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