
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All of these are current challenges in autonomous robotics,
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and yet the bee's doing it
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with incredible computational efficiency.
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In particular,
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we want to
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be able to reproduce, for example,
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the collision avoidance or navigation dependencies
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would be in robot form.
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Let's imagine autonomous
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drones that we could use in exploration.
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or agriculture, or in mining.
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At least eight people have been killed
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after a magnitude 6.1 earthquake struck the Philippines.
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For example, trying to deploy drones
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to search for survivors of an earthquake
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or something like that.
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Time is gonna be of the essence.
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You want to automate as much of the process as possible,
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have fully autonomous flight and navigation for the robots,
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then we could have some real benefits
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in that kind of technology.
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That would be the Holy Grail for so much robotics.
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Bees have solved that with this minute brain.
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We're finding that actually
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bee navigation may be a lot more map-like
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than people have previously assumed.
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I mean, the idea of a mental map is
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that you have kind of representation of the
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relationship between points in space.
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That seems like a much higher level kind
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of cognitive ability
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than people have typically assumed bees
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and other insects are able to employ.
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We've been looking at an algorithm
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inspired by how the honeybee brain works,
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what's called an optic flow estimator,
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which basically tells you how fast things
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are moving across the visual field, and you can use that.
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You know, as you will have seen from looking out
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of the window on a train, for example,
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when things are close to you, they move much faster,
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apparently, across your visual field,
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and you can use that as depth information,
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of depth cue, or information that
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you're about to crash into something,
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but you could also use it for a variety
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of other applications,
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like using it to estimate how far you've traveled,
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or how fast you're traveling.
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and again, these are tremendously useful for navigation
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and for flight control, flight regulation.
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Whether we like it or not,
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we're in this robotic revolution.
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It's happening, it will only accelerate even further.
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What interests me is the capacity for safe robotics.
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If we're gonna have a system that is trustworthy,
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we need to understand how that system works
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very, very, very deeply.
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If we're starting our robotic systems
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in the basis of a deeply understood system
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like the bee brain, to me, we have a system that
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is more intrinsically understood,
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and I think, therefore,
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potentially safer and more trustworthy
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than some of the current approaches in robotics.
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(bees buzzing)
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I suspect I'm not alone in saying this,
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but I think that in the arc of understanding of
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the bee brain, we're at the most exciting point.
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We're really getting to the point where we can put,
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not just the bee brain, but insect brains together
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as an information flow system.
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Just being able to translate what we've learned
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from the bee as a hypothesis to
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help us analyze a human brain and mammalian brains,
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that's the value of the work I'm doing with bees.
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We all have an attachment to cats and dogs
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because they're so naturally empathic.
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When you look at a bee's face, it gives nothing away.
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It gives you nothing.
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It's face is a blank mask.
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I have as warm a relationship with bees
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because I developed so much respect for them.
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When I work with bees, usually I'm working
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with just one individual bee,
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who I've paint marked or number marked so I know who she is.
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In the course of that day,
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you get this really privileged insight into
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the kind of intelligence that this animal has,
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and you realize how astonishing it is,
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and what a cognitive, and elegant,
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and beautiful entity this animal is.
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(soft classical music)
19850
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