- Hi there, I'm Frank Graff.

Artificial intelligence can do a lot of things, including help people walk, beetles as you've never seen them, and meet the godmother of the digital image.

We're all about innovation science, next on SciNC.

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Beetles, not the band, are the most diverse and largest group of insects on the planet.

There are more than 400,000 species of beetles.

The insects make up 25% of all living creatures.

They predate the dinosaurs.

But you've never seen beetles like this.

Here's entomologist Adrian Smith with the North Carolina Museum of Natural Sciences.

- To my eye, there aren't very many insects that look as impressive in flight as a scarab beetle does.

This scarab, a grapevine or spotted June beetle, is a huge and lumbering insect.

Yet in the air, the details of its movements are sophisticated and graceful.

If this one were to fly near you, you'd hear a frantic buzz and flicker of its wings as it went past.

But here, seeing them like this at 6,000 frames per second, each wing is a steadied oar rowing through the air, carrying the beetles in an arching and upward flight path.

(gentle music) In this video, you'll see flight footage from 12 species across 10 taxonomic families.

And that's just a tiny sliver of overall beetle biodiversity.

And if you live in the Eastern US like I do, it's likely you can find all these beetles in and around your neighborhood.

Okay, let's start with two more scarab beetles.

This is an oriental beetle.

And watch what happens with its wings as it preps for flight.

First, the hardened protective forewings, the elytra, are raised out of the way.

Underneath are the flight-powering hind wings, but they're folded in half.

To unfold them, the beetle starts flapping and it takes them two to three wing beats to snap into place.

These beetles are one of two scarab species that over the last 100 years have expanded their range to become one of the more common garden pests in the Eastern US.

The other scarab that might be in your garden is this, the Japanese beetle.

These, despite being pests, are pretty attractive beetles.

They're easily identified by their iridescent green body and those white tufts of hair lining the sides of their abdomen.

Like the last two species you just saw, Japanese beetles fly in the same style, leading with their fanned-out antennae and trailing by dragging behind their long, tarsal claws.

This is something completely different, a water scavenger beetle.

These beetles are aquatic, but the adults are agile flyers and will fly to disperse between habitats.

If you put out an insect-attracting light at night, they're also known to come to those too.

Look at its legs as it flies and notice the slender spike of a last segment.

That's actually a cluster of long, fan-like hairs that form a paddle it uses when it swims.

And here's what that looks like.

When they're swimming, only the middle and hind legs are involved.

The front legs are folded up and held up against the body.

This alternating side-to-side stroke distinguishes them from other diving beetles, which use both legs in unison.

The silver shine you see underneath their bodies is a layer of air they keep trapped and clinging to their cuticle.

These beetles also have a keel that runs under the midline of their body.

From the side, it looks like a sharp, spine-like rudder underneath them as they swim.

This next one is a sap-feeding beetle called the picnic beetle.

Their name comes from an attraction to fruits, vinegar, and fermented drinks.

In natural habitats, they're attracted to this, fermented sap oozing out of a distressed tree.

Picnic beetles feed on the sap, but they also help themselves to snack on some of the other insects there, too.

As it flies, you can see how their hind wings, as they're outstretched, are nearly twice the length of the elytra that they're usually folded up underneath.

This is a jewel beetle, and it's the only beetle in this video whose hind wings aren't folded up.

You'll see when it raises up the wings that the hind wings are a perfect match in size to the front wings.

Once they're raised, you can also catch glimpses of the iridescent emerald green abdomen.

Jewel beetles are named that because a lot of them have brightly colored iridescent bodies.

This one was found hanging out amongst the leaves of a persimmon tree.

Jewel beetles are known to feed on pollen, nectar, and foliage, and quick to take to the wing like this if their perch on a stem or a leaf is disturbed.

Next is a tumbling flower beetle.

These beetles are also common on vegetation and flowers, and when you see them, they usually look wedge-shaped with a humpback, but in flight, they look a bit different.

As these ones raise their wings, look between the thorax and abdomen, and you can see how the two body segments separate.

You can see it a little bit better on this shot, where the beetle takes off and flies in profile to the camera.

The common name tumbling flower beetle comes from what they do to rapidly escape from a predator.

In these shots, the scary predator is a paintbrush.

The first shot on the left is going to show you the tumble in slightly slower than real time, and then on the right, you'll see it again slowed down even more.

What's happening is the beetles are rotating beyond their head, and then they rapidly rotate them back across their bodies to flick themselves off the ground and into a tumble.

They're able to do that because their hind legs attach to their bodies via a screw-like joint that allows them to rotate 270 degrees.

These beetles are one of only a few insects with legs that are able to rotate and move in a straight line.

These beetles are one of only a few insects with leg joints that work like that.

Now, these are cool sequences.

These are tiger beetles.

Tiger beetles do everything fast.

They're the fastest running insect, going about the same speed as your eye would when jogging, and apparently, they don't like to waste time when getting into a flight either.

Most beetles will raise their wings, position their bodies, and tiger beetles do all that at once while leaping into the air.

Compare what you just saw the tiger beetle do with this, a clay-colored leaf beetle.

Both the recording speed of 6,000 frames per second and the playback speed of 30 frames per second are the same for each sequence.

But as you see here, a more normal beetle like this takes its time when getting ready to fly.

This one raises its wings and positions its body at a 45-degree angle.

The takeoff is more or less a vertical one from that standing, outstretched posture.

There's no jump, and the wing beats just lift the beetle into the air.

(gentle music) One thing you'll notice with all this video is that a lot of beetles have more or less the same strategy and style of orienting their bodies in flight.

For instance, these three beetles from three different taxonomic families.

On the left is a soldier beetle, and in the middle is a small, flat cerebicid, or longhorn beetle, and on the right is a checkered beetle.

These three have similar elongated body shapes and all take to the air standing and belly first with their legs fully outstretched.

(gentle music) So those were some of the most interesting beetles I've been able to film over the last couple of years, and you can be sure this won't be the last beetle video that I make.

But for now, thanks for watching.

Beetles, but not the band.

Not a day goes by without hearing something good or bad about artificial intelligence, or AI.

We know the computer system can perform tasks that typically require human intelligence, including research, data processing, digital image creation, the list goes on.

Producer Evan Howell shows us research into how AI can help with the basic human function of walking.

(gentle music) - Scientists are teaching robots how to walk, run, move, just by showing them a video like this.

It's a breakthrough designed to help people with mobility issues.

- We leverage AI, we want to use AI to harness the power of simulation.

- Artificial intelligence.

Researchers at NC State have created a new kind of exoskeleton that already knows how humans move thanks to AI.

Exoskeletons have been around for a while.

Traditionally, the systems were bulky, heavy, and focused on the lower limbs.

Those older models also required each user to experiment with them, while lab technicians calibrated the machinery that ultimately helped them walk.

(gentle music) The new system is lighter, smaller, smarter.

- So overall, our exoskeleton is a personal mobility assistance device, so everyone can use it, including able-bodied individuals, and also people with a disability.

So it's because it's very lightweight and portable.

Many exoskeletons, they are very, very heavy.

Many exoskeletons, they are about 20 to 30 pounds.

Our exoskeleton is about five, six pounds, so much lighter and much smaller.

- Everybody uses their muscles in slightly different ways to do most things.

In fact, there are a few hundred muscles in the body you use together simultaneously.

Those older exoskeleton models focus just on muscle groups in the lower limbs, while ignoring others needed for an activity, and that wasn't exactly helpful.

The AI teaching this exoskeleton changes everything.

These researchers have used it to develop a device a person can use virtually straight out of the box.

It's kind of like a Segway, remember those?

They came out years ago, and about after a minute of practice, you can just sort of go.

Su envisions the device to be something like that, since the system already knows how humans move, it just needs to be calibrated for the individual user.

- So usually, it takes one or two hours to do human experiments to figure out how the robot controller, the software, can coordinate how to assist the human locomotion, like walking or running, but in our case, we do everything in a computer simulation.

It doesn't require any human experiments, and the robot can be deployed immediately.

(upbeat music) - The aha moment for this new type of exoskeleton was to have AI analyze how an able-bodied person walked, ran, and climbed stairs.

Researchers then had the computer imitate the motion and muscle coordination, and use that data to train what they call a microcontroller on each side.

As a user moves, the exoskeleton adjusts and fine-tunes itself to the user's movements.

Importantly, it learns how much force is needed and when to apply it.

The AI calibrates itself to walk through a host of variables like timing and how much power to give the user, and thanks to AI and machine learning, it has learned the minute workings of around 200 muscles in the body, top to bottom.

- We don't want the robot to generate the assistance too early or too late, right?

Think about if a person are helping you to walk, the timing, when to provide this assistance is crucial.

If it's too early, maybe the person can fall, or too late, the person can also fall, right?

After this AI part figures out the activity, then the AI part will generate the appropriate assistance with appropriate magnitude and appropriate timing.

(upbeat music) - Patients who have suffered strokes or related neurological issues, or elderly individuals who may be at risk for falls, may be helped by the discovery.

Su says there are even applications for those with cerebral palsy.

Right now, it's just a one-size-fits-all device, and the team hopes to make it available for more people in about a year.

So this is the second generation, is that right?

- Yes, this is our second generation of it.

We try to make it as lightweight and more compact, back in our first generation, and it's also more adjustable to different types of body sizes as well.

- So can you feel it moving your legs?

- Yeah, so whenever I try to lift up my thigh here, whenever I walk, it will give you a quick jolt, try to lift up your leg, and whenever for your stepping back down, it will push your leg back down, so it can straighten out your legs whenever you need it to be.

- It's the actuator and microcontroller that move the exoskeleton.

The user moves, the microcontroller senses that movement, and adds however much power the device thinks the user needs.

It learns how humans put the best foot forward, as it were.

And what's different here is that this robot, this device, can actually move your legs backwards as well.

- Yes, exactly.

So a lot of passives, so devices like with springs, it's only in one direction.

So these power ones are definitely very useful in both lifting up your leg, and also lowering and straightening out and supporting your leg as well.

- But when a user puts the exoskeleton on, human and machine are in sync.

- The more data you give it, the smarter it kind of gets.

So it's really trying to fit your gait profile, essentially.

So the more you walk, the more it kind of picks up that data and tries to fit that curve almost kind of thing.

- So Sue is optimistic about the future.

He says that by doing all the testing and simulation, his team has streamlined the process, which he says will make these exoskeletons more affordable and accessible.

And he says, who knows?

A device that helps people with paralysis walk might be just around the corner.

- We take and send so many images using our phone that we take it for granted.

It wasn't always that way.

- And a Duke math professor who is known as the godmother of the digital image created the algorithm that made it happen.

President Joe Biden presented her with the National Science Award for the discovery.

We live in a digital world.

A new study by the company Photo Tutorial estimates 1.9 trillion photos are taken every year.

5.3 billion photos taken every day.

61,000 photos taken per second.

The study also estimates we share a staggering 14 billion images every day through social media.

And we can create and share those digital images of our world thanks to Duke University math professor Ingrid Dobschi.

- I'm exhilarated by human creativity.

It's wonderful.

I mean, we use so many applications all the time.

- Dobschi is known as the godmother of the digital image because of her pioneering work in signal processing.

And it's all thanks to math because a digital signal conveys information just like a math equation.

- And that's the beauty of mathematics that the patterns and formalisms you learn have applications in many places.

That's the reason we invent mathematics to begin with.

I mean, mathematics start with us realizing that things in different circumstances have things, have patterns, have concepts in common.

- Dobschi's great mathematical discovery was the creation of what's known as the Dobschi wavelet.

Essentially, wavelets allow computers to function much like the human eye.

The image provides greater resolution and detail at the focal point and leaves the rest of the image a little more blurred.

- Here is where I need to be really sharp.

And here, and that can be at the edge of the image, but on the whole image, it tells me, here's where I need to be sharp, but it typically doesn't need to be sharp everywhere.

- The algorithm Dobschi created allowed images to be compressed.

It was called JPEG 2000, the first version of what we know as a JPEG file.

She created it in 1987 when she was 33.

- The image itself has been compressed.

It has been transformed into a version that will take less memory.

- Here's how it works.

Take this image of sailboats.

The image itself is made up of tens of thousands of picture elements.

We know them as pixels.

Pixels are the smallest single component in a digital image.

So these are the pixels then?

- Pixels.

- Yeah, okay.

- And each represents a number.

81, and this could be 83, and so on.

And many of them will be very similar because something similar is going on in the image.

- Dobschi's algorithm assigns a number corresponding to the grayscale for each pixel.

But look closely.

Many of those pixels are pretty similar to each other.

The water is about the same color.

So are the cliffs, the trees, and much of the sky.

83 and 85 that were here, if I replace them by 84, those, that's a gray level you wouldn't see a difference.

It's fine to think of each of these as 84.

And you can see that by the fact that when I compute their difference, that's just two.

It's very little.

- When there's a dramatic color change in the image, there's a big change in the number assigned.

- There may be here 147, and then 145, and then so on.

So this is something from dark to light.

- You say this is a big change in the grayscale.

- Yeah.

- The wavelet's algorithm then averages out the differences, which highlight the action in an image.

- But here, their difference is much more.

It's 62.

- Not much is lost if all that ocean with a similar color is blurry.

The focus of the picture are the sailboats.

And so when I compute things like that, I've replaced every two numbers by just one.

And the red differences tell me where I don't have to care about the fact that I forgot information.

But they say here, please, remember me, 64, 62.

Remember that I was a big difference.

You need me in order to keep track of that sudden transition.

And so you can compress by retaining only half the original amount of numbers and the differences where they're big.

- Remember, it's all about mathematics.

And in math terms, a signal is something that conveys information.

Signal processing and the creation of wavelets deals with the geometry of information rather than the geometry of shapes, motions, and forces.

Mathematics brings it all together.

- I realized that all this mathematics that I had learned for correspondence between quantum and classical mechanics was also useful for signal processing, which is a completely different thing.

But in both cases, you try to think in quantum mechanics of particles and waves, things that have both these properties.

In signal processing, you have things that are very localized in time, but you also think of frequency.

You think of a music note, the notes on a sheet of paper on bars.

It tells you what notes to play when.

Notes is how many oscillations per time unit.

Well, that is not something that happens right now.

It happens over a little time.

But on the other hand, you want to know when.

So it has a duality there, and it's that duality that is similar to what you have in quantum mechanics.

- While technology has moved on from the original JPEG 2000, basic concepts of wavelets and signal processing survive and are now contributing to a variety of fields.

- So I have worked with people in many different fields.

I mean, in neuroscience, in geophysics, in biology, in art history even, because I like learning things.

I love listening to people who are good at something that they do and that I don't know.

- Now to an innovative way to share science information.

Meet the social media science teacher.

(upbeat music) - Round one.

Is this your clavicle or your ulna?

However, when I slam it on the counter, the arrows change direction because of refraction.

I'm a fourth generation North Carolina public school teacher.

I say producers.

- Producers.

- Consumers.

- Consumers.

- So it's March, 2020.

Schools had just closed because of the pandemic.

And I noticed a dip in my online Zoom attendance.

And I just started thinking, what could I possibly do to make my class more exciting, more engaging, and get kids to come back to school?

Let's play "What's Under My Microscope," round one.

That was the genesis of Miss B TV.

It's me doing something that I thought might allow me to create engaging science videos that my students could watch.

And it might make them say, ooh, I should go to science class today.

When you're an elementary school science teacher, you do what it takes to get these kids to pay attention to you.

Nice walking.

(squeaking) I'm at 3.2 million followers.

It's been amazing.

I never in a million years expected to be able to reach and teach millions of people through a TikTok channel.

This gray stuff is our lichen.

What I love most about this job is the fact that kids are innately curious about the world around them.

And in my job, I just get to tap into that curiosity.

I get to let them explore these things that they've always wondered about.

- What makes her a really good science teacher is that she helps us understand things that we don't really understand.

We get to do science experience.

- I was A Charlatan of the Year.

What did make my selection extra special was they said, we are putting you on the cover.

To say I'm honored is just the understatement of the century.

The world really needs science and scientists.

This is the most important subject, in my opinion.

It's everywhere.

It's in everyone's lives.

We need students who grow up interested in science who will go on to pursue STEM careers.

I want kids to be excited about what they're learning.

I want to put science in their hands.

I wanna make it tangible.

I want them to see what they're doing apply to the real world.

- Try it for yourself and follow for more science videos.

- And that's it for SciENcy for this week.

If you want more SciENcy, be sure to follow us online.

I'm Frank Graff.

Thanks for watching.

(upbeat music) ♪ ♪ - SciNC is supported by a generous bequest gift from Dan Kerrigan and the Gaia Earth Balance Endowment through the Gaston Community Foundation.

- Quality public television is made possible through the financial contributions of viewers like you who invite you to join them in supporting PBS NC.